Cross-validation of IASI/MetOp derived tropospheric D with TES … · 2018. 12. 7. · the TES,...

Atmos. Meas. Tech., 8, 1447–1466, 2015

www.atmos-meas-tech.net/8/1447/2015/

doi:10.5194/amt-8-1447-2015

© Author(s) 2015. CC Attribution 3.0 License.

Cross-validation of IASI/MetOp derived tropospheric δD

with TES and ground-based FTIR observations

J.-L. Lacour1, L. Clarisse1, J. Worden2, M. Schneider3, S. Barthlott3, F. Hase3, C. Risi4, C. Clerbaux1,5,

D. Hurtmans1, and P.-F. Coheur1

1Spectroscopie de l’Atmosphère, Service de Chimie Quantique et Photophysique, Université Libre de Bruxelles,

Brussels, Belgium2Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA3Institute for Meteorology and Climate Research (IMK-ASF), Karlsruhe Institute of Technology, Karlsruhe, Germany4UPMC Univ. Paris 06, CNRS/INSU – UMR8539, LMD-IPSL, Paris, France5UPMC Univ. Paris 06; Université Versailles St-Quentin, CNRS/INSU, LATMOS-IPSL, Paris, France

Correspondence to: J.-L. Lacour ([email protected])

Received: 2 October 2014 – Published in Atmos. Meas. Tech. Discuss.: 12 November 2014

Revised: 18 February 2015 – Accepted: 26 February 2015 – Published: 20 March 2015

Abstract. The Infrared Atmospheric Sounding Interferom-

eter (IASI) flying onboard MetOpA and MetOpB is able

to capture fine isotopic variations of the HDO to H2O ra-

tio (δD) in the troposphere. Such observations at the high

spatio-temporal resolution of the sounder are of great inter-

est to improve our understanding of the mechanisms con-

trolling humidity in the troposphere. In this study we aim to

empirically assess the validity of our error estimation previ-

ously evaluated theoretically. To achieve this, we compare

IASI δD retrieved profiles with other available profiles of

δD, from the TES infrared sounder onboard AURA and from

three ground-based FTIR stations produced within the MU-

SICA project: the NDACC (Network for the Detection of

Atmospheric Composition Change) sites Kiruna and Izaña,

and the TCCON site Karlsruhe, which in addition to near-

infrared TCCON spectra also records mid-infrared spectra.

We describe the achievable level of agreement between the

different retrievals and show that these theoretical errors are

in good agreement with empirical differences. The compar-

isons are made at different locations from tropical to Arc-

tic latitudes, above sea and above land. Generally IASI and

TES are similarly sensitive to δD in the free troposphere

which allows one to compare their measurements directly.

At tropical latitudes where IASI’s sensitivity is lower than

that of TES, we show that the agreement improves when tak-

ing into account the sensitivity of IASI in the TES retrieval.

For the comparison IASI-FTIR only direct comparisons are

performed because the sensitivity profiles of the two observ-

ing systems do not allow to take into account their differ-

ences of sensitivity. We identify a quasi negligible bias in

the free troposphere (−3 ‰) between IASI retrieved δD with

the TES, which are bias corrected, but important with the

ground-based FTIR reaching −47 ‰. We also suggest that

model-satellite observation comparisons could be optimized

with IASI thanks to its high spatial and temporal sampling.

1 Introduction

Water vapour in the troposphere has a central role in the cli-

mate system (Pierrehumbert et al., 2007; Sherwood et al.,

2010). Yet there are important uncertainties associated with

the mechanisms controlling tropospheric water vapour dis-

tribution throughout the globe, leading to systematic biases

in actual representations (Soden and Bretherton, 1994; Brog-

niez and Pierrehumbert, 2007; Allan et al., 2003; Bates and

Jackson, 1997; Pierce et al., 2006) and an important spread in

future climate predictions (Soden and Held, 2006; de Forster

and Collins, 2004). In particular, the cloud feedback is re-

sponsible for most of the spread in the different climate mod-

els (Cess et al., 1990; Dufresne and Bony, 2008) because of

the various representations of associated processes in the dif-

ferent models. Recently, Sherwood et al. (2014) showed that,

among 43 climate models, the different ways of simulating

Published by Copernicus Publications on behalf of the European Geosciences Union.

1448 J.-L. Lacour et al.: Cross-validation of IASI/MetOp δD retrievals

convective mixing between the lower and middle tropical tro-

posphere was responsible for about half of the variance in cli-

mate sensitivity. It is thus crucial to improve representation

of hydrological processes.

Observations of water vapour isotopologues have the po-

tential to reveal information on the processes controlling hu-

midity. The different water isotopologues are indeed charac-

terized by distinct vapour pressures and are therefore sen-

sitive to phase changes: the heavy isotopologues (H218O,

HDO) preferentially condense while the light (H216O) pref-

erentially evaporates. Hence, the heavy-to-light isotopologue

ratio provides useful information on the air mass history and

can be used to constrain hydrological processes (Strong et al.,

2007; Worden et al., 2007; Samuels-Crow et al., 2014; Risi

et al., 2012a, b; Noone, 2012). The ratio is commonly ex-

pressed in δ notation:

δD= 1000

(HDOH2O

VSMOW− 1

), (1)

where VSMOW (Vienna Standard Mean Ocean Water) is the

reference standard for water isotope ratios (Craig, 1961).

Among the different methods to determine the isotopic

composition of water vapour, it has been shown that re-

mote sensing instruments can be used to infer estimates of

δD at a sufficient precision for scientific applications (Risi

et al., 2012b), with the advantage that they provide mea-

surements over regions and at altitudes that are not easily

accessible. Space sounders also have the potential to pro-

vide global distributions (Worden et al., 2007; Frankenberg

et al., 2009, 2013; Boesch et al., 2013). The Infrared At-

mospheric Sounding Interferometer (IASI) (Clerbaux et al.,

2009) onboard the MetOp meteorological satellite is partic-

ularly suited for measuring δD owing to its unique sampling

characteristics (Schneider and Hase, 2011; Lacour et al.,

2012). Indeed, IASI samples the atmosphere almost every-

where on the globe twice a day with a ground pixel size of

12 km at nadir.

Because of their inherent lack of vertical sensitivity, mea-

surements derived from remote sounding instruments consti-

tute a more or less complicated function of the quantity of

the interest (Rodgers and Connor, 2003) and can not be re-

garded as true values. The regularization procedure used in

the retrievals is in fact often such that they constitute the most

probable estimate given the measurement and some a pri-

ori statistical information. Moreover retrieved quantities de-

pend also on several parameters of the inversion such as the

a priori, the spectroscopic line database, the spectral range

etc. For all these reasons, the validity of quantities derived

from remote sensing instruments always needs to be evalu-

ated against other observations. It is at the same time crucial

to document how different remote sensing products compare

between them. In this paper we assess the validity of δD

vertical profiles retrieved from IASI at ULB by comparing

them with other available profiles of δD in the troposphere.

We use the term “cross-validation” according to von Clar-

mann (2006) for this exercise as we compare IASI vertical

profiles against profiles from other remote sounding instru-

ments which do not constitute absolute values of the state

of the atmosphere. Our study is similar to the recent cross-

validation of IASI δD retrievals from KIT with ground-based

FTIRs (Wiegele et al., 2014). We note that there has recently

been an increasing number of absolute measurements of tro-

pospheric δD (Schneider et al., 2014; Herman et al., 2014),

which will be essential to validate δD profiles retrieved from

the remote sounders and thus to ensure the optimal use of

the latter, which are for now often limited to relative vari-

ations analyses (Risi et al., 2012b). In this study, although

we do not use the absolute measurements, we perform the

cross-validation with respect to instruments which have been

evaluated against them. This allows us to infer some prelim-

inary conclusions on how our retrievals would compare to

these references.

For the cross-validation of IASI, we use δD profiles from

the TES instrument onboard Aura (Worden et al., 2012)

and from ground-based FTIRs from the MUSICA network

(Schneider et al., 2012) which are both sensitive to δD in the

same part of the troposphere as IASI. We do not perform the

comparison with other space sounders, which provide δD re-

trievals in the upper troposphere or near the surface where

IASI is generally less sensitive (Lacour et al., 2012; Schnei-

der and Hase, 2011).

The main purpose of the cross-validation exercise pre-

sented here is to verify that two profiles from two different re-

mote sounding instruments agree within their respective lim-

itations (Rodgers and Connor, 2003), that is to say that the

estimated profiles are well characterized by their error and

sensitivity matrices. In Sect. 2 we introduce the methodology

employed to adequately intercompare the different instru-

ment products. Specifics of the δD retrievals (also referred

to as HDO / H2O ratio retrieval) are also documented in this

section. We then give a brief overview of the different instru-

ments in Sect. 3. In Sects. 4 and 5 we detail the results of the

comparison between IASI and TES and between IASI and

the ground-based FTIRs, respectively.

2 Methodology to intercompare δD profiles

In this study we mainly follow the Rodgers and Con-

nor (2003) methodology developed to intercompare indirect

measurements. Its application to δD retrievals is described

below.

2.1 Retrieval of the HDO / H2O ratio

Retrieving the HDO / H2O ratio at a sufficient quality from

remote sounding instruments is challenging since the re-

trieval needs to be precise enough to capture the fine iso-

topic variations and sensitive over the large dynamical range

Atmos. Meas. Tech., 8, 1447–1466, 2015 www.atmos-meas-tech.net/8/1447/2015/

J.-L. Lacour et al.: Cross-validation of IASI/MetOp δD retrievals 1449

of water vapour concentrations in the troposphere. This re-

quirement is antagonistic with the general formulation of the

optimal estimation as the precision of the retrieval highly de-

pends on the applied statistical constraint which itself lim-

its the range of possible states. One way of overcoming this

limitation is to introduce an inter-constraint between the two

water isotopologues and to perform the retrieval on a loga-

rithmic scale (Schneider et al., 2006; Worden et al., 2006).

The different retrieval products we use here (Lacour et al.,

2012; Worden et al., 2012; Schneider et al., 2012) have been

obtained applying this constrained approach. One difficulty

introduced by the constrained retrieval is the posterior char-

acterization of the δD profiles as the averaging kernels and

error covariance matrices obtained are indeed representative

of the retrieved states log(H2O) and log(HDO) and can not

be directly applied to δD.

Schneider et al. (2012) have developed an elegant method

to characterize the vertical profiles of H2O and δD for

retrievals which constrain the ratio log(HDO / H2O). This

method allows one to transform the products obtained

in the {log(H2O), log(HDO)} space into a proxy state

{log(humidity), δD}. It is then possible to provide proxy error

covariance matrices and averaging kernels for the δD profile

which in turn facilitates its use for geophysical analyses.

In addition, the method allows for a minimization of the

cross dependence of the H2O retrieval on the δD retrieval

and vice versa (Schneider et al., 2012). As retrieved H2O and

δD exhibit different vertical sensitivities (the sensitivity to

δD being limited compared to H2O) and are thus not fully

representative of the same air mass, Schneider et al. (2012)

recommend distinguishing two types of products. A product

(type 1) for an optimal use of H2O vertical profiles alone and

a product (type 2) for consistent H2O and δD data which are

likely to be used together and need to be representative of the

same air mass. This is achieved by reducing the H2O profile

to the δD retrieval sensitivity. In this paper we use this proxy

state (type 2) to characterize δD profiles in terms of averaging

kernels and error covariance matrices and all retrievals have

therefore been a posteriori corrected to obtain a product of

type 2. Specifically, according to Schneider et al. (2012) this

is done by

x̂∗ = P−1CP(x̂− xa

)+ xa, (2)

with xa the a priori state vector, x̂ the estimated state vector

{log(H2O), log(HDO)} the profiles originally retrieved and

x̂∗ the corrected state vector {log(H2O), log(HDO)} that is

used to compute the δD ratio of type 2. For the description of

P and C matrices we refer to Schneider et al. (2012). These

matrices ensure the reduction of vertical sensitivity and res-

olution of the H2O profile as well as a correction of the cross

dependence. Averaging kernels and error covariance matri-

ces from the different retrievals have all been transformed

into the {log(humidity), δD} proxy space.

2.2 Transformation between grids

A cross-validation exercise should compare like with like

and consists of applying corrections to make the different

retrievals comparable. A first step required for the cross-

validation involves the adjustment of the different vertical

grids on which the retrievals are performed. The state vec-

tors, the error covariance matrices as well as the averaging

kernel matrices need to be represented on the same grids to

be comparable. The state vector and the error covariance ma-

trices can be transformed into a coarser or a finer grid. In-

deed, following Rodgers (2000) the state vector x on a fine

grid is related to a reduce vector z on a coarser grid as

x =Wz+ εWx (3)

with W the interpolation matrix and εW x the error induced

by the interpolation (Calisesi et al., 2005). The transforma-

tion of the state vector on a fine grid to a state vector on a

coarser grid can be obtained via

z=W∗x, (4)

where W∗ is the pseudo inverse matrix of W. The error co-

variance matrix can be resampled on the coarser grid as fol-

lows:

Sz =W∗SxW∗T . (5)

For the averaging kernels, the interpolation is more compli-

cated. For example, Calisesi et al. (2005) also use the linear

transformation to resample the AVK on different grids as fol-

lows:

Az =W∗AxW. (6)

The equation has been used to transform averaging kernels

on different grids in the case of retrieved profiles from limb

sounders (Ceccherini et al., 2003; Calisesi et al., 2005) which

are characterized by high vertical resolution compared to

nadir sounders. In our study, as can be seen in Fig. 1, the IASI

grid is coarser than the one used for TES and FTIRs. We aim

at representing the other retrievals on the same grid as IASI

since extrapolation would lead to additional error. Applying

Eq. (6) to TES averaging kernels led to satisfying interpo-

lated averaging kernel matrices. In the case of the FTIR how-

ever, this could not be applied without a significant degrada-

tion of the matrix owing to the configuration of levels for the

FTIR grid. To have the FTIR AVK on the IASI vertical grid

we therefore interpolated the eigenvectors of the AVK. First,

the FTIR averaging kernels matrix is decomposed into its

eigenvectors (AVK=VDV−1); second, the leading eigen-

vectors are interpolated on the IASI grid (V′=WV); and

third, the FTIR averaging kernels are reconstructed with

the interpolated eigenvectors but with the eigenvalues corre-

sponding to the original AVK (AVK′=V′DV′−1

). The AVK′

obtained is then used for the comparison.

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1450 J.-L. Lacour et al.: Cross-validation of IASI/MetOp δD retrievalsJ.-L. Lacour et al.: Cross-validation of IASI/MetOp δD retrievals 3

0

1

2

3

4

5

6

7

8

9

10

IASI TES FTIR (Kalsruhe)

Alti

tude

[km

]

Figure 1.Retrieval grids of the different retrievals: IASI/ULB (red),TES averaged from retrievals above sea (purple), FTIR at Karlsruhe(green).

its the range of possible states. One way of overcoming thislimitation is to introduce an inter constraint between the twowater isotopologues and to perform the retrieval on a log-arithmic scale (Schneider et al., 2006; Worden et al., 2006).The different retrieval products we use here (Lacour et al.,2012; Worden et al., 2012; Schneider et al., 2012) have beenobtained applying this constrained approach. One difficultyintroduced by the constrained retrieval is the posterior char-acterization of theδD profiles as the averaging kernels anderror covariance matrices obtained are indeed representativeof the retrieved stateslog(H2O) andlog(HDO) and can notbe directly applied toδD.

Schneider et al. (2012) have developed an elegant methodto characterize the vertical profiles ofH2O and δDfor retrievals which constrain the ratiolog(HDO / H2O).This methods allows to transform the products obtainedin the {log(H2O), log(HDO)} space into a proxy state{log(humidity), δD}. It is then possible to provide proxy er-ror covariance matrices and averaging kernels for theδD pro-file which in turn facilitates its use for geophysical analyses.

In addition, the method allows for a minimization of thecross dependence of theH2O retrieval on theδD retrievaland vice versa (Schneider et al., 2012). As retrievedH2O andδD exhibit different vertical sensitivities (the sensitivity toδD being limited compared toH2O) and are thus not fullyrepresentative of the same air mass, Schneider et al. (2012)recommend to distinguish two types of products. A product(type 1) for an optimal use ofH2O vertical profiles alone anda product (type 2) for consistentH2O andδD data which arelikely to be used together and need to be representative of thesame air mass. This is achieved by reducing theH2O profileto theδD retrieval sensitivity. In this paper we use this proxystate (type 2) to characterizeδD profiles in terms of averag-ing kernels and error covariance matrices and all retrievals

have therefore been a posteriori corrected to obtain a productof type 2. Specifically, according to Schneider et al. (2012)this is done by:

x̂∗ = P−1CP(x̂−xa)+ xa, (2)

with xa the a priori state vector,̂x the estimated state vector{log(H2O), log(HDO)} the profiles originally retrieved andx̂∗ the corrected state vector{log(H2O), log(HDO)} that isused to compute theδD ratio of type 2. For the description ofP andC matrices we refer to Schneider et al. (2012). Thesematrices ensure the reduction of vertical sensitivity and reso-lution of theH2O profile as well as a correction of the crossdependence. Averaging kernels and error covariance matri-ces from the different retrievals have all been transformedinto the{log(humidity), δD} proxy space.

2.2 Transformation between grids

A cross-validation exercise should compare like with likeand consists of applying corrections to make the differentretrievals comparable. A first step required for the cross-validation involves the adjustment of the different verticalgrids on which the retrievals are performed. The state vec-tors, the error covariance matrices as well as the averagingkernels matrices need to be represented on the same gridsto be comparable. The state vector and the error covariancematrices can be transformed into a coarser or a finer grid. In-deed, following Rodgers (2000) the state vectorx on a finegrid is related to a reduce vectorz on a coarser grid as:

x = Wz + ǫWx (3)

with W the interpolation matrix andǫWx the error inducedby the interpolation (Calisesi et al., 2005). The transforma-tion of the state vector on a fine grid to a state vector ona coarser grid can be obtained via:

z = W∗x (4)

whereW∗ is the pseudo inverse matrix ofW. The error co-variance matrice can be re sampled on the coarser grid asfollows:

Sz = W∗SxW∗T. (5)

For the averaging kernels, the interpolation is more compli-cated. For example, Calisesi et al. (2005) use also the lineartransformation to resample the AVK on different grids as fol-lows:

Az = W∗AxW. (6)

The equation has been used to transform averaging ker-nels on different grids in the case of retrieved profiles fromlimb sounders (Ceccherini et al., 2003; Calisesi et al., 2005)which are characterized by high vertical resolution compared

Figure 1. Retrieval grids of the different retrievals: IASI/ULB (red),

TES averaged from retrievals above sea (purple), FTIR at Karlsruhe

(green).

2.3 Expected difference between retrievals

The difference between two retrievals (now on the same

grids) is given by Rodgers and Connor (2003) as

δ = x̂1− x̂2 = (A1−A2)(x− xc)+ εx1− εx2

, (7)

with A1 and A2 the averaging kernel matrices of the two re-

trievals being compared, x the state vector and xc the mean

of the comparison ensemble. The latter, together with the

covariance matrix Sc, describe the ensemble of states over

which the comparison is performed (Rodgers and Connor,

2003). We document how this ensemble is generated in the

next subsection. The covariance of δ (Eq. 7) is given by

Sδ = (A1−A2)T Sc (A1−A2)+Sx1

+Sx2, (8)

with Sc the covariance matrix describing the comparison en-

semble, and Sx the error covariance matrix due to observa-

tional error. Equation (8) evaluates the expected difference

between two retrievals. The first term describes the contri-

bution coming from the different vertical sensitivities of the

two instruments and the two other terms, the respective con-

tributions from the error covariances of each retrieval.

When the two retrievals to be compared exhibit very dif-

ferent vertical sensitivity profiles, the expected error can be

very large. When it gets close to the expected natural vari-

ability of the quantity of interest, the comparison loses some

significance. To deal with such situations one might reduce

the effect of the smoothing error on the comparison by sim-

ulating one profile with the vertical sensitivity of the other.

If the retrieval 2 is optimal with respect to the comparison

ensemble and the retrieval 1 with less vertical sensitivity, the

retrieved profile 2 can be smoothed with the averaging ker-

nels of retrieval 1 to give

x̂12 = xc+A1

(x̂2− xc

). (9)

The averaging kernel matrix associated with x̂12 is then

A1 A2. Equation (8) becomes

Sδ12= (A1−A1A2)Sc(A1−A1A2)

T+Sx1

+A1Sx2AT1 .. (10)

By doing so, the smoothing error will be smaller than in the

direct comparison.

2.3.1 Correction for the use of different a priori

The different retrieved profiles of δD have been adjusted to

take into account the use of different a priori by adding to

each retrieved profile the term (A− I) (xa− xc) (Rodgers

and Connor, 2003) with xc being the mean profile of the

comparison ensemble which we defined as the a priori pro-

file of TES for the IASI-TES comparison and as the FTIR a

priori profile for the IASI-FTIR comparison.

2.3.2 Usefulness of the comparison and choice of the

comparison ensemble

As said above, the comparison is useful if the difference

between the two compared retrieved profiles is lower than

the natural expected variability of δD. The latter is evalu-

ated here by comparing covariance matrices with daily δD

profiles from the isotope-enabled atmospheric model LMDZ

(Risi et al., 2010). The model, nudged with ECMWF re-

analysed winds, has demonstrated capabilities to reproduce

reasonably well δD distributions throughout the globe (Risi

et al., 2012b). We consider in our analysis the expected nat-

ural variability of δD at a quasi global scale (from 60◦ S to

60◦ N) but also at regional scales whenever relevant.

We also use the quasi global covariance matrix as the com-

parison ensemble covariance matrix Sc (Eqs. 10 and 8) which

should describe the real ensemble of atmospheric possible

states as well as possible (Rodgers and Connor, 2003).

2.4 Comparison of the δD–humidity relation

δD profiles alone do not provide information on hydrological

processes. They become useful when analysed together with

humidity variations as this combination will determine an

enrichment or depletion of the air mass accompanying a hu-

midifying or drying process. In a first approximation isotopic

composition of water vapour follows a Rayleigh distillation

curve (Rayleigh, 1902) which predicts a continuous deple-

tion of the heavy isotopologue during condensation. This re-

lation can be approximated to the following linear relation

(Noone, 2012):

(δD− δD0)≈ (α− 1) lnq

q0

, (11)

with q the specific humidity, α the effective fractionation co-

efficient and the subscript 0 describing the initial conditions

(isotopic composition and mixing ratio of the source depend-

ing on latitude and temperature).



Observations of δD and H2O are especially interesting

when they show deviations from Rayleigh distillation curves.

For example, mixing of different air masses will give the

resulting air mass an isotopic signature more enriched than

a Rayleigh distillation for a same q (Noone et al., 2011;

Galewsky et al., 2007). In contrast, re-evaporation of rain

drops in convective environments enhances the depletion of

the heavy isotopologue in water vapour (Worden et al., 2007;

Risi et al., 2008), resulting in a more depleted isotopic sig-

nature. These simple examples are two extremes in the pro-

cesses affecting isotopic composition. In general the isotopic

composition is determined by a complex interplay between

enriching and depleting processes.

Analysis of retrieved δD from remote sounders needs to be

considered carefully as the retrieval of H2O influences the re-

trieved values of δD. This is especially true in our case where

a statistical constraint is added between HDO and H2O. Even

if the influence of H2O retrieval on δD is minimized by ap-

plying the methodology of Schneider et al. (2012) it is im-

portant to verify that observations of δD together with hu-

midity can actually show some deviations from Rayleigh

curves. For example in their cross-validation and validation

study, Schneider et al. (2014) and Wiegele et al. (2014) show

that remote sensing products and in situ measurement exhibit

similar anomalies in the δD–q space, demonstrating that the

former are indeed sensitive to hydrological conditions. We

also address this issue in the present paper by comparing the

observations from the different instruments in the q–δD dia-

grams and by analysing the spatio-temporal variations of the

q–δD relation.

3 Products overview

3.1 IASI

IASI is a Fourier transform spectrometer flying onboard the

European meteorological polar-orbit MetOp satellite. It mea-

sures thermal infrared radiation emitted by the Earth and the

atmosphere with a spectral resolution of 0.5 cm−1 (apodized)

and a low radiometric noise of 0.1–0.2 K (in the spectral

range used for the retrieval) for a reference blackbody at

280 K (Hilton et al., 2012; Clerbaux et al., 2009). The sam-

pling characteristics of the instrument (a measurement al-

most everywhere twice a day) result in about 1.2 million

spectra per day. Currently there is no algorithm available

which is capable of processing this volume of data for δD in

near-real-time but there are two different retrieval schemes

that have been developed to retrieve δD from IASI spec-

tra for limited periods or regions: the one we are concerned

with in this paper, developed at Université Libre de Brux-

elles (ULB) with the radiative transfer code “Atmosphit”

and the one developed at KIT within the MUSICA project,

which applies the radiative transfer and retrieval code PROF-

FIT. Both retrieval schemes are optimized to constrain the

log(HDO / H2O) ratio but present significant differences. The

main differences are the spectral range used and the strength

of the statistical constraint used: at KIT, a wide range of

the IASI spectra is used in the retrieval (1190→ 1400 cm−1)

with a strong statistical constraint while at ULB the re-

trieval uses a shorter spectral range (1195→ 1253 cm−1) and

a moderate statistical constraint. More details can be found in

Lacour et al. (2012) for the ULB retrieval and in Schneider

and Hase (2011) for the KIT retrieval. In what follows the

IASI retrieval we refer to is the one developed at ULB. The

retrieved profiles have been theoretically characterized and

evaluated against model simulations in Lacour et al. (2012)

for scenes above the oceans. It has been shown that the re-

trieved profiles were sufficiently sensitive and precise in the

free troposphere with an error on the 3–6 km layer evaluated

to 38 ‰ on a single measurement basis. In the present study,

scenes above land and sea from tropical to Arctic latitudes

are considered. Note that only measurements from MetOpA,

the first of the series of MetOp satellites, are analysed.

3.2 TES

The TES instrument aboard the Aura satellite since 2004

(Beer et al., 2001) is, like IASI, a Fourier transform spec-

trometer measuring the thermal infrared radiation emitted by

the Earth and the atmosphere. The spectral region covered by

TES ranges from 650 to 3050 cm−1. The spectral resolution

of TES (apodized spectral resolution of 0.1 cm−1) is higher

than that of IASI (0.5 cm−1), while the instrumental noise

is larger. The TES sampling (limb and nadir measurements)

is characterized with 3 different observational modes (global

survey, step-and-stare, transect) allowing for different spatial

coverage. In global survey mode TES takes one nadir ob-

servation every 180 km approximately. We used TES V005

Lite data (Worden et al., 2012) which are bias corrected for

a suspected problem in HDO spectroscopic parameters. The

TES retrieval scheme uses a wide spectral range from 1190 to

1320 cm−1. This version of TES data was recently validated

with aircraft measurements above Alaska by Herman et al.

(2014) and a remaining bias of +37 ‰ has been identified.

Observations of δD from TES available at a global scale from

September 2004 have already been widely used to study hy-

drological processes.

3.3 Ground-based FTIR

The project MUSICA (MUlti-platform remote Sensing of

Isotopologues for investigating the Cycle of Atmospheric

water) aims to provide tropospheric H2O and δD data sets

from different instruments (Schneider et al., 2012). It is

subdivided in three components: (1) the ground-based re-

mote sensing component (ground-based FTIR from NDACC

network), (2) the space-based component (IASI-KIT) and

(3) an in situ component with cavity ring-down measure-

ments. Here we work with component (1) of MUSICA



Figure 2. Illustration of the collocation between TES and IASI measurements for the IASI descending orbit (PM) on 18 January 2011 abovethe Pacific and Indian Oceans. TES and IASI ground pixels are represented by square and ellipses respectively. The colourscale indicate theretrieved values ofδD at 5.5 km. The background is a MODIS picture taken the same day. On the right panel, IASI pixels are represented attheir real sizes.

(isotopic composition and mixing ratio of the source depend-ing on latitude and temperature).

Observations ofδD and H2O are especially interestingwhen they show deviations from Rayleigh distillation curves.For example, mixing of different air masses will give theresulting airmass an isotopic signature more enriched thana Rayleigh distillation for a sameq (Noone et al., 2011;Galewsky et al., 2007). In contrast, re-evaporation of raindrops in convective environment enhance the depletion ofheavy isotopologue in water vapour (Worden et al., 2007;Risi et al., 2008), resulting in a more depleted isotopic sig-nature. These simple examples are two extremes in the pro-cesses affecting isotopic composition. In general the isotopiccomposition is determined by a complex interplay betweenenriching and depleting processes.

Analysis of retrievedδD from remote sounders needs to beconsidered carefully as the retrieval ofH2O influences the re-trieved values ofδD. This is especially true in our case wherea statistical constraint is added betweenHDO and H2O.Even if the influence ofH2O retrieval onδD is minimizedby applying the methodology of Schneider et al. (2012) itis important to verify that observations ofδD together withhumidity can actually show some deviations from Rayleighcurves. For example in their cross-validation and validationstudy, Schneider et al. (2014) and Wiegele et al. (2014) showthat remote sensing products and in situ measurement exhibitsimilar anomalies in theδD–q space, demonstrating that theformer are indeed sensitive to hydrological conditions. Wealso address this issue in the present paper by comparing theobservations from the different instruments in theq–δD dia-grams and by analysing the spatio temporal variations of theq–δD relation.

3 Products overview

3.1 IASI

IASI is a Fourier Transform Spectrometer flying on board theEuropean meteorological polar-orbit MetOp satellite. It mea-sures thermal infrared radiation emitted by the Earth and theatmosphere with a spectral resolution of 0.5cm−1 (apodized)and a low radiometric noise of 0.1–0.2K (in the spectralrange used for the retrieval) for a reference blackbody at280K (Hilton et al., 2012; Clerbaux et al., 2009). The sam-pling characteristics of the instrument (a measurement al-most everywhere twice a day) result in about 1.2 millionspectra a day. Currently there is no algorithm available whichis capable to process this volume of data forδD in near-real-time but there are two different retrieval schemes that havebeen developed to retrieveδD from IASI spectra for limitedperiods or regions: the one we are concerned with in this pa-per, developed at Université Libre de Bruxelles (ULB) withthe radiative transfer code “Atmosphit” and the one devel-oped at KIT within the MUSICA project, which applies theradiative transfer and retrieval code PROFFIT. Both retrievalschemes are optimized to constrain thelog(HDO / H2O) ra-tio but present significant differences. The main differencesare the spectral range used and the strength of the statisticalconstraint used: at KIT, a wide range of the IASI spectra isused in the retrieval (1190→1400 cm−1) with a strong sta-tistical constraint while at ULB the retrieval uses a shorterspectral range (1195→1253 cm−1) and a moderate statisticalconstraint. More details can be found in Lacour et al. (2012)for the ULB retrieval and in Schneider and Hase (2011) forthe KIT retrieval. In what follows the IASI retrieval we re-fer to is the one developed at ULB. The retrieved profiles

Figure 2. Illustration of the collocation between TES and IASI measurements for the IASI descending orbit (PM) on 18 January 2011 above

the Pacific and Indian oceans. TES and IASI ground pixels are represented by square and ellipses respectively. The colour scale indicates the

retrieved values of δD at 5.5 km. The background is a MODIS picture taken the same day. On the right panel, IASI pixels are represented at

their real sizes.

and use ground-based FTIR measurement from 3 NDACC

stations: Izaña (28.3◦ N, 16.5◦W, 2367 m a.s.l.), Karlsruhe

(49.1◦ N, 8.4◦ E, 111 m a.s.l.) and Kiruna (67.8◦ N, 20.4◦ E,

419 m a.s.l.). δD observations from these sites have been used

previously for a comparison with IASI using the KIT re-

trieval scheme (Wiegele et al., 2014).

4 Comparison IASI vs. TES

4.1 Data sets description and collocation criterion

With its exceptional sampling characteristics, IASI provides

a huge amount of data which requires important computing

resources and appropriate algorithms to fully treat it (Hurt-

mans et al., 2012). For the retrieval of HDO / H2O ratios these

resources are, for the time being, limited and thus IASI δD

availability is also limited. For this cross-validation two δD

data sets are considered: (1) the full year 2010 along a merid-

ional gradient in the Atlantic (from−60◦ S to 60◦ N and from

30 to 25◦W) that we will refer to MD data set, (2) the pe-

riod 2010–2012 above the Indian and Pacific oceans (15◦ S

to 10◦ N and from 65 to 155◦ E) hereafter called the PIO data

set. To illustrate the difference between TES and IASI sam-

pling note that the PIO data set from March 2010 to Decem-

ber 2010 includes about 20 000 δD retrievals from TES and

4.5 million from IASI (cloud free measurements).

For each TES measurement, IASI measurement was se-

lected if it was taken within a radius of 0.5◦ for the PIO

data set and 1◦ for the MD data set as there was less data.

Fig. 2 illustrates the spatial collocation of TES (squares) with

IASI (ellipses) measurements for the descending orbit (PM)

on 18 January 2011 above the maritime continent. Only IASI

pixels that are within the red circles (right panel of Fig. 2) are

considered for the comparison. It is not possible to have less

than 4 h difference between the two instruments as this cor-

responds to the time delay between their day and night over-

pass times. The temporal collocation is such that we compare

TES daytime measurement (13:30) only with IASI daytime

measurement (09:30) and the same for the evening/night. In

addition to these criteria, we also carried out a filtering on the

air mass history based on backward trajectory analysis. For

each TES measurement, backward trajectory was computed

with HYSPLIT (Draxler and Hess, 1998). The data was re-

jected if the position of the air mass four hours before the

TES measurement was too far (2.5◦) from the IASI measure-

ment. This 2.5◦ threshold has been defined by analysing the

statistical differences between the TES and IASI integrated

3–6 km column and the distance of the air mass. We found

that a spatial mismatch above 2.5◦ led indeed to significant

differences.

Despite the strict collocation criterion used, some mis-

matches due to the natural variability of δD could arise. The

spatial mismatch within circles of 0.5 to 1◦ is assumed to be

inferior to the error on IASI retrieval and is thus unlikely

to control the total difference expected between TES and

IASI. For example, the 1σ standard deviation at 4.5 km on

IASI retrieved profiles within cell of 1◦×1◦ is about 22 ‰.

In Wiegele et al. (2014), the authors estimated the error due

to spatial mismatch for similar distances of about 18 ‰. The

impact of a temporal mismatch is more difficult to estimate

and might affect the total difference budget to some extent,

especially above the maritime continent where convection

has a pronounced diurnal cycle.



Comparison of one TES observation vs. several IASI

observations

Generally, intercomparison studies are carried out by com-

paring one observation vs. another observation. Because the

observational error on the IASI retrieval is relatively impor-

tant (38 ‰ in the free troposphere; Lacour et al., 2012) com-

pared to TES, to the FTIR and also compared to the expected

natural variability of δD, the comparison between a couple

of δD profiles could have limited utility. To cope with that,

we chose to average all the IASI measurements fulfilling the

collocation criteria with one TES δD observation. By doing

so, the IASI observational error is lowered by the square root

ofN , the number of observations. Likewise, the error covari-

ance matrix of the IASI error of Eqs. (8) and (10) is divided

by N . Generally the number of IASI observations available

around one TES observation ranges from 1 to 15.

4.2 Retrieval characteristics

Figure 3 shows typical averaging kernels for IASI and TES at

tropical latitudes. These averaging kernels correspond to δD

proxy averaging kernels (Schneider et al., 2012). For IASI,

the resolution of the averaging kernels is quite coarse, about

4–5 km and the information of the retrieval comes mainly

from the 0–3 and 3–7 km layers. The peaks of the averaging

kernels are not perfectly located at their nominal altitude es-

pecially above 6 km indicating that the retrieved state above

that altitude is mainly sensitive to variations of the real state

at lower altitude. The degrees of freedom (DOFS) for this

typical retrieved profiles of IASI is 1.7. Compared to IASI,

TES averaging kernels show better resolved structures with

a finer resolution and the averaging kernels all peak at their

nominal height. The degree of freedom of 2.2 indicates two

decorrelated levels of information, one in the lowest tropo-

sphere (0–3 km) and another in the free troposphere.

This situation is representative of tropical latitudes and in-

dicates there a better sensitivity of TES to δD. The vertical

sensitivity is however affected by local conditions such as hu-

midity content, temperature profiles and surface temperature.

Figure 4 shows the degrees of freedom for TES and IASI

along the meridional gradient data set. One can see that the

IASI DOFS present fewer variations than TES with latitude.

More specifically, DOFS for IASI varies only between 1.5

and 2, while TES DOFS vary between high values (2–2.3)

at tropical latitudes and lower values (0.5–2) at higher lati-

tudes. The stability of the δD DOFS from IASI, as we ex-

plain in Appendix A, is due to a compensating effect of bet-

ter sensitivity with increasing surface temperature but lower

sensitivity with increasing humidity. Yet the higher sensitiv-

ity of IASI over TES at high latitude remains surprising and

requires further investigations.


have been theoretically characterized and evaluated againstmodel simulations in Lacour et al. (2012) for scenes abovethe oceans. It has been shown that the retrieved profiles weresufficiently sensitive and precise in the free troposphere withan error on the 3–6km layer evaluated to 38 ‰ on a singlemeasurement basis. In the present study, scenes above landand sea from tropical to Arctic latitudes are considered. Notethat only measurements from MetOpA, the first of the seriesof MetOp satellites, are analysed.

3.2 TES

The TES instrument aboard the Aura satellite since 2004(Beer et al., 2001) is, like IASI, a Fourier Transform Spec-trometer measuring the thermal infrared radiation emittedbythe Earth and the atmosphere. The spectral region covered byTES ranges from 650 to 3050cm−1. The spectral resolutionof TES (apodized spectral resolution of 0.1cm−1) is higherthan that of IASI (0.5cm−1), while the instrumental noiseis larger. The TES sampling (limb and nadir measurements)is characterized with 3 different observational modes (globalsurvey, step-and-stare, transect) allowing for differentspatialcoverage. In global survey mode TES takes one nadir ob-servation every 180km approximately. We used TES V005Lite data (Worden et al., 2012) which are bias corrected fora suspected problem in HDO spectroscopic parameters. TheTES retrieval scheme uses a wide spectral range from 1190 to1320cm−1. This version of TES data was recently validatedwith aircraft measurements above Alaska by Herman et al.(2014) and a remaining bias of +37 ‰ has been identified.Observations ofδD from TES available at a global scale fromSeptember 2004 have already been widely used to study hy-drological processes.

3.3 Ground-based FTIR

The project MUSICA (MUlti-platform remote Sensing ofIsotopologues for investigating the Cycle of Atmosphericwater) aims to provide troposphericH2O and δD datasetsfrom different instruments (Schneider et al., 2012). It issubdivided in three components: (1) the ground-based re-mote sensing component (ground-based FTIR from NDACCnetwork), (2) the space-based component (IASI-KIT) and(3) an in situ component with cavity ring-down measure-ments. Here we work with component (1) of MUSICAand use ground-based FTIR measurement from 3 NDACCstations: Izana (28.3◦ N, 16.5◦ W, 2367m a.s.l.), Karlsruhe(49.1◦ N, 8.4◦ E, 111m a.s.l.) and Kiruna (67.8◦ N, 20.4◦ E,419m a.s.l.). δD observations from these sites have beenused previously for a comparison with IASI using the KITretrieval scheme (Wiegele et al., 2014).

−0.2 0 0.1 0.2 0.3 0.40

1

2

3

4

5

6

7

8

9

10

Alti

tude

[km

]

IASI − DOFS=1.7

−0.2 0 0.1 0.2 0.3 0.40

1

2

3

4

5

6

7

8

9

10

Alti

tude

[km

]

TES − DOFS=2.2

Figure 3. Typical averaging kernels in{δD} proxy space for IASI(left panel) and for TES (right panel) for a tropical scene (2.5◦ N).The nominal heights of the kernels are marked by filled circles.

4 Comparison IASI vs. TES

4.1 Datasets description and collocation criterion

With its exceptional sampling characteristics, IASI providesa huge amount of data which requires important comput-ing resources and appropriate algorithms to fully treat it(Hurtmans et al., 2012). For the retrieval ofHDO / H2O ra-tios these resources are, for the time being, limited and thusIASI δD availability is also limited. For this cross-validationtwo δD datasets are considered: (1) the full year 2010 alonga meridional gradient in the Atlantic (from−60◦ S to 60◦ Nand from 30 to 25◦ W) that we will refer to MD dataset, (2)the period 2010–2012 above the Indian and Pacific Oceans(15◦ S to 10◦ N and from 65 to 155◦ E) hereafter called PIOdataset. To illustrate the difference between TES and IASIsampling note that, the PIO dataset from march 2010 to De-cember 2010 includes about 20 000δD retrievals from TESand 4.5 millions from IASI (cloud free measurements).

For each TES measurement, IASI measurement was se-lected if it was taken within a radius of 0.5◦ for the PIOdataset and 1◦ for the MD dataset as there was less data. TheFigure 2 illustrates the spatial collocation of TES (squares)with IASI (ellipses) measurements for the descending or-bit (PM) on 18 January 2011 above the maritime continent.Only IASI pixels that are within the red circles (right panelof Figure 2) are considered for the comparison. It is not pos-sible to have less than 4 h difference between the two instru-ments as this corresponds to the time delay between theirday and night overpass times. The temporal collocation issuch that we compare TES daytime measurement (13.30)only with IASI daytime measurement (9.30) and the samefor the evening/night. In addition to these criteria, we alsodid a filtering on the airmass history based on backwardtrajectories analysis. For each TES measurement, a backward

Figure 3. Typical averaging kernels in {δD} proxy space for IASI

(left panel) and for TES (right panel) for a tropical scene (2.5◦ N).

The nominal heights of the kernels are marked by filled circles.

4.3 Expected difference

For this comparison the retrievals of IASI and TES have

been (1) a posteriori corrected for the cross-correlation in-

terferences between H2O and δD, (2) TES data have been

re-gridded on the IASI grid and (3) corrected for the use of

different a priori. To compute the expected agreement we use

the quasi global Sc computed from the LMDZ model. Note

that IASI and TES retrievals are not optimal with regard to

the comparison ensemble defined by Sc since they each use

different a priori covariance matrices. The Sc is more loose

than the one (Sa) used in TES retrievals and more constrained

than the one used in IASI retrievals. The same Sc is used for

the entire intercomparison.

Figure 5 shows for the retrievals above the PIO data set

the total expected difference (black curve) from the compar-

ison IASI vs. TES and its different contributions from the

observational and smoothing error. For the PIO data set TES

retrievals have more sensitivity to δD, we thus smoothed TES

retrieved profiles with IASI averaging kernels for a more

like-with-like comparison.

The direct comparison (no smoothing) is shown on the left

panel of Fig. 5 and the smoothed comparison on the right

panel. The total expected difference (black curve) of the di-

rect comparison ranges from 120 ‰ at the lowest layer to

55 ‰ at 4.5 km, increasing again up to 68 ‰ at 7.5 km. The

total expected difference is largely controlled by IASI obser-

vational error in the 0–2 km layer and above 6 km. In the free

troposphere the difference of vertical sensitivities (smooth-

ing error) between the two sounders also has an impact on

the direct comparison. Note that IASI’s observational error

exceeds the δD global variability above 7 km and at 0.5 km,



-30° -15° 0° 15° 30° 45° 60°0.5

1.0

1.5

2.0

2.5 TES IASI

D

OFS

Latitude

Figure 4. TES (purple) and IASI (red) degrees of freedom forδDalong the meridional gradient.

trajectory was computed with HYSPLIT (Draxler and Hess,1998). The data was rejected if the position of the airmassfour hours before the TES measurement was too far (2.5◦)from the IASI measurement. This 2.5◦ threshold has beendefined by analysing the statistical differences between theTES and IASI integrated 3–6km column and the distance ofthe airmass. We found that a spatial mismatch above 2.5◦ ledindeed to significant differences.

Despite the strict collocation criterion used, some mis-match due to the natural variability ofδD could arise. Thespatial mismatch within circles of 0.5 to 1◦ is assumed to beinferior to the error on IASI retrieval and is thus unlikely tocontrol the total difference expected between TES and IASI.For example, the 1 sigma standard deviation at 4.5 km onIASI retrieved profiles within cell of 1◦×1◦ is about 22‰. InWiegele et al. (2014), the authors estimated the error due tospatial mismatch for similar distances of about 18‰. The im-pact of a temporal mismatch is more difficult to estimate andmight affect the total difference budget to some extent espe-cially above the maritime continent where convection has apronounced diurnal cycle.

Comparison of one TES observation vs. several IASI ob-servations

Generally, intercomparison studies are carried out by com-paring one observation vs. another observation. Because theobservational error on the IASI retrieval is relatively impor-tant (38 ‰ in the free troposphere, Lacour et al., 2012) com-pared to TES, to the FTIR and also compared to the expectednatural variability ofδD, the comparison between a coupleof δD profiles could have limited utility. To cope with that,we chose to average all the IASI measurements fulfilling thecollocation criteria with one TESδD observation. By doingso, the IASI observational error is lowered by the squarerootof N , the number of observations. Likewise, the error covari-ance matrix of the IASI error of Eqs. (8) and (10) is divided

by N . Generally the number of IASI observations availablearound one TES observation ranges from 1 to 15.


Figure 3 shows typical averaging kernels for IASI and TES attropical latitudes. These averaging kernels correspond toδDproxy averaging kernels (Schneider et al., 2012). For IASI,the resolution of the averaging kernels is quite coarse, about4–5km and the information of the retrieval comes mainlyfrom the 0–3 and 3–7km layers. The peaks of the averagingkernels are not perfectly located at their nominal altitudees-pecially above 6km indicating that the retrieved state abovethat altitude is mainly sensitive to variations of the real stateat lower altitude. The degrees of freedom (DOFS) for thistypical retrieved profiles of IASI is 1.7. Compared to IASI,TES averaging kernels show better resolved structures witha finer resolution and the averaging kernels all peak at theirnominal height. The degree of freedom of 2.2 indicate twodecorrelated levels of information, one in the lowest tropo-sphere (0–3km) and another one in the free troposphere.

This situation is representative of tropical latitudes andin-dicates there a better sensitivity of TES toδD. The verticalsensitivity is however affected by local conditions such ashumidity content, temperature profiles, and surface temper-ature. Figure 4 shows the degrees of freedom for TES andIASI along the meridional gradient dataset. One can see thatthe IASI DOFS presents less variations than TES with lati-tude. More specifically, DOFS for IASI varies only between1.5 and 2 while TES DOFS vary between high values (2–2.3) at tropical latitudes and lower values (0.5–2) at higherlatitudes. The stability of theδD DOFS from IASI, as we ex-plain in Appendix A, is due to a compensating effect of bet-ter sensitivity with increasing surface temperature but lowersensitivity with increasing humidity. Yet the higher sensitiv-ity of IASI over TES at high latitude remains surprising andrequires further investigations.


For this comparison the retrievals of IASI and TES have been(1) a posteriori corrected for the cross-correlation interfer-ences betweenH2O and δD, (2) TES data have been re-gridded on IASI grid and (3) corrected for the use of differenta priori. To compute the expected agreement we use the quasiglobalSc computed from LMDZ model. Note that IASI andTES retrievals are not optimal with regard to the comparisonensemble defined bySc since they each use different a pri-ori covariance matrices. TheSc is more loose than the one(Sa) used in TES retrievals and more constrained than theone used in IASI retrievals. The sameSc is used for the en-tire intercomparison.

Figure 5 shows for the retrievals above the PIO dataset thetotal expected difference (black curve) from the comparisonIASI vs. TES and its different contributions from the obser-

Figure 4. TES (purple) and IASI (red) degrees of freedom for δD

along the meridional gradient.

and this is because the a priori covariance matrix (Sa) used in

the IASI retrieval is larger than the Sc used for the compari-

son. This error budget indicates that the direct comparison is

relevant in the free troposphere when it refers to the expected

natural variability of δD at global scale (dark blue bold line).

However at a more regional scale (here the tropical variabil-

ity given by the light blue bold line) the direct comparison is

less significant since the total expected difference (55 ‰) is

very close to the expected natural variability of δD (∼ 70 ‰).

The right panel of Fig. 5 shows a similar error budget but

accounting for the difference in sensitivity between instru-

ments. One can see that the smoothing contribution is sig-

nificantly reduced compared to the direct comparison. TES

observational error is also reduced mainly because the fine

structures have been removed by the IASI averaging kernels.

This does not, however, affect the total expected difference

since this error was already relatively small. The total ex-

pected difference is now only controlled by IASI’s observa-

tional error and is reduced to 38 ‰ at 3.5 km.

4.4 Expected vs. real differences

In the previous section we have described the differences ex-

pected from the comparison between TES and IASI based

on the theoretical error budgets of the different retrievals. In

this section we compare the theoretical error budget with the

real differences between TES and IASI δD retrieved profiles.

Those are taken as the SD of the difference TES-IASI in the

δD profiles and are plotted as a green line in Fig. 5. For the

direct comparison, we find that the real difference is lower

than the expected one below 7 km. This indicates that the dif-

ference TES-IASI at these altitudes is in agreement with the

theoretical error budget. The fact that the real difference ex-

ceeds the expected one above 7 km could be due to an under-

estimation of the IASI’s observational error (since all other

contributions are mostly negligible). When smoothing TES

retrieved profiles with IASI averaging kernels the real differ-

ences decrease in the free troposphere where the smoothing


0 20 40 60 80 100 1200

1

2

3

4

5

6

7

8

9

10TES smoothed with IASI AVK

Alti

tude

[km

]

Error in permil

Direct comparison

0 20 40 60 80 100 120

Total expected difference From IASI observational error From TES observational error From different vertical sensitivity (smoothing error) Global variability Tropical variability Real difference

Figure 5. Expected difference of the IASI and TES retrieval attropical latitudes and its different contribution sourcesaccordingto Eq. (8) for the direct comparison (left) and to Eq. (10) forthesmoothed comparison (right). The squareroot of the diagonal ele-ments of theSδ matrix as well as the different contribution matricesare plotted. Real differences are also shown in green.

vational and smoothing error. For the PIO dataset TES re-trievals have more sensitivity toδD, we thus smoothed TESretrieved profiles with IASI averaging kernels for the morelike with like comparison.

The direct comparison (no smoothing) is shown on the leftpanel of Fig. 5 and the smoothed comparison on the rightpanel. The total expected difference (black curve) of the di-rect comparison ranges from 120 ‰ at the lowest layer to55 ‰ at 4.5km, increasing again up to 68 ‰ at 7.5km. Thetotal expected difference is largely controlled by IASI obser-vational error in the 0–2km layer and above 6km. In the freetroposphere the difference of vertical sensitivities (smooth-ing error) between the two sounders also has an impact inthe direct comparison. Note that IASI’s observational errorexceeds theδD global variability above 7km and at 0.5km,and this is because the a priori covariance matrix (Sa) used inthe IASI retrieval is larger than theSc used for the compari-son. This error budget indicates that the direct comparisonisrelevant in the free troposphere when it refers to the expectednatural variability ofδD at global scale (dark blue bold line).However at a more regional scale (here the tropical variabil-ity given by the light blue bold line) the direct comparison isless significant since the total expected difference (55 ‰) isvery close to the expected natural variability ofδD (∼ 70 ‰).

The right panel of Fig. 5 shows a similar error budget butaccounting for the difference in sensitivity between instru-ments. One can see that the smoothing contribution is sig-

nificantly reduced compared to the direct comparison. TESobservational error is also reduced mainly because the finestructures have been removed by the IASI averaging kernels.This does however not affect the total expected differencesince this error was already relatively small. The total ex-pected difference is now only controlled by IASI’s observa-tional error and is reduced to 38 ‰ at 3.5km.


In the previous section we have described the differences ex-pected from the comparison between TES and IASI basedon the theoretical error budgets of the different retrievals. Inthis section we compare the theoretical error budget with thereal differences between TES and IASIδD retrieved profiles.Those are taken as the SD of the difference TES-IASI in theδD profiles and are plotted as green line in Fig. 5. For the di-rect comparison, we find that the real difference is lower thanthe expected one below 7km. This indicates that the differ-ence TES-IASI at these altitudes is in agreement with thetheoretical error budget. The fact that the real differenceex-ceeds the expected one above 7km could be due to an under-estimation of the IASI’s observational error (since all othercontributions are mostly negligible). When smoothing TESretrieved profiles with IASI averaging kernels the real differ-ences decrease in the free troposphere where the smoothingerror was important. As for the non-smoothed comparison,the real difference remains below the theoretical one over theentire 0–7km range.

While these figures are indicative of the error budget abovethe Indian and Pacific Oceans, the variations in sensitivityare such that the budget will depend on humidity and tem-perature conditions. However, we found that the results pre-sented in Fig. 5 are generally representative of all observa-tions above the oceans. In the following sub-section we pro-vide a more statistical view on the agreement between TESand IASI.

4.4.1 Statistics of the agreement between IASI and TES

In this subsection we compare IASI to TES statistically forthe MD and PIO datasets. We focus on retrievedδD valuesat 4.5km which is the altitude where IASI is the most sen-sitive above the oceans. For the PIO dataset we documentthe agreement for both the direct and the smoothed compar-isons. For the MD dataset we only consider the direct com-parison because the sensitivity of TES – depending on thelatitude (Fig. 4) – is sometimes higher and sometimes lowerthan IASI sensitivity. As we discussed in Sect. 4.2 the directcomparison is meaningful since the expected differences aresubstantially smaller than the natural variability at a globalscale. We summarize the results from the comparison be-tween IASI and TES in Table 1, in terms of1σ SD, slopeof the major axis regression (m) and Pearson correlation co-efficient (r).

Figure 5. Expected difference of the IASI and TES retrieval at

tropical latitudes and its different contribution sources according to

Eq. (8) for the direct comparison (left panel) and to Eq. (10) for the

smoothed comparison (right panel). The square root of the diago-

nal elements of the Sδ matrix as well as the different contribution

matrices are plotted. Real differences are also shown in green.

error was important. As for the non-smoothed comparison,

the real difference remains below the theoretical one over the

entire 0–7 km range.

While these figures are indicative of the error budget above

the Indian and Pacific oceans, the variations in sensitivity

are such that the budget will depend on humidity and tem-

perature conditions. However, we found that the results pre-

sented in Fig. 5 are generally representative of all observa-

tions above the oceans. In the following subsection we pro-

vide a more statistical view on the agreement between TES

and IASI.

4.4.1 Statistics of the agreement between IASI and TES

In this subsection we compare IASI to TES statistically for

the MD and PIO data sets. We focus on retrieved δD values

at 4.5 km which is the altitude where IASI is the most sen-

sitive above the oceans. For the PIO data set we document

the agreement for both the direct and the smoothed compar-

isons. For the MD data set we only consider the direct com-

parison because the sensitivity of TES – depending on the

latitude (Fig. 4) – is sometimes higher and sometimes lower

than IASI sensitivity. As we discussed in Sect. 4.2 the direct

comparison is meaningful since the expected differences are

substantially smaller than the natural variability at a global



Table 1. Comparison between IASI and TES δD at different heights for the PIO and MD data sets. σ (diff) is the SD of the difference between

TES and IASI, in ‰. r is the Pearson correlation coefficient and m is the slope of the major axis regression TES vs. IASI (a value of m

greater than one indicates that TES variability is greater than IASI variability). Direct comparison∗ is for the comparison restricted to the

TES and IASI data having similar sensitivities (see text for details).

Data set Altitude [km] m r σ (diff) [‰]

Direct Smoothed Direct Smoothed Direct Smoothed

PIO

0.5 0.09 6.24 0.13 0.30 91 72

2.5 0.93 1.73 0.41 0.44 44 34

3.5 1.18 1.12 0.50 0.55 41 30

4.5 1.21 0.81 0.55 0.61 43 35

5.5 1.27 0.79 0.57 0.39 42 41

8.5 0.22 4.27 0.25 0.25 66 50

Direct Direct∗ Direct Direct∗ Direct Direct∗

MD

0.5 0.38 0.37 0.28 0.27 71 72

2.5 0.80 0.93 0.60 0.61 56 54

3.5 0.98 1.18 0.67 0.73 49 35

4.5 0.95 1.02 0.62 0.76 46 37

5.5 1.04 1.12 0.47 0.59 68 50

8.5 0.16 0.16 0.29 0.40 84 72

scale. We summarize the results from the comparison be-

tween IASI and TES in Table 1, in terms of 1σ SD, slope

of the major axis regression (m) and Pearson correlation co-

efficient (r).

For the PIO data set we found a SD of the differ-

ence of 43 ‰ for the direct comparison which decreases to

35 ‰ when TES retrievals are smoothed with IASI averag-

ing kernels. These values are in line with the theoretical es-

timations of the error. The correlation coefficients have val-

ues of 0.55 and 0.61 for the direct and smoothed comparison

respectively. These values for the correlation are driven by

the low signal-to-noise ratio of the compared quantities. In-

deed, we calculated that we would expect a correlation coef-

ficient no larger than 0.7 if we were to compare TES retrieved

profiles with the same profiles perturbed by a random noise

of 35 ‰. The correlation coefficient found for the IASI-TES

comparison is coherent with this and demonstrates that TES

and IASI δD co-vary well together. The slopes of the regres-

sion curves indicate that the TES variability is higher than the

IASI one before the smoothing, but lower when the smooth-

ing is taken into account.

For the MG data set, we only report statistics of the di-

rect comparison but we distinguish a case with all collocated

measurements and another (column “Direct” in Table 1) with

only the collocated retrievals which have similar degrees of

freedom (DOFSIASI=DOFSTES± 0.3). When all the mea-

surements are taken into account we find at 4.5 km a SD of

46 ‰ in agreement with the theoretical error estimate. The

correlation coefficient of 0.67 for this data set is significantly

higher than for the PIO data set due to the larger amplitude of

variations of δD along the meridional gradient (higher signal-

to-noise ratio). The SD of the differences and the correlation

coefficient are improved to 37 ‰ and 0.76 when only consid-

ering retrievals with similar degrees of freedom.

4.4.2 Systematic difference between IASI and TES

We calculate the mean bias for the 3–6 km layer as the

mean difference between IASI and TES. We find a bias of

+20 ‰ when using the non-smoothed data from PIO and

MD data sets together and a bias of −3 ‰ when TES re-

trievals are smoothed with IASI averaging kernels (consid-

ering only collocated measurements where TES sensitivity

is higher than IASI). The significant bias found for the non-

smoothed data is probably due to the low vertical resolution

of IASI. The averaging kernels indicate indeed that IASI is

sensitive to a thicker layer of the atmosphere than TES which

is likely to give a more enriched signal because of the mixing

with information from the lowest layers. The bias when TES

is smoothed according to IASI sensitivity is almost negligi-

ble. Although this may appear an encouraging result it is also

questionable as TES data V005 are bias corrected, for uncer-

tainties in spectroscopic line strength (Herman et al., 2014;

Worden et al., 2011). As we use the same spectroscopic pa-

rameters for IASI retrieval, the high level of agreement could

suggest another origin than spectroscopy for the bias applied

to TES δD.

An accurate estimation of the bias on δD retrieved profiles

from IASI would require further investigations including di-

rect comparisons with available in situ profiles of δD in the

troposphere (Schneider et al., 2014; Herman et al., 2014).

Here, the purpose is to qualitatively document the bias be-

tween the different δD products.



4.5 Spatio-temporal variations of the δD–log(q)

relation

For the MD data set we analyse δD–q relations at 4.5 km

from each instrument for bins of 10◦, in terms of the correla-

tion coefficient between δD and log(q) and the slope of the

regression curve δD vs. log(q). The variations of these pa-

rameters along the meridional gradient are shown in Fig. 6.

The two instruments present very coherent variations of the

δD–q relation. We also see that for each instrument the cor-

relation coefficient δD–q varies strongly with latitude. In the

case of a perfect Rayleigh distillation, δD would have a corre-

lation coefficient of 1 with log(q) (Eq. 11). The values found

for TES and IASI are the closest to 1 at 5◦ S and significantly

lower at other latitudes, indicating that processes different

than Rayleigh distillation are at play.

With the PIO data set we investigate both spatial and tem-

poral variations of the δD–q relation at 3.5 and 5.5 km. We

distinguish 3 different areas each of 30◦ longitudes (from

west to east: A, B and C) in the entire data set and we

also separate winter (DJF) from summer (JJA). The collo-

cated pairs corresponding to these categories are plotted in

Fig. 7. In this case, TES profiles (H2O and δD) have been

smoothed with IASI averaging kernels. We also plot the

Rayleigh distillation curve (purple line) according to Eq. (11)

with q0=3× 10−2 mol mol−1 and δD0=−70 ‰ which de-

termine a lower limit for Rayleigh processes occurring at

these latitudes. Above this curve, Rayleigh processes for

drier source term and mixing processes can explain the iso-

topic composition. Below, only depleting processes can be at

the origin of the observed values.

At 5.5 km, the seasonal and longitudinal patterns observed

by TES and IASI are very similar. In particular one can see

that for zone A the difference between the high δD values

in summer and low values in winter are very different than

what is observed in zone B with a majority of points below

the Rayleigh distillation curve in DJF. In zone C, both instru-

ments show a clear amount effect (enhancement of the de-

pletion with high water vapour content) although IASI H2O

values seem slightly drier than TES.

At 3.5 km the seasonal and longitudinal variations are co-

herent between the two instruments, but the general agree-

ment is less good than at 5.5 km. For example, an amount

effect is well observed for each zone for TES while it can

only be clearly seen in IASI retrievals in zone C. The reason

of these differences is probably due to the better sensitivity

of TES at these altitudes and below.

4.6 Comparison instrument–model

One of the specific applications of satellite measurements

of δD is to evaluate performances of isotope-enabled GCM.

TES observations have for example previously been used to

evaluate GCM at a global scale (Yoshimura et al., 2011; Risi

et al., 2012b) while IASI observations have been compared


0.2

0.4

0.6

0.8-30° -20° -10° 0° 10° 20° 30° 40° 50°

-30 -20 -10 0 10 20 30 40 500

100

200

300

400

500

r (lo

g(q)

, D

)

TES IASI

slop

e (m

)

Latitude

TES IASI

Figure 6.Top panel: variation of the correlation coefficient betweenlog(q) and δD at 4.5km along the meridional gradient for TES(purple) and IASI (red). Bottom panel: variation of the slope of thelinear regression betweenlog(q) andδD (spatial and temporal vari-ability within the 10◦ bin) along the meridional gradient for TES(purple) and IASI (red).

For the PIO dataset we found a SD of the difference of43 ‰ for the direct comparison which decreases to 35 ‰when TES retrievals are smoothed with IASI averaging ker-nels. These value are in line with the theoretical estimationsof the error. The correlation coefficients have values of 0.55and 0.61 for the direct and smoothed comparison respec-tively. These values for the correlation are driven by the lowsignal to noise ratio of the compared quantities. Indeed, wecalculated that we would expect a correlation coefficient notlarger than 0.7 if we were to compare TES retrieved profileswith the same profiles perturbed by a random noise of 35 ‰.The correlation coefficient found for the IASI-TES compari-son is coherent with this and demonstrate that TES and IASIδD co-vary well together. The slopes of the regression curvesindicate that the TES variability is higher than IASI one be-fore the smoothing, but lower when the smoothing is takeninto account.

For the MG dataset, we only report statistics of the di-rect comparison but we distinguish a case with all collocatedmeasurements and another (bottom row in Table 1) with onlythe collocated retrievals which have similar degrees of free-dom (DOFSIASI = DOFSTES± 0.3). When all the measure-ments are taken into account we find a SD of 46 ‰ in agree-ment with the theoretical error estimate. The correlation co-efficient of 0.67 for this dataset is significantly higher thanfor the PIO dataset due to the larger amplitude of variationsof δD along the meridional gradient (higher signal to noiseratio). The SD of the differences and the correlation coeffi-cient are improved to 37 ‰ and 0.76 when only consideringretrievals with similar degrees of freedom.

4.4.2 Systematic difference between IASI and TES

We calculate the mean bias for the 3–6km layer as themean difference between IASI and TES. We find a bias of+20 ‰ when using the non-smoothed data from PIO andMD datasets together and a bias of−3 ‰ when TES re-trieval are smoothed with IASI averaging kernels (consid-ering only collocated measurements where TES sensitivityis higher than IASI). The significant bias found for the non-smoothed data is probably due to the low vertical resolutionof IASI. The averaging kernels indicate indeed that IASI issensitive to a thicker layer of the atmosphere than TES wichis likely to give a more enriched signal because of the mixingwith information from the lowest layers. The bias when TESis smoothed according to IASI sensitivity is almost negligi-ble. Although this may appear an encouraging result it is alsoquestioning as TES data V005 are bias corrected, for uncer-tainties in spectroscopic line strength (Herman et al., 2014;Worden et al., 2011). As we use the same spectroscopic pa-rameters for IASI retrieval, the high level of agreement couldsuggest another origin than spectroscopy for the bias appliedto TESδD.

An accurate estimation of the bias onδD retrieved pro-files from IASI would require further investigations includ-ing direct comparisons with available in situ profiles ofδD in the troposphere (Schneider et al., 2014; Herman et al.,2014). Here, the purpose is to qualitatively document the biasbetween the differentδD products.

4.5 Spatio-temporal variations of theδD–log(q) rela-tion

For the MD dataset we analyseδD–q relations at 4.5km fromeach instrument for bins of 10◦, in terms of the correlationcoefficient betweenδD and log(q) and the slope of the re-gression curveδD vs. log(q). The variations of these param-eters along the meridional gradient are shown in Fig. 6. The2 instruments present very coherent variations of theδD–qrelation. We also see that for each instrument the correlationcoefficientδD–q varies strongly with latitude. In the case ofa perfect Rayleigh distillation,δD would have a correlationcoefficient of 1 withlog(q) (Eq. 11). The values found forTES and IASI are the closest to 1 at 5◦ S and significantlylower at other latitudes, indicating that processes differentthan Rayleigh distillation are at play.

With PIO dataset we investigate both spatial and temporalvariations of theδD–q relation at 3.5 and 5.5km. We dis-tinguish 3 different areas each of 30◦ longitudes (from Westto East: A, B and C) in the entire dataset and we also sepa-rate winter (DJF) from summer (JJA). The collocated pairscorresponding to these categories are plotted in Fig. 7. Inthis case, TES profiles (H2O andδD) have been smoothedwith IASI averaging kernels. We also plot the Rayleighdistillation curve (purple line) according to Eq. (11) withq0 = 3.10−2 mol mol−1 andδD0 =−70 ‰ which determine

Figure 6. Top panel: variation of the correlation coefficient between

log(q) and δD at 4.5 km along the meridional gradient for TES (pur-

ple) and IASI (red). Bottom panel: variation of the slope of the lin-

ear regression between log(q) and δD (spatial and temporal vari-

ability within the 10◦ bin) along the meridional gradient for TES

(purple) and IASI (red).

to LMDZ at regional scales (Lacour et al., 2012; Pommier

et al., 2014). Moreover, because of the integrated nature of

the isotopologues ratio, models are often useful for interpret-

ing the measurements. We take the opportunity of this cross-

validation study to briefly investigate the differences that can

arise from the comparison of a GCM with TES or with IASI.

The goal here is twofold: (1) document how the instruments

will differ in instrument–model comparisons and (2) illus-

trate the impact of IASI sampling in model–observation com-

parisons.

We use the GCM LMDZ (Risi et al., 2010) that we con-

sider as the reality. We also consider retrieved profiles from

IASI and TES as the reality. The model outputs are thus not

smoothed with any instrument vertical sensitivity. This is not

an usual approach but it allows one to have an idea of how

close observations are from reality. Indeed by not taking the

instrument sensitivity into account during the comparison,

retrievals are considered as an estimate of the true state with

an error contribution due to the smoothing, rather than es-

timate of a state smoothed by the averaging kernels (which

is done when smoothing model outputs with averaging ker-

nels) (Rodgers, 2000). We use the Pearson correlation coef-

ficient as a metric of the agreement between LMDZ and the

retrieved δD between 3 and 6 km, and the results are reported

in Table 2. We have subdivided the MD data set in 2 different

latitudinal groups according to the TES sensitivity: tropical

observations located between 15◦ S and 15◦ N and subtropi-

cal to mid-latitude observations located between 15 and 45◦


J.-L. Lacour et al.: Cross-validation of IASI/MetOp δD retrievals 145710 J.-L. Lacour et al.: Cross-validation of IASI/MetOp δD retrievals

l)k)

h)

j)

i)g)

f)e)d)

c)b)

CBAIA

SITE

SD

[per

mil]

JJA DJF

Longitude

At 5.5 kma)

At 3.5 km

q vmr [mmol/mol]

155°E125°E95°E65°ETE

S

IASI

D [p

erm

il]

q vmr [mmol/mol]q vmr [mmol/mol]

q vmr [mmol/mol] q vmr [mmol/mol] q vmr [mmol/mol]

D [p

erm

il]D

[per

mil]

Figure 7. Spatio temporal variations of theδD–q relation for the PIO dataset. RetrievedδD andq are separated in 3 longitudinal boxes of30◦ (A, B, C) from 65 to 155◦ E to highlight spatial variations. Winter (DJF, blue squares) and summer (JJA, red squares) are also separatedto highlight seasonal variations.(a–c) correspond to IASI retrieved values at 5.5km, and (g–i) to IASI retrieved values at 3.5km. (d–f)correspond to TES retrieved values at 5.5km, and(j–l) to TES retrieved values at 3.5km. The purple line represents a Rayleigh distillationcurve computed according to Eq. (11) withq0 = 0.03 mol mol−1 andδD0 =−70 ‰.

a lower limit for Rayleigh processes occurring at these lati-tudes. Above this curve, Rayleigh processes for drier sourceterm and mixing processes can explain the isotopic composi-tion. Below, only depleting processes can be at the origin ofthe observed values.

At 5.5km, the seasonal and longitudinal patterns observedby TES and IASI are very similar. In particular see that forzone A the difference between the highδD values in sum-mer and low values in winter are very different than whatis observed in zone B with a majority of points below theRayleigh distillation curve in DJF. In zone C, both instru-ments show a clear amount effect (enhancement of the de-pletion with high water vapour content) although IASIH2Ovalues seem slightly drier than TES.

At 3.5km the seasonal and longitudinal variations are co-herent between the two instruments, but the general agree-ment is less good than at 5.5km. For example, an amounteffect is well observed for each zone for TES while it canonly be clearly seen in IASI retrievals in zone C. The reason

of these differences is probably due to the better sensitivityof TES at these altitudes and below.

4.6 Comparison instrument–model

One of the specific applications of satellite measurements ofδD is to evaluate performances of isotopes-enabled GCM.TES observations have for example previously been usedto evaluate GCM at a global scale (Yoshimura et al., 2011;Risi et al., 2012b) while IASI observations have been com-pared to LMDZ at regional scales (Lacour et al., 2012;Pommier et al., 2014). Moreover because of the integratednature of the isotopologues ratio, models are often usefulto interpret the measurements. We take the opportunity ofthis cross-validation study to briefly investigate the differ-ences that can arise from the comparison of a GCM withTES or with IASI. The goal here is twofold: (1) documenthow the instruments will differ in instrument–model com-parisons and (2) illustrate the impact of IASI sampling inmodel–observation comparisons.

Figure 7. Spatio-temporal variations of the δD–q relation for the PIO data set. Retrieved δD and q are separated in three longitudinal

boxes of 30◦ (A, B, C) from 65 to 155◦ E to highlight spatial variations. Winter (DJF, blue squares) and summer (JJA, red squares) are

also separated to highlight seasonal variations. (a–c) correspond to IASI retrieved values at 5.5 km, and (g–i) to IASI retrieved values at

3.5 km. (d–f) correspond to TES retrieved values at 5.5 km, and (j–l) to TES retrieved values at 3.5 km. The purple line represents a Rayleigh

distillation curve computed according to Eq. (11) with q0= 0.03 mol mol−1 and δD0=−70 ‰.

in both hemispheres. Note also that the comparison TES-

LMDZ considers one TES observation vs. one LMDZ cell,

and that this results in a worse agreement than previous stud-

ies that generally average TES observations over time and/or

space.

For the PIO data set the values found in Table 2 show

that the comparison LMDZ vs. TES shows a better correla-

tion coefficient (0.26) than for the LMDZ vs. IASI compar-

ison (0.15). This is also true for the MD data set at tropical

latitudes with a slightly higher correlation coefficient of 0.46

and 0.30 for TES and IASI respectively. In contrast, for the

subtropical to mid-latitude observations, we find a better cor-

relation coefficient for the LMDZ vs. IASI comparison (0.42)

compared to the LMDZ vs. TES comparison (0.30). The bet-

ter agreement between LMDZ (reality) and IASI above 15◦

makes sense since we observe a significant decrease in TES

sensitivity at these latitudes (see Fig. 4).

With the PIO data set we investigate how the number of

available observations can impact a model–instrument com-

parison. This is interesting because the number of daily IASI

observations in one model cell (3.75◦× 2.53◦) on a given day

can be very large. Indeed, from the histogram in Fig. 8 we

see that there is about 25 % of the LMDZ cells that contains

1 to 10 observations and about 12 % that contains 90 obser-

vations or more. The average number of observations avail-

able per cell is 46. The correlation coefficient between IASI

and LMDZ increases compared to a one-to-one comparison,

due on one hand to the decrease of the observational error

by√(N) when averaging several observations, and on the

other hand to the better sampling of the model cell by IASI

that allows to capture the variability of δD within this cell.



Table 2. Pearson correlation coefficients between LMDZ and TES/IASI for the PIO and MD data sets at 4.5 km.

Orbit Comparison r N

Pacific and Indian oceans

DayTES vs. LMDZ 0.26 5636

IASI vs. LMDZ 0.15 5636

NightTES vs. LMDZ 0.25 5636

IASI vs. LMDZ 0.16 5 636

Meridional gradient

Tropics: 15◦ S–15◦ NTES vs. LMDZ 0.46 556

IASI vs. LMDZ 0.30 556

Subtropics to mid-latitudes: 15–45◦TES vs. LMDZ 0.30 591

IASI vs. LMDZ 0.42 591J.-L. Lacour et al.: Cross-validation of IASI/MetOp δD retrievals 11

10 20 30 40 50 60 70 80 900

5

10

20

25

Perc

enta

ge

Number of observations

0.20

0.25

0.30

0.35

0.40

0.45

0.50

r (LMD

Z,IASI)

mean number of obs.

Figure 8. On the background (purple): histogram in percent ofthe number of IASI observations available per model cell fortheLMDZ-IASI comparison (daily values) above the Pacific and Indianoceans dataset. In green, correlation coefficient betweenδD simu-lated by LMDZ and averagedδD from all observations available inthe cell in function of the number of observations available.

We use the GCM LMDZ (Risi et al., 2010) that we con-sider as the reality. We also consider retrieved profiles fromIASI and TES as the reality. The model outputs are thus notsmoothed with any instrument vertical sensitivity. This isnotan usual approach but it allows having an idea of how closeobservations are from reality. Indeed by not taking the in-strument sensitivity into account during the comparison, re-trievals are considered as an estimate of the true state withan error contribution due to the smoothing, rather than es-timate of a state smoothed by the averaging kernels (whichis done when smoothing models outputs with averaging ker-nels) (Rodgers, 2000). We use the Pearson correlation co-efficient as a metric of the agreement between LMDZ andthe retrievedδD between 3 and 6km, and the results are re-ported in Table 2. We have subdivided the MD dataset in 2different latitudinal groups according to the TES sensitivity:tropical observations located between 15◦ S and 15◦ N andsubtropical to mid-latitudes observations located between 15and 45◦ in both hemisphere. Note also that the comparisonTES-LMDZ considers one TES observation vs. one LMDZcell, and that this results in a worse agreement than previousstudies that generally average TES observations over timeand/or space.

For the PIO dataset the values found in Table 2 show thatthe comparison LMDZ vs. TES shows a better correlationcoefficient (0.26) than for the LMDZ vs. IASI comparison(0.15). This is also true for the MD dataset at tropical lati-tudes with slightly higher correlation coefficient of 0.46 and0.30 for TES and IASI respectively. In contrast, for the sub-tropical to mid-latitudes observations, we find a better corre-lation coefficient for the LMDZ vs. IASI comparison (0.42)compared to the LMDZ vs. TES comparison (0.30). The bet-ter agreement between LMDZ (reality) and IASI above 15◦

makes sense since we observe a significant decrease in TESsensitivity at these latitudes (see Fig. 4).

With the PIO dataset we investigate how the number ofavailable observations can impact a model–instrument com-parison. This is interesting because the number of daily IASIobservations in one model cell (3.75◦× 2.53◦) on a givenday can be very large. Indeed, from the histogram in Fig. 8we see that there is about 25 % of the LMDZ cells that con-tains 1 to 10 observations and about 12 % that contains 90observations or more. The average number of observationsavailable per cell is 46. The correlation coefficient betweenIASI and LMDZ increases compared to a one to one com-parison, due on one hand, to the decrease of the observa-tional error by

√(N) when averaging several observations

and on the other hand to the better sampling of the model cellby IASI that allows to capture the variability ofδD withinthis cell. When including less than 10 observations the cor-relation coefficient is below 0.25 but it increases up to 0.5when including more than 90 observations. This is importantand suggests that model–observation comparison could belargely improved by exploiting the unprecedented samplingof IASI.

To conclude this section we show in Figure 9 how the twoinstruments mapδD andH2O variations above the Pacificand Indian oceans. Since TES sampling is relatively sparse,collocated retrieved values ofδ at 5.5 km are averaged onthree months periods on cells of 2.5◦×2.5◦. In that figure,TES retrieved values are not smoothed with IASI averagingkernel in order to ensure that TES retrieved profiles are notdegraded to a lower sensitivity.δ variations are representedin relative values with respect to the mean of the dataset toavoid the impact of the bias on the comparison. One can seeon Figure 9 that seasonal distributions ofδ (panels a) and b))are very similar for both instruments with the spatial struc-tures of enriched and depleted zones being quasi identical.The same comparison with water vapour shows that humidand dry structures are also very similar in the TES and IASIretrievals and that the spatial structures ofδ andH2O exhibitvery different patterns.

5 Comparison IASI vs. FTIR

5.1 Datasets description and collocation criterion

Three ground-based NDACC-FTIRs of the MUSICA net-work have been selected at different latitudes: Kiruna, Karl-sruhe and Izana. We consider FTIR and IASI observationscollocated when there are no more than three hours betweenthe two measurements and when the IASI observation is lo-cated in a radius of 1.5◦ around the measurement sites. Wehave applied the same approach than for the IASI-TES com-parison to make the comparison the most significant possibleand when several IASI observations fulfilled the collocationcriteria, we have averaged them to reduce the observational

Figure 8. On the background (purple): histogram in percent of

the number of IASI observations available per model cell for the

LMDZ-IASI comparison (daily values) above the Pacific and Indian

oceans data set. In green, the correlation coefficient between δD

simulated by LMDZ and averaged δD from all observations avail-

able in the cell in function of the number of observations available.

When including less than 10 observations the correlation co-

efficient is below 0.25 but it increases up to 0.5 when includ-

ing more than 90 observations. This is important and sug-

gests that model–observation comparison could be largely

improved by exploiting the unprecedented sampling of IASI.

To conclude this section we show in Fig. 9 how the two

instruments map δD and H2O variations above the Pacific

and Indian oceans. Since TES sampling is relatively sparse,

collocated retrieved values of δ at 5.5 km are averaged on

three-month periods on cells of 2.5◦× 2.5◦. In that figure,

TES retrieved values are not smoothed with IASI averaging

kernels in order to ensure that TES retrieved profiles are not

degraded to a lower sensitivity. δ variations are represented

in relative values with respect to the mean of the data set

to avoid the impact of the bias on the comparison. One can

see on Fig. 9 that seasonal distributions of δ (panels a and b)

are very similar for both instruments with the spatial struc-

tures of enriched and depleted zones being quasi identical.

The same comparison with water vapour shows that humid

and dry structures are also very similar in the TES and IASI

retrievals and that the spatial structures of δ and H2O exhibit

very different patterns.

5 Comparison IASI vs. FTIR

5.1 Data sets description and collocation criterion

Three ground-based NDACC-FTIRs of the MUSICA net-

work have been selected at different latitudes: Kiruna, Karl-

sruhe and Izaña. We consider FTIR and IASI observations

collocated when there are no more than three hours between

the two measurements and when the IASI observation is lo-

cated in a radius of 1.5◦ around the measurement sites. We

have applied the same approach as that for the IASI-TES

comparison to make the comparison the most significant pos-

sible and when several IASI observations fulfilled the collo-

cation criteria, we have averaged them to reduce the obser-

vational error. FTIRs and IASI δD profiles correspond to the

years 2010, 2011 and 2012.


Representative averaging kernels for the three ground-based

FTIR are plotted in Fig. 10 in comparison with the corre-

sponding IASI averaging kernels. The IASI averaging ker-

nels exhibit similar sensitivity profiles from high latitude

to subtropical latitudes with degrees of freedom of 1.7, 1.9

and 1.7 at Kiruna, Karlsruhe and Izaña, respectively. As dis-

cussed before, the IASI retrieval sensitivity to δD comes

from the free troposphere and also from the lowest lay-

ers of the atmosphere. At Arctic latitude (Kiruna) the IASI

sensitivity close to the surface is the highest, probably ow-

ing to a favourable thermal contrast (Pommier et al., 2014).

The FTIR averaging kernels exhibit similar sensitivity than

IASI in terms of information content with DOFS of 1.5, 1.2

and 1.7 for Kiruna, Karlsruhe and Izaña respectively. The

profiles of vertical sensitivities however significantly differ:

Kiruna and Karlsruhe FTIR are mainly sensitive in the first



Figure 9. Seasonal distributions of δD and H2O for the PIO data set at 5.5 km (2010) as seen by TES (a, c) and by IASI (b, d). Only

collocated pairs are used to compute the seasonal averages. The values are relative differences with respect to the mean of each data set.

Figure 10. Averaging kernels in {δD} proxy space for the three different sites of the comparison: (a) and (b) for Kiruna, (c) and (d) for

Karlsruhe and (e) and (f) for Izaña. (a), (c) and (e) corresponding to IASI and (b), (d) and (f) to the ground-based FTIR.

layers of the atmosphere and at Izaña, the FTIR exhibits sen-

sitivity in the 3–5 km layer and also above 6 km.


The expected differences for the direct IASI-FTIR compari-

son are calculated according to Eq. (8) in the same way as for

TES comparisons. The same Sc covariance matrix was also

used. To evaluate the significance of the cross-validation, we

compare the expected differences (black curve) in Fig. 11 at

the three sites with the global δD variability (dark blue curve)

but also with the regional variabilities (respectively green,

brown and cyan curves for Kiruna, Karlsruhe and Izaña).

The variabilities were calculated from LMDZ model pro-

files within a given 20◦ latitudinal band. We can see from

Fig. 11 that Kiruna and Karlsruhe present very similar error

budgets mainly controlled by IASI observational error while

at Izaña the smoothing error also impacts the expected dif-

ference. For this comparison, we found that the smoothing of

one instrument averaging kernels with the other was not pro-

ductive. The comparison can thus not be optimized to take

into account the different vertical sensitivities of the two in-

struments and only the direct comparison is discussed next.




error. FTIRs and IASIδD profiles correspond to the years2010, 2011 and 2012.


Representative averaging kernels for the three ground-basedFTIR are plotted in Fig. 10 in comparison with the corre-sponding IASI averaging kernels. The IASI averaging ker-nels exhibit similar sensitivity profiles from high latitudeto subtropical latitudes with degrees of freedom of 1.7, 1.9and 1.7 at Kiruna, Karlsruhe and Izana, respectively. As dis-cussed before, the IASI retrieval sensitivity toδD is comingfrom the free troposphere and also from the lowest layersof the atmosphere. At Arctic latitude (Kiruna) the IASI sen-sitivity close to the surface is the highest, probably owingto a favourable thermal contrast (Pommier et al., 2014). TheFTIR averaging kernels exhibit similar sensitivity than IASIin terms of information content with DOFS of 1.5, 1.2 and1.7 for Kiruna, Karlsruhe and Izana respectively. The profilesof vertical sensitivities however significantly differ: Kirunaand Karlsruhe FTIR are mainly sensitive in the first layers ofthe atmosphere and at Izana, the FTIR exhibits sensitivity inthe 3 to 5km layer and also above 6km.


The expected differences for the direct IASI-FTIR compari-son are calculated according to Eq. (8) in the same way as forTES comparisons. The sameSc covariance matrix was alsoused. To evaluate the significance of the cross-validation,wecompare the expected differences (black curve) in Fig. 11 atthe three sites with the globalδD variability (dark blue curve)but also with the regional variabilities (respectively green,brown and cyan curves for Kiruna, Karlsruhe and Izana).The variabilities were calculated from LMDZ model pro-files within a given 20◦ latitudinal band. We can see fromFig. 11 that Kiruna and Kaslruhe present very similar errorbudgets mainly controlled by IASI observational error whileat Izana the smoothing error also impacts the expected dif-ference. For this comparison, we found that the smoothing ofone instrument averaging kernels with the other was not pro-ductive. The comparison can thus not be optimized to takeinto account the different vertical sensitivities of the two in-struments and only the direct comparison is discussed next.

The error difference budgets are shown in Fig. 11, repre-sentative of an average of the error budgets of a one monthperiod. We note from Fig. 11 that the observational errorsfrom the FTIR and from IASI are very different. For bothsites the FTIR observational error is indeed lower than 20 ‰throughout the vertical profile while IASI observational er-ror ranges from 20 ‰ around 3–4km to 80 ‰ in the uppertroposphere. It is interesting here that the IASI observationalerror is significantly smaller in the lower troposphere com-pared to the error budget discussed previously in Fig. 5. This

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Alti

tude

, km

expected difference [permil]

Regional variability (40°N-60°N)

IzanaKalsruheKiruna

Tropical variability

Figure 11.Same as Fig. 5 but for the comparison between IASI andthe ground-based FTIR of Kiruna (left) and Karlsruhe (right).

is mainly due to the fact that the two sites are on the conti-nent, where the sensitivity of IASI to near surfaceδD is betterdue to more favourable thermal contrast. It is also interestingto notice that the IASI observational error in the lower tropo-sphere does not exceed theδD variability at global scale andat a regional scale. This indicates that IASI retrievals pro-vide relevantδD measurements in these conditions even inthe boundary layer.

For Kiruna and Karlsruhe, the total expected difference islowest in the free troposphere (about 20 ‰ for Kiruna and35 ‰ for Karlsruhe) and highest in the upper troposphere.Compared to the regional expected variability ofδD, thecomparison might be considered useful below 5km sinceboth budgets show expected difference lower than theδDvariability at regional level.

At Izana, the total expected difference ranges from 90 ‰at 2.5km to about 60 ‰ at 4.5km. At higher altitude the totalexpected error exceeds the natural variability ofδD. In thiscase it is not only the IASI observational error that dominatesthe total difference expected. From 2.5 to 4km the smooth-ing error is indeed large and contributes with both IASI andFTIR observational error. From 4 to 6km the FTIR observa-tional error becomes less important while at higher altitudeit is the IASI observational error that becomes predominantagain. The comparison appears significant with respect to thevariability of δD at global scale but not at regional scale.


The real difference between the 2 instruments are calculatedas the SD of the difference for each level for the correspond-ing time period of the computed error budgets. As in theIASI vs. TES comparison the SD profiles are plotted (greencurves) on the error budget in Fig. 11 for the three sites.

We find that the SD profiles of the difference follow wellthe error profiles expected from the theoretical error (al-though with small deviations at Karlsruhe and Izana). This

Figure 11. Same as Fig. 5 but for the comparison between IASI and the ground-based FTIR of Kiruna (left panels) and Karlsruhe (right

panels).

The error difference budgets are shown in Fig. 11, repre-

sentative of an average of the error budgets of a one month

period. We note from Fig. 11 that the observational er-

rors from the FTIR and from IASI are very different. For

both sites the FTIR observational error is indeed lower than

20 ‰ throughout the vertical profile while IASI observa-

tional error ranges from 20 ‰ around 3–4 km to 80 ‰ in

the upper troposphere. It is interesting here that the IASI ob-

servational error is significantly smaller in the lower tropo-

sphere compared to the error budget discussed previously in

Fig. 5. This is mainly due to the fact that the two sites are

on the continent, where the sensitivity of IASI to near sur-

face δD is better due to more favourable thermal contrast. It

is also interesting to note that the IASI observational error

in the lower troposphere does not exceed the δD variability

at a global scale and at a regional scale. This indicates that

IASI retrievals provide relevant δD measurements in these

conditions even in the boundary layer.

For Kiruna and Karlsruhe, the total expected difference is

lowest in the free troposphere (about 20 ‰ for Kiruna and

35 ‰ for Karlsruhe) and highest in the upper troposphere.

Compared to the regional expected variability of δD, the

comparison might be considered useful below 5 km since

both budgets show expected difference lower than the δD

variability at regional level.

At Izaña, the total expected difference ranges from 90 ‰ at

2.5 km to about 60 ‰ at 4.5 km. At higher altitude the total

expected error exceeds the natural variability of δD. In this

case it is not only the IASI observational error that dominates

the total difference expected. From 2.5 to 4 km the smooth-

ing error is indeed large and contributes with both IASI and

FTIR observational error. From 4 to 6 km the FTIR observa-

tional error becomes less important while at higher altitude

it is the IASI observational error that becomes predominant

again. The comparison appears significant with respect to the

variability of δD at a global scale but not regional scale.


The real difference between the two instruments is calculated

as the SD of the difference for each level for the correspond-

ing time period of the computed error budgets. As in the

IASI vs. TES comparison the SD profiles are plotted (green

curves) on the error budget in Fig. 11 for the three sites.

We find that the SD profiles of the difference follow well

the error profiles expected from the theoretical error (al-

though with small deviations at Karlsruhe and Izaña). This

indicates that the error budget and sensitivity characteriza-

tion are realistic and correct.

5.5 Statistics of the agreement between FTIRs and

IASI

Figure 12 gives a scatter plot of IASI vs. FTIR observations

for the three different sites. The data refer to the δD at 2.5 km

for Kiruna and Karlsruhe and at 5.5 km for Izaña, which are

the altitudes for which the two instruments share the most

sensitivity. The SD of the difference between IASI and FTIR

for all the collocated measurements are 24, 35 and 55 ‰ for

Kiruna, Karlsruhe and Izaña respectively which is in very

good agreement with theoretical expected difference. The


J.-L. Lacour et al.: Cross-validation of IASI/MetOp δD retrievals 146114 J.-L. Lacour et al.: Cross-validation of IASI/MetOp δD retrievals

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r = 0.68std = 55 permilb = 47 permil

FTIR

IASI

direct comp.

direct comp.

Izana

Kalsruhe

m = 1.45r = 0.75std = 24 permilb = 107 permil

IASI

Kiruna

direct comp.

Figure 12. Scatter plot of IASI vs. FTIRδD from top to bottomfor Kiruna (2.5km), Karlsruhe (2.5km) and Izana (5.5km). Wegive the slopes of the major axis regression curves (m), the Pearsoncorrelation coefficient (r), the SD of the difference and the meanbias (b, FTIR-IASI).

indicates that the error budget and sensitivity characteriza-tion are realistic and correct.

5.5 Statistics of the agreement between FTIRs and IASI

Figure 12 gives a scatter plot of IASI vs. FTIR observationsfor the three different sites. The data refer to theδD at 2.5kmfor Kiruna and Karlsruhe and at 5.5km for Izana, which arethe altitudes for which the two instruments share the mostsensitivity. The SD of the difference between IASI and FTIRfor all the collocated measurements are 24, 35 and 55 ‰ forKiruna, Karlsruhe and Izana respectively which is in verygood agreement with theoretical expected difference. Thecorrelation coefficients of 0.75, 0.77 and 0.68 indicate thatδD retrieved from both instruments co-vary well together.The smaller correlation coefficient of 0.68 at Izana comparedto Kiruna and Karlsruhe is logical due to the larger differenceexpected at this site. The slope of the regression curves indi-cate that the amplitude ofδD variations is more important forIASI than FTIR at Kiruna and Karlsruhe. But that the oppo-site prevails at Izana.

For the three sites, IASIδD are biased low compared toFTIR. The mean bias values (FTIR-IASI) are 107, 72 and47 ‰ for Kiruna, Karlsruhe and Izana respectively. Since weare not considering exactly the same atmosphere in the dif-ferent locations due to the impossibility of smoothing oneretrieval with the averaging kernels of the other the valuescan not quantitatively be compared between them. Quali-tatively, this bias appear to decrease with altitude and thevalue of 47 ‰ found at Izana is close to what has been foundin the recent absolute validation of ground-based FTIR bySchneider et al. (2014) where the authors found a high biasof the Izana ground-based FTIR of+70 ‰ in the middle tro-posphere. In Wiegele et al. (2014), the authors used the sameFTIR data to cross-validate the IASI/MUSICA product (re-trieved at KIT/IMK-ASF) and found for all sites a consis-tently low bias. A direct comparison between the here pre-sented IASI/ULB product and the IASI/MUSICA productwould be interesting, but is out of the scope of this paper.

5.6 Variations of the log(q)–δD relation

To analyze the consistency of the humidity–δD relation be-tween IASI and ground-based FTIR observations we followa similar approach than for comparison with TES. The ideais to see if IASI and ground based FTIRs show coherent vari-ations in thelog(q)–δD space. We plot on Fig. 13δD vs. hu-midity for the three different sites.δD (at 2.5km for Kirunaand Karlsruhe and at 5.5km for Izana) are given in terms ofrelative variations to remove the biases discussed above. Inthe 3 first panels the different seasons are differentiated bycolours. To better visualize spatial variations the comparisonis also provided for all sites together but with colours to dis-tinguish each (right panel). Since the different retrievals arenot considering the same atmosphere this is a qualitative ap-proach.

The extreme right panel of Fig. 13 shows that the threedifferent sites exhibit very different distributions in the δD–log(q) space. The amplitude of variations are very similarfor IASI and the ground-based FTIR. The variability is thelargest at Izana with 400 ‰ between the minimum and max-imum values, due to the fact that the retrieved value referto the free troposphere (5.5km) where the true variability isindeed expected to be large. The amplitude of variations isthe lowest for Kiruna. At this site for which no winter col-located points were available, we observe a good agreementbetween the two distributions. The amplitudes of variations(for δD andH2O) for both instruments are similar as well asthe seasonal differences although in the case of IASI the sea-sonal patterns appear to be more scattered. At Karlsruhe thegeneral patterns agree best despite a steeper slope for IASIand shows well differenciated seasonal differences for bothinstruments. At Izana IASI retrievals are more scattered thanthe FTIR ones owing to the larger observational error fromIASI.

Figure 12. Scatter plot of IASI vs. FTIR δD from top to bottom for

Kiruna (2.5 km), Karlsruhe (2.5 km) and Izaña (5.5 km). We give

the slopes of the major axis regression curves (m), the Pearson cor-

relation coefficient (r), the SD of the difference and the mean bias

(b, FTIR-IASI).

correlation coefficients of 0.75, 0.77 and 0.68 indicate that

δD retrieved from both instruments co-vary well together.

The smaller correlation coefficient of 0.68 at Izaña compared

to Kiruna and Karlsruhe is logical due to the larger difference

expected at this site. The slope of the regression curves indi-

cate that the amplitude of δD variations is more important for

IASI than FTIR at Kiruna and Karlsruhe. But that the oppo-

site prevails at Izaña.

For the three sites, IASI δD are biased low compared to

FTIR. The mean bias values (FTIR-IASI) are 107, 72 and

47 ‰ for Kiruna, Karlsruhe and Izaña respectively. Since we

are not considering exactly the same atmosphere in the dif-

ferent locations due to the impossibility of smoothing one re-

trieval with the averaging kernels of the other the values can

not quantitatively be compared between them. Qualitatively,

this bias appears to decrease with altitude and the value of

47 ‰ found at Izaña is close to what has been found in the re-

cent absolute validation of ground-based FTIR by Schneider

et al. (2014) where the authors found a high bias of the Izaña

ground-based FTIR of +70 ‰ in the middle troposphere.

In Wiegele et al. (2014), the authors used the same FTIR

data to cross-validate the IASI/MUSICA product (retrieved

at KIT/IMK-ASF) and found for all sites a consistently low

bias. A direct comparison between the IASI/ULB product

presented here and the IASI/MUSICA product would be in-

teresting, but is out of the scope of this paper.

5.6 Variations of the log(q)–δD relation

To analyse the consistency of the humidity–δD relation be-

tween IASI and ground-based FTIR observations we follow

a similar approach as that for comparison with TES. The idea

is to see if IASI and ground-based FTIRs show coherent vari-

ations in the log(q)–δD space. We plot on Fig. 13 δD vs. hu-

midity for the three different sites. δD (at 2.5 km for Kiruna

and Karlsruhe and at 5.5 km for Izaña) are given in terms of

relative variations to remove the biases discussed above. In

the first three panels the different seasons are differentiated

by colours. To better visualize spatial variations the compar-

ison is also provided for all sites together but with colours to

distinguish each (right panel). Since the different retrievals

are not considering the same atmosphere this is a qualitative

approach.

The extreme right panel of Fig. 13 shows that the three

different sites exhibit very different distributions in the δD–

log(q) space. The amplitudes of variations are very similar

for IASI and the ground-based FTIR. The variability is the

largest at Izaña with 400 ‰ between the minimum and max-

imum values, due to the fact that the retrieved value refers

to the free troposphere (5.5 km) where the true variability is

indeed expected to be large. The amplitude of variations is

the lowest for Kiruna. At this site for which no winter col-

located points were available, we observe a good agreement

between the two distributions. The amplitudes of variations

(for δD and H2O) for both instruments are similar as well as

the seasonal differences, although in the case of IASI the sea-

sonal patterns appear to be more scattered. At Karlsruhe the

general patterns agree best despite a steeper slope for IASI

and shows well differentiated seasonal differences for both

instruments. At Izaña IASI retrievals are more scattered than

the FTIR ones owing to the larger observational error from

IASI.

Overall Fig. 13 shows that IASI and the ground-based

FTIR reproduce similar spatial and seasonal variations in

humidity–δD relationships. We can safely conclude that the

two instruments probe the same hydrological processes in the

same way.

6 Conclusions

In this study we have cross-validated δD profiles retrieved

from IASI spectra with profiles from TES and three ground-



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Figure 13.Distributions of IASI (top) and FTIR (bottom) observationsin thelog(q)–δD space for the three different sites (from left to right:Kiruna, Karlsruhe and Izana). The colours refer to seasons.Distributions for the three sites together are given on the right panel, with coloursdifferentiating the sites: brown is for Izana, green for Karlsruhe and yellow for Kiruna.δD values are presented in relative variations. Pearsoncorrelation coefficient betweenδD andlog(q) are also documented in the bottom of the plots.

Overall Fig. 13 shows that IASI and the ground-basedFTIR reproduce similar spatial and seasonal variations inhumidity–δD relationships. We can safely conclude that thetwo instruments probe the same hydrological processes in thesame way.

6 Conclusions

In this study we have cross-validatedδD profiles retrievedfrom IASI spectra with profiles from TES and three ground-based FTIRs. We provided a comprehensive and detailed es-timation of error differences expected from the comparisonsbetween the different instruments. Generally, we find that thetotal difference between TES and IASI, and between IASIand the ground-based FTIR is controlled by IASI observa-tional error and by the smoothing error due to the differ-ences in sensitivity of the instruments. In the comparisonwith the ground-based FTIRs, only a direct comparison wasperformed because it was not possible to simulate one re-trieval with the averaging kernels of the other. The relevanceof the different comparisons was analysed regarding the ex-pected natural variability ofδD at a global scale and alsoat regional scale. Except at Izana, all the comparisons exhibitdifferences lower than expected natural variability at regionalscales.

We have further verified the theoretical consistency of ourerror estimations and showed that they were consistent with

the real differences inδD measured by the various instru-ments. This successful cross-validation of IASI has been per-formed at various locations from tropical to Arctic latitudesabove sea and land giving us excellent confidence in the re-trieved profiles from IASI at global scale. Moreover, spatio-temporal variations of the humidity–δD relation were anal-ysed and show coherent variations among the instruments,indicating that the latter were sensitive to the hydrologicalprocesses in the same way.

The cross-validation exercise performed here also allowedus to better characterize IASI retrievals. Above sea, we haveshown that IASI retrieval exhibit large error in the lower andupper troposphere exceeding the expected natural variabilityof δD. The retrieved profile is on the contrary exploitablein the free troposphere where the error is minimized. Aboveland, the large thermal contrast reduces the error in the lowestlayers and allows retrieving profiles ofδD down to the nearsurface with sufficient precision, as demonstrated with thecomparison at Kiruna and Karlsruhe.

By analysing the emprirical differences between IASI andthe other sounders, we found a small bias with TES (−3 ‰in the free troposphere) and an important bias with the FTIR(−47 ‰ in the free troposphere).

Finally, we have investigated the impact of IASI sam-pling in a model–instrument comparison and showed that thedaily agreement between model and IASI was greatly im-proved when using all IASI observations available in a modelcell. This suggests that model evaluation against observations

Figure 13. Distributions of IASI (top panels) and FTIR (bottom panels) observations in the log(q)–δD space for the three different sites

(from left to right: Kiruna, Karlsruhe and Izaña). The colours refer to seasons. Distributions for the three sites together are given on the right

panel, with colours differentiating the sites: brown is for Izaña, green for Karlsruhe and yellow for Kiruna. δD values are presented in relative

variations. Pearson correlation coefficients between δD and log(q) are also documented in the bottom of the plots.

based FTIRs. We provided a comprehensive and detailed es-

timation of error differences expected from the comparisons

between the different instruments. Generally, we find that the

total difference between TES and IASI, and between IASI

and the ground-based FTIR is controlled by IASI observa-

tional error and by the smoothing error due to the differ-

ences in sensitivity of the instruments. In the comparison

with the ground-based FTIRs, only a direct comparison was

performed because it was not possible to simulate one re-

trieval with the averaging kernels of the other. The relevance

of the different comparisons was analysed regarding the ex-

pected natural variability of δD at a global scale and also

at regional scale. Except at Izaña, all the comparisons exhibit

differences lower than expected natural variability at regional

scales.

We have further verified the theoretical consistency of our

error estimations and showed that they were consistent with

the real differences in δD measured by the various instru-

ments. This successful cross-validation of IASI has been per-

formed at various locations from tropical to Arctic latitudes,

above sea and land, giving us excellent confidence in the re-

trieved profiles from IASI at a global scale. Moreover, spatio-

temporal variations of the humidity–δD relation were anal-

ysed and show coherent variations among the instruments,

indicating that the latter were sensitive to the hydrological

processes in the same way.

The cross-validation exercise performed here also allowed

us to better characterize IASI retrievals. Above sea, we have

shown that IASI retrievals exhibit large error in the lower and

upper troposphere, exceeding the expected natural variability

of δD. The retrieved profile is, on the contrary, exploitable

in the free troposphere where the error is minimized. Above

land, the large thermal contrast reduces the error in the lowest

layers and allows one to retrieve profiles of δD down to the

near surface with sufficient precision, as demonstrated with

the comparison at Kiruna and Karlsruhe.

By analysing the empirical differences between IASI and

the other sounders, we found a small bias with TES (−3 ‰ in

the free troposphere) and an important bias with the FTIR

(−47 ‰ in the free troposphere).

Finally, we have investigated the impact of IASI sam-

pling in a model–instrument comparison and showed that the

daily agreement between model and IASI was greatly im-

proved when using all IASI observations available in a model

cell. This suggests that model evaluation against observations

could be optimized with IASI more than with other sounders

(in the free troposphere).



Appendix A: Sensitivity change along the meridional

gradient for IASI retrieval

Since IASI presents some sensitivity to surface we expect

a change in sensitivity with decreasing surface temperature.

This change is not visible on Fig. 4, in this appendix we fur-

ther investigate this apparent contradiction. In Fig. A1, we

used all available IASI data along the meridional gradient

and average the degrees of freedom for H2O and δD on lati-

tude bins. For H2O there is an increase in sensitivity with sur-

face temperature and a small decrease is observed with high

water vapour content. For δD we also observe a significant

increase in DOFS with latitude but with a more significant

drop off in sensitivity with high water vapour content. This

could explain why IASI sensitivity is more constant with lat-

itudinal variations than TES.


Table 1.Comparison between IASI and TESδD at different heightsfor the PIO and MD datasets.σ(diff) is the SD of the difference be-tween TES and IASI, in ‰.r is the pearson correlation coefficientand m is the slope of the major axis regression TES vs. IASI (avalue of m greater than one indicates that TES variability isgreaterthan IASI variability). Direct comparison* is for the comparison re-stricted to the TES and IASI data having similar sensitivities (seetext for details).

Dataset Altitude [km] m r σ(diff) [‰]Direct Smoothed Direct Smoothed Direct Smoothed

PIO

0,5 0,09 6,24 0,13 0,30 91 722,5 0,93 1,73 0,41 0,44 44 343,5 1,18 1,12 0,50 0,55 41 304,5 1,21 0,81 0,55 0,61 43 355,5 1.27 0,79 0,57 0,39 42 418,5 0,22 4,27 0,25 0,25 66 50

Direct Direct* Direct Direct* Direct Direct*

MD

0,5 0,38 0,37 0,28 0,27 71 722,5 0,80 0,93 0,60 0,61 56 543,5 0,98 1,18 0,67 0,73 49 354,5 0,95 1,02 0,62 0,76 46 375,5 1,04 1,12 0,47 0,59 68 508,5 0,16 0,16 0,29 0,40 84 72

Table 2. Pearson correlation coefficients between LMDZ andTES/IASI for the PIO and MD datasets at 4.5km.

orbit comparison r N

Pacific Indian OceansDay

TES vs. LMDZ 0.26 5636IASI vs. LMDZ 0.15 5636

NightTES vs. LMDZ 0.25 5636IASI vs. LMDZ 0.16 5636

Meridional gradientTropics: 15◦ S–15◦ N

TES vs. LMDZ 0.46 556IASI vs. LMDZ 0.30 556

Subtropics to mid-latitudes: 15–45◦TES vs. LMDZ 0.30 591IASI vs. LMDZ 0.42 591

could be optimized with IASI more than with other sounders(in the free troposphere).

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q vmr m

ol/mol

Figure A1. Variation of the degrees of freedom for IASIδD re-trieval along the latitudinal gradient (red) and mixing ratio of watervapour at 4.5km.

Appendix A: Sensitivity change along the meridionalgradient for IASI retrieval

Since IASI presents some sensitivity to surface we expecta change in sensitivity with decreasing surface temperature.This change is not visible on Fig. 4, in this appendix we fur-ther investigate this apparent contradiction. In Fig. A1, weused all available IASI data along the meridional gradientand average the degrees of freedom forH2O andδD on lati-tude bins. ForH2O there is an increase in sensitivity with sur-face temperature and a small decrease is observed with highwater vapour content. ForδD we also observe a significantincrease in DOFS with latitude but with a more significantdrop off in sensitivity with high water vapour content. Thiscould explain why IASI sensitivity is more constant with lat-itudinal variations than TES.

Acknowledgements. IASI has been developed and built under theresponsibility of the “Centre National d’Etudes Spatiales” (CNES,France). It is flown on-board the Metop satellites as part of the EU-METSAT Polar System. The IASI L1 data are received throughthe EUMETCast near real-time data distribution service. The re-search in Belgium was funded by the F.R.S.-FNRS, the BelgianState Federal Office for Scientific, Technical and Cultural Affairs(Prodex arrangement 4000111403 IASI.FLOW). L. Clarisse and P.-F. Coheur are respectively Research Associate (Chercheur Quali-fié) and Senior Research Associate (Maître de Recherches) withF.R.S.-FNRS. C. Clerbaux is grateful to CNES for scientific col-laboration and financial support. The ground-based FTIR retrievalshave been performed in the framework of the project MUSICA,which is funded by the European Research Council under the Eu-ropean Community’s Seventh Framework Programme (FP7/2007-2013)/ERC Grant agreement number 256961.

References

Allan, R. P., Ringer, M. A., and Slingo, A.: Evaluation of moisturein the Hadley Centre climate model using simulations of HIRS

Figure A1. Variation of the degrees of freedom for IASI δD re-

trieval along the latitudinal gradient (red) and mixing ratio of water

vapour at 4.5 km.



Acknowledgements. IASI has been developed and built under the

responsibility of the “Centre National d’Etudes Spatiales” (CNES,

France). It is flown onboard the MetOp satellites as part of the

EUMETSAT Polar System. The IASI L1 data are received through

the EUMETCast near real-time data distribution service. The

research in Belgium was funded by the F.R.S.-FNRS, the Belgian

State Federal Office for Scientific, Technical and Cultural Affairs

(Prodex arrangement 4000111403 IASI.FLOW). L. Clarisse and

P.-F. Coheur are respectively Research Associate (Chercheur

Qualifié) and Senior Research Associate (Maître de Recherches)

with F.R.S.-FNRS. C. Clerbaux is grateful to CNES for scientific

collaboration and financial support. The ground-based FTIR

retrievals have been performed in the framework of the project

MUSICA, which is funded by the European Research Council

under the European Community’s Seventh Framework Programme

(FP7/2007-2013)/ERC Grant agreement number 256961.

Edited by: L. Hoffmann

References

Allan, R. P., Ringer, M. A., and Slingo, A.: Evaluation of moisture

in the Hadley Centre climate model using simulations of HIRS

water-vapour channel radiances, Q. J. Roy. Meteorol. Soc., 129,

3371–3389, doi:10.1256/qj.02.217, 2003.

Bates, J. J. and Jackson, D. L.: A comparison of water vapor obser-

vations with AMIPI simulations, J. Geophys. Res., 102, 21837–

21852, doi:10.1029/97JD01769, 1997.

Beer, R., Glavich, T. A., and Rider, D. M.: Tropospheric emission

spectrometer for the Earth Observing System’s Aura satellite,

Appl. Optics, 40, 2356–2367, 2001.

Boesch, H., Deutscher, N. M., Warneke, T., Byckling, K., Cogan,

A. J., Griffith, D. W. T., Notholt, J., Parker, R. J., and Wang, Z.:

HDO / H2O ratio retrievals from GOSAT, Atmos. Meas. Tech., 6,

599–612, doi:10.5194/amt-6-599-2013, 2013.

Brogniez, H. and Pierrehumbert, R. T.: Intercomparison of tropical

tropospheric humidity in GCMs with AMSU-B water vapor data,

Geophys. Res. Lett., 34, L17812, doi:10.1029/2006GL029118,

2007.

Calisesi, Y., Soebijanta, V. T., and van Oss, R.: Regrid-

ding of remote soundings: formulation and application to

ozone profile comparison, J. Geophys. Res., 110, D23306,

doi:10.1029/2005JD006122, 2005.

Ceccherini, S., Carli, B., Pascale, E., Prosperi, M., Raspollini, P.,

and Dinelli, B. M.: Comparison of measurements made with two

different instruments of the same atmospheric vertical profile,

Appl. Optics, 42, 6465–6473, doi:10.1364/AO.42.006465, 2003.

Cess, R. D., Potter, G. L., Blanchet, J. P., Boer, G. J., Del Ge-

nio, A. D., Déqué, M., Dymnikov, V., Galin, V., Gates, W. L.,

Ghan, S. J., Kiehl, J. T., Lacis, A. A., Le Treut, H.,

Li, Z.-X., Liang, X.-Z., McAvaney, B. J., Meleshko, V. P.,

Mitchell, J. F. B., Morcrette, J.-J., Randall, D. A., Rikus, L.,

Roeckner, E., Royer, J. F., Schlese, U., Sheinin, D. A., Slingo, A.,

Sokolov, A. P., Taylor, K. E., Washington, W. M., Wether-

ald, R. T., Yagai, I., and Zhang, M.-H.: Intercomparison and

interpretation of climate feedback processes in 19 atmospheric

general circulation models, J. Geophys. Res., 95, 16601–16615,

doi:10.1029/JD095iD10p16601, 1990.

Clerbaux, C., Boynard, A., Clarisse, L., George, M., Hadji-Lazaro,

J., Herbin, H., Hurtmans, D., Pommier, M., Razavi, A., Turquety,

S., Wespes, C., and Coheur, P.-F.: Monitoring of atmospheric

composition using the thermal infrared IASI/MetOp sounder, At-

mos. Chem. Phys., 9, 6041–6054, doi:10.5194/acp-9-6041-2009,

2009.

Craig, H.: Isotopic variations in meteoric waters, Science, 133,

1702–1703, doi:10.1126/science.133.3465.1702, 1961.

de Forster, P. and Collins, M.: Quantifying the water vapour feed-

back associated with post-Pinatubo global cooling, Clim. Dy-

nam., 23, 207–214, doi:10.1007/s00382-004-0431-z, 2004.

Draxler, R. R. and Hess, G. D.: An overview of the HYSPLIT4

modeling system for trajectories, dispersion and deposition,

Aust. Meteorol. Mag., 47, 295–308, 1998.

Dufresne, J.-L. and Bony, S.: An assessment of the primary

sources of spread of global warming estimates from cou-

pled atmosphere? Ocean models, J. Climate, 21, 5135–5144,

doi:10.1175/2008JCLI2239.1, 2008.

Frankenberg, C., Yoshimura, K., Warneke, T., Aben, I., Butz, A.,

Deutscher, N., Griffith, D., Hase, F., Notholt, J., Schnei-

der, M., Schrijver, H., and Rockmann, T.: Dynamic pro-

cesses governing lower-tropospheric HDO / H2O ratios as ob-

served from space and ground, Science, 325, 1374–1377,

doi:10.1126/science.1173791, 2009.

Frankenberg, C., Wunch, D., Toon, G., Risi, C., Scheepmaker, R.,

Lee, J.-E., Wennberg, P., and Worden, J.: Water vapor isotopo-

logue retrievals from high-resolution GOSAT shortwave infrared

spectra, Atmos. Meas. Tech., 6, 263–274, doi:10.5194/amt-6-

263-2013, 2013.

Galewsky, J., Strong, M., and Sharp, Z. D.: Measurements of water

vapor D /H ratios from Mauna Kea, Hawaii, and implications for

subtropical humidity dynamics, Geophys. Res. Lett., 34, L22808,

doi:10.1029/2007GL031330, 2007.

Herman, R. L., Cherry, J. E., Young, J., Welker, J. M., Noone, D.,

Kulawik, S. S., and Worden, J.: Aircraft validation of Aura Tro-

pospheric Emission Spectrometer retrievals of HDO / H2O, At-

mos. Meas. Tech., 7, 3127–3138, doi:10.5194/amt-7-3127-2014,

2014.

Hilton, F., Armante, R., August, T., Barnet, C., Bouchard, A.,

Camy-Peyret, C., Capelle, V., Clarisse, L., Clerbaux, C.,

Coheur, P.-F., Collard, A., Crevoisier, C., Dufour, G., Ed-

wards, D., Faijan, F., Fourrié, N., Gambacorta, A., Goldberg, M.,

Guidard, V., Hurtmans, D., Illingworth, S., Jacquinet-Husson, N.,

Kerzenmacher, T., Klaes, D., Lavanant, L., Masiello, G., Ma-

tricardi, M., McNally, A., Newman, S., Pavelin, E., Payan, S.,

Péquignot, E., Peyridieu, S., Phulpin, T., Remedios, J., Schlüs-

sel, P., Serio, C., Strow, L., Stubenrauch, C., Taylor, J., Tobin, D.,

Wolf, W., and Zhou, D.: Hyperspectral earth observation from

IASI: five years of accomplishments, B. Am. Meteorol. Soc., 93,

347–370, doi:10.1175/BAMS-D-11-00027.1, 2012.

Hurtmans, D., Coheur, P.-F., Wespes, C., Clarisse, L.,

Scharf, O., Clerbaux, C., Hadji-Lazaro, J., George, M.,

and Turquety, S.: FORLI radiative transfer and retrieval

code for IASI, J. Quant. Spectrosc. Ra., 113, 1391–1408,

doi:10.1016/j.jqsrt.2012.02.036, 2012.

Lacour, J.-L., Risi, C., Clarisse, L., Bony, S., Hurtmans, D.,

Clerbaux, C., and Coheur, P.-F.: Mid-tropospheric δD obser-

vations from IASI/MetOp at high spatial and temporal resolu-


http://dx.doi.org/10.1256/qj.02.217

http://dx.doi.org/10.1029/97JD01769

http://dx.doi.org/10.5194/amt-6-599-2013

http://dx.doi.org/10.1029/2006GL029118

http://dx.doi.org/10.1029/2005JD006122

http://dx.doi.org/10.1364/AO.42.006465

http://dx.doi.org/10.1029/JD095iD10p16601

http://dx.doi.org/10.5194/acp-9-6041-2009

http://dx.doi.org/10.1126/science.133.3465.1702

http://dx.doi.org/10.1007/s00382-004-0431-z

http://dx.doi.org/10.1175/2008JCLI2239.1

http://dx.doi.org/10.1126/science.1173791



http://dx.doi.org/10.1029/2007GL031330


http://dx.doi.org/10.1175/BAMS-D-11-00027.1

http://dx.doi.org/10.1016/j.jqsrt.2012.02.036


tion, Atmos. Chem. Phys., 12, 10817–10832, doi:10.5194/acp-

12-10817-2012, 2012.

Noone, D.: Pairing measurements of the water vapor isotope ratio

with humidity to deduce atmospheric moistening and dehydra-

tion in the tropical mid-troposphere, J. Climate, 25, 4476–4494,

doi:10.1175/JCLI-D-11-00582.1, 2012.

Noone, D., Galewsky, J., Sharp, Z. D., Worden, J., Barnes, J.,

Baer, D., Bailey, A., Brown, D. P., Christensen, L., Crosson, E.,

Dong, F., Hurley, J. V., Johnson, L. R., Strong, M., Toohey, D.,

Van Pelt, A., and Wright, J. S.: Properties of air mass mixing and

humidity in the subtropics from measurements of the D / H iso-

tope ratio of water vapor at the Mauna Loa Observatory, J. Geo-

phys. Res., 116, D22113, doi:10.1029/2011JD015773, 2011.

Pierce, D. W., Barnett, T. P., Fetzer, E. J., and Gleckler, P. J.: Three-

dimensional tropospheric water vapor in coupled climate mod-

els compared with observations from the AIRS satellite system,

Geophys. Res. Lett., 33, L21701, doi:10.1029/2006GL027060,

2006.

Pierrehumbert, R. T., Brogniez, H., and Roca, R.: On the Relative

Humidity of the Earth’s Atmosphere, Princeton University Press,

Princeton, NJ, 2007.

Pommier, M., Lacour, J.-L., Risi, C., Bréon, F. M., Clerbaux, C.,

Coheur, P.-F., Gribanov, K., Hurtmans, D., Jouzel, J., and Za-

kharov, V.: Observation of tropospheric δD by IASI over west-

ern Siberia: comparison with a general circulation model, At-

mos. Meas. Tech., 7, 1581–1595, doi:10.5194/amt-7-1581-2014,

2014.

Rayleigh, L.: On the distillation of binary mixtures, Philos. Mag.,

4, 521–537, 1902.

Risi, C., Bony, S., and Vimeux, F.: Influence of convective processes

on the isotopic composition of precipitation and water vapor in

the tropics: 2. Physical interpretation of the amount effect, J.

Geophys. Res., 113, D19306, doi:10.1029/2008JD009943, 2008.

Risi, C., Bony, S., Vimeux, F., and Jouzel, J.: Water-stable isotopes

in the LMDZ4 general circulation model: model evaluation for

present-day and past climates and applications to climatic in-

terpretations of tropical isotopic records, J. Geophys. Res., 115,

D12118, doi:10.1029/2009JD013255, 2010.

Risi, C., Noone, D., Worden, J., Frankenberg, C., Stiller, G.,

Kiefer, M., Funke, B., Walker, K., Bernath, P., Schneider, M.,

Bony, S., Lee, J., Brown, D., and Sturm, C.: Process-evaluation

of tropospheric humidity simulated by general circulation mod-

els using water vapor isotopic observations: 2. Using isotopic di-

agnostics to understand the mid and upper tropospheric moist

bias in the tropics and subtropics, J. Geophys. Res., 117, D05304,

doi:10.1029/2011JD016623, 2012a.

Risi, C., Noone, D., Worden, J., Frankenberg, C., Stiller, G.,

Kiefer, M., Funke, B., Walker, K., Bernath, P., Schnei-

der, M., Wunch, D., Sherlock, V., Deutscher, N., Griffith, D.,

Wennberg, P. O., Strong, K., Smale, D., Mahieu, E., Barthlott, S.,

Hase, F., Garcia, O., Notholt, J., Warneke, T., Toon, G.,

Sayres, D., Bony, S., Lee, J., Brown, D., Uemura, R., and

Sturm, C.: Process-evaluation of tropospheric humidity simu-

lated by general circulation models using water vapor isotopo-

logues: 1. Comparison between models and observations, J. Geo-

phys. Res., 117, D05303, doi:10.1029/2011JD016621, 2012b.

Rodgers, C. D.: Inverse Methods for Atmospheric Sounding: The-

ory and Practise, World Scientific, Singapore, 2000.

Rodgers, C. D. and Connor, B. J.: Intercomparison of re-

mote sounding instruments, J. Geophys. Res., 108, 4116,

doi:10.1029/2002JD002299, 2003.

Samuels-Crow, K. E., Galewsky, J., Hardy, D. R., Sharp, Z. D.,

Worden, J., and Braun, C.: Upwind convective influences on

the isotopic composition of atmospheric water vapor over the

tropical Andes, J. Geophys. Res.-Atmos., 119, 7051–7063,

doi:10.1002/2014JD021487, 2014.

Schneider, M. and Hase, F.: Optimal estimation of tropospheric

H2O and δD with IASI/METOP, Atmos. Chem. Phys., 11,

11207–11220, doi:10.5194/acp-11-11207-2011, 2011.

Schneider, M., Hase, F., and Blumenstock, T.: Ground-based re-

mote sensing of HDO / H2O ratio profiles: introduction and vali-

dation of an innovative retrieval approach, Atmos. Chem. Phys.,

6, 4705–4722, doi:10.5194/acp-6-4705-2006, 2006.

Schneider, M., Barthlott, S., Hase, F., González, Y., Yoshimura,

K., García, O. E., Sepúlveda, E., Gomez-Pelaez, A., Gisi, M.,

Kohlhepp, R., Dohe, S., Blumenstock, T., Wiegele, A., Christner,

E., Strong, K., Weaver, D., Palm, M., Deutscher, N. M., Warneke,

T., Notholt, J., Lejeune, B., Demoulin, P., Jones, N., Griffith, D.

W. T., Smale, D., and Robinson, J.: Ground-based remote sens-

ing of tropospheric water vapour isotopologues within the project

MUSICA, Atmos. Meas. Tech., 5, 3007–3027, doi:10.5194/amt-

5-3007-2012, 2012.

Schneider, M., González, Y., Dyroff, C., Christner, E., Wiegele, A.,

Barthlott, S., García, O. E., Sepúlveda, E., Hase, F., Andrey, J.,

Blumenstock, T., Guirado, C., Ramos, R., and Rodríguez, S.:

Empirical validation and proof of added value of MUSICA’s tro-

pospheric δD remote sensing products, Atmos. Meas. Tech., 8,

483–503, doi:10.5194/amt-8-483-2015, 2015.

Sherwood, S. C., Roca, R., Weckwerth, T. M., and An-

dronova, N. G.: Tropospheric water vapor, convection, and cli-

mate, Rev. Geophys., 48, RG2001, doi:10.1029/2009RG000301,

2010.

Sherwood, S. C., Bony, S., and Dufresne, J.-L.: Spread in model

climate sensitivity traced to atmospheric convective mixing, Na-

ture, 505, 37–42, doi:10.1038/nature12829, 2014.

Soden, B. J. and Bretherton, F. P.: Evaluation of water vapor dis-

tribution in general circulation models using satellite observa-

tions, J. Geophys. Res., 99, 1187–1210, doi:10.1029/93JD02912,

1994.

Soden, B. J. and Held, I. M.: An assessment of climate feedbacks

in coupled ocean atmosphere models, J. Climate, 19, 3354–3360,

doi:10.1175/JCLI3799.1, 2006.

Strong, M., Sharp, Z. D., and Gutzler, D. S.: Diagnosing moisture

transport using D / H ratios of water vapor, Geophys. Res. Lett.,

34, L03404, doi:10.1029/2006GL028307, 2007.

von Clarmann, T.: Validation of remotely sensed profiles of atmo-

spheric state variables: strategies and terminology, Atmos. Chem.

Phys., 6, 4311–4320, doi:10.5194/acp-6-4311-2006, 2006.

Wiegele, A., Schneider, M., Hase, F., Barthlott, S., García, O.

E., Sepúlveda, E., González, Y., Blumenstock, T., Raffalski,

U., Gisi, M., and Kohlhepp, R.: The MUSICA MetOp/IASI

H2O and δD products: characterisation and long-term compar-

ison to NDACC/FTIR data, Atmos. Meas. Tech., 7, 2719–2732,

doi:10.5194/amt-7-2719-2014, 2014.

Worden, J., Bowman, K., Noone, D., Beer, R., Clough, S., Elder-

ing, A., Fisher, B., Goldman, A., Gunson, M., Herman, R., Ku-

lawik, S. S., Lampel, M., Luo, M., Osterman, G., Rinsland, C.,




http://dx.doi.org/10.1175/JCLI-D-11-00582.1

http://dx.doi.org/10.1029/2011JD015773

http://dx.doi.org/10.1029/2006GL027060


http://dx.doi.org/10.1029/2008JD009943

http://dx.doi.org/10.1029/2009JD013255

http://dx.doi.org/10.1029/2011JD016623

http://dx.doi.org/10.1029/2011JD016621

http://dx.doi.org/10.1029/2002JD002299

http://dx.doi.org/10.1002/2014JD021487






http://dx.doi.org/10.1029/2009RG000301

http://dx.doi.org/10.1038/nature12829

http://dx.doi.org/10.1029/93JD02912

http://dx.doi.org/10.1175/JCLI3799.1

http://dx.doi.org/10.1029/2006GL028307




Rodgers, C., Sander, S., Shephard, M., and Worden, H.: Tropo-

spheric Emission Spectrometer observations of the tropospheric

HDO / H2O ratio: estimation approach and characterization, J.

Geophys. Res., 111, D16309, doi:10.1029/2005JD006606, 2006.

Worden, J., Noone, D., and Bowman, K.: Importance of rain evap-

oration and continental convection in the tropical water cycle,

Nature, 445, 528–532, doi:10.1038/nature05508, 2007.

Worden, J., Noone, D., Galewsky, J., Bailey, A., Bowman, K.,

Brown, D., Hurley, J., Kulawik, S., Lee, J., and Strong, M.:

Estimate of bias in Aura TES HDO/H2O profiles from com-

parison of TES and in situ HDO/H2O measurements at the

Mauna Loa observatory, Atmos. Chem. Phys., 11, 4491–4503,

doi:10.5194/acp-11-4491-2011, 2011.

Worden, J., Kulawik, S., Frankenberg, C., Payne, V., Bowman, K.,

Cady-Peirara, K., Wecht, K., Lee, J.-E., and Noone, D.: Pro-

files of CH4, HDO, H2O, and N2O with improved lower tro-

pospheric vertical resolution from Aura TES radiances, Atmos.

Meas. Tech., 5, 397–411, doi:10.5194/amt-5-397-2012, 2012.

Yoshimura, K., Frankenberg, C., Lee, J., Kanamitsu, M., Wor-

den, J., and Röckmann, T.: Comparison of an isotopic atmo-

spheric general circulation model with new quasi-global satellite

measurements of water vapor isotopologues, J. Geophys. Res.,

116, D19118, doi:10.1029/2011JD016035, 2011.


http://dx.doi.org/10.1029/2005JD006606

http://dx.doi.org/10.1038/nature05508



http://dx.doi.org/10.1029/2011JD016035

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