Meso-γ 3D-Var Assimilation of Surface measurements : Impact on short-range high-resolution...

Meso-Meso-γ 3D-Var Assimilation of Surface γ 3D-Var Assimilation of Surface measurementsmeasurements: :

Impact on short-range high-resolution Impact on short-range high-resolution simulationssimulations

Geneviève JaubertGeneviève Jaubert, Ludovic Auger, Nathalie Colombon, , Ludovic Auger, Nathalie Colombon, Véronique Ducrocq, François BouttierVéronique Ducrocq, François Bouttier

CNRM/GAME Météo-France and CNRSCNRM/GAME Météo-France and CNRS

G.Jaubert Météo-France WSN05 5-9 september 2005 Toulouse France

Introduction

Feasibility study for:• the assimilation part of the next operational NWP high-resolution system of Meteo-France AROME • high-resolution reanalyses of the field phase of cooperative experiments

Validation on the Gard torrential event (8-9 sept.2002) and the MAP IOP08 frontal case (20-21 oct.1999)


Hybrid assimilation systemFirst guess: large scale analysis (ECMWF or ARPEGE)Two domains: Mesoscale domain mesh 10 km Convective scale domain mesh: 2.5 km1 analysis or 6-hour assimilation with 1 hour step

Meso-NH configuration: Two nested models mesh: 10 and 2.5 km 3 ice types Explicit convection at 2.5 km

Aladin 3D-Var Analysis

Meso-NH model

IncrementsGuessAssimilation

cycle

Meso-NH simulation


Experimental design06 UTC 12 UTC 12 UTC

24 hours Simulation

REF12

ANA12 UTC mesoscale analysisand

12 UTC convective scale analysis

12 UTC Large scale analysis

ASSIM

REF0606 UTC Large scale analysis

Assimilation step 1 hour

07 08 10 11 1209

06 UTC mesoscale analysis

and06 UTC convective

scale analysis


Data used in the analyses

MAP IOP8 frontal case


Gard torrential event

Surface data (3000 observations)surface pressure 2m temperature and humidity, 10m windhourly measurements

Radiosounding data (60 messages) Temperature, humidity, wind, geopotential06 UTC and 12 UTC

Mesoscale domain

Convective scale domain

2800 km

The background error statisticsBased on the background errors covariances used for the ALADIN analyses

Mesoscale domain (mesh 10 km)Mesoscale domain (mesh 10 km):ALADIN statistics except at low levels:larger variances except for the rotational wind geostrophic balance reduced at low levels


720 km

Convective scale domain (mesh 2.5 km)Convective scale domain (mesh 2.5 km)ALADIN horizontal length scales reducedLarger variances at low levels

Vertical profiles of background errors stdev

METEOSAT IR 20020908 18 UTC

ASSIM

REF12

ANA

DATA

Gard torrential rain event

12h precipitation statisticsT+0 T+12

Simulation

REF12

ANA

ASSIM

Correlation

0.20

0.62

0.13

Bias

-3.80

-4.81

-2.31

12h accumulated rain Convective scale domain8 sep 2002 12 UTC 9 sep 2002 00 UTC T+0 T+12

ASSIM

REF12

ANA

DATA

Gard torrential rain event


Simulation

REF12

ANA

ASSIM

ETS

0.1 mm 20 mm

0.14 0.18

0.16 0.31

0.12 0.19

Correlation

0.20

0.62

0.13

Bias

-3.80

-4.81

-2.31

12h accumulated rain Convective scale domain8 sep 2002 12 UTC 9 sep 2002 00 UTC T+0 T+12


METEOSAT WV 991021: 00 UTC

12h accumulated rain Convective scale domain20 oct 18 UTC 21 oct 06 UTC T+6 T+18

DATA

ASSIM

REF12

ANA



Simulation

REF06

REF12

ANA

ASSIM

Correlation

0.61

0.66

0.68

0.70

Bias

3.31

4.10

1.98

0.76


METEOSAT WV 991021: 00 UTC

12h accumulated rain Convective scale domain20 oct 18 UTC 21 oct 06 UTC T+6 T+18

DATA

ASSIM

REF12

ANA


DATA

ASSIM

REF12

ANA

ETS

0.1 mm 20 mm

0.66 0.30

0.65 0.34

0.65 0.35

0.63 0.36


Simulation

REF06

REF12

ANA

ASSIM

Correlation

0.61

0.66

0.68

0.70

Bias

3.31

4.10

1.98

0.76

Comparaison with the surface measurements (convective scale domain)



Gard torrential event

Bias

Stdev

Bias

Stdev--- REF12 --- ANA --- ASSIM

2 m Temperature

--- REF12 --- ANA --- ASSIM

Bias

Stdev

2 m Humidity

Bias

Stdev


MAP IOP8 frontal caseComparaison with the 13 radiosoundings included in the convective scale domain at 18 UTC (T + 6 hours)

Mixing ratio (g/kg)

Bias

Stdev

Temperature (K)

Bias

Stdev

Conclusion

The assimilation of the surface measurements reduces the bias of temperature and humidity at low levels

Improvement only if the background error covariance matrice is tuned to limit the contribution of the balanced part of the flow at low levels

Better results if both the surface measurements and the altitude data are used


The mesoscale assimilation improved: the forecast of precipitation the forecast of temperature in the first 3 km

Meso-γ 3D-Var Assimilation of Surface measurements : Impact on short-range high-resolution...

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