Mapping NWP Model Output with Radar Observations in ... · xxxTx xx cN q where ρ is the air...

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Mapping NWP Model Output with Radar Observations in Complex Terrain * Ruping Mo National Laboratory for Coastal and Mountain Meteorology, Environment Canada, Vancouver, BC, Canada Paul I. Joe Cloud Physics and Severe Weather Research Section, Environment Canada, Toronto, ON, Canada Jason A. Milbrandt Atmospheric Numerical Prediction Research, Environment Canada, Dorval, QB, Canada Corresponding author’s address: Ruping Mo National Laboratory for Coastal and Mountain Meteorology Environment Canada 201-401 Burrard Street Vancouver, BC V6C 3S5 Canada E-mail: [email protected] Technical Report 2012-001 National Laboratory for Coastal and Mountain Meteorology 31 May 2012 * This report is based on a poster, presented at CMOS/AMS Congress 2012 (May 29 to June 1), Montreal, Quebec, Canada; see Fig. 7 at the end of the report.

Transcript of Mapping NWP Model Output with Radar Observations in ... · xxxTx xx cN q where ρ is the air...

Mapping NWP Model Output with Radar Observations in Complex Terrain*

Ruping Mo

National Laboratory for Coastal and Mountain Meteorology, Environment Canada,

Vancouver, BC, Canada

Paul I. Joe

Cloud Physics and Severe Weather Research Section, Environment Canada,

Toronto, ON, Canada

Jason A. Milbrandt

Atmospheric Numerical Prediction Research, Environment Canada,

Dorval, QB, Canada

Corresponding author’s address:

Ruping Mo National Laboratory for Coastal and Mountain Meteorology Environment Canada 201-401 Burrard Street Vancouver, BC V6C 3S5 Canada E-mail: [email protected]

Technical Report 2012-001

National Laboratory for Coastal and Mountain Meteorology 31 May 2012

* This report is based on a poster, presented at CMOS/AMS Congress 2012 (May 29 to June 1), Montreal, Quebec, Canada; see Fig. 7 at the end of the report.

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1. Introduction

High-resolution radar observations and numerical weather prediction (NWP) model

guidance played the crucial roles in supporting weather services for the Vancouver 2010

Olympic and Paralympic Winter Games (Joe et al. 2010; Mailhot et al. 2010). In

particular, a special C-band Doppler radar installed near Whistler provided the critical

real-time observations of the detailed atmospheric conditions in a mountainous area for

the Olympic forecasting operations (Fig. 1). This radar also captured a variety of unusual

orographic weather patterns that could be better understood through a further analysis of

high-resolution NWP model output, if these patterns were correctly predicted by the

model.

Figure 1: The Whistler radar (VVO) and the complex terrain surround it.

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To facilitate such a radar-model comparison approach, this study presents some

practical procedures to calculate the model counterparts of the radar measurements.

These procedures consist of two principal components: (a) mimicking the radar

reflectivity and radial velocity based on the available high-resolution NWP variables, and

(b) mapping them on a radar PPI (Plan Position Indicator) or RHI (Range Height

Indicator) display for direct comparison with the corresponding radar measurements.

Their usefulness as a set of tools to interpret radar measurements and validate model

predictions over complex terrain is demonstrated in a case study of orographic flow

observed in the Whistler area during the Vancouver 2010 Olympic Winter Games. Their

application in supporting the operational forecast is also highlighted in this study.

2. Radar and model descriptions

The Whistler radar (ID: VVO) shown in Fig. 1 was installed at the junction of three

critical valleys to support the forecasting operations during the 2010 Olympic and

Paralympic Winter Games (Joe et al. 2010). This C-band Doppler radar provided areal

and vertical coverage of precipitation and wind in a mountainous area. It was configured

for local coverage (≤ 60 km) and using short pulses (0.65 μs, 125-m range bins) to

mitigate the effects of topographical clutter. A special scan strategy was taken to obtain

useful data in the 11°–15° elevation angles, because the radar beam in this area generally

clears the terrain above 12°. Specific RHI scans were also taken over key sites and down

critical valleys for the Olympic operations. The scan interval was set to 10 minutes. Fig. 2

show some scan examples taken at 1500 UTC, 16 February 2010.

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Figure 2: Whistler radar (VVO) 13° PPI displays of reflectivity and radial velocity (top panel) and a 187° RHI display of radial velocity (bottom panel) at 1500 UTC, 16 February 2010.

A mesoscale deterministic prediction system was developed by Environment

Canada for the Vancouver 2010 Olympic and Paralympic Winter Games. It was run in

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the limited-area model configuration with a 58-level hybrid vertical discretization

(Mailhot et al. 2010). The system consisted of three one-way-nested grids at 15-km, 2.5-

km, and 1-km grid spacings, run twice a day and integrated for at least 19 h. It used the

double-moment version of the Milbrandt-Yau (MY2) microphysics package to treat grid-

scale clouds and precipitation (Milbrandt and Yau 2005, 2006). This Olympic system has

been adapted for the current operational 2.5-km resolution model, run routinely over

several domains across Canada.

Specifically, the MY2 scheme partitions the total hydrometeor spectrum into six

categories (cloud, rain, ice, snow, graupel, and hail), predicts changes to the mass mixing

ratio (qx) and the total number concentration (NTx) for category x, and then describes the

particle size distribution with a two-parameter gamma distribution function of the form

(1 )( ) (1 ) (1)x x xDx Tx x xN D N D e

where Nx(D) is the total number concentration per unit volume of particles of diameter D

for the category x, λx is the slope parameter, αx is the dispersion parameter, and Γ is the

gamma function. It can be shown that

1/3(4 ) (1 ) (2)x x x Tx x xc N q

where ρ is the air density and cx is a constant.

3. Model equivalent radar reflectivity and radial velocity

Radar-observed precipitation and wind fields in complex terrain can be very complicated.

The most obvious influence is the orography which can force upward movement and

result in orographic precipitation on the windward slope. The type, amount, intensity and

duration of these precipitation events are strongly controlled by the barrier width, slope

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steepness and updraft speed. The topography can also channel the flow or accelerate it

through valley constrictions. In addition, drainage and upslope flows can arise due to

diurnal solar radiation variations.

The MY2 cloud microphysics package was used in the high-resolution NWP

model to explicitly predict the cloud and precipitation microphysical processes in the

atmosphere. To compare model predictions with radar measurements, one can diagnose

the radar reflectivity factor Zx and the equivalent radar reflectivity Zex from the MY2

scheme outputs as (see Milbrandt and Yau 2005)

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(6 )(5 )(4 )( ) 6, 0.224 , (3)

(3 )(2 )(1 )x x x x x

x ex xx x x x Tx w

q cZ Z Z

c N

where ρw is the density of water.

The equivalent radar velocity, R , is obtained by projecting the 3D velocity of

model hydrometeors to the local radial directions at a data point, i.e.,

( sin cos )cos( ) ( )sin( ) (4)R Tu w w

where u, υ, w are the eastward, northward, and upward velocity components of the air

motion, Tw is a non-negative bulk terminal fall velocity of the hydrometeors, is

horizontal angle between north and the radar scanning direction, θ is the scanning

elevation angle, and δ is the angular correction at the data point due to the curvature of

the earth (see Fig. 3). In this study, the bulk terminal fall velocity is calculated as a

reflectivity-weighted variable

(5)T x Zx xx xw Z w Z

where Zxw is the reflectivity-weighted fall velocity for the x-category hydrometeor, given

by (see Milbrandt and Yau 2006)

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(7 )

1 2

0 (7 )

(7 ) (6)

(7 ) ( )

x

x x

x x xZx x b

x x x

bw a

f

with 0 being the surface air density, and ax, bx, fx being the fall speed parameters given

in Milbrandt and Yau (2005).

Under the assumptions that the atmospheric refractivity index is linearly

dependent on height and 1dh ds , where h is height above radar and s is arc distance

from the radar, Doviak and Zrnić (1993) obtained the following three equations to relate

h, s, and δ to the slant range r and elevation angle θ (see Fig. 3):

2 2 1/ 2( 2 sin ) , (7)e e eh r a ra a

arcsin cos ( ) , (8)e es a r a h

arctan cos ( sin ) , (9)er a r

where 04( ) / 3ea a h is the effective radius of the earth, with a as the earth’s radius and

0h as the radar antenna height.

For a data point on a PPI display with given s and θ, its height and slant range are

given as

cos cos( / ) 1 , (10)e eh a s a

( )sin( / ) cos . (11)e er a h s a

For a data point on an RHI display with given h and s, its slant range is also

obtained from Eq. (11), with elevation angle obtained from

( ) cos( / )arctan . (12)

( )sin( / )e e e

e e

a h s a a

a h s a

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Equations (7)–(12) can be used to determine the location and radar-measurable

parameters at a grid point on a PPI or RHI display. Then any 3D model variable can be

interpolated to this grid point, and the corresponding radial velocity can be calculated

using Eq. (4).

Figure 3: Equivalent earth model (left panel) and some selected radar beams (right panel) in an atmosphere with refractive index linearly dependent on height.

4. A case study

On 16 February 2010, poor weather conditions in the Whistler area had a significant

impact on the Olympic alpine skiing competitions. In the early morning near 1500 UTC,

a cold front had just moved across the area, and an apparent southwest low-level jet (LLJ)

was observed by the Whistler radar at about 3 km ASL (Fig. 2). This post-frontal LLJ

was also well predicted by the 1-km resolution NWP model, as shown in Figures 4 & 5.

Because Doppler radar can only detect radial velocity, understanding the flow structure

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from the radar image is not trivial problem. In Fig. 4b, the model wind vectors are also

plotted to facilitate the interpretation of the predicted radial velocity. The same wind

vectors are plotted in Fig. 4d over a topographical background to highlight the impact of

the complex terrain. For example, Fig. 4d indicates that the LLJ was locally enhanced

near some crests of the mountains. Comparing Fig. 5 with Fig. 2 shows that the predicted

LLJ was at a slightly higher elevation.

Figure 4: Equivalent radar products on the 13° PPI display of the Whistler radar, predicted by the 1-km resolution NWP model valid at 1500 UTC, 16 February 2010. In both (b) and (d), the vectors represent the model winds on the PPI. In (c), the air vertical velocity is indicated by color shading.

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Figure 5: Equivalent radar radial velocity on the 187° RHI display of the Whistler radar, predicted by the 1-km resolution NWP model valid at 1500 UTC, 16 February 2010. The black (white) dot line indicates the freezing level (the lowest scan in Fig. 2).

5. Operational applications

The Whistler radar was installed to support the Vancouver 2010 Olympic and

Paralympic Winter Games, during which intensive meteorological data had been

collected. The algorithms of equivalent radar measurements given in this study can be

applied to these Olympic data in further investigation of winter weather processes the in

complex terrain of the west coast of Canada.

The algorithms have also been used, in operational mode, to generate equivalent

radar products of the 2.5-km NWP model for the four radars in British Columbia. A

convenient website has been developed to present these products (Fig. 6). Forecasters can

used these products to perform a quick model validation and to guide their forecast

decision accordingly.

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Figure 6: A website developed to host the equivalent radar products of the operational 2.5-km resolution NWP model. The model images are updated and archived soon after the model run has been completed, and can be compared with real-time radar observations.

6. Concluding remarks

To compare high-resolution radar data with high-resolution NWP model data, it is

necessary to compute the model counterparts of reflectivity and radial velocity. The

procedures described in this study allow converting predicted hydrometeor velocity

( , , Tu w w ) on model grids into radial velocity υR in radar PPI or RHI grids. They are

particularly useful in complex terrain, where the contribution of ( Tw w ) could be

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important when radar scan with a relatively large elevation angle θ is necessary.

However, the accuracy of these procedures relies on the assumptions that the atmospheric

refractive index is linearly dependent on height and tanθ << 1. In addition, the effect of

the beam broadening has not been taken into account.

This report is based on a poster (see Fig. 7), presented at CMOS/AMS Congress

2012 (May 29 to June 1), Montreal, Quebec, Canada.

Figure 7: Poster presented at CMOS/AMS Congress 2012.

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References

Doviak, R. J., and D. S. Zrnić, 1993: Doppler Radar and Weather Observations. 2nd ed.

Academic Press, 562 pp.

Joe, P., and Coauthors, 2010: Weather services, science advances, and the Vancouver

2010 Olympic and Paralympic Winter Games. Bull. Amer. Met. Soc., 91, 31–36.

Mailhot, J., and Coauthors, 2010: Environment Canada’s experimental numerical weather

prediction systems for the Vancouver 2010 Winter Olympic and Paralympic

Games. Bull. Amer. Met. Soc., 91, 1073–1085.

Milbrandt, J. A., and M. K. Yau, 2005: A multimoment bulk microphysics

parameterization. Part I and Part II. J. Atmos. Sci., 62, 3051–3081.

Milbrandt, J. A., and M. K. Yau, 2006: A multimoment bulk microphysics

parameterization. Part III: Control simulation of a hailstorm. J. Atmos. Sci., 63,

3014–3136.