USE OF PRECIPITATION INFORMATION FROM SPACEBORNE RADAR FOR VERIFICATION AND ASSIMILATION IN THE ECMWF MODEL A.Benedetti, P. Lopez, E. Moreau, P. Bauer, F. Chevallier, M. Janiskova’, A. Tompkins

Outline

•

Precipitation assimilation activities

at ECMWF •Brief overview of the Tropical Rainfall Measuring Mission (TRMM) • Overview of the 1D-Var retrievals from the TRMM Microwave Imager (TMI) • Validation of Rainrate/Brightness Temperature retrievals using the TRMM Precipitation Radar •Outline of the

1D+4DVar

approach • Use of radar reflectivities for assimilation • Preliminary results • Discussion and conclusions

Precipitation assimilation at ECMWF

Goal: To assimilate observations related to precipitation and clouds in ECMWF’s 4D-Var system including parameterizations of atmospheric moist processes.

A bit of history:  Work on precipitation assimilation at ECMWF initiated by Mahfouf and Marécal  1D-Var on TMI and SSM/I rainfall rates (RR) (M&M 2000).

 Indirect 1D+4D-Var assimilation of RR more robust than direct 4D-Var.

 1D+4D-Var assimilation of RR is able to improve humidity but also the dynamics in the forecasts (M&M 2002).

More recent developments:      New simplified convection scheme (Lopez 2003) New simplified cloud scheme (Tompkins & Janisková 2003) used in 1D-Var Microwave Radiative Transfer Model (Bauer & Moreau 2002) Assimilation experiments of direct measurements from TRMM and SSM/I (TB or Z) instead of indirect retrievals of rainfall rates, in a 1D+ 4D-Var framework.

Use of Precipitation Radar data to validate 1D-Var and 1D+4D-Var results.

T

ROPICAL

R

AINFALL

M

EASURING

M

ISSION

• Operational since 1997; provides rain observations between 35S-35N • Instruments on board (still working): Microwave Imager (TMI) : surface rainrate from Brightness Temperatures (Tb) Precipitation Radar (PR) : rainrate profiles from Reflectivities (Z) - Visible and Infrared Scanner (VIRS) - Lightning Imaging Sensor (LIS) PR IMAGE OF TROPICAL CYCLONE ZOE, December 2002, 165-180E/0-20S http://trmm.gsfc.nasa.gov/

1D-Var retrievals from TRMM data V A L I D A T I O N 1D-Var on Brightness Temp.

1D-Var on TMI rain rates TMI Brightness Temp (Tb) Retrieval algorithm (2A12,PATER) R E T R I V A L “Observed” rainfall rates Observations interpolated on model’s T511 Gaussian grid moist physics + radiative transfer moist physics

1D-Var (TCWV, snow and rainfall rates)

background T,q v

Radar Forward Model Evaluation of 1D-var

background T,q v PR reflectivity

Validation of 1D-Var retrievals of rainfall from TMI radiances and TRMM Rainrates Model FG

T, q Moist physics

FG rain and snow rates Rainfall

from TRMM Algorithms (2A12, PATER, etc.)

Observed Radiances (TMI) Model FG

T, q Moist physics + radiative transfer

FG ‘rainy radiance’ 1D-Var retrievals of rainfall and snowfall rate 1D-Var retrievals of rainfall and snowfall rate TRMM-PR observations

+

Forward radar model= equivalent reflectivity 1D-Var retrieval evaluation

Forward radar model

• Based on

Mie look-up tables

for the computation of reflectivity, assumes a Marshall-Palmer distribution for rain and snow particles and includes treatment of bright band at 273K • 3D radar reflectivity at 14 GHz is computed via bilinear interpolation at the given model temperature and rain/snow content at each model grid point and vertical level • Model rain/snow contents are computed from precipitation fluxes assuming a fixed fall velocity

Background 1D-Var results PATER obs 1D-Var/BT 1D-Var/RR Case of tropical cyclone ZOE (26 December 2002 @1200 UTC) TMI data Surface rainfall rates (mm hr -1 )

1D-Var/RR PATER 1D-Var results 1D-Var/BT Case of tropical cyclone ZOE (26 December 2002 @1200 UTC)

Total Column Water Vapour increments (top , kg m -2 ) and mean profiles of temperature and specific humidity increments (bottom)

Evaluation of 1D-Var results using PR data Background PR obs 1D-Var/RR 1D-Var/BT Case of tropical cyclone ZOE (26 December 2002 @1200 UTC)

14 GHz Radar Reflectivity at ~2km (dBZ)

Evaluation of 1D-Var results using PR data Background PR obs 1D-Var/RR 1D-Var/BT Case of tropical cyclone ZOE (26 December 2002 @1200 UTC)

14 GHz Radar Reflectivity Cross section (dBZ)

Evaluation of 1D-Var results using PR data

22°S 24°S 26°S 28°S 22°S 24°S 26°S 28°S

PR obs

174°W

100 4 1000 996 1000

172°W 170°W 168°W

1 00 8

176°W 174°W

100 4

172°W 172°W

1000

170°W 170°W 168°W 168°W

1 00 8 996 1000

176°W 174°W 172°W 170°W 168°W 166°W 166°W 166°W 166°W 45 40 22°S 35 22°S 30 24°S 27.5

24°S 26°S 25 22.5

26°S 28°S 20 15 10 28°S

Background 100 4

172°W

1000 996 1000

176°W 174°W 172°W 172°W

100 4

45 40 22°S 35 24°S 30 27.5

26°S 25 22.5

28°S 20 15 10 22°S 24°S 26°S 28°S

1000 996 1000

176°W 174°W 172°W 170°W 168°W

1 00 8

170°W 170°W 168°W 168°W

1 00 8

170°W 168°W 166°W 166°W 166°W 45 40 22°S 35 30 24°S 27.5

26°S 25 22.5

28°S 20 15 10 45 40 22°S 35 24°S 30 27.5

26°S 25 22.5

28°S 20 15 10 166°W

Case of tropical cyclone AMI (14 January 2003 @1800 UTC)

14 GHz Radar Reflectivity at ~2km (dBZ) and Mean Sea Level Pressure (hPa)

Evaluation of 1D-Var results using PR data

400 400 500 500 600 600 700 700 800 800 900 174 O W 28 O S 10 15 20 22.5

172 O W 27 O S 25 27.5

26 O S 30 170 O W 35 25 O S 40 400

1D-Var/RR Z RR analysis (dBZ) AMI 2003-01-14 18:00:00

45 168 O W 24 O S 900 174 O W 28 O S 10 400 15 20 22.5

172 O W 27 O S 25 26 O S 27.5

30 170 O W 35 25 O S 40 45 168 O W 24 O S 500 500 600 600 700 700 800 900 174 O W 28 O S 10 15 20 22.5

27 O S 172 O W 25 26 O 27.5

S 30 170 O W 35 25 O S 40 800 45 168 O W 24 O S 900 174 O W 28 O S 10 15 20 22.5

27 O S 172 O W 25 26 O S 27.5

30 170 O W 35 25 O S 40 45 168 O W 24 O S 14 GHz Radar Reflectivity Cross Section (dBZ)

Statistical evaluation of 1D-Var results

Bias (solid) and rms (dashed) as a function of reflectivity Scatterplot of model Z vs obs • Background has higher bias than retrievals • Observations tend to show larger values (this could be also due to the fact that PR only ‘sees’ rain ) • Little difference between 1D-Var/RR and 1D-Var/BT

Background 1D-Var/BT 1D-Var/RR

•PR Data from 21 tropical cyclones that were observed between January and April 2003) were used to evaluate the retrieval results.

•The 1D-Var/BT and 1D-Var RR were run for all cases and statistics were collected

Statistical evaluation of 1D-Var results

Heidke Skill Score Probability distribution functions

HSS=1 good skill HSS=0 poor skill

• Retrievals are more skillful than background • 1D-Var/BT slightly more skillful than 1D-Var/RR at large reflectivity values

PR obs Background 1D-Var/BT 1D-Var/RR

Ongoing Research and Future Validation Work

• TRMM-Precipitation Radar data is a viable tool to make quantitative assessments regarding the quality of ECMWF precipitation retrievals. • Global PR data analysis with an improved averaging statistics is currently being investigated.

to obtain more robust • PR data will be further used for evaluation of the TMI 1D+4D-Var analysis and subsequent forecast • Plans to use the PR data to study the spatial distribution of precipitation for verification of the forecast model are also ongoing research

1D+4D-Var assimilation of TRMM data 1D-Var on TBs or reflectivities TMI TBs or TRMM-PR reflectivities 1D-Var on TMI or PR rain rates Retrieval algorithm (2A12,2A25) “Observed” rainfall rates Observations interpolated on model’s T511 Gaussian grid moist physics + radiative transfer or reflectivity model

1D-Var (T,q increments)

moist physics background T,q v TCWV obs



TCWV bg

   z q v dz

4D-Var

background T,q v

1D-Var on TRMM/Precipitation Radar data

2A25 Rain Background Rain 1D-Var Analysed Rain 2A25 Reflect .

Background Reflect.

1D-Var Analysed Reflect .

Tropical Cyclone Zoe (26 December 2002 @1200 UTC) Vertical cross-section of rain rates (top, mm h -1 ) and reflectivities (bottom, dBZ): observed (left), background (middle), and analysed (right).

Black isolines on right panels = 1D-Var specific humidity increments .

400 500 600 700 800 900 Close-ups on 1D-Var using PR reflectivities with different error assumptions on obs

TRMM PR reflectivity (dBZ)

173 O E 13 O S 174 O E 12 O S 175 O E 11 O S 176 O E 10 O S 177 O E 20 178 O E 9 O S 15 32.5

30 27.5

25 22.5

45 40 37.5

35 400

1D-Var 25% error at all levels Model reflectivity (dBZ) and humidity increments (g/kg) err=constant 25%, all levels

45

1 .5

0 0 .2

0 .5

1 2 2 .2

.2

40

.5

3 0 .5

3

37.5

500 35 32.5

600

0 .2

.5

0 0 .2

1 2 1 2

700 800

.5

.5

0 .5

0 .2

0 .2

30 27.5

25 22.5

900 173 O E 13 O S

0.2

174 O E 12 O S 175 O E 11 O S 176 O E 10 O S 177 O E 20 178 O E 9 O S 15 400

Model reflectivity (dBZ) fist guess

45 40 37.5

500 600 700 35 32.5

30 27.5

25 22.5

800 900 173 O E 13 O S 174 O E 12 O S 175 O E 11 O S 176 O E 10 O S 177 O E 20 178 O E 9 O S 15 400

1D-Var 50% error at all levels Model reflectivity (dBZ) and humidity increments (g/kg) err=constant 50%, all levels

45

1 .2

.2

0 .5

0 .2

.2

0 0 .2

.2

.5

.2

0 .5

40

2 2 0 .5

37.5

500 35

1

600 700 800

0 .2

.5

1 0 .5

1 .5

0 .5

2 0 .2

32.5

30 27.5

25 22.5

900

.5

173 O E 13 O S 174 O E 12 O S 175 O E

0 .2

11 O S 176 O E

0 .5

10 O S 177 O E 20 178 O E 9 O S 15

1D-Var retrievals using PR: observations at one level only vs full profile

TRMM PR reflectivity (dBZ)

400 500 600 700 800 900 173 O E 13 O S 174 O E 12 O S 175 O E 11 O S 176 O E 10 O S 177 O E 400

Model reflectivity (dBZ) fist guess

45 500 600 700 800 900 173 O E 13 O S 174 O E 12 O S 175 O E 11 O S 176 O E 10 O S 177 O E 27.5

25 22.5

20 178 O E 9 O S 15 40 37.5

35 32.5

30 20 178 O E 9 O S 15 45 40 37.5

35 32.5

30 27.5

25 22.5

1D-Var obs at all levels

400

Model reflectivity (dBZ) and humidity increments (g/kg) err=constant 25%, all levels 1 .5

0 0 .2

.5

0 1 2 .2

2 .2

.5

0 .5

3 3

500 600 700 800 900 173 O E 13 O S

0 .2

.5

0.2

174 O E 12 O S

0 .5

0 .2

1

175 O E

2

11 O S

.5

176 O E

1 0 .5

0 .2

2

10 O S 177 O E

.2

0

45 40 25 22.5

20 178 O E 9 O S 15 37.5

35 32.5

30 27.5

400

1D-Var obs at level 48 (~2km) Model reflectivity (dBZ) and humidity increments (g/kg) err=constant 25% , level 28 only .2

.2

0 .5

.2

.2

0 0 .5

1 1

500

-0 .2

0 .2

600 700 800 900

.5

173 O E 13 O S

-0 .2

0 .2

.5

174 O E 12 O S

1 0.2

175 O E

0 .5

11 O S 176 O E

2 1 0 .5

0 .2

10 O S 177 O E 27.5

25 22.5

20 178 O E 9 O S 15 45 40 37.5

35 32.5

30

Background and 1D-Var increments in Total Column Water Vapour (pseudo-obs for 4D-Var) from PR reflectivities

0° 10°S 20°S 170°E 180° kg/m2 TCWV increments (kg/m^2) 175°E 0° 75 70 65 60 55 10°S 50 45 40 35 10°S 15°S 25 20 170°E 175°E

Increments indicate an overall moistening confined along the satellite track

kg/m2 25 20 1 -1 -2 -3 5 3 2 -10 -20 -25

4D-Var differences in Total Column Water Vapour and Mean Sea Level Pressure (MSLP) Between experiment with PR data and control experiment (no PR data)

0° 0° 10°S 20°S 170°E 180° 180° kg/m2 20 10 0° 5 3 2 1 -0.5

-1 -2 -3 -10 -20

No initial impact on the dynamics is evident in the analysis. At 1200UTC, changes in Mean Sea Level Pressure are developing and appear to persist well into the forecast indicating a shift in the location of the storm with respect to the control run.

180° kg/m2 20 10 0° 5 3 2 1 10°S -1 -0.5

-0.5

-1 -2 -3 20°S -10 -20 170°E 180° Forecast: 28 Dec. 2003, 1200UTC 170°E 180° kg/m2 20 0° -0 .5

10 0° 5 3 2 1 10°S -0 .5

-2 -1 -0 .5

-0 .5

-0.5

-1 -2 -3 20°S -0.5

-10 -20 170°E 180°

Comparison 1D+4D-Var assimilation of TRMM-PR rain rates/reflectivities: Impact on analysed and forecast TCWV and MSLP (Experiment – Control) (Tropical Cyclone Zoe, 26-28 December 2002) Analysis at 300UTC, Dec 26 Forecast at 1200UTC, Dec 26.

Forecast at 1200UTC, Dec 28.

168E 10S 1D+4D-Var assimilation of TRMM-PR and TMI observations: Impact on tropical cyclone Zoe track forecast (26-31 December 2002) ZOE TRACK FORECAST (BASE: 2002122612) 170E 172E 174E 176E 178E 10S 0 Comparison of forecast tracks from: 6 12 24 18 control run (no TRMM data), 12S 12S 48 60 36 - observations, 14S 72 84 TMI-TB TMI-RAIN PR-RAIN PR-Z CONTROL OBS 14S - 1D+4D on TMI TBs, - 1D+4D on TMI Rain Rates, 96 - 1D+4D on TRMM/PR Rain Rates, 16S 16S 108 - 1D+4D on TRMM/PR Reflectivities 18S 168E 170E 172E 174E 176E 18S 178E Coloured labels indicate forecast times (in hours) -As suggested by the MSLP changes, the track forecasts are improved when TRMM observations are assimilated in rainy areas especially when using TMI Brightness Temperatures.

-Despite the smaller spatial coverage of TRMM/PR data (200-km swath) compared to that of TMI data (780-km swath), the impact of these type of observations is non-negligible.

1D+4D-Var assimilation of precipitation: preliminary conclusions Observations TMI RR TMI TB TRMM/PR RR TRMM/PR Z pros computationally cheap sensitivity to RR, cloud and WV flexibility of channels land and ocean, vertical info land and ocean, vertical info cons only if rainy background & over ocean algorithm-dependent (2A12, PATER,…) computational cost of RTM over ocean only limited spatial coverage limited spatial coverage All four methods manage to converge in various meteorological situations (large scale/convective precipitation, tropics/mid-latitudes).

   4D-Var is able to digest TCWV retrievals produced by 1D-Var on TMI and TRMM/PR observations in rainy areas.

The significant impact on the humidity field seen at analysis time can be kept during the forecast, and the dynamics is affected accordingly. In the studied TC case, assimilating TMI and TRMM/PR observations improve the TC track and minimum MSLP forecasts.

1D+4D-Var assimilation of precipitation: preliminary conclusions (2)

TRMM/PR Rain Rates versus TRMM/PR Reflectivities ?

 Observational errors may be easier to prescribe for reflectivities than for 2A25 derived rain rates.

  Inclusion of vertical correlations of observation errors has a marginal impact on the 1D-Var results.

The extra computational cost for running the reflectivity model is reasonable.

TMI versus TRMM/PR ?

 Including the information on the vertical distribution of rainfall contained in the TRMM/PR observations improves the 1D-Var retrieved rain rate profiles.

 Despite their smaller spatial coverage, the impact of TRMM/PR data is comparable to that of TMI data.

 TRMM/PR data can be used over land and ocean areas, whereas TMI data are currently restricted to ocean (surface emissivity over land).

1D+4D-Var assimilation of precipitation: prospects • Cycle 1D+4D-Var assimilation areas over several months:  of TRMM and SSM/I observations in rainy global scores, study of specific events, assessment of the different 1D-Var methods.

• • Improve the determination of observation and model error statistics.

Address the issue related to the use of satellite passive microwave data over land .

• Assess the potential of the assimilation of ground-based radar data , but problem of availability (non real-time, country-dependent)?

• • Until when will TRMM observations be available?

Looking forward to GPM (global coverage, better temporal resolution, information on atmospheric ice?).

• 1D+4D-Var assimilation of SSM/I (and TMI data ?) expected to become operational in 2004.

Some statistics…..

We defined a ‘confusion matrix’ for grid points where first guess and 1D-var BT and RR retrievals hit/miss with respect to PR Observed YES Observed NO Predicted YES A C Predicted NO B D Then we defined the Heidke Skill Score (HSS): 2(AD-BC) B*B + C*C + 2*A*D + (B+C)*(A+D)