1. 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

2. 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

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

4. TROPICAL RAINFALL MEASURING MISSION 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/

5. 1D-Var retrievals from TRMM data Evaluation of 1D-var 1D-Var (TCWV, snow and rainfall rates) moist physics + radiative transfer background T,q v “Observed” rainfall rates Retrieval algorithm (2A12,PATER) 1D-Var on Brightness Temp.1D-Var on TMI rain rates Observations interpolated on model’s T511 Gaussian grid TMI Brightness Temp (Tb) Radar Forward Model PR reflectivity RETRIVALRETRIVAL VALIDATIONVALIDATION

6. -Based on Mie look-up tables for the computation of reflectivity and extinction, assumes a Marshall-Palmer distribution for rain and snow particles -Includes treatment of bright band at 273K -Table entries are categorized according to rain/snow contents, temperature, frequency and hydrometeor category. -3D radar reflectivity at 14 GHz is computed via bilinear interpolation at the given model temperature and rain/snow content at each grid point and vertical level -Model 3D rain/snow contents are computed from precipitation fluxes assuming a fixed fall velocity (see Excursus I). Rainfall from TRMM Algorithms (2A12, PATER, etc.) Observed Radiances (TMI) Model FG T, q Forward radar model= equivalent reflectivity 1D-Var retrievals of rainfall and snowfall rate FG ‘rainy radiance’ 1D-Var retrievals of rainfall and snowfall rate TRMM-PR observations 1D-Var retrieval evaluation Validation of 1D-Var retrievals of rainfall from TMI radiances and TRMM Rainrates Moist physics Moist physics + radiative transfer FG rain and snow rates + Model FG T, q

7. 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 Forward radar model

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

9. 1D-Var results 1D-Var/RR PATER1D-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)

10. Evaluation of 1D-Var results using PR data Case of tropical cyclone ZOE (26 December 2002 @1200 UTC) 14 GHz Radar Reflectivity at ~2km (dBZ) Background 1D-Var/RR1D-Var/BT PR obs

11. Evaluation of 1D-Var results using PR data Case of tropical cyclone ZOE (26 December 2002 @1200 UTC) 14 GHz Radar Reflectivity Cross section (dBZ) Background 1D-Var/RR1D-Var/BT PR obs

12. Evaluation of 1D-Var results using PR data 1D-Var/RR1D-Var/BT PR obsBackground Case of tropical cyclone AMI (14 January 2003 @1800 UTC) 14 GHz Radar Reflectivity at ~2km (dBZ) and Mean Sea Level Pressure (hPa)

13. Evaluation of 1D-Var results using PR data 1D-Var/RR 1D-Var/BT PR obsBackground 14 GHz Radar Reflectivity Cross Section (dBZ)

14. Statistical evaluation of 1D-Var results 1D-Var/RR 1D-Var/BT Background 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 Bias (solid) and rms (dashed) as a function of reflectivity 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 Scatterplot of model Z vs obs

15. Statistical evaluation of 1D-Var results 1D-Var/RR 1D-Var/BT Background Heidke Skill Score Retrievals are more skillful than background 1D-Var/BT slightly more skillful than 1D-Var/RR at large reflectivity values HSS=1 good skill HSS=0 poor skill PR obs Probability distribution functions

16. 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 to obtain more robust statistics is currently being investigated. 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 Ongoing Research and Future Validation Work

17. 1D+4D-Var assimilation of TRMM data 4D-Var 1D-Var (T,q increments) moist physics + radiative transfer or reflectivity model background T,q v “Observed” rainfall rates Retrieval algorithm (2A12,2A25) 1D-Var on TBs or reflectivities1D-Var on TMI or PR rain rates Observations interpolated on model’s T511 Gaussian grid TMI TBs or TRMM-PR reflectivities

18. 1D-Var on TRMM/Precipitation Radar data 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. 2A25 RainBackground Rain1D-Var Analysed Rain 2A25 Reflect. Background Reflect. 1D-Var Analysed Reflect.

19. Close-ups on 1D-Var using PR reflectivities with different error assumptions on obs 1D-Var 25% error at all levels 1D-Var 50% error at all levels

20. 1D-Var retrievals using PR: observations at one level only vs full profile 1D-Var obs at all levels 1D-Var obs at level 48 (~2km)

21. Background and 1D-Var increments in Total Column Water Vapour (pseudo-obs for 4D-Var) from PR reflectivities TCWV guess (kg/m^2) Increments indicate an overall moistening confined along the satellite track TCWV increments (kg/m^2)

22. 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) Analysis: 26 Dec. 2003, 0300UTC Forecast: 26 Dec. 2003, 1200UTC 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. Forecast: 28 Dec. 2003, 1200UTC

23. 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 with PR rain rates Forecast at 1200UTC, Dec 26.Forecast at 1200UTC, Dec 28. with PR reflectivities

24. 1D+4D-Var assimilation of TRMM-PR and TMI observations: Impact on tropical cyclone Zoe track forecast (26-31 December 2002) Comparison of forecast tracks from: - control run (no TRMM data), - observations, - 1D+4D on TMI TBs, - 1D+4D on TMI Rain Rates, - 1D+4D on TRMM/PR Rain Rates, - 1D+4D on TRMM/PR Reflectivities 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.

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

26. 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: 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.

27. 1D+4D-Var assimilation of precipitation: prospects Cycle 1D+4D-Var assimilation of TRMM and SSM/I observations in rainy areas over several months:  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.

28. 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) Some statistics…..