1. Satellite Data Assimilation in Cloudy and Precipitation Conditions Fuzhong Weng NOAA/NESDIS/Center for Satellite Applications and Research and Sr. Scientist, US Joint Center for Satellite Data Assimilation The 4 th International Precipitation Working Group Meeting October 13-17, 2008

2. Content JCSDA Program Update Satellite Data Utilization in GFS Improving Uses of Satellite Data in Cloudy Conditions Summary and Conclusions

3. JCSDA Partners Pending In 2001 the Joint Center was established 2 by NASA and NOAA and in 2002, the JCSDA expanded its partnerships to include the U.S. Navy and Air Force weather agencies. 2 Joint Center for Satellite Data Assimilation: Luis Uccellini, Franco Einaudi, James F. W. Purdom, David Rogers: April 2000.

4. JCSDA Science Priorities I Improve Radiative Transfer Models II Prepare for Advanced Operational Instruments III Assimilating Observations of Clouds and Precipitation IV Assimilation of Land Surface Observations from Satellites V Assimilation of Satellite Oceanic Observations VI Assimilation for air quality forecasts

5. SATELLITE DATA STATUS in NCEP GFS – May 2008 Jason AltimeterImplemented into NCEP GODAS AIRS with All Fields of ViewImplemented – 1 May MODIS WindsImplemented– 1 May NOAA-18 AMSU-AImplemented– 1 May NOAA-18 MHSImplemented– 1 May NOAA-17 SBUV Total Ozone4 December 2007 NOAA-17 SBUV Ozone ProfileImplemented– ??? SSMI/S RadiancesPreliminary forecast assessment completed GOES 1x1 sounder radiancesImplemented 29 May 2007 METOP AMSU-A, MHS, HIRSImplemented 29 May 2007 COSMIC/CHAMP Implemented (COSMIC – 1 May) CHAMP Data in prep. MODIS Winds v2.Test and Development METOP IASI/ASCATPreliminary forecast assessment completed AMSR/E RadiancesPreliminary forecast assessment completed AIRS/MODIS Sounding Channels Assim.Data in Preparation JMA high resolution windsImplemented 4 December 2007 GOES Hourly Winds, SW WindsTo be Tested GOES 11 and 12 Clear Sky Rad. Assim(6.7µm)To be Tested MTSAT 1R Wind Assim.Data in Preparation AURA OMITest and Development TOPEX,ERS-2 ENVISAT ALTIMETERTest and Development (Envisat) ERS-2 (dead) TOPEX implemented in NCEP GODAS FY – 2CData in Preparation

6. Satellite Data Ingested into Models and Future Data Stream

7. JCSDA Budget Initiative Summary: Requested increase in JCSDA funding will accelerate uses of satellite measurements under cloudy and precipitation conditions and will improve the skill for forecasts up to 10 days in length, and predict the intensity and track of severe weather forecasts. Currently, temperature and moisture profiles in cloudy regions are poorly understood and difficult to extract from the available satellite data due to a lack of capabilitiyies in assimilating satellite cloudy and rain-affected radiances. FY08 NOAA Budget: $3.3M and other leveraged resources: $1.5M FY09 NOAA Budget: +$0.6M

8. Community Radiative Transfer Model Support over 100 Sensors GOES-R ABI Metop IASI/HIRS/AVHRR/AMSU/MHS TIROS-N to NOAA-18 AVHRR TIROS-N to NOAA-18 HIRS GOES-8 to 13 Imager channels GOES-8 to 13 sounder channel 08-13 Terra/Aqua MODIS Channel 1-10 METEOSAT-SG1 SEVIRI Aqua AIRS Aqua AMSR-E Aqua AMSU-A Aqua HSB NOAA-15 to 18 AMSU-A NOAA-15 to 17 AMSU-B NOAA-18 MHS TIROS-N to NOAA-14 MSU DMSP F13 to15 SSM/I DMSP F13,15 SSM/T1 DMSP F14,15 SSM/T2 DMSP F16 SSMIS NPP ATMS Coriolis Windsat TiROS-NOAA-14 SSU “Technology transfer made possible by CRTM is a shining example for collaboration among the JCSDA Partners and other organizations, and has been instrumental in the JCSDA success in accelerating uses of new satellite data in operations” – Dr. Louis Uccellini, Director of National Centers for Environmental Prediction

9. GFS Impacts: Anomaly Correlations

10. Pre-operational implementation run PRYnc (assimilation of operational obs ), PRYc (PRYnc + COSMIC refractivity) We assimilated around 1,000 COSMIC profiles per day Anomaly correlation as a function of forecast day (geopotential height) rms error (wind)

11. Cloud Detection Algorithm & Assimilation Impact Over oceans: SSM/I CLW heritage algorithm (Weng &Grody, 1994), where SSMIS TBs are remapped to SSM/I TB (Yan &Weng, TGRS, 2008) Over land: a newly developed cloud detection algorithm is used. SSMIS IWP algorithm is developed by Sun & Weng (2008, TGRS) based on the AMSU IWP heritage algorithm by Zhao & Weng (2002, JAM).

12. ASCAT Impacts

13. Required Improvements for Assimilation of Passive Microwave Satellite Data Better bias correction Improved surface emissivity model Better cloud detection algorithms Direct cloudy radiance assimilation

14. Variational Bias Correction p: predictor b : bias correction coefficient Update the bias inside the assimilation system by finding corrections that minimize the systematic radiance departures while simultaneously improving the fit to other observed data inside the analysis flow. Major predictors  Scan angle or scan position  Lapse rate (  )  Lapse rate squared (  2 )  Cloud liquid water

15. METOP AMSUA F16 SSMIS (UPP) O – B Histograms for QC Passed Data over (Cloud-free) Oceans N18 AMSUA wobc wbc

16. Cloud Detection Algorithm & Assimilation Impact SSM/I and AMSU CLW algorithms (Weng &Grody, 1994, JGR; Weng et al., 2001. TGRS) MHS and SSMIS IWP algorithm (Zhao & Weng, 2002, JAM; Sun & Weng, 2008, TGRS)

17. Atmospheric Transmittance (a) Atmospheric Transmittance at 52.8 GHz (b) Atmospheric Transmittance at 183  7 GHz (c) Atmospheric Transmittance at 183  3 GHz(d) Atmospheric Transmittance at 183  1 GHz

18. Impacts of Snow & Sea Ice Emissivity SSMIS and MHS include several sounding channels sensitive to variable emissivity especially over snow and sea ice conditions Improved snow and sea ice emissivity models result in around 60% of SSMIS and MHS sounding data passing QC The impact of the MHS data using the new emissivity model is positive a positive impact a positive impact

19. Assimilation of Cloudy and Rain-Affected Radiances – Current Approaches JCSDA  Radiances in cloudy areas (rainy pixels rejected) are handled as clear pixels in forward calculation  Radiance biases due to clouds are corrected through bias correction algorithms ECMWF  LWP is first retrieved from simple algorithms and used to check if radiances are clear or cloudy/rainy. Clear pixels directly go to 4dvar  For cloudy/rainy pixels, it goes to 1dvar for better refinement in LWP, TPW, and other parameters. TPW in rainy areas is assimilated in 4dvar  Impacts of TPW on other analysis fields (T, Q, and Wind) are done through cloud and moisture physics in 4dvar system Metoffice  Atmospheric parameters under cloudy and precipitating conditions are retrieved from 1dvar and the 1dvar convergence flag is used to control the radiances into 4dvar, also rain and non-rain pixels  4dvar process include several key hydrometeor parameters (no precipitation) and TL/AD from cloud and moisture physics.

20. Note: Data over thick cloudy area are screened out but those over thin cloudy area have been assimilated without including cloudy radiance computation QC Issues in Handling Cloudy Radiances AMSU Cloud-free Data Over OceanAMSU Data Passed through QC

21. New Considerations in Cloudy Radiance Assimilation at JCSDA Develop forward radiative transfer and Jacobian models including clouds and precipitation Use 1dvar quality control of satellite radiances Extent the control variables with more hydrometeor parameters Incorporate cloud and moisture physics in minimization processes Improve bias corrections using more predictors (e.g. LWP and RWP) from observations and/or moisture physics

22. Direct SSMIS Cloudy Radiance Assimilation The initial temperature field from control run (left panels) w/o uses of SSMIS rain-affected radiances and test run (right panels) using SSMIS rain-affected radiances DMSP F-16 SSMIS radiances is at the first time assimilated using NCEP 3Dvar data analysis. The new data assimilation improves the analysis of surface minimum pressure and temperature fields for Hurricane Katrina. Also, Hurricane 48-hour forecast of hurricane minimum pressure and maximum wind speed was significantly improved from WRF model Significance: Direct assimilation of satellite radiances under all weather conditions is a central task for Joint Center for Satellite Data Assimilation (JCSDA) and other NWP centers. With the newly released JCSDA Community Radiative Transfer Model (CRTM), the JCSDA and their partners will be benefited for assimilating more satellite radiances in global and mesoscale forecasting systems and can improve the severe storm forecasts in the next decade Control Experiment

23. Katrina Warm Core Evolution through NCEP GSI Analysis

24. Uses of 1dvar for QC

25. MIRS Environmental Data Records SDR/EDRPOES/METOP AMSU-A/B; MHS DMSP SSMIS NPOESS ATMS/MIS Radiances Temp. profile Moist. profile Total precipitable water* Hydr. profile Precip rate* Snow cover* Snow water equivalent* Sea ice * Cloud water* Ice water* Land temp* Surface emis* Soil moisture/Wetness Index *currently from MSPPS only

26. 1DVAR including All hydrometeors MIRS LWP ECMWF

27. MiRS T vs. RAOB (Ocean)

28. MiRS T vs. RAOB (Land)

29. MiRS Q vs. RAOB (Ocean)

30. MiRS Q vs. RAOB (Land)

31. Minimization Process including Moisture Physics If the control variables are only a subset of atmospheric variables, cloud hydrometeors are derived from the control variables Then, Jacobian, i.e. radiance gradient relative to the control variable will be also affected by moisture physics

32. Water vapor Clouds Precipitation Large scale condensation Convective condensation Precipitation production (Rain or snow) (water or ice) Water vapor Clouds LiquidIce Cloud Condensation Cloud Evaporation RainSnow Accretion Auto- conversion Aggregation Melting Accretion Evaporation of rain Evaporation of snow Falling out Zhao and Carr (1997) Simplified Arakawa Schubert scheme Zhao and Carr (1997) Sundqvist et al. (1989) ) Including GFS Cloud and Moisture Physics FW, TL, and AD models based on Zhao and Carr (1997) microphysics scheme exist in the current GDAS system and will be tested in 1dvar system (off-line test). 1dvar is a simple version of GSI 3dvar (background covariance matrix is derived from NMC method)

33. Summary and Conclusions NOAA is taking a new initiative on assimilation of cloudy and rain-affected radiances through JCSDA program Microwave sensor data from POES, DMSP, EOS are vital for global medium range forecasts and have produced largest impacts through better bias correction, snow and sea ice emissivity models 1dvar system including cloudy and rain-affected radiances are developed and used in NESDIS operation for sounding products. Impacts from uses of rain-affected radiances in variational analysis are encouraging for storm intensity prediction and better moisture field

34. Acknowledgements Dr. Banghua Yan, JCSDA/Perot System – Emissivity model and cloud detection, bias correction Dr. Min-jeong Kim, JCSDA/CIRA – TL/Adjoint moisture physics Dr. Sid-Boukabara, NESDIS – 1DVAR Dr. John Derber, JCSDA/NCEP – 3dvar/4dvar and bias corrections Drs. Yong Han (NESIDS), Paul vanDelst (NCEP), Mar Liu (JCSDA), Yong Chen (JCSDA/CIRA): CRTM team