The Joint Center For Satellite Data Assimilation JCSDA Helping to improve Climate, Weather, Ocean and Air Quality

1. Note prep for 4d-cts data, rad ass for EnKF etc.Note prep for 4d-cts data, rad ass for EnKF etc.

3. Overview JCSDA – Background The Satellite Program – The Challenge JCSDA NPOESS Preparation for NPOESS using Heritage Instruments Plans/Future Prospects Summary

4. Background The value of satellite Observations

5. Begin with 500 mb day 5 anomaly correction from NCEP global forecast system. If the coeff. is greater than 60%, we can say the model can predict the synoptic scale features with a reasonable skill. Overall, we see a steady growth of NWP prediction skill in last few decades. This growth is due to 1) the advance in model physics, 2) an increasing computing power that allows us run high resolution models, and 3) using more and more satellite data. After 1998, you see a sudden jump in the skills both in N/S hemisphere. This is mainly attributed to the first assimilation of AMSU. In SH, there is not a lot of conventional data, satellite measurements have filled in the data void areas. Today, we have seen the ACs in SH and NH are approaching to the same level. Begin with 500 mb day 5 anomaly correction from NCEP global forecast system. If the coeff. is greater than 60%, we can say the model can predict the synoptic scale features with a reasonable skill. Overall, we see a steady growth of NWP prediction skill in last few decades. This growth is due to 1) the advance in model physics, 2) an increasing computing power that allows us run high resolution models, and 3) using more and more satellite data. After 1998, you see a sudden jump in the skills both in N/S hemisphere. This is mainly attributed to the first assimilation of AMSU. In SH, there is not a lot of conventional data, satellite measurements have filled in the data void areas. Today, we have seen the ACs in SH and NH are approaching to the same level.

8. The Challenge 5-Order Magnitude Increase in Satellite Data Over 10 Years

10.  5-Order Magnitude Increase in satellite Data Over 10 Years Currently, we use ~105 observations per day in operational models out of total of more than 106. There are estimated to be ~1011 observations per day by 2010. This estimate does NOT include GIFTS or Doppler Wind Lidar. This volume of data is outside our current capacity and design bracket for most data assimilation systems. Last column represents using 10 percent of the data volume (note logarithmic scale). That is what feasible for scientific and logistical reasons. Note that the ordinate in the left panel has a logarithmic scale.Currently, we use ~105 observations per day in operational models out of total of more than 106. There are estimated to be ~1011 observations per day by 2010. This estimate does NOT include GIFTS or Doppler Wind Lidar. This volume of data is outside our current capacity and design bracket for most data assimilation systems. Last column represents using 10 percent of the data volume (note logarithmic scale). That is what feasible for scientific and logistical reasons. Note that the ordinate in the left panel has a logarithmic scale.

14. The JCSDA History, Mission, Vision, ….

15. History

16. JCSDA Partners

17. JCSDA Mission and Vision Mission: Accelerate and improve the quantitative use of research and operational satellite data in weather. ocean, climate and environmental analysis and prediction models Vision: A weather, ocean, climate and environmental analysis and prediction community empowered to effectively assimilate increasing amounts of advanced satellite observations and to effectively use the integrated observations of the GEOSS

18. JCSDA SCIENCE PRIORITIES Science Priority I - Improve Radiative Transfer Models - Atmospheric Radiative Transfer Modeling – The Community Radiative Transfer Model (CRTM) - Surface Emissivity Modeling Science Priority II - Prepare for Advanced Operational Instruments Science Priority III -Assimilating Observations of Clouds and Precipitation - Assimilation of Precipitation - Direct Assimilation of Radiances in Cloudy and Precipitation Conditions Science Priority IV - Assimilation of Land Surface Observations from Satellites Science Priority V - Assimilation of Satellite Oceanic Observations Science Priority VI – Assimilation for air quality forecasts

19. Goals – Short/Medium Term Increase uses of current and future satellite data in Numerical Weather and Climate Analysis and Prediction models Develop the hardware/software systems needed to assimilate data from the advanced satellite sensors Advance common NWP models and data assimilation infrastructure Develop a common fast radiative transfer system(CRTM) Assess impacts of data from advanced satellite sensors on weather and climate analysis and forecasts (OSEs,OSSEs) Reduce the average time for operational implementations of new satellite technology from two years to one Point 2 AIRS, IASI, CRIS, GIFTS, GOES RPoint 2 AIRS, IASI, CRIS, GIFTS, GOES R

20. Major Accomplishments Common assimilation infrastructure at NOAA and NASA Community radiative transfer model Common NOAA/NASA land data assimilation system Interfaces between JCSDA models and external researchers Snow/sea ice emissivity model – permits 300% increase in sounding data usage over high latitudes – improved polar forecasts MODIS winds, polar regions, - improved forecasts - Implemented AIRS radiances assimilated – improved forecasts - Implemented Improved physically based SST analysis - Implemented Preparation for advanced satellite data such as METOP (IASI,AMSU,MHS…), , NPP (CrIS, ATMS….), NPOESS, GOES-R data underway. Advanced satellite data systems such as DMSP (SSMIS), CHAMP GPS, COSMIC GPS, WindSat tested for implementation. Impact studies of POES AMSU, HIRS, EOS AIRS/MODIS, DMSP SSMIS, WindSat, CHAMP GPS on NWP through EMC parallel experiments active Data denial experiments completed for major data base components in support of system optimisation OSSE studies completed Strategic plans of all Partners include 4D-VAR

21. Satellite Data used in NWP HIRS sounder radiances AMSU-A sounder radiances AMSU-B sounder radiances GOES sounder radiances GOES, Meteosat, GMS winds GOES precipitation rate SSM/I precipitation rates TRMM precipitation rates SSM/I ocean surface wind speeds ERS-2 ocean surface wind vectors Quikscat ocean surface wind vectors AVHRR SST AVHRR vegetation fraction AVHRR surface type Multi-satellite snow cover Multi-satellite sea ice SBUV/2 ozone profile and total ozone Altimeter sea level observations (ocean data assimilation) AIRS MODIS Winds …

24. NPOESS The Instruments Applications -NWP/Environmental Analysis and Prediction -Ocean Analysis and Prediction -Climate – Short/Long Term Prediction, Reanalyses (tuning, calibration) Preparations for NPOESS– The CRTM Preparations for NPOESS- Assimilating Data from Heritage Instruments

25. NPOESS The Instruments Applications -NWP/Environmental Analysis and Prediction -Ocean Analysis and Prediction -Climate – Short/Long Term Prediction, Reanalyses (tuning, calibration)

30. NPOESS Applications -NWP/Environmental Analysis and Prediction -Ocean Analysis and Prediction -Climate – Short/Long Term Prediction, Reanalyses (tuning, calibration) Note Multi-Variate Multi-Instrument Analysis (Radiance) Observations Tuned/Cross Calibrated -Differences due to Radiative Transfer Error , Model Error (bias), Observation Error, Calibration Error, …. Modern Analysis Methods Aid Use Of Asynoptic Observations

31. JCSDA NPOESSPreparation Preparations for NPOESS– The CRTM Preparations for NPOESS- Assimilating Data from Heritage Instruments

32. PREPARATION FOR NPOESS:Development and Implementation of the Community Radiative Transfer Model (CRTM)

33. Community Contributions Community Research: Radiative Transfer science AER. Inc: Optimal Spectral Sampling (OSS) Method NRL – Improving Microwave Emissivity Model (MEM) in deserts NOAA/ETL – Fully polarmetric surface models and microwave radiative transfer model UCLA – Delta 4 stream vector radiative transfer model UMBC – aerosol scattering UWisc – Successive Order of Iteration CIRA/CU – SHDOMPPDA UMBC SARTA Princeton Univ – snow emissivity model improvement NESDIS/ORA – Snow, sea ice, microwave land emissivity models, vector discrete ordinate radiative transfer (VDISORT), advanced double/adding (ADA), ocean polarimetric, scattering models for all wavelengths Core team (JCSDA - STAR/EMC): Smooth transition from research to operations Maintenance of CRTM (OPTRAN/OSS coeffs., Emissivity upgrade) CRTM interface Benchmark tests for model selection Integration of new science into CRTM

34. CRTM has been integrated into the GSI at NCEP/EMC Beta version CRTM has been released to the public CRTM with OSS (Optimal Spectral Sampling) has been preliminarily implemented and is being evaluated and improved.

37. NPOESS/JCSDA The NPOESS Instruments : CrIS ATMS VIIRS OMPS CMIS GPSOS-demanifest

63. Discussion and Conclusions Overall positive impact Post NESDIS QC used, particularly for gross errors cf. background and for winds above tropopause Implemented in JCSDA Partner Organisations JCSDA is ready for VIIRS polar wind assimilation

66. SSMIS Radiance Assimilation

67. SSM/IS Radiance Assimilation in GSI

68. NCEP AMSR-E Radiance Assimilation

69. AMSR-E Radiance Assimilation in GSI

71. JCSDA WindSat Testing Coriolis/WindSat data is being used to assess the utility of passive polarimetric microwave radiometry in the production of sea surface winds for NWP Study accelerates NPOESS preparation and provides a chance to enhance the current global system Uses NCEP GDAS

72. JCSDA WindSat Testing Experiments Control with no surface winds (Ops minus QuikSCAT) Operational (QuikSCAT only) WindSat only QuikSCAT & WindSat winds Assessment underway

73. Ops(no AIRS, no aqua amsu, quikscat), Ops + windsatOps(no AIRS, no aqua amsu, quikscat), Ops + windsat

74. Ops(no AIRS, no aqua amsu, quikscat), Ops + windsatOps(no AIRS, no aqua amsu, quikscat), Ops + windsat

75. Ops(no AIRS, no aqua amsu, quikscat), Ops + windsatOps(no AIRS, no aqua amsu, quikscat), Ops + windsat

76. Assimilation of GPS RO observations at JCSDA Lidia Cucurull, John Derber, Russ Treadon, Jim Yoe…

78. GPS RO / COSMIC

79. GPS RO /COSMIC : The COnstellation of Satellites for Meteorology, Ionosphere, and Climate A Multinational Program Taiwan and the United States of America A Multi-agency Effort NSPO (Taiwan), NSF, UCAR, NOAA, NASA, USAF Based on the GPS Radio Occultation Method

80. GPS RO / COSMIC : Goals are to provide: Limb soundings with high vertical resolution All-weather operating capability Measurements of Doppler delay based on temperature and humidity variations, convertible to bending angle, refractivity, and higher order products (i.e., temperature/humidity) Suitable for direct assimilation in NWP models Self-calibrated soundings at low cost for climate benchmark

81. Information content from1D-Var studiesIASI (Infrared Atmospheric Sounding Interferometer)RO (Radio Occultation) - METOP Normalized error is an indication of how the measurement has improved upon the forecast. It is the measurement error divided by the forecast error. So if the forecast error is 1K and the measurement error is 0.5K, the normalized error is 0.5. The forecast error varies with altitude and latitude; it is ~1.2K in the extratropics and ~0.9K in the tropics. A normalized error of less than 1.0 improves the forecast.Normalized error is an indication of how the measurement has improved upon the forecast. It is the measurement error divided by the forecast error. So if the forecast error is 1K and the measurement error is 0.5K, the normalized error is 0.5. The forecast error varies with altitude and latitude; it is ~1.2K in the extratropics and ~0.9K in the tropics. A normalized error of less than 1.0 improves the forecast.

82. GPS RO / COSMIC (cont’d): COSMIC launched April 2006 Lifetime 5 years Operations funded through March 08

85. Summary NPOESS will provide higher spatial and spectral resolution data for environmental and climate applications These data, used with modern data assimilation methods, will lead to significantly improved weather, ocean, climate and air quality forecasts. Experience has shown for early data exploitation it is vital JCSDA is involved in CAL/VAL activity and early data evaluation. Community RTM and emissivity model being expanded to include NPP then NPOESS instruments. Risk Reduction/OSSE Studies have been undertaken in support of NPOESS Work on AIRS, AMSU, AVHRR and MODIS assimilation as a prelude to using CrIS, ATMS and VIIRS on NPOESS is ongoing. Assimilation of GPS (CHAMP/COSMIC/GPSOS/GRAS) well advanced and will improve upper troposphere reanalyses.

86. Summary Preparation for a polarimetric scanner/imager well underway using SSMI, SSMIS, AMSR(E) and WINDSAT observations. OMPS will be added to O3 sensing suite Modern data assimilation methods will aid in calibration/cross calibration, QC, climate analysis (use asynoptic obs),…… Some important environmental/climate observations still require attention Aerosols Solar irradiance Sea level Earth radiation budget

87. Closing Remarks The next decade of the meteorological (multipurpose) satellite program promises to be as exciting as the first, given the opportunities provided by new observations, modern data assimilation techniques, improved environmental modeling capacity and burgeoning computer power. NPOESS will provide essential observations for improved environmental (ocean, atmosphere, climate) modeling and for improved climate data sets JCSDA will be well positioned to exploit the NPOESS component of the GEOSS in terms of: Assimilation science Modeling science. Computing power