Slide1: The National Polar-orbiting Operational Environmental Satellite System (NPOESS) Data Assimilation and NWP Benefits Dr. Stephen Lord Director, Environmental Modeling Center National Centers for Environmental Prediction ﴀ January 13, 2004


Data Assimilation and NWP Benefits : Data Assimilation and NWP Benefits EMC Director since March 2000 25 Years Experience in Atmospheric Modeling/Numerical Weather Prediction Post doctoral appointment at UCLA Nine years at Miami’s Hurricane Research Division/Atlantic Meteorological and Oceanographic Laboratory Active member of EMC’s development staff since 1989 Active research in tropical cyclone analysis and forecasting EMC Deputy Director, 1994-1997 EMC Acting Director, 1997-2000 Ph.D. Atmospheric Sciences, UCLA Dr. Stephen Lord Director, Environmental Modeling Center National Centers for Environmental Prediction


Overview: Overview Importance of Data Assimilation and Modeling NPOESS Data Volume and Instruments Preparation for the NPOESS Era The NASA-NOAA-DoD Joint Center for Satellite Data Assimilation Applied research areas Summary


Generic Weather Forecast Process: Generic Weather Forecast Process Observations Gridded Analysis Background (model) Information Forecast Model Output Forecast Postprocessed Information Forecaster (NWS, commercial) Save Lives & Property Weather-Sensitive Commerce ($2+ T) Every National Weather Service (NWS) forecast of more than six hours requires using environmental obs through Data Assimilation and Modeling (DAAM) Improvements to the Nation’s weather, water and climate forecasts come from: 1) DAAM 2) Increased computational resources 3) Improved observations through data sources such as NPOESS Communications, Dissemination


Improvements in 500 mb Forecasts Through DAAM: Improvements in 500 mb Forecasts Through DAAM


Five Order of Magnitude Increase in Satellite Data Over Next Ten Years: Five Order of Magnitude Increase in Satellite Data Over Next Ten Years Count (Millions) Daily Satellite Observation Count 2000 1990 2010 2010-10%of obs 2002 100 M obs NPOESS Era Data Volume 2003 125 M obs


NPOESS Instruments: NPOESS Instruments VIIRS Visible / Infrared Imager / Radiometer Suite CMIS Conical Scanning Microwave Imager / Sounder CrIS Cross-track Infrared Sounder ATMS Advanced Technology Microwave Sounder SESS Space Environment Sensor Suite GPSOS GPS Occultation Sensor OMPS Ozone Mapping and Profiler Suite ADCS Advanced Data Collection System SARSAT Search and Rescue Satellite-Aided Tracking APS Aerosol Polarimetry Sensor ERBS Earth Radiation Budget Sensor SS Survivability Sensor ALT Radar Altimeter TSIS Total Solar Irradiance Sensor


Expected NPOESS Instrument Impact on NWS Forecast Performance: Expected NPOESS Instrument Impact on NWS Forecast Performance P: Primary S: Secondary N: None


NASA-NOAA-DOD Joint Center for Satellite Data Assimilation (JCSDA): NASA-NOAA-DOD Joint Center for Satellite Data Assimilation (JCSDA) NOAA, NASA, DOD partnership Mission Accelerate and improve the quantitative use of research and operational satellite data in weather and climate prediction models Current generation data Prepare for next-generation (NPOESS, METOP, research) instruments Supports applied research Partners University, Government and Commercial Labs


Joint Center for Satellite Data Assimilation (JCSDA): Joint Center for Satellite Data Assimilation (JCSDA)


Overview of JCSDA Applied Research Areas: Overview of JCSDA Applied Research Areas Advanced radiative transfer Clouds and precipitation Assess impacts of current instruments Improve sea surface temperature data Enhance land surface data and assimilation Observing System Simulation Experiments (OSSEs) Improve data assimilation techniques


Advanced Radiative Transfer[Tahara, VanDelst, McMillin, Han]: Advanced Radiative Transfer [Tahara, VanDelst, McMillin, Han] Increased accuracy Improved computation efficiency Required for huge increase in data volume Add effects of Aerosols Trace gases Reflection, scattering and absorption by clouds RMS=0.08 Mean=0.0017 OPTRAN fits to Line-by Line RT


Overview of JCSDA Applied Research Areas: Overview of JCSDA Applied Research Areas Advanced radiative transfer Clouds and precipitation Assess impacts of current instruments Improve sea surface temperature data Enhance land surface data sets Observing System Simulation Experiments (OSSEs) Improve data assimilation techniques


Clouds & Precipitation: Clouds & Precipitation Most fruitful area for improved weather forecasts “Where the action is” Most difficult region for accurate remotely sensed data At least a 10 year development program, with incremental improvements along the way Both Microwave (MW) and Infrared (IR) data needed Improve use of data above cloud tops Improved model physics required to allow use of advanced observations Model initialization for tropics (operational at NCEP) Hurricane forecasting MJO Improved initialization in active mid-latitude weather regions will result


Overview of JCSDA Applied Research Areas: Overview of JCSDA Applied Research Areas Advanced radiative transfer Clouds and precipitation Assess impacts of current instruments Improve sea surface temperature data Enhance land surface data sets Observing System Simulation Experiments (OSSEs) Improve data assimilation techniques


Clouds & Precipitation (cont)[Treadon, Moorthi, Derber]: Clouds & Precipitation (cont) [Treadon, Moorthi, Derber]


Assess Impacts of Current Instruments: Assess Impacts of Current Instruments MW has largest impact on forecast scores IR useful in cloud free areas and for cloud top determinations Results in Improved data sampling algorithms Focused direction for future applied research Improved knowledge of entire observing system and how to extract more information from all observations to improve forecasts Experiments ongoing with computing sponsored by NPOESS Program


Improve Sea Surface Temperature (SST) data: Improve Sea Surface Temperature (SST) data Advanced algorithms provide Increased accuracy Better time and space coverage More reliable bias correction and data delivery Better compatibility with independent (buoy) observations Unified treatment of marine boundary layer and ocean surface for remote sensing Preliminary results encouraging Operational implementation at NCEP expected by end of calendar 2004


Improve Sea Surface Temperature Data [X. Li & Derber]: Improve Sea Surface Temperature Data [X. Li & Derber] SST Difference 29-28 October 2003 - Experiment SST Difference 29-28 October 2003 - Control New physical retrieval from AVHRR data, cast as variational problem OPTRAN RTM & Linear Tangent Model Eventual direct use of AVHRR (and other) radiance data RMS and Bias Fits to Independent Buoy SST Data NOAA-16 AVHRR data only Northern Hemisphere Ex. Tropics


Enhance Land Surface Data and Assimilation: Enhance Land Surface Data and Assimilation Critical for determining water cycle Both climate and weather applications Improved surface emissivity model Greater time and space resolution Algorithms for advanced instruments (e.g. MODIS) Global land surface data assimilation


Improved Surface Emissivity Model for Snow [Yan, Okamoto and Weng): Improved Surface Emissivity Model for Snow [Yan, Okamoto and Weng) Annual Mean RMS TB Difference (Obs – Simulated) SnowEM Operational


Enhance Land Surface Data and Assimilation: Enhance Land Surface Data and Assimilation Critical for determining water cycle Both climate and weather applications Improved surface emissivity model Greater time and space resolution Algorithms for advanced instruments (e.g. MODIS) Global land surface data assimilation


Enhance Land Surface Data and Assimilation [cont]: Improved land surface data sets AVHRR: global 1-km vegetation class and weekly fields of LAI and green vegetation fraction (Gallo and Loveland) GOES: daily realtime 4-km N. America snow cover fraction (Zeng) MODIS: Global 1-km fixed field of deep snow max albedo and snow albedo functions for Noah Land Surface Model (Zeng) Global 1-km fixed field of vegetation classes and weekly fields of LAI, green vegetation fraction (Friedl) Global Land Data Assimilation System (GLDAS) Transition to NCEP operations (Houser & Mitchell) Enhance Land Surface Data and Assimilation [cont]


Observing System Simulation Experiments (OSSEs): Observing System Simulation Experiments (OSSEs) Prepare for advanced data Formatting Understanding and formulation of observational errors Initial quality control algorithms Estimated relative forecast impacts (in simulated world) Understanding and formulation of observational errors Assists requirements definition and instrument design


OSSEsCalibration of Simulated Data Impacts Vs Real [Masutani, Woollen, Terry, Yang]: OSSEs Calibration of Simulated Data Impacts Vs Real [Masutani, Woollen, Terry, Yang] 500 hPa Height Anomaly Correlation 72 hour forecasts


Observing System Simulation Experiments (OSSEs): Observing System Simulation Experiments (OSSEs) Prepare for advanced data Formatting Understanding and formulation of observational errors Initial quality control algorithms Estimated relative forecast impacts (in simulated world) Understanding and formulation of observational errors Assists requirements definition and instrument design


OSSEs Observational Error FormulationSurface & Upper Air [Woollen, Masutani]: OSSEs Observational Error Formulation Surface & Upper Air [Woollen, Masutani] time With random error: Data rejection rate too small (top) Fit of obs too small (bottom) Percent improvement over Control Forecast (without DWL) Open circles: RAOBs simulated with systematic representation error Closed circles: RAOBs simulated with random error Orange: Best DWL Purple: Non- Scan DWL 4 0 -4


Improve Data Assimilation Techniques[Derber, Treadon, Parrish, Wu, KleistFerrier, Moorthi]: Improve Data Assimilation Techniques [Derber, Treadon, Parrish, Wu, Kleist Ferrier, Moorthi] Extract additional information from observations Unified global and regional system Improve model physics Improve model and observational error estimates Assess computational resources for future data assimilation systems


Examples of Instrument-Specific Development: Examples of Instrument-Specific Development GPS Occultation (COSMIC) NCAR-sponsored Post-Doc at JCSDA High vertical resolution, low horizontal res. (different from any other satellite data) Forward model to derive index of refraction developed Preparing for use of data within NCEP analysis Preparing for COSMIC using CHAMP and SAC-C data (available 12/03) Atmospheric InfraRed Sounder (AIRS) Currently in parallel testing at NCEP Representative of preparation needed for NPOESS instruments Lessons learned AMSR Provides new information in microwave range for ocean and land surface data assimilation Processing capability at NESDIS Formatted raw (radiance) data into WMO standard BUFR SST, wind speed products available for evaluation; others becoming available through NASA Marshall Space Flight Center FY04 Delivery of L1b radiances and L2 products Impact studies


Examples of Instrument-Specific Development: Examples of Instrument-Specific Development GPS Occultation (COSMIC) NCAR-sponsored Post-Doc at JCSDA High vertical resolution, low horizontal res. (different from any other satellite data) Forward model to derive index of refraction developed Preparing for use of data within NCEP analysis Preparing for COSMIC using CHAMP and SAC-C data (available 12/03) Atmospheric InfraRed Sounder (AIRS) Currently in parallel testing at NCEP Representative of preparation needed for NPOESS instruments Lessons learned AMSR Provides new information in microwave range for ocean and land surface data assimilation Processing capability at NESDIS Formatted raw (radiance) data into WMO standard BUFR SST, wind speed products available for evaluation; others becoming available through NASA Marshall Space Flight Center FY04 Delivery of L1b radiances and L2 products Impact studies


Testing AIRS Data in the NCEP Global Data Assimilation System (GDAS): Testing AIRS Data in the NCEP Global Data Assimilation System (GDAS) T254/L64 (operational resolution) Model top at 0.2 hPa Uses AIRS data available by operational cut-offs 2:45hr after analysis time - early analysis (forecasts) 6hr after analysis time – final analysis (assimilation) 6 hr cycle (analysis every 3 hrs using data from +/- 3 hr) 254 out of 281 channels 73-86 removed (Channels peak too high) 1937-2109 removed (non-LTE) 2357 removed (Large obs-background diffs) Shortwave channels downweighted (wavenumber > 2000) or removed (wavenumber > 2400) during day Can’t simulate reflected solar radiation


AIRS Testing at NCEP [Derber, Treadon]: AIRS Testing at NCEP [Derber, Treadon] Red: control Black: AIRS Solid: cntl Dotted: AIRS Black: 12 h Red: 36 h Small Positive Impact Parallel testing has begun at NCEP Results are first attempt at full resolution, with cycling Some minor initial adjustments have been made to system (and more may be made in future) Initial results show small positive/neutral impact Testing will continue and additional improvements and uses (such as for SST and cloud analysis) will be developed Neutral Impact


Lessons Learned from AIRS: Lessons Learned from AIRS Long term support for development of data assimilation prior to launch essential Data format should be worked out with users as early as possible Production of simulated data prior to real data availability allows development of data stream Consistency in use of data and formats greatly simplifies problem


Examples of Instrument-Specific Development: Examples of Instrument-Specific Development GPS Occultation (COSMIC) NCAR-sponsored Post-Doc at JCSDA High vertical resolution, low horizontal res. (different from any other satellite data) Forward model to derive index of refraction developed Preparing for use of data within NCEP analysis Preparing for COSMIC using CHAMP and SAC-C data (available 12/03) Atmospheric InfraRed Sounder (AIRS) Currently in parallel testing at NCEP Representative of preparation needed for NPOESS instruments Lessons learned AMSR Provides new information in microwave range for ocean and land surface data assimilation Processing capability at NESDIS Formatted raw (radiance) data into WMO standard BUFR SST, wind speed products available for evaluation; others becoming available through NASA Marshall Space Flight Center FY04 Delivery of L1b radiances and L2 products Impact studies


AMSR Preparation: AMSR Preparation Team composition NESDIS Lead: Ralph Ferraro NCEP Lead: William Gemmill NESDIS members: Gene Legg, P. Haggerty, W. Chen, W. Wolff NCEP members: B. Katz, J. Woolen NASA: A. Hou Joint Center Activities Prepare and evaluate AMSR-E products for use by NCEP Devise and conduct product impact studies Operational capability to be delivered: L1b and L2 AMSR-E and BUFR files Funded by NASA


AMSR Products: AMSR Products SST Surface Wind Speed Land Surface Temperature Soil Moisture


Summary: Summary Current preparation for NPOESS (and NPP) data will enable prompt and timely use of these advanced data Preparation includes Formatting (with international participation) Data integration into analysis infrastructure Quality control and performance monitoring Scientific development must focus on both remote sensing technology and model A process of continuous and incremental improvement All aspects of environment (atmosphere, ocean, land, hydrology, ice, chemistry) are now critical Often, neutral or slight positive impact for a new instrument is to be expected Preparation maximizes potential for impact on the nation’s weather and climate forecasts Inclusive Data Assimilation And Modeling (DAAM) effort will result in clear improvements in the 3-10 day range by the time NPOESS instruments are launched Major savings for industries directly affected by weather and climate factors will continue to increase as better weather products are developed


Summary (cont]: Summary (cont] The JCSDA will continue to be a focus for scientific development for the weather and climate modeling community Advanced work on new instruments, using OSSEs and other simulated data, can accelerate preparations for new instruments