1 Use of Ocean Surface Winds in NCEP’s Global Data Assimilation System Stephen J. Lord Director NCEP Environmental Modeling Center NCEP: “where America’s climate, weather, and ocean services begin”

2 Overview Satellite data used in NWP and NWP applications The NASA-NOAA-DOD Joint Center for Satellite Data Assimilation –JCSDA-sponsored data impact studies –Impact of QuikSCAT and Windsat (L. Bi et al, U. Wisconsin and JCSDA) –Improved use of surface wind observations

3 The Environmental Forecast Process Observations Analysis Model Forecast Post-processed Model Data Forecaster User (public, industry…) Numerical Forecast System Data Assimilation

4 Satellite data used in NCEP’s operational data assimilation systems 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 AIRS MODIS Winds Altimeter sea level observations (ocean data assimilation and wave data assimilation system)

5 POES Data Delivery Locations Received (M) GFS Data Cutoff NAM Data Cutoff Next-generation Satellite Data Delivery

6 NCEP Forecast Systems and Mission Applications Forecast Systems –Global Forecast System (GFS) & GDAS –North American Model (NAM) & RDAS –Rapid Update Cycle (RUC) –Global Ensemble System (GEN) –Short-Range Ensemble Forecast System (SREF) –Air Quality Forecast System –Hurricane System (HUR)* –Real Time Ocean Forecast System (RTOFS) –Global and Regional Wave System (WAV) –Ice Drift System (ICE) –Climate Forecast System (CFS) * System does not have associated data assimilation system

7 Five Order of Magnitude Increase in Satellite Data Over Next Ten Years Count (Millions) Daily Satellite & Radar Observation Count %of obs M obs NPOESS Era Data Volume M obs Level 2 radar data 2 B M obs

8 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

9 February 2001 –SSM/I precipitation estimates in physical initialization (preparation for TRMM data) May 2001 –Inclusion of cloud liquid water in model and data assimilation October 2001 –TRMM TMI precipitation estimates added to physical initialization January 2002 –QuikSCAT data added (3-8% improvement in 10 m winds vs. mid- latitude deep ocean buoys at h; 7-17% improvement for MSLP) October 2002 –Preparation for AIRS (upgraded OPTRAN, cloud detection, data thinning algorithms) June 2005 –AIRS data added (center spot, reduced channels) November 2005 –MODIS winds added Research Data Added to NCEP Operational Atmospheric Data Assimilation

10 Data Assimilation Impacts in the NCEP GDAS (cont) AMSU and “All Conventional” data provide nearly the same amount of improvement to the Northern Hemisphere.

11 N. Hemisphere 1000 mb ht anomaly correlation AMSU: 0.5 day improvement at 5 days

12 Jung and Zapotocny JCSDA Funded by NPOESS IPO Satellite data ~ 10-15% impact Better Worse Better EPAC ATL Impact of REMOVING Satellite Data NOT Statistically Significant

13 Assimilating and determining the impact of sea surface winds measured by WindSat/Coriolis data in the Global Forecast System Li Bi Tom Zapotocny John Le Marshall Michael Morgan James Jung 31 May 2006

14 Goals of the Study Run GFS with QuikSCAT (cntrl254) Run GFS without QuikSCAT (noqscat254) Run GFS with Windsat & QuikSCAT Study statistical properties of QuikSCAT and Windsat products

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17 Tropical Winds

18 WindSat and QuikSCAT Wind Fields WindSat QuikSCAT

19 JCSDA Community Radiative Transfer Model (CRTM) Upgrades for –Major Science upgrades available for immediate testing & further development Scattering by clouds Surface optics Multiple stream (impacts surface emissivity and reflection) Aerosol absorption and scattering (Weaver, JCSDA AO) Preparation for advanced instruments (IASI, CrIS, etc) –Laying foundation for advanced applications ( ) Begin assimilation of cloudy radiances Requires major –Computing and human resources for complete evaluation of impact –Evaluation and upgrades to forecast model (for forecast cloud properties) –Bias correction and QC development (partial cloudiness, etc) –Code structure and performance Execution efficiency, memory footprint To prepare for new absorption models (e.g. OSS) from JCSDA AO investigators (e.g. AER) Increase flexibility for future changes Establish and refine testing procedures (offline and in GSI)

20 Final Comments Surface Vector Winds (SVWs) are not a major driver of NWP skill Nonetheless, SVWs provide a useful supplement to sounding data and other wind retrievals for specific ocean phenomena (e.g. hurricanes) Forecaster use of SVWs is a major consideration Preliminary results QuikSCAT appears to be a better instrument than Windsat Future SVW capability should match QuikSCAT capability

21 Thanks Questions?

22 Planning for FY09 Integration and Testing of New Observations 1.Data Access (routine, real time)3 months 2.Formatting and establishing operational data base1 month 3.Extraction from data base1 month 4.Analysis development (I) 6-18 months 5.Preliminary evaluation2 months 6.Quality control3 months 7.Analysis development (II) 6-18 months 8.Assimilation testing and forecast evaluation1 month 9.Operational implementation6 months 10.Maintain system*1 person “till death do us part” * Scientific improvements, monitoring and quality assurance Total Effort: person months per instrument

23 Facilitating Steps Continue to increase support for –Computing –Community-based data assimilation and model advances at NOAA and NASA Begin to support –Altimetry and surface wind instruments and data assimilation –Quality control (Operations) –Use of imagery and tracers (e.g. ozone) as proxy for direct wind observations –“Coupled” data assimilation of atmosphere, land, ocean Increase prioritization and planning efforts for FY09 –Understanding observing system impacts –New instrument classes Unique measurements Cover under observed aspects of atmosphere, ocean, land –Atmospheric winds –Coastal ocean data assimilation –Air quality & atmospheric monitoring –Land Surface data assimilation with direct use of radiances –Example Wind lidar

24 Doppler Wind Lidar (DWL) Impact Forecast hour % % Wind Anomaly Correlation Differences from Conv. Data Only TOVS + Best DWL TOVS only TOVS + Best DWL TOVS only

25 Impact of Observations and Numerical Forecast System Technology Growth on Global Forecasts Obs only NFS Tech Growth + Obs NFS Tech Growth: Computing Data Assim. Models Ensembles

26 ECMWF Improvement in medium-range forecast skill NFS Tech Growth + Obs 12-month running mean anomaly correlation (%) of 500hPa height forecasts Obs only

27 Current Satellite Data Assimilation Development (cont) Improved use of satellite data for SST analysis –Improved AVHRR QC and bias correction (Xu Li, A. Harris) –Addition of simplified ocean mixed-layer model (EMC-MMAB, GMAO) –Use of microwave instruments (e.g. AMSR-E) Upgrades to ozone assimilation –GOME and current NASA, NOAA instruments (CPC, JSDI; Stajner, GMAO, AO) Land surface data assimilation –Use of GMAO Catchment model as multi-Land Surface Model (LSM) system (together with Noah, VIC and Sacramento LSMs) –Collaboration on advanced Ensemble Kalman Filter (EKF) techniques Ocean data assimilation –Use of altimeter data (EMC, Behringer) –Impacts on S/I forecasting (EMC, Behringer) –GMAO uses Poseidon isopycnal model but will test developments in MOM-4 Observing system design and impacts –Analysis adjoint diagnostic tools –Observing System Simulation Experiments (OSSEs) for Understanding interaction between observing system and DA system Defining potential impact of and preparing for future instruments

28 Global Data Assimilation Observations Processing Definitions –Received: The number of observations received operationally per day from providers (NESDIS, NASA, Japan, Europeans and others) and maintained by NCEP’s Central Operations. Counted observations are those which could potentially be assimilated operationally in NCEP’s data assimilation system. Observations from malfunctioning instruments are excluded. –Selected: Number of observations that is selected to be considered for use by the analysis (data numbers are reduced because the intelligent data selection identifies the best observations to use). Number excludes observations that cannot be used due to science deficiencies. –Assimilated: Number of observations that are actually used by the analysis (additional reduction occurs because of quality control procedures which remove data contaminated by clouds and those affected by surface emissivity problems, as well as other quality control decisions)

29 Global Data Assimilation Observations Processing (cont) 2002July 2005 Notes November 2005 Operations Received123 M 169.0MNov increase attributed to additional AIRS, MODIS winds, NOAA-18 and NOAA-17 SBUV data M Selected 19 M 23.6 M26.9 M Assimilated 6 M 6.7 M8.1 M

30 Current Satellite Data Assimilation Development at the JCSDA Community Radiative Transfer Model (CRTM) –NESDIS/ORA leads scientific development –EMC transitions development to operations & maintains operational codes –GMAO focuses on applications to NASA instruments used in research DA systems –Examples of CTRM applications AIRS MODIS WindSat SSM/IS AMSR OMI ATMS IASI CrIS OMPS JCSDA partnership for COSMIC –Project management (NESDIS) –Data delivery, formatting (UCAR, NCEP Central Ops) –Scientific algorithms and QC (JCSDA, NESDIS, UCAR) –Testing with CHAMP data prior to launch with DA system (JCSDA, EMC, UCAR)