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Turning Observations into Knowledge: NASA Weather Research Program

Learn about NASA's Weather Research Program and its collaboration with NOAA to invest in weather research and development. Discover how algorithms in satellite data assimilation and retrieval are applied to short and medium-range weather forecasts.

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Turning Observations into Knowledge: NASA Weather Research Program

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  1. NASA’s Weather Research ProgramNWS SRH SOO-NASA/SPoRT Joint Workshop Dr. Tsengdar Lee July 11-13, 2006

  2. Turning Observations into Knowledge Products

  3. System of Systems Framework

  4. NASA’s Weather Research Activities • Under Earth Science Research Division/Research and Analysis Program and Applied Science Program • Invest in basic and applied weather research and development • Collaborate closely with NOAA colleagues • Developed algorithms in satellite data assimilation and retrieval • Applied directly to short and medium range weather forecast • SPoRT and JCSDA are two of the major investments

  5. Turning Observations into Knowledge Products

  6. 2.5 Gbps lambda SONET OC48 (2.5 Gbps) SONET OC12 (622 Mbps) SONET OC3 (155 Mbps) Mission Support Backbone GRC GRC CIEF CIEF Midwest Midwest DC DC ARC JPL JPL HQ HQ CIEF CIEF OC OC 48 48 Bay OC OC 48 48 Core Lambda Services Lambda Services CIEF DFRC DFRC ARC GSFC GSFC LRC LRC Bay CIEF CIEF CIEF CIEF JSC JSC MSFC MSFC South South South South Central Central East East WSC KSC KSC SSC SSC MAF MAF WSTF WSC

  7. Interactive Visual Supercomputing

  8. Storage Area Network NASA Mission Support Network Ideal Architecture VisionData Centric, Multi-Tiered Compute Environment Multi-tiered Platforms High Speed Research Network High Speed Access to Other Sites Common Front End Next Generation Platforms Visualization Environment Capacity Systems Capacity Systems Capability Systems GB/s Hierarchical Storage Management Shared High Speed Disk

  9. Collaboration with Science Mission Computing and Modeling and Analysis Research • Project Columbia computing facility • World’s fourth fastest computer with 51.8 Teraflops throughput • 10240 processors • Earth Science modeling and data assimilation has been the prime usage of the systems

  10. Establishing a Modeling Environment

  11. Project FastPath

  12. TRL DefinitionsNASA Technology Readiness Level

  13. NCEP’s Role in the Model Transition Process EMC and NCO have critical roles in the transition from NOAA R&D to operations Applied research Other Agency, Academia Effort Service Centers (TPC) Field Offices EMC NOAA Research (GFDL/URI) NCO EMC Basic Research Observation System Test Beds JHT JCSDA Service Centers Life cycle Support User OPS 1 2 3 4 5 6 7 8 OPS Support Svc Centrs User R&D Operations Delivery 5. Level II:- Preliminary Testing (DA/Higher Resolution) 4. Level I:- Preliminary Testing (Lower Resolution) 6. EMC Pre- Implementation Testing (Packaging and Calibration) 7. NCO Pre- Implementation Testing 1..Identified for Selection 2. Code/Algorithm Assessment and/or Development 3. Interface with Operational Codes 8. Implementation Delivery Transition from Research to Operations Launch List – Model Implementation Process Concept of Operations Requirements Criteria

  14. SPoRT’s Role in the R&O Process Effort NOAA Research NWS Observation System

  15. Highlights

  16. Data Category Number of AIRS Channels Total Data Input to Analysis Data Selected for Possible Use Data Used in 3D VAR Analysis (Clear Radiances) ~200x106 radiances (channels) ~2.1x106 radiances (channels) ~0.85x106 radiances (channels) AIRS Data Impact on NCEP GFS N.H. S.H. Current preliminary impact study shows that the use of a small fraction (~0.5%) of AIRS clear only data can provide significant 3 to 6 –day forecast skill improvement in both northern & southern Hemispheres

  17. JCSDA Road Map (2002 - 2010) 3D VAR -----------------------------------------------------4D VAR By 2010, a numerical weather prediction community will be empowered to effectively assimilate increasing amounts of advanced satellite observations The radiances can be assimilated under all conditions with the state-of-the science NWP models Resources: NPOESS sensors ( CMIS, ATMS…) GIFTS, GOES-R OK Required Advanced JCSDA community-based radiative transfer model, Advanced data thinning techniques The CRTM include cloud, precipitation, scattering The radiances from advanced sounders will be used. Cloudy radiances will be tested under rain-free atmospheres, more products (ozone, water vapor winds) AIRS, ATMS, CrIS, VIIRS, IASI, SSM/IS, AMSR, WINDSAT, GPS ,more products assimilated Science Advance A beta version of JCSDA community-based radiative transfer model (CRTM) transfer model will be developed, including non-raining clouds, snow and sea ice surface conditions Improved JCSDA data assimilation science The radiances of satellite sounding channels were assimilated into EMC global model under only clear atmospheric conditions. Some satellite surface products (SST, GVI and snow cover, wind) were used in EMC models AMSU, HIRS, SSM/I, Quikscat, AVHRR, TMI, GOES assimilated Pre-JCSDA data assimilation science Radiative transfer model, OPTRAN, ocean microwave emissivity, microwave land emissivity model, and GFS data assimilation system were developed 2002 2003 2008 2009 2010 2007 2004 2005 2006

  18. Short Term Priorities • MODIS: MODIS AMV assessment and enhancement. Accelerate assimilation into operational models. • AIRS: Improved utilization of AIRS • Improve data coverage of assimilated data. Improve spectral content in assimilated data. • Improve QC using other satellite data (e.g. MODIS, AMSU) • Investigate using cloudy scene radiances and cloud clearing options • Improve RT Ozone estimates • Reduce operational assimilation time penalty (Transmittance Upgrade) • SSMIS: Collaborate with the SSMIS CALVAL Team to jointly help assess SSMIS data. Accelerate assimilation into operational model as appropriate

  19. Some Major Accomplishments • Common assimilation infrastructure at NOAA and NASA • Common NOAA/NASA land data assimilation system • Interfaces between JCSDA models and external researchers • Community radiative transfer model-Significant new developments, New release June • Snow/sea ice emissivity model – permits 300% increase in sounding data usage over high latitudes – improved polar forecasts • Advanced satellite data systems such as EOS (MODIS Winds, Aqua AIRS, AMSR-E) tested for implementation • MODIS winds, polar regions - improved forecasts. Current Implementation • Aqua AIRS - improved forecasts. Current Implementation • Improved physically based SST analysis • Advanced satellite data systems such as • DMSP (SSMIS), • CHAMP GPS • being tested for implementation • Impact studies of POES AMSU, Quikscat, GOES and EOS AIRS/MODIS with JCSDA data assimilation systems completed.

  20. SPoRT Center Structure

  21. MODIS / AMSR-E • MODIS imagery • orbital track map • single visible image (250m) • natural color 3 ch. composite (500m) • long wave infrared - 11m (1000m) • short wave infrared – 3.9m (1000m) • 11m - 3.9m– fog product (1000m) • water vapor - 6.7m (1000m) • MODIS products • cloud top pressure (5km) • precipitable water (5km) • lifted index (5km) • land surface temperature (LST) – 1 km • SST - single time and composite – 1km • AMSR-E products (5km) • rain rates (instantaneous) • convective fraction • SST • precipitable water • ocean surface wind speed

  22. Terra / Aqua Data Availability MODIS (on the NASA Terra and Aqua polar orbiting satellites) provides up to 4 passes a day for a given region Terra: nominal 10:30am (d) / 10:30pm (a) overpass time Aqua: nominal 1:30pm (a) / 1:30am (d) overpass Orbital tracks - recent past and future orbital visualizations available in AWIPS Latency - most MODIS data and products are available on the Southern Region server within 30 minutes of collection – additional 10-15 minute delay based on ftp scripts

  23. COMPOSITE May 28, 2004 MODIS/AMSR-E Data Access in AWIPS • Data provided in D2D • access like GOES satellite data • correspond to WFO coverage areas at highest resolution • Examples: • color composites • TPW • SSTs & composite SST • rain rates Previews available http://weather.msfc.nasa.gov/sport/sport_observations.html

  24. ADAS ADAS 24h WRF simulation 3h WRF simulation 21 00 00 Impact of MODIS SSTs on Mesoscale Weather • Methodology: • 2 km resolution with 51 levels • Physics differences from operational WRF: • No cumulus parameterization • WSM 6-class microphysics scheme • 24h simulations run daily for May 2004 • Parallel runs for both the RTG SSTs and the MODIS SST composites MODIS SST- RTG SST (K) 14 May 2004

  25. Hurricane Katrina 06 UTC August 29, 2005 • WRF Hurricane Forecasts • In collaboration with Goddard Space Flight Center, run test cases to determine if WRF forecasts are sensitive to SSTs • Domain configured like May 2004 runs • 24 – 48 h forecasts • Initialized with 40 km NAM analyses • NAM 3h forecasts used for LBCs • Parallel forecasts with either RTG SSTs or MODIS SST composite 42h forecast of 3h accumulated precip (in) New Orleans, LA Radar Reflectivity

  26. Summary • Use of MODIS SST composites is currently ongoing in operational WRF forecasts • May 2004 simulations and hurricane forecasts provide the opportunity to determine the impact of MODIS SSTs on regional forecasts • Preliminary work suggests that the WRF model appears to respond appropriately to high-resolution SST data • Greatest impact of MODIS SSTs is seen in the marine boundary layer

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