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WE1.L10 - USE OF NASA DATA IN THE JOINT CENTER FOR SATELLITE DATA ASSIMILATION
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WE1.L10 - USE OF NASA DATA IN THE JOINT CENTER FOR SATELLITE DATA ASSIMILATION

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  • CPR: Cloud profiling radar CALIOP is an active lidar
  • - Overall (average) impacts similar in all systems: magnitudes and relative contributions to forecast error reduction
  • All results except bottom right (which shows total number of observations assimilated during the study period) are based on a 24-hr global forecast error measure that combines errors in wind, temperature and surface pressure into a single scalar measure with units of energy per unit mass (J/kg). In top two panels, negative values imply beneficial impact (error reduction) on the 24-h forecast due to assimilation of a given data type. - Top left panel shows the (daily average) total impact of each observing system, given all the observations assimilated of each type. - Top right panel shows the average impact per-observation, obtained by dividing the total impact (top left) by the total number of observations assimilated for a given data type (bottom right). - Bottom left panel shows fraction of each data type that provides benefit to the 24-h forecast. Notice that only a small majority (less than 54%) of the observations of each type actually improve the forecast in most cases….but this small majority is enough to provide significant benefit to the forecast. The fact that the majority of beneficial observations tends to be small reflects the expected statistical nature of the assimilation scheme. This result is common for all data assimilation systems currently used. - Bottom right panel shows the total number of each observation type assimilated during the one-month-plus study period.

WE1.L10 - USE OF NASA DATA IN THE JOINT CENTER FOR SATELLITE DATA ASSIMILATION WE1.L10 - USE OF NASA DATA IN THE JOINT CENTER FOR SATELLITE DATA ASSIMILATION Presentation Transcript

  • USE OF NASA DATA IN THE JOINT CENTER FOR SATELLITE DATA ASSIMILATION (JCSDA) Lars Peter Riishojgaard, Director, JCSDA and Sid Ahmed Boukabara, Deputy Director, JCSDA (Presenter) With contributions from: M. Rienecker, P. Phoebus, S. Lord, J. Zapotocny, E. Liu, R. Gelaro, V. Kumar, C.D. Peters-Lidard, R. Vogel, F. Weng and many others IGARSS Conference, Honolulu, Hawaii, July, 2010 Paper #4161 (WE1.L10.3)
  • Contents Overview of Satellite Data Used at JCSDA partners 2 Land Data Assimilation Highlights 5 JCSDA Structure, Objectives & Science Priorities 1 Ocean Data Assimilation Highlights 4 Impact Study Experiments 6 Atmospheric Data Assimilation Highlights 3 Conclusions/Summary 7
  • Mission: … to accelerate and improve the quantitative use of research and operational satellite data in weather, ocean, climate and environmental analysis and prediction models. Vision: An interagency partnership working to become a world leader in applying satellite data and research to operational goals in environmental analysis and prediction JCSDA Structure and Objectives IGARSS Conference, Honolulu, Hawaii, July, 2010 NASA/Earth Science Division US Navy/Oceanographer and Navigator of the Navy and NRL NOAA/NESDIS NOAA/NWS NOAA/OAR US Air Force/Director of Weather
  • JCSDA Science Priorities
    • Radiative Transfer Modeling (CRTM)
    • Preparation for assimilation of data from new instruments
    • Clouds and precipitation
    • Assimilation of land surface observations
    • Assimilation of ocean surface observations
    • Atmospheric composition; chemistry and aerosol
    Driving the activities of the Joint Center since 2001, approved by the Science Steering Committee Overarching goal: Help the operational services improve the quality of their prediction products via improved and accelerated use of satellite data and related research IGARSS Conference, Honolulu, Hawaii, July, 2010
  • JCSDA accomplishments
    • Common assimilation infrastructure (between NOAA, NASA, AFWA)
    • Community radiative transfer model (NOAA, Navy, NASA, AFWA)
    • Common NOAA/NASA/AFWA land data assimilation system (NOAA, NASA, AFWA)
    • Numerous new satellite data assimilated operationally, e.g. MODIS (winds and AOD), AIRS and IASI hyperspectral IR radiances, GPSRO sensors (COSMIC, GRAS, GRACE), SSMI/S, Windsat, Jason-2,…
    • Advanced sensors tested for operational readiness, e.g. ASCAT, MLS, SEVIRI (radiances),…
    • Ongoing methodology improvement for sensors already assimilated, e.g. AIRS, GPSRO, SSMI/S,…
    • Improved physically based SST analysis
    • Adjoint sensitivity diagnostics
    • Emerging OSSE capability in support of COSMIC-2, JPSS, GOES-R, Decadal Survey and other missions
    IGARSS Conference, Honolulu, Hawaii, July, 2010
  • Contents Overview of Satellite Data Used at JCSDA partners 2 Land Data Assimilation Highlights 5 JCSDA Structure, Objectives & Science Priorities 1 Ocean Data Assimilation Highlights 4 Atmospheric Data Assimilation Highlights 3 Impact Study Experiments 6 Conclusions/Summary 7
  • Satellite Data Used at JCSDA Partners (NCEP, Navy, GMAO): Polar Orbiters: Microwave Imagers / Scatterometers / IR/WV Imagers NASA Sensors Navy/FNMOC (NOGAPS with NAVDAS-AR 4DVAR) NOAA/NCEP (GFS with GSI 3DVAR) NASA/GMAO (GEOS-5 w/ 3DVAR) SSM/I Wind/radiances (2) OPS Not used (Instrument problem) OPS SSM/I TPW/radiances (2) OPS Not used (Instrument problem) OPS SSMIS Wind/radiances (2-3) OPS Preparing for testing SSMIS TPW/radiances (2-3) OPS Preparing for testing QuikScat Marine Surface Winds (0) OPS/sensor failed OPS/sensor failed OPS/sensor failed ASCAT Marine Surface Winds (1) OPS Testing WindSat Marine Surface Winds (1) OPS OPS OPS WindSat TPW (1) OPS MODIS IR Atmospheric Motion Vector (AMV) Winds (2) OPS OPS OPS MODIS WV AMV Winds (2) OPS OPS OPS AVHRR IR AMV Winds (2) OPS Testing ERS-2 (1) OPS Not used (Instrument problem) AMSR-E (1) (parameter??) Testing to begin soon Preparing for testing passive TRMM TMI Precip No OPS OPS SSM/I Precip Not used (Instrument problem) SSMIS Precip Preparing for testing Totals
  • Satellite Data Used at JCSDA Partners (NCEP, Navy, GMAO): Polar Orbiters: Microwave Sounders/ IR Sounders/ GPS Profilers NASA Sensors Navy/FNMOC (NOGAPS with NAVDAS-AR 4DVAR) NOAA/NCEP (GFS with GSI 3DVAR) NASA/GMAO (GEOS-5 w/ 3DVAR) AMSU-A (6) OPS OPS as quality permits OPS AMSU-B/MHS (4) testing OPS as quality permits OPS HIRS No OPS as quality permits OPS AIRS (1) OPS OPS OPS IASI (1) OPS OPS OPS SSMIS Lower Atmosphere Sounding (LAS) T b (2-3) OPS GPS Precipitable Water (PW) No OPS regional Monitored global COSMIC GPS Radio Occultation (RO) testing OPS (refractivity) Bending angle monitored OPS GRAS RO testing testing CHAMP/GRACE RO testing GRACE Testing; CHAMP data not received Totals
  • Satellite Data Used at JCSDA Partners (NCEP, Navy, GMAO): Ocean Sensors: Sea Surface data for NWP, Climate, and/or Ocean Prediction NASA Sensors Navy/FNMOC (NOGAPS with NAVDAS-AR 4DVAR) NOAA/NCEP (GFS with GSI 3DVAR) NASA/GMAO (GEOS-5 w/ 3DVAR) NOAA AVHRR SST (GAC/LAC) OPS OPS METOP AVHRR SST (GAC/LAC) OPS Testing GOES SST (2) OPS OPS MSG SST (2) (Meteosat) OPS MTSAT SST AMSR-E SST (Aqua) OPS (at NAVO….not at FNMOC yet) Testing AATSR SST (Envisat) OPS MODIS SST (2) (Terra/Aqua) No SSM/I Sea Ice Concentration ( 4) (F-11, F-13, F-14, F-15) OPS OPS (F-15 only) SSMIS Sea Ice Concentration (4) (F-16, F-17, F-18) OPS Testing SMOS Sea-Surface Salinity (SSS) MODIS Surface chlorophyll (2) (Terra/Aqua) Testing R&D system Coastal Zone Color Scanner (CZCS) Surface chlorophyll Testing R&D system SeaWiFS Surface chlorophyll Testing R&D system
  • Satellite Data Used at JCSDA Partners (NCEP, Navy, GMAO): Ocean Sensors: Altimeters NASA Sensors Navy/FNMOC (GNCOM, NCOM w/ NCODA) NOAA/NCEP NASA/GMAO (ODAS-2 and ODAS-3 with MOM4) (2) Jason Altimeter Significant Wave Height (SWH) OPS --2 OPS -J1 Testing - J2 Envisat –Altimeter SWH OPS Testing (2) Jason Altimeter Sea Surface Height (SSH) OPS --2 OPS -J1 Testing - J2 OPS (data from AVISO) Envisat –Altimeter SSH OPS OPS OPS TOTAL
  • Satellite Data Used at JCSDA Partners (NCEP, Navy, GMAO): Aerosol/Trace Gas Assimilation Sensors NASA-related Sensors Navy/FNMOC ( NAAPS, COAMPS w/ NAVDAS-AOD) NOAA/NCEP NASA/GMAO (GEOS-5 w/ 3DVAR) AF/AFWA MODIS Aerosol Optical Depth (2) (Terra/Aqua) OPS Preparation for Testing SBUV O3 testing OPS as quality permits OPS EnviSat O3 No MLS O3 Available in real time R&D System OMI O3 OPS R&D System GOME Testing TOTAL
  • Satellite Data Used at JCSDA Partners (NCEP, Navy, GMAO): Land Surface Assimilation Sensors NASA Sensors imbedded Navy/FNMOC (No LS DA for our LSM at this time*) NOAA/NCEP GSI, no LIS at this time NASA/GMAO (EnKF with Catchment LSM) Soil Moisture (AMSR-E) No Testing R&D system Soil Moisture (SMOS) No To be Tested Soil Moisture (SMAP) No Not used Snow Cover (IMS, multiple sensors) No OPS R&D system Snow Depth (AFWA, multiple sensors) No OPS R&D system Land Surface/Skin Temperature No No R&D system AVHRR Green Vegetation Fraction (GVF, 5-year monthly climatologies No Testing AVHRR GVF (weekly, near real time) No Testing MODIS –IGBP land use (vegetation) class (static field) No Testing MODIS maximum deep-snow albedo (static field No To be tested MODIS snow-free albedo (sub-monthly static fields) No To be tested TOTAL
  • Summary of NASA-related Sensors Used Operationally (at one or several JCSDA partners)
    • Implemented Operationally
      • MODIS (IR sensor: multiple products)
      • QuickSCAT (MW scatterometer: sensor failed)
      • TRMM/TMI (MW Imager)
      • AIRS
      • AMSR-E*
      • Jason (NASA/CNES sensor)
      • OMI*
    • Under Testing/To be tested:
      • SeaWIFS
      • SMAP
      • MLS
    *Non-NASA sensors onboard NASA platforms
  • Contents Overview of Satellite Data Used at JCSDA partners 2 Land Data Assimilation Highlights 5 JCSDA Structure, Objectives & Science Priorities 1 Ocean Data Assimilation Highlights 4 Atmospheric Data Assimilation Highlights 3 Impact Study Experiments 6 Conclusions/Summary 7
  • Atmospheric Data Assimilation Highlight: GEOS-5/GSI estimate of cloud top height from AIRS compared with CloudSat and CALIPSO
    • Due to large differences in footprint size between AIRS and CPR/CALIOP, the CTH validation is done only in regions A and C where the clouds are more uniform.
    • In general, GSI retrieved CTHs from AIRS are underestimated for optically thick clouds.
    • Difficulties in retrieving CTH in multi-layer cloud region.
    • Next: Include MODIS cloud products for further validation.
    Slide courtesy of Emily Liu CloudSat/CALIPSO track GSI retrieved cloud top height (CTH) from AIRS A B C A B C A B C
  • Ozone Assimilation in GEOS/GSI System ( Steven Pawson)
    • Activities in GEOS-5/GSI include:
    • Assimilation of SBUV, OMI and MLS ozone observations
    • Improvements to system:
      • observation operator for OMI (+TOMS/GOME/etc.) kernels
      • Background error covariance models (beginning)
    • Investigations of ozone structure in the UTLS and the troposphere
    • Impacts of assimilating MLS profiles on AIRS radiances
    • OSSEs for NPP-OMPS:
      • MLS+OMI system is baseline
      • Generation, retrieval and assimilation of limb profiler observations
    Impact (% change) of O 3 from OMI data Expected impact (% change) with kernels Present system omits the decrease in sensitivity to low tropospheric ozone in OMI – this is being built into H operator, with expected reduction in impact of OMI ozone in middle troposphere. Results for Jan 2006.
  • Contents Overview of Satellite Data Used at JCSDA partners 2 Land Data Assimilation Highlights 5 JCSDA Structure, Objectives & Science Priorities 1 Ocean Data Assimilation Highlights 4 Atmospheric Data Assimilation Highlights 3 Impact Study Experiments 6 Conclusions/Summary 7
  • Ocean Data Assimilation Highlight: Differences between RTOFS SSH analysis and Ssalto/Duacs (independent) SSH analysis Left panel: with JASON-1/JASON-2/ENVISAT, Right panel : without JASON-2 T he right panel shows presence of larger differences in the Gulf Stream region which may lead to formation of spurious mesoscale features. I ncreased variability W/O JASON-2 With JASON-2 Slide courtsey of S. Lord RTOFS: Real-Time Ocean Forecast System
  • Contents Overview of Satellite Data Used at JCSDA partners 2 Land Data Assimilation Highlights 5 JCSDA Structure, Objectives & Science Priorities 1 Ocean Data Assimilation Highlights 4 Atmospheric Data Assimilation Highlights 3 Impact Study Experiments 6 Conclusions/Summary 7
  • Land Data Assimilation Highlight: Assimilation of multi-sensor snow observations into a land surface model
    • A blended, multi-sensor snow dataset (ANSA) was generated by utilizing the MODIS and AMSR-E retrieved snow datasets.
    • These multi-sensor snow observations are employed in the NASA/NOAA/AFWA common Land Information System (LIS).
    • The evaluation of assimilation runs against in-situ observations of snow depth and SWE demonstrate improvements as a result of data assimilation
    ANSA snow map 15 January 2007 Courtesy of S. Kumar et al.
  • Another Land Data Application Highlight: New land surface emissivity for infrared assimilation: CRTM land bias improvement & positive forecast impact
    • Univ. Wisconsin (Seemann & Borbas) spectral infrared emissivity is derived from MODIS-channel emissivity retrievals (monthly composite, 416 wavenumbers)
    • Comparison of CRTM simulation to observation shows reduced Tb bias for desert regions when using this emissivity dataset.
    Tb difference (K) CRTM sim minus MODIS obs (3.96 µm) CRTM run with current emis CRTM run with U.Wisc. emis Less bias with U.Wisc emis Slide courtesy of R. Vogel, Y. Chen, Q. Liu, Y. Han, F. Weng JCSDA & NESDIS/STAR Sahara Desert MODIS Used to validate the JCSDA Community Radiative Transfer Model (CRTM) over land surfaces Forecast impact with GSI shows improved forecast in Southern Hemisphere CRTM current IR emis = black line U.Wisc. new IR emis = red line
  • Contents Overview of Satellite Data Used at JCSDA partners 2 Land Data Assimilation Highlights 5 JCSDA Structure, Objectives & Science Priorities 1 Ocean Data Assimilation Highlights 4 Atmospheric Data Assimilation Highlights 3 Impact Study Experiments 6 Conclusions/Summary 7
  • Fcst Error Reduction (J/kg) NASA GEOS-5 Navy NOGAPS Global domain: 00+06 UTC assimilations Jan 2007 Comparison of Data Impacts in Navy and NASA Forecast Systems using Adjoint Tools: Daily average 24-h observation impacts  AMSU-A,  Raob,  Satwind and  Aircraft have largest impact in all systems All obs types, except SSMI speeds in GEOS-5, are beneficial Fcst Error Reduction (J/kg) NASA Sensors imbedded        
  • Impacts of Various Observing Systems in GEOS-5.5.1 24-hr Forecasts from 00z Analyses on 28 Jan – 02 March 2010 Adjoint-Based Global Forecast Error Measure Total Impact Impact Per Observation Observation Count Fraction of Beneficial Observations Forecast Error Reduction (J/kg) Forecast Error Reduction (1e-6 J/kg) Improves Forecast Degrades Forecast Ron Gelaro, GMAO  AMSU-A,  Raob,  Satwind and  Aircraft have largest impact in all systems NASA Sensors
  • Data Impact Studies Using NOGAPS/NAVDAS-AR at FNMOC Per OB Impacts NASA Sensors
  • Summary IGARSS Conference, Honolulu, Hawaii, July, 2010
    • NASA Satellite Data are used in many ways in the JCSDA:
      • Operational Implementation in NWP assimilation models
      • Testing-mode implementation in Operational models
      • Used to validate/improve of some components of operational NWP models (such as CRTM)
    • NASA Satellite Data used for multitude of data assimilation activities:
      • Atmospheric data assimilation (sounding, cloud, ozone, air quality, etc)
      • Ocean data assimilation
      • Land data assimilation
    • NASA Sensors Used include: TMI, MODIS, AIRS, AMSR-E, Jason, OMI
    • Effort is on-going to assimilate more NASA sensors and others (both existing and future sensors): NPP (CrIS, ATMS, VIIRS), SEVIRI, GPM, SMAP, ADM