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Satellite Instrument Calibration and Data Assimilation. Fuzhong Weng, Acting Chief Satellite Meteorology and Climatology Division NOAA/NESDIS/Center for Satellite Applications and Research. NOAA Satellite Conference, NCWCP, College Park, MD April 10, 2013. Why Calibration is Critical.

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Satellite Instrument Calibration and Data Assimilation

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    1. Satellite Instrument Calibration and Data Assimilation Fuzhong Weng, Acting Chief Satellite Meteorology and Climatology Division NOAA/NESDIS/Center for Satellite Applications and Research NOAA Satellite Conference, NCWCP, College Park, MD April 10, 2013

    2. Why Calibration is Critical Climate Data Records • Calibration:is a process of quantitatively defining the satellite sensor responses to known signal inputs that are traceable to established reference standards, and converting the Earth observation raw signal to Sensor Data Records (SDRs). • Calibrated SDRs from RDR are the fundamental building blocks for all satellite products, including the radiances for data assimilation in Numerical Weather Prediction (NWP), reanalysis, and fundamental climate data records (FCDRs) for climate change detection. CDR Environmental Data Records EDR Sensor Data Records SDR Raw Data Records RDR Calibration is the centerpiece of data quality assurance and is part of the core competency of any satellite program

    3. NOAA Satellite Calibration Tasks • Conduct prelaunch analyses of thermal vacuum data and provide recommendations for improving instrument design • Quantify the uncertainty in radiometric calibration (e.g. precision and accuracy) for all categories of instruments • Quantify the uncertainty in spectral calibration for hyperspectral instruments • Quantify the errors in instrument geolocation and channel-to-channel co-registration • Develop a long-term monitoring (LTM) system for trending the instrument performance (e.g. noise, spacecraft and instrument housekeeping ) • Analyze the root-cause for the instrument anomalies and provide the recommendations for mitigating the performance risk associated with all the anomalies

    4. STAR ICVS-LTM System Instrument Performance Monitoring System (IPMS) SDR Quality Assurance System (SQAS) EDR Quality Assurance System (EQAS) ATMS ATMS ATMS CrIS CrIMSS CrIS NPP/JPSS NPP/JPSS VIIRS NPP/JPSS VIIRS VIIRS OMPS OMPS OMPS AMSU CERES CERES MHS NPP/JPSS Spacecraft NOAA/Metop AVHRR GCOM-W AMSR-2 HIRS GCOM-W AMSU NOAA/Metop AMSR-2 MHS NOAA/Metop AVHRR DMSP SSMIS GCOM-W AIRS AMSR-2 Radiative Transfer Model IASI • Output Products: • SQAS Analysis Data • Sensor Data Global Distribution • Sensor Data Global Bias Distribution • LTM Trending Plots • Warning Notification DMSP • Input Data Sources: • GRAVITE (TDR/SDR) • CLASS (TDR/SDR) • DDS (Level 1B) SSMIS • Output Products: • T/Q Profiles • Aerosol Products • Cloud Products • Ozone Products • Surface Products • Energy Budget • Input Data Sources: • EMC (GFS/GDAS) • ECMWF (GFS/GDAS) • CLASS (TDR/SDR) • DDS (Level 1B) • Input Data Sources: • GRAVITE (RDR/TDR) • CLASS (TDR/SDR) • DDS (Level 1B) • Output Products: • IPMS Analysis Data • LTM Trending Plots • Warning Notification AMSU MHS AVHRR Satellite Data and Application Demonstration System (DADS) HIRS Climate Predictions and Projections • Instrument Status Trending • Sensor Performance Trending • Spacecraft Operational Status • Sensor/SC Diagnostic Datasets IPMS SQAS EQAS NWP Global Forecasts • Sensor Data Global Distribution • Sensor Data Global Bias Distribution • Sensor Data Global Bias Trending • Satellite retrieval products • Inter-sensor calibrated CDR products • High Impact Events Imager Hurricanes and High Impact Events Regional Forecasts

    5. Spacecraft Monitoring Example • Instrument temperatures obtained from S/C telemetry (right) • S/C PUMA Battery 1 Voltage real time variation during the last 24 hours and LTM trending since launch (below)

    6. ATMS Monitoring Example • Lunar intrusion effects on ATMS space view readings and channels are different (right) • ATMS 4-Wire PRTs anomalies are observed in individual readings of all bands (below)

    7. CrIS SDR Data Quality Monitoring Large imagery part over Australia hot scene caused by invalid bit-trim flag

    8. VIIRS Degradation Monitoring Samples VIIRS Focal Plane Aperture Temperature

    9. Suomi NPP TDR/SDR Algorithm Schedule C C C C C C C C C C C C C C

    10. Hurricane Sandy Warm Core Anomaly Ascending 1730 UTC, 29 October 2012 At 1800 UTC Oct 29 Max Wind: 90 MPH, Min Pressure: 940 hPa Cross section along Latitude 38.1 N Cross section along Longitude 72.9 W

    11. Cross-Track Infrared Sounder (CrIS) SDR Status • CRIS SDR provisional product review was held on October 23-24, 2012 and the panel recommended its provisionalmaturity level • SDR provisional product: • NEdNs are well below specifications • Spectral uncertainty: < 2 ppm, well below specification • Radiometric uncertainty: ~0.1K, well below specification • Geolocation error: < 1.0 km below specification

    12. NOAA AMSR-2 Calibration Status • AMSR-2 SDR data (aka level 1) are processed at NOAA/STAR • Biases with respect to TMI and CRTM simulations are evaluated. It is found that AMSR2 brightness temperatures from 6 to 18 GHz have warm biases which are also non-linear. • Algorithms have been developed to detect and Radio Frequency Interference (RFI) signals in AMSR2 data

    13. AMSR-2 Experimental Rain Water Path 2012.10.26.06 2012.10.27.06 2012.10.29.06 2012.10.28.06

    14. TV Signals Reflected by Ocean – RFI AMSR2 Geostationary TV Satellite TV signals reflected by ocean Signals from Geostationary TV Satellite Satellite downlink beam coverage Emission by ocean Geostationary satellite TV signals reflected by ocean surface is a major source of maritime RFI. RFI signals are mixed with natural emission from pixels interfered by reflected TV signals.

    15. Community Radiative Transfer Model (CRTM) for Satellite Data Assimilation • Atmospheric gaseous absorption • Band absorption coeff trained by LBL spectroscopy data with sensor response functions • Variable gases ( H2O, CO2, O3 etc) . • Zeeman splitting effects near 60 GHz • Cloud/precipitation scattering and emission • Fast LUT optical models at all phases including non-spherical ice particles • Gamma size distributions • Aerosol scattering and emission • GOCART 5 species (dust, sea salt, organic/black carbon, ) • Lognormal distributions with 35 bins • Surface emissivity/reflectivity • Two-scale microwave ocean emissivity • Large scale wave IR ocean emissivity • Land mw emissivity including vegetation and snow • Land IR emissivity data base • Radiative transfer scheme • Tangent linear and adjoints • Inputs and outputs at pressure level coordinate • Advanced double and adding scheme • Other transfer schemes such as SOI, Delta Eddington “Technology transfer made possible by CRTM is a shining example for collaboration among the JCSDA Partners and other organizations, and has been instrumental in the JCSDA success in accelerating uses of new satellite data in operations” – Dr. Louis Uccellini, Director of National Centers for Environmental Prediction

    16. Satellite microwave sounding data – provide hurricane thermal/moisture structure for improving intensity forecast (SSMIS/AMSU-A/MHS/ATMS) Satellite infrared sounding data – provide environmental thermal and moisture structure for track and precipitation forecast (HIRS/CrIS/AIRS/IASI) Ocean surface wind and temperature from satellite scatterometer and passive microwave imager – provide surface energy flux and surface vortex (ASCAT/AMSR2) GPSRO refractivity and bending angle – provide tropical cyclonegenesis information (COSMIC/GRAS) Geostationary sounder and imager – provide real-time monitoring and tracking of all severe weather events with a high temporal and spatial resolutions (e.g. GOES etc). Satellite Data Critical for Improving Hurricane and Coastal Precipitation Forecasts

    17. HWRF Model and Data Assimilation System • HWRF Model: • 2012 NCEP-Trunk version 934 • Three telescoping domains: • Outer domain: 27km: 75x75o; • Inner domain: 9km ~11x10o • Inner-most domain: 3km inner-most nest ~6x6o • Revised Model Level and Top: • Vertical levels: 61 • Model top: 0.5 hPa • Data Assimilation System: • HWRF 6 hour forecasts • GSI (3DVAR) • The Hurricane Weather Research and Forecasting (HWRF) Model dynamical core is designed based on the WRF model using NCEP Non-Hydrostatic Mesoscale Model (NMM) core with a movable high-resolution nested grid (telescopic) • Regional-Scale, Moving Nest, Ocean-Atmosphere Coupled Modeling System. Horizontal resolution: 27 km outer grid, 9 km inner grid, 42 vertical levels • Non-Hydrostaticsystem of equations formulated on a rotated latitude-longitude, Arakawa E-grid and a vertical, pressure hybrid (sigma_p-P) coordinate. • Advanced HWRF 3D Variational analysis that includes vortex relocation, correction to winds, MSLP, temperature and moisture in the hurricane region and adjustment to actual storm intensity. • Uses SAS convection scheme, GFS/GFDL surface, boundary layer physics, GFDL/GFS radiation and Ferrier Microphysical Scheme. • Ocean coupled modeling system (POM/HYCOM).

    18. ATMS Weighting Functions STAR HWRF Top Pressure (hPa) NCEP HWRF Top Our approach: Raise the model top to allow for more satellite data assimilated into hurricane forecast model ATMS Weighting Function

    19. Control Experiment – L61 • Conventional Data: • Radiosondes, aircraft reports (AIREP/PIREP, RECCO, MDCRS-ACARS, TAMDAR, AMDAR), Surface ship and buoy observations , Surface observations over land, Pibalwinds,Wind profilers, VAD wind, Dropsondes • Satellite Instrument Data: • AMSU-A (channel 5-14) from NOAA-18, NOAA-19 and METOP-A • HIRS from NOAA-19 and METOP-A • AIRS from EOS Aqua • ASCAT from METOP-A • GPSRO from GRAS/COSMIC

    20. Hurricane Sandy Forecasts • Control : L61 • Sensitivity ExperimentsATMS: L61+ATMS • IASI: L61+IASI • CrIS: L61+CrIS • Forecast Period: 1800 UTC Oct 22, 2012 - • 1800 UTC Oct 29, 2012 • Total Cycles: 29 HWRF FST Turn on GSI 5-day Forecast 0000 UTC 1800 UTC 0000 UTC day5

    21. Impacts of Assimilation of NOAA/METOP Data on Hurricane Sandy’s Track CONV Only L61

    22. Impacts of Assimilation of ATMS Radiances on Hurricane Sandy’s Track L61:Control Run L61+ATMS

    23. Comparison of Temperature Increments from ATMS and AMSU-A Shaded: ATMS Red contour: AMSU-A Black contour: Conventional ATMS and AMSU-A (NOAA-19) produce largest temperature innovation in storm regions in similar magnitudes and complementary in spatial coverage

    24. Impacts of Assimilation of IR Hyperspectral Sounder Radiances on Hurricane Sandy’s Track L61 L61+IASI L61+CrIS

    25. Multiple Forecasts of Max. Wind Speed for Hurricane Sandy ATMS L61 IASI CrIS

    26. NOAA satellite instruments are well calibrated for operational applications and environmental data stewardship. SuomiNPP ATMS is very unique in resolving hurricane warm core features through its high spatial oversampling and additional channels. 2012 HWRF/GSI is re-configured with more vertical layers and higher model top for direct satellite radiance assimilation. In general, control and sensitivity experiments show that uses of ATMS/CrIS data in HWRF improve the forecasts in both hurricane intensity and track. When hurricane is near landfall, satellite data always have impacts on track, especially with ATMS. Satellite data show significant impacts on three day’s track forecasts over open water. It appears that CrIS has also significantly large impact on track forecasts. For conventional data only, hurricane track forecast errors increase rapidly when hurricane is near landfall. Summary and Conclusions