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  • Slide 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
  • Slide 2
  • 2 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. Why Calibration is Critical Calibration is the centerpiece of data quality assurance and is part of the core competency of any satellite program Sensor Data Records Environmental Data Records Climate Data Records Raw Data Records RDR SDR EDR CDR
  • Slide 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 3
  • Slide 4
  • NOAA/Metop ATMS CrIS VIIRS OMPS CERES AMSR-2 SSMIS AMSU MHS AVHRR HIRS GCOM-W DMSP AMSR-2 SSMIS AMSU MHS AVHRR HIRS GCOM-W ATMS CrIS VIIRS OMPS NPP/JPSS DMSP Input Data Sources: GRAVITE (RDR/TDR) CLASS (TDR/SDR) DDS (Level 1B) Input Data Sources: GRAVITE (RDR/TDR) CLASS (TDR/SDR) DDS (Level 1B) Input Data Sources: EMC (GFS/GDAS) ECMWF (GFS/GDAS) CLASS (TDR/SDR) DDS (Level 1B) Input Data Sources: EMC (GFS/GDAS) ECMWF (GFS/GDAS) CLASS (TDR/SDR) DDS (Level 1B) NPP/JPSS NOAA/Metop Climate Predictions and Projections Hurricanes and High Impact Events Satellite Data and Application Demonstration System (DADS) Global Forecasts Regional Forecasts NWP Instrument Status Trending Sensor Performance Trending Spacecraft Operational Status Sensor/SC Diagnostic Datasets Instrument Status Trending Sensor Performance Trending Spacecraft Operational Status Sensor/SC Diagnostic Datasets IPMS Sensor Data Global Distribution Sensor Data Global Bias Distribution Sensor Data Global Bias Trending Sensor Data Global Distribution Sensor Data Global Bias Distribution Sensor Data Global Bias Trending SQAS Satellite retrieval products Inter-sensor calibrated CDR products High Impact Events Imager Satellite retrieval products Inter-sensor calibrated CDR products High Impact Events Imager EQAS Radiative Transfer Model NPP/JPSS ATMS CERES OMPS VIIRS CrIMSS GCOM-W AMSR-2 Instrument Performance Monitoring System (IPMS) SDR Quality Assurance System (SQAS) SDR Quality Assurance System (SQAS) EDR Quality Assurance System (EQAS) EDR Quality Assurance System (EQAS) STAR ICVS-LTM System AIRS AVHRR MHS AMSU IASI Input Data Sources: GRAVITE (TDR/SDR) CLASS (TDR/SDR) DDS (Level 1B) Input Data Sources: GRAVITE (TDR/SDR) CLASS (TDR/SDR) DDS (Level 1B) Output Products: IPMS Analysis Data LTM Trending Plots Warning Notification Output Products: IPMS Analysis Data LTM Trending Plots Warning Notification Output Products: SQAS Analysis Data Sensor Data Global Distribution Sensor Data Global Bias Distribution LTM Trending Plots Warning Notification Output Products: SQAS Analysis Data Sensor Data Global Distribution Sensor Data Global Bias Distribution LTM Trending Plots Warning Notification Output Products: T/Q Profiles Aerosol Products Cloud Products Ozone Products Surface Products Energy Budget Output Products: T/Q Profiles Aerosol Products Cloud Products Ozone Products Surface Products Energy Budget NPP/JPSS Spacecraft NOAA/Metop
  • Slide 5
  • ICVS-LTM System Components NPP spacecraft JPSS spacecraft * NPP ATMS/CrIS/VIIRS/OMPS NOAA-18/-19/MetOP-A AMSU-A/MHS/AVHRR/HIRS DMSP F-16/17/18 SSMIS GOES-11/12/13/14/15 Sounder AMSR2, MetOp-B, DMSP F19 * NPP ATMS/CrIS NOAA-18/-19/MetOP-A AMSU-A/MHS DMSP F-16/17/18 SSMIS AMSR2, MetOP-B, DMSP F19 * Spacecraft Instrument Data Quality * Available after launch
  • Slide 6
  • Suomi NPP Spacecraft Parameters CrIS Sensor I/F 1 Temperature CrIS Sensor I/F 2 Temperature CrIS Structure Near Survival Heater Temperature CrIS PCE Temperature CrIS Interferometer Electronics Temperature CrIS SSM Temperature ATMS G-Band RX Shelf Temperature ATMS Scan Drive Mechanism Temperature ATMS Baseplate Temperature ATMS Calibration Target Bench Temperature ATMS V-Band RX Shelf Temperature ATMS Coldplate Operational Temperature (via DSEP ECC 1) ATMS Coldplate Operational Temperature (via DSEP ECC 2) ATMS Coldplate Operational Heater Deadband (via DSEP ECC 1) ATMS Coldplate Operational Heater Deadband (via DSEP ECC 2) ATMS Coldplate Operational Heater Operational Setpoint (via DSEP ECC 1) ATMS Coldplate Operational Heater Operational Setpoint (via DSEP ECC 2) S/C Telemetry for ATMS (11) OMPS Sensor I/F 1 Temperature OMPS Sensor I/F 2 Temperature OMPS Total Column Thermistor Temperature OMPS Nadir Profiler Thermistor Temperature OMPS Limb Thermistor Temperature S/C Telemetry for OMPS (5) VIIRS Sensor I/F 1 Temperature VIIRS Sensor I/F 2 Temperature VIIRS Telescope Moter Survival Heater Temperature VIIRS Aft Optics Housing Temperature VIIRS EM Coldplate Temperature VIIRS EM Nadir Panel Temperature VIIRS Coldplate Temperature (via DSEP ECC 1) VIIRS Coldplate Temperature (via DSEP ECC 2) VIIRS Electronics Coldplate Heater Deadband (via DSEP ECC 1) VIIRS Electronics Coldplate Heater Deadband (via DSEP ECC 2) VIIRS Electronics Coldplate Heater Operational Setpoint (via DSEP ECC 1) VIIRS Electronics Coldplate Heater Operational Setpoint (via DSEP ECC 2) S/C Telemetry for VIIRS (12) S/C Telemetry for CrIS (6)
  • Slide 7
  • Spacecraft Monitoring Example o Instrument temperatures obtained from S/C telemetry (right) o S/C PUMA Battery 1 Voltage real time variation during the last 24 hours and LTM trending since launch (below)
  • Slide 8
  • ATMS Monitoring Example o Lunar intrusion effects on ATMS space view readings and channels are different (right) o ATMS 4-Wire PRTs anomalies are observed in individual readings of all bands (below)
  • Slide 9
  • ATMS Bias Monitoring o ATMS angular dependent bias character obtained from RTM (right) o ATMS daily global images (below) and data quality distribution
  • Slide 10
  • CrIS SDR Data Quality Monitoring Large imagery part over Australia hot scene caused by invalid bit-trim flag
  • Slide 11
  • VIIRS Degradation Monitoring Samples 11 VIIRS Focal Plane Aperture Temperature
  • Slide 12
  • 12 Suomi NPP TDR/SDR Algorithm Schedule C CCCCCCCCC CC C C Sensor Beta Provisional (Review Date)Validated (planned) CrIS May 7, 2012October 23, 20122013 ATMS February 22, 2012October 23 20122013 OMPS-EV March 12, 2012 October 23, 2012 2013 VIIRSMay 2, 2012 October 23, 20122013
  • Slide 13
  • Hurricane Sandy Warm Core Anomaly Ascending 1730 UTC, 29 October 2012 Cross section along Longitude 72.9 WCross section along Latitude 38.1 N At 1800 UTC Oct 29 Max Wind: 90 MPH, Min Pressure: 940 hPa 13
  • Slide 14
  • Cross-Track Infrared Sounder (CrIS) SDR Status CRIS SDR provisional product review was held on October 23-24, 2012 and the panel recommended its provisional maturity 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 14
  • Slide 15
  • Total clear sky observation points ~400000 Blue: after nonlinearity coefficient change but before spectral coefficient change Black: before nonlinearity and spectral coefficient changes Red: after nonlinearity coefficient and spectral coefficient changes The achieved uniformity of the spectral and radiometric uncertainties cross the 9 FOVs is important for NWP to maximize the use of the radiance data CrIS Individual FOV Bias wrt NWP Simulations Courtesy of Yong Chen, STAR 15
  • Slide 16
  • 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
  • Slide 17
  • 2012.10.26.06 2012.10.27.06 2012.10.28.06 2012.10.29.06 AMSR-2 Experimental Rain Water Path
  • Slide 18
  • TV Signals Reflected by Ocean RFI Satellite downlink beam coverage Geostationary TV Satellite 18 AMSR2 Emission by ocean Signals from Geostationary TV Satellite TV signals reflected 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.
  • Slide 19
  • Community Radiative Transfer Model (CRTM) for Satellite Data

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