Satellite Instrument Calibration and Data Assimilation Fuzhong Weng
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 • 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 EDR SDR RDR ta te Da a m i Cl rds Reco l enta m n o r Envi ecords R Data ta r Da o s n Se rds Reco Data Raw ds r Reco Calibration is the centerpiece of data quality assurance and is part of the core competency of any satellite program 2
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 coregistration • 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
STAR ICVS-LTM System Instrument Performance Monitoring System (IPMS) SDR Quality Assurance System (SQAS) ATMS Cr. IS NPP/JPSS VIIRS NPP/JPSS OMPS NOAA/Metop GCOM-W AVHRR HIRS SSMIS Input Data Sources: • GRAVITE (RDR/TDR) • CLASS (TDR/SDR) • DDS (Level 1 B) Output Products: • IPMS Analysis Data • LTM Trending Plots • Warning Notification DMSP VIIRS OMPS CERES MHS GCOM-W HIRS MHS DMSP NPP/JPSS AVHRR AMSU AMSR-2 Cr. IMSS VIIRS AMSU NPP/JPSS Spacecraft GCOM-W ATMS OMPS CERES NOAA/Metop EDR Quality Assurance System (EQAS) AMSR-2 AMSU AMSR-2 MHS NOAA/Metop SSMIS AVHRR AIRS IASI Radiative Transfer Model Input Data Sources: • EMC (GFS/GDAS) • ECMWF (GFS/GDAS) • CLASS (TDR/SDR) • DDS (Level 1 B) Output Products: • SQAS Analysis Data • Sensor Data Global Distribution • Sensor Data Global Bias Distribution • LTM Trending Plots • Warning Notification Input Data Sources: • GRAVITE (TDR/SDR) • CLASS (TDR/SDR) • DDS (Level 1 B) Output Products: • T/Q Profiles • Aerosol Products • Cloud Products • Ozone Products • Surface Products • Energy Budget Satellite Data and Application Demonstration System (DADS) • Instrument Status Trending • Sensor Performance Trending • Spacecraft Operational Status • Sensor/SC Diagnostic Datasets IPMS SQAS • Sensor Data Global Distribution • Sensor Data Global Bias Trending EQAS NWP • Satellite retrieval products • Inter-sensor calibrated CDR products • High Impact Events Imager Global Forecasts Climate Predictions and Projections Regional Forecasts Hurricanes and High Impact Events
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)
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)
Cr. IS SDR Data Quality Monitoring Large imagery part over Australia hot scene caused by invalid bit-trim flag
VIIRS Degradation Monitoring Samples VIIRS Focal Plane Aperture Temperature 11
Suomi NPP TDR/SDR Algorithm Schedule Sensor Cr. IS ATMS OMPS-EV VIIRS Beta May 7, 2012 February 22, 2012 March 12, 2012 C May 2, 2012 C Provisional (Review Date) C October 23, 2012 October 23, 2012 Validated (planned) 2013 12
Hurricane Sandy Warm Core Anomaly Ascending 1730 UTC, 29 October 2012 At 1800 UTC Oct 29 Max Wind: 90 MPH, Min Pressure: 940 h. Pa Cross section along Latitude 38. 1 N Cross section along Longitude 72. 9 W 13
Cross-Track Infrared Sounder (Cr. IS) 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: • NEd. Ns are well below specifications • Spectral uncertainty: < 2 ppm, well below specification • Radiometric uncertainty: ~0. 1 K, well below specification • Geolocation error: < 1. 0 km below specification 14
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 AMSR 2 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 AMSR 2 data
AMSR-2 Experimental Rain Water Path 2012. 10. 26. 06 2012. 10. 28. 06 2012. 10. 27. 06 2012. 10. 29. 06
TV Signals Reflected by Ocean – RFI Geostationary TV Satellite AMSR 2 TV signals reflected by ocean Emission by ocean Signals from Geostationary TV Satellite downlink beam coverage Geostationary satellite TV signals reflected by ocean surface is a major source of maritime RFI signals are mixed with natural emission from pixels interfered by reflected TV signals. 18
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 ( H 2 O, CO 2, O 3 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 19
Satellite Data Critical for Improving Hurricane and Coastal Precipitation Forecasts • 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/Cr. IS/AIRS/IASI) • Ocean surface wind and temperature from satellite scatterometer and passive microwave imager – provide surface energy flux and surface vortex (ASCAT/AMSR 2) • 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). 22
HWRF Model and Data Assimilation System HWRF Model: • 2012 NCEP-Trunk version 934 • Three telescoping domains: Outer domain: 27 km: 75 x 75 o; Inner domain: 9 km ~11 x 10 o Inner-most domain: 3 km inner-most nest ~6 x 6 o Revised Model Level and Top: • Vertical levels: 61 • Model top: 0. 5 h. Pa Data Assimilation System: • HWRF 6 hour forecasts • GSI (3 DVAR) • 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 highresolution 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 latitudelongitude, Arakawa E-grid and a vertical, pressure hybrid (sigma_p-P) coordinate. • Advanced HWRF 3 D 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. 23 • Ocean coupled modeling system (POM/HYCOM).
ATMS Weighting Functions Pressure (h. Pa) STAR HWRF Top NCEP HWRF Top ATMS Weighting Function Our approach: Raise the model top to allow for more satellite data assimilated into hurricane forecast model 24
Control Experiment – L 61 Conventional Data: Radiosondes, aircraft reports (AIREP/PIREP, RECCO, MDCRS-ACARS, TAMDAR, AMDAR), Surface ship and buoy observations , Surface observations over land, Pibal winds, 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 25
Hurricane Sandy Forecasts Control : L 61 Sensitivity Experiments ATMS: L 61+ATMS IASI: L 61+IASI Cr. IS: L 61+Cr. IS Forecast Period: 1800 UTC Oct 22, 2012 1800 UTC Oct 29, 2012 Total Cycles: 29 HWRF FST 1800 UTC Turn on GSI 0000 UTC 5 -day Forecast 0000 UTC day 5 26
Impacts of Assimilation of NOAA/METOP Data on Hurricane Sandy’s Track L 61 CONV Only 28
Impacts of Assimilation of ATMS Radiances on Hurricane Sandy’s Track L 61: Control Run L 61+ATMS 29
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 30
Impacts of Assimilation of IR Hyperspectral Sounder Radiances on Hurricane Sandy’s Track L 61+IASI L 61+Cr. IS 31
Multiple Forecasts of Max. Wind Speed for Hurricane Sandy L 61 ATMS IASI Cr. IS 33
Summary and Conclusions • NOAA satellite instruments are well calibrated for operational applications and environmental data stewardship. • Suomi NPP 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/Cr. IS 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 Cr. IS has also significantly large impact on track forecasts. • For conventional data only, hurricane track forecast errors increase rapidly when hurricane is near landfall. 35
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