Satellite Instrument Calibration and Data Assimilation Fuzhong Weng, Acting Chief Satellite...
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Transcript of Satellite Instrument Calibration and Data Assimilation Fuzhong Weng, Acting Chief Satellite...
Satellite Instrument Calibration and Data Assimilation
Fuzhong Weng, Acting ChiefSatellite Meteorology and Climatology Division
NOAA/NESDIS/Center for Satellite Applications and Research
NOAA Satellite Conference, NCWCP, College Park, MDApril 10, 2013
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• 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
RecordsRDR
SDR
EDR
CDR
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
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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:•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
IPMS • 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
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)
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)
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:•T/Q Profiles•Aerosol
Products•Cloud
Products•Ozone
Products•Surface
Products•Energy
Budget
NPP/JPSS Spacecraft
NOAA/Metop
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)
CrIS SDR Data Quality Monitoring
Large imagery part over Australia hot scene caused by invalid bit-trim flag
VIIRS Degradation Monitoring Samples
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VIIRS Focal Plane Aperture Temperature
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Suomi NPP TDR/SDR Algorithm Schedule
C
CCCCCCCCCCCCC
Sensor Beta Provisional (Review Date) Validated (planned)
CrIS May 7, 2012 October 23, 2012 2013ATMS February 22, 2012 October 23 2012 2013
OMPS-EV March 12, 2012 October 23, 2012 2013VIIRS May 2, 2012 October 23, 2012 2013
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
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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
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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
2012.10.26.06 2012.10.27.06
2012.10.28.06 2012.10.29.06
AMSR-2 Experimental Rain Water Path
TV Signals Reflected by Ocean – RFI
Satellite downlink beam coverage
Geostationary TV Satellite
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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.
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
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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/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).
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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).23
Pre
ssur
e (h
Pa)
ATMS Weighting Function
NCEP HWRF Top
STAR HWRF Top
ATMS Weighting Functions
Our approach: Raise the model top to allow for more satellite data assimilated into hurricane forecast model 24
Control Experiment – L61
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
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Hurricane Sandy Forecasts
Control : L61
Sensitivity Experiments
ATMS: L61+ATMS
IASI: L61+IASI
CrIS: L61+CrIS
Forecast Period: 1800 UTC Oct 22, 2012 -
1800 UTC Oct 29, 2012
Total Cycles: 29
1800 UTC 0000 UTC 0000 UTC day55-day Forecast
HWRF FST Turn on GSI
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CONV OnlyL61
Impacts of Assimilation of NOAA/METOP Data on Hurricane Sandy’s Track
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L61:Control Run
Impacts of Assimilation of ATMS Radiances on Hurricane Sandy’s Track
L61+ATMS
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Comparison of Temperature Increments from ATMS and AMSU-A
Shaded: ATMS Red contour: AMSU-ABlack contour: Conventional
ATMS and AMSU-A (NOAA-19) produce largest temperature innovation in storm regions in similar magnitudes and complementary in spatial coverage
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L61+IASIL61 L61+CrIS
Impacts of Assimilation of IR Hyperspectral Sounder Radiances on Hurricane Sandy’s Track
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Multiple Forecasts of Max. Wind Speed for Hurricane Sandy
L61
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IASI
ATMS
CrIS
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/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.
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