Using Satellite Data to Improve Operational Atmospheric Constituents Forecasting Capabilities
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Transcript of Using Satellite Data to Improve Operational Atmospheric Constituents Forecasting Capabilities
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Using Satellite Data to Improve Operational Using Satellite Data to Improve Operational Atmospheric Constituents Forecasting Atmospheric Constituents Forecasting
CapabilitiesCapabilities
Shobha KondraguntaShobha Kondragunta,, NOAA/NESDIS/STARNOAA/NESDIS/STARXiaoyang Zhang, UMD - CICS @ NOAA/NESDIS/STARXiaoyang Zhang, UMD - CICS @ NOAA/NESDIS/STAR
Arlindo da Silva, NASA Goddard Space Flight CenterArlindo da Silva, NASA Goddard Space Flight CenterSarah Lu, Sarah Lu, IMSG @ NOAA/NWS/NCEPIMSG @ NOAA/NWS/NCEP
Hyun Cheol Kim, NOAA/ARLHyun Cheol Kim, NOAA/ARL
Project OverviewProject Overview
Joint NASA/GMAO, NESDIS/STAR, and NWS/NCEP project to:Joint NASA/GMAO, NESDIS/STAR, and NWS/NCEP project to:» Develop near real time biomass burning emissions product covering Develop near real time biomass burning emissions product covering
the whole globe from polar and geostationary satellites for NEMS-the whole globe from polar and geostationary satellites for NEMS-GFS-GOCARTGFS-GOCART
– Globally, biomass burning is one of the primary sources of Globally, biomass burning is one of the primary sources of aerosols; burning varies seasonally, geographically and is either aerosols; burning varies seasonally, geographically and is either natural (e.g., forest fires induced by lightning) or human induced natural (e.g., forest fires induced by lightning) or human induced (e.g., agricultural burning for land clearing). Satellites can provide (e.g., agricultural burning for land clearing). Satellites can provide this information on a real time basis. this information on a real time basis.
» Develop and deploy a global aerosol prediction system that can in the Develop and deploy a global aerosol prediction system that can in the future assimilate satellite-derived atmospheric composition parametersfuture assimilate satellite-derived atmospheric composition parameters
Meet Research (NASA) to Operations (NOAA) goals of the JCSDA Meet Research (NASA) to Operations (NOAA) goals of the JCSDA
» QFED code transition from NASA to NOAAQFED code transition from NASA to NOAA
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Blended Polar and Geo Blended Polar and Geo BBEP FlowchartBBEP Flowchart
Terra+Aqua MODIS fire detections
QFEDv2
Blended global biomass burning emission
Simulating AOD using NEMS-GFS-GOCART
Geostationary satellite fire detections
MODIS fire FRP with cloud adjustment
MODIS fire emissions
MODIS AOD
Calibrating Fire emissions
Scaling MODIS fire emissions
Simulating diurnal FRP
Fire emissions
Scaling fire emissions
GBBEP-GeoQFED: Quick Fire Emission Dataset from MODIS fire data
GBBEP-Geo: Global Biomass Burning Emissions Product from Multiple Geostationary Satellites
FRP Datasets from MODIS and FRP Datasets from MODIS and Geostationary Satellites Geostationary Satellites
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Satellite/Sensor Algorithm Version
Spatial Resolution
Temporal Resolution
Terra/MODIS & Aqua/MODIS
Collection 5 1 km Daily (4 times)
GOES-E and -W
V65 4 km nadir 30 min
Metosat-9 SEVIRI
V65 3 km 15 min
MTSAT Imager V65 4 km 30 min
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Biomass Burning Emission Biomass Burning Emission Derived from Fire Radiative Derived from Fire Radiative
Power Power
Fire Radiative Power (FRP)Fire Radiative Power (FRP)
FRP can also be empirically derived from 3.9 um radianceFRP can also be empirically derived from 3.9 um radiance
Fire Radiative EnergyFire Radiative Energy
Biomass combustedBiomass combusted
EmissionsEmissions
MIRbkLMIRhLa
sampAFRP ,,
FRP = AσT4
dtFRPFREt
t2
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BC = FRE*β
E = BC*EF
σ -- 5.67x10-8 Js-1K-4
A – area brunedT – fire temperature-- 0.368±0.015 kg/MJ Lh – radiance at 3.9 umLbk – background radianceEF – emissions factorsa -- constant
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Scaling factors determined by comparing model simulated AOD with observed MODIS AOD
CEterra=a1Eterra
CEaqua=a2Eaqua
CEgoes_E=a3Egoes_E
CEgoes_w=a4Egoes_W
CEmetosat=a5Emetosat
CEmtsat=a6Emtsat
Calibration of Multiple Calibration of Multiple Satellite-based Fire Satellite-based Fire
Emissions Emissions
CE represents scaled emissions and E is satellite-dependent fire emissions. Parameters a1-a6 are scaling factors calculated by comparing year-long model-simulated AOD and MODIS AOD
NASA Quick Fire NASA Quick Fire Emission Dataset (QFED)Emission Dataset (QFED)
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QFEDv2—Calculated from MODIS FRP for various biome types and tuned using scaling factors which are obtained by comparing GFS-GOCART-modeled AOD with MODIS observed AOD.Emissions are tuned respectively for Terra MODIS and Aqua MODIS, which are then combined to produce daily global emissions.
Final QFED product at 0.25x0.3125 degree is merged from Terra and Aqua daily fire emissions of BC, OC, SO2, CO, CO2, PM2.5
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GBBEP-Geo: Simulating GBBEP-Geo: Simulating Diurnal FRP from Diurnal FRP from
Geostationary SatellitesGeostationary Satellites
Averaged from DOY 257-305, 2009
GOES-E and GOES-W
METEOSAT MTSAT
Diurnal FRP patterns are simulated by combining the available instantaneous FRP observations within a day and a set of representative climatological diurnal patterns of FRP for various ecosystems
Climatological diurnal patterns of GOES FRP
GBBEP-Geo: Global Biomass GBBEP-Geo: Global Biomass Burning Emissions Product from Burning Emissions Product from Multiple Geostationary SatellitesMultiple Geostationary Satellites
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Hourly fire emissions for CO, OC, BC, CO2, SO2, PM2.5Limited coverage in high latitudes and no coverage in most regions across India and parts of boreal Asia
No spatial coverage
MODIS AOD for MODIS AOD for Comparison with Comparison with
Modeled AODModeled AOD
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Spatial patterns of MODIS AOD and Hazard Mapping System (HMS) fire hot spots.
Calibration of Simulated Calibration of Simulated FRP between GOES-E and FRP between GOES-E and
GOES-WGOES-W
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Estimating satellite-scaling factor between GOES-E and GOES-W
Calibration of GBBEP-geo Calibration of GBBEP-geo with Scaled QFEDv2with Scaled QFEDv2
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GBBEP-geo can be calibrated to be equivalent to QFEDv2 to generate global blended fire emissions because the QFEDv2 has been scaled using MODIS AOD.
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• Model Configuration: Forecast model: Global Forecast System (GFS) based
on NOAA Environmental Modeling System (NEMS), NEMS-GFS
Aerosol model: NASA Goddard Chemistry Aerosol Radiation and Transport Model, GOCART
• Phased Implementation: Dust-only guidance is established in Q4FY12 Full-package aerosol forecast after real-time global
smoke emissions are available and tested (JSDI project)
• NRT Dust Forecasts 5-day dust forecast once per day (at 00Z), output
every 3 hour, at T126 L64 resolution ICs: Aerosols from previous day forecast and
meteorology from operational GDAS GRIB2 products in 1x1 degree: 3-d distribution of
dust aerosols (5 bins from 0.1 – 10 µm) and 2-d aerosol diagnosis fields (e.g., aerosol optical depth, surface mass concentration)
Operational since Sept 2012
NEMS GFS Aerosol Component (NGAC)NCEP’s global interactive atmosphere-aerosol
forecast system
Acknowledge: Development and operational implementation of NGAC represents a successful “research to operations” project sponsored by NASA Applied Science Program, JCSDA and NWS
NGAC experiments using smoke emissions from GFEDv2, QFEDv2, and GBBEP-Geo
for 2011
Seasonal variations: Surface mass concentration for dust aerosols (top) and carbonaceous aerosols using QFED2 (bottom) for different season (July on the left and March on the right).The model is still running for GBBEP-Geo results.
NGAC experiments using smoke emissions from GFEDv2, QFEDv2, and GBBEP for
2011 Biomass Burning: QFED V2 versus GFED (global map on the left and regional map over Africa on the right) and time series of OC/BC surface concentration over Africa (black for QFED2 and green for GFED)
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Summary and Ongoing Summary and Ongoing WorkWork
• NGAC model run with QFED2 emissions for 2011 completed• Model output will be compared to MODIS AOD to determine
scaling factors• NGAC model run with GBBEP-Geo emissions for 2011 is currently
ongoing • QFED code successfully implemented on STAR computers.• Other approaches to determine scaling factors (inter-satellite
calibration) will be used to minimize the model simulations needed.• Product validation is critical. Direct validation is currently not possible
because of the lack of reliable in-situ measurements. We will work on inter-comparison with other biomass burning emission products.
• Algorithm Critical Design Review (CDR) to be held in May 2013