NOAA Fire Emissions Product: The Blended Global Biomass Burning Emissions Product from MODIS and Geostationary Satellites (GBBEPx)

Transcription

1 3 rd IBBI Workshop April 2014 Schloss Ringberg, Bavaria, Germany NOAA Fire Emissions Product: The Blended Global Biomass Burning Emissions Product from MODIS and Geostationary Satellites (GBBEPx) Xiaoyang Zhang GSCE, South Dakota State University Shobha Kondragunta NOAA/NESDIS/STAR Arlindo Da Silva - NASA Sarah Lu - IMSG@NOAA/NWS/NCEP Hyuncheol Kim - IMSG@NOAA/ARL 1

2 Outline Background Global Biomass Burning Emissions Product from Geostationary satellites (GBBEP-Geo) Quick Fire Emissions Product (QFED) Blended global biomass burning emissions product (GBBEPx) Summary 2

3 Background NOAA/NWS/NCEP is developing capabilities to provide global aerosol forecasts. The global model needs near-real time biomass burning emissions at a global coverage. Quick fire emissions data (QFED) are generated from polar-orbiting satellites of Terra and Aqua MODIS in NASA. MODIS provides high spatial resolution of fire data but it only observes surface 4 times a day. Global biomass burning emissions product from a network of geostationary satellites (GBBEP-Geo) is generated in NOAA. The fires are detected every minutes but with a coarser spatial resolution and without coverage at high latitude. A blended QFED and GBBEP-Geo (GBBEPx) could better serve as an input for NCEP to improve regional and global aerosol/air quality monitoring and forecasting. 3

4 Flowchart of Blending QFED and GBBEP-Geo QFEDv2 Terra+Aqua MODIS fire detections MODIS fire FRP with cloud adjustment QFED: Quick Fire Emission Dataset from MODIS fire data GBBEP-Geo: Global Biomass Burning Emissions Product from Multiple Geostationary Satellites GBBEP-Geo Geostationary satellite fire detections Simulating diurnal FRP MODIS fire emissions calibrated with GFEDv2 and MODIS AOD Blended global biomass burning emission Fire emissions Adjusting Fire emissions to QFEDv2 GFS-Global Forecast System NEMS-NOAA Environmental Modeling System GOCART--NASA Goddard Chemistry Aerosol Radiation and Transport Model Simulating AOD using NEMS-GFS Scaling fire emissions MODIS AOD Tuning blended fire emissions NEMS-GFS- GOCART forecast 4

5 Biomass Burning Emissions from Fire Radiative Power Fire Radiative Power (FRP) is theoretically a function of fire size and fire temperature which is closely related to brightness temperature observed from satellite thermal bands (Wooster, 2002). FRE is fire radiative energy (MJ), an integration of FRP during a certain time period of biomass burning. It represents the dry fuel mass combusted within a given burned area or a fire pixel. E=A M C F =FRE β F E ---biomass burning emissions (kg) A ---burned area (km 2 ) M --biomass density/fuel loading (kg.km -2 ) C combustion factor F emission factors β - a combustion rate per unit energy released. 5

6 Fire Data from Geostationary Satellites for Producing GBBEP-Geo -- WF_ABBA (Wildfire Automated Biomass Burning Algorithm) product Satellite/Sensor GOES-E: WF_ABBA fire product Coverage: America GOES-W: WF_ABBA fire product Coverage: America Metosat SEVIRI: WF_ABBA fire product Coverage: Africa and Europe MTSAT Imager: WF_ABBA fire product Coverage: Eastern Asia Algorithm Version Spatial Resolution Parameters in fire pixels V65 4 km FRP Latitude Longitude Land cover type V65 4 km FRP Latitude Longitude Land cover type V65 3km FRP Latitude Longitude Land cover type V65 4 km FRP Latitude Longitude Land cover type Temporal Resolution 30 min 30 min 15 min 30 min 6

7 Quality in WF_ABBA Product Fire detection rate (%) Meteosat MTSAT GOES Flag0 Flag1 Flag2 Flag3 Flag4 Flag5 FRP Quality category Flag0-- good quality fire pixel Flag2--cloud contaminated fire pixel Flag4-- medium-probability fire pixel Flag1-- saturated fire pixel Flag3-- high-probability fire pixel Flag5-- low-probability fire pixel 7

8 Reconstruction of Diurnal FRP in a Fire Pixel -- Climatologic Diurnal Patterns FRP (MW) Forests Savannas Shrublands Grasslands Croplands Local solar time (hour) Climatologic diurnal FRP (average data from ) fitted using the Discrete Fourier Transform model for various ecosystems in North America. The FRP is observed with good quality and with satellite viewing angle less than 40 degree. 8

9 Reconstruction of Diurnal FRP in a Fire Pixel -- Example Reconstructed pattern Detected fire without FRP estimate No fire detection Detectd FRP FRP(MW) Forest at W and 44.49N in 2006 August UTC time (hour) It is assumed that the shape of FRP diurnal pattern is similar in a given ecosystem and that the diurnal pattern of FRP for a given fire pixel can be reconstructed by fitting the climatological diurnal curve corresponding to that ecosystem to the timely detected fire FRP values. 9

10 GBBEP-Geo: Hourly Biomass Burning Aerosols (PM2.5) Estimated from GOES+MET09+MTS01 FRP on Sept. 16, 2009 GOES MET09 MTS01

11 Quick Fire Emission Dataset (QFED) from MODIS DATA in NASA QFEDv2 Calculated from (1) MODIS FRP for various biome types, (2) combustion factor obtained by comparing with GFED product, and (3) emission factors scaled 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. Finally, QFED product at 0.25x degree is merged from Terra and Aqua daily fire emissions of BC, OC, SO2, CO, CO2, PM2.5 11

12 MODIS Data for QFED Product Satellite/Sensor Terra/MODIS : MOD14- Thermal Anomalies/Fire products Terra/MODIS: MOD 03 - Geolocation Data Set Aqua/MODIS : MYD14- Thermal Anomalies/Fire products Terra/MODIS: MYD 03 - Geolocation Data Set Algorithm Version Spatial Resolution Parameters in fire pixels Collection 5 1 km FRP Latitude Longitude Sample andline Collection 5 1km Number of fire pixels Cloud pixels Clear land pixels Collection 5 1 km FRP Latitude Longitude Sample line Collection 5 1km Number of fire pixels Cloud pixels Clear land pixels NASA ftp site: nrt1.modaps.eosdis.nasa.gov Temporal Resolution Daily (2 times) Daily (2 times) Daily (2 times) Daily (2 times) 12

13 E GFED QFED fx = χme GFED fx QFED E fx = χme fx QFED Calibrated Using GFED and MODIS AOD QFED calibrated using GFED: E GFED fx = χ m E χ MOD14 =1.38 for Terra MODIS χ MyD14 = 0.47 for Aqua MODIS. QFED fx QFED calibrated using MODIS AOD: Modeled AOT from NASA Goddard Earth Observing System Model (GEOS-5) comparing with the MODIS AOT q = a_bb*q_bb + a_an*q_an q = MODIS AOD q_bb = Blended_Emissions AOD NOBB AOD q_an = NOBB a_bb = Tuning factor for fire emission a_an = Tuning factor for anthropogenic 13 emission

14 Scaling (Calibrating) GBBEP-Geo Using QFEDv2 Satellite-based scaling factors calibration of GBBEP-Geo using QFEDv2 E QFED = a 1 E GOES E QFED = a 2 E Metosat E QFED = a 3 E MTSAT CE GOES = a 1 E GOES CE Metosat =a 2 E Metosat CE MTS = a 3 E MTSAT CE is scaled (calibrated) emissions E is emissions from different satellites 14

15 Determination of Scaling Factors for GBBEP-Geo Comparison of daily PM2.5 emissions between GBBEP-Geo and QFEDv2 NA--North America SA South America AF Africa AS-Asia AU--Australia

16 Near Real Time Biomass Burning Emissions Product Blended from Polar-orbiting and Geostationary Satellites gbbpgeo_qfed2.emis_bc nc4 gbbpgeo_qfed2.emis_oc nc4 gbbpgeo_qfed2.emis_co nc 4 gbbpgeo_qfed2.emis_pm nc4 gbbpgeo_qfed2.emis_co nc4 gbbpgeo_qfed2.emis_so nc4 2day/BBEP/

17 Example of Implementation from Current Processing at NOAA File name for hourly GBBEP-Geo: GBBEP_Geo.Hourly_emissions nc4 The product include: Latitude Longitude Fire radiative energy Dry mass burned PM2.5 BC CO CO2 OC SO2 Ecosystem Version Year Month Day 17

18 Forecast with Biomass Burning Emissions in July 2011 (6 hourly) 18

19 Future Development Plan Algorithm Improvement FRP could be underestimated because of satellite viewing zenith angles, large pixel size, cloud and smoke obscuration, and other detection failure. Innovative approaches are needed to improve the FRP estimation. Polar-orbit satellite FRP and geostationary satellite FRP should be fused to enhance the calculation of diurnal and spatial pattern. The fused diurnal FRP is expected to increase the quality of biomass burning emissions. Further Algorithm Validation» Validating model-simulated AOD using satellite observations from MODIS, CALIPSO, MISR and VIIRS» Validating surface biomass burning emissions using fieldbased observations of biomass combustion. 19

20 Summary GBBEP-Geo is developed using diurnal patterns of FRP which reduces impacts of missing fire observations caused by cloud cover, sensor saturation, etc. QFEDv2 is calibrated by taking MODIS AOD as a reference and GBBEP-Geo is then calibrated using QFEDv2. These two datasets are blended to generate a global biomass burning emissions product, which is expected to meet well the requirement of global aerosol forecasting (NEMS-GFS-GOCART). The operational product also keeps GBBEP-Geo data which provide hourly emissions for individual fire pixels without any model-based calibration. This dataset, containing FRE, DM, PM2.5, BC, CO, CO2, OC, SO2, and ecosystem types, would be useful for other models. Validation is still needed in our next step. This will involve: (1) to validate modelsimulated AOD using satellite observations from MODIS, CALIPSO, MISR and VIIRS; (2) to validate surface biomass burning emissions using field-based observations of biomass combustion. 20