Production of proxy datasets in support of GOES-R algorithm development

Hillger, Donald W.; Brummer, Renate; Grasso, Louie; Sengupta, Manajit; DeMaria, Robert; DeMaria, Mark

doi:10.1117/12.828489

Cited by 1 publication

(2 citation statements)

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“…The third data set used to assess the WF_ABBA FRP‐based biomass burning emissions is the GOES‐R fire proxy simulated at CIRA (Cooperative Institute for Research in the Atmosphere), Colorado State University. This proxy simulates 4 GOES‐R ABI (Advanced Baseline Imager) bands (2.25 μ m, 3.9 μ m, 10.35 μ m, and 11.2 μ m) that include fire hot spots using a high‐resolution Regional Atmospheric Modeling System (RAMS) model [ Grasso et al , 2008; Hillger et al , 2009]. The artificial fires were laid out in a regular grid size of 400 m and a temporal resolution of 5 min.…”

Section: Methodsmentioning

confidence: 99%

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Near‐real‐time global biomass burning emissions product from geostationary satellite constellation

Zhang¹,

Kondragunta²,

Ram³

et al. 2012

J. Geophys. Res.

100

101

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[1] Near-real-time estimates of biomass burning emissions are crucial for air quality monitoring and forecasting. We present here the first near-real-time global biomass burning emission product from geostationary satellites (GBBEP-Geo) produced from satellite-derived fire radiative power (FRP) for individual fire pixels. Specifically, the FRP is retrieved using WF_ABBA V65 (wildfire automated biomass burning algorithm) from a network of multiple geostationary satellites. The network consists of two Geostationary Operational Environmental Satellites (GOES) which are operated by the National Oceanic and Atmospheric Administration, the Meteosat second-generation satellites (Meteosat-09) operated by the European Organisation for the Exploitation of Meteorological Satellites, and the Multifunctional Transport Satellite (MTSAT) operated by the Japan Meteorological Agency. These satellites observe wildfires at an interval of 15-30 min. Because of the impacts from sensor saturation, cloud cover, and background surface, the FRP values are generally not continuously observed. The missing observations 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. Finally, the simulated diurnal variation in FRP is applied to quantify biomass combustion and emissions in individual fire pixels with a latency of 1 day. By analyzing global patterns in hourly biomass burning emissions in 2010, we find that peak fire season varied greatly and that annual wildfires burned 1.33 Â 10 12 kg dry mass, released 1.27 Â 10 10 kg of PM2.5 (particulate mass for particles with diameter <2.5 mm) and 1.18 Â 10 11 kg of CO globally (excluding most parts of boreal Asia, the Middle East, and India because of no coverage from geostationary satellites). The biomass burning emissions were mostly released from forest and savanna fires in Africa, South America, and North America. Evaluation of emission result reveals that the GBBEP-Geo estimates are comparable with other FRP-derived estimates in Africa, while the results are generally smaller than most of the other global products that were derived from burned area and fuel loading. However, the daily emissions estimated from GOES FRP over the United States are generally consistent with those modeled from GOES burned area and MODIS (Moderate Resolution Imaging Spectroradiometer) fuel loading, which produces an overall bias of 5.7% and a correlation slope of 0.97 AE 0.2. It is expected that near-real-time hourly emissions from GBBEP-Geo could provide a crucial component for atmospheric and chemical transport modelers to forecast air quality and weather conditions.

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Section: Methodsmentioning

confidence: 99%

“…The GOES‐R ABI imagery at an approximately 2 × 2 km resolution was simulated using the anticipated point spread function from the 400 m fires [ Grasso et al , 2008; Hillger et al , 2009; Schmidt et al , 2010]. Based on these simulated instantaneous radiance, the WF_ABBA was used to process the detection of fire characteristics.…”

Section: Methodsmentioning

confidence: 99%