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John F. Weaver, Dan Lindsey, Dan Bikos, Chris C. Schmidt, and Elaine Prins

Abstract

This paper demonstrates the proper use of geostationary satellite imagery in wildland fire detection. The roles of both the visible and the 3.9-μm channels are emphasized. Case studies from June 2002 are presented to illustrate techniques that can be utilized in both the detection and short-range forecasting processes. The examples demonstrate that, when utilized correctly, the sensitivity of the shortwave infrared channel to subpixel heat sources can often result in detections that match the timelines of human observations. Finally, a derived satellite product that increases the detection rate of wildland fires from space is described.

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Gary P. Ellrod, Rao V. Achutuni, Jaime M. Daniels, Elaine M. Prins, and James P. Nelson III

The Geostationary Operational Environmental Satellite-8 (GOES-8), the first in the GOES I–M series of advanced meteorological satellites was launched in April 1994 and became operational at 75 °W longitude the following year. GOES-8 features numerous improvements over prior GOES platforms such as 1) improved resolution in the infrared (IR) and water vapor bands, 2) reduced instrument noise, 3) 10-bit visible and IR digitization, 4) greater image frequency, 5) more spectral bands, and 6) an independent sounder. A qualitative and quantitative comparison of the imager data from GOES-8 and GOES-7 shows that imagery from the newer spacecraft is superior in most respects. Improvements in resolution and instrument noise on GOES-8 provide sharper, cleaner images that allow easier detection of significant meteorological or oceanographic features. Infrared temperature comparisons between GOES-8 and GOES-7 were within 0.5°–2.0°C for all IR bands, indicating consistency between the two spacecraft. Visible band albedos from GOES-8 were at least 50% greater than GOES-7 for a wide range of scenes, suggesting that output from the GOES-7 visible detectors had degraded since its launch in 1987. Products derived from GOES-8 imager data for observing fog at night, fire detection, heavy precipitation estimation, and upper-level winds based on cloud or water vapor motion have been shown to be superior to similar products from GOES-7. Early difficulties with image registration and IR striping were alleviated after the first year. Based on the performance of GOES-8, future spacecraft in the GOES I–M series can be expected to provide many years of useful service to meteorologists, oceanographers, and the environmental monitoring community.

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Elaine M. Prins, Christopher S. Velden, Jeffrey D. Hawkins, F. Joseph Turk, Jaime M. Daniels, Gerald J. Dittberner, Kenneth Holmlund, Robbie E. Hood, Arlene G. Laing, Shaima L. Nasiri, Jeffery J. Puschell, J. Marshall Shepherd, and John V. Zapotocny
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Jassim Al-Saadi, James Szykman, R. Bradley Pierce, Chieko Kittaka, Doreen Neil, D. Allen Chu, Lorraine Remer, Liam Gumley, Elaine Prins, Lewis Weinstock, Clinton MacDonald, Richard Wayland, Fred Dimmick, and Jack Fishman

Accurate air quality forecasts can allow for mitigation of the health risks associated with high levels of air pollution. During September 2003, a team of NASA, NOAA, and EPA researchers demonstrated a prototype tool for improving fine particulate matter (PM2.5) air quality forecasts using satellite aerosol observations. Daily forecast products were generated from a near-real-time fusion of multiple input data products, including aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS)/Earth Observing System (EOS) instrument on the NASA Terra satellite, PM2.5 concentration from over 300 state/local/national surface monitoring stations, meteorological fields from the NOAA/NCEP Eta Model, and fire locations from the NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) Geostationary Operational Environmental Satellite (GOES) Wildfire Automated Biomass Burning Algorithm (WF_ABBA) product. The products were disseminated via a Web interface to a small group of forecasters representing state and local air management agencies and the EPA. The MODIS data improved forecaster knowledge of synoptic-scale air pollution events, particularly over oceans and in regions devoid of surface monitors. Forecast trajectories initialized in regions of high AOD offered guidance for identifying potential episodes of poor air quality. The capability of this approach was illustrated with a case study showing that aerosol resulting from wildfires in the northwestern United States and southwestern Canada is transported across the continent to influence air quality in the Great Lakes region a few days later. The timing of this demonstration was selected to help improve the accuracy of the EPA's AIRNow (www.epa.gov/airnow/) next-day PM2.5 air quality index forecast, which began on 1 October 2003. Based on the positive response from air quality managers and forecasters, this prototype was expanded and transitioned to an operational provider during the summer of 2004.

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