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AIR QUALITY FORECAST VERIFICATION USING SATELLITE DATA
Citation:
KONDRAGUNDA, P. L., L. J. MCQUEEN, C. KITTAKA, A. PRADOS, P. CIREN, I. LASZLO, B. PIERCE, R. HOFF, AND J. SZYKMAN. AIR QUALITY FORECAST VERIFICATION USING SATELLITE DATA. JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY. American Meteorological Society, Boston, MA, 47:1-18, (2008).
Impact/Purpose:
Our main objective is to assess the exposure of selected ecosystems to specific atmospheric stressors. More precisely, we will analyze and interpret environmental quality (primarily atmospheric) data to document observable changes in environmental stressors that may be associated with legislatively-mandated emissions reductions.
Description:
NOAA 's operational geostationary satellite retrievals of aerosol optical depths (AODs) were used to verify National Weather Service (NWS) experimental (research mode) particulate matter (PM2.5) forecast guidance issued during the summer 2004 International Consortium for Atmospheric Research on Transport and Transformation/New England Air Quality Study (ICARTT/NEAQS) field campaign. The forecast period was encompassed by long range transport of smoke from fires burning in Canada and Alaska and a regional-scale sulfate event over the Gulf of Mexico and the eastern United States (U.S). Over the 30-day time period for which daytime hourly forecasts were compared to observations, the categorical (event defined as AOD > 0.65) forecast accuracy was between 60% and 100% with a mean of -80%. Hourly normalized mean bias (forecasts -observations) ranged between -50% and +50% with forecasts being biased high when observed AODs were small and biased low when observed AODs were high. Normalized Mean Errors are between 50% and 100% with the errors on the lower end during July 18-22,2004 time period when a regional scale sulfate event occurred. Spatially, the errors are small over the regions where sulfate plumes were present. Correlation coefficient (r) also showed similar features (spatially and temporally) with a peak value of -0.6 during July 18- 22,2004 time period. The dominance of long-range transport of smoke into the US during the summer of 2004, which the model lacked due to its static boundary conditions, skewed the model forecast performance. Enhanced accuracy and reduced normalized mean errors during the time period when a sulfate event prevailed shows that the forecast system is very capable of issuing PM2.5 forecasts for urban/industrial pollution events. 2