Analog ensemble scheme for objective, short-term cloud forecasting

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Tuesday, 19 January 2010
Timothy J. Hall, The Aerospace Corporation, Silver Spring, MD; and R. N. Thessin, G. J. Bloy, and C. N. Mutchler

Handout (577.6 kB)

Analog forecasting is well established in meteorology. In recent years, investigators have begun to automate analog forecasting using modern statistical techniques. In this study, we developed a fuzzy, k-nearest neighbors (k-nn) method to identify an ensemble of historical analogs matching a set of atmospheric parameters. This ensemble was then used to make a short-term, probabilistic prediction of cloud-free conditions. Forecasts were produced in this manner for 1, 2, 3, 4 and 5 hours into the future. Analogs were discovered in a database comprised of a multi-year, half-hourly time series of atmospheric conditions. Atmospheric parameters in the database include cloud features or patterns identified in meteorological satellite imagery as well as variables extracted from data assimilation-derived Eta model analyses. Our investigation focused on six different local and regional targets spread across a variety of weather regimes within the continental United States. Performance of our scheme exceeded both basic persistence and the persistence probability for all targets, at all forecast times.