Generalized Exponential Markov and Model Output Statistics: A Comparative Verification

Thomas J. Perrone Techniques Development Laboratory, Office of Systems Development, National Weather Service, NOAA, Silver Spring, MD 20910

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Robert G. Miller Techniques Development Laboratory, Office of Systems Development, National Weather Service, NOAA, Silver Spring, MD 20910

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Abstract

We performed a comparative verification of Model Output Statistics (MOS) against Generalized Exponential Markov (GEM), a single station forecasting technique which uses only the surface observation and climatology as input. The verification was performed under three conditions: a “scientific” comparison, where both techniques use the same observation as input; an “operational” comparison, where GEM uses a later observation than does MOS, to simulate the situation where a National Weather Service (NWS) forecaster preparing to make an aviation forecast has a later observation; and a “special operational” comparison, pitting GEM against MOS derived from the previous Limited Area Fine Mesh (LFM) cycle, to simulate the “mid-morning update” operational situation in the NWS where the aviation forecast must be made using previous LFM cycle MOS guidance. Verifications for ceiling, visibility, total cloud amount, temperature, dew-point depression, and wind speed/direction were performed on a full yew of data (a warm season and a cool one) for 21 stations across the United States. GEM demonstrates improvement over MOS for the operational and special operational comparisons, with strongest showing on the major aviation elements—ceiling, visibility, and total cloud amount. For these major aviation elements, a skill crossover between GEM and MOS lies between 5 and 8 hours, and between 3 to 5 hours for the remaining elements. For ceiling and visibility, we also performed experiments blending the GEM and MOS probabilities, and found the resulting categorical, as well as probabilistic, forecasts superior to these produced by GEM or MOS alone.

Abstract

We performed a comparative verification of Model Output Statistics (MOS) against Generalized Exponential Markov (GEM), a single station forecasting technique which uses only the surface observation and climatology as input. The verification was performed under three conditions: a “scientific” comparison, where both techniques use the same observation as input; an “operational” comparison, where GEM uses a later observation than does MOS, to simulate the situation where a National Weather Service (NWS) forecaster preparing to make an aviation forecast has a later observation; and a “special operational” comparison, pitting GEM against MOS derived from the previous Limited Area Fine Mesh (LFM) cycle, to simulate the “mid-morning update” operational situation in the NWS where the aviation forecast must be made using previous LFM cycle MOS guidance. Verifications for ceiling, visibility, total cloud amount, temperature, dew-point depression, and wind speed/direction were performed on a full yew of data (a warm season and a cool one) for 21 stations across the United States. GEM demonstrates improvement over MOS for the operational and special operational comparisons, with strongest showing on the major aviation elements—ceiling, visibility, and total cloud amount. For these major aviation elements, a skill crossover between GEM and MOS lies between 5 and 8 hours, and between 3 to 5 hours for the remaining elements. For ceiling and visibility, we also performed experiments blending the GEM and MOS probabilities, and found the resulting categorical, as well as probabilistic, forecasts superior to these produced by GEM or MOS alone.

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