92nd American Meteorological Society Annual Meeting (January 22-26, 2012)

Wednesday, 25 January 2012
Evaluating Tropical Cyclogenesis Forecasts in Four Global Models
Hall E (New Orleans Convention Center )
Daniel J. Halperin, Florida State University, Tallahassee, FL; and H. E. Fuelberg, R. E. Hart, J. Cossuth, and R. Truchelut

Tropical cyclone (TC) forecasts rely heavily on output from numerical models. Each model in the suite of models used by forecasters has its own strengths and weaknesses. Some research has investigated the skill of the various models with respect to track, with the assumption that a TC already exists. However, little research has considered how well (or poorly) global models forecast TC genesis. A few studies have considered the Western North Pacific Basin, but there have been numerous upgrades to the numerical models since then. One recent study examined the North Atlantic Basin, but it analyzed only a small sample of storms. This paper will analyze TC cyclogenesis in four global models (GFS, NOGAPS, UKMET, and CMC) over seven seasons (2004-2010) in the North Atlantic Basin. All model indicated TCs will be counted and classified as a hit, miss, or false alarm. The method of finding TCs in the model environment is based on a mixture of methods used previously in the literature. Hits are defined as when a model predicts genesis within 24 hours of the National Hurricane Center best track genesis time and within five degrees latitude and longitude of the best track genesis location. False alarm case 1 is defined as when the model is predicting genesis at a location where a TC already exists in the best track, but the timing of genesis is off (i.e., more than 24 hours from the best track genesis time). False alarm case 2 is defined as model indicated TCs that never develop.

Results will show which model best predicted TC genesis (with the acknowledgement that the “best” model can change from year to year) and whether recent upgrades to the models have yielded improved TC genesis forecasts. Basic statistics will be conducted on the results, including skill scores. The results will be subdivided into geographical regions and analyzed spatially and temporally. This may provide insight regarding regions where a model performs best and whether forecast skill decreases with increased forecast hour, as one would expect.

Future research will investigate the “whys” behind the results presented here. Synoptic patterns will be analyzed, and commonalities among each category (hits, misses, and false alarms) will be investigated.

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