Predicting Subseasonal Tropical Cyclone Activity using NOAA and ECMWF Reforecasts


Tropical cyclones are extreme events with enormous and devastating consequences on life, property, and our economies. As a result, large-scale efforts have been devoted to improving tropical cyclone forecasts with lead times ranging between a few days to months. More recently, subseasonal forecasts (e.g., 2-6 weeks lead time) of tropical cyclones have received greater attention. Here, we study whether bias-corrected, subseasonal tropical cyclone reforecasts of the GEFS and ECMWF models are skillful in the Atlantic basin. We focus on the peak hurricane season, July-November, using the reforecast years 2000-2019. Model reforecasts of accumulated cyclone energy (ACE) are produced, and validated, for lead times of 1-2 weeks and 3-4 weeks. Weeks 1-2 forecasts are substantially more skillful than a 31-day moving-window climatology, while weeks 3-4 forecasts still exhibit positive skill throughout much of the hurricane season. Furthermore, the skill of the combination of the two models is found to be an improvement with respect to either individual model. In addition to the GEFS and ECMWF model reforecasts, we develop a statistical modeling framework which solely relies on daily sea surface temperatures. The reforecasts of ACE from this statistical model is capable of producing better skill than the GEFS or ECMWF model, individually, and it can be leveraged to further enhance the model combination reforecast skill for the 3-4 week lead time.