Improving Hurricane Intensity Forecasts

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This figure shows the average error of hurricane intensity model forecasts after their biases predicted by PRIME are used to correct each forecast on a case-by-case basis. The dashed lines show average model error without the bias correction and the solid lines show the error when the corrections are applied. See Bhatia et al. (2017), below.

From 2013-2018, our research group was supported by the Joint Hurricane Testbed for two successive projects intended to improve hurricane intensity forecasts. In the first project, we developed a statistical model to predict the biases and errors of the forecast of individual forecast models based on the synoptic environment, which could then be used to correct those forecasts or weight them accordingly. This model is known as Prediction of Model Intensity Error (PRIME). In the second project, we used an Observing System Simulation Experiment (OSSE) approach to consider the low-intensity bias of estimates of hurricane intensity from airborne observations, due to the fact that these measurements can only sample a small fraction of the area of the storm. Through funding from the Hurricane Forecast Improvement Project (HFIP), we also made improvements to the boundary layer parameterizations in one of the hurricane prediction forecast models, HWRF.

Some recent publications:

Klotz, B. W., and D. S. Nolan, 2019: SFMR surface wind undersampling over the tropical cyclone lifecycle. Mon. Wea. Rev., 147, 247-268.

Bhatia, Kieran T., David S. Nolan, Andrea B. Schumacher, and Mark DeMaria, 2017: Improving Tropical Cyclone Intensity Forecasts with PRIME. Wea. Forecasting, 32, 1353-1377.

Bhatia, Kieran T., and David S. Nolan, 2015: Prediction of intensity model error (PRIME) for Atlantic basin tropical cyclones. Wea. Forecasting, 30, 1845-1865.

Zhang, Jun A., David S. Nolan, Robert F. Rogers, and Vijay Tallapragada, 2015: Evaluating the impact of improvements in the boundary layer parameterization on hurricane intensity and structure forecasts in HWRF. Mon. Wea. Rev., 143, 3136-3155.

Bhatia, Kieran T., David S. Nolan, 2013: Relating the skill of tropical cyclone intensity forecasts to the synoptic environment. Wea. Forecasting, 28, 961–980.