Further Improvements to the Statistical Hurricane Intensity Prediction Scheme Using Tropical Cyclone Rainfall and Structural Features

Abstract

The Statistical Hurricane Intensity Prediction Scheme (SHIPS) is a multiple regression model for forecasting tropical cyclone (TC) intensity [both central pressure (Pmin) and maximum wind speed (Vmax)]. To further improve the accuracy of the Japan Meteorological Agency version of SHIPS, five new predictors associated with TC rainfall and structural features were incorporated into the scheme. Four of the five predictors were primarily derived from the hourly Global Satellite Mapping of Precipitation (GSMaP) reanalysis product, which is a microwave satellite-derived rainfall dataset. The predictors include the axisymmetry of rainfall distribution around a TC multiplied by ocean heat content (OHC), rainfall areal coverage, the radius of maximum azimuthal mean rainfall, and total volumetric rain multiplied by OHC. The fifth predictor is the Rossby number. Among these predictors, the axisymmetry multiplied by OHC had the greatest impact on intensity change, particularly, at forecast times up to 42 h. The forecast results up to 5 days showed that the mean absolute error (MAE) of the Pmin forecast in SHIPS with the new predictors was improved by over 6% in the first half of the forecast period. The MAE of the Vmax forecast was also improved by nearly 4%. Regarding the Pmin forecast, the improvement was greatest (up to 13%) for steady-state TCs, including those initialized as tropical depressions, with slight improvement (2%–5%) for intensifying TCs. Finally, a real-time forecast experiment utilizing the hourly near-real-time GSMaP product demonstrated the improvement of the SHIPS forecasts, confirming feasibility for operational use.

Development and Evaluation of an Evolutionary Programming-Based Tropical Cyclone Intensity Model

“…However, as noted above, this type of a persistence forecast can lead to large errors, such as at the onset or ending of RI and RW. While the importance of keeping DELV is also supported by its use in both the OCD5 and SHIPS models (Knaff et al 2003;DeMaria and Kaplan 1994;Shimada et al 2018), it is worth investigating whether other information can be leveraged so that the model has a priori knowledge of when a persistence forecast may not be warranted. One method to do this would be to introduce a variable such as the difference between the intensity of the storm and its maximum potential intensity, as is done in the LGEM model (DeMaria 2009).…”

Tropical Cyclone Intensity Prediction in the Western North Pacific Basin Using SHIPS and JMA/GSM

“…Future studies will include adding new predictors to further improve the prediction accuracy of SHIPS. For example, Shimada et al (2018) demonstrated improved accuracy by using a microwave satellite-derived rainfall dataset from the Global Satellite Mapping of Precipitation (GSMaP, Kubota et al 2007;Aonashi et al 2009). Another future study will include the use of the JMA official TC track forecasts instead of JMA/GSM track predictions when calculating environmental predictors such as the ocean heat content and/or vertical wind shear.…”

Tropical cyclone intensity prediction based on recurrent neural networks

“…where s ′ ( • ) is the derivative of s( • ) and diag( • ) denotes extending a vector to a diagonal matrix. Equations (6) and 7indicate that the outputs h t at time t are related to those at the previous time (h k ), while W can directly describe such relationship. The solutions for U and V are similar to (6) and 7.…”

“…Equations (6) and 7indicate that the outputs h t at time t are related to those at the previous time (h k ), while W can directly describe such relationship. The solutions for U and V are similar to (6) and 7. Once the parameter set u is determined, our prediction model has been established.…”