TL;DR
A Tropical Cyclone Formation Region Prediction Model based on Convolutional Neural Network is developed to predict TC formation region from 1 to 9-month leads during 1979–2021, offering a promising tool for risk assessment and early preparedness in the Western North Pacific.
A deep learning model is employed that translates dynamical circulation predictions into precipitation estimates and provides physically interpretable explanations for AI-derived regional climate projections, facilitating evidence-based assessment before observational data become available.
Reliable typhoon forecasting demands both predictive accuracy and physical transparency. This study develops a tree-based framework for simultaneous multi-step prediction of typhoon trajectory and intensity in Western North Pacific (WNP), and applies SHapley additive explanations (SHAP) to quantify the contribution of...
The NOAA's experimental Seasonal Forecast System (SFS) reforecasts, based on two versions of the Unified Forecast System (UFS), prototypes P8 and HR3, are evaluated for skill in predicting western North Pacific (WNP) seasonal tropical cyclone (TC) activity over 40 years of April initialized seasonal reforecasts . TC...
The 2020 Atlantic tropical cyclone (TC) season was unprecedented, producing a record storm count that exceeded the seasonal forecasted ranges, despite their anticipation of high activity. Here we assess the likelihood of the extreme 2020 season given the observed sea surface temperature (SST) forcing by examining a h...
Accurate prediction of summer precipitation is vital for disaster prevention in eastern China. However, skillful forecasts one year ahead remain a significant challenge. This study evaluates the predictability of summer precipitation over eastern China one year ahead in the Japanese SINTEX-F2 model. Evaluations indic...
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