A Deep Learning-Based Bias Correction Model for Tropical Cyclone Track and Intensity towards Forecasting of the TianXing Large Weather Model

Accurate forecasting of tropical cyclone (TC) tracks and intensities is essential. Although the TianXing large weather model, a 6-hourly forecasting model surpassing operational forecasts, exhibits superior performance, its TC forecasts still require enhancement. Prediction errors persist due to biases in the training data and smoothing effects in data-driven methods. To address this, we introduce CycloneBCNet, a deep learning model designed to correct TianXing’s TC forecasts biases by leveraging spatial and temporal data. CycloneBCNet utilizes the simpler yet better video prediction (SimVP) framework with spatial attention to highlight cyclone core regions in forecast fields. It also incorporates TC trend information (center position, maximum wind speed, and minimum sea-level pressure) via a long short-term memory (LSTM) module. These TC vectors are derived from post-processed TianXing forecasts. By fusing features from forecast fields and TC vectors, CycloneBCNet corrects biases across multiple lead times. At a 96-hour lead time, track error reduces from 162.4 to 86.4 km, wind speed error from 17.2 to 6.69 m s-1, and pressure error from 22.2 to 9.36 hPa. Interpretability analysis shows that CycloneBCNet adjusts its attention across forecast lead times. Intensity corrections prioritize inner-core dynamics, particularly the eye and eyewall, while track corrections shift from lower-level variables and the cyclone’s core to broader environmental factors and mid- to upper-level features as the forecast duration increases. These findings demonstrate that CycloneBCNet effectively captures key TC dynamics consistent with meteorological principles, including the dominance of near-surface conditions for intensity and the increasing influence of steering currents on track prediction.