Post-Processing Neural Weather Model Outputs for Tropical Cyclone Intensity Forecasts

Numerical Weather Prediction (NWP) models, which integrate physical equations forward in time, are the traditional tools for simulating atmospheric processes and forecasting weather in modern meteorology. With recent advancements in deep learning, Neural Weather Models (NeWMs) have emerged as competent medium-range NWP emulators with reported performances that compare favorably to state-of-the-art NWP models. However, they are commonly trained on reanalysis with limited spatial resolution (e.g., 0.25° horizontal grid spacing) and thus smooth out key features associated with a number of weather phenomena. For example, tropical cyclones—among the most impactful weather events due to their devastating effects on human activities—are challenging to forecast, as extrema like wind gusts, which serve as proxies for tropical cyclone intensity, are smoothed in deterministic forecasts at 0.25° resolution. To address this, we use our best global observational estimate of wind gusts and minimum sea level pressure to train models that post-process NeWM outputs and enable accurate and reliable forecasts of TC intensity. We present a tracking-independent post-processing algorithm and show that even naïve, linear models extract useful information from NeWM model outputs beyond what is present in the initial conditions used to roll out NeWM predictions. We explore how the NeWM’s spatial context may further improve the forecast through masking and convolutional architectures. Our post-processing framework thus presents a step towards democratization of tropical cyclone intensity forecasting, given the reduction in computational requirements for producing global weather forecasts with NeWMs compared to traditional NWP approaches and the algorithmic simplicity of the tracking-independent approach.