In the 1920s, Numerical Weather Prediction (NWP) emerged as a significant development in weather forecasting. NWP plays a crucial role in various industries such as transportation, logistics, agriculture, and energy production, enabling better economic planning. Accurate weather predictions have also resulted in saving numerous lives by providing advance warnings of severe catastrophes. Over the years, weather forecasts have improved in quality due to advancements in computational power, better understanding of small-scale phenomena, and improved atmospheric observations.
In 1922, Lewis Fry Richardson made the first dynamically modeled numerical weather prediction using a slide rule and a table of logarithms. It took him six weeks to produce a 6-hour forecast for a single location. By the 1950s, early electronic computers significantly increased the speed of forecasting, making operational predictions helpful for future forecasts.
Data-driven Deep Learning (DL) models have gained popularity in weather forecasting due to their lower processing costs compared to cutting-edge NWP models. These DL models have been trained using climate model outputs, general circulation models (GCM), reanalysis products, or a combination of both. By removing biases present in NWP models and enabling the production of large ensembles for probabilistic forecasting and data assimilation at a lower computing cost, data-driven models have the potential to enhance weather forecasts.
However, most data-driven weather models use low-resolution data for training, which results in the loss of fine-scale physical information. To be truly effective, data-driven models should provide forecasts with the same or better resolution as the state-of-the-art numerical weather models. High-resolution data can significantly improve the predictions of data-driven models for variables with complex fine-scale structures.
To address these challenges, researchers from various institutions have created FourCastNet, a Fourier-based neural network forecasting model. FourCastNet produces global data-driven forecasts of important atmospheric variables at a resolution of 0.25, enabling direct comparison with high-resolution models like the ECMWF’s Integrated Forecasting System (IFS). The model has shown success in accurately forecasting variables like surface winds and precipitation up to one week in advance and offers higher resolution compared to current DL-based global weather models. It also demonstrates comparable or superior performance to the IFS model for lead periods of up to three days and surpasses it for longer lead periods.
The use of FourCastNet allows the generation of large ensembles, providing well-calibrated and constrained uncertainties in extreme weather events. This enables better probabilistic weather forecasting, early warnings of extreme occurrences, and rapid evaluation of their effects.
For more information, please refer to the research paper.
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