![]() ![]() The researcher trained 100 epochs (cycles) using hourly samples of weather data from 1979-2021 for specific time intervals.Įach of the sub-models that resulted required 16 days of training on 192 V100 graphics cards. ![]() This resulted in a minimization of the number of iterations for predicting a meteorological condition at a specific time and a reduction in erroneous forecasts. Using a hierarchical, temporal, aggregation strategy, the model was trained for different forecast intervals using 1-hour, 3-hour, 6-hour, and 24-hour intervals. The model uses a 3D Earth-Specific Transformer (3DEST) architecture to process complex non-uniform 3D meteorological data. The model can predict in seconds fine-grained meteorological features including humidity, wind speed, temperature, and sea level pressure. Huawei said the Pangu Weather model showed higher precision compared to traditional numerical prediction methods for forecasts of 1 hour to 7 days, with a prediction speed gain of 10,000 times. ![]() It is the first AI prediction model with higher precision than traditional numerical prediction methods. The Pangu Weather model is developed by the Huawei CLOUD team. The paper, titled “Accurate medium-range global weather forecasting with 3D neural networks” provides independent verifications of these capabilities. The model allows a 10,000x improvement in prediction speed, reducing global weather prediction time to just seconds. Pangu-Weather is an AI prediction model to demonstrate higher precision than traditional numerical weather forecast methods. The paper describes how to develop a precise and accurate global AI weather forecast system based on deep learning using 43 years of data, which appeared in the prestigious journal on July 5, 2023. One of the world’s top scientific journals, Nature has published a paper about the Huawei Cloud Pangu weather AI model. ![]()
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