| CPC G06N 3/08 (2013.01) [G06N 3/044 (2023.01)] | 18 Claims |
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1. A method for training a neural network (NN)-based climate forecasting model on a pre-processed multi-model ensemble of global climate simulation data from a plurality of global climate simulation models (GCMs), comprising:
determining a common spatial scale and a common temporal scale for the multi-model ensemble of global climate simulation data, wherein the multi-model ensemble of global climate simulation data comprises simulation data generated from at least two GCMs;
spatially re-gridding the multi-model ensemble of global climate simulation data to the common spatial scale;
temporally homogenizing the multi-model ensemble of global climate simulation data to the common temporal scale;
separating the spatially re-gridded, temporally homogenized multi-model ensemble of global climate simulation data into long-term signals or trends, global warming signals, seasonal signals, and other signal components caused by similar contributing climate factors;
generating synthetic simulation data through a climatology augmentation process by altering the long-term signal or trends within a feasible range;
augmenting the spatially re-gridded, temporally homogenized multi-model ensemble of global climate simulation data with the synthetic simulation data, to generate a spatially re-gridded, temporally homogenized, and augmented multi-model ensemble of global climate simulation data;
training, during a first training phase, the NN-based climate forecasting model using a first member time series of the spatially re-gridded, temporally homogenized, and augmented multi-model ensemble of global climate simulation data, wherein each input and corresponding desired output at a target lead time used in the first training phase are selected from the first member time series of the spatially re-gridded, temporally homogenized, and augmented multi-model ensemble of global climate simulation data; and
training, during a second training phase, the NN-based climate forecasting model using a second member time series of the spatially re-gridded, temporally homogenized, and augmented multi-model ensemble of global climate simulation data, wherein each input and corresponding desired output at the target lead time used in the second training phase are selected from the second member time series of the spatially re-gridded, temporally homogenized, and augmented multi-model ensemble of global climate simulation data;
wherein at least one training phase uses a member time series containing the synthetic simulation data.
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