Systems and methods of data preprocessing and augmentation for neural network climate forecasting models

A system for training a neural network (NN)-based climate forecasting model, comprising:

• at least one processor;
• and
• a non-transitory physical storage medium for storing program code and accessible by the processor, the program code when executed by the processor causes the processor to:
• generate a multi-model ensemble of global climate simulation data by combining simulation data generated from at least two of a plurality of global climate models (GCMs);
• determine a common spatial scale and a common temporal scale for the multi-model ensemble of global climate simulation data;
• spatially re-grid the multi-model ensemble of global climate simulation data to the common spatial scale;
• temporally homogenize the multi-model ensemble of global climate simulation data to the common temporal scale;
• augment the spatially re-gridded, temporally homogenized multi-model ensemble of global climate simulation data with synthetic simulation data, to generate a spatially re-gridded, temporally homogenized, and augmented multi-model ensemble of global climate simulation data, wherein the synthetic simulation data is generated from the spatially re-gridded, temporally homogenized multi-model ensemble of global climate simulation data;
• and
• train the NN-based climate forecasting model using the spatially re-gridded, temporally homogenized, and augmented multi-model ensemble of global climate simulation data, wherein the NN-based climate forecasting model comprises a predictive neural network, wherein for a given input into the NN-based climate forecasting model, a forecast output at a target lead time is generated by the NN-based climate forecasting model based on the given input, wherein the forecast output at the target lead time is compared to a corresponding desired output at the target lead time during the training, and wherein the given input and the corresponding desired output at the target lead time are selected from the multi-model ensemble of global climate simulation data.

A non-transitory physical storage medium for training a neural network (NN)-based climate forecasting model, the storage medium comprising program code stored thereon, that when executed by a processor causes the processor to:

• generate a multi-model ensemble of global climate simulation data by combining simulation data generated from at least two of a plurality of global climate models (GCMs);
• determine a common spatial scale and a common temporal scale for the multi-model ensemble of global climate simulation data;
• spatially re-grid the multi-model ensemble of global climate simulation data to the common spatial scale;
• temporally homogenize the multi-model ensemble of global climate simulation data to the common temporal scale;
• augment the spatially re-gridded, temporally homogenized multi-model ensemble of global climate simulation data with synthetic simulation data, to generate a spatially re-gridded, temporally homogenized, and augmented multi-model ensemble of global climate simulation data, wherein the synthetic simulation data is generated from the spatially re-gridded, temporally homogenized multi-model ensemble of global climate simulation data;
• and
• train the NN-based climate forecasting model using the spatially re-gridded, temporally homogenized, and augmented multi-model ensemble of global climate simulation data, wherein the NN-based climate forecasting model comprises a predictive neural network, wherein for a given input into the NN-based climate forecasting model, a forecast output at a target lead time is generated by the NN-based climate forecasting model based on the given input, wherein the forecast output at the target lead time is compared to a corresponding desired output at the target lead time during the training, and wherein the given input and the corresponding desired output at the target lead time are selected from the multi-model ensemble of global climate simulation data.