| CPC G06N 3/08 (2013.01) [G01W 1/10 (2013.01); G06N 3/045 (2023.01)] | 20 Claims |
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1. A method for training a generative adversarial network (GAN) to generate an ensemble of forecast data for a target meteorological variable at a target spatial resolution, comprising:
pre-processing global numerical weather forecast data to generate an ensemble of corrector input data at an input spatial resolution,
wherein the input spatial resolution is lower than the target spatial resolution,
wherein the GAN comprises a generator deep neural network (G-DNN) and a discriminator deep neural network (D-DNN), and
wherein the G-DNN comprises a corrector deep neural network (C-DNN) followed by a super-resolver deep neural network (SR-DNN) having an output spatial resolution at the target spatial resolution;
retrieving observational data for the target meteorological variable at the input spatial resolution, wherein the observational data for the target meteorological variable corresponds to the ensemble of corrector input data over geopatch-time indices;
pre-training the C-DNN, using a first C-DNN loss function independent of the SR-DNN and D-DNN, wherein the first C-DNN loss function is computed based on a first C-DNN output generated from the ensemble of corrector input data, and the observational data;
pre-training the SR-DNN, using a second SR-DNN loss function independent of the D-DNN, wherein the second SR-DNN loss function is computed based on the first C-DNN output, the observational data, and a SR-DNN output generated from the first C-DNN output; and
training the GAN, using a third D-DNN loss function computed based on a second C-DNN output generated from the ensemble of corrector input data and a random vector input.
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