Creating Synthetic Satellite Cloud Data Based on GAN Method

doi:10.16182/j.issn1004731x.joss.20-0055

Abstract

Abstract: To create the synthetic satellite cloud data in the domain of Meteorology, a method based on Generative Adversarial Networks (GAN) is proposed. Depending on ability of the nonlinear mapping and the information extraction of raster data with the deep learning network, a deep generative adversarial network model is proposed to extract the corresponding information between the numerical weather prediction(NWP) products and the satellite cloud data, and then the appropriate elements of the NWP product are chosen as the input to synthesize the corresponding satellite cloud data. The experiments are conducted on the re-analysis products of the European Centre for Medium-Range Weather Forecasts (ECMWF) and FY-4A satellite cloud date.The results show that the proposed method is effective to create synthetic satellite cloud data by using the NWP products.

Key words: deep learning, generative adversarial networks, numerical weather prediction products, satellite cloud data, simulation

CLC Number:

TP391.9

Cheng Wencong, Shi Xiaokang, Wang Zhigang. Creating Synthetic Satellite Cloud Data Based on GAN Method[J]. Journal of System Simulation, 2021, 33(6): 1297-1306.

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