preprints.org > environmental and earth sciences > atmospheric science and meteorology > doi: 10.20944/preprints202408.0961.v1 (registering DOI)

Preprint Article Version 1 This version is not peer-reviewed

Version 1 : Received: 13 August 2024 / Approved: 13 August 2024 / Online: 13 August 2024 (14:41:57 CEST)

Developing short-term forecasting model for global atmospheric temperature and wind in the near space is crucial for understanding atmospheric dynamics and supporting human activities in this region. While numerical models have been extensively developed, deep learning techniques have recently shown promise in improving atmospheric forecasting accuracy. In this study, convolutional long short-term memory (ConvLSTM) and convolutional gated recurrent unit (ConvGRU) neural networks were applied to build for short-term global-scale forecasting model of atmospheric temperature and wind in the `near space based on the MERRA-2 reanalysis dataset from 2010-2022. The model results showed that the ConvGRU model outperforms the ConvLSTM model in the short-term forecast results. The ConvGRU model achieved a root mean square error in the first three hours of approximately 1.8 K for temperature predictions, and errors of 4.2 m/s and 3.8 m/s for eastward and northward wind predictions, respectively

Global atmospheric temperature and wind in the near space; ConvGRU; ConvLSTM; MERRA‐2 reanalysis dataset

Environmental and Earth Sciences, Atmospheric Science and Meteorology

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