Diffusion-based Composite Meteorological Element Regional Weather Generator
Pages 273 - 279
Abstract
To generate credible day-scale regional meteorological data sequences for supporting surface process model research, this paper describes a diffusion model based on the field of image generation. The model combines precipitation, maximum and minimum temperatures, average temperature, relative humidity, and solar radiation in a day-scale regional weather generator. Its neural network adopts a dual-frame U-Net model, preserving correlations in the sequence. The experiments are conducted based on data from the Northeast region of China spanning from 2001 to 2011. Evaluation metrics employed in the study include spatial correlations and composite data correlations, aiding in assessing the similarity between model-generated data and actual historical data. This study provides a promising approach for better understanding the regional occurrence of compound meteorological elements and offers a robust tool for future surface process research.
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Published In
December 2023
371 pages
ISBN:9798400709203
DOI:10.1145/3639631
Copyright © 2023 ACM.
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Association for Computing Machinery
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Published: 16 February 2024
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ACAI 2023
ACAI 2023: 2023 6th International Conference on Algorithms, Computing and Artificial Intelligence
December 22 - 24, 2023
Sanya, China
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Overall Acceptance Rate 173 of 395 submissions, 44%
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