Research on Machine Learning-Based Multi-source Precipitation Data Fusion

DOI

10.2991/978-94-6463-040-4_105How to use a DOI?

Keywords

Artificial intelligence; Data analysis; Machine learning; Gaussian process regression

Abstract

With the development of artificial intelligence (AI) in recent years, meteorological departments have also begun to improve algorithms and revise short-term forecasts via AI, expecting to timely capture meteorological clues in massive weather data, to “prevent meteorological disasters”, and “calculate precipitation faster and more accurately”. At present, AI has been initially applied to the meteorological field, especially to the analysis of massive meteorological data. For instance, the AI-based data analysis technology can rapidly judge the cloud type and the meteorological prototype in satellite images. The AI-based data fusion technology contributes to more three-dimensional and refined atmosphere data, which improves the temporal and spatial resolutions of precipitation data. If the big data in AI are used to analyze typhoons and identify the typhoon track and source, the errors resulting from the naked-eye observation of images by meteorologists can be avoided, thus considerably improving the scientificity and accuracy of weather forecasts. During data fusion, the severe convective weather characteristics reflected by massive historical precipitation data can be learned through machine learning methods to predict the evolution trend of disastrous weather within the future 1 to 2 h. Furthermore, precipitation data errors are corrected through AI data analysis, and a daily precipitation fusion dataset with a spatial resolution of 1 km is obtained.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Hengliang Guo
AU  - Yu Fu
AU  - Yaohuan Yang
AU  - Yuanyuan Yue
AU  - Menggang Kou
AU  - Wenyu Zhang
PY  - 2022
DA  - 2022/12/27
TI  - Research on Machine Learning-Based Multi-source Precipitation Data Fusion
BT  - Proceedings of the 2022 3rd International Conference on Artificial Intelligence and Education (IC-ICAIE 2022)
PB  - Atlantis Press
SP  - 690
EP  - 696
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-040-4_105
DO  - 10.2991/978-94-6463-040-4_105
ID  - Guo2022
ER  -