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System Development & Application
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 9
2773-2780

Precipitation nowcasting based on multiple factors and explainability analysis

Chen Long
Peng Jing
Hu Xuefei
Huang Zhan'ao
Li Xiaojie
School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
DOI: 10.19734/j.issn.1001-3695.2024.01.0030

Abstract

The current methods for short-time precipitation nowcasting are based on radar echo extrapolation model, without fully considering the close influence of other meteorological factors on the evolution of precipitation generation and cancellation, thus limiting the accuracy of the forecasts. To address the above issues, this paper produced a short-time precipitation nowcasting dataset, and proposed the MFPNM(multiple factors precipitation nowcasting model). Based on data from the Fengyun-4B satellite, the dataset toke quantitative precipitation estimation as the forecast object and contained four background meteorological factors. Taking the TransUNet as the backbone of the model, this model proposed the parallel dual encoder to extract the high-dimensional spatio-temporal features of the forecast object and the background meteorological data, respectively. Besides, it constructed the content coding module to encode the spatial features of the background data as the learnable positional embedding of the high-dimensional feature vectors of the forecast object. It used a Transformer module to construct the global relationship between the high-dimensional features of the sequence data for better sequence prediction. The metrics used in this paper included critical success index, false alarm rate, root-mean-square error, and structural similarity, etc. The MPFNM was evaluated on two datasets(the proposed dataset and an open-source dataset) and outperformed the baseline models, and it was analyzed for explainability through the SHAP technique. The experimental results and explainability analysis show that the model has better forecasting accuracy and reliability.

Foundation Support

国家自然科学基金资助项目(42075142,42130608)
国家重点研发计划资助项目(2020YFA0608000)
四川省科技计划资助项目(2022YFG0029,2023YFG0101,2024YFG0001)
成都信息工程大学科技创新能力提升计划资助项目(KYTD202330)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.01.0030
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 9
Section: System Development & Application
Pages: 2773-2780
Serial Number: 1001-3695(2024)09-029-2773-08

Publish History

[2024-05-14] Accepted Paper
[2024-09-05] Printed Article

Cite This Article

陈龙, 彭静, 胡雪飞, 等. 基于多要素的短临降水预报及可解释性分析 [J]. 计算机应用研究, 2024, 41 (9): 2773-2780. (Chen Long, Peng Jing, Hu Xuefei, et al. Precipitation nowcasting based on multiple factors and explainability analysis [J]. Application Research of Computers, 2024, 41 (9): 2773-2780. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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