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Li Dan, Lin Wen, Liu Qun, et al. Application of machine learning to statistical evaluation of artificial rainfall enhancement. J Appl Meteor Sci, 2024, 35(1): 118-128. DOI: 10.11898/1001-7313.20240110.
Citation: Li Dan, Lin Wen, Liu Qun, et al. Application of machine learning to statistical evaluation of artificial rainfall enhancement. J Appl Meteor Sci, 2024, 35(1): 118-128. DOI: 10.11898/1001-7313.20240110.
shu

Application of Machine Learning to Statistical Evaluation of Artificial Rainfall Enhancement

  • 1.

    Fujian Institute of Meteorological Sciences, Fuzhou 350001

  • 2.

    Fujian Key Laboratory of Severe Weather, Fuzhou 350001

  • 3.

    Key Laboratory for Strait Disaster Weather of CMA, Fuzhou 350001

  • 4.

    School of Mathematics and Statistics, Minnan Normal University, Zhangzhou 363000

  • 5.

    Gutian Meteorological Station of Ningde, Fujian, Ningde 352000

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    Abstract
  • As an important part of weather modification operation, the scientific effectiveness evaluation of artificial rainfall enhancement has gradually attracted attentions of government and public. In order to evaluate effects of artificial rainfall enhancement objectively and quantitatively, combing linear fitting, polynomial regression, spline regression and 3 other machine learning methods including decision tree, support vector machine and neural network, the relationship model between the rainfall in the target area and the contrast area is established based on rainfall data and operation information of recent 10 years in Fujian. Different historical regression statistical test schemes of rainfall enhancement effects are compared and analyzed, aiming to further optimize the best natural rainfall estimation algorithm based on alterable contrast area with statistical method, which can provide reference for the assessment of artificial rainfall enhancement effects. Results show that historical rainfall data samples are mainly concentrated in the weak rainfall grade. Using multiple regression methods (linear regression, polynomial regression and spline regression), the piecewise statistics of rainfall data does not significantly improve the linear regression model between two regions, and its root mean square error (RMSE) is generally higher than the statistical results. By comparing various machine learning and linear regression models, it is found that CNN and quomial regression perform relatively well when the regional average surface rainfall is taken as the statistical variable, with the determination coefficient of CNN being 0.516 and RMSE being 1.097 mm. Each statistical model is greatly improved after six root square transformations of rainfall data. The performance of the model established by CNN method is relatively optimal, with determination coefficient R2 up to 0.658 and RMSE only , followed by SVM statistical model based on sixth-order root square transform data, with R2 being 0.41 and RMSE being . In order to further overcome the time asynchronization and uneven spatial distribution of rainfall in the two regions, the convolutional neural network CNN optimizers (RMSP, ADAM and SGD) are used to establish the contrast-target region rainfall relationship model based on the grid data of natural rainfall plane. Comparison results show that the ADAM optimizer model is the best with the RMSE of 0.61 mm, and its ability to estimate natural rainfall in the affected area is enhanced, by which method the disturbance of the heavy rainfall center will be reduced.
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  • Fig  1.   Gutian artificial rainfall effect evaluation base in Fujian

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    Fig  2.   Fitting relationship between topography similarity and rainfall correlation coefficient in target area and contrast area

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    Fig  3.   Artificial rainfall target area, floating contrast area and the best-contrast area of Gutian

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    Fig  4.   Sample size of different rainfall intensity

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    Fig  5.   Root mean square error of rainfall estimation for different rainfall intensity by different models

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    Fig  6.   Root mean square error and determination coefficient of rainfall estimation based on total samples by different models

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    Fig  7.   Regional grid and automatic weather station in target area and contrast area

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    Fig  8.   Area rainfall distribution with different methods at 1900 BT 9 Apr 2016

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    Fig  9.   Comparison of rainfall estimation among 3 machine learning optimizers

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    Table  1   Sample size of different rainfall categories in 2014-2023

    降水等级 雨强/(mm·h-1) 样本量 样本比例/%
    弱降水 [0.1, 5) 25207 95.98
    一般降水 [5, 10) 780 2.97
    中等降水 [10, 25) 269 1.02
    强降水 [25, +∞) 7 0.03
    DownLoad: CSV
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    Article views: 771 PDF downloads: 167 Cited by: 4
    • Received : 2023-10-09
    • Accepted : 2023-11-30
    • Published : 2024-01-30
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