Monthly precipitation forecast in the Dongjiang Basin based on CFSv2 products and machine learning

ZHUANG Shengjie; WANG Dagang; LIN Yongen; LIN Zequn; CHEN Runting

doi:10.13471/j.cnki.acta.snus.ZR20230016

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Monthly precipitation forecast in the Dongjiang Basin based on CFSv2 products and machine learning

Research articles | 更新时间：2024-09-29

- Monthly precipitation forecast in the Dongjiang Basin based on CFSv2 products and machine learning
- Acta Scientiarum Naturalium Universitatis Sunyatseni Vol. 63, Issue 4, Pages: 9-18(2024)
- 作者机构：
  
  1.中山大学地理科学与规划学院，广东广州 510006
  2.广东省粤北岩溶区碳水耦合野外科学观测研究站，广东广州 510006
- 作者简介：
- 基金信息：
- DOI：10.13471/j.cnki.acta.snus.ZR20230016
  CLC： P457.6
- Published：25 July 2024，
  
  Published Online：24 April 2024，
  
  Received：15 November 2023，
  
  Accepted：13 March 2024
- 稿件说明：
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庄胜杰,王大刚,林泳恩等.基于CFSv2产品和机器学习的东江流域月降水预报[J].中山大学学报(自然科学版)(中英文),2024,63(04):9-18.

ZHUANG Shengjie,WANG Dagang,LIN Yongen,et al.Monthly precipitation forecast in the Dongjiang Basin based on CFSv2 products and machine learning[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2024,63(04):9-18.
庄胜杰,王大刚,林泳恩等.基于CFSv2产品和机器学习的东江流域月降水预报[J].中山大学学报(自然科学版)(中英文),2024,63(04):9-18. DOI： 10.13471/j.cnki.acta.snus.ZR20230016.

ZHUANG Shengjie,WANG Dagang,LIN Yongen,et al.Monthly precipitation forecast in the Dongjiang Basin based on CFSv2 products and machine learning[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2024,63(04):9-18. DOI： 10.13471/j.cnki.acta.snus.ZR20230016.

摘要

中长期降水预报一直以来是研究水文气象变化的热点，其精度与可靠性不高的问题亟待解决。以东江流域为研究对象，通过距平相关系数ACC、标准化均方根误差NRMSE、平均绝对误差MAE和多模型稳定性指数MSI评估CFSv2模式产品在月尺度的预测精度与稳定性，采用CFSv2模式降水预报、CFSv2模式预报因子结合机器学习模型预报2种方法预测未来降水。结果表明，不同预见期下，CFSv2模式降水预测与实测降水量具有较高的相关性，对于枯水期的预测效果好于汛期，但随着起报时间发生改变，降水预测的差异性较大，模型稳定性较差；CFSv2模式预报因子结合机器学习模型提高了预测的稳定性，相较于CFSv2模式降水预测，MSI从0.45降低到0.25，在很大程度上减小了由于起报时间改变产生的预报随机性。研究成果可为中长期降水预测提供一种新的思路，并为中长期水文预报和水资源管理提供决策依据。

Abstract

Mid to long-term precipitation forecasting has always been a hot topic in hydro-meteorological research，with the issue of low accuracy and reliability needing urgent solutions. This study focuses on the Dongjiang Basin and evaluates the prediction accuracy and stability of CFSv2 model products at the monthly scale using the anomaly coefficient of correlation （ACC），normalized root mean square error （NRMSE），mean absolute error （MAE），and the multi-model stability index （MSI）. Two methods，namely the CFSv2 model precipitation forecast and the machine learning model forecast combined with CFSv2 model predictors，are employed to predict future precipitation. The results show that under different lead times，the CFSv2 model precipitation forecast exhibits a high correlation with observed precipitation，performing better during the dry season compared to the flood season. However，there is significant variability in precipitation forecasts and poor model stability with changes in the initial time. Combining CFSv2 model predictors with machine learning models improves the forecast stability，reducing the MSI from 0.45 to 0.25 and effectively reducing the randomness in forecasts caused by changes in the initial time. The findings contribute to providing a new approach for mid to long-term precipitation forecasting and offer decision-making support for mid to long-term hydrological forecasting and water resource management.

关键词

CFSv2中长期预报机器学习产品评估

Keywords

CFSv2mid to long-term forecastmachine learningproduct evaluation

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