点击左上角“MDPI环境与地球科学”关注我们，为您推送更多最新研究。
降水预测在提高气象预报精度、灾害预警、农业生产指导、水资源管理、环境监测以及城市规划和建设等方面都具有重要的意义。
这份“降水预测”方向的文献清单，可以为您提供相关研究的灵感！
1
Improved Monthly and Seasonal Multi-Model Ensemble Precipitation Forecasts in Southwest Asia Using Machine Learning Algorithms
利用机器学习算法改进西南亚地区月和季节多模式集合降水预报
识别二维码
免费阅读英文原文
Pakdaman, M.; Babaeian, I.; Bouwer, L.M. Improved Monthly and Seasonal Multi-Model Ensemble Precipitation Forecasts in Southwest Asia Using Machine Learning Algorithms. Water 2022, 14, 2632.
2
A WRF/WRF-Hydro Coupled Forecasting System with Real-Time Precipitation–Runoff Updating Based on 3Dvar Data Assimilation and Deep Learning
基于3Dvar数据同化和深度学习的实时降水-径流更新的WRF/WRF-Hydro水文耦合预报系统
识别二维码
免费阅读英文原文
Liu, Y.; Liu, J.; Li, C.; Liu, L.; Wang, Y. A WRF/WRF-Hydro Coupled Forecasting System with Real-Time Precipitation–Runoff Updating Based on 3Dvar Data Assimilation and Deep Learning. Water 2023, 15, 1716.
3
Spatio-Temporal Characteristics and Trend Prediction of Extreme Precipitation—Taking the Dongjiang River Basin as an Example
极端降水时空特征及趋势预测——以东江流域为例
识别二维码
免费阅读英文原文
Li, N.; Chen, X.; Qiu, J.; Li, W.; Zhao, B. Spatio-Temporal Characteristics and Trend Prediction of Extreme Precipitation—Taking the Dongjiang River Basin as an Example. Water 2023, 15, 2171.
4
A Method for Monthly Extreme Precipitation Forecasting with Physical Explanations
一种具有物理解释的月极端降水预报方法
识别二维码
免费阅读英文原文
Yang, B.; Chen, L.; Singh, V.P.; Yi, B.; Leng, Z.; Zheng, J.; Song, Q. A Method for Monthly Extreme Precipitation Forecasting with Physical Explanations. Water 2023, 15, 1545.
5
Rainfall Prediction Rate in Saudi Arabia Using Improved Machine Learning Techniques
利用改进的机器学习技术对沙特阿拉伯降雨量进行预测
识别二维码
免费阅读英文原文
Baljon, M.; Sharma, S.K. Rainfall Prediction Rate in Saudi Arabia Using Improved Machine Learning Techniques. Water 2023, 15, 826.
6
Projections of Mean and Extreme Precipitation Using the CMIP6 Model: A Study of the Yangtze River Basin in China
使用CMIP6模型预测平均降水量和极端降水量：中国长江流域的研究
识别二维码
免费阅读英文原文
Zhu, C.; Yue, Q.; Huang, J. Projections of Mean and Extreme Precipitation Using the CMIP6 Model: A Study of the Yangtze River Basin in China. Water 2023, 15, 3043.
7
Assessing the Forecasting Accuracy of a Modified Grey Self-Memory Precipitation Model Considering Scale Effects
考虑尺度效应改进灰色自记忆降水模型的预报准确性评估
识别二维码
免费阅读英文原文
Meng, F.; Sun, Z.; Yang, L.; Yu, K.; Wang, Z. Assessing the Forecasting Accuracy of a Modified Grey Self-MemoryPrecipitation Model Considering Scale Effects. Water 2022, 14,1647.
8
Trends and Drivers of Flood Occurrence in Germany: A Time Series Analysis of Temperature, Precipitation, and River Discharge
德国洪水发生的趋势和驱动因素：温度、降水和河流流量的时间序列分析
识别二维码
免费阅读英文原文
Alobid, M.; Chellai, F.; Szűcs, I. Trends and Drivers of Flood Occurrence in Germany: A Time Series Analysis of Temperature, Precipitation, and River Discharge. Water 2024, 16, 2589.
9
Combined Forecasting Model of Precipitation Based on the CEEMD-ELM-FFOA Coupling Model
基于CEEMD-ELM-FFOA耦合模型的降水组合预测模型
识别二维码
免费阅读英文原文
Zhang, X.; Wu, X. Combined Forecasting Model of Precipitation Based on the CEEMD-ELM-FFOA Coupling Model. Water 2023, 15, 1485.
10
Development of Monthly Scale Precipitation-Forecasting Model for Indian Subcontinent using Wavelet-Based Deep Learning Approach
基于小波深度学习方法的印度次大陆月尺度降水预测模型的开发
识别二维码
免费阅读英文原文
Yeditha, P.K.; Anusha, G.S.; Nandikanti, S.S.S.; Rathinasamy, M. Development of Monthly Scale Precipitation-Forecasting Model for Indian Subcontinent using Wavelet-Based Deep Learning Approach. Water 2023, 15, 3244.
11
RLNformer: A Rainfall Levels Nowcasting Model Based on Conv1D_Transformer for the Northern Xinjiang Area of China
RLNformer：基于Conv1D_Transformer的中国北疆地区降雨量临近预报模型
识别二维码
免费阅读英文原文
Liu, Y.; Liu, S.; Chen, J. RLNformer: A Rainfall Levels Nowcasting Model Based on Conv1D_Transformer for the Northern Xinjiang Area of China. Water 2023, 15, 3650.
12
Gated Attention Recurrent Neural Network: A Deeping Learning Approach for Radar-Based Precipitation Nowcasting
门控注意力循环神经网络：基于雷达的降水临近预报的深度学习方法
识别二维码
免费阅读英文原文
Wu, G.; Chen, W.; Jung, H. Gated Attention Recurrent Neural Network: A Deeping Learning Approach for Radar-Based Precipitation Nowcasting. Water 2022, 14, 2570.
13
A Methodology for the Prediction of Extreme Precipitation in Complex Terrains: A Case Study of Central Southwest China
复杂地形下极端降水预测方法：以中国西南中部地区为例
识别二维码
免费阅读英文原文
Lei, S.; Yu, S.; Sun, J.; Wang, Z.; Liao, Y. A Methodology for the Prediction of Extreme Precipitation in Complex Terrains: A Case Study of Central Southwest China. Water 2024, 16, 427.
14
Evaluation of High-Intensity Precipitation Prediction Using Convolutional Long Short-Term Memory with U-Net Structure Based on Clustering
基于聚类的U-Net结构卷积长短期记忆对强降水预测的评价
识别二维码
免费阅读英文原文
Kwon, T.; Yoon, S.-S.; Shin, H.; Yoon, S. Evaluation of High-Intensity Precipitation Prediction Using Convolutional Long Short-Term Memory with U-Net Structure Based on Clustering. Water 2024, 16, 97.
15
Aeolus Data Validation for an Extreme Precipitation Event in Greece with the COSMO NWP Model
使用COSMO NWP模型对希腊极端降水事件的Aeolus数据验证
识别二维码
免费阅读英文原文
Avgoustoglou, E.; Matsangouras, I.; Pytharoulis, I.; Nastos, P. Aeolus Data Validation for an Extreme Precipitation Event in Greece with the COSMO NWP Model. Water 2023, 15, 3820.
精选视频
往期回顾
论文选题灵感：“高级氧化技术在水处理中的应用”研究方向 | MDPI Water
版权声明：
本文内容由MDPI中国办公室编辑负责撰写，详细内容请以英文原版为准。如需转载，请于公众号后台留言咨询。
由于微信订阅号推送规则更新，建议您将“MDPI环境与地球科学”设为星标，便可在消息栏中便捷地找到我们，及时了解最新开放出版动态资讯！
点击左下方“阅读原文”，进入期刊主页。
喜欢今天的内容？不如来个 "三连击" ☞【分享，点赞，转发】
