降雨监测与预报技术在防洪减灾中的应用进展
Application progress of rainfall monitoring and forecasting techniques in flood control and disaster reduction
原文林 1杨逸凡 1赵小棚 1郭进军 1胡少伟1
作者信息
- 1. 郑州大学水利与交通学院,河南郑州 450001
- 折叠
摘要
洪水灾害突发性强,成灾速度快,对人民生命和财产安全造成较大的威胁.降雨作为洪水灾害致灾因子,数据的精确度对防洪减灾具有重要意义.以降雨监测与预报技术为切入点,对雨量站点观测、天气雷达降雨估计及预报、降雨数值预报、卫星遥感反演的现状进行了总结,通过分析时空降尺度方法及多源数据融合技术在降雨监测与预报中的应用,揭示了其在提升降雨数据"量"与"型"准确度方面的效果.研究表明:降雨监测与预报技术在当前取得了显著进展,但在山丘区和城市环境空间的复杂地形方面仍面临分辨率受到限制及精确性、时效性不足的问题.多源数据融合能提高降雨数据精度、时空覆盖能力和预测准确性,优化算法模型、融合"空-天-地"多源数据形成高分辨率预报是未来的研究方向.
Abstract
Flood disasters are highly sudden and develop rapidly,posing significant threats to people's lives and properties safe-ty.Rainfall is a disaster-causing factor for floods,and the accuracy of rainfall data is of great significance for flood control and disaster reduction.Focusing on rainfall monitoring and forecasting technology,we summarized the current state of rain gauge obser-vations,weather radar rainfall estimation and forecasting,numerical weather prediction,and satellite remote sensing inversion.By analyzing the application of spatiotemporal downscaling methods and multi-source data fusion technology in rainfall monitoring and forecasting,we reveal their effectiveness in improving the accuracy of both the"quantity"and"type"of rainfall data.Re-search indicates that significant progress has been made in rainfall monitoring and forecasting technology.However,challenges re-main in complex terrains,such as mountainous and urban environments,where resolution is limited and accuracy and timeliness are insufficient.Multi-source data fusion can improve the accuracy of rainfall data,spatiotemporal coverage,and prediction accu-racy.Therefore,optimizing algorithm models and integrating multi-source data from"space-sky-ground"to create high-res-olution forecasts is a future research direction.
关键词
降雨监测/降雨预报/防洪减灾/卫星遥感/天气雷达/数值预报/降尺度/多源数据融合Key words
rainfall monitoring/rainfall forecasting/flood control and disaster reduction/satellite remote sensing/weather ra-dar/numerical prediction/downscaling/multi-source data fusion引用本文复制引用
基金项目
国家重点研发计划项目(2022YFC3004401)
出版年
2024
国家科技期刊平台
NSTL
万方数据