Collaborative Research: ITR: Framework for Optimal Merging of Multi-sensor Spatial Data and Multi-model, Multi-analysis Ensemble Forecasts of Heavy Precipitation and Floods

合作研究：ITR：强降水和洪水多传感器空间数据和多模型、多分析集合预报的优化合并框架

• 批准号: 0311858
• 负责人: T. Krishnamurti
• 金额: $ 20.45万
• 依托单位: Florida State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-08-15 至 2006-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0311858&HistoricalAwards=false
• 关键词: Collaborative; Research; ITR; Framework; Optimal

The aim of this project is to integrate satellite and ground-based data with numerical weather prediction models, hydrological models and neural network models of precipitation and flooding, in order to develop techniques for forecasting heavy precipitation and flooding that are tailored to local regions. The focus regions will be river basins in Appalachia, Nepal, Mozambique and Brazil where there are significant orographic influences on precipitation. The project is a collaboration between a hydrologist and a meteorologist and involves support personnel with expertise in information technology. The work combines scientific computing and machine learning techniques with meteorology and hydrology. The forecasting of heavy floods in areas adjacent to mountainous regions is an important problem in many places around the world. If successful, the research would develop an approach that could then be adapted to operational use for such forecasting.

该项目的目的是将卫星和地面数据与数值天气预报模型、水文模型以及降水和洪水的神经网络模型结合起来，以便开发适合当地区域的强降水和洪水预报技术。 重点区域将是阿巴拉契亚、尼泊尔、莫桑比克和巴西的河流流域，这些地区的地形对降水有重大影响。 该项目是一名水文学家和一名气象学家之间的合作，并涉及具有信息技术专门知识的支助人员。 这项工作将科学计算和机器学习技术与气象学和水文学相结合。 在世界许多地方，对邻近山区的大洪水进行预报是一个重要问题。 如果成功，该研究将开发一种方法，然后可以适应此类预测的操作使用。