Arulraj Malarvizhi, Petković Veljko, Wen Susan, Ferraro Ralph R, Meng Huan
Earth System Science Interdisciplinary Center/Cooperative Institute for Satellite Earth System Studies, University of Maryland, College Park, Maryland, USA.
College of Computer, Mathematical, and Natural Sciences, University of Maryland, College Park, Maryland, USA.
Sci Data. 2024 Sep 27;11(1):1042. doi: 10.1038/s41597-024-03877-x.
Satellite-based Quantitative Precipitation Estimates (QPE) are indirect estimates of precipitation rates and as such are often prone to errors, warranting a need for characterizing the associated uncertainties before being used in application-specific studies. Moreover, multiple satellite-based QPE products are offered through different agencies, each with their own specifications, formats and requirements, posing a challenge to understanding the products uncertainties. This manuscript presents a standardized validation system named NPreciSe - NOAA Satellite-based Precipitation Validation System, which assesses the performance of satellite-based precipitation products in near real-time over the continental United States. NPreciSe is coupled with a user-interactive web platform and built using an open-source software, Python. It is structured to help (1) the end-users determine the best satellite QPE for their specific application, and (2) the algorithm developers identify systematic biases in QPE retrievals. This manuscript presents the capabilities of the NPreciSe, discusses the methodology adopted in developing the standardized validation system, and introduces the web portal.
基于卫星的定量降水估计（QPE）是对降水率的间接估计，因此往往容易出错，这就需要在将其用于特定应用研究之前对相关不确定性进行表征。此外，不同机构提供了多种基于卫星的QPE产品，每种产品都有自己的规格、格式和要求，这给理解产品的不确定性带来了挑战。本文介绍了一个名为NPreciSe的标准化验证系统——美国国家海洋和大气管理局（NOAA）基于卫星的降水验证系统，该系统可在美国大陆近实时评估基于卫星的降水产品的性能。NPreciSe与一个用户交互式网络平台相结合，并使用开源软件Python构建。其结构旨在帮助（1）最终用户为其特定应用确定最佳的卫星QPE，以及（2）算法开发者识别QPE反演中的系统偏差。本文介绍了NPreciSe的功能，讨论了开发标准化验证系统所采用的方法，并介绍了网络门户。
