NPreciSe - An Automated Satellite Precipitation Product Assessment Tool.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-09-27 DOI:10.1038/s41597-024-03877-x

Malarvizhi Arulraj, Veljko Petković, Susan Wen, Ralph R Ferraro, Huan Meng

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Abstract

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.

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NPreciSe - 卫星降水产品自动评估工具。

基于卫星的定量降水估算（QPE）是对降水率的间接估算，因此往往容易出现误差，因此在用于特定应用研究之前需要确定相关不确定性的特征。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，这给了解产品的不确定性带来了挑战。本手稿介绍了一个标准化验证系统，名为 NPreciSe - NOAA 星基降水验证系统，该系统对美国大陆上空的星基降水产品性能进行近实时评估。NPreciSe 与用户交互式网络平台相结合，使用开源软件 Python 构建。它的结构可帮助（1）最终用户确定最适合其特定应用的卫星 QPE，以及（2）算法开发人员识别 QPE 检索中的系统性偏差。本手稿介绍了 NPreciSe 的功能，讨论了开发标准化验证系统所采用的方法，并介绍了门户网站。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.