Crowd-sourced observations for short-range numerical weather prediction: Report from EWGLAM / SRNWP Meeting 2019
Crowd-sourced observations for short-range numerical weather prediction: Report from EWGLAM / SRNWP Meeting 2019
复制标题
短期数值天气预报的众包观测：EWGLAM / SRNWP 2019 年会议报告
DOI：
10.1002/asl.1031
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发表时间：
2021
影响因子：
3
通讯作者：
Hintz K
中科院分区：
文献类型：
--
作者：
Hintz K
众源观测（CSO）为数值天气预报（NWP）提供了巨大的潜力。本文基于2019年EWGLAM会议的特别会议，综述了该领域的进展、挑战和机遇，重点关注高分辨率有限区域模型（LAM）。介绍了CSO的两个主要应用领域：数据同化和验证。数据同化发展的一部分集中在智能手机压力观测，这代表了大量的数据。然而，必须特别注意数据保护和观察的质量。本文介绍了两个例子：丹麦的SMAPS实验和美国的uWx实验。另一个数据同化主题是使用低成本天气传感器进行公民观测;这里介绍了挪威使用Netatmo站的一个例子。另一个应用领域是使用CSO进行模型验证。英国开发的一种新方法是应用社交媒体数据来检测恶劣天气事件。这种方法特别重要，因为LAM NWP模型的一个未来应用领域是面向影响的警报。
Crowd‐sourced observations (CSO) offer great potential for numerical weather prediction (NWP). This paper offers a synthesis of progress, challenges and opportunities in this area based on a special session of the EWGLAM Meeting in 2019, concentrating on high‐resolution limited‐area models (LAMs). Two main application areas of CSO are described: data assimilation and verification. One part of data assimilation developments concentrates on smartphone pressure observations, which represent a large volume of data. However, special care has to be taken about data protection and the quality of observations. In this paper, two examples are presented: the SMAPS experiment from Denmark and the uWx experiment from the United States. Another data assimilation topic is citizen observations with low‐cost weather sensors; here an example from Norway is presented using Netatmo stations. The other application area is the use of CSO for model verification. One novel method developed in the United Kingdom is applying social media data to detect severe weather events. This approach is especially important because one future application area of LAM NWP models is impact‐oriented warnings.