Relationship between rainfall variability and the predictability of radar rainfall nowcasting models

Liu, Zhenzhen, Dai, Qiang and Zhuo, Lu 2019. Relationship between rainfall variability and the predictability of radar rainfall nowcasting models. Atmosphere 10 (8) , 458. 10.3390/atmos10080458

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Abstract

Radar rainfall nowcasts are subject to many sources of uncertainty and these uncertainties change with the characteristics of a storm. The predictive skill of a radar rainfall nowcasting model can be difficult to understand as sometimes it appears to be perfect but at other times it is highly inaccurate. This hinders the decision making required for the early warning of natural hazards caused by rainfall. In this study we define radar spatial and temporal rainfall variability and relate them to the predictive skill of a nowcasting model. The short-term ensemble prediction system model is configured to predict 731 events with lead times of one, two, and three hours. The nowcasting skill is expressed in terms of six well-known indicators. The results show that the quality of radar rainfall nowcasts increases with the rainfall autocorrelation and decreases with the rainfall variability coefficient. The uncertainty of radar rainfall nowcasts also shows a positive connection with rainfall variability. In addition, the spatial variability is more important than the temporal variability. Based on these results, we recommend that the lead time for radar rainfall nowcasting models should change depending on the storm and that it should be determined according to the rainfall variability. Such measures could improve trust in the rainfall nowcast products that are used for hydrological and meteorological applications.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Earth and Environmental Sciences
Additional Information:	This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Publisher:	MDPI
ISSN:	2073-4433
Date of First Compliant Deposit:	21 October 2022
Date of Acceptance:	8 August 2019
Last Modified:	21 May 2023 17:15
URI:	https://orca.cardiff.ac.uk/id/eprint/153223

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