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.
Highlights
Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Water and Environmental Management Research Centre, Department of Civil Engineering, University of Bristol, Bristol BS8 1TR, UK
A clear understanding of the performance of the radar rainfall nowcasting model can enable stakeholders to be more confident in their decision making
It is important to determine the features of storms that affect the skill of radar rainfall nowcasting models
Summary
“Nowcasting” refers to automated weather forecasts for precipitation with a short term lead time (0–6 h) as well as high spatial (e.g., 1 km) and temporal (e.g., 5 min) resolutions [1,2,3]. The statistical-based uncertainty model may overestimate the uncertainty because external uncertainty may be introduced through gauge rainfall error or gauge representative error Due to these limitations, which affect both the deterministic and probabilistic forms, stakeholders can face difficulties when using radar rainfall nowcast products for practical applications. The spatial and temporal variability of storm motion and evolution will affect the skill of a nowcasting model This changing predictive skill can be difficult to understand as sometimes the models appear to be perfect while at other times they are highly inaccurate. We explore the key indicators that represent storm features with a close relationship to radar rainfall nowcasts By understanding this relationship, stakeholders will be able to interpret the skill of rainfall nowcasting models based on given rainfall observations. The conclusion section summarizes the key findings, limitations, and future work
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