Abstract
A high-resolution Weather Research and Forecasting (WRF) model for a heavy rainfall case is configured and the performance of the precipitation forecasting is evaluated. Sensitivity tests were carried out by changing the model configuration, such as domain size, sea surface temperature (SST) data, initial conditions, and lead time. The numerical model employs one-way nesting with horizontal resolutions of 5 km and 1 km for the outer and inner domains, respectively. The model domain includes the capital city of Seoul and its suburban megacities in South Korea. The model performance is evaluated via statistical analysis using the correlation coefficient, deviation, and root mean squared error by comparing with observational data including, but not limited to, those from ground-based instruments. The sensitivity analysis conducted here suggests that SST data show negligible influence for a short range forecasting model, the data assimilated initial conditions show the more effective results rather than the non-assimilated high resolution initial conditions, and for a given domain size of the forecasting model, an appropriate outer domain size and lead time of <6 h for a 1-km high-resolution domain should be taken into consideration when optimizing the WRF configuration for regional torrential rainfall events around Seoul and its suburban area, Korea.
Highlights
Over the last several decades, dangerous weather events such as severe rain, heavy snow, drought, and heat wave caused by climate change have been concentrated over densely populated urban and industrial areas
Default Model Results outer domain were obtained by spatial interpolation of the KLAPS with 15-km resolution for the outer domain and 5-km data-assimilated one-way nested inner domain from the Korea Meteorology Administration (KMA) data, such as radar, Automated Weather Stations (AWS), satellite, and profile data
The lead time experiment for the high-resolution forecasting model in this study indicates that a lead time
Summary
Over the last several decades, dangerous weather events such as severe rain, heavy snow, drought, and heat wave caused by climate change have been concentrated over densely populated urban and industrial areas. In Korea, localized torrential rainfall has caused flash floods and mountain landslides, sometimes resulting in heavy casualties and property loss [1]. To prevent such substantial damage, it is important to predict heavy rainfall over densely populated and industrial areas within a short time range. The 1-km resolution forecasting model has a highly resolved model configuration, and its forecasting skills are sensitive to the input data and configuration. Such information plays an important role in improving the skills of the forecasting model for local atmospheric circulation
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
