Evaluation of Rainfall Forecasts with Heavy Rain Types in the High-Resolution Unified Model over South Korea

• Published in: Weather and forecasting Vol. 34; no. 5; pp. 1277 - 1293
• Main Authors: Song, Hwan-Jin, Lim, Byunghwan, Joo, Sangwon
• Format: Journal Article
• Language: English
• Published: Boston American Meteorological Society 01.10.2019
• Subjects: Accuracy; Atmospheric precipitations; Clouds; Cluster analysis; Clustering; Cold; Data assimilation; Data collection; Disasters; Evaluation; High resolution; Mathematical models; Natural disasters; Precipitation; Rain; Rainfall; Rainfall forecasting; Rainfall intensity; Resolution; Socioeconomic factors; Vector quantization; Weather forecasting; Wind; South Korea; United Kingdom > UK; United States > US; Korean Peninsula
• ISSN: 0882-8156; 1520-0434
• DOI: 10.1175/WAF-D-18-0140.1

Heavy rainfall events account for most socioeconomic damages caused by natural disasters in South Korea. However, the microphysical understanding of heavy rain is still lacking, leading to uncertainties in quantitative rainfall prediction. This study is aimed at evaluating rainfall forecasts in the Local Data Assimilation and Prediction System (LDAPS), a high-resolution configuration of the Unified Model over the Korean Peninsula. The rainfall of LDAPS forecasts was evaluated with observations based on two types of heavy rain events classified from K-means clustering for the relationship between surface rainfall intensity and cloud-top height. LDAPS forecasts were characterized by more heavy rain cases with high cloud-top heights (cold-type heavy rain) in contrast to observations showing frequent moderate-intensity rain systems with relatively lower cloud-top heights (warm-type heavy rain) over South Korea. The observed cold-type and warm-type events accounted for 32.7% and 67.3% of total rainfall, whereas LDAPS forecasts accounted for 65.3% and 34.7%, respectively. This indicates severe overestimation and underestimation of total rainfall for the cold-type and warm-type forecast events, respectively. The overestimation of cold-type heavy rainfall was mainly due to its frequent occurrence, whereas the underestimation of warm-type heavy rainfall was affected by both its low occurrence and weak intensity. The rainfall forecast skill for the warm-type events was much lower than for the cold-type events, due to the lower rainfall intensity and smaller rain area of the warm-type. Therefore, cloud parameterizations for warm-type heavy rain should be improved to enhance rainfall forecasts over the Korean Peninsula.