Abstract
The skill of the Weather Research and Forecasting (WRF) model in predicting extreme rainfall events (EREs) is investigated for two different initialization times: 1200 UTC (12-hour lead time) and 0000 UTC (zero lead time). A high resolution (3-km) WRF model configuration is used to simulate 38 EREs in Karnataka, a state in southwest India. These EREs were identified by analyzing daily accumulated rainfall recorded by telemetric rain-gauge (TRG) stations over the region, specifically during the prominent rainy season in the state (June to November) for the period 2015–2019. EREs were defined using the 99.993rd percentile threshold for each subdivision and year. The model forecasts of rainfall and other meteorological variables were verified against high density TRG (6480 stations) and telemetric weather station (860 stations) observations throughout the Karnataka. The model performance was assessed through computation of quantitative error measures and statistical skill scores. Consistently, both sets of experiments reveal a propensity of WRF model to overestimate rainfall in the coastal region, with simulations initialized at 0000 UTC displaying a more pronounced wet bias compared to those at 1200 UTC. However, simulations initialized at 0000 UTC are more skillful than 1200 UTC in predicting the frequency of rainfall occurrences. Validation of other meteorological variables indicates that the model tends to underpredict temperature and overpredict relative humidity, especially during night hours, while it overpredicts wind speed over the region. Quantification of the model errors against in-situ measurements are crucial before deploying these models for operational applications and a deeper understanding of these errors will aid in further improvements to the model.
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