Monday, 13 January 2020
Hall B1 (Boston Convention and Exhibition Center)
California experienced several atmospheric river events during the 2018-2019 winter which led to millions of dollars in damages and created threats of flooding and mudslides. Accurate model forecasts are needed for forecasters and stakeholders to make critical decisions that protect lives and property during these types of events. Mesoscale models including the High Resolution Rapid Refresh (HRRR) and the Coupled Ocean-Atmosphere Mesoscale Prediction System (COAMPS) offer localized forecasts due to their fine scale grid spacing that can be useful when making forecasts. However, these models have many differences including grid spacing, initialization times, physical processes, and domain coverage. The proposed study seeks to identify how the differences between the models affects the accuracy of the outputted forecasts during atmospheric river events. The fractions skill score (FSS), equitable threat score (ETS), and bias score will be calculated to determine the performance of each model for 24 forecasts periods during five atmospheric river events as well as regular winter precipitation for the 2018-2019 winter. Forecasted total precipitation, winds, and simulated brightness temperatures will be compared to observations to evaluate how skillful the models are when forecasting large-scale phenomena such as atmospheric rivers compared to regular winter precipitation conditions. Systematic forecast errors will be investigated for correlations with environmental features such as atmospheric stability and integrated vapor transport (IVT). Resampling techniques will be applied to determine statistical significance of the skill scores.
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