Evaluation of physical parameterizations for atmospheric river induced precipitation and application to long-term reconstruction based on three reanalysis datasets in Western Oregon

Abstract Dynamically downscaled precipitationis often used for evaluating sub-daily precipitationbehavior on a watershed-scale and for the input to hydrological modeling because of its increasing accuracy and spatiotemporal resolution. Despite these advantages, physical parameterizations in regional models and systematic biases due to the dataset used for boundary conditions greatly influence the quality of downscaled precipitationdata. The present paper aims to evaluate the performance and the sensitivities of physical parameterizations of the Weather Research and Forecasting (WRF) model to simulate extreme precipitationassociated with atmospheric rivers(ARs) over the Willamette watershed in Oregon. Also investigated was whether the optimized WRF configuration for extreme events can be used for long-term reconstruction using different boundary condition datasets. Three reanalysis datasets, the Twentieth Century Reanalysis version 2c (20CRv2c), the European Center for Medium-Range Weather Forecasts (ECMWF) twentieth century reanalysis (ERA20C), and the Climate Forecast SystemReanalysis (CFSR), which have different spatial resolutions and dataset periods, were used to simulate precipitationat 4 km resolution. Sensitivity analyses showed that AR precipitationis most sensitive to the microphysicsparameterization. Among 13 microphysicsschemes investigated, the Goddard and the Stony-Brook University schemes performed the best regardless of the choice of reanalysis. Reconstructed historical precipitationwith the optimized configuration showed better accuracies during the wet season than the dry season. With respect to simulations with CFSR, it was found that the optimized configuration for AR precipitationcan be used for long-term reconstruction with small biases. However, systematic biases in the reanalysis datasets may still lead to uncertainties in downscaling precipitationin a different season with a single configuration.