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

Kinya Toride; Yoshihiko Iseri; Angela M Duren; John F England; M Levent Kavvas

doi:10.1016/j.scitotenv.2018.12.214

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

Sci Total Environ. 2019 Mar 25:658:570-581. doi: 10.1016/j.scitotenv.2018.12.214. Epub 2018 Dec 16.

Authors

Kinya Toride¹, Yoshihiko Iseri², Angela M Duren³, John F England⁴, M Levent Kavvas²

Affiliations

¹ Department of Civil and Environmental Engineering, University of California, Davis, 1 Shields Ave, Davis, CA 95616, USA. Electronic address: ktoride@ucdavis.edu.
² Department of Civil and Environmental Engineering, University of California, Davis, 1 Shields Ave, Davis, CA 95616, USA.
³ U.S. Army Corps of Engineers, Portland District, Portland, OR, USA.
⁴ U.S. Army Corps of Engineers, Risk Management Center, Lakewood, CO, USA.

PMID: 30580212
DOI: 10.1016/j.scitotenv.2018.12.214

Abstract

Dynamically downscaled precipitation is often used for evaluating sub-daily precipitation behavior 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 precipitation data. 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 precipitation associated 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 System Reanalysis (CFSR), which have different spatial resolutions and dataset periods, were used to simulate precipitation at 4 km resolution. Sensitivity analyses showed that AR precipitation is most sensitive to the microphysics parameterization. Among 13 microphysics schemes investigated, the Goddard and the Stony-Brook University schemes performed the best regardless of the choice of reanalysis. Reconstructed historical precipitation with 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 precipitation can be used for long-term reconstruction with small biases. However, systematic biases in the reanalysis datasets may still lead to uncertainties in downscaling precipitation in a different season with a single configuration.

Keywords: Atmospheric river; Microphysics; Model evaluation/performance; Pacific Northwest; Precipitation; Reanalysis.