Orographic Quantitative Precipitation Forecast Sensitivity to the Choice of Microphysics Parameterization during a U.S.West Coast Atmospheric River Event

Abstract

The Center for Western Weather and Water Extremes provides high-resolution forecasts of Atmospheric Rivers (ARs) along the U.S. West Coast. Using a 34-year reforecast dataset, we assess the sensitivity of orographic quantitative precipitation forecasts (QPF) associated with ARs to bulk microphysics parameterization (BMP). A 14-member, 168-hour reforecast ensemble of an AR event from 7–10 January 2017 is produced by varying only the BMP. Advances in microphysics parameterization have led to improved multi-moment schemes as well as a shift in the treatment of ice-phase hydrometeors. Rather than tracking the evolution of a prescribed ice class, some newer schemes track a single ice class and the evolution of its properties. These newer schemes, such as the Predicted Particle Properties schemes, the Ice-Spheroids Habit Model with Aspect-ratio Evolution scheme, and National Taiwan University parameterization may improve operational forecasts. We compare several BMPs to identify potential forecast improvements during a landfalling AR. Compared to precipitation observations and analyses, the QPF from the ensemble exhibited a general positive bias along the windward slopes of the Sierra Nevada Mountains, with a negative QPF bias in the lee (recognizing observational uncertainties). Several of the newer BMPs reduced this bias couplet near the Sierra Nevada. Our analysis identified a strong correlation between QPF and the drying ratio in the ensemble. Some newer schemes featured up to a 5% lower drying ratio, consistent with bias reduction and more realistic representation of cold-season orographic precipitation in this case. Further case studies are needed to generalize findings.