Numerical Investigations of Atmospheric Rivers and the Rain Shadow over the Santa Clara Valley
WRF, microphysics, drop size distribution, rainfall rate, atmospheric rivers, radar
Climate | Meteorology | Oceanography and Atmospheric Sciences and Meteorology
This study investigated precipitation distribution patterns in association with atmospheric rivers (ARs). The Weather Research and Forecasting (WRF) model was employed to simulate two strong atmospheric river events. The precipitation forecasts were highly sensitive to cloud microphysics parameterization schemes. Thus, radar observed and simulated ZH and ZDR were evaluated to provide information about the drop-size distribution (DSD). Four microphysics schemes (WSM-5, WSM-6, Thompson, and WDM-6) with nested simulations (3 km, 1 km, and 1/3 km) were conducted. One of the events mostly contained bright-band (BB) rainfall and lasted less than 24 h, while the other contained both BB and non-bright-band (NBB) rainfall, and lasted about 27 h. For each event, there was no clear improvement in the 1/3 km model, over the 1 km model. Overall, the WDM-6 microphysics scheme best represented the rainfall and the DSD. It appears that this scheme performed well, due to its relative simplicity in ice and mixed-phase microphysics, while providing double-moment predictions of warm rain microphysics (i.e., cloud and rain mixing ratio and number concentration). The other schemes tested either provided single-moment predictions of all classes or double-moment predictions of ice and rain (Thompson). Considering the shallow nature of precipitation in atmospheric rivers and the high-frequency of the orographic effect enhancing the warm rain process, these assumptions appear to be applicable over the southern San Francisco Bay Area.
Dalton Behringer and Sen Chiao. "Numerical Investigations of Atmospheric Rivers and the Rain Shadow over the Santa Clara Valley" Atmosphere (2019). https://doi.org/10.3390/atmos10030114
This article was originally published by MDPI in Atmosphere, volume 10, issue 3. © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
This article is also available online at the following link: https://doi.org/10.3390/atmos10030114