A study of the fidelity of simulated warm rain microphysics over the coastal terrain of the Pacific Northwest

Abstract

Significant advancements in microphysical parameterization schemes have taken place over the last two decades. Yet, over that time, improvements to warm rain physics have largely been neglected in favor of improving ice processes. As early as Colle et al. (1999), there was evidence of too little precipitation falling in environments conducive to warm rain. In subsequent years, data from Colle et al. (2000), Minder et al. (2008), Conrick and Mass (2019a,b), and others have suggested that warm rain may suffer from deficiencies and, more importantly, these biases are model agnostic (MM5 and WRF) and occur regardless of parameterization scheme. The Pacific Northwest is an excellent natural laboratory to study warm rain. An abundance of terrain-forced winter precipitation has encouraged field experiments in the region, most recently the Olympic Mountains Experiment (OLYMPEX; 2015-16). Observational evidence from OLYMPEX (Zagrodnik et al. 2018, 2019, and 2021) has shown that warm rain processes are fundamental to overall precipitation production near terrain, yet evaluations have demonstrated that WRF struggles to produce the proper microphysical characteristics of warm rain: small drop diameters and large number concentrations. This study demonstrates that warm-sector environments experience the largest underprediction of precipitation and the greatest microphysical biases relative to other types of environments. Surface-level evidence is presented for a warm rain bias by examining drop size distributions (DSDs) over short-term and extended periods, highlighting that WRF retains cold rain characteristics even when warm rain is occurring. Satellite observations from GPM are also used as an evaluation tool to demonstrate that WRF underpredicts cloud and rain water contents. To find a solution to the regional precipitation biases, it is shown that a lognormal distribution is optimal for describing cloud water during warm rain events. When used during warm rain periods, the lognormal distribution and associated model changes boost precipitation and reduce microphysical deficiencies, offering a potential fix to a decades-old problem. Implementation of a lognormal cloud water distribution improves precipitation simulations for a case study in February 2016 and also improves enhances the realism of long-term simulated warm rain over an approximately one-month period.