WRF Simulations of the 20–22 January 2007 Snow Events over Eastern Canada: Comparison with In Situ and Satellite Observations

Abstract

One of the grand challenges of the Global Precipitation Measurement (GPM) mission is to improve cold season precipitation measurements in middle and high latitudes through the use of high-frequency passive microwave radiometry. For this, the Weather Research and Forecasting (WRF) model with the Goddard microphysics scheme is coupled with a satellite data simulation unit (WRF-SDSU) that has been developed to facilitate over-land snowfall retrieval algorithms by providing a virtual cloud library and microwave brightness temperature (Tb) measurements consistent with the GPM Microwave Imager (GMI). This study tested the Goddard cloud microphysics scheme in WRF for two snowstorm events, a lake effect and a synoptic event, that occurred between 20 and 22 January 2007 over the Canadian CloudSAT/CALIPSO Validation Project (C3VP) site in Ontario, Canada.The 24h-accumulated snowfall predicted by the WRF model with the Goddard microphysics was comparable to the observed accumulated snowfall by the ground-based radar for both events. The model correctly predicted the onset and ending of both snow events at the CARE site. WRF simulations capture the basic cloud properties as seen by the ground-based radar and satellite (i.e., CloudSAT, AMSU-B) observations as well as the observed cloud streak organization in the lake event. This latter result reveals that WRF was able to capture the cloud macro-structure reasonably well.Sensitivity tests utilizing both the 2ice (ice and snow) and 3ice (ice, snow and graupel) Goddard microphysical schemes were also conducted. The domain-and timeaverage cloud species profiles from WRF simulations with both microphysical schemes show identical results (due to weak vertical velocities and therefore the absence of large precipitating liquid or ice particles like graupel). However, both microphysics schemes produced an appreciable amount of liquid water while the C3VP aircraft measurements show much less liquid water than the model in both snow events. These results indicate that additional research is needed to improve the current cloud microphysics scheme for 2 the extreme cold environment in high latitudes. Future aircraft observations are also needed to verify the abscence of graupel in high-latitude in-land snow events.

Sensitivity of Lake-Effect Snowfall to Lake Ice Cover and Temperature in the Great Lakes Region

“…All simulations employ the Goddard microphysics and Mellor-Yamada-Janic planetary boundary layer schemes, as these parameterizations have demonstrated success in modeling LES events in previous studies (Shi et al 2010). See Table 1 for the complete suite of physics schemes used.…”

On Polarimetric Radar Signatures of Deep Convection for Model Evaluation: Columns of Specific Differential Phase Observed during MC3E*

“…Caniaux et al (1995); Gray (2000); Lang et al (2003); Mechem et al (2006); Braun et al (2010); Fridlind et al (2012); Zeng et al (2013); Mrowiec et al (2015)). Additionally, contoured frequency with altitude (CFAD) plots of reflectivity are used to evaluate differences in modeled and observed storm microphysics and dynamics (Lang et al 2003;Blossey et al 2007;Matsui et al 2009;Shi et al 2010;Lang et al 2011;Tao et al 2014;Matsui et al 2015). These methods have proven valuable in illustrating gross differences between modeled and observed storm characteristics; however, they do not provide specific information on, for example, the prevalence, spatial distribution, and strength of deep convective updrafts.…”

Evaluation of cloud microphysics schemes in simulations of a winter storm using radar and radiometer measurements

“…[6] This methodology of integrating a NWP model with a forward radiative transfer model has recently been used to evaluate model simulations and to improve model microphysics schemes [e.g., Matsui et al, 2009;Han et al, 2010;Li et al, 2010;Shi et al, 2010]. It is also one of the key components in algorithm development to retrieve or assimilate remote sensing data [e.g., Olson et al, 2001;Tong and Xue, 2005;Zupanski et al, 2011;Blahak et al, 2011].…”