Evaluation and Comparison of Microphysical Algorithms in ARW-WRF Model Simulations of Atmospheric River Events Affecting the California Coast

Abstract

Numerical prediction of precipitation associated with five cool-season atmospheric river events in northern California was analyzed and compared to observations. The model simulations were performed by using the Advanced Research Weather Research and Forecasting Model (ARW-WRF) with four different microphysical parameterizations. This was done as a part of the 2005-06 field phase of the Hydrometeorological Test Bed project, for which special profilers, soundings, and surface observations were implemented. Using these unique datasets, the meteorology of atmospheric river events was described in terms of dynamical processes and the microphysical structure of the cloud systems that produced most of the surface precipitation. Events were categorized as ''bright band'' (BB) or ''nonbright band'' (NBB), the differences being the presence of significant amounts of ice aloft (or lack thereof) and a signature of higher reflectivity collocated with the melting layer produced by frozen precipitating particles descending through the 08C isotherm.The model was reasonably successful at predicting the timing of surface fronts, the development and evolution of low-level jets associated with latent heating processes and terrain interaction, and wind flow signatures consistent with deep-layer thermal advection. However, the model showed the tendency to overestimate the duration and intensity of the impinging low-level winds. In general, all model configurations overestimated precipitation, especially in the case of BB events. Nonetheless, large differences in precipitation distribution and cloud structure among model runs using various microphysical parameterization schemes were noted. FIG. 4. Simulated (a) wind profile at BBY and (b) precipitation accumulation by the model run using the Lin microphysics for the period 0000 UTC 30 to 2200 UTC 31 Dec 2005. Wind flags, barbs, and isotachs as in Fig. 3. AUGUST 2009 J A N K O V E T A L . FIG. 8. Cloud water, cloud ice, rain, snow, and graupel mixing ratios (310 23 kg kg 21 ) averaged over five events and the American River basin for the four different microphysics schemes. AUGUST 2009 J A N K O V E T A L . FIG. 13. Snow (see color bar), rain (white contours) and graupel (black contours) mixing ratios (kg kg 21 ), and 08C temperature (red) and wet bulb temperature (green) lines for the 30 Dec 2005 event at CZD for model runs using the (a) Lin, (b) WSM6, (c) Thompson, and (d) Schultz microphysics. Melting level heights (asterisks) from available OAK soundings. Both shading and contours are scaled by a factor of 10 24 . AUGUST 2009 J A N K O V E T A L .

Influence of microphysical schemes on atmospheric water in the Weather Research and Forecasting model

“…Several studies (Jankov et al, 2005(Jankov et al, , 2009Otkin and Greenwald, 2008;Mercader et al, 2010;Argüeso et al, 2011;Awan et al, 2011) have assessed WRF microphysical schemes by simulating a real case scenario with multiphysics options and comparing the results with observations. Other studies have used a much simpler setup to investigate the effects of mountain geometry and upstream conditions on orographic precipitation (Chen and Lin, 2005;Rotunno, 2005, 2006;Pathirana et al, 2005;Watson and Lane, 2012), using however only one or two microphysical parameterizations.…”

Observations of Wintertime U.S. West Coast Precipitating Systems with W-Band Satellite Radar and Other Spaceborne Instruments

“…Since such differentiating is straightforward with radar measurements, the relative occurrence of AR warm rain can be evaluated statistically. The estimate of this occurrence as well as an occurrence of mixed rainfall (i.e., a mix of BB and warm rain) from CloudSat measurements might lead to better parameterizing cloud and precipitation microphysics in AR models as the rainfall regime prevalence was shown to be sensitive to a choice of a model microphysical parameterization scheme (Jankov et al 2009). …”

“…Another important AR modeling issue is water substance partitioning in BB rainfall (Jankov et al 2009). CloudSat measurements can provide some useful observational information regarding correspondence between the rainfall and the ice phase in precipitating clouds.…”

Microphysics parameterization sensitivity of the WRF Model version 3.1.7 to extreme precipitation: evaluation of the 1997 New Year’s flood of California

“…Moreover, microphysical schemes play a significant role if the horizontal resolution of the interested domain is finer than 9 km. Recent studies have been conducted for the evaluation of the microphysics schemes of the WRF Model with respect to winter precipitation in California, with a focus on atmospheric river phenomena (Jankov et al, 2007;Jankov et al, 2009; Tan, 2010;Jankov et al, 2011;Han et al, 2013), 25 which is the reason for the extreme precipitation that occurs along the West coast of the US (Zhu and Newell, 1998;Ralph et al, 2004; Tan, 2010;Houze, 2012). Jankov et al (2007) Hereupon, the main goal of this study is to evaluate hourly quantitative extreme precipitation forecasting (QEPF) performance of the WRF Model for the most extreme atmospheric river event in the history of California.…”