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Gregory Thompson, Roy M. Rasmussen, and Kevin Manning

Abstract

This study evaluates the sensitivity of winter precipitation to numerous aspects of a bulk, mixed-phase microphysical parameterization found in three widely used mesoscale models [the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), the Rapid Update Cycle (RUC), and the Weather Research and Forecast (WRF) model]. Sensitivities of the microphysics to primary ice initiation, autoconversion, cloud condensation nuclei (CCN) spectra, treatment of graupel, and parameters controlling the snow and rain size distributions are tested. The sensitivity tests are performed by simulating various cloud depths (with different cloud-top temperatures) using flow over an idealized two-dimensional mountain. The height and width of the two-dimensional barrier are designed to reproduce an updraft pattern with extent and magnitude consistent with documented freezing-drizzle cases. By increasing the moisture profile to saturation at low temperatures, a deep, precipitating snow cloud is also simulated. Upon testing the primary sensitivities of the microphysics scheme in two dimensions as reported in the present study, the MM5 with the modified scheme will be tested in multiple case studies and the results will be compared to observations in a forthcoming companion paper, Part II.

The key results of this study are 1) the choice of ice initiation schemes is relatively unimportant for deep precipitating snow clouds but more important for shallow warm clouds having cloud-top temperature greater than −13°C, 2) the assumed snow size distribution and associated snow diffusional growth along with the assumed graupel size distribution and method of transforming rimed snow into graupel have major impacts on the mass of cloud water and formation of freezing drizzle, and 3) a proper simulation of drizzle using a single-moment scheme and exponential size distribution requires an increase in the rain intercept parameter, thereby reducing rain terminal velocities to values more characteristic of drizzle.

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Douglas A. Wesley, Roy M. Rasmussen, and Ben C. Bernstein

Abstract

The Longmont anticyclone, a region of low-level anticyclonic turning and convergence during episodes of northerly winds along the Front Range of the Rocky Mountains, is documented for a snow event that occurred during the Winter Icing and Storms Project. The complex terrain in this region, especially the barrier to the west and the sloping Cheyenne Ridge to the north, is critical for the formation of this mesoscale feature. Upward motions related to this persistent convergent region downstream of the Cheyenne Ridge can strongly influence local snowfall distributions. The particular event studied in this manuscript was weakly forced on the synoptic scale. Through close examination of Doppler radar, special sounding and surface mesonetwork data, the effects of the Longmont anticyclone on snowfall were determined. The results of the analyses suggest that the convergence triggered convective snowbands in a region of delayed postfrontal cold advection at low levels. A series of mesoscale model simulations predicted the behavior of low-level northerly flow along the Front Range and demonstrated the role of the terrain during the development of the Longmont anticyclone. The results of these simulations were compared to the case study results.

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Pablo A. Mendoza, Balaji Rajagopalan, Martyn P. Clark, Kyoko Ikeda, and Roy M. Rasmussen

Abstract

Statistical postprocessing techniques have become essential tools for downscaling large-scale information to the point scale, and also for providing a better probabilistic characterization of hydrometeorological variables in simulation and forecasting applications at both short and long time scales. In this paper, the authors assess the utility of statistical postprocessing methods for generating probabilistic estimates of daily precipitation totals, using deterministic high-resolution outputs obtained with the Weather Research and Forecasting (WRF) Model. After a preliminary assessment of WRF simulations over a historical period, the performance of three postprocessing techniques is compared: multinomial logistic regression (MnLR), quantile regression (QR), and Bayesian model averaging (BMA)—all of which use WRF outputs as potential predictors. Results demonstrate that the WRF Model has skill in reproducing observed precipitation events, especially during fall/winter. Furthermore, it is shown that the spatial distribution of skill obtained from statistical postprocessing is closely linked with the quality of WRF precipitation outputs. A detailed comparison of statistical precipitation postprocessing approaches reveals that, although the poorest performance was obtained using MnLR, there is not an overall best technique. While QR should be preferred if skill (i.e., small probability forecast errors) and reliability (i.e., match between forecast probabilities and observed frequencies) are target properties, BMA is recommended in cases when discrimination (i.e., prediction of occurrence versus nonoccurrence) and statistical consistency (i.e., equiprobability of the observations within their ensemble distributions) are desired. Based on the results obtained here, the authors believe that future research should explore frameworks reconciling hierarchical Bayesian models with the use of the extreme value theory for high precipitation events.

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Gregory Thompson, Paul R. Field, Roy M. Rasmussen, and William D. Hall

Abstract

A new bulk microphysical parameterization (BMP) has been developed for use with the Weather Research and Forecasting (WRF) Model or other mesoscale models. As compared with earlier single-moment BMPs, the new scheme incorporates a large number of improvements to both physical processes and computer coding, and it employs many techniques found in far more sophisticated spectral/bin schemes using lookup tables. Unlike any other BMP, the assumed snow size distribution depends on both ice water content and temperature and is represented as a sum of exponential and gamma distributions. Furthermore, snow assumes a nonspherical shape with a bulk density that varies inversely with diameter as found in observations and in contrast to nearly all other BMPs that assume spherical snow with constant density. The new scheme’s snow category was readily modified to match previous research in sensitivity experiments designed to test the sphericity and distribution shape characteristics. From analysis of four idealized sensitivity experiments, it was determined that the sphericity and constant density assumptions play a major role in producing supercooled liquid water whereas the assumed distribution shape plays a lesser, but nonnegligible, role. Further testing using numerous case studies and comparing model results with in situ and other observations confirmed the results of the idealized experiments and are briefly mentioned herein, but more detailed, microphysical comparisons with observations are found in a companion paper in this series (Part III, forthcoming).

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Lulin Xue, Jiwen Fan, Zachary J. Lebo, Wei Wu, Hugh Morrison, Wojciech W. Grabowski, Xia Chu, István Geresdi, Kirk North, Ronald Stenz, Yang Gao, Xiaofeng Lou, Aaron Bansemer, Andrew J. Heymsfield, Greg M. McFarquhar, and Roy M. Rasmussen

Abstract

The squall-line event on 20 May 2011, during the Midlatitude Continental Convective Clouds (MC3E) field campaign has been simulated by three bin (spectral) microphysics schemes coupled into the Weather Research and Forecasting (WRF) Model. Semi-idealized three-dimensional simulations driven by temperature and moisture profiles acquired by a radiosonde released in the preconvection environment at 1200 UTC in Morris, Oklahoma, show that each scheme produced a squall line with features broadly consistent with the observed storm characteristics. However, substantial differences in the details of the simulated dynamic and thermodynamic structure are evident. These differences are attributed to different algorithms and numerical representations of microphysical processes, assumptions of the hydrometeor processes and properties, especially ice particle mass, density, and terminal velocity relationships with size, and the resulting interactions between the microphysics, cold pool, and dynamics. This study shows that different bin microphysics schemes, designed to be conceptually more realistic and thus arguably more accurate than bulk microphysics schemes, still simulate a wide spread of microphysical, thermodynamic, and dynamic characteristics of a squall line, qualitatively similar to the spread of squall-line characteristics using various bulk schemes. Future work may focus on improving the representation of ice particle properties in bin schemes to reduce this uncertainty and using the similar assumptions for all schemes to isolate the impact of physics from numerics.

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