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Neil Laird, Alicia M. Bentley, Sara A. Ganetis, Andrew Stieneke, and Samantha A. Tushaus

Abstract

The frequency, timing, and environmental conditions of lake-effect (LE) precipitation over Lake Tahoe and Pyramid Lake in northern California and western Nevada were examined for the 14 winters (September–March) from 1996/97 through 2009/10. Weather Surveillance Radar-1988 Doppler (WSR-88D) data from Reno, Nevada (KRGX), were used to identify 62 LE events. LE precipitation occurred as single bands extending downwind from overlake areas, and as isolated regions of overlake precipitation with little or no extension over land. Mesoscale vortices were also identified during both Lake Tahoe and Pyramid Lake LE events. An average of 4.4 LE events occurred each winter in the Lake Tahoe and Pyramid Lake region, with events occurring most frequently in October. LE events had an average duration of 6.3 h, approximately half the duration of LE events observed over Lake Champlain, the New York State Finger Lakes, or the Great Salt Lake. The observed conditions during LE events in the Lake Tahoe and Pyramid Lake region typically had 1) mean surface air temperatures below freezing, 2) mean surface wind speeds of <2.0 m s−1 (notably weaker than during lake effect in other areas), 3) a mean lake–air temperature difference of 11.5°C, and 4) a mean lake–700-hPa temperature difference of 20.9°C.

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Samantha A. Tushaus, Derek J. Posselt, M. Marcello Miglietta, Richard Rotunno, and Luca Delle Monache

Abstract

Recent idealized studies examined the sensitivity of topographically forced rain and snowfall to changes in mountain geometry and upwind sounding in moist stable and neutral environments. These studies were restricted by necessity to small ensembles of carefully chosen simulations. Research presented here extends earlier studies by utilizing a Bayesian Markov chain Monte Carlo (MCMC) algorithm to create a large ensemble of simulations, all of which produce precipitation concentrated on the upwind slope of an idealized Gaussian bell-shaped mountain. MCMC-based probabilistic analysis yields information about the combinations of sounding and mountain geometry favorable for upslope rain, as well as the sensitivity of orographic precipitation to changes in mountain geometry and upwind sounding. Exploration of the multivariate sensitivity of rainfall to changes in parameters also reveals a nonunique solution: multiple combinations of flow, topography, and environment produce similar surface rainfall amount and distribution. Finally, the results also divulge that the nonunique solutions have different sensitivity profiles, and that changes in observation uncertainty also alter model sensitivity to input parameters.

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Derek J. Posselt, Xuanli Li, Samantha A. Tushaus, and John R. Mecikalski

Abstract

Dual-polarization Doppler radar has proven useful for the estimation of hydrometeor content and the classification of hydrometeor type. Recent studies have leveraged dual-polarization-specific information to produce improved assimilated cloud and precipitation fields from the warm rain (above freezing) portion of deep convective storms. While the strengths of dual-polarization radar observations have been conclusively shown for rain and hail hydrometeors, it is less clear how much information is provided in mixed-phase and ice-only regions.

In this paper, a Markov chain Monte Carlo (MCMC) algorithm is used to examine the information content of dual-polarization-specific variables in the ice-phase region of a convective storm. Results are used to quantify how much information is added by specific differential phase and radar correlation coefficient, as well as how this information is degraded when the assumed particle size distribution and particle density are allowed to vary. It is found that dual-polarization-specific observations (K dp and ρhv) provide significant information on rimed ice content, and moderate information on pristine ice, especially where snow mass is more than 10% of the total volume hydrometeor mass. There is a significant reduction in information content for rain and a near-complete loss of information for graupel–hail and snow when the particle size distribution and ice particle densities are not well known, and there are systematic changes in radar information gain and loss with changes in hydrometeor mass. The results highlight the need for a thorough exploration of forward model sensitivities prior to performing radar data assimilation.

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Mark S. Kulie, Lisa Milani, Norman B. Wood, Samantha A. Tushaus, Ralf Bennartz, and Tristan S. L’Ecuyer

Abstract

The first observationally based near-global shallow cumuliform snowfall census is undertaken using multiyear CloudSat Cloud Profiling Radar observations. CloudSat snowfall observations and snowfall rate estimates from the CloudSat 2C-Snow Water Content and Snowfall Rate (2C-SNOW-PROFILE) product are partitioned between shallow cumuliform and nimbostratus cloud structures by utilizing coincident cloud category classifications from the CloudSat 2B-Cloud Scenario Classification (2B-CLDCLASS) product. Shallow cumuliform (nimbostratus) snowfall events comprise about 36% (59%) of snowfall events in the CloudSat snowfall dataset. The remaining 5% of snowfall events are distributed between other categories. Distinct oceanic versus continental trends exist between the two major snowfall categories, as shallow cumuliform snow-producing clouds occur predominantly over the oceans. Regional differences are also noted in the partitioned dataset, with over-ocean regions near Greenland, the far North Atlantic Ocean, the Barents Sea, the western Pacific Ocean, the southern Bering Sea, and the Southern Hemispheric pan-oceanic region containing distinct shallow snowfall occurrence maxima exceeding 60%. Certain Northern Hemispheric continental regions also experience frequent shallow cumuliform snowfall events (e.g., inland Russia), as well as some mountainous regions. CloudSat-generated snowfall rates are also partitioned between the two major snowfall categories to illustrate the importance of shallow snow-producing cloud structures to the average annual snowfall. While shallow cumuliform snowfall produces over 50% of the annual estimated surface snowfall flux regionally, about 18% (82%) of global snowfall is attributed to shallow (nimbostratus) snowfall. This foundational spaceborne snowfall study will be utilized for follow-on evaluative studies with independent model, reanalysis, and ground-based observational datasets to characterize respective dataset biases and to better quantify CloudSat snowfall detection and quantitative snowfall estimate uncertainties.

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