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Gregory Thompson and Trude Eidhammer

Abstract

Aerosols influence cloud and precipitation development in complex ways due to myriad feedbacks at a variety of scales from individual clouds through entire storm systems. This paper describes the implementation, testing, and results of a newly modified bulk microphysical parameterization with explicit cloud droplet nucleation and ice activation by aerosols. Idealized tests and a high-resolution, convection-permitting, continental-scale, 72-h simulation with five sensitivity experiments showed that increased aerosol number concentration results in more numerous cloud droplets of overall smaller size and delays precipitation development. Furthermore, the smaller droplet sizes cause the expected increased cloud albedo effect and more subtle longwave radiation effects. Although increased aerosols generally hindered the warm-rain processes, regions of mixed-phase clouds were impacted in slightly unexpected ways with more precipitation falling north of a synoptic-scale warm front. Aerosol impacts to regions of light precipitation, less than approximately 2.5 mm h−1, were far greater than impacts to regions with higher precipitation rates. Comparisons of model forecasts with five different aerosol states versus surface precipitation measurements revealed that even a large-scale storm system with nearly a thousand observing locations did not indicate which experiment produced a more correct final forecast, indicating a need for far longer-duration simulations due to the magnitude of both model forecast error and observational uncertainty. Last, since aerosols affect cloud and precipitation phase and amount, there are resulting implications to a variety of end-user applications such as surface sensible weather and aircraft icing.

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Gary M. Lackmann and Gregory Thompson

Abstract

Environments that accompany mesoscale snowbands in extratropical cyclones feature strong midlevel frontogenesis and weak symmetric stability, conditions conducive to vigorous ascent. Prior observational and numerical studies document the occurrence of upward vertical velocities in excess of 1 m s−1 near the comma head of winter cyclones. These values roughly correspond to the terminal fall velocity of snow; snow lofting has been measured directly with vertically pointing radars. Here, we investigate the occurrence of lower-tropospheric snow lofting near mesoscale bands and its contribution to snowfall heterogeneity. We test the hypothesis that hydrometeor lofting substantially influences snowfall distributions by analyzing the vertical snow flux in case-study simulations, by computing snow trajectories, and by testing sensitivity of simulated snowbands to parameterized snow terminal fall velocity and advection. These experiments confirm the presence of upward snow flux in the lower troposphere upstream of simulated mesoscale snowbands for two events (27 January 2015 and 2 February 2016). The band of lower-tropospheric lofting played a more important role in the January 2015 case relative to the February 2016 event. Lofting enhances the horizontal advection of snow by increasing hydrometeor residence time aloft, influencing the surface snowfall distribution. Experimental simulations illustrate that while lofting and advection influence the snow distribution, these processes reduce snowfall heterogeneity, contrary to our initial hypothesis. Our findings indicate that considerable horizontal displacement can occur between the locations of strongest ascent and heaviest surface snowfall. Numerical forecasts of snowbands are sensitive to formulations of terminal fall velocity of snow in microphysical parameterizations due to this lofting and transport process.

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Gregory Thompson, Randy Bullock, and Thomas F. Lee

Abstract

Overprediction of the spatial extent of aircraft icing is a major problem in forecaster products based on numerical model output. Dependence on relative humidity fields, which are inherently broad and smooth, is the cause of this difficulty. Using multispectral satellite analysis based on NOAA Advanced Very High Resolution Radiometer data, this paper shows how the spatial extent of icing potential based on model output can be reduced where there are no subfreezing cloud tops and, therefore, where icing is unlikely. Fifty-one cases were analyzed using two scenarios: 1) model output only and 2) model output screened by a satellite cloud analysis. Average area efficiency, a statistical validation measure of icing potential using coincident pilot reports of icing, improved substantially when satellite screening was applied.

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Gregory G. Garner and Anne M. Thompson

Abstract

Air quality forecasts produced by the National Air Quality Forecast Capability (NAQFC), human air quality forecasters, and persistence are evaluated for predictive skill and economic value when used to inform decisions regarding pollutant emission and exposure. Surface ozone forecasts and observations were collected from 40 monitors representing eight forecast regions throughout Washington, D.C.; Virginia; and Maryland over the 2005–09 ozone seasons (April–October). The skill of the forecasts are quantified using discrete statistics, such as correlation, mean bias, and root-mean-square error, and categorical statistics, such as exceedance hit rate, false alarm rate, and critical success index. The value of the forecasts are quantified using a decision model based on costs to protect the public against a poor air quality event and the losses incurred if no protective measures are taken. The results indicate that the most skillful forecast method is not necessarily the most valuable forecast method. Air shed managers need to consider multiple forecast methods when deciding on multiple protective measures, because a single measure of forecast skill can often hide the user’s sensitivity to forecast error for a specific decision.

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Tianqi Zuo, Alison D. Nugent, and Gregory Thompson

Abstract

In recent decades, a significant rainfall decline over the island of Hawai‘i has been noted, with many hypothesizing that the drying is associated with the volcanic aerosols emitted from the Kīlauea volcano. While it is clear that volcanic emissions can create hazardous air quality for Hawaiian communities, the impacts on rainfall are less clear. Here we investigate the impact of volcanic aerosol emissions on Hawai‘i Island rainfall. Based on observed daily rainfall and SO2 emissions, it is found that days with high SO2 emissions have on average 8 mm day−1 less rainfall downstream of the Kīlauea volcano. Sensitivity studies with varying volcanic aerosol emission sources from the Kīlauea vent locations have also been conducted by the Weather Research and Forecasting (WRF) Model in order to examine the detailed physical processes. Consistent with SO2 air quality observations, it is found that the diurnal change in aerosol number concentration is strongly dependent on the diurnal variation of local circulations. The added aerosols are lofted into the orographic convection where they modify the microphysical properties of the warm clouds by increasing the cloud droplet number concentration, decreasing the cloud droplet size, increasing cloud water content, and enhancing cloud evaporation. The volcanic aerosols also delay precipitation production and modify the spatial distribution of rainfall on the downstream mountainside. The modification of precipitation on an island has far-reaching consequences. For this reason, we work to quantify the sensitivity of the orographic precipitation to volcanic aerosols and move beyond hypothesized relationships to work toward understanding the underlying problem.

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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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Kyoko Ikeda, Matthias Steiner, and Gregory Thompson

Abstract

Accurate prediction of mixed-phase precipitation remains challenging for numerical weather prediction models even at high resolution and with a sophisticated explicit microphysics scheme and diagnostic algorithm to designate the surface precipitation type. Since mixed-phase winter weather precipitation can damage infrastructure and produce significant disruptions to air and road travel, incorrect surface precipitation phase forecasts can have major consequences for local and statewide decision-makers as well as the general public. Building upon earlier work, this study examines the High-Resolution Rapid Refresh (HRRR) model’s ability to forecast the surface precipitation phase, with a particular focus on model-predicted vertical temperature profiles associated with mixed-phase precipitation, using upper-air sounding observations as well as the Automated Surface Observing Systems (ASOS) and Meteorological Phenomena Identification Near the Ground (mPING) observations. The analyses concentrate on regions of mixed-phase precipitation from two winter season events. The results show that when both the observational and model data indicated mixed-phase precipitation at the surface, the model represents the observed temperature profile well. Overall, cases where the model predicted rain but the observations indicated mixed-phase precipitation generally show a model surface temperature bias of <2°C and a vertical temperature profile similar to the sounding observations. However, the surface temperature bias was ~4°C in weather systems involving cold-air damming in the eastern United States, resulting in an incorrect surface precipitation phase or the duration (areal coverage) of freezing rain being much shorter (smaller) than the observation. Cases with predicted snow in regions of observed mixed-phase precipitation present subtle difference in the elevated layer with temperatures near 0°C and the near-surface layer.

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Gregory Thompson, Marcia K. Politovich, and Roy M. Rasmussen

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Recent advances in high-performance computing have enabled higher-resolution numerical weather models with increasingly complex data assimilation and more accurate physical parameterizations. With respect to aircraft and ground icing applications, a weather model’s cloud physics scheme is responsible for the direct forecasts of the water phase and amount and is a critical ingredient to forecasting future icing conditions. In this paper, numerical model results are compared with aircraft observations taken during icing research flights, and the general characteristics of liquid water content, median volume diameter, droplet concentration, and temperature within aircraft icing environments are evaluated. The comparison reveals very promising skill by the model in predicting these characteristics consistent with observations. The application of model results to create explicit forecasts of ice accretion rates for an example case of aircraft and ground icing is shown.

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Edward A. Brandes, Kyoko Ikeda, Gregory Thompson, and Michael Schönhuber

Abstract

Terminal velocities of snow aggregates in storms along the Front Range in eastern Colorado are examined with a ground-based two-dimensional video disdrometer. Power-law relationships for particles having equivalent volume diameters of 0.5–20 mm are computed for the temperatures −1°, −5°, and −10°C. Fall speeds increase with temperature. Comparison with relationships found in the literature suggests that temperature-dependent relations may be surrogates for relations based on aggregate composition (e.g., plates, columns, or dendrites) and the degree of riming.

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Gregory Thompson, Roelof T. Bruintjes, Barbara G. Brown, and Frank Hage

Abstract

The purpose of the Federal Aviation Administration’s Icing Forecasting Improvement Program is to conduct research on icing conditions both in flight and on the ground. This paper describes a portion of the in-flight aircraft icing prediction effort through a comprehensive icing prediction and evaluation project conducted by the Research Applications Program at the National Center for Atmospheric Research. During this project, in- flight icing potential was forecast using algorithms developed by RAP, the National Weather Service’s National Aviation Weather Advisory Unit, and the Air Force Global Weather Center in conjunction with numerical model data from the Eta, MAPS, and MM5 models. Furthermore, explicit predictions of cloud liquid water were available from the Eta and MM5 models and were also used to forecast icing potential.

To compare subjectively the different algorithms, predicted icing regions and observed pilot reports were viewed simultaneously on an interactive, real-time display. To measure objectively the skill of icing predictions, a rigorous statistical evaluation was performed in order to compare the different algorithms (details and results are provided in Part II). Both the subjective and objective comparisons are presented here for a particular case study, whereas results from the entire project are found in Part II. By statistically analyzing 2 months worth of data, it appears that further advances in temperature and relative-humidity-based algorithms are unlikely. Explicit cloud liquid water predictions, however, show promising results although still relatively new in operational numerical models.

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