The growth of the wind industry in recent years has motivated investigation into wind farm interference with the operation of the nationwide Weather Surveillance Radar-1988 Doppler (WSR-88D) network. Observations of a wind farm were taken with a Doppler on Wheels (DOW) during the DOW Radar Observations at Purdue Study (DROPS), a largely studentled field program that took place in the fall of 2009. The DOW sampled clear-air weather and precipitation at locations within 5 km of the Benton County, Indiana, wind farm to determine the wind turbines' effect on Doppler velocity and ref lectivity data. These data were analyzed and compared with data from the Indianapolis WSR-88D (KIND) and a local television station (WLFI) radar. In precipitation, the DOW data show velocity couplets that have the appearance of isolated tornadic vortices. Under clear-air sampling, significant multipath scattering is evident, but no velocity couplets would meet the DOW-equivalent tornado detection algorithm criteria. Broader impacts of these findings are discussed, and suggestions are made for additional studies that would explore how to mitigate these impacts.

A practical approach is recommended for identifying and archiving tornado events, based on the use of definitions that label all vortices as either type I, II, or III tornadoes. This methodology will provide a more meaningful tornado climatology in Storm Data, which separates and classifies all vortices associated in any manner with cumuliform clouds. Tornadoes produced within the mesocyclone of discrete supercell storms, with strong local updrafts (SLUs), will be classified as type I tornadoes. Frequently, these type I tornadoes result from the interaction of the SLU with strong rear-flank downdrafts (RFDs), or with shear vortices in the PBL. Tornadoes produced in association with quasi-linear convective systems (QLCS) will be classified as type II tornadoes (including cold pool, rear-inflow jets, bookend, and mesovortex events along the line). All other vortex types (including landspouts, waterspouts, gustnadoes, cold air vortices, and tornadoes not associated with mesocyclones or QLCS) will be labeled as type III tornadoes. A general discussion is provided that further clarifies the differences and categorization of these three classifications (which encompass 15 tornado species), along with a recommendation that NOAA adopt this taxonomy in operational and data archiving practices. Radar analysis and field observations, combined with storm-scale meteorological expertise, should allow for the official ''typing'' of tornado reports by NOAA personnel. Establishment of such a climatological database in Storm Data may be of value in assessing the effects (if any) of twenty-first-century global warming on U.S. tornado trends.

Lake-effect storms (LES) produce substantial snowfall in the vicinity of the downwind shores of the Great Lakes. These storms may take many forms; one type of LES event, lake to lake (L2L), occurs when LES clouds/snowbands develop over an upstream lake (e.g., Lake Huron), extend across an intervening landmass, and continue over a downstream lake (e.g., Lake Ontario). The current study examined LES snowfall in the vicinity of Lake Ontario and the atmospheric conditions during Lake Huron-to-Lake Ontario L2L days as compared with LES days on which an L2L connection was not present [i.e., only Lake Ontario (OLO)] for the cold seasons (October–March) from 2003/04 through 2013/14. Analyses of snowfall demonstrate that, on average, significantly greater LES snowfall totals occur downstream of Lake Ontario on L2L days than on OLO days. The difference in mean snowfall between L2L and OLO days approaches 200% in some areas near the Tug Hill Plateau and central New York State. Analyses of atmospheric conditions found more-favorable LES environments on L2L days relative to OLO days that included greater instability over the upwind lake, more near-surface moisture available, faster wind speeds, and larger surface heat fluxes over the upstream lake. Last, despite significant snowfalls on L2L days, their average contribution to the annual accumulated LES snowfall in the vicinity of Lake Ontario was found to be small (i.e., 25%–30%) because of the relatively infrequent occurrence of L2L days.

Self-organizing maps (SOMs) have been shown to be a useful tool in classifying meteorological data. This paper builds on earlier work employing SOMs to classify model analysis proximity soundings from the near-storm environments of tornadic and nontornadic supercell thunderstorms. A series of multivariate SOMs is produced wherein the input variables, height, dimensions, and number of SOM nodes are varied. SOMs including information regarding the near-storm wind profile are more effective in discriminating between tornadic and nontornadic storms than those limited to thermodynamic information. For the best-performing SOMs, probabilistic forecasts derived from matching near-storm environments to a SOM node may provide modest improvements in forecast skill relative to existing methods for probabilistic forecasts.

In the Great Lakes region, total cold-season snowfall consists of contributions from both lake-effect systems (LES) and non-LES snow events. To enhance understanding of the regional hydroclimatology, this research examined these separate contributions with a focus on the cold seasons (October–March) of 2009/2010, a time period with the number of LES days substantially less than the mean, and 2012/2013, a time period with the number of LES days notably greater than the mean, for the regions surrounding Lakes Erie, Michigan, and Ontario. In general, LES snowfall exhibited a maximum contribution in near-shoreline areas surrounding each lake while non-LES snowfall tended to provide a more widespread distribution throughout the entire study regions with maxima often located in regions of elevated terrain. The percent contribution for LES snowfall to the seasonal snowfall varied spatially near each lake with localized maxima and ranged in magnitudes from 10% to over 70%. Although total LES snowfall amounts tended to be greater during the cold season with the larger number of LES days, the percent of LES snowfall contributing to the total cold-season snowfall was not directly dependent on the number of LES days. The LES snowfall contributions to seasonal totals were found to be generally larger for Lakes Erie and Ontario during the cold season with a greater number of LES days; however, LES contributions were similar or smaller for areas in the vicinity of Lake Michigan during the cold season with a smaller number of LES days.