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ABSTRACT High-impact weather events, such as severe thunderstorms, tornadoes, and hurricanes, cause significant disruptions to infrastructure, property loss, and even fatalities. High-impact events can also positively impact society, such as [...]

The Weather Service Radar-1988 Doppler (WSR-88D) network within the United States has recently been upgraded to include dual-polarization capability. Among the expectations that have resulted from the upgrade is the ability to discriminate between different precipitation types in winter precipitation events. To know how well any such algorithm performs and whether new algorithms are an improvement, observations of winter precipitation type are needed. Unfortunately, the automated observing systems cannot discriminate between some of the more important types. Thus, human observers are needed. Yet, to deploy dedicated human observers is impractical because the knowledge needed to identify the various precipitation types is common among the public. To most efficiently gather such observations would require the public to be engaged as citizen scientists using a very simple, convenient, nonintrusive method. To achieve this, a simple 'app' called mobile Precipitation Identification Near the Ground (mPING) was developed to run on 'smart' phones or, more generically, web-enabled devices with GPS location capabilities. Using mPING, anyone with a smartphone can pass observations to researchers at no additional cost to their phone service or to the research project. Deployed in mid-December 2012, mPING has proven to be not only very popular, but also capable of providing consistent, accurate observational data., WHY THIS IS SUCH A GREAT IDEA. Late in 2011 a planned upgrade of the Weather Service Radar-1988 Doppler (WSR-88D) radar network began in earnest (www.roc.noaa.gov /WSR88D/PublicDocs/DualPol/DPstatus.pdf). This upgrade adds [...]

A cloud model ensemble forecasting approach is developed to create forecasts that describe the range and distribution of thunderstorm lifetimes that may be expected to occur on a particular day. Such forecasts are crucial for anticipating severe weather, because long-lasting storms tend to produce more significant weather and have a greater impact on public safety than do storms with brief lifetimes. Eighteen days distributed over two warm seasons with 1481 observed thunderstorms are used to assess the ensemble approach. Forecast soundings valid at 1800, 2100, and 0000 UTC provided by the 0300 UTC run of the operational Meso Eta Model from the National Centers for Environmental Prediction are used to provide horizontally homogeneous initial conditions for a cloud model ensemble made up from separate runs of the fully three-dimensional Collaborative Model for Mesoscale Atmospheric Simulation. These soundings are acquired from a 160 km x 160 km square centered over the location of interest; they are shown to represent a likely, albeit biased, range of atmospheric states. A minimum threshold value for maximum vertical velocity of 8 m [s.sup.-1] within the cloud model domain is used to estimate storm lifetime. Forecast storm lifetimes are verified against observed storm lifetimes, as derived from the Storm Cell Identification and Tracking algorithm applied to Weather Surveillance Radar--1988 Doppler (WSR-88D) data from the National Weather Service (reflectivity exceeding 40 dB[Z.sub.e]). Probability density functions (pdfs) are estimated from the storm lifetimes that result from the ensemble. When results from all 18 days are pooled, a vertical velocity threshold of 8 m [s.sup.-1] is found to generate a forecast pdf of storm lifetime that most closely resembles the pdf that describes the collection of observed storm lifetimes. Standard 2 x 2 contingency statistics reveal that, on identifiable occasions, the ensemble model displays skill in comparison with the climatologic mean in locating where convection is most likely to occur. Contingency statistics also show that when storm lifetimes of at least 60 min are used as a proxy for severe weather, the ensemble shows considerable skill at identifying days that are likely to produce severe weather. Because the ensemble model has skill in predicting the range and distribution of storm lifetimes on a daily basis, the forecast pdf of storm lifetime is used directly to create probabilistic forecasts of storm lifetime, given the current age of a storm.