Assessment of the Pseudo Geostationary Lightning Mapper Products at the Spring Program and Summer Experiment

Since 2010, the de facto Geostationary Lightning Mapper (GLM) demonstration product has been the PseudoGeostationary Lightning Mapper (PGLM) product suite. Originally prepared for the Hazardous Weather Testbed's Spring Program (specifically the Experimental Warning Program) when only four groundbased lightning mapping arrays were available, the effort now spans collaborations with several institutions and eight collaborative networks. For 2013, NASA's Shortterm Prediction Research and Transition (SPoRT) Center and NOAA's National Severe Storms Laboratory have worked to collaborate with each network to obtain data in realtime. This has gone into producing the SPoRT variant of the PGLM that was demonstrated in AWIPS II for the 2013 Spring Program. Alongside the PGLM products, the SPoRT / Meteorological Development Laboratory's total lightning tracking tool also was evaluated to assess not just another visualization of future GLM data but how to best extract more information while in the operational environment. Specifically, this tool addressed the leading request by forecasters during evaluations; provide a time series trend of total lightning in realtime. In addition to the Spring Program, SPoRT is providing the PGLM "mosaic" to the Aviation Weather Center (AWC) and Storm Prediction Center. This is the same as what is used at the Hazardous Weather Testbed, but combines all available networks into one display for use at the national centers. This year, the mosaic was evaluated during the AWC's Summer Experiment. An important distinction between this and the Spring Program is that the Summer Experiment focuses on the national center perspective and not at the local forecast office level. Specifically, the Summer Experiment focuses on aviation needs and concerns and brings together operational forecaster, developers, and FAA representatives. This presentation will focus on the evaluation of SPoRT's pseudoGLM products in these separate test beds. The emphasis will be on how future GLM observations can support operations at both the local and national scale and how the PGLM was used in combination with other lightning data sets. Evaluations for the PGLM were quite favorable with forecasters appreciating the high temporal resolution, the ability to look for rapid increases in lightning activity ahead of severe weather, as well as situational awareness for where convection is firing and for flight routing

A Statistical Analysis of Icing Prediction in Complex Terrains

The issue of icing has been around for decades in aviation industry, and while notable improvements have been made in the study of the formation and process of icing, the prediction of icing events is a challenge that has yet to be completely overcome. Low level icing prediction, particularly in complex terrain, has been bumped to the back burner in an attempt to perfect the models created for in-flight icing. However, over the years there have been a number of different, non-model methods used to better refine the variable involved in low-level icing prediction. One of those methods comes through statistical analysis and modeling, particularly through the use of the Classification and Regression Tree (CART) techniques. These techniques examine the statistical significance of each predictor within a data set to determine various decision rules. Those rules in which the overall misclassification error is the smallest are then used to construct a decision tree and can be used to create a forecast for icing events. Using adiabatically adjusted Rapid Update Cycle (RUC) interpolated sounding data these CART techniques are used in this study to examine icing events in the White Mountains of New Hampshire, specifically on the summit of Mount Washington. The Mount Washington Observatory (MWO), which sits on the summit and is manned year around by weather observers, is no stranger to icing occurrences. In fact, the summit sees icing events from October all the way until April, and occasionally even into May. In this study, these events are examined in detail for the October 2010 to April 2011 season, and five CART models generated for icing in general, rime icing, and glaze icing in attempt to create a decision tree or trees with a high predictive accuracy. Also examined in this study for the October 2010 to April 2011 icing season is the Air Weather Service Pamphlet(AWSP) algorithm, a decision tree model currently in use by the Air Force to predict icing events. Producing an icing forecast with this model requires the user to manually work through each branch. Previous work to this end was completed by Stanley et al. 2002, and the goal of this study is to further that work by automating the AWSP using the adiabatically adjusted RUC interpolated sounding data as a test set in an attempt to produce an effective automated forecast tool for icing events in complex terrain