TITLE

A Perfect Prognosis Scheme for Forecasting Warm-Season Lightning over Florida

AUTHOR(S)
Shafer, Phillip E.; Fuelberg, Henry E.
PUB. DATE
June 2008
SOURCE
Monthly Weather Review;Jun2008, Vol. 136 Issue 6, p1817
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
This study develops and evaluates a statistical scheme for forecasting warm-season lightning over Florida. Four warm seasons of analysis data from the Rapid Update Cycle (RUC) and lightning data from the National Lightning Detection Network are used in a perfect prognosis technique to develop a high-resolution, gridded forecast guidance product for warm-season cloud-to-ground (CG) lightning over Florida. The most important RUC-derived parameters are used to develop equations producing 3-hourly spatial probability forecasts for one or more CG flashes, as well as the probability of exceeding various flash count percentile thresholds. Binary logistic regression is used to develop the equations for one or more flashes, while a negative binomial model is used to predict the amount of lightning, conditional on one or more flashes occurring. The scheme is applied to output from three mesoscale models during an independent test period (the 2006 warm season). The evaluation is performed using output from the National Centers for Environmental Prediction (NCEP) 13-km RUC (RUC13), the NCEP 12-km North American Mesoscale Model, and local high-resolution runs of the Weather Research and Forecasting (WRF) Model for a domain over south Florida. Forecasts from all three mesoscale models generally show positive skill through the 2100–2359 UTC period with respect to a model containing only climatology and persistence (L-CLIPER) and persistence alone. A forecast example using the high-resolution WRF Model is shown for 16–17 August 2006. Although the exact timing and placement of forecast lightning are not perfect, there generally is good agreement between the forecasts and their verification, with most of the observed lightning occurring within the higher forecast probability contours.
ACCESSION #
32874781

 

Related Articles

  • Information-Based Skill Scores for Probabilistic Forecasts. Ahrens, Bodo; Walser, André // Monthly Weather Review;Jan2008, Vol. 136 Issue 1, p352 

    The information content, that is, the predictive capability, of a forecast system is often quantified with skill scores. This paper introduces two ranked mutual information skill (RMIS) scores, RMISO and RMISY, for the evaluation of probabilistic forecasts. These scores are based on the concept...

  • Development of a perfect prognosis probabilistic model for prediction of lightning over south-east India. Rajeevan, M; Madhulatha, A; Rajasekhar, M; Bhate, Jyoti; Kesarkar, Amit; Rao, B // Journal of Earth System Science;Apr2012, Vol. 121 Issue 2, p355 

    A prediction model based on the perfect prognosis method was developed to predict the probability of lightning and probable time of its occurrence over the south-east Indian region. In the perfect prognosis method, statistical relationships are established using past observed data. For real time...

  • An Intraseasonal Prediction Model of Atlantic and East Pacific Tropical Cyclone Genesis. Slade, Stephanie A.; Maloney, Eric D. // Monthly Weather Review;Jun2013, Vol. 141 Issue 6, p1925 

    A real-time statistical model based on the work of Leroy and Wheeler is developed via multiple logistic regression to predict weekly tropical cyclone activity over the Atlantic and east Pacific basins. The predictors used in the model include a climatology of tropical cyclone genesis for each...

  • Improved Seasonal Probability Forecasts. Kharin, Viatcheslav V.; Zwiers, Francis W. // Journal of Climate;Jun2003, Vol. 16 Issue 11, p1684 

    A simple statistical model of seasonal variability is used to explore the properties of probability forecasts and their accuracy measures. Two methods of estimating probabilistic information from an ensemble of deterministic forecasts are discussed. The estimators considered are the...

  • Estimation of Predictability with a Newly Derived Index to Quantify Similarity among Ensemble Members. Yamada, Tomohito J.; Koster, Randal D.; Kanae, Shinjiro; Oki, Taikan // Monthly Weather Review;Jul2007, Vol. 135 Issue 7, p2674 

    This study reveals the mathematical structure of a statistical index, Ω, that quantifies similarity among ensemble members in a weather forecast. Previous approaches for quantifying predictability estimate separately the phase and shape characteristics of a forecast ensemble. The diagnostic...

  • Development and testing of the GRAPES regional ensemble-3DVAR hybrid data assimilation system. Chen, Lianglü; Chen, Jing; Xue, Jishan; Xia, Yu // Journal of Meteorological Research;Dec2015, Vol. 29 Issue 6, p981 

    Based on the GRAPES (Global/Regional Assimilation and Prediction System) regional ensemble prediction system and 3DVAR (three-dimensional variational) data assimilation system, which are implemented operationally at the Numerical Weather Prediction Center of the China Meteorological...

  • Sensitivity of Idealized Supercell Simulations to Horizontal Grid Spacing: Implications for Warn-on-Forecast. Potvin, Corey K.; Flora, Montgomery L. // Monthly Weather Review;Aug2015, Vol. 143 Issue 8, p2998 

    The Warn-on-Forecast (WoF) program aims to deploy real-time, convection-allowing, ensemble data assimilation and prediction systems to improve short-term forecasts of tornadoes, flooding, lightning, damaging wind, and large hail. Until convection-resolving (horizontal grid spacing Δ x < 100...

  • Statistical Prediction of Weekly Tropical Cyclone Activity in the Southern Hemisphere. Leroy, Anne; Wheeler, Matthew C. // Monthly Weather Review;Oct2008, Vol. 136 Issue 10, p3637 

    A statistical prediction scheme, employing logistic regression, is developed to predict the probability of tropical cyclone (TC) formation in zones of the Southern Hemisphere during forthcoming weeks. Through physical reasoning, examination of previous research, and some new analysis, five...

  • Long-range forecasting of intermittent streamflow. van Ogtrop, F. F.; Vervoort, R. W.; Heller, G. Z.; Stasinopoulos, D. M.; Rigby, R. A. // Hydrology & Earth System Sciences Discussions;2011, Vol. 8 Issue 1, p681 

    Long-range forecasting of intermittent streamflow in semi-arid Australia poses a number of major challenges. One of the challenges relates to modelling zero, skewed, non-stationary, and non-linear data. To address this, a probabilistic statistical model to forecast streamflow 12 months ahead is...

Share

Read the Article

Courtesy of THE LIBRARY OF VIRGINIA

Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics