Thunderstorm nowcasting is an important function for the severe weather forecaster. The usual method is for radar to detect thunderstorms, and for downstream systems to generate ’threat areas’ for issuing warnings. However, these threat areas are difficult for other systems to work with as they are not mathematically well defined. In this paper we develop an algorithm, THESPA (THunderstorm Envi...

The probabilistic distribution of the thunderstorm phenomenon during the pre-monsoon season (1 March to 18 June) over Pune, a tropical Indian station, has been examined with the help of Markov chain models using daily thunderstorm data for a period of 11 years (1970–80). The data have also been tested using Akaike’s information criterion. This test has clearly indicated that the first-order Mar...

UNLABELLED ThunderSTORM is an open-source, interactive and modular plug-in for ImageJ designed for automated processing, analysis and visualization of data acquired by single-molecule localization microscopy methods such as photo-activated localization microscopy and stochastic optical reconstruction microscopy. ThunderSTORM offers an extensive collection of processing and post-processing metho...

Design wind loads are partly based on extreme value analyses of historical wind data, and limitations on the quantity and spatial resolution of wind data pose a significant challenge in such analyses. A promising source of recent wind speed and direction data is the automated surface observing system (ASOS), a network of about 1000 standardized US weather stations. To facilitate the use of ASOS...

As a thunderstorm approaches a fixed asset (90% of the time) where people are located, VHF cloud lightning mapping provides 10s of minutes of lead time before the first cloud-to-ground strokes reach this asset. At times when a thunderstorm develops directly over the fixed asset (~10% of the time), the cloud flash detection efficiency exceeding 90% maximizes lead time during thunderstorm growth ...

The thunderstorms are typical mesoscale systems dominated by intense convection. Mesoscale models are essential for the accurate prediction of such high-impact weather events. In the present study, an attempt has been made to compare the simulated results of three thunderstorm events using NMM and ARW model core of WRF system and validated the model results with observations. Both models perfor...

In this paper we consider two different approaches for spatial stochastic modeling of thunderstorms. Thunderstorm cells are represented using germ-grain models from stochastic geometry, which are based on Cox or doubly-stochastic cluster processes. We present methods for the operational fitting of model parameters based on available point probabilities and thunderstorm records of past periods. ...

A simplified stochastic model based on airmass thunderstorm rainfall data from the 58-square-mile Walnut Gulch Experimental Watershed in south eastern Arizona is being developed at the Southwest Watershed Research Center, Tucson, Ariz. Records from the 95 rain gage network on this watershed pro vide valuable information on airmass thunderstorm rainfall in the Southwestern United States. Proba b...

INTRODUCTION Three-dimensional radar reflectivity data and the algorithms used to help meteorologists interpret these data are extremely important in nowcasting. However, a number of inherent problems arise when tracking thunderstorm cells with 3-dimensional reflectivity. These problems include (1) identifying cells in a complex multi-cellular thunderstorm environment, (2) detecting thunderstor...

This paper presents a study developed at SIMEPAR (Paraná state weather service) using lightning data for electrical thunderstorm nowcasting. Thunderstorm electrical data collected at SIMEPAR such as lightning location, time of occurrence, current intensity, polarity, etc, are stored in real-time in a relational database. As a first step of this study, Microsoft Business Intelligence bundled wit...