Storm cell

Abstract: Accurate storm identification and tracking are basic and essential parts of radar and severe weather warning operations in today’s operational meteorological community. Improvements over the original WSR-88D storm series algorithm have been made with the Storm Cell Identification and Tracking algorithm (SCIT). This paper discusses the SCIT algorithm, a centroid tracking algorithm with improved methods of identifying storms (both isolated and clustered or line storms). In an analysis of 6561 storm cells, the SCIT algorithm correctly identified 68% of all cells with maximum reflectivities over 40 dB Z and 96% of all cells with maximum reflectivities of 50 dBZ or greater. The WSR-88D storm series algorithm performed at 24% and 41%, respectively, for the same dataset. With better identification performance, the potential exists for better and more accurate tracking information. The SCIT algorithm tracked greater than 90% of all storm cells correctly. The algorithm techniques and results of a detailed performance evaluation are presented. This algorithm was included in the WSR-88D Build 9.0 of the Radar Products Generator software during late 1996 and early 1997. It is hoped that this paper will give new users of the algorithm sufficient background information to use the algorithm with confidence.

Abstract: An enhanced hail detection algorithm (HDA) has been developed for the WSR-88D to replace the original hail algorithm. While the original hail algorithm simply indicated whether or not a detected storm cell was producing hail, the new HDA estimates the probability of hail (any size), probability of severe-size hail (diameter ≥19 mm), and maximum expected hail size for each detected storm cell. A new parameter, called the severe hail index (SHI), was developed as the primary predictor variable for severe-size hail. The SHI is a thermally weighted vertical integration of a storm cell’s reflectivity profile. Initial testing on 10 storm days showed that the new HDA performed considerably better at predicting severe hail than the original hail algorithm. Additional testing of the new HDA on 31 storm days showed substantial regional variations in performance, with best results across the southern plains and weaker performance for regions farther east.

Abstract: [1] The urban canopy of excess heat, water vapor, and roughness can affect the evolution of weather systems, as can land vegetative processes. High-resolution simulations were conducted using the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS®) to investigate the impact of urban and land vegetation processes on the prediction of the mesoscale convective system (MCS) observed on 30 July 2003 in the vicinity of Oklahoma City (OKC), Oklahoma. The control COAMPS model (hereinafter CONTROL) used the Noah land surface model (LSM) initialized with the Eta Data Assimilation System and incorporates an urban canopy parameterization (UCP). Experiments assessed the impact of land vegetative processes by (1) adding a canopy resistance scheme including photosynthesis (GEM) to the Noah LSM and (2) replacing the UCP with a simpler urban surface characterization of roughness, albedo, and moisture availability (NOUCP). The three sets of simulations showed different behaviors for the storm event. The CONTROL simulation propagated two storm cells through the OKC urban region. The NOUCP also resulted in two cells, although the convective intensity was weaker. The GEM simulation produced one storm cell west of the downtown region, whose intensity and timing were closer to the observed. To understand the relative roles of the urban and vegetation interaction processes, a factor separation experiment was performed. The urban model improved the ability to represent the MCS, and the enhanced representation of vegetation further improved the model performance. The enhanced performance may be attributed to better representation of the urban-rural heterogeneities and improved simulation of the moisture fluxes and upstream inflow boundaries.

Abstract: Thunderstorms in the United States cause over 100 deaths and $10 billion (U.S. dollars) in damage per year, much of which is attributable to straight-line (nontornadic) wind. This paper describes a machine-learning system that forecasts the probability of damaging straight-line wind (≥50 kt or 25.7 m s−1) for each storm cell in the continental United States, at distances up to 10 km outside the storm cell and lead times up to 90 min. Predictors are based on radar scans of the storm cell, storm motion, storm shape, and soundings of the near-storm environment. Verification data come from weather stations and quality-controlled storm reports. The system performs very well on independent testing data. The area under the receiver operating characteristic (ROC) curve ranges from 0.88 to 0.95, the critical success index (CSI) ranges from 0.27 to 0.91, and the Brier skill score (BSS) ranges from 0.19 to 0.65 (>0 is better than climatology). For all three scores, the best value occurs for the smallest dist...