Meteorologists use shapes and movements of clouds in satellite images as indicators of several major types of severe storms. Yet, because satellite image data are in increasingly higher resolution, both spatially and temporally, meteorologists cannot fully leverage the data in their forecasts. Automatic satellite image analysis methods that can find storm-related cloud patterns are thus in demand. We propose a machine learning and pattern recognition-based approach to detect “comma-shaped” clouds in satellite images, which are specific cloud distribution patterns strongly associated with cyclone formulation. In order to detect regions with the targeted movement patterns, we use manually annotated cloud examples represented by both shape and motion-sensitive features to train the computer to analyze satellite images. Sliding windows in different scales ensure the capture of dense clouds, and we implement effective selection rules to shrink the region of interest among these sliding windows. Finally, we evaluate the method on a hold-out annotated comma-shaped cloud data set and cross match the results with recorded storm events in the severe weather database. The validated utility and accuracy of our method suggest a high potential for assisting meteorologists in weather forecasting.
Taking advantages of these two ideas, Dixon and Wiener developed the renowned centroid-based storm nowcasting algorithm, named Thunderstorm Identification, Tracking, Analysis and Nowcasting (TITAN) @cite_3 . This method consists of two steps: identifying the isolated storm cells and forecasting possible centroid locations. Compared with former methods, TITAN can model and track some storm merging and splitting cases. This method, however, can have large errors if the cloud shape changes quickly @cite_37 . Some later works attempted to model the storm identification process mathematically. For instance, @cite_5 and @cite_9 used statistical features of the radar reflection to classify regions into storm or storm-less classes.
@cite_5 @cite_37 @cite_9 @cite_3
2047243358 2069037608 2016184960
Abstract A technique to identify storms and capture scalar features within the geographic and temporal extent of the identified storms is described. The identification technique relies on clustering grid points in an observation field to find self-similar and spatially coherent clusters that meet the traditional understanding of what storms are. From these storms, geometric, spatial, and temporal features can be extracted. These scalar features can then be data mined to answer many types of research questions in an objective, data-driven manner. This is illustrated by using the technique to answer questions of forecaster skill and lightning predictability. Abstract A number of tools are available today – and more are currently being developed – to discriminate, within a particular storm, areas with hail precipitation using data provided by conventional meteorological radar systems. However, at a supraregional level most of the methods that have traditionally been employed to identify hailstorms often obtain ambiguous results. On the other hand, many currently available systems for data extraction and processing make it relatively easy to calculate a large number of variables derived from radar parameters for each storm analyzed and at different stages in its development. The questions are now: is it possible to select and or classify these variables according to their ability to discriminate hailstorms from non-hail storms? And if so, would the combination of several of these variables enable us to develop new and improved discriminating tools? These questions have prompted the use of mathematical methods for discrimination, such as logistic regression and linear discriminant analysis. In both models the stepwise method was used to construct the mathematical equation automatically. Data from a C-band radar system installed in the NW of the Iberian Peninsula were used to set up the two models. The two models share a number of variables (VIL, maximum reflectivity, height of the maximum reflectivity and ddBZ_max dt.), but the discriminant model makes use additionally of the top and the tilt of the storm. The results have been assessed not only in terms of the precision indices (the models have a probability of detection of 84.9 and 86.8 , respectively), but also with respect to their accuracy, sufficiency relation, resolution and discrimination. Finally, the logistic model has been calibrated for S-band radar systems. The results show a very satisfactory probability of detection, demonstrating that these methods may also be effective in this type of system. Abstract A methodology is presented for the real-time automated identification, tracking, and short-term forecasting of thunderstorms based on volume-scan weather radar data. The emphasis is on the concepts upon which the methodology is based. A “storm” is defined as a contiguous region exceeding thresholds for reflectivity and size. Storms defined in this way are identified at discrete time intervals. An optimization scheme is employed to match the storms at one time with those at the following time, with some geometric logic to deal with mergers and splits. The short-term forecast of both position and size is based on a weighted linear fit to the storm track history data. The performance of the detection and forecast were evaluated for the summer 1991 season, and the results are presented.