Research On Position And Formation Extrapolation Algorithm For Convective Storm Cells

Convective weather refers to a sudden and extremely destructive weather, which isoften accompanied by thunderstorms, hail, tornadoes, strong local convective rainfalland other weather disasters. Doppler weather radar reflectivity figure shows that thereis an obvious correlation between severe convective storm cell the occurrence of suchweather conditions. In nowadays work, Doppler radar reflectivity image of cloud is avery important data to make forecast, and the main mode of the current convectiveweather forecast is also based on storm cell tracking and extrapolating.To retain the morphological characteristics of storm cell as much as possible anextrapolation algorithm called rotation, decomposition, bilateral dilation andcomposition extrapolation (RDBCE) is presented to obtain an extrapolated Dopplerradar reflectivity image. The algorithm was coded in VS2010MFC and OpenCV,integrated use of computer vision, image recognition technology and meteorologicalscientific knowledge, convective storm cell automatic identification and extrapolationmodel was designed as following:First, get2storm cells of consecutive time and obtain the position offset andangular offset. Based on the hierarchical structure of a storm cell, a color reflectivityimage is first decomposed into multiple binary images. By matching the currentimage with its predecessor, expanding and shrinking areas can be identified. Bilateraldilation algorithm is then implemented to predict changes in image contours.Meanwhile the rotation and orientation prediction of a storm cell is represented by anellipse stretching along its long axis. After the composition, rotation and translation ofsub-images, a final extrapolated Doppler radar reflectivity image which retains thevariation trends and morphological characteristics is generated.For storm cloud which is composed of multiple overlapping cells, we designed amerging extrapolation model. Unlike the traditional TREC algorithm using local areachanges to represent internal morphological changes, the algorithm introduced in thispaper purposes an innovative approach to forecast morphological changes of a stormcell. Test results show that the similarity rate between6minutes-extrapolated imageand the actual image is up to70%，among which storm clouds with multiple cores isimproved by8%on average compared to the TREC algorithm used in operationalsystems. Furthermore, no significant discrepancy has been identified betweenextrapolated image and actual image.