Research On The Approach Of Hail Detection And Nowcasting In Guizhou Region Based On Storm Numerical Model

An effective hail detection algorithm suitable for Guizhou region has been established based on the analysis of the radar statistical characters of hail storms and the application of other comprehensive techniques by using 3D merged reflectivity of multiple radars over Guizhou ,hail events observation data of 504 hail prevention spots in Guizhou , and NCEP reanalysis data. A nowcasting technique for hail storm based on storm numerical model and radar data assimilation also has been established, which is used to forecast the moving and evolution of hail storm during 3 hours in Guizhou region. The main contents and conclusions are as follows:1. The climatic statistical characters of hail in Guizhou and the relationship between distribution of hail and some topographical factors, such as elevation, slope grade, slope aspect and terrain incision depth, has been studied by using climatic statistical analysis and some GIS techniques, such as digital terrain analysis, zonal statistics and image classification with historical hail records of 84 meteorological stations over 44 years in Guizhou province and the 1:1000000 resolution DEM data of china. It is shown, natural logarithm of mean annual hail days conforms to normality distribution .The elevation is the major topographical factor which influence the distribution of hail primarily, the annual mean hail days increase with the increase of elevation and it increase remarkably as the elevation increase to about 1000 -1500 meters. Micro topographical factors, such as slope grade and slope aspect are not remarkable factors to the difference of annual mean hail days, but topography rising over large area and windward slope of warm moist air is favorable to hail. Terrain incision depth is not remarkable factors to the difference of annual mean hail days also. Difference of latitude is also one of the factors which influence the difference of annual mean hail days. The model for annual mean hail days from the three remarkable factors and the map of hail hazard evaluation are credible via statistical test and comparison to historical hail reports over countryside spots.2. The evaluation databases for the WSR-88D hail detection algorithm have been built via the methodology defining specific conditions a storm cell must meet to be included in the evaluation by using hail observation data of 504 hail prevention spots in Guizhou and Doppler radar data of Guiyang during 8 of severe hail events from 2005 to 2006,after that, The algorithms were evaluated using the probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) statistics. It has shown that, (Probability of severe hail)POSH=30% got the highest CSI score in Guizhou region, but this threshold did not always get the best performance in these 8 of severe hail events. The difference of WTSM (Warning Threshold Selection Model) in different climatic region is the main reason why the default POSH algorithm got a bad performance. An improved WTSM would more accurately predict the optimum SHI threshold for each day. It would help ensure that the POSH threshold of 50% always corresponds to the largest possible CSI on each day. The re-evaluation of the improved POSH algorithm has shown that it has decreased the hail detection FAR, and get a higher performance of severe hail detection in Guizhou.3. Approach of Local space interpolation has been used to remap raw radar reflectivity fields onto a 3D Cartesian grid with high resolution, and maximum scheme has been used to combine multiple-radar reflectivity fields. Based on 3D merged reflectivity product, some severe hail diagnose factors, such as grid-based VIL, grid-based density of VIL, grid-based SHI (severe hail index) and grid-based POSH, have been calculated .The grid-based severe hail detection algorithm has been improved with the statistics of these severe hail diagnose factors and hail topographical factors. It is shown that the grid-based severe hail diagnose product would more accurately detect severe hail storm in a severe hail weather case which occurred in West-Northern and central Guizhou .4. For the purpose of researching the impact of assimilation of microphysical adjustments using reflectivity of Doppl er radar, ARPS model, ARPS3DVAR and a complex cloud analysis procedure are used to assimilate CINRAD/CD Doppler radar data of a severe storm weather case which occurred in Northern Guizhou province. The assimilation and predictions use a 3km grid nested inside a 15km one. There are three numerical experiments with different settings for retrieving cloud hydrometeors using reflectivity of Doppler radar in our studying. Results show that these two experiments with complex cloud analysis procedure can analyze a suitable distribution of cloud water and hydrometeors, and the distribution of potential temperature and vertical velocity response to the adjustment of hydrometeors perfectly. When compare to the EXPR_NOREF experiment without complex cloud analysis, because the CNTL-SMITH-REFONLY experiment which used the complex cloud analysis has analyzed a suitable field of cloud water and hydrometeors in model's initial time, capture the major features of convective storm, so duration of spin up time has be decreased remarkably and the major structure and features of the storm in initial time and over 1h forecasting time can be simulated suitably, but the experiment without cloud analysis must cost 2 or 3 hours to analyze a field of hydrometeors which has error position. The assimilation of reflectivity of Doppler radar has acted as key role in the simulation of severe convective storm.5. Based on grid-based hail detection algorithm ,storm numerical model and radar data assimilation, A nowcasting technique for hail storm ,which using model radar reflectivity retrieving from hydrometeors as the hail forecasting factors and grid-based hail detection algorithm as the hail forecasting model, has been established, It can be used to forecasting the hail location and size. Comparing with other hail forecasting approaches, this new approach get more meaningful and more comprehensive forecasting factors, a more stable forecasting model and easy way to build the hail forecasting model. The new approach was successfully applied in a severe hail weather case, which forecasted accurately the position of severe hail storms during 3 hours from initial time.