A Preliminary Study On CINRAD And Satellite Data Assimilation And Applications Oriented To Lightning Storm Forecast

Herein main purpose is preliminary study on CINRAD (Chinese Next Generation Weather Radar) and satellite data assimilation and applications oriented to lightning storm forecast. Based on data of NCEP, CINRAD net, FY2C meteorological satellite, LLS(Lightning Location System) nets, AWS(Automatic Weather Station) nets, remote sensing observations assimiliaton and lightning storm forecast are studied and analyzed preliminarily with diagnosis, numerical simulation and theories, leading to the following main results.1) Radar data indirect assimilation experiments: for rainstorm forecast, dual-Doppler radar radial velocity data through MUSCAT (Multiple-Doppler Synthesis and Continuity Adjustment Technique) method are assimilated into numerical model ARPS. Compared with non-assimilation experiment, initial wind fields are improved effectively. Followed with integral time, indirect assimilation experiments can predict mesoscale structures of wind, temperature and so on. While as a whole, adjusted humidity fields are not very clear, furthermore, the retrieval area limit of dual-Doppler radar is exit, all of these result in simulated rainfall locations are different from observation in situ. While indirect assimilation experiment is better than that of non-assimilation one in rainfall center, the former appears positive role.2) Radar observations direct assimilation experiments: By means of ARPS-3DVar and ADAS (ARPS Data Analysis/Assimilation System), CINRAD observations are assimilated only with NCEP reanalysis data for the forecast of rainstorm. Results show that meteorological element fields are improved observably by assimilation experiments. And more assimilation times lead to better wind field, water vapor field and the like, which are closer to observations in situ, especially for water vapor field, leading to the fact that short-time precipitation simulation is also closer to observations. All of these results are better than that of indirect assimilation experiments.Thereinto radar radial velocity data improve mostly upon initial wind field with obvious mesoscale structures. The corresponding assimilation experiment improves effectively on water vapor field, causing clear water vapor center, just in actual rainstorm center. After 1h integral, reflectivity data's role on wind field is clear, and mesoscale structure can be simulated continually, similarly with radial velocity data assimilation experiment.For radial velocity data, followed with the beginning of integral, for example, after one hour, radial velocity data assimilation experiment appears water vapor centers with great values, but these are not concord with actual condition.It is showed that reflectivity data assimilation experiments are briefly marked on improving forecast fields than that of radial velocity.3) FY2C satellite observations indirect assimilation experiments: CMW (Cloud Motion Wind) data from TCFM (Tracking Cloud with Combined Fourier Phase Analysis and maximum Correlation) method and CMA (China Meteorological Administration) released CMW data are assimilated into numerical model respectively. For the example of typhoon, CMW data from TCFM method assimilation experiment adjusts effectively on initial vertical wind field, resulting intense ascent motions at eye wall of the typhoon MATSA, whatever in lower, middle or upper levels, lasting out for hours. These ascent motions are just at the center of modeled water vapor fields, which leading to severe storm and rainfall at eye wall of typhoon. It is presented that indirect assimilation experiment of CMW data from TCFM method improves effectively not only on vertical velocity field but for precipitation.4) Based on theory analysis, E-I_r relationship is brought forward with two electrification mechanisms. One is inductive mechanism, i.e., cloud particles collide with precipitation particles then flick away, the other is noninductive mechanism because of different temperature, i.e., bigger supercooled cloud particles collide and freeze with hail particles combined with ice scraps. By means of CINRAD and FY2C data assimilation, precipitation intensity I_r can be obtained, then electric field intensity E, discharge time and locations can be achieved with supposed threshold 300KV/m. Though it can not distinguish between IC (Intra-Cloud) lightning and CG (Cloud-to-Ground) lightning, results are still comparable to LLS observations.5) Example analysis shows that the relationship between precipitation data from AMS and CG data from LLS is not very clear. The probability of the two appears at the same time period and same location is very small. Rainfall is closer to -CG than that of +CQ their crests and troughs match, crests and troughs of -CG are earlier than that of rainfall, roughly 10min. +CG maybe relative to severe storm like spout, hail and the like.