Study On Key Techniques To Data Preprocessing For Geostationary Lightning Mapper

Lighting is one of most splendid and important atmospheric phenomena, which isclosely related with numerous factors in global changes. Rainfall could be predictedthrough statistical relationship between lightning frequency and rainfall rate. Thegeneration, development and variation characteristics of strong supercell storms couldbe forecasted through characteristics of positive cloud-to-ground, so are creation anddissolution of tornado and hailstorm. In addition, the evolution process of strongweather phenomena could be also predicted. During researches on simulation ofglobal warming, cloud coverage including distribution and structure is limited by thelow spatial resolution and relatively simple convective parameters, which could besolved by regional and global lightning data observed.Lighting motoring stations have been built up rather densely around the world.Although these stations could detect and count the frequencies of lighting and evendetect lighting spectra and radio signals in their neighborhoods, it is hard for them toprovide global distribution of the lighting and satellite observation could just make upthis disadvantage. Optical Transient Detector (OTD), the first optical lighting imagemapper on the satellite, was launched by American, and after it Lighting ImagingSystem (LIS ) was set up, which is still at work and the most widely used lighting data.However it has a limited ability for it is mounted on the low-orbit satellite that leadsto limited orbit periods.It is planned in American that a stationary satellite GOSE-R will be launchedabroad GLM in 2014, and a meteorological satellite FY-4 to be launched by ourcountry will also carry the lighting image sensor. The lighting's data productsalgorithm is a science and technology frontier internationally which has been studiedin USA but still a blank field in our country. Therefore, the key techniques of datapreprocessing on geostationary lightning mapper have been studied in this thesis,which would provide technical supports for ground system contribution ofmeteorological satellite FY-4's lighting imaging sensor in the near future.Considering LIS data, physical features of flash and spatial variations in China,flash signals and noise as well as the radiative transfer of lighting impulse are simulated, based on which the influence of cloud on detecting ability of stationarysatellites is considered. The effect of cloud top'height on lighting position isconsidered by a flash position correction model developed by STK. The algorithm oflighting products is developed, which employs pixel filtering, clustering algorithmand particle filtering to filter the false signals and generate the lighting products.LIS lighting data from 1998 to 2010 is reanalyzed, and the Flash density datasetis generated, which is used to do the EOF analysis in order to suggest the spatial andtemporal distribution of lighting in China. Moreover, the rainfall grid data is generatedby the Kriging interpolation, which is used to do the SVD decomposition in order toanalyze the correlation between rainfall and lighting. The results show that there existsignificantly regional differences for lighting distribution around the country. Highdensity zones include middle and western Area of Shandong, Guangdong, Guangxi,Guizhou, southern part of Sichuan as well as middle and northern part of Hainan. Theocean is less dense. The lighting also presents latitudinal characteristic, and the Tropicof Cancer and its northern part is denser. In addition, the flash density is related to thedistance with the ocean, which peaks at transitional zones of land and ocean and thenshow a general downward tendency, the far the distance from the ocean the lower thefrequency will be. What's more, the lighting is closely associated with rainfall. Whenthe Flash frequencies increase (decrease), the rainfall will increase (decrease); on thecontrary when the rainfall increases (decrease), the frequencies willincrease(decrease), and the influence of frequencies on rainfall is greater. The biggerthe time scale of meteorological field anomaly is, the higher correlation between thefrequencies and rainfall will be, but the correlation coefficient is rather steady in thesame Area.Because there is no lighting data from geostationary satellite, proxy data hasbeen simulated during the study on data preprocessing. First, the view field of lightingimage sensor simulated by STK covers the study area. Knowing basic electronicstructure, the charging procession and the method for satellites to detect the lighting, aradiation transmission model within the cloud is established and used to simulatefeatures of lighting signals captured by the sensor on top of cloud. Analyzing falselighting signals from LIS, false lighting signals are simulated by Monte Carlo Method.Based on the work mentioned above, proxy data is finally generated considering thefrequency distribution of regional lighting that has been dected.Using a lightning signal transfer model within the cloud and a cloud top heightcorrection model for lighting position, the influence of cloud on detecting ability of stationary satellites is considered, which mainly includes how cloud particle effectflash impulse of cloud top and how the height of cloud top effect the position accuracy.The volume, optical thickness, and shape of cloud, even where the lightning occurwithin the cloud will exert influence on lighting energy and its lasting time, and that'sjust why continuous images are needed to detect to all the lightings. The longitudinaland latitudinal deviations are positively related with cloud height. The higher thecloud is, the greater the positional deviations of edge pixels will be. The longitudinaldeviations effected by cloud top height show the center longitude as the axis ofsymmetry, and gradually expand to the direction of northwest and northeast; thelatitudinal deviations increase with the latitude degrees. Thus the cloud height must becorrected for lighting position when using the lighting image sensor on stationarysatellites to detect the lighting.The data collected by lighting image sensor consist of so many false signals thatthey should be removed by filtering algorithm. Based on simulated data, pixel-pixelfiltering algorithm is developed considering the characteristics of stationary satellitesdetect the lighting after analyzing the false signals from LIS data. However, it couldnot totally filter them. As to generate data products and filter, the clustering algorithmis designed, which is applied to Event of LIS data. And the clustering results coincidewith Group and Flash data. While clustering, part of noise is clustered into data ofLevel 2, and this problem could be solved by particle filtering. The theory of particlefiltering holds collective Flash of both time and space scale, and get rid of isolatedFlash which leave the actual lighting to largest extent and accept the noise to smallestextent so that the possibility of errors is lowest. The algorithm also keeps the rejectedFlash for participating testing and calculating again. To some degree, the accuracy ofalgorithm is influenced by the simulation data, which could be adjusted appropriatelyaccording to experiment data after indoor lighting test and launching of satellites.