Algorithm Research And Application Of Meteorological Spatial Interpolation

Spatial interpolation method provides meteorological data for each gridmeteorological element data in the study area. The discrete meteorological points in spacewas used to convert a continuous surface. So that the distribution of meteorological tomodel study. However, in many meteorological spatial interpolation methods, there is nouniversal optimal interpolation methods fit all meteorological elements and a variety ofgeographical conditions. The current study and solve urgent question is: how to select theappropriate current optimal spatial interpolation methods according to specific conditionssuch as the number of regional meteorological station, the regional spatial distribution andspecific meteorological factors. To improve the accuracy and efficiency of theinterpolation.Based on spatial interpolation methods proposed adaptive discrete points interpolationand temporal interpolation method based on time series. First，In order to enhance thedegree of the precision of weather forecast, the research carry on the spatial interpolationalgorithm. Joined the time series in the Spatio-Temporal interpolation, It was moreaccordance with the spatial and temporal characteristics of meteorological elements, andsolved the lack of real-time data caused by a variety of force majeure from the source (suchas instrument meteorological site failure, transmission line failure, etc.). Based on theobservation data of over900weather stations in Chongqing City of nearly a year, Itconducted a comparative study of interpolation methods, and confirmed the feasibility ofthe algorithm. Second, To obtain meteorological data outside the meteorologicalobservation stations and higher precision interpolation, the research carry on the spatialinterpolation algorithm. Combined with lack of real-time data in meteorological stationsbased on space interpolation algorithm for obtained, This paper presents a newinterpolation method based on the actual demand——Adaptive discrete pointsinterpolation: It can make up for statistical bias of the current spatial interpolationalgorithm. And through the observation data of nearly one year by more than900weatherstations in Chongqing Municipality. It completed comparison validation about DiscretePoints Interpolation using294key substations as focus of attention location. Finally it isconcluded that optimal interpolation scheme of each concern.Finally, the method of adaptive discrete points interpolation and temporalinterpolation method based on time series was applied to Chongqing Electric Power meteorological warning system in real time. The study found that it increased the area ofnon-meteorological observation site meteorological data interpolation accuracy. Theestablishment and operation of the system, which strengthens Chongqing Electric PowerCompany’s ability to guard against severe weather.