FY-3Satellite Imager Data Inversion And Observation System Simulation Experiments On Data Assimilation

Application of the weather satellite data in the operation system is acknowledged as one of the most important aspects of improving the accuracy of numerical weather forecast. The study of radiance data direct assimilation in the meso-scale numerical forecast field in China is lagging behind other developed countries that it is still in the preliminary stage. So far, the variational method is mostly used, but as the development of four-dimensional assimilation, ensemble kalman filter got more and more attention and been studied a lot. How to assimilate satellite data using ensemble kalman filter method, which is a new way to improve the model initial field and forecast accuracy, is a very significant research subject.This assimilation system is based on EnSRF, by the torrential rain once in the Beijing area simulation to test programs on satellite data assimilation and rainstorm simulation. The main conclusions are:The implementation of the inversion results show that the surface temperature inversion products and MODIS temperature products to achieve a high degree of correlation. Compared to the same day MODIS data inversion temperature, in a variety of atmospheric conditions, surface conditions are the same case, the accuracy is significantly lower, may be low because the the FY3brightness temperature of product quality sake. Soil moisture, tree-covered area inversion result is low, the error, which proved unable to detect X-band high vegetation cover soil moisture. Surface type identification the MODIS and FY3information can be used to identify high-precision surface types, the classification of its products, spatial resolution can reach a precision of less than1km.In an ideal inspection system test the correctness of aspects to Beijing7.21rainstorm, for example, comparative study of30members of the ensemble forecasting results, EnKF assimilation and actual results for the three heavy rain simulation for each physical field, through comparison, the near-surface wind field by stochastic turbulence, specific humidity may be affected by the water phase physics parameterization schemes of accuracy, thus this assimilation system for ten meters wind,2m specific humidity and temperature field assimilative capacity of slightly less than for other elements of the assimilative capacity of the surface pressure assimilation three results are relatively close, but on the ground heat flux assimilation effect is obvious, even better than the ensemble. Overall, the assimilation effect is applied for forecasting has improved effect, indicating assimilation system code basically correct, assimilation is feasible. Conclusion for the Beijing area is just one example of precipitation the results, the conclusion is a limited. So you want to be more rigorous conclusions, the need to use more occur in different places and at different times of a case to verify. The main problems are a small number of ensemble members, the background error covariance is underestimated, too intensive observation points in the horizontal direction, resulting in large false correlation; land surface soil moisture and temperature during physical processes, binding mechanism, which variation of diurnal and late assimilation of the need for further analysis; initial characterization of the initial background error mode scrambling and soil moisture and temperature error characteristics are the same, that is, the representation of the initial background error adequacy require further in-depth analysis.