Time Serise Reconstruction Of Surface Temperature Base On Data Assililation

The satellite-derived environmental parameters play important roles in global change and regional resources and environment researches. Atmosphere effects and sensor limitations often lead to data products of inherently variable quality. The main goals of time series data reconstruction are to remove cloud affected observations and create gapless dataset at a prescribed time with multiple spatial-temporal interpolation and statistical methods. The conventional reconstructing algorithms include mean diurnal variation (MDV), nonlinear regressions (NLR), look up tables (LUT), dynamic linear regression (DLR), artificial neural network (ANN) and so on. The paper aims to develop an algorithm of time series reconstruction of surface temperature based on data assimilation according to the current existing problems of precision instability, results unsatisfactoriness and simplistic effect evaluation methods with the remote sensing surface temperature as basic parameters. Used the daily surface temperature data derived of static meteorological satellite of GMS-5(5km/day), as the inputs data. The data assimilation system took Kalman Filter as the assimilation method. A complete set of global optimal surface temperature time series data sets were obtained by correcting the surface temperature values with the regional ground-based observations constantly. This method was implemented by the computer program and was applied and validated in the Beijing-Tianjin-Hebei region. The results show that accuracy of the reconstructed surface temperature data series was improved significantly in terms of mean and standard deviation. A higher consistency was achieved between the variable regulations in a year of the reconstructed solar surface temperature data and the ground observations, the surface temperature data set which space complete and time continuous has been built. This paper provides a new method of time series data reconstruction of the surface energy parameter. It will be validated and promoted in more regions.