Meteorological Data Simulation Using Kalman Filter Estimation Model

[摘要] For regional weather forecasting and spatial meteorological/environmental studies, the effectiveness of the data collection is reduced if one or more of these concurrent observations are missing or not recorded. By processing the available meteorological data for the Kingdom of Saudi Arabia, it is found that the large amount of information on meteorological variables is either partly missing or not recorded for a time period. It is, therefore, necessary to simulate such missing records using appropriate estimation model.The estimation models cited in the literature are reviewed and Kalman filter estimation model is selected for simulation purpose. Shannon's entropy concept is used to estimate the parameters of the model. Methodology is then applied to simulate missing records on temperature, pressure and humidity. The estimation performance of the model is evaluated by computing mean and variance of both observed and simulated values as well as on the basis of statistical theory of error variance.

[学科分类] 大气科学