A New Progressive EOFs Quality Control Method for Surface Pressure Data Based on the Barometric Height and Biweight Average Correction

Abstract

When assimilating surface pressure data in synoptic-scale models, we find the utilization rate of surface pressure data in zones with complex terrains is not high. Therefore, it is particularly important and urgent to carry out quality control of surface pressure data. Numerical weather prediction model analysis and forecasting provide essential data that can be compared with surface observations. The main adverse effects on surface pressure quality control include elevation differences between the model terrain and the observation stations and continuous outliers with the same characteristics in the initiation stage of quality control. Therefore, we propose a progressive empirical orthogonal function (EOF) with simulated observation (EOFs) combining barometric height correction (BHC) and biweight average correction (BAC) methods for the quality control of surface pressure data in this study. From the quality control results of the surface pressure data in regions with complex topography in China during June–August 2013, it was found that the BHC method could effectively reduce the deviations caused by elevation differences between the model terrain and the observation stations, and the BAC method could obviously reduce systematic deviations due to physical processes and the parameterization schemes of the models. The BHC-BAC method integrated the advantages of both methods and had the best correction effect. When continuous outliers with the same characteristics occurred in the initiation stage of quality control, the progressive EOF method might unreasonably eliminate observations. However, the progressive EOFs method could effectively solve this problem and had better performance in data quality control. The progressive EOFs quality control method with the combined BHC-BAC method could obviously reject outliers. The observation increment (deviations between observations and background field) after quality control by the progressive EOFs method was the closest to normal distribution, satisfying the Gaussian distribution assumption of data assimilation.