A Two-Stage Quality Control Method for 2-m Temperature Observations Using Biweight Means and a Progressive EOF Analysis

The relatively smooth terrain embedded in the numerical model creates an elevation difference against the actual terrain, which in turn makes the quality control of 2-m temperature difficult when forecast or analysis fields are utilized in the process. In this paper, a two-stage quality control method is proposed to address the quality control of 2-m temperature, using biweight means and a progressive EOF analysis. The study is made to improve the quality control of the observed 2-m temperature collected by China and its neighboring areas, based on the 6-h T639 analysis from December 2009 to February 2010. Results show that the proposed two-stage quality control method can secure the needed quality control better, compared with a regular EOF quality control process. The new method is, in particular, able to remove the data that are dotted with consecutive errors but showing small fluctuations. Meanwhile, compared with the lapse rate of temperature method, the biweight mean method is able to remove the systematic bias generated by the model. It turns out that such methods make the distributions of observation increments (the difference between observation and background) more Gaussian-like, which ensures the data quality after the quality control.