Application of Quality Control Method Based on Spatial Gradient Information in Assimilation of Infrared High Spectrum Data AIRS

In this study, using nonhydrostatic mesoscale Weather Research Forecast (WRF) Version 3.4 model, a new quality control (QC) method is introduced that uses infrared high spectrum Atmospheric Infrared Sounder (AIRS) data based on spatial gradient information.To access the effect of the new QC method on numerical typhoon simulation, assimilation and simulation experiments were performed using Typhoon Megi (2010).The results show that if using only the previous QC method provided by the WRF data assimilation system, some data points with gradient anomalies exceeding the threshold value are assimilated into the model.These data points may be contaminated by clouds or misidentified and can be regarded as “bad” points.When such bad data are assimilated into the numerical model, large errors can be brought into the model, which can significantly affect the quality of the initial analysis field.In the new QC method, gradient information checking should be done first to find and eliminate the bad data points.The new method results in a more accurate initial field and improvement in the simulated typhoon tracks.Therefore, the new QC method based on spatial gradient information has a positive effect on improving assimilation results and plays an important role in the numerical simulation.