The Quality Control Of FY-3A Microwave Humidity Sounder Data

Assimilation and application of meteorological satellite remote sensing data to improve the accuracy of numerical weather prediction has been widely recognized. Due to data assimilation of meteorological satellite remote sensing data as much as possible to accurately reflect the true atmospheric state, so the need to control the quality of the data in the data assimilation, excluding the observation noise is relatively large and the radiation transfer model can not accurately simulate data.The Scan-angle dependence of global FY-3A MWHS observed brightness temperature (O) and simulated brightness temperature (B) by the Community Radiative Transfer Model based on 6 h forecast fields of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) from 28 Aug. to 6 Sep.2010 are analyzed in this study. The observations of channel 3 and channel 4 are discontinuous with scan angle. The observations at two sides of nadir along the same MWHS scan line are unsymmetrical. The peak height of the channel weighting functions is more close to surface; the more serious is the unsymmetrical. This is mainly due to the polar track satellite orbit inclination angle of 90 degrees is not strict, such as FY-3A satellite orbit inclination angle is 98.81 degrees, resulting in cross track across the instrument each scan line of observation field caused by different latitude.Scan-angle and latitudinal-dependence of observation increment for MWHS channel 3 to 5 within five-degree latitudinal bands are given quantitatively. This scan-bias correction coefficient can be used directly for MWHS data assimilation.In this paper,a comparison between the simulated brightness temperatures with MWHS measurements clearly shows that MWHS observations from the three sounding channels contain a scan-angle-dependent cohesive noise along the instrument scan-line.This noise does not cancel out when a large amount of data over a sufficiently long period of time is averaged,which eliminates the possibility of such a noise to arise from the natural variability of the atmosphere and surface.Although the causes of noise is not yet clear,a principle component analysis is used for the characterization of this cohesive noise using one-month FY-3A MWHS data.lt is shown that the MWHS cohesive noise is primarily contained in the first principal component (PC) mode,a five-point smoother is then applied to the first PC,which effectively removes such a data noise in the MWHS data.The principal component analysis method to reconstruct original data and provides a very effective data filter for us, not only easy to use, and the result is accurate and stable. The filtering method can be used not only to deal with the whole orbit satellite observation data, can also be applied to the quality control of satellite trajectory took a observation data...