The theory of classical variational assimilation assumes that biases are unbiased Gaussian. This article investigates the bias characteristic estimate and the bias correction of Geostationary Interferometric Infrared Sounder (GIIRS) on board the FY-4A satellite. Quality-control procedures for GIIRS long-wave temperature channels brightness temperature include cloud detection based on the Advanced Geosynchronous Radiation Imager (AGRI) and outlier removal. The mean biases for most channels are within ±2 K after quality control except for the contaminated channels. Statistical evaluation of the first-guess departures of GIIRS observations in GRAPES (Global/Regional Assimilation and Prediction Enhanced System) global 4D-Var reveal that biases for the long-wave temperature channels depend on fields of view (FOVs) and latitudinal distribution. Additionally, the diurnal variation of biases is obvious only for the upper tropospheric channels, and the biases for high tropospheric channels are smaller than the biases for low tropospheric channels. Finally, off-line bias correction that was used in this study accounts for the field-of-regard (FOR) dependence and the diurnal variation bias characteristics of GIIRS. After bias correction, the results show that biases of long-wave temperature sounding channels are reduced to ±0.02 K, and the standard deviations are less than 1 K except for the contaminated channels. The probability density function of the differences between observations and simulations for some common assimilation channels is closer to the unbiased Gaussian distribution.
Tropical cyclone (TC) forecasts depend highly on the accuracy of atmospheric temperature and humidity structures in the initial field of the model. Satellite sounder observations, such as microwave and infrared (IR) sounders, are the main source of temperature and water vapor information over the tropical ocean where there are relatively sparse conventional in situ observations (Li & Liu, 2009;Wang et al., 2014Wang et al., , 2017. Compared with IR imagers, hyperspectral IR sounders, such as the Infrared Atmospheric Sounder Interferometer (IASI), the Atmospheric Infrared Sounder (AIRS) and the Cross-track Infrared Sounder (CrIS) (Menzel et al., 2018), have thousands of channels that can provide accurate and detailed atmospheric temperature and humidity information with high vertical resolution (Wang et al., 2007).The assimilation of hyperspectral IR sounder data has shown positive impacts on numerical weather prediction (NWP) (
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