The evaluation of FY4A's Geostationary Interferometric Infrared Sounder (GIIRS) long‐wave temperature sounding channels using the GRAPES global 4D‐Var

Abstract

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.

Impact of High Temporal Resolution FY‐4A Geostationary Interferometric Infrared Sounder (GIIRS) Radiance Measurements on Typhoon Forecasts: Maria (2018) Case With GRAPES Global 4D‐Var Assimilation System

“…In addition, only the GIIRS LW channels are assimilated in this study. The bias correction (BC) for GIIRS is based on its detector dependent biases; more details about the GIIRS preprocessing (e.g., hamming apodization) and BC steps can be found in Yin et al (2020). In addition, a background check based on the differences between observations and simulations (|O-B| < 3K) is used in the quality control (QC).…”

Effects of CO2 Changes on Hyperspectral Infrared Radiances and Its Implications on Atmospheric Temperature Profile Retrieval and Data Assimilation in NWP

“…However, the CO 2 concentration is not predicted in the current NWP models. The version of the RTTOV model that includes predictors such as temperature, water vapor and ozone is widely used in the operational DA system of the ECMWF Integrated Forecast System (IFS) model [31] and the Chinese Global/Regional Assimilation PrEdiction (GRAPES) model [32]. In comparison, the radiance contribution from CO 2 gas absorption and emission is calculated with the fixed CO 2 concentration profile in the RTTOV model.…”

Exploration of a Future NOAA Infrared Sounder in Geostationary Earth Orbit

“…The China Meteorological Administration (CMA) launched the Geostationary Interferometric Infrared Sounder (GIIRS) on board the FY-4A on 1 May 2018. The value of these new type of high temporal observations to improve the quality of atmospheric wind profiles and the forecast of track and intensity of tropical cyclones has been recently demonstrated [9][10][11][12][13][14]. EUMETSAT is planning to launch the IRS onboard the MTG-S satellite towards the end of 2023 [15][16][17][18][19].…”