Biases Characteristics Assessment of the Advanced Geosynchronous Radiation Imager (AGRI) Measurement on Board Fengyun–4A Geostationary Satellite

Jia Zhu,Wei Guo,Jiong Shu

doi:10.3390/rs12182871

Abstract

The Chinese Fengyun–4A geostationary meteorological satellite was successfully launched on 11 December 2016, carrying an Advanced Geostationary Radiation Imager (AGRI) to provide the observations of visible, near infrared, and infrared bands with improved spectral, spatial, and temporal resolution. The AGRI infrared observations can be assimilated into numerical weather prediction (NWP) data assimilation systems to improve the atmospheric analysis and weather forecasting capabilities. To achieve data assimilation, the first and crucial step is to characterize and evaluate the biases of the AGRI brightness temperatures in infrared channels 8–14. This study conducts the assessment of clear–sky AGRI full–disk infrared observation biases by coupling the RTTOV model and ERA Interim analysis. The AGRI observations are generally in good agreement with the model simulations. It is found that the biases over the ocean and land are less than 1.4 and 1.6 K, respectively. For bias difference between land and ocean, channels 11–13 are more obvious than water vapor channels 9–10. The fitting coefficient of linear regression tests between AGRI biases and sensor zenith angles manifests no obvious scan angle–dependent biases over ocean. All infrared channels observations are scene temperature–dependent over the ocean and land.

Highlights

In recent years, the new generation of geostationary meteorological satellites has developed quickly, and geostationary meteorological data are playing an increasingly important role in monitoring severe weather, lighting, air pollution development, and surface solar irradiance, and assimilating data by numerical weather prediction (NWP) models [1,2,3,4,5]
The Advanced Geostationary Radiation Imager (AGRI) is an instrument similar to the Advanced Himawari Imager (AHI) on board the Japanese satellite Himawari–8 [12], the Advanced Baseline Imager (ABI) on board the U.S Geostationary Operational Environmental Satellite (GOES)–R [13], and the Flexible Combined Imager (FCI) which will be on board the European Meteosat Third Generation Imaging (MTG–I) satellite [14]
Despite the study of the AGRI infrared data biases assessment in a selected region based on the Weather Research and Forecasting model data assimilation (WRFDA) and the radiative transfer for TIROS Operational Vertical Sounder (RTTOV) [35], there are still no publications concerning the assessment of full disk AGRI infrared data bias characteristics based on the FY–4A observation and cloud mask (CLM) data

Summary

Introduction

The new generation of geostationary meteorological satellites has developed quickly, and geostationary meteorological data are playing an increasingly important role in monitoring severe weather, lighting, air pollution development, and surface solar irradiance, and assimilating data by numerical weather prediction (NWP) models [1,2,3,4,5]. Despite the study of the AGRI infrared data biases assessment in a selected region based on the Weather Research and Forecasting model data assimilation (WRFDA) and the radiative transfer for TIROS Operational Vertical Sounder (RTTOV) [35], there are still no publications concerning the assessment of full disk AGRI infrared data bias characteristics based on the FY–4A observation and cloud mask (CLM) data. The full disk ocean area and selected land area clear sky AGRI data biases are assessed by the difference between observations and simulations which are obtained by directly using RTTOV and ERA Interim analysis, providing preparation for the assimilation application of FY–4A AGRI data to the NWP model.

AGRI Instrument

11 Long-wave IR 12

Results

AGRI Data Biases Distribution over Ocean and Land

Conclusions