FENGYUN-4A Advanced Geosynchronous Radiation Imager Layered Precipitable Water Vapor Products’ Comprehensive Evaluation Based on Quality Control System

Abstract

A physical retrieval algorithm has been developed for deriving the layered precipitable water vapor (LPWs) product from infrared radiances of the Advanced Geosynchronous Radiation Imager (AGRI) onboard FengYun-4A (FY-4A), the first of the new generation of Chinese geostationary weather satellites (FengYun-4, or FY-4 Series). The FY-4A AGRI LPWs are evaluated with different types of reference datasets based on Quality Control System (QCS), including those from Himawari-8 AHI (Advanced Himawari Imager), MODIS (Moderate Resolution Imaging Spectroradiometer), Radiosonde, ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis v5), NCEP (National Centers for Environmental Prediction) reanalysis and CMA (China Meteorological Administration) forecast product from global medium range numerical weather prediction (NWP) system. QCS is one of the important components of FY-4A ground segment, which mainly focuses on the satellite products’ evaluation and validation. It is found that the AGRI LPW product has a good agreement with different evaluating sources and the quality is favorable and stable. With the capability of frequent (5-min interval) observations over the East Asia and Western Pacific regions, the AGRI LPW products can be used to investigate the atmospheric temporal and spatial variations in the pre-landfall environment for typhoons. The QCS is a useful tool to monitor, evaluate, and validate the AGRI LPW products.

Retrieval of Atmospheric Water Vapor Content in the Environment from AHI/H8 Using Both Physical and Random Forest Methods—A Case Study for Typhoon Maria (201808)

“…The algorithm procedure of cloud mask products please refer to [34] and its accuracy has been asserted in Wang [35]. Cloud mask product is used as a prerequisite in the operational macroscopic and microphysical property of cloud parameters and atmospheric parameters retrieval algorithms [36,37].…”

“…The ERA5 dataset has very good quality, it is the first reanalysis to use a 10-member ensemble to assess atmospheric uncertainties through the 4D-Var data assimilation system, and it also uses many observations for data assimilation, thus can provide atmospheric conditions more accurately. The accuracy of the temperature and water vapor profiles has been confirmed by various assessments from remote sensing and radiosonde observations [25,37], and using ERA5 data as a reference to analyze vertical meteorological fields is highly advantageous [31]. The ERA5 data have a much improved spatial and temporal resolutions compared to its predecessor, and the vertical stratification has been increased from 60 to 137 layers.…”