Effects of joint assimilation of FY-4A AGRI and ground-based microwave radiometer on heavy rainfall prediction

Abstract

As the latest generation of Chinese Geostationary Weather Satellites, Fengyun-4 carries the Advanced Geosynchronous Radiation Imager (AGRI), which has more spectral bands and higher temporal and spatial resolution than the Visible Infrared Spin-Scan Radiometer (VISSR) onboard geostationary satellite FY-2. Direct assimilation of the FY-4A AGRI datasets has been proved to be an efficient way to improve heavy rainfall simulation. We aim to assess the joint assimilation of AGRI infrared radiance and ground-based MWR (Microwave Radiometer) data on short-duration heavy rainfall prediction. RTTOV (Radiative Transfer for the TIROS Operational Vertical Sounder) is used as the observational operator for FY-4A AGRI data assimilation. The data assimilation interface is built in WRFDA 4.3 to achieve direct assimilation of FY4A AGRI radiance. The forecasting effectiveness of the joint assimilation for a typical heavy rainfall event over northern China is analyzed with four simulation experiments. The main conclusions are: 1) Assimilating MWR data can improve the initial humidity condition in the middle-lower layers, while AGRI radiance assimilation favors the initial humidity correction in the middle-upper layers. The joint assimilation of two datasets can remarkably improve the initial humidity condition in the entire column. 2) Data assimilation effectively improves the 6-h accumulated rainfall simulation. The joint assimilation of AGRI radiance and MWR data is superior to assimilating either of them. The joint assimilation significantly improves the rainfall forecast over the Beijing area, where the seven MWRs are distributed. 3) Data assimilation experiments present similar effects on predicted and initial humidity conditions. The MWR_DA experiment (only assimilate MWR data) markedly improves the humidity forecast in the middle-lower layers, while AGRI_DA (only assimilate AGRI data) is effective in the middle-upper layers. The joint assimilation of AGRI radiance and MWR data could skillfully correct the humidity distribution in the entire layers, allowing for more accurate heavy rainfall prediction. This paper provides a valuable basis for further improving the application of FY-4A AGRI radiance in numerical weather models.

The Impact of Assimilating Cirrus‐Effected Infrared Satellite Radiance From the FY‐4A AGRI on Water Vapor Analysis and Rainstorm Forecasting

“…Shi et al. (2023) reported that the joint assimilation of AGRI radiance data and ground‐based microwave radiometer data can subtly correct the humidity distribution throughout a layer, resulting in more accurate heavy rain forecasts. Therefore, the joint use of microwave instruments onboard satellites that are sensitive to low‐level water vapor, with the AGRI, may yield a more significant contribution in the future.…”

“…As a next step, we can explore the use of the fast RTM models developed for the Fengyun satellites(Yao et al, 2020), cloud-phase products for AGRI instruments(Liu et al, 2023) and bias correction schemes with cloud variables(Feng & Pu, 2022;Okamoto et al, 2019) to further improve the effect of AGRI CER assimilation.On the other hand, the incorporation of radiosonde data below 400 hPa in the cirrus cloud region can also improve the effectiveness of AGRI assimilation. However, such observations are scarce and unavailable over the ocean Shi et al (2023). reported that the joint assimilation of AGRI radiance data and ground-based microwave radiometer data can subtly correct the humidity distribution throughout a layer, resulting in more accurate heavy rain forecasts.…”

Analysis of lightning activity characteristics and its relationship with precipitation in a strong convective weather process in Jiangsu Province

“…In recent years, new types of detection data have been put into wide application and achieved fruitful research results in the proximity warning of short-term heavy precipitation weather. Shi et al (2023) evaluated the results of joint assimilation of AGRI infrared radiance and ground-based MWR (microwave radiometer) data in short-term heavy precipitation prediction, and found that the joint assimilation of AGRI radiance and MWR data improves the simulation of the 6-hourly cumulative precipitation effectively and improves the precipitation prediction significantly in the Beijing area; Van et al (2022) measured the relationship between raindrop distribution and rainfall intensity by using an optical raindrop spectral rangefinder and derived relevant metrics. At present, in terms of short-term early warning, the atmospheric electric field instrument of the new detection instrument such as short data aging and far smaller than radar detection range, can make up for the shortcomings of radar detection data in time and space resolution, and also provide technical support for severe convective weather monitoring and early warning to some extent.…”

Impacts of Fengyun-4A and Ground-Based Observation Data Assimilation on the Forecast of Kaifeng’s Heavy Rainfall (2022) and Mechanism Analysis of the Event

“…FY-4A, equipped with advanced instruments like the Geostationary Interferometric Infrared Sounder (GIIRS) and the Lightning Mapping Imager (LMI), offers unprecedented atmospheric data quality, including improved spectral bands and spatial resolutions compared to its predecessors [27,28]. This has enabled more accurate atmospheric analyses, particularly in the assimilation of AGRI infrared radiance and ground-based microwave radiometer (MWR) data, as demonstrated by studies on the prediction of short-duration heavy rainfall events [29][30][31]. Their findings indicate that the joint assimilation of these data sources significantly enhances moisture distribution accuracy across atmospheric layers, thereby improving the precision of intense precipitation forecasting.…”