The distribution and variation of top-of-atmosphere longwave cloud radiative forcing (LCRFTOA) has drawn a significant amout of attention due to its importance in understanding the energy budget. Advancements in sensor and data processing technology, as well as a new generation of geostationary satellites, such as the FengYun-4A (FY-4A), allow for high spatial resolutions that are crucial for real-time radiation monitoring. Nevertheless, there is a distinct lack of official top-of-atmosphere outgoing longwave radiation products under clear-sky conditions (OLRclear). Consequently, this study addresses the challenge of constructing LCRFTOA data with high spatiotemporal resolution over the full disk region of FY-4A. After simulating the influence of atmospheric parameters on OLRclear based on SBDART radiation transfer model (RTM), we developed a model for estimating OLRclear using infrared channels from the advanced geosynchronous radiation imager (AGRI) onboard the FY-4A satellite. The OLRclear results showed an RMSE of 5.05 W/m2 and MBE of 1.59 W/m2 compared to ERA5. Besides, the calculated LCRFTOA results were validated against instantaneous, daily average, and monthly average ERA5 and CERES LCRFTOA products, supporting the validity of the algorithm proposed in this paper. Finally, the changes in LCRFTOA due to variaty cloud heights (high, medium, and low cloud) were analyzed. This study provides the basis for comprehensive studies on the characteristics of top-of atmosphere radiation.

This study aims to evaluate the combined assimilation of AGRI infrared radiance and ground-based MWR (Microwave Radiometer) data for predicting short-duration heavy rainfall. The WRFDA 4.3 framework is utilized to establish the data assimilation interface. The structural characteristics of this joint assimilation approach are analyzed using a typical heavy rainfall event in the Kaifeng region of Central China. The findings indicate the following: (1) The joint assimilation of FY-4A AGRI and ground-based MWR data effectively corrects the initial moisture content in model simulations. Data assimilation significantly improves the simulation of 24-hour accumulated rainfall. Furthermore, the joint assimilation of AGRI radiance and MWR data outperforms assimilating either of them individually; (2) In the heavy rainfall stage, from the low level to the high level, strong upward movement occurs in the center of the rainstorm. The water vapor in the process of this rainstorm mainly comes from the westward air transport in front of the high trough. The water vapor is continuously transported to the heavy rainfall belt through this channel, providing sufficient water vapor for the maintenance of the rainstorm; (3) The combination of the interaction between high and low level vorticity and divergence in the atmosphere and the vertical upward movement, the emergence of low level jet and the generation of unstable energy in the atmospheric environment jointly lead to the occurrence of the rainstorm process; and (4) The terrain-sensitive experiment results show that, the existence of the topography of Taihang Mountain changes the precipitation in Kaifeng area, and reducing the terrain height will lead to a 50%-60% decrease in the overall precipitation of the precipitation center, and the precipitation range will also be greatly reduced. An increase of more than 50% in terrain height will lead to an increase in precipitation center, precipitation range and precipitation intensity, and an increase of 10%-20% in overall precipitation; An increase of more than 75% in the terrain height will cause the rain belt to shift eastward by about 0.5°E, and the precipitation center will shift significantly eastward. When the terrain height increased by more than 100%, the precipitation did not continue to increase, which was basically the same as the control test. This paper provides a valuable basis for further improving the application of FY-4A AGRI radiance and ground-based microwave radiometer in numerical weather models.

Soil moisture (SM) products derived from passive satellite missions are playing an increasingly important role in agricultural applications, especially in crop monitoring and disaster warning. Evaluating the dependability of those products before they can be used on a large scale is crucial. In this study, we assessed the level 2 (L2) SM product from the Chinese Fengyun-3C (FY-3C) radiometer against in situ measurements collected from the Chinese Automatic Soil Moisture Observation Stations (CASMOS) during a one-year period from January 1 to December 31, 2016 in Henan, which is an agricultural province in China. Four statistical parameters were used to evaluate the products’ reliability: mean difference, root-mean-square error (RMSE), unbiased RMSE (ubRMSE), and the correlation coefficient. These statistical indicators revealed that the FY-3C L2 SM product generally did not agree with the in situ SM data from CASMOS. The time-series analysis further indicated that the correlations and estimated error were highly related to the growing periods of the crops in our study area. FY-3C L2 SM data tended to overestimate soil moisture during May, August, and September, when the crops reach their maximum vegetation density, and tended to underestimate the soil moisture content during the rest of the year. The averaged correlation coefficient between FY-3C SM and the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index was 0.55, which demonstrates that the vegetation water content of the crops considerably influences the SM product. To improve the accuracy of the FY-3C SM product, an improved algorithm that can filter out the influences of the crops should be applied in the future.

Land surface temperature (LST) is a crucial parameter in climate and ecology, exerting significant influence on meteorological conditions, ecosystems, and human life. LST data sources are diverse, with remote sensing being the prevailing means of acquisition. FY-4A/AGRI offers high-quality LST products for East Asia. In this study, we evaluated the performance of the product in Hunan Province and conducted refined analysis of surface heat resources based on the 4km/1h resolution product over a two-year period. The results demonstrate that the FY-4A LST product effectively captures surface temperature (R=0.893), albeit with a relatively high error level (Bias=-6.295℃; RMSE=8.58℃), particularly in capturing high LST values. The performance of this product is superior in the eastern flat terrain area of Hunan Province compared to its performance in the western mountainous region due to environmental conditions causing systematic errors that contribute to instability in detection deviation for this product. Surface heat resources are more abundant in eastern Hunan Province than in mountainous areas located west and southwardly, and the detailed distribution of them at finer scales is mainly influenced by the terrain and climate conditions. There is no obvious seasonal difference in the distribution of heat resources except in winter, and rapid urbanization within Chang-Zhu-Tan urban agglomeration during two years has significantly altered the spatial distribution pattern of surface heat resources across Hunan Province. These findings provide a quantitative baseline for assessing FY-4A satellite's detection capability while serving as a reference for further application of its LST products and establishing foundations for divisional classification and utilization strategies pertaining to surface heat resources within Hunan Province.