Advanced Geosynchronous Radiation Imager Research Articles

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

The advancement of Numerical Weather Prediction (NWP) is pivotal for enhancing high-impact weather forecasting and warning systems. However, due to the high spatial and temporal inhomogeneity, the moisture field is difficult to describe by initial conditions in NWP models, which is the essential thermodynamic variable in the simulation of various physical processes. Data Assimilation techniques are central to addressing these challenges, integrating observational data with background fields to refine initial conditions and improve forecasting accuracy. This study evaluates the effectiveness of integrating observations from the Fengyun-4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) and ground-based microwave radiometer (MWR) in forecasts and mechanism analysis of a heavy rainfall event in the Kaifeng region of central China. Our findings reveal that jointly assimilating AGRI radiance and MWR data significantly enhances the model’s humidity profile accuracy across all atmospheric layers, resulting in improved heavy rainfall predictions. Analysis of the moisture sources indicates that the storm’s water vapor predominantly originates from westward air movement ahead of a high-altitude trough, with sustained channeling towards the rainfall zone, ensuring a continuous supply of moisture. The storm’s development is further facilitated by a series of atmospheric processes, including the interplay of high and low-level vorticity and divergence, vertical updrafts, the formation of a low-level jet, and the generation of unstable atmospheric energy. Additionally, this study examines the influence of Tai-hang Mountain’s terrain on precipitation patterns in the Kaifeng area. Our experiments, comparing a control setup (CTL) with varied terrain heights, demonstrate that reducing terrain height by 50–60% significantly decreases precipitation coverage and intensity. In contrast, increasing terrain height enhances precipitation, although this effect plateaus when the elevation increase exceeds 100%, closely mirroring the precipitation changes observed with a 75% terrain height increment.

Statistical Characteristics and Synoptic Patterns of Convection Initiation over the Middle Reaches of the Yangtze River Basin as Observed Using the Fengyun-4A Satellite

Abstract Convection initiations (CIs) observed using the advanced geosynchronous radiation imager on the Chinese Fengyun-4A satellite were identified over the middle reaches of the Yangtze River basin during warm season (May–September) of 2018–21. A hybrid objective tracking algorithm combining the conventional area overlapping with the Kalman filter method was applied. Subsequently, spatial and temporal variations in the identified CIs and their synoptic circulation patterns were analyzed. The frequency of CIs was highest in August and lowest in May. Nearly 81% of CIs occurred during noon–afternoon (1100–1859 LST), with the highest frequency in the southern mountains of the study region, whereas the CIs with relatively low frequency moved to the plains from afternoon to morning (1700–1059 LST). The diurnal variation of CIs throughout the study region exhibited a unimodal structure, with a peak appearing at noon (1200–1259 LST). CIs during noon–afternoon in July and August had faster cloud-top cooling rates. The synoptic circulations without tropical cyclones during noon–afternoon hours were classified into four patterns by hierarchical clustering; two dominant patterns (i.e., SW-Flows and S-Flows) had broader areas of higher most unstable convective available potential energy (MUCAPE), whereas the 0–3-km shear (SHR3) was the weakest in the S-Flows pattern. It was clear that the high-frequency areas of CIs were most likely to occur in stronger MUCAPE and weaker SHR3 environments, and CIs were more controlled by thermally unstable environments. We further illustrated that CIs tend to concentrate in unstable and moisture flux convergence areas affected by mountains.

FY‐4A/AGRI Infrared Brightness Temperature Estimation of Precipitation Based on Multi‐Model Ensemble Learning

AbstractSatellite infrared detectors cannot penetrate clouds, especially precipitating clouds. Improving precipitation estimation accuracy based on infrared brightness temperature has always been important but challenging. In this paper, based on the infrared brightness temperature of the Advanced Geosynchronous Radiation Imager (AGRI) onboard China's Feng‐Yun 4A satellite, we develop and evaluate a new precipitation estimation method. First, using static data, physical characteristics of clouds, cloud image texture features, temporal motion features, and AGRI infrared channel brightness temperature, we construct features for a machine learning model. Then, we develop precipitation estimation methods. Precipitation is estimated in two steps: classification and regression. We employ a random forest classification model to identify whether there is precipitation in a given field of view. If there is precipitation, a multi‐model ensemble regression learning method is used to estimate the areas with this precipitation. The ensemble learning method uses convex optimization to integrate prediction results based on the optimization of hyperparameters of five basic models (i.e., those of random forest, XGBoost, LightGBM, decision tree, and extra tree models). Furthermore, two regression stacking ensemble models—the Least Absolute Shrinkage and Selection Operator (herein referred to as Stacking1‐LASSO) and K‐nearest neighbor (herein referred to as Stacking2‐KNN)—are used to predict the results of the aforementioned basic models. The results of basic models are used as inputs of these two stacking models. Finally, based on the Integrated Multi‐satellitE Retrievals for GPM (IMERG) precipitation product and rain gauge precipitation data, we conduct precipitation estimation experiments and evaluate our methods. The results show that ensemble learning models have greater accuracy in estimating precipitation than the basic models. When using IMERG precipitation as the target precipitation, ensemble learning models can estimate the central area of heavy precipitation during typhoons Ampil and Maria. The ensemble learning estimation effect is better than that of Stacking2‐KNN. Moreover, when rain gauge data is used as the target precipitation, ensemble learning can also estimate the center of heavy precipitation and with good consistency with recorded satellite brightness temperature data.

A Deep-Learning and Transfer-Learning Hybrid Aerosol Retrieval Algorithm for FY4-AGRI: Development and Verification over Asia

The Advanced Geosynchronous Radiation Imager (AGRI) is a mission-critical instrument for the Fengyun series of satellites. AGRI acquires full-disk images every 15 min and views East Asia every 5 min through 14 spectral bands, enabling the detection of highly variable aerosol optical depth (AOD). Quantitative retrieval of AOD has hitherto been challenging, especially over land. In this study, an AOD retrieval algorithm is proposed that combines deep learning and transfer learning. The algorithm uses core concepts from both the Dark Target (DT) and Deep Blue (DB) algorithms to select features for the machine-learning (ML) algorithm, allowing for AOD retrieval at 550 nm over both dark and bright surfaces. The algorithm consists of two steps: ①A baseline deep neural network (DNN) with skip connections is developed using 10 min Advanced Himawari Imager AODs as the target variable, and ②Sunphotometer AODs from 89 ground-based stations are used to fine-tune the DNN parameters. Out-of-station validation shows that the retrieved AOD attains high accuracy, characterized by a coefficient of determination (R2) of 0.70, a mean bias error (MBE) of 0.03, and a percentage of data within the expected error (EE) of 70.7%. A sensitivity study reveals that the top-of-atmosphere reflectance at 650 and 470 nm, as well as the surface reflectance at 650 nm, are the two largest sources of uncertainty impacting the retrieval. In a case study of monitoring an extreme aerosol event, the AGRI AOD is found to be able to capture the detailed temporal evolution of the event. This work demonstrates the superiority of the transfer-learning technique in satellite AOD retrievals and the applicability of the retrieved AGRI AOD in monitoring extreme pollution events.

Thundercloud structures detected and analyzed based on coherent Doppler wind lidar

Abstract. The studies of intracloud discharges may shed light on the microphysical structure of thunderclouds, as both the magnitude and the sign of charge separation due to graupel collides with ice crystals within the strong updrafts are influenced by the surrounding environment. Here, a compact all-fiber coherent Doppler wind lidar (CDWL) working at the 1.5 µm wavelength is applied for probing the dynamics and microphysics structure of thunderstorms. Thanks to the precise spectrum measurement, multi-component spectra signals of thunderstorms can be analyzed by the CDWL. The spectrum width, skewness, and Doppler velocity of CDWL is used to separate and identify the particle composition and polarity. In the experiment, the thundercloud properties are detected by the CDWL, 10.6 cm Doppler weather radar (DWR), and Advanced Geosynchronous Radiation Imager (AGRI) onboard Fengyun-4 satellites. In particular, the spectrum width and skewness of the thundercloud below the 0 ∘C isotherm are increased, and when a cloud-ground lightning occurs, there is additional graupel with a velocity greater than 5 m s−1. It indicates that this region is a melting layer, and lightning activity changes the motion characteristics of graupel, affecting the charge structure of the whole thundercloud. In general, our findings provide details on the velocity, phase, and composition of particles in the outside updraft region of the thunderstorm. The identification and analysis of graupel is particularly important. It is proved that the precise spectrum of CDWL is a promising indicator for studying the charge structure of thunderstorms.

Reconstruction of Land Surface Temperature Derived from FY-4A AGRI Data Based on Two-Point Machine Learning Method

Land surface temperature (LST) is one of the most important parameters of the interface between the earth surface and the atmosphere, and it plays a significant role in many research fields, such as agriculture, climate, hydrology, and the environment. However, the thermal infrared band of remote sensors is easily affected by clouds and aerosols, leading to many data gaps in LST products, which restricts the subsequent application of these products. In this paper, Beijing, China, is selected as the study area, and the LST data retrieved from Fengyun 4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) are reconstructed based on the two-point machine learning method. Firstly, the two-point machine learning model is built to reconstruct the theoretical clear-sky LST from simulated and actual images, and the accuracy of the reconstruction results is evaluated compared with the random forest algorithm and the inverse distance weighted method. Secondly, the actual LST under the influence of clouds is reconstructed by using the ERA5 reanalysis LST data as the auxiliary data, and the reconstruction accuracy is then evaluated by the field measurement LST data. The experimental results show that (1) the prediction accuracy of the two-point machine learning method is higher than that of the random forest method in both simulated data and actual data experiments; (2) the R2 of reconstructed LST under theoretical clear-sky conditions is 0.6860 and the root mean square error (RMSE) is 2.9 K, while the R2 of the reconstructed accuracy of actual LST under clouds is 0.7275 and the RMSE is 2.6 K, i.e., the RMSE decreases by 10.34%; (3) the two-point machine method combined with the auxiliary ERA5 LST data can well reconstruct LST under cloudy conditions and present a reasonable LST distribution.

A Parallax Shift Effect Correction Based on Cloud Top Height for FY-4A Lightning Mapping Imager (LMI)

The Lightning Mapping Imager (LMI) onboard the Fengyun-4A (FY-4A) satellite is the first independently developed satellite-borne lightning imager in China. It enables continuous lightning detection in China and surrounding areas, regardless of weather conditions. The FY-4A LMI uses a Charge-Coupled Device (CCD) array for lightning detection, and the accuracy of lightning positioning is influenced by cloud top height (CTH). In this study, we proposed an ellipsoid CTH parallax correction (ECPC) model for lightning positioning applicable to FY-4A LMI. The model utilizes CTH data from the Advanced Geosynchronous Radiation Imager (AGRI) on FY-4A to correct the lightning positioning data. According to the model, when the CTH is 12 km, the maximum deviation in lightning positioning caused by CTH in Beijing is approximately 0.1177° in the east–west direction and 0.0530° in the north–south direction, corresponding to a horizontal deviation of 13.1558 km, which exceeds the size of a single ground detection unit of the geostationary satellite lightning imager. Therefore, it is necessary to be corrected. A comparison with data from the Beijing Broadband Lightning Network (BLNET) and radar data shows that the corrected LMI data exhibit spatial distribution that is closer to the simultaneous BLNET lightning positioning data. The coordinate differences between the two datasets are significantly reduced, indicating higher consistency with radar data. The correction algorithm decreases the LMI lightning location deviation caused by CTH, thereby improving the accuracy and reliability of satellite lightning positioning data. The proposed ECPC model can be used for the real-time correction of lightning data when CTH is obtained at the same time, and it can be also used for the post-correction of space-based lightning detection with other cloud top height data.

Comparison of FY-4A/AGRI SST with Himawari-8/AHI and In Situ SST

The Fengyun-4A (FY-4A) satellite is a new-generation geostationary meteorological satellite developed by China. The advanced geosynchronous radiation imager (AGRI), one of the key payloads onboard FY-4A, can monitor sea surface temperature (SST). This paper compares FY-4A/AGRI SST with in situ and Himawari-8/advanced Himawari imager (AHI) SST. The study area spans 30°E–180°E, 60°S–60°N, and the study period is from January 2019 to December 2021. The matching time window of the three data is 30 min, and the space window is 0.1°. The quality control criterion is to select all clear sky and well-distributed matchups within the study period, removing the influence of SST fronts. The results of the difference between FY-4A/AGRI and in situ SST show a bias of −0.12 °C, median of −0.05 °C, standard deviation (STD) of 0.76 °C, robust standard deviation (RSD) of 0.68 °C, and root mean square error (RMSE) of 0.77 °C for daytime and a bias of 0.00 °C, median of 0.05 °C, STD of 0.78 °C, RSD of 0.72 °C, and RMSE of 0.78 °C for nighttime. The results of the difference between FY-4A/AGRI SST and Himawari-8/AHI SST show a bias of 0.04 °C, median of 0.10 °C, STD of 0.78 °C, RSD of 0.70 °C, and RMSE of 0.78 °C for daytime and the bias of 0.30 °C, median of 0.34 °C, STD of 0.81 °C, RSD of 0.76 °C, and RMSE of 0.86 °C for nighttime. The three-way error analysis also indicates a relatively larger error of AGRI SST. Regarding timescale, the bias and STD of FY-4A/AGRI SST show no seasonal correlation, but FY-4A/AGRI SST has a noticeable bias jump in the study period. Regarding spatial scale, FY-4A/AGRI SST shows negative bias at the edge of the AGRI SST coverage in the Pacific region near 160°E longitude and positive bias in high latitudes of the southern hemisphere. The accuracy of FY-4A/AGRI SST depends on the satellite zenith angle and water vapor. Further research on the FY-4A/AGRI SST retrieval algorithm accounting for the variability of water vapor will be conducted.

Impacts of Assimilating FY-4A AGRI Clear-Sky Water Vapor Radiance on Short-Range Weather Prediction during Indian Summer Monsoon

ABSTRACT This study aims to assess the impacts of assimilating the clear-sky radiances from the Advanced Geosynchronous Radiation Imager (AGRI) onboard the Fengyun-4A (FY-4A) satellite on 72-h forecasts. First, we compare the water vapour (WV) brightness temperature (TB) in July 2018 from the National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) analyses and Weather Research and Forecasting (WRF) forecasts with the clear-sky AGRI WV channel observations. The results suggest that the NCEP GDAS analyses are more consistent with the AGRI observations than the WRF forecasts, and the AGRI observations can be utilized to improve the initial conditions of the WRF model by data assimilation, especially in water vapour and surface temperature. After the preliminary verification, two identical cycling assimilation experiments are performed with and without the AGRI WV TB assimilation for a month. The results reveal that the WRF forecasts with AGRI TB assimilation are closer to satellite observations than the first guess in both WV channels. The validation with WV channel data of the Microwave Humidity Sounder (MHS) and SAPHIR sensors indicates that after the AGRI data assimilation, the errors are smaller than in the control experiment (without AGRI assimilation). The assimilation of the TB observed by AGRI WV channels shows a remarkable positive impact on moisture forecasts at middle and upper levels. Comparison of the TB from the WRF forecasts with the MHS observations suggests that the AGRI WV channel assimilation can improve the predictions. Overall, assimilating AGRI WV observations can positively influence the WRF analyses and forecasts.

High-Frequency Observations of Cyanobacterial Blooms in Lake Taihu (China) from FY-4B/AGRI

China’s FY-4B satellite, launched on 3 June 2021, is a new-generation geostationary meteorological satellite. The Advanced Geosynchronous Radiation Imager (AGRI) onboard FY-4B has 15 spectral channels, including 2 visible (470 and 650 nm), 1 near infrared (825 nm), and 3 shortwave infrared (1379, 1610, and 2225 nm) bands, which can be used to observe the Earth system with the highest spatial resolution of 500 m and 15 min temporal resolution. In this study, FY-4B/AGRI observations were applied for the first time to monitor cyanobacterial blooms in Lake Taihu, China. The AGRI reflectance at visible and near-infrared bands was first corrected to surface reflectance using the 6S radiative transfer model. Due to the similar spectral reflectance characteristics to those of land-based vegetation, the normalized difference vegetation index (NDVI) and some other remote sensing vegetation indices are usually used for the retrieval of cyanobacterial blooms. The fractional vegetation cover (FVC) of algae, defined as the fraction of green vegetation in the nadir view, was adopted to depict the status and trend of cyanobacterial blooms. NDVI and FVC, the two remote sensing indices developed for the retrieval of land vegetation, were used for the detection of cyanobacteria blooms in Lake Taihu. Finally, the FVC derived from AGRI measurements was compared with that obtained from the Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite to validate the effectiveness of our method. It was found that atmospheric correction can substantially improve the determination of the normalized difference vegetation index (NDVI) values of cyanobacterial blooms in the lake. As a proof of the robustness of the algorithm, the NDVIs are both derived from both AGRI and AHI and their magnitudes are similar. In addition, the distribution of cyanobacterial blooms derived from AGRI FVC is highly consistent with that derived from FY-3D/MERSI and EOS/MODIS. While a lower spatial resolution of FY-4B/AGRI might restrict its capability in capturing some spatial details of cyanobacterial blooms, the high-frequency measurements can provide information for the timely and effective management of aquatic ecosystems and help researchers better quantify and understand the dynamics of cyanobacterial blooms. In particular, AGRI can provide greater details on the diurnal variation in the distribution of cyanobacterial blooms owing to the high temporal resolution.

Nonlinear Bias Correction of the FY-4A AGRI Infrared Radiance Data Based on the Random Forest

Bias correction is a key prerequisite for radiance data assimilation. Directly assimilating the radiance observations generally involves large systematic biases affecting the numerical prediction accuracy. In this study, a nonlinear bias correction scheme with Random Forest (RF) technology is firstly proposed based on the Fengyun-4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) channels 9–10 observations in the Weather Research and Forecasting Data Assimilation (WRFDA) system. Two different settings of the predictors are additionally designed and evaluated based on the performance of the RF model. It seems that an apparent scene temperature-dependent bias could be effectively resolved by the RF scheme when applying the RF method with newly added predictors. Results suggest that the proposed nonlinear scheme of RF performs better than the linear scheme does in terms of reducing the systematic biases. A more idealized error distribution of observation minus background (OMB) is found in the RF-based experiments that measure the nonlinear relationship between the OMB biases and the predictors when using the Gaussian distribution as the reference. Furthermore, the RF scheme shows a consistent improvement in bias correction with the potential to ameliorate the atmospheric variables of analyses.

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

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.

Estimation of downwelling surface longwave radiation for all-weather skies from FengYun-4A geostationary satellite data

ABSTRACT Fengyun-4A (FY-4A) is the latest generation of China’s geostationary satellite. The Advanced Geosynchronous Radiation Imager (AGRI) onboard FY-4A can provide high-precision, high-frequency observation data, which makes a new possibility for estimating the downwelling surface longwave radiation (DSLR) with high spatial and temporal resolution. This work presents a new method for estimating DSLRs under all-sky conditions using a genetic algorithm–artificial neural network (GA-ANN) algorithm based on brightness temperature (BT) from the FY-4A AGRI infrared channels and near-surface air temperature and dew point temperature from ERA5 reanalysis data. Based on the verification results of two independent observation sites, it is shown that the bias and RMSE are - 4.31 W/m2 and 35.28 W/m2, respectively. Compared the CERES SYN all-sky DSLR product with the DSLR estimated by the new method, the bias and RMSE are 0.86 W/m2 and 26.87 W/m2, respectively, and the new method has a higher spatial resolution (4 km), which can display more details of spatial variation.

A cloud optical and microphysical property product for the advanced geosynchronous radiation imager onboard China's Fengyun-4 satellites: The first version

Fengyun-4 (FY-4), the latest collection of Chinese geostationary meteorological satellites, monitors the Eastern Hemisphere with high spatiotemporal resolutions. This study developed a cloud optical and microphysical property product for the Advanced Geosynchronous Radiation Imager (AGRI) onboard the FY-4 satellites. The product focuses on cloud optical thickness (COT) and cloud effective radius (CER) using a bi-spectral retrieval algorithm, and also includes cloud mask and phase using machine learning (ML) algorithms as prerequisites for COT and CER retrievals. The ML-based algorithm develops four independent models using Random Forest methods for cloud mask, liquid water, ice, and mixed-phase/multi-layer clouds, respectively. A two-habit ice and sphere water cloud model are employed to give their optical properties. Look-up tables of cloud reflectance in the COT and CER sensitive channels are built for efficient forward simulations, and the retrieval is performed by an optimal estimation algorithm. Compared with collocated active observations, the cloud mask and phase results give true positive rates of ∼95% and ∼85% and are more sensitive to mixed-phase clouds. Meanwhile, the AGRI-based COT and CER agree closely with those given by the collocated MODIS and AHI cloud products, and the correlation coefficients between MODIS and the AGRI results are 0.76 and 0.63 for COT and CER, respectively. The COT and CER retrievals will be persistently maintained and improved as the operational product for FY-4/AGRI.摘要风云四号作为中国新一代静止气象卫星, 提供了高时空分辨率的监测产品. 本文介绍风云四号搭载的先进地球同步轨道辐射成像仪AGRI的云光学和微物理特性产品. 该产品包含了基于双光谱通道反演的云光学厚度和云粒子有效半径产品, 以及基于机器学习的云识别和云相态产品. 与时空匹配的主动卫星观测结果对比显示, 该产品的云识别和云相态的准确率分别在95%和85%; 该产品提供的云光学厚度和云有效粒径与经典的MODIS产品的相关系数达到0.76和0.63. 团队将持续优化和更新该云光学和微物理特性定量产品, 服务风云四号卫星定量应用.

Inter‐Calibration of Geostationary Imager Infrared Bands Using a Hyperspectral Sounder on the Same Platform

AbstractGeostationary (GEO) imager Infrared (IR) bands are often intercalibrated with hyperspectral IR sounders onboard low earth orbit (LEO) satellites. Due to viewing geometry and time differences between GEO and LEO, the inter‐calibration is limited to certain viewing angles and times. When a GEO hyperspectral IR sounder and GEO imager are on the same platform, the inter‐calibration can be conducted from all angles and at all times. In this study, the Geostationary Interferometric Infrared Sounder (GIIRS) is used to inter‐calibrate the Advanced Geosynchronous Radiation Imager (AGRI), both onboard FengYun‐4A. Radiance inter‐comparisons between GIIRS and AGRI under homogeneous scenes, including both clear and cloudy situations, indicate that the differences are within 1 K. The differences have obvious dependence on scene radiance, less dependence on the cloudy situation, and the local zenith angle when it is less than 70°. However, they exhibit significant diurnal characteristics.

Analysis and Evaluation of the Layered Precipitable Water Vapor Data from the FENGYUN-4A/AGRI over the Southeastern Tibetan Plateau

The Layered Precipitable Water Vapor (LPW) product derived from the Advanced Geosynchronous Radiation Imager (AGRI) onboard the first of the Chinese new generation geostationary satellite Fengyun-4A (FY-4A) has great significance for weather forecasting and climate monitoring of the Tibetan Plateau. To analysis and evaluation the reliability of the FY-4A/AGRI LPW, with respect to the complex terrain on the Southeastern Tibetan Plateau, the atmospheric precipitable water vapor values were calculated based on the radiosonde observations (RAOB TPW) of 11 radiosonde stations in the research area from 2019 to 2020, and a comparative analysis was performed with the FY-4A/AGRI LPW. The results indicated that: (1) FY-4A/AGRI LPW and RAOB TPW demonstrate excellent consistency in all of the vertical height layers, but the atmospheric precipitable water vapor was underestimated by FY-4A/AGRI LPW; (2) The mean values of FY-4A/AGRI LPW in various months were all lower than those of RAOB TPW. The low layer FY-4A/AGRI LPW was the most stable in precision from the dimension of months; and (3) The precision of FY-4A/AGRI LPW, and the deviation between FY-4A/AGRI LPW and RAOB TPW were related with RDLS. The evaluation results of the study demonstrated that FY-4A/AGRI LPW underestimated the total water vapor in the research area, but the Bias and RMSE values were relatively low. FY-4A/AGRI LPW had a relatively high precision, and the data from it had superior quality and stability in terms of time changes and spatial distribution. Therefore, the product can perfectly reflect the spatial and temporal variation of the atmospheric water vapor on the Southeastern Tibetan Plateau.

Assimilation of FY-4A GIIRS radiance observations in the forecast of Typhoon Bavi

We assimilated radiance observations from the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the FengYun-4A geostationary satellite to evaluate their impact on the forecast of Typhoon Bavi using WRFDA. The temperature channels with high information content, representing 90% of the information content of all temperature channels, were selected for assimilation. All radiance observations above the cloud-top were assimilated by comparing the channel height to the cloud-top height coming from the product of the Advanced Geosynchronous Radiation Imager (AGRI). The assimilation of the GIIRS observations decreased the root-mean-square error of the temperature by 2% and improved the precipitation forecast. The rain band in southeast China was reproduced well, thus showing that infrared hyperspectral radiance observations have added value in improving the circulation around typhoons and, therefore, provide better forecasts. The increased relative humidity in the upper layer and stronger typhoon outflow were found to related to the intensify of typhoon in the analysis compared with the control experiment.

Estimating Hourly All-Weather Land Surface Temperature From FY-4A/AGRI Imagery Using the Surface Energy Balance Theory

Thermal infrared (TIR) observations from geostationary satellites enable the retrievals of the diurnal variations in land surface temperatures (LST) linked to the land-atmosphere energy exchange and water cycle. However, cloud cover obstructs TIR signals and leads to missing gaps that generally account for more than 50% of TIR LST maps. This study proposed an effective method to estimate hourly all-weather LST from the Advanced Geosynchronous Radiation Imager (AGRI) data under the framework of surface energy balance (SEB) theory. An improved temperature and emissivity separation algorithm was first used to obtain the high-quality clear-sky LST, which plays a decisive role in ensuring the accuracy of recovered cloudy-sky LST. Then we proposed a unique way to solve the temperature difference (ΔLST) between the cloudy-sky LST and hypothetical clear-sky LST caused by cloud radiative effects (CRE). The bias (RMSE) of the estimated AGRI hourly cloudy-sky LST is 0.10 K (3.71 K) during the daytime, and -0.20 K (2.73 K) during the nighttime, respectively. The overall bias (RMSE) of the estimated AGRI all-weather LST is 0.02 K (2.84 K). The estimated hourly all-weather LST not only captures the rapid variation in diurnal LST but is also promising for temporal upscaling. The temporally upscaled daily mean LST shows a bias (RMSE) of 0.03 K (1.35 K). This study provides a promising solution to generate diurnal hourly all-weather LST for AGRI and other geostationary satellites.

Evaluation and Improvement of FY-4A/AGRI Sea Surface Temperature Data

The advanced geosynchronous radiation imager (AGRI) aboard the Chinese Fengyun-4A (FY-4A) satellite can provide operational hourly sea surface temperature (SST) product. However, the temporal and spatial variation of the errors for this product is still unclear. In this article, FY-4A/AGRI SST is evaluated using the in situ SST from 2019-2021, and a cumulative distribution function matching method is adopted to reduce the errors. Statistical results show that the mean bias and root-mean-square error (RMSE) of FY-4A/AGRI SST are −0.37 °C and 0.98 °C, the median and robust standard deviation (RSD) are −0.30 °C and 0.90 °C. The variations in daily and monthly errors are large and there are no prominent seasonal variations during the period analyzed. There are negative biases exceeding −1.0 °C in low-mid latitude regions and larger positive biases in southern high latitude region. There are dependencies of satellite SST minus in situ SST on satellite zenith angle and on SST itself. After the bias correction, the bias and RMSE are reduced to −0.02 °C and 0.72 °C, and the median and RSD are reduced to 0.00 °C and 0.60 °C. On the time scale, the fluctuation ranges of bias and median are smaller. The difference of satellite SST minus in situ SST can reflect the diurnal variation of SST. The biases are generally within ±0.2 °C in full disk. The error dependencies on satellite zenith angle and SST are also greatly reduced.

Assessment on the Diurnal Cycle of Cloud Covers of Fengyun-4A Geostationary Satellite Based on the Manual Observation Data in China

Complicated and regionally representative diurnal cycle characteristics of clouds may introduce some errors into the cloud mask (CLM) algorithm of Geostationary (GEO) meteorological satellite imaging system, which are very difficult to be assessed by using analogous products of fixed-passing polar-orbiting satellites. In this investigation, the diurnal cycle of the performance of CLM algorithm of the Advanced Geosynchronous Radiation Imager onboard the China Fengyun-4A satellite (FY-4A/AGRI) is validated by using manually observed cloud covers (CC) at twenty-five ground-based stations in China. The results indicate that the CCs calculated by the FY-4A/AGRI cloud mask algorithm are overestimated at 11:00 BJT (Beijing Time) and 14:00 BJT (around noon) and underestimated at 08:00 BJT and 20:00 BJT (in the morning and evening) at most stations. In summer, compared with other seasons, the cloud covers obtained from the FY-4A/AGRI over northern China and the Tibetan Plateau are much better consistent with the manual observations, but the situation is the opposite in southern China. However, the CC results retrieved at vegetation surface by FY-4A/AGRI show the best and stable performance. Due to that the two independent cloud tests induce most of overestimations, some sensitivity experiments for the CLM algorithm are conducted. The results show that the best improvement effect is achieved after only closing one cloud test using the 3.8 μm band. Many extremely overestimated cloud cover samples (about 56.3%) are eliminated. After that, the FY-4A/AGRI cloud mask product is more reasonable compared with the corresponding infrared and visible imageries.