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The passive microwave land surface emissivity (MLSE) plays a crucial role in retrieving various land surface and atmospheric parameters and in Numerical Weather Prediction models. The retrieval accuracy of MLSE depends on many factors, including the consistency of the input data acquisition time. The FengYun-3D (FY-3D) polar-orbiting meteorological satellite, equipped with passive microwave and infrared bands, offers time-consistent data crucial for MLSE retrieval. This study proposes a physics-based MLSE retrieval algorithm using all the input data from the FY-3D satellite. Based on the retrieved MLSE, the spatial distribution of the MLSE is closely correlated with the land cover types and topography. Lower emissivities prevailed over barren or sparsely vegetated regions, river basins, and coastal areas. Higher emissivities dominated densely vegetated regions and mountainous areas. Moderate emissivities dominated grasslands and croplands. Lower-frequency channels showed larger emissivity differences with different polarizations than those of higher-frequency channels in barren or sparsely vegetated regions. The MLSE across densely vegetated land areas, mountainous areas, and deserts showed small seasonal variations. However, woody savannas, grasslands, croplands, and seasonal snow-covered areas showed noticeable seasonal variations. For most land cover types, the differences between vertically and horizontally polarized emissivities remained relatively constant across seasons. However, certain grasslands in eastern Inner Mongolia and southern Mongolia showed clear seasonal variations. It is very difficult to verify the MLSE on a large scale. Consequently, the possible error sources in the retrieved MLSE were analyzed, including the brightness temperature errors (correlation coefficient ranging from 0.92 to 0.99) and the retrieved land surface temperature errors (Root Mean Square Error was 3.34 K and relation coefficient was 0.958). [ABSTRACT FROM AUTHOR]

The thermal infrared data of Fengyun 4A (FY-4A) geostationary meteorological satellite can be used to retrieve hourly land surface temperature (LST). In this paper, seven candidate algorithms are compared and evaluated. The Ulivieri (1985) algorithm is determined to be optimal for the algorithm of FY-4A LST official products. The refined algorithm coefficients for distinguishing dry and moist atmosphere were established for daytime and nighttime, respectively. Then, FY-4A LST official products under clear-sky conditions are produced. The validation results show that: (1) Compared with in-situ measured LST data at the HeBi crop measurement network, the root mean square errors (RMSE) were 2.139 and 2.447 K. Compared with in-situ measured LST data at Naqu alpine meadow site of Tibet plateau, the RMSE was 2.86 K. (2) When compared with the MODIS LST product, the RMSE was 1.64, 2.17, 2.6, and 1.73 K in March, July, October, and December, respectively. By the bias long-time change at a single site, RMSE of the XLHT (city) and GZH (desert) sites were 2.735 and 2.97 K, respectively. Overall, the preferred algorithm exhibits good accuracy and meets the required accuracy of the FY-4A mission. [ABSTRACT FROM AUTHOR]

In this paper, we developed a unified and operational cloud mask algorithm for the new-generation geostationary (GEO) meteorological satellite imagers of the Advanced Geostationary Radiation Imager (AGRI) aboard Fengyun-4A (FY-4A) and the Advanced Himawari Imager (AHI) aboard Himawari-8 (H08). We investigated the all-round performance of the cloud mask algorithm. Spatiotemporally, the algorithm matches the official Collection-6 cloud mask products of a Moderate Resolution Imaging Spectroradiometer (MODIS) from both the Terra and Aqua platforms, which we employed as the benchmark for performing intercomparisons and validations. The robust cloud mask algorithm can show high consistency between FY-4A/AGRI and H08/AHI. The MODIS-based validation results suggest that cloudy scene identification is better than that observed for clear skies for both FY-4A/AGRI and H08/AHI; there is also a relatively low false-alarm ratio (FAR). Moreover, the algorithm is more reliable during daytime hours, with a hit rate (HR) of approximately 92% for both FY-4A/AGRI and H08/AHI. We found slightly higher accuracy in cloud-masking results over water than those over land. Furthermore, we found that more than 67% of the matched pixels for both advanced GEO imagers had no bias when taking MODIS as the benchmark. Overall, HR values were approximately 91.04% and 91.82% for FY-4A/AGRI and H08/AHI, respectively. These results confirm the high quality of the algorithm for retrieving real-time cloud mask products. [ABSTRACT FROM AUTHOR]

With the development and the improvement of meteorological satellites, different instruments have significantly enhanced the ability to observe clouds over large spatial regions. Recent geostationary satellite radiometers, e.g., Advanced Himawari Imager (AHI) and Advanced Geosynchronous Radiation Imager (AGRI) onboard the Himawari-8 and the Fengyun-4A satellite, respectively, provide observations over similar regions at higher spatial and temporal resolutions for cloud and atmosphere studies. To better understand the reliability of AHI and AGRI retrieval products, we compare their cloud products with collocated Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products, especially in terms of the cloud optical thickness (COT) and cloud effective radius (CER). Our comparison indicates that cloud mask and cloud phase of these instruments are reasonably consistent, while clear differences are noticed for COT and CER results. The average relative differences (RDs) between AHI and AGRI ice COT and that of MODIS are both over 40%, and the RDs of ice CER are less than 20%. The consistency between AHI and MODIS water cloud results is much better, with the RDs of COT and CER being 29% and 9%, respectively, whereas the RDs of AGRI COT and CER are still larger than 30%. Many factors such as observation geometry, cloud horizontal homogeneity, and retrieval system (e.g., retrieval algorithm, forward model, and assumptions) may contribute to these differences. The RDs of COTs from different instruments for homogeneous clouds are about one-third smaller than the corresponding RDs for inhomogeneous clouds. By applying unified retrieval systems based on the forward radiative transfer models designed for each particular band, we find that 30% to 70% of the differences among the results from different instruments are caused by the retrieval system (e.g., different treatments or assumptions for the retrievals), and the rest may be due to sub-pixel inhomogeneity, parallax errors, and calibration. [ABSTRACT FROM AUTHOR]

Based on the infrared TBB from 2010 to 2014 of the geostationary meteorological satellite FY-2E, the climatic characteristics of summer convection over the Tibetan Plateau and its surrounding areas r are analyzed. The analysis shows that in May, the main convection occurred in the eastern edge of the Tibetan Plateau, and then with the Asian summer monsoon, the strongest convection (severe convection) occurs in the southeast part of the plateau part in June. In late summer, the strong southwest wind brought abundant moisture to the eastern and central area of the plateau through the topographic gap and forms a belt of convection there. In the western part of the plateau, area with convection frequency greater than 6% reaches the southern plateau at about 37th pentad, and gradually moves northward until the end of July. In the middle part of the plateau, convection (severe convection) becomes active since the early (mid) June, and maintain the whole late summer with three northward movements until reaching 34°N. Convections in the eastern part of the Tibetan Plateau is relatively active since the beginning of May and the northward stretching time is slightly later than that over the central part of the plateau. Two high intra-seasonal variability centers are located in the middle branch of the Brahmaputra and the southeastern part of the plateau. Summer convective activities are very uneven in these regions and prone to drought and flood disasters. The first leading mode of the convection frequency is the reverse mode over the Indian monsoon region and the southeastern part of the plateau while the second leading mode reflects the variation over the western part of the plateau，the India continent west of 80°E and the South Asian continent east of 80°E. [ABSTRACT FROM AUTHOR]

The Advanced Geostationary Radiation Imager (AGRI) onboard the first of China's next-generation geostationary meteorological satellite series (FY-4A) can provide high temporal resolution layer precipitable water (LPW) products. The AGRI LPWs, including total precipitable water (TPW), low (PW_low), middle (PW_mid), and high (PW_high) levels precipitable water, were first validated using a year radiosonde data. The results show that AGRI LPWs generally agree well with radiosonde derived LPWs, and the accuracy of LPWs demonstrates obvious spatial and temporal patterns. All LPWs at 00 UTC were underestimated and the root means square error (RMSE) of TPW, PW_low, PW_mid, and PW_high at 00 UTC are 6.04, 1.79, 3.32, and 2.68 mm, while the correlation coefficients (R) are 0.953, 0.958, 0.925, and 0.887, respectively. In contrast, AGRI LPWs at 12 UTC have no obvious under- or over-estimation, and shows a lower RMSE (1.49–4.4 mm) and a higher correlation (0.934–0.971). PW_high shows the largest mean absolute percentage error (MAPE) (>39%), while the MAPE of the other three LPWs is less than 29%. The RMSE of all four AGRI LPWs generally increase with water vapor, while R and MAPE decrease with water vapor. Thus, the AGRI LPWs over low latitudes with high water vapor content show larger RMSE while lower MAPE and R than mid and high latitudes. The accuracy of AGRI LPWs displays a seasonal pattern, with higher RMSE, lower R and MAPE in summer wet months, while lower RMSE, higher R and MAPE in winter dry months. • FY-4A AGRI layer precipitable water products were firstly validated using radiosonde data. • The accuracy of AGRI layer precipitable water varies obviously with space and time. • All four AGRI layer precipitable water products at 12 UTC are better than 00 UTC. • The monthly mean absolute percentage error of high level precipitable water is larger than 25% and can reach up to 66%. [ABSTRACT FROM AUTHOR]