Satellite-derived surface upward longwave radiation (SULR) is essential for monitoring the global surface radiation budget, ecological processes and climate change. However, the widely-used SULR products derived from thermal infrared (TIR) remote sensing exhibit spatial discontinuities because TIR signals cannot penetrate cloud cover. Conventional cloud-sky SULR estimation approaches often utilize post-processed reanalysis data as inputs, which could not meet the real-time requirement of operational system. This study proposes a lightweight cloud-sky SULR real-time estimation method for the Fengyun-4A (FY-4A) geostationary satellite using a Light Gradient Boosting Machine (LightGBM) model. The daytime cloud-sky SULR is estimated by applying the established relationship between auxiliary variables and clear-sky SULR to cloudy conditions, while the nighttime cloud-sky SULR values are estimated by applying the determined relationship between input variables and a publicly accessible, gap-filled SULR product. The model inputs include (1) spatial-temporal location record data, (2) multiple surface characteristic parameters generated from previous-year data, and (3) two categories of operational FY-4A radiation products, with both components being available in real-time. Validation against six Heihe Watershed Allied Telemetry Experimental Research (HiWATER) sites demonstrates that the reconstructed cloud-sky SULR achieves acceptable RMSE (MBE) values of 33.4 W/m2(1.5 W/m2) for daytime and 25.2 W/m2(4.7 W/m2) for nighttime conditions. Therefore, the proposed lightweight method could improve the spatial coverage of current FY-4A SULR product and further promote real-time SULR-related applications.

This study develops an integrated framework for all-sky surface longwave downward radiation (SLDR) estimate for the MERSI-II imager onboard the Fengyun-3D (FY-3D) satellite. The framework comprises a hybrid method for the clear-sky SLDR estimate and a cloud base temperature (CBT)-based single-layer cloud model (SLCM) for the cloudy-sky SLDR estimate. In-situ validation indicates that the hybrid method yields a bias/RMSE of -0.78/21.70 W/m2, whereas the SLCM achieves a bias/RMSE of 5.79/23.61 W/m2. The bias/RMSE of the all-sky SLDR is 3.37/22.93 W/m2. The estimated all-sky instantaneous SLDR was combined with ERA5 temporal information to derive daily SLDR using a bias-corrected sinusoidal integration method, yielding a bias of 0.04 W/m2and an RMSE of 16.77 W/m2. These results demonstrate the robustness of the proposed framework and its substantial potential in generating both instantaneous and daily SLDR products at 1 km spatial resolution.

The outgoing longwave radiation (OLR) at the top of the atmosphere is an important parameter for the Earth's radiation energy balance. Based on FY3B satellite's VIRR instrument observations, a long-term OLR dataset at the top of the atmosphere during 2012-2019 was produced with a spatial resolution of 5 km. Data evaluation revealed that the long-term orbital drift caused a time shift in satellite observations and lead to the attenuation of observed radiation values over time, severely restricting the climatic application of Fengyun satellite long sequence data. In this study, an algorithm for correcting orbital drift errors was designed to address this issue. The corrected dataset was evaluated using the Clouds and the Earth's Radiant Energy System (CERES) as a reference, and the long-term stability of the Fengyun satellite's OLR dataset was analyzed by discussing the annual and seasonal variation rates of OLR. The correlation analysis was conducted to characterize its ability, providing objective evidence and dataset assurance for climate research.

Surface albedo plays a significant role in Earth’s energy budget and global climate change. The spaceborne remote sensing technique is efficient for deriving and monitoring long-term surface albedo over large regions. Numerous satellite surface albedo products have been established and used for climate change research. However, the surface daily mean albedo is not regularly produced from satellite observations, even though it is more critical than instantaneous albedo for calculating daily shortwave radiation budget. Compared to polar-orbiting satellites, the geostationary satellites offer greater potential for mapping daily albedo with more diurnal observations. This study proposes an innovative multisensor combined direct estimation algorithm, which takes advantage of multiple clear-sky observations from new-generation geostationary satellite sensors FengYun-4 Advanced Geostationary Radiation Imager (AGRI) and Himawari-8 Advanced Himawari Imager (AHI) taken during the same day to improve the surface albedo estimation. Compared to albedo estimates derived from a single sensor, the root mean squared error (RMSE) decreases from 0.030 to 0.022 at OzFlux sites and from 0.040 to 0.031 at Heihe sites. Moreover, an information index of top-of-atmosphere (II_TOA) reflectance is proposed to quantify the amount of information that multiangular TOA observations carry in the surface albedo estimation. As a result, combining observations from two sensors increases such information by 20%, as compared to observations from a single sensor. This study demonstrates the combined multisensor direct estimation method has significant potential for improving surface albedo estimation.

Longwave downward radiation (LWDR) is an important parameter that modulates the earth’s radiation and energy balance, and is also a key variable that affects the global warming. Currently, although many reanalysis LWDR products and satellite-based algorithms are available, their coarse spatio-temporal resolutions as well as the difficulties in organizing the corresponding driving parameters seriously limit their applications. As China’s new generation geostationary satellite, Fengyun-4A (FY-4A) provides higher spatial and temporal resolutions (4 km @nadir, 15min at full disk mode) at longwave infrared channels which can routinely monitor the changes of the earth’s radiation in near real-time and therefore provide great potentials in generating various high-accuracy radiation products. Unfortunately, the existing official LWDR products of FY-4A can only provide estimates under clear skies, and its accuracy still has much room for improvement. For above-mentioned points, an improved general all-sky parameterization algorithm is proposed based on readily available input variables, such as land surface temperature (LST), column water vapor (CWV) and cloud-top temperature (CTT). Then the new algorithm is applied to FY-4A aiming to derive believable all-sky LWDR. The validation results show that, the new algorithm does show a noticeable improvement over the original one by reducing the relatively large errors in LWDR under conditions of extremely cold and dry (flux range <150 W/m²), as well as the large bias in the polar and high altitude regions. Moreover, the new method can generate more reliable LWDR than that of FY-4A official product in terms of both spatio-temporal continuity and accuracy, with RMSE less than 22 W/m² and bias less than 0.5 W/m² under all-sky conditions. The easy-to-use and believable performance of the new algorithm provide an opportunity to accurately derive all-sky LWDR from FY-4A and similar satellite missions with high resolutions.

In the above article[1], the following corrections to text citations should be noted. In Sections II “DATA” and IV “RESULTS AND ANALYSIS,” the citation [13] is changed to [24] and all text citations for [24] through [45] link to the latter citation.Table Iprovides the incorrect citation as shown in the published article along with the reference to which it should direct. In addition, the citation [27] in the above article[1]is revised to[4]in this Errata.

Massive MIMO is one of the promising techniques to improve the spectral efficiency and network performance in future 5G networks. Compared to FDD, it is relatively easier to realize downlink massive MIMO for TDD as downlink channel information can be obtained via uplink-downlink channel reciprocity. This paper provides our field test results of massive MIMO system with a base station prototype equipped with 64 transmit antennas. Significant throughput gain is observed by performing 3D-beamforming to current LTE-Advanced handsets by using standard-transparent Multiuser(MU) MIMO techniques. With the massive MIMO base station prototype, MU-MIMO is realized by multiplexing maximum of eight handsets in spatial domain considering both azimuth and elevation directions. In addition to the field trial test results, future potential enhancements for TDD massive MIMO system are discussed. Evaluation results of evaluating some enhancements on uplink reference signal are provided.

This paper researches on the method how to retrieve the water-leaving reflectance from Chinese HJ1 CCD imagery. The instrument characteristics are first analyzed and then the monochromic atmosphere correction equation is described. Using the radiative transfer model, the correction parameters are worked out by varying different conditions such as sun-observation geometry, aerosol models and so on. The results are stored in look-up table (LUT). Moreover, the MODIS data are used to make certain aerosol load and water vapor volume, which are the input parameters of the pixel-by-pixel procedure. The retrievals of water-leaving reflectance are finally compared to the in Situ measurement and MODIS results. It is found that the correction accuracy is good especially in the blue and green bands. This paper demonstrates that this method is a simple and efficient approach and well worth applying to future conventional production.