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• Sun interference is studied on the 10 year-operation of Earth observation satellite. • Impact by Sun on Geostationary Ocean Color Imager (GOCI) data reception is examined. • Sun outage is prevented by GOCI imaging time adjustment in real satellite operation. • Simulation and measurement of sun interference are compared by quantitative analysis. • Prevention from sun outage in GOCI image reception is validated by operation results. A practical approach has been studied to cope with the sun interference of Earth observation geostationary satellite and the results of 10-year satellite operation for preventing sun outage are presented with detailed analysis in this paper. The Communication Ocean Meteorological Satellite (COMS) is equipped with the Geostationary Ocean Color Imager (GOCI), which performs ocean observation mission in the geostationary orbit. After the launch of the COMS, this study was initiated to prevent image data loss due to sun interference in receiving GOCI image data at the ground station of the principle user site under the operation configuration of single satellite and single ground station. This paper fully covers the entire research and the operation process which have been conducted over the 10 years from 2011 to 2021, including unexpected changes of operational environment such as ground station antenna change and satellite longitude position change. The specific characteristics of sun interference on the COMS operation is analysed through the theoretical simulation on the strength and the occurrence time of sun interference affecting the GOCI image data reception. The simulation outcomes are used to specify trigger level, date and time of sun outage in the COMS operation. From the concept that the sun outage could be prevented by adjusting the GOCI imaging time in advance, a GOCI special operation plan was established for the practical way of the sun outage prevention that was optimized for the COMS mission and could be applied to actual satellite operation. The approach of this study is verified by confirming the success of the sun outage prevention after the execution of the GOCI special operation plan. The verification is based on the operation results of the satellite link Radio Frequency (RF) signal performance and the image reception success status which has been obtained during the 10 years of the COMS operation. And, this study shows good consistency between the simulation outcomes and the operational measurements as well as the operational flexibility that it is possible to cope with the sun outage successfully even in the unexpected operational changes during the 10 years. [ABSTRACT FROM AUTHOR]

This study shows that geostationary satellites are critical to estimate the accurate cloud feedback strength over the tropical western Pacific (TWP). Cloud feedback strength was calculated by the simultaneous relation between cloud cover and sea surface temperature (SST) over the TWP [120°E–170°E, 20°S–20°N]. During 2011–2018, the cloud cover was obtained by geostationary earth orbit satellite (GEO) and low‐level earth orbit satellite (LEO) (AGEO, ALEO), and the NOAA's all‐sky SST (To) was weighted with the clear‐sky fraction observed by GEO and LEO (TwGEO; TwLEO). The linear regression coefficients between clouds and SST are very different: −7.93%K−1 (AGEO/TwGEO), −6.94%K−1 (ALEO/TwGEO), −1.35%K−1 (AGEO/TwLEO), −0.69%K−1 (ALEO/TwLEO), −0.02 %K−1 (AGEO/To), and −0.50 %K−1 (ALEO/To). Among these, only the TwGEO values provided a valid cloud feedback signal. This is because GEO's field of view is large enough to simultaneously capture cloud cover over the entire TWP. Plain Language Summary: Geostationary satellites are essential for accurately estimating cloud feedback strength over the tropical western Pacific (TWP). Cloud feedback strength is the change in cloudiness that results from a change in sea surface temperature (SST). When using data from both geostationary and low‐earth orbit satellites, the resulting cloud feedback signals are very different. This is because geostationary satellites have a large enough field of view to capture cloud cover over the entire TWP, while low‐earth orbit satellites do not. Therefore, geostationary satellites are the only reliable source of data for estimating cloud feedback strength over the TWP. This is important because cloud feedback is a major uncertainty in climate models. Key Points: In the tropical western Pacific (TWP), the cloud‐sea surface temperature (SST) relation has been subject to the analysis methods with satellite observationsThe negative relationship is revealed only when the daily SST is weighted with the clear‐sky fraction from a geostationary satelliteThis disparity arises from the capability of geostationary satellites to simultaneously capture a snapshot of the entire TWP area [ABSTRACT FROM AUTHOR]

Airborne ground-level particulate matter of size <2.5 μm (PM 2.5) is a potential health hazard, even with short-term exposure. To quantify the amount of ambient PM 2.5 , there have been many advances in the use of satellite observations in estimating PM 2.5 concentrations over broader areas. Although hindcasts have successfully estimated PM 2.5 concentrations at locations with no ground-based observations, nowcasts are crucial in preventing potential exposure to PM 2.5 in real time. In this study, a near-real-time hourly PM 2.5 prediction framework was designed using a geostationary earth orbit satellite, Geostationary Ocean Color Imager-II (GOCI-II) aerosol products, and random forest (RF) models to yield hourly PM 2.5 predictions over East Asia 10 times daily at 2.5 km resolution. The framework includes an oversampling stage, which increases the number of infrequent PM 2.5 occurrences. The algorithm was tested over a one-year period, during which predictions and observations were strongly correlated (R2 = 0.79) with a relative root mean square error of 41.99%. The results were comparable to or better than those of previous hindcast models. Uncertainties in prediction stemmed mainly from the number of valid predictions and the absolute volume of training samples. Due to the high temporal and spatial resolution, predicted PM 2.5 concentrations successfully filled in regions with no ground-based stations. The products also identified concentration gradients within sub-districts, and their temporal changes during daytime. Prediction with a model with a training dataset from an hour ago still produced robust results. The products may be used in releasing health hazard alerts through real-time monitoring of air quality. • A framework of NRT ground-level PM 2.5 concentration prediction was developed. • NRT PM 2.5 prediction showed good agreement to observations, even in cold months. • High spatiotemporal resolution of GOCI-II yielded predictions within sub-districts. • NRT PM 2.5 prediction is consistent to an untrained independent observation data. • Including the most recent data in training was important in autumn and winter. [ABSTRACT FROM AUTHOR]

This paper considers geolocation of a stationary radio frequency emitter which is being steered by multiple antennas installed on a geostationary satellite using received signal strength metric. The difference in the signal strengths is measured by the antennas and subsequently plotted as lines of position on the surface of the earth. Intersection of these two or more lines of position indicates the location of the terrestrial radio frequency transmitters. This problem is appropriately modelled using a satellite tool kit that simulates the space environment involving satellites, antennas, emitters, etc in a realistic and integrated manner. Accuracy and size of the geolocation area depend on the distance between emitters and the receiver and also on the contour widths geometry. Results of geolocation accuracy are compared by installing the radio frequency emitter at increasing latitudes and at varying contour widths. It is observed that the emitters placed at lower latitudes and having smaller contour widths provided higher accuracy in geolocation that validates the proposed formulation. [ABSTRACT FROM AUTHOR]

This article presents a novel technique for automatically locating tropical cyclone (TC) centers based on top cloud motions in consecutive geostationary satellite images. The high imaging rate and spatial resolution images of the Gaofen-4 geostationary satellite enable us to derive pixel-wise top cloud motion data of TCs, and from the data, TC spiral centers can be accurately determined based on an entirely different principle from those based on static image features. First, a physical motion field decomposition is proposed to eliminate scene shift and TC migration in the motion data without requiring any auxiliary geolocation data. This decomposition does not generate the artifacts that appear in the results of the previously published motion field decomposition. Then, an algorithm of a motion direction-based index embedded in a pyramid searching structure is fully designed to automatically and effectively locate the TC centers. The test shows that the TC concentric motions are more clearly revealed after the proposed motion field decomposition and the located centers are in good agreement with the cloud pattern centers in a visual sense and also with the best track data sets of four meteorological agencies. [ABSTRACT FROM AUTHOR]

• High temporal resolution GHI were estimated from satellite Himawari-8. • The increased frequency of satellite observations improves accuracy of GHI estimates. • Estimates at 10-min interval can capture rapid weather changes within an hour. The emergence of new-generation geostationary satellites (such as Himawari-8 or FenYun-4) provides us an opportunity to obtain more accurate global horizontal irradiance (GHI) data. In this study, instantaneous GHI estimates (with a spatio-temporal resolution of 10 min and 5 km) are produced using the cloud products from the new-generation geostationary satellite Himawari-8 with a physically based algorithm. The hourly, daily, and monthly GHI estimates are aggregated using "snapshots" (or instantaneous estimates) at two different sampling frequencies (i.e., 1 h and 10 min), and validated against surface radiometry measurements collected in China. The root mean square errors (RMSE) for hourly, daily and monthly GHI estimates calculated using 1-h snapshots were 106.6, 27.9, and 17.7 W m−2, respectively, whereas those for hourly, daily and, monthly GHI calculated using 10-min snapshots decreased to 94.0, 24.0, and 16.6 W m−2, respectively. This result demonstrates that the accuracies of hourly, daily, and monthly GHI estimates are improved by increasing the frequency of satellite observations from 1 h (frequency of the previous-generation geostationary satellites) to 10 min (frequency of the new-generation geostationary satellites). [ABSTRACT FROM AUTHOR]

Submesoscale processes are key in understanding physical and biological phenomena near the surface, but there remains a lack of observational evidence over large areas. We used hourly images from a geostationary satellite that can resolve variation in surface ocean color over an area of few hundred kilometers. The temporal variation in the surface chlorophyll a distribution captured by the satellite images was first used to generate a submesoscale‐permitting velocity field, from which we calculated the turbulence statistics such as kinetic energy spectra, velocity structure functions, and energy flux. Application to the April scenes in the East/Japan Sea showed that the kinetic energy spectra had a transition scale at 50 km that suggested two spectral regimes following k−3 and k−5/3, implying the coexistence of quasi‐geostrophic turbulence and surface quasi‐geostrophic turbulence. The chlorophyll a scalar spectrum suggested two spectral regimes of k−5/3 and k−1 with a transition at 3 km. Plain Language Summary: The primary question that motivated this work was how to evaluate near‐surface submesoscale (i.e., between mesoscale and microscale) turbulence in an Eulerian framework over an area of a few hundred kilometers. As this has not been possible with conventional observational platforms, we suggested a novel approach using a geostationary satellite that observed ocean color (Geostationary Ocean Color Imager, specifically in this study). A submesoscale‐permitting velocity field was generated for the East/Japan Sea by processing satellite chlorophyll a observations, which was used to compute various turbulence statistics. Since the Eulerian observation has a sufficient number of data points, we were able to discern different physical properties over a broad area during a short observation period. To our best knowledge, the derivation of submesoscale statistics from the geostationary ocean color images has not been attempted for such a large area. Our findings may provide a timely and systematic approach for validating numerical models and evaluating the near‐surface submesoscale turbulence that plays an important role in smaller‐scale physical and biological processes. Key Points: Satellite chlorophyll a observations are used to calculate Eulerian statistics and evaluate submesoscale turbulence over a large areaKinetic energy spectra and velocity structure functions are computed based on the derived submesoscale‐permitting velocity fieldKinetic energy spectra suggest that quasi‐geostrophic and surface quasi‐geostrophic turbulence dominate in the East/Japan Sea in April [ABSTRACT FROM AUTHOR]

A large number of photovoltaic (PV) power systems have been adopted in Japan after a feed‐in tariff was introduced in 2012. However, PV power generation data from residential rooftop and/or ground‐mounted PV systems, and larger MW‐size PV plants have not been measured accurately in real‐time. This is because PV power monitoring instruments (eg, smart meters) have not collected a sufficient amount of power generation data. In order to realize adequate safety control of electric power systems under high PV‐penetration conditions, it is important to fully understand the temporal and spatial variations associated with PV power generation. In this study, we estimated the PV power generation for a regional area (ie, prefecture or municipality) in terms of PV power installation capacity and satellite‐estimated solar irradiance using a Japanese geostationary satellite, Himawari‐8. The satellite‐derived regionally integrated PV power estimations were validated with reference data provided by electric power companies. The validation results showed that these estimations were comparable to the reference data, provided by the Kyushu Electric Power Company Inc. (Kyushu) and the Tokyo Electric Power Company Inc. (TEPCO). However, the results also identified slight overestimations of PV power in the spring and summer seasons. An advantage of the proposed method is that it does not require land‐based monitoring instruments, which can lead to increased operational cost savings for PV power systems. Furthermore, in consideration of future PV power penetration scenarios, it is suggested that PV power in excess of regional power demands could be generated under the same weather conditions. Regional PV power generation (or prefecture and/or municipality regions) are estimated based on PV system installation capacity and satellite‐estimated solar irradiance by using a geostationary satellite. [ABSTRACT FROM AUTHOR]

This paper presents a new algorithm to retrieve the aerosol optical depth (AOD) from a Himawari-8 Advanced Himawari Imager (AHI). Six typical aerosol models that derived from the long-term ground-based observations of East Asia are used in AOD retrieval. To accurately determine the surface reflectance, improved channel relationships between red, blue, and shortwave infrared (SWIR) are built up according to the infrared Normalized Difference Vegetation Index (NDVISWIR). Based on the new derived aerosol models and improved channel relationships, AOD over East Asian is retrieved by using the AHI data. The results are compared with Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products (MOD04 and MYD04) and yielded a correlation coefficient lager than 0.8 (R = 0.87 and 0.92, respectively). In addition, the retrieved AOD values are also validated by ground-based measurements at 12 Aerosol Robotic Network (AERONET) locations and revealed a good agreement between them (R = 0.86). [ABSTRACT FROM AUTHOR]

In many disciplines, knowledge on the accuracy of Land Surface Temperature (LST) as an input is of great importance. One of the most efficient methods in LST evaluation is cross validation. Well-documented and validated polar satellites with a high spatial resolution can be used as references for validating geostationary LST products. This study attempted to investigate the discrepancies between a Moderate Resolution Imaging Spectro-radiometer (MODIS) and Indian National Satellite (INSAT-3D) LSTs for high temperatures, focusing on six deserts with sand dune land cover in the Middle East from 3 March 2015 to 24 August 2016. Firstly, the variability of LSTs in the deserts of the study area was analyzed by comparing the mean, Standard Deviation (STD), skewness, minimum, and maximum criteria for each observation time. The mean value of the LST observations indicated that the MYD-D observation times are closer to those of diurnal maximum and minimum LSTs. At all times, the LST observations exhibited a negative skewness and the STD indicated higher variability during times of MOD-D. The observed maximum LSTs from MODIS collection 6 showed higher values in comparison with the last versions of LSTs for hot spot regions around the world. After the temporal, spatial, and geometrical matching of LST products, the mean of the MODIS--INSAT LST differences was calculated for the study area. The results demonstrated that discrepancies increased with temperature up to +15.5 K. The slopes of the mean differences were relatively similar for all deserts except for An Nafud, suggesting an effect of View Zenith Angle (VZA). For modeling the discrepancies between two sensors in continuous space, the Diurnal Temperature Cycles (DTC) of both sensors were constructed and compared. The sample DTC models approved the results from discrete LST subtractions and proposed the uncertainties within MODIS DTCs. The authors proposed that the observed LST discrepancies in high temperatures could be the result of inherent differences in LST retrieval algorithms. [ABSTRACT FROM AUTHOR]

The paper presents currently developing method of volcanic ash detection and retrieval for the Geostationary Korea Multi-Purpose Satellite (GK-2A). With the launch of GK-2A, aerosol remote sensing including dust, smoke, will begin a new era of geostationary remote sensing. The Advanced Meteorological Imager (AMI) onboard GK-2A will offer capabilities for volcanic ash remote sensing similar to those currently provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. Based on the physical principles for the current polar and geostationary imagers are modified in the algorithm. Volcanic ash is estimated in detection processing from visible and infrared channel radiances, and the comparison of satellite-observed radiances with those calculated from radiative transfer model. The retrievals are performed operationally every 15 min for volcanic ash for pixel sizes of 2 km. The algorithm currently under development uses a multichannel approach to estimate the effective radius, aerosol optical depth (AOD) simultaneously, both over water and land. The algorithm has been tested with proxy data generated from existing satellite observations and forward radiative transfer simulations. Operational assessment of the algorithm will be made after the launch of GK-2A scheduled in 2018. [ABSTRACT FROM AUTHOR]

Geostationary satellites observe the earth surface and atmosphere with a short repeat time, thus, providing aerosol parameters with high temporal resolution, which contributes to the air quality monitoring. Due to the limited information content in satellite data, and the coupling between the signals received from the surface and the atmosphere, the accurate retrieval of multiple aerosol parameters over land is difficult. With the strategy of taking full advantage of satellite measurement information, here we propose a neural network AEROsol retrieval framework for geostationary satellite (NNAeroG), which can potentially be applied to different instruments to obtain various aerosol parameters. NNAeroG was applied to the Advanced Himawari Imager on Himawari-8 and the results were evaluated versus independent ground-based sun photometer reference data. The aerosol optical depth, Ångström exponent and fine mode fraction produced by the NNAeroG method are significantly better than the official JAXA aerosol products. With spectral bands selection, the use of thermal infrared bands is meaningful for aerosol retrieval. [ABSTRACT FROM AUTHOR]

Abstract: The feasibility and technology requirements of geostationary (GEO) satellite-to-ground free space quantum key distribution (QKD) are investigated. The losses budget and bit rates of the system show that GEO satellite-to-ground QKD is feasible and a globe secure QKD network can be set up through this system. [Copyright &y& Elsevier]

Abstract: This paper proposes a new autonomous stationkeeping system suitable for geostationary satellite operation and presents the results of the computer simulations conducted to verify the proposed system. The proposed on-board stationkeeping system receives pseudo-range signal from the ground equipments located at two different positions with a long baseline, determines the orbit error in real-time, and generates the orbit control command. To minimize the complexity of the on-board stationkeeping logic and to improve reliability, a simple orbit controller has been designed, which generates a series of control signal making the orbit roughly follow the predetermined reference range data. The reference range data are assumed to be generated through a ground based computer simulation and embedded or uploaded with time tag. Finally, the performance of the proposed system has been verified through computer simulations. [Copyright &y& Elsevier]

A combined CCD-method for determination of position of geostationary satellites (GSS) is suggested. Its efficiency and high accuracy was confirmed by regular observations. Using small telescopes, equipped with CCD-cameras, one can control geostationary orbits to displace GSS in order to avoid collisions with space debris. [ABSTRACT FROM AUTHOR]

A ranging and positioning system using a geostationary satellite has been developed and its performance has been evaluated using an aircraft. This system can be applied to oceanic air traffic control and can be used at a ground station to determine positions of aircraft. An experimental system consists of a geostationary satellite, a ground station, an aircraft, and a pseudo-satellite, which is installed inside the ground station sod used as the substitute for another geostationary satellite. The Engineering Test Satellite V (ETS-V) of Japan was in the experiment. It is known that the positioning error increases a user approaches the equator in a positioning system using two geostationary satellites. This fact has been confirmed in this paper by experiment. It has also been shown that positioning errors can be decreased by using relative positioning m which the position of the ground station is used as a reference station. The use of old orbit elements increases the error of the predicted position of the geostationary satellite and therefore increases the positioning error of the aircraft. This has also been confirmed in the experiment. Furthermore, it has been shown that, in relative positioning, positioning errors do not increase much even with old orbit elements and the epoch of orbit elements does not have much influence on positioning errors. [ABSTRACT FROM AUTHOR]

This study describes a high-speed correction method for geolocation information of geostationary satellite data for accurate physical analysis. Geostationary satellite observations with high temporal resolution provide instantaneous analysis and prompt reports. We have previously reported the quasi real-time analysis of solar radiation at the surface and top of the atmosphere using geostationary satellite data. Estimating atmospheric parameters and surface albedo requires accurate geolocation information to estimate the solar radiation accurately. The physical analysis algorithm for Earth observations is verified by the ground truth. In particular, downward solar radiation at the surface is validated by pyranometers installed at ground observation sites. The ground truth requires that the satellite observation data pixels be accurately linked to the location of the observation equipment on the ground. Thus, inaccurate geolocation information disrupts verification and causes complex problems. It is difficult to determine whether error in the validation of physical quantities arises from the estimation algorithm, satellite sensor calibration, or a geolocation problem. Geolocation error hinders the development of accurate analysis algorithms; therefore, accurate observational information with geolocation information based on latitude and longitude is crucial in atmosphere and land target analysis. This method provides the basic data underlying physical analysis, parallax correction, etc. Because the processing speed is important in geolocation correction, we used the phase-only correlation (POC) method, which is fast and maintains the accuracy of geolocation information in geostationary satellite observation data. Furthermore, two-dimensional fast Fourier transform allowed the accurate correction of multiple target points, which improved the overall accuracy. The reference dataset was created using NASA's Shuttle Radar Topography Mission 1-s mesh data. We used HIMAWARI-8/Advanced HIMAWARI Imager data to demonstrate our method, with 22,709 target points for every 10-min observation and 5826 points for every 2.5 min observation. Despite the presence of disturbances, the POC method maintained its accuracy. Column offset and line offset statistics showed stability and characteristic error trends in the raw HIMAWARI standard data. Our method was sufficiently fast to apply to quasi real-time analysis of solar radiation every 10 and 2.5 min. Although HIMAWARI-8 is used as an example here, our method is applicable to all geostationary satellites. The corrected HIMAWARI 16 channel gridded dataset is available from the open database of the Center for Environmental Remote Sensing (CEReS), Chiba University, Japan. The total download count was 50,352,443 on 8 July 2020. Our method has already been applied to NASA GeoNEX geostationary satellite products. [ABSTRACT FROM AUTHOR]

The new generation of geostationary satellite sensors is producing an unprecedented amount of Earth observations with high temporal, spatial and spectral resolutions, which enable us to detect and assess abrupt surface changes. In this study, we developed the land surface directional reflectance and albedo products from Geostationary Operational Environment Satellite-R (GOES-R) Advanced Baseline Imager (ABI) data using a method that was prototyped with the Moderate Resolution Imaging Spectroradiometer (MODIS) data in a previous study, and was also tested with data from the Advanced Himawari Imager (AHI) onboard Himawari-8. Surface reflectance is usually retrieved through atmospheric correction that requires the input of aerosol optical depth (AOD). We first estimated AOD and the surface bidirectional reflectance factor (BRF) model parameters simultaneously based on an atmospheric radiative transfer formulation with surface anisotropy, and then calculated the "blue-sky" surface broadband albedo and directional reflectance. This algorithm was implemented operationally by the National Oceanic and Atmospheric Administration (NOAA) to generate the GOES-R land surface albedo product suite with a daily updated clear-sky satellite observation database. The "operational" land surface albedo estimation from ABI and AHI data was validated against ground measurements at the SURFRAD sites and OzFlux sites and compared with the existing satellite products, including MODIS, Visible infrared Imaging Radiometer (VIIRS), and Global Land Surface Satellites (GLASS) albedo products, where good agreement was found with bias values of −0.001 (ABI) and 0.020 (AHI) and root-mean-square-errors (RMSEs) less than 0.065 for the hourly albedo estimation. Directional surface reflectance estimation, evaluated at more than 74 sites from the Aerosol Robotic Network (AERONET), was proven to be reliable as well, with an overall bias very close to zero and RMSEs within 0.042 (ABI) and 0.039 (AHI). Results show that the albedo and reflectance estimation can satisfy the NOAA accuracy requirements for operational climate and meteorological applications. [ABSTRACT FROM AUTHOR]