Search

A large portion of cloud scenes over the globe shows multiple layers composed of different phases, in general with ice clouds on the top and liquid water clouds beneath. Such multi‐layer (ML) clouds constitute major challenges in cloud observations and weather and climate modeling. This study improved a threshold algorithm for detecting ice‐over‐water ML clouds using geostationary satellites. Optimal thresholds were established for the spectral characteristics of the Advanced Himawari Imager (AHI) and the Advanced Geostationary Radiation Imager (AGRI), accounting for differences between land and ocean surfaces. Validation with collocated space radar and lidar measurements indicated the identification accuracies of approximately 82% over the land and 76% over the ocean. Annual distributions of ML clouds inferred by AHI and AGRI exhibited strong similarity. Furthermore, 6 years of hourly observations revealed distinct monthly and daily variations in ice‐over‐water clouds over the Asia–Pacific region. The ML cloud monthly variations were similar to those of the seasonal convection cycle, with occurrence frequencies over the typical regions higher in summer (maximum ∼27%) and lower (minimum 6%–10%) in winter. Regarding daily variations, ice‐over‐water clouds occurred more frequently around local noon over most of the six time zones (from UTC + 06 to UTC + 11) throughout all seasons. The refined spatiotemporal distribution of ML clouds, particularly the daily variations, is possible to improve our understanding of cloud vertical distributions and radiative effects, and has the potential to promote subsequent validation and parameterization of cloud overlapping in global climate modeling. Plain Language Summary: ML clouds with complex vertical structures introduce significant challenges in global climate models owing to a lack of accurate observations and universally applicable theories. While active satellite instruments provide valuable information about ML clouds, they are limited by temporal resolution and spatial discontinuity. This study improves on a passive geostationary satellite spectral imager‐based method for detecting ice‐over‐water clouds. Based on the advantages of geostationary satellites, the annual and monthly distributions, as well as daily variations of ice‐over‐water clouds, are revealed. Our results provide a valuable observational foundation for investigating ML cloud properties. Key Points: New thresholds of multi‐layer cloud detection algorithms for Advanced Himawari Imager and Advanced Geostationary Radiation Imager are introduced and validatedDaytime spatial and temporal variations of multi‐layer clouds over the Asia‐Pacific region are presentedThere are substantial daily variations (up to over 15%) on the multi‐layer cloud occurrence frequencies [ABSTRACT FROM AUTHOR]

• During non-glint season, the main source of light reflected by GEO satellites is MLI. • GEO satellite reflectance spectra remain stable at varying phase angles. • Normalized reflectance spectra remain stable despite solar declination changes. Geostationary orbit (GEO) satellites have extensive application in fields like communications and navigation due to their stationary properties. Using additional methods to identify and characterise GEO satellites is crucial for enhancing cataloguing capabilities and verifying their operation status. Reflectance spectroscopy is a promising technique for characterising satellites, but a deeper understanding of the primary factors influencing satellite reflectance spectra is required. So we carried out slitless spectroscopic observations on six GEO satellites using the 80 cm high-precision telescope of the Purple Mountain Observatory located in Yaoan, Yunnan. Reflectance spectra and by-product light curves were then obtained after necessary spectral extraction and correction procedures. Our experiment and observation lasted over ten months, and this paper utilizes a subset of data with high Signal-to-Noise Ratio(SNR). Based on the reflectance spectral data obtained, we discovered that: The dominant factor impacting the reflectance spectra is the Multi-layer insulation(MLI) on the surface of three-axis stabilized GEO satellites. Reflectance spectral trends of GEO satellites do not change significantly over the course of a night with changes in the sun-satellite-observatory phase angle. The normalised reflectance spectral trends remain unchanged despite variations in the solar declination. The aforementioned findings establish a strong basis for utilising reflectance spectra to recognise and classify non-cooperative GEO satellites. [ABSTRACT FROM AUTHOR]

In this study, we present the first results of detecting ionospheric irregularities using non-typical GPS observations recorded onboard the Geostationary Operational Environmental Satellites (GOES) mission operating at ~35,800 km altitude. Sitting above the GPS constellation, GOES can track GPS signals only from GPS transmitters on the opposite side of the Earth in a rather unique geometry. Although GPS receivers onboard GOES are primarily designed for navigation and were not configured for ionospheric soundings, these GPS measurements along links that traverse the Earth's ionosphere can be used to retrieve information about ionospheric electron density. Using the radio occultation (RO) technique applied to GPS measurements from the GOES–16, we analyzed variations in the ionospheric total electron content (TEC) on the links between the GPS transmitter and geostationary GOES GPS receiver. For case-studies of major geomagnetic storms that occurred in September 2017 and August 2018, we detected and analyzed the signatures of storm-induced ionospheric irregularities in novel and promising geostationary GOES GPS observations. We demonstrated that the presence of ionospheric irregularities near the GOES GPS RO sounding field of view during geomagnetic disturbances was confirmed by ground-based GNSS observations. The use of RO observations from geostationary orbit provides new opportunities for monitoring ionospheric irregularities and ionospheric density. [ABSTRACT FROM AUTHOR]

Due to the external environment and the buoyancy of cyanobacteria, the inhomogeneous vertical distribution of phytoplankton in eutrophic lakes affects remote sensing reflectance (Rrs) and the inversion of surface chlorophyll-a concentration (Chla). In this study, vertical profiles of Chla(z) (where z is the water depth) and field Rrs (Rrs_F) were collected and utilized to retrieve the vertical profiles of Chla in Lake Chaohu in China. Chla(z) was categorized into vertically uniform (Type 1: N = 166) and vertically non-uniform (Type 2: N = 58) types. Based on the validation of the atmospheric correction performance of the Geostationary Ocean Color Imager (GOCI), a Chla(z) inversion model was developed for Lake Chaohu from 2011 to 2020 using GOCI Rrs data (Rrs_G). (1) Five functions of non-uniform Chla(z) were compared, and the best result was found for Chla(z) = a × exp(b × z) + c (R2 = 0.98, RMSE = 38.15 μg/L). (2) A decision tree of Chla(z) was established with the alternative floating algae index (AFAIRrs), the fluorescence line height (FLH), and wind speed (WIN), where the overall accuracy was 89% and the Kappa coefficient was 0.79. The Chla(z) inversion model for Type 1 was established using the empirical relationship between Chla (z = surface) and AFAIRrs (R2 = 0.58, RMSE = 10.17 μg/L). For Type 2, multivariate regression models were established to estimate the structural parameters of Chla(z) combined with Rrs_G and environmental parameters (R2 = 0.75, RMSE = 72.80 μg/L). (3) There are obvious spatial variations in Chla(z), especially from the water surface to a depth of 0.1 m; the largest diurnal variations were observed at 12:16 and 13:16 local time. The Chla(z) inversion method can determine Chla in different layers of each pixel, which is important for the scientific assessment of phytoplankton biomass and lake carbon and can provide vertical information for the short-term prediction of algal blooms (and the generation of corresponding warnings) in lake management. [ABSTRACT FROM AUTHOR]

• 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]

Convective initiation (CI) nowcasting in subtropical regions often faces challenges, such as complex physical processes and imbalanced samples of CI events, resulting in a high false alarm ratio (FAR). In this paper, we propose a Storm Warning System with Physics‐Augmentation (SWASP) based on the random forest algorithm and cloud physical conditions, using Himawari‐8 Advanced Himawari Imager data from April to September 2019 in South China. The cloud physical conditions (e.g., cloud‐top cooling rates) were investigated to establish regional thresholds for convection occurrence. Ancillary information, including elevation, satellite zenith angle, and latitude, was also incorporated into the SWASP model. Compared to conventional methods, the SWASP model exhibits an improved probability of detection by 0.11 and 0.08 and a decreased FAR by 0.38 and 0.44 for daytime and nighttime forecasts. Moreover, the SWASP model enables the detection of local convective storm systems about 30 min to 1 hr ahead of radar detection in typical convective storm cases. This study contributes to further advancements of the SWASP model by incorporating physical conditions and emphasizes the potential application of geostationary satellites in convective early warnings. Key Points: A Storm Warning System with Physics‐Augmentation (SWASP) has been proposed, which utilizes the random forest algorithmSWASP enhances the probability of detection and reduces false alarm ratios in storm nowcastingChanges in cloud‐top height are identified as the critical factor for accurate storm nowcasting [ABSTRACT FROM AUTHOR]

The On-orbit geometric calibration of geostationary cameras is literally considered a primary prerequisite for the quantitative applications involving accurate geometric positioning, tracking and identification of space-aeronautics moving targets. Affected by the solar illumination angles and the orbital heat flux of the satellite, the thermal environment around the camera turns out to be of difference in different imaging time and areas, which will cause uncertain spatial thermal deformation among the installation structures, and then change the camera's geometric positioning model and the final positioning accuracy inevitably. In terms of the problems mentioned above, a novel installation angles estimation method based on Time-Series Prediction Method is proposed to correct the positioning error caused by the spatial thermal deformation of the geostationary satellites. The installation angles estimation algorithm based on Time-Series Prediction Method model is described elaborately. This proposed method overcomes the dependence of conventional methods on the quantity and quality of GCPs, and is able to estimate the angle biases of the installation structure of the camera. Experimental results show that 92.5% and 97.63% of the positioning errors in the testing dataset corrected by the proposed method can be within 2 pixels and 3 pixels, respectively, which is better than that of ± 18 pixels before correction. Generally, this method could be a supplementary to the conventional methods to correct the positioning error caused by spatial thermal deformation of geostationary payloads. [ABSTRACT FROM AUTHOR]

This study reviewed the legal and regulatory improvements implemented by the International Telecommunication Union (ITU) and major countries in response to the evolving radio environments due to the increase in the number of non-geostationary satellite networks. In addition, domestic regulations, such as the radio waves act and radio regulations related to geostationary and non-geostationary satellite networks, were examined. The proposed improvements to these regulations are discussed, considering the technical characteristics of satellite networks and spectrum management. [ABSTRACT FROM AUTHOR]

Geostationary satellite data enable frequent observations of the Earth's surface, facilitating the rapid monitoring of land covers and changes. However, optical signals over vegetation, represented by the vegetation index (VI), exhibit an anisotropic effect due to the diurnal variation in the solar angle during data acquisition by geostationary satellites. This effect, typically characterized by the bi-directional reflectance distribution function (BRDF), can introduce uncertainties in vegetation monitoring and the estimation of phenological transition dates (PTDs). To address this, we investigated the diurnal variation in the normalized difference vegetation index (NDVI) with solar angles obtained from geostationary satellites since the image had fixed observation angles. By establishing a temporal conversion relationship between instantaneous NDVI and daily NDVI at the local solar noon (LSNVI), we successfully converted NDVIs obtained at any time during the day to LSNVI, increasing cloud-free observations of NDVI by 34%. Using different statistics of the time series vegetation index, including LSNVI, daily averaged NDVI (DAVI), and angular corrected NDVI (ACVI), we extracted PTD at five typical sites in China. The results showed a difference of up to 41.5 days in PTD estimation, with the highest accuracy achieved using LSNVI. The use of the proposed conversion approach, utilizing time series LSNVI, reduced the root mean square error (RMSE) of PTD estimation by 9 days compared with the use of actual LSNVI. In conclusion, this study highlights the importance of eliminating BRDF effects in geostationary satellite observations and demonstrates that the proposed angular normalization method can enhance the accuracy of time series NDVI in vegetation monitoring. [ABSTRACT FROM AUTHOR]

Geostationary satellites have the capability to offer AOD products with a higher frequency of observation within a given period, thus improving the geographical coverage of averaged AOD products compared to polar satellites. Moreover, the averaged AOD of geostationary satellites is more reflective of the average aerosol load as compared to AOD products derived from polar orbit satellites. Despite this, there is still an absence of comprehensive research on the comparative representativeness of AOD mean products from geostationary and polar orbit satellites, and most current research only focuses on retrieval accuracy. This paper compares the geographical coverage of averaged AOD products released by the Advanced Baseline Imager (ABI) sensor on the Geostationary Operational Environmental Satellite-R (GOES-R) (09:00 UTC-23:00 UTC) with AOD products released by the Moderate-resolution Imaging Spectroradiometer (MODIS) (16:00 UTC and 19:00 UTC) on hourly, daily, semi-monthly and monthly scales. Moreover, the Aerosol Robotic Network datasets were used to evaluate the accuracy of ABI AOD and to propose usage suggestions. In terms of daily AOD products, the AOD daily mean generated by the geostationary satellite GOES-R/ABI consistently outperforms the AOD daily mean generated by MODIS AOD in terms of spatial coverage. However, on monthly scales, the difference is no longer significant. With regard to accuracy, it is proved that when the time scale of averaging is gradually expanded, root mean square error (RMSE) and mean absolute error (MAE) gradually decrease. On the season scale, ABI AOD exhibits the highest level of accuracy during the autumn season (September, October, November); on the spatial scale, ABI AOD exhibits the best accuracy in North America. Therefore, if the daily, semi-monthly or monthly averaged ABI AOD datasets are used, the recommended time is autumn, and the study area is North America. [ABSTRACT FROM AUTHOR]

Real-time monitoring of rainfall areas based on satellite remote sensing is of vital importance for extreme rainfall research and disaster prediction. In this study, a new rainfall area identification algorithm was developed for the new generation of geostationary satellites with high spatial and temporal resolution and rich bands. As the main drivers of the rainfall process, the macro and micro physical properties of clouds play an important role in the formation and development of rainfall. We considered differences in the absorption capacity of the water vapor absorption channels in the infrared band and introduced a sensitivity difference of rainfall area in water vapor channels to construct a sensitive detection of the water vapor region. The results of this algorithm were evaluated using Global Precipitation Measurement (GPM) satellite products and CloudSat measurements in various scenarios, with hit rates of 70.03% and 81.39% and false alarm rates of 2.05% and 21.34%, respectively. Spatiotemporal analysis revealed that the types of upper clouds in the rainfall areas mainly consisted of deep convection, cirrostratus, and nimbostratus clouds. Our study provides supporting data for weather research and disaster prediction, as well as an efficient and reliable method for capturing temporal and spatial features. [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]

This paper outlines a multi-system approach for ground-based optical observations and the characterization of satellites in geostationary orbit. This multi-system approach is based on an in-depth analysis of the key factors to consider for light curve analysis of Earth's orbiting satellites. Light curves have been observed in different spectral bands using two different systems. The first system is specialized for astronomical observations and consists of a telescope equipped with an sCMOS camera and Sloan photometric filters. In contrast, the second system is a more cost-effective solution designed for professional non-astronomical applications, incorporating DSLR cameras equipped with RGB channels associated with a Bayer mask and photographic lenses. This comparative analysis aims to highlight the differences and advantages provided by each system, stressing their respective performance characteristics. The observed light curves will be presented as a function of the phase angle, which depends on the relative positions of the observer, the object, and the Sun. This angle plays an important role in optimizing the visibility of Earth's orbiting satellites. Finally, multiband observations of different satellites will be compared to seek an associated spectral signature, which may allow the identification of structurally similar objects through optical observations. [ABSTRACT FROM AUTHOR]

The infrared sounder (IRS) instrument is an infrared Fourier-transform spectrometer that will be on board the Meteosat Third Generation series of the future European Organization for the Exploitation of Meteorological Satellite's geostationary satellites. It will measure the radiance emitted by the Earth at the top of the atmosphere using 1,960 channels. The IRS will provide high spatial- and temporal-frequency four-dimensional information on atmospheric temperature and humidity, winds, clouds, and surfaces, as well as on the chemical composition of the atmosphere. The assimilation of these new observations represents a great challenge and opportunity for the improvement of numerical weather prediction (NWP) forecast skill, especially for mesoscale models such as the Applications de la Recherche à l'Opérationnel à Méso-Echelle (AROME) at Météo-France. The objectives of this study are to prepare for the assimilation of the IRS in this system and to evaluate its impact on the forecasts when added to the currently assimilated observations. By using an observing system simulation experiment constructed for a mesoscale NWP model. This observing system simulation experiment framework makes use of synthetic observations of both IRS and the currently assimilated observing systems in AROME, constructed from a known and realistic state of the atmosphere. The latter, called the "nature run", is derived from a long and uninterrupted forecast of the mesoscale model. These observations were assimilated and evaluated using a 1 hr update cycle three-dimensional variational data assimilation system over 2-month periods, one in the summer and one in the winter. This study demonstrates the benefits that can be expected from the assimilation of IRS observations into the AROME NWP system. The assimilation of only 75 channels over oceans increases the total amount of observations used in the AROME three-dimensional variational data assimilation system by about 50%. The IRS impact in terms of forecast scores was evaluated and compared for the summer and winter periods. The main findings are as follows: (a) over both periods the assimilation of these observations leads to statistically improved forecasts over the whole atmospheric column; (b) for the summer-season experiment, the forecast ranges up to >48 hr are improved; (c) for the winter-season experiment, the impact on the forecasts is globally positive but is smaller than the summer period and extends only to 24 hr. Based on these results, it is foreseen that the addition of future IRS observations in the AROME NWP systems will significantly improve mesoscale weather forecasts. [ABSTRACT FROM AUTHOR]

The cool skin effect refers to the phenomenon where the surface skin temperature of the ocean is always slightly cooler than the temperature of the water directly underneath due to the ubiquitous cooling processes at the ocean surface, especially in the absence of solar radiation. The cool skin effect plays a critical role in the estimation of heat, momentum, and gas exchange between the air and the sea. However, the scarcity of observational data greatly hinders the accurate assessment of the cool skin effect. Here, the matchup data from the new generation geostationary satellite Himawari-8 and in situ sea surface temperature (SST) observations are used to evaluate the performance and dependence on the cool skin effect in the low/mid-latitude oceans. Results show that the intensity of the cool skin effect as revealed by Himawari-8 (−0.16 K) is found to be relatively weaker than previously published cool skin models based on in situ concurrent observations. A considerable amount of warm skin signals has been detected in the high-latitude oceans (e.g., Southern Ocean) under the circumstances of positive air–sea temperature difference and high wind, which may be the main cause of discrepancies with previous thoughts on the cool skin effect. [ABSTRACT FROM AUTHOR]

Herein, the impact of a hyperspectral infrared sounder on a geostationary satellite (GeoHSS) in a regional numerical weather prediction system is investigated during the Baiu seasons in 2017, 2018, and 2020, including events of heavy rainfall. The reanalysis-based observing system simulation experiment (OSSE) technique uses ERA5 as the pseudo-truth atmospheric profile. Temperature and relative humidity pseudo-observations are generated using one-dimensional variational retrieval scheme based on the spectral characteristics of the GeoHSS. Verification against radiosondes shows improvements at various altitudes and forecast times (FTs). Although wind pseudo-observations are not assimilated, winds are also impacted through assimilation cycles and forecasts. Furthermore, the precipitation forecasts show an improving trend with a notable impact to extend the forecasts' lead time. Case studies show impacts on precipitation primarily during longer FTs, accompanied by improved prediction of depressions on the Baiu front and upper-level troughs. These are due to large-scale impacts from the pseudo-observations with a comprehensive coverage over clear-sky areas, propagating to precipitation areas through the assimilation cycle and forecasts. However, the prediction of an event of small-scale localized heavy rainfall is insufficient even at short forecast ranges due to a limited spatial resolution. Experiments show that extracting information in the lower atmosphere is critical and that the impact on upper-level environments is sensitive to using observations in cloudy areas. [ABSTRACT FROM AUTHOR]

Recently, as the satellite data service market has grown significantly, satellite demand has been rapidly increasing. Demand for geostationary satellites with weather observation, communication broadcasting, and GPS missions is also increasing. Completing the charging process of the Li-ion battery during the sun period is one of the main tasks of the electrical power system in geostationary satellites. In the case of the electrical power system of low Earth orbit satellites, the Li-ion battery is connected to the DC/DC converter output, and the charging process is completed through CV control. However, in the case of the regulated bus of the DET type, which is mainly used in the electrical power system of geostationary satellites, a Li-ion battery is connected to the input of the DC/DC converter. Therefore, a method other than the CV control of the DC/DC converter is required. This paper proposes a control algorithm for tapering charging of the Li-ion battery in the regulated bus of the DET type for Li-ion battery charge completion operation required by space-level design standards. In addition, the proposed control algorithm is verified through an experiment on a geostationary satellite's ground electrical test platform. The experiment verified that it has a power conversion efficiency of 99.5% from the solar array to the battery. It has 21 tapering steps at the equinox and 17 tapering steps at the solstice. [ABSTRACT FROM AUTHOR]

Cloud base height (CBH) is crucial for parameterizing the cloud vertical structure (CVS), but knowledge concerning the temporal and spatial distribution of CBH is still poor owing to the lack of large-scale and continuous CBH observations. Taking advantage of high temporal and spatial resolution observations from the Advanced Himawari Imager (AHI) on board the geostationary Himawari-8 satellite, this study investigated the climatology of CBH by applying a novel CBH retrieval algorithm to AHI observations. We first evaluated the accuracy of the AHI-derived CBH retrievals using the active measurements of CVS from the CloudSat and CALIPSO satellites, and the results indicated that our CBH retrievals for single-layer clouds perform well, with a mean bias of 0.3 ± 1.9 km. Therefore, the CBH climatology was compiled based on AHI-derived CBH retrievals for single-layer clouds for the time period between September 2015 and August 2018. Overall, the distribution of CBH is tightly associated with cloud phase, cloud type, and cloud top height and also exhibits significant geographical distribution and temporal variation. Clouds at low latitudes are generally higher than those at middle and high latitudes, with CBHs peaking in summer and lowest in winter. In addition, the surface type affects the distribution of CBH. The proportion of low clouds over the ocean is larger than that over the land, while high cloud occurs most frequently over the coastal area. Due to periodic changes in environmental conditions, cloud types also undergo significant diurnal changes, resulting in periodic changes in the vertical structure of clouds. [ABSTRACT FROM AUTHOR]

The current geostationary satellite images have a time resolution of minutes. It is not clear how useful they are for detecting diurnal and seasonal changes. We tested this by comparing lake surface temperatures (LSTs) from Himawari-8 Advanced Himawari Imager (AHI) images with in situ measurements in Lake Kasumigaura, Japan, for one year. LST products of the Sentinel-3 satellites were also compared. We found that instantaneous LSTs can be estimated with an root mean square difference (RMSE) of 0.6°C. A large amount of data (N = 102–103) is available for each month, but N differs greatly in the range of 216–2771. A good agreement (RMSE of 0.9°C) was found between monthly mean LSTs from Himawari-8 and in situ measurements, and it is better than the RMSE of 1.2–1.7°C found for the Sentinel-3 LST products. The diurnal change detection was more difficult due to insufficient data numbers around noon. Practical remedies for this issue are suggested. [ABSTRACT FROM AUTHOR]

The use of geostationary meteorological satellites for land remote sensing has attracted much attention after the launch of the Himawari-8 satellite equipped with a sensor with enhanced land observation capabilities. In the context of land remote sensing, geolocation errors are often a critical issue, especially in mountainous regions, where a precise orthorectification process is required to maintain high geometric accuracy. The present work addresses the issues related to orthorectification of the new-generation geostationary Earth orbit (GEO) satellites by applying an algorithm known as the ray-tracing indirect method to the data acquired by the Advanced Himawari Imager (AHI) aboard the Himawari-8 satellite. The orthorectified images of the AHI were compared with data from the Sentinel-2 Multispectral Instrument (MSI). The comparison shows a clear improvement of the geometric accuracy, especially in high-elevation regions located far from the subsatellite point. The results indicate that approximately 7.3% of the land pixels are shifted more than 3 pixels during the orthorectification process. Furthermore, the maximum displacement after the orthorectification is up to 7.2 pixels relative to the location in the original image, which is of the Tibetan Plateau. Moreover, serious problems caused by occlusions in the images of GEO sensors are clearly indicated. It is concluded that special caution is needed when using data from GEO satellites for land remote sensing in cases where the target is in a mountainous region and the pixels are located far from the subsatellite point. [ABSTRACT FROM AUTHOR]

Convective storms and lightning are among the most important weather phenomena that are challenging to forecast. In this study, a novel multi-task learning (MTL) encoder-decoder U-net neural network was developed to forecast convective storms and lightning with lead times for up to 90 min, using GOES-16 geostationary satellite infrared brightness temperatures (IRBTs), lightning flashes from Geostationary Lightning Mapper (GLM), and vertically integrated liquid (VIL) from Next Generation Weather Radar (NEXRAD). To cope with the heavily skewed distribution of lightning data, a spatiotemporal exponent-weighted loss function and log-transformed lightning normalization approach were developed. The effects of MTL, single-task learning (STL), and IRBTs as auxiliary input features on convection and lightning nowcasting were investigated. The results showed that normalizing the heavily skew-distributed lightning data along with a log-transformation dramatically outperforms the min-max normalization method for nowcasting an intense lightning event. The MTL model significantly outperformed the STL model for both lightning nowcasting and VIL nowcasting, particularly for intense lightning events. The MTL also helped delay the lightning forecast performance decay with the lead times. Furthermore, incorporating satellite IRBTs as auxiliary input features substantially improved lightning nowcasting, but produced little difference in VIL forecasting. Finally, the MTL model performed better for forecasting both lightning and the VIL of organized convective storms than for isolated cells. [ABSTRACT FROM AUTHOR]

Clear‐sky radiances (CSRs) derived from observations made by imager sensors on board geostationary satellites are widely used in most operational numerical weather prediction systems. CSRs have data on tropospheric water vapour and temperatures, and the products at water vapour bands are generally assimilated into global data assimilation systems. In another band, known as the CO2 band (13.3–13.4 μm), CSRs are not used widely yet, despite having a wealth of information about temperatures in the mid‐ and low troposphere. This is mainly because of the high surface sensitivity of this band, which makes it difficult to accurately simulate brightness temperatures when there are non‐negligible errors in the surface parameters in the models. This article quantitatively investigated the surface sensitivities of CO2 and water vapour bands, which have the sensitivity under dry atmospheric conditions, and developed retrieval of land surface temperature (LST) from window band CSRs to obtain a more accurate simulated brightness temperature. Additionally, it was discovered that the retrieved LST outperformed that from the model in short‐range forecasts for the low‐water‐vapour band (7.3 μm) CSR data assimilation, and throughout the forecasting period, especially in the tropics, for the CO2 band CSR data assimilation. We also examined an unexpected improvement in the low troposphere in the model's LST trials, and we concluded that it was related to the relationship between the LST and atmospheric temperature biases. [ABSTRACT FROM AUTHOR]

Spatially continuous surface air temperature (SAT) is of great significance for various research areas in geospatial communities, and it can be reconstructed by the SAT estimation models that integrate accurate point measurements of SAT at ground sites with wall-to-wall datasets derived from remotely sensed observations of spaceborne instruments. As land surface temperature (LST) strongly correlates with SAT, estimation models are typically developed with LST as a primary input. Geostationary satellites are capable of observing the Earth's surface across large-scale areas at very high frequencies. Compared to the substantial efforts to estimate SAT at daily or monthly scales using LST derived from MODIS, very limited studies have been performed to estimate SAT at high-temporal scales based on LST from geostationary satellites. Estimation models for hourly SAT based on the LST derived from FY-4A, the first geostationary satellite in China's new-generation meteorological observation mission, were developed for the first time in this study. The models were fully cross-validated for a very large-scale region with diverse geographic settings using random forest, and specified differently to explore the influence of time and location variables on model performance. Overall predictive performance of the models is about 1.65–2.08 K for sample-based cross-validation, and 2.22–2.70 K for site-based cross-validation. Incorporating time or location variables into the hourly models significantly improves predictive performance, which is also confirmed by the analysis of predictive errors at temporal scales and across sites. The best-performing model with an average RMSE of 2.22 K was utilized for reconstructing maps of SAT for each hour. The hourly models developed in this study have general implications for future studies on large-scale estimating of hourly SAT based on geostationary LST datasets. [ABSTRACT FROM AUTHOR]

Clouds can block solar radiation from reaching the surface, so timely and effective cloud cover test and forecasting is critical to the operation and economic efficiency of photovoltaic (PV) plants. Traditional cloud cover algorithms based on meteorological satellite observation require many auxiliary data and computing resources, which are hard to implement or transplant for applications at PV plants. In this study, a portable and fast cloud mask algorithm (FCMA) is developed to provide near real-time (NRT) spatial-temporally matched cloud cover products for PV plants. The geostationary satellite imager data from the Advanced Himawari Imager aboard Himawari-8 and the related operational cloud mask algorithm (OCMA) are employed as benchmarks for comparison and validation. Furthermore, the ground-based manually observed cloud cover data at seven quintessential stations at 08:00 and 14:00 BJT (Beijing Time) in 2017 are employed to verify the accuracy of cloud cover data derived from FCMA and OCMA. The results show a high consistency with the ground-based data, and the average correlation coefficient (R) is close to 0.85. Remarkably, the detection accuracy of FCMA is slightly higher than that of OCMA, demonstrating the feasibility of FCMA for providing NRT cloud cover at PV plants. [ABSTRACT FROM AUTHOR]

Oceanic mesoscale eddies continuously regulate the horizontal and vertical transport of mass, heat, salt, carbon, and nutrients throughout the ocean system owing to their ubiquity, three-dimensionality, and long-term persistence. Although satellites have been the main platforms used to observe mesoscale eddies and chlorophyll-a (Chla) distributions, they cannot support submesoscale physical–biological interactions. Contemporary satellite observations of Eulerian velocity fields are unable to resolve submesoscale processes that govern vertical migration and mixing, which are crucial for controlling the nutrients and light for phytoplankton in the surface layer. We explored the physical–biological interaction between the anticyclonic mesoscale eddy and the Chla secondary bloom that occurred after the spring bloom in the East/Japan Sea using the Geostationary Ocean Color Imager (GOCI). The GOCI currents were generated using GOCI Chla data and were used to map streamlines, vorticity, and divergence to characterize the surface current near the eddy. In the early spring bloom period, the eddy interior showed Chla depletion as the eddy was trapped externally. We found that the second bloom period coincided with a higher divergence or upwelling period in the eddy core, and a sharp Chla peak was observed when wind-induced Ekman suction was pronounced. This study describes the first satellite observation of surface layer divergence inside an anticyclonic mesoscale eddy with internal Chla blooms, utilizing a submesoscale-permitting GOCI-based surface current. [ABSTRACT FROM AUTHOR]

The early detection and rapid extinguishing of forest fires are effective in reducing their spread. Based on the MODIS Thermal Anomaly (MOD14) algorithm, we propose an early stage fire detection method from low-spatial-resolution but high-temporal-resolution images, observed by the Advanced Himawari Imager (AHI) onboard the geostationary meteorological satellite Himawari-8. In order to not miss early stage forest fire pixels with low temperature, we omit the potential fire pixel detection from the MOD14 algorithm and parameterize four contextual conditions included in the MOD14 algorithm as features. The proposed method detects fire pixels from forest areas using a random forest classifier taking these contextual parameters, nine AHI band values, solar zenith angle, and five meteorological values as inputs. To evaluate the proposed method, we trained the random forest classifier using an early stage forest fire data set generated by a time-reversal approach with MOD14 products and time-series AHI images in Australia. The results demonstrate that the proposed method with all parameters can detect fire pixels with about 90% precision and recall, and that the contribution of contextual parameters is particularly significant in the random forest classifier. The proposed method is applicable to other geostationary and polar-orbiting satellite sensors, and it is expected to be used as an effective method for forest fire detection. [ABSTRACT FROM AUTHOR]

Near-real-time precipitation retrieval plays an important role in the study of the evolutionary process of precipitation and the prevention of disasters caused by heavy precipitation. Compared with ground-based precipitation observations, the infrared precipitation estimations from geostationary satellites have great advantages in terms of geographical coverage and temporal resolution. However, precipitation retrieved from multispectral infrared data still faces challenges in terms of accuracy, especially in extreme cases. In this paper, we propose a new paradigm for satellite multispectral infrared data retrieval of precipitation and construct a new model called PrecipGradeNet. This model uses FY-4A L1 FDI data as the input, IMERG precipitation data as the training target, and improves the precipitation retrieval accuracy by grading the precipitation intensity through Res-UNet, a semantic segmentation network. To evaluate the precipitation retrieval of the model, we compare the retrieval results with the FY-4A L2 QPE operational product to the IMERG precipitation. IMERG is considered as the ground truth. We evaluate the precipitation retrieval from the precipitation fall area identification, the precipitation intensity interval discrimination, and the precipitation quantification. Experimental results show that PrecipGradeNet has better overall performance compared with the FY-4A QPE product in precipitation fall area identification with POD increased by 48% and CSI and HSS improved by 21% and 14%. PrecipGradeNet also has better performance in light precipitation with POD increased by 114% and CSI and HSS improved by 64% and 52%, and better overall precipitation quantification, with RMSE and CC improved by 16% and 15%. In addition, PrecipGradeNet avoids the overall bias in the low and extreme high precipitation cases. Therefore, the new paradigm proposed in this paper has the potential to improve the retrieval accuracy of satellite precipitation estimation products. This study suggests that the application of semantic segmentation methods may provide a new path to correct the intensity bias of the satellite-based precipitation products. [ABSTRACT FROM AUTHOR]

In this paper, we discuss a novel retrieval of ionospheric electron density profiles using the Radio Occultation (RO) technique applied to measurements captured by the Global Positioning System (GPS) receivers onboard two Geostationary Operational Environmental Satellites (GOES). The GOES satellites operate at ~35,800 km altitude and are primarily weather satellites that operationally contribute continuous remote-sensing data for real-time weather forecasting, as well as near-Earth environment monitoring and Sun observations. The GPS receivers onboard GOES-16 and GOES-17 satellites can track GPS signals propagated through the Earth's atmosphere, and although the receivers are primarily designed for navigation and station-keeping maneuvers, these GPS measurements that traverse the Earth's atmosphere can be used to retrieve the ionospheric electron density profiles. This process poses a range of technical challenges. GOES RO links are different from the traditional low Earth orbit (LEO) RO geometry since the receiver is located in an orbit that is higher in altitude than the GPS constellation of transmitters. Additionally, the GPS receivers onboard GOES satellites provide only single-frequency GPS L1 observations and have clocks much less stable than those typically used for RO measurements. The geographical distribution of the retrieved GEO-based RO profiles was found to be uniquely constrained and repeatable based on the relative geostationary fixed positions in the Earth-Centered Earth Fixed reference frame with respect to the GPS constellation orbiting at lower altitude, and significantly different from the coverage patterns of LEO-based RO missions. We demonstrate the successful application of the proposed RO profiling technique with a statistically significant set of GPS observations from GOES-16 and GOES-17 satellites over several years of data collection. This enabled us to retrieve more than 10,000 ionospheric electron density profiles with a maximum altitude of up to 1000–2000 km, much higher than any existing LEO-based RO mission. We demonstrate good performance of GEO-based RO measurements for properly specifying the vertical distribution of ionospheric plasma density by comparing the profiles dataset from the GOES RO experiment with independent reference observations – ground-based ionosondes and LEO-based RO missions, as well as model simulation results provided by the empirical International Reference Ionosphere model. Over multiple years of observations, statistical analysis of discrepancies between the ionospheric F2 layer peak parameters (peak density and height) derived from geosynchronous GOES observations and reference measurements was conducted. This analysis reveals a very good agreement between GOES RO electron density profiles and independent types of measurements in both the F2 peak and the profile shape. [ABSTRACT FROM AUTHOR]

The Geostationary Environment Monitoring Spectrometer (GEMS) is the world's first geostationary instrument that monitors hourly gaseous air pollutant levels, including nitrogen dioxide (NO 2). Using the first-of-its-kind capabilities of GEMS NO 2 data, we examined how well GEMS NO 2 levels can explain the spatiotemporal variabilities in hourly NO 2 concentrations in the Republic of Korea for the year 2022. A correlation analysis between hourly GEMS NO 2 levels and ground NO 2 concentrations showed a higher spatial correlation [Pearson r = 0.56 (SD = 0.20)] than a temporal one [Pearson r = 0.42 (SD = 0.14)], on average. To take advantage of the enhanced spatial predictability of GEMS NO 2 data, we employed a mixed effects model to allow hour-specific relationships between GEMS NO 2 and NO 2 concentrations on a given day in each region and subsequently estimated hourly NO 2 concentrations in all urban and rural areas. The 10-fold cross validation demonstrated R2 = 0.72, mean absolute error (MAE) = 3.7 ppb, and root mean squared error (RMSE) = 5.5 ppb. The hourly variations of the relationships were attributed particularly to those of wind speed among meteorological parameters considered in this study. The spatial distributions of hourly estimated NO 2 concentrations were highly correlated between hours [average r = 0.91 (SD = 0.06)]. Nonetheless, they represented the diurnal patterns of urban versus rural NO 2 contrasts during the day [urban/rural NO 2 ratios from 1.22 (5 p.m.) to 1.37 (12 p.m.)]. The newly retrieved GEMS NO 2 data enable temporally as well as spatially resolved NO 2 exposure assessment. In combination with the time-activity patterns of individual subjects, the GEMS NO 2 data can generate 'sub-population' exposure estimates and therefore enhance health effect studies. [Display omitted] • GEMS NO 2 data showed high performance of estimating hourly NO 2 air pollution. • The spatial distribution of NO 2 was highly correlated between hours on a given day. • Urban-rural NO 2 differences were considerable by hour. [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]

The surface longwave downward radiation (SLDR) is the crucial input parameter for many land surface process models. To address the unavailability of the official cloudy-sky SLDR product of the FengYun-4A (FY-4A) satellite, this study developed an integrated framework to estimate the hourly all-time cloud-sky SLDR from the advanced geostationary radiation imager (AGRI) onboard FY-4A. Firstly, we developed a novel method that directly estimate the all-time cloud base height (CBH) by the Light Gradient Boosting Machine technique and achieved an accurate CBH estimate. Subsequently, the cloud base temperature (CBT), calculated from the estimated CBH and MARRA2 temperature profiles, was used to drive a single-layer cloud model (SLCM) and realize the cloudy-sky SLDR estimate. Validation results across 51 flux sites showed the bias and RMSE of the estimated all-time cloudy-sky SLDR are −0.06 and 22.61 W/m2, respectively, outperforming mainstream SLDR products. In addition, the CBT-based SLCM performed well under different cloud types, elevations, and land types, demonstrating the robustness and generalizability of the integrated framework for estimating the all-time cloudy-sky SLDR. • We propose an integrated framework to estimate all-time cloud-sky SLDR for FY 4A/AGRI. • A novel direct retrieval method is developed to estimate AGRI CBH during both daytime and nighttime. • The bias and RMSE of the derived cloudy sky SLDR over 51 sites located in East Asia are −0.06 W/m2 and 22.61 W/m2. • This study provides an alternative solution for AGRI which does not have the cloudy-sky SLDR product yet. [ABSTRACT FROM AUTHOR]

High‐temporal‐resolution geostationary satellite infrared measurements are always used to capture and predict typical characteristics at the cloud top of rapidly developing strong storms at the pre‐convection or convection initiation (CI) stage. However, the large false alarm rate of CI nowcasting is difficult to avoid due to the complex and unpredictable trigger factors. Although the microwave measurement technique can observe thick clouds and even the precipitation within clouds due to the weaker atmospheric extinction effect on microwave, microwave data from polar‐orbiting satellites are rarely used to observe the CI due to their relatively low temporal resolution. In this study, we analyze several previously unknown CI characteristics over the East Asia region from 2016 to 2019 based on spatially and temporally matched Advanced Technology Microwave Sounder data. These typical CI samples are initially identified by using continuous infrared images from the Himawari‐8 geostationary satellite. The results show that there is a distinct cloud optical depth at the pre‐convection stage in the western (deep cloud clusters) and eastern (shallow cloud clusters) Tibetan Plateau (TP). The shallow precipitating cloud clusters of the CI over the eastern TP are possibly attributed to the favorable local dynamic and thermal conditions stem from the Asian monsoon. Another notable finding shows that the fast‐developing CI over the ocean has thick clouds compared with the samples over the land. Overall, the unique CI characteristics found from microwave observations in this study indicate that the future geostationary microwave sounder technologies will almost certainly provide some new findings and enhance early warning capabilities about convection. Key Points: This study uses microwave sounder data to explore some previously unknown convection initiation (CI) characteristicsMore relatively shallow cloud clusters at the pre‐convection stage are found based on microwave observations in the eastern Tibetan PlateauCompared with the samples over the land, strong CI that develops rapidly over the ocean has thicker clouds [ABSTRACT FROM AUTHOR]

Layered cloud cover (LCC), that is, cloud cover at different levels, is crucial for estimating cloud radiative effects and modeling climate change. However, accurate LCC characterization using passive satellite measurements is challenging because of the difficulties in resolving cloud vertical structures. In this study, we developed a novel method to estimate LCC from geostationary satellite radiometric measurements. The proposed method resolves cloud vertical structures by retrieving cloud-top and cloud-base heights for both single- and multi-layer clouds; thus, better estimating LCC. Our results agreed well with active satellite measurements, showing identification accuracies of 86%, 90%, and 91% for high, medium, and low clouds, respectively. Additionally, our LCC estimates derived from satellite measurements were used to evaluate those from atmospheric reanalysis. The annual averaged total, high, medium, and low cloud covers given by our methods were 0.681, 0.393, 0.356, and 0.455, respectively, while those from ERA-5 were 0.623, 0.415, 0.274, and 0.392, respectively. These results indicate that the total cloud cover determined by ERA-5 was lower than that derived from satellite measurements, potentially as a result of medium and low-level clouds. [ABSTRACT FROM AUTHOR]

The Advanced Geostationary Radiation Imager (AGRI) is one of the main imaging sensors on the Fengyun-4A (FY-4A) satellite. Due to the combination of high spatial and temporal resolution, the AGRI is suitable for continuously monitoring atmospheric aerosol. Existing studies only perform AOD retrieval on the dark target area of FY-4A/AGRI, and the full disk AOD retrieval is still under exploration. The Neural Network AEROsol Retrieval for Geostationary Satellite (NNAeroG) based on the Fully Connected Neural Network (FCNN) was used to retrieve FY-4A/AGRI full disk aerosol optical depth (AOD). The data from 111 ground-based Aerosol Robotic Network (AERONET) and Sun–Sky Radiometer Observation Network (SONET) sites were used to train the neural network, and the data from 28 other sites were used for independent validation. FY-4A/AGRI AOD data from 2017 to 2020 were validated over the full disk and three different surface types (vegetated areas, arid areas, and marine and coastal areas). For general validation, the AOD predicted by the application of NNAeroG to FY-4A/AGRI observations is consistent with the ground-based reference AOD data. The validation of the FY-4A/AGRI AOD versus the reference data set shows that the root-mean-square error (RMSE), mean absolute error (MAE), R squared (R2), and percentage of data with errors within the expected error ± (0.05 + 15%) (EE15) are 0.237, 0.145, 0.733, and 58.7%, respectively. The AOD retrieval accuracy over vegetated areas is high but there is potential for improvement of the results over arid areas and marine and coastal areas. AOD retrieval results of FY-4A/AGRI were compared under fine and coarse modes. The retrieved AOD has low accuracy in coarse mode but is better in coarse–fine mixed mode and fine mode. The current AOD products over the ocean of NNAeroG-FY4A/AGRI are not recommended. Further development of algorithms for marine areas is expected to improve the full disk AOD retrieval accuracy. [ABSTRACT FROM AUTHOR]

Knowledge about tropical cyclone (TC) size is essential for disaster prevention and mitigation strategies, but due to the limitations of observations, TC size data from the open ocean are scarce. In this paper, several models are developed to identify TC size parameters, including the radius of maximum wind (RMW) and the radii of 34 (R34), 50 (R50), and 64 (R64) knot winds, using various machine learning algorithms based on infrared channel imagery of geostationary meteorological satellites over the western North Pacific (WNP). Through evaluation and verification, the trained and optimized support vector machine models are proposed for RMW and R34, whereas the general regression neural network models are set up for R50 and R64. According to the independent-sample evaluations against aircraft observations (1981 - 1987)/Joint Typhoon Warning Center best track data (2017 - 2019), the mean absolute errors of R34, R50, R64, and RMW are 54/58, 34/38, N/A/21, and 25/25 km, respectively. The corresponding median errors are 39/46, 34/31, N/A/17, and 17/19 km, respectively. There is an overall slight underestimation of the parameters, which needs to be analyzed and improved in a future study. Despite aircraft observations of TCs in the WNP having ceased in the late 1980s, this new dataset of TC sizes enables a thorough estimation of wind structures covering a period of 40 years [ABSTRACT FROM AUTHOR]

Volcanic explosive eruptions inject several different types of particles and gasses into the atmosphere, giving rise to the formation and propagation of volcanic clouds. These can pose a serious threat to the health of people living near an active volcano and cause damage to air traffic. Many efforts have been devoted to monitor and characterize volcanic clouds. Satellite infrared (IR) sensors have been shown to be well suitable for volcanic cloud monitoring tasks. Here, a machine learning (ML) approach was developed in Google Earth Engine (GEE) to detect a volcanic cloud and to classify its main components using satellite infrared images. We implemented a supervised support vector machine (SVM) algorithm to segment a combination of thermal infrared (TIR) bands acquired by the geostationary MSG-SEVIRI (Meteosat Second Generation—Spinning Enhanced Visible and Infrared Imager). This ML algorithm was applied to some of the paroxysmal explosive events that occurred at Mt. Etna between 2020 and 2022. We found that the ML approach using a combination of TIR bands from the geostationary satellite is very efficient, achieving an accuracy of 0.86, being able to properly detect, track and map automatically volcanic ash clouds in near real-time. [ABSTRACT FROM AUTHOR]

High-frequency imaging characteristics allow a geostationary satellite (GSS) to capture the diurnal variation in vegetation canopy reflectance spectra, which is of very important practical significance for monitoring vegetation via remote sensing (RS). However, the observation angle and solar angle of high-frequency GSS RS data usually differ, and the differences in bidirectional reflectance from the reflectance spectra of the vegetation canopy are significant, which makes it necessary to normalize angles for GSS RS data. The BRDF (Bidirectional Reflectance Distribution Function) prototype library is effective for the angle normalization of RS data. However, its spatiotemporal applicability and error propagation are currently unclear. To resolve this problem, we herein propose a synthetic angle normalization model (SANM) for RS vegetation canopy reflectance; this model exploits the GSS imaging characteristics, whereby each pixel has a fixed observation angle. The established model references a topographic correction method for vegetation canopies based on path-length correction, solar zenith angle normalization, and the Minnaert model. It also considers the characteristics of diurnal variations in vegetation canopy reflectance spectra by setting the time window. Experiments were carried out on the eight Geostationary Ocean Color Imager (GOCI) images obtained on 22 April 2015 to validate the performance of the proposed SANM. The results show that SANM significantly improves the phase-to-phase correlation of the GOCI band reflectance in the morning time window and retains the instability of vegetation canopy spectra in the noon time window. The SANM provides a preliminary solution for normalizing the angles for the GSS RS data and makes the quantitative comparison of spatiotemporal RS data possible. [ABSTRACT FROM AUTHOR]

Battery technology has been used for satellites since the first satellite sputnik-1 was launched in 1957. The majority of larger satellite’s (geostationary or communication satellites) lives range from 7 to 15 years. During the lifetime of satellites, the batteries used must complete 1000 to 33000 cycles without any problems or likelihood of maintenance. There are three battery technologies, Li-ion, Ni-H2 and Ni-Cd, that are well proven for Geostationary satellite applications. Energy density, lifetime, weight, ampere-hour capacity, depth of discharge, ruggedness and recharge-ability, battery management, thermal management, and self-discharge are main parameters that should be considered when comparing electrical and thermal performance of these three battery technologies. The purpose of this study is to compare the thermal control system for these three batteries for three-axis stabilized geostationary satellites. In particular, the thermal dissipation was compared, which is the temperature range required for battery operation. Thermal analysis was performed for Li-ion batteries using ThermXL software, and showed a temperature results variation ranging between 10.9°C and 32.7°C. The temperature during the battery module was not greater that its qualification temperature results. [ABSTRACT FROM AUTHOR]

Córdoba Province in Argentina is a global hotspot for deep hail-producing storms. Previous studies of hail formation and detection largely relied on satellite snapshots or modeling studies, but lacked hail validation, relying instead on proxy metrics. To address this limitation, this study used hail collected in the mountainous Córdoba region in collaboration with the citizen science program "Cosecheros de Granizo 2018–2020" including from a record-breaking hail event and from the 2018–2019 RELAMPAGO field campaign. Three cases including a MCS and two supercells, which have verified hail in different environment locations relative to the Sierras de Córdoba, were analyzed for multi-spectral signatures in GOES-16 satellite data. Brightness temperatures decreased over time after convective initiation, reaching values cooler than the tropopause with variations around those values of different magnitudes. Overall, all cases exhibited a slight weakening of the updraft and strong presence of smaller ice crystal sizes just prior to the hail report, especially for the larger hailstones. The results demonstrate promise in using satellite proxies for hail detection in multiple environments for different storm modes. The long-term goal is to better understand hail-producing storms and unique challenges of forecasting hail in this region. [ABSTRACT FROM AUTHOR]

Live fuel moisture content (LFMC) is a crucial variable affecting fire ignition and spread. Satellite remote sensing has been effective in estimating LFMC over large spatial scales, but continuous sub-daily (e.g., every 10 mins to hourly during daylight) LFMC monitoring from space is yet to be accomplished. Using the geostationary satellite Himawari-8 temporally dense observations every 10 mins, this study designed a generalized reduced gradient (GRG) numerical optimization method coupled with PROSAILH_5B radiative transfer model (RTM) to track the sub-daily LFMC dynamics. This method simultaneously accounted for the changing sun-target-sensor geometry bi-directional reflectance distribution function (BRDF) effect on Himawari-8 AHI reflectance. LFMC field measurements from Australia and China validated the LFMC estimation from Himawari-8 AHI. In addition, they were also compared to estimates from two broadly used polar-orbiting satellites, the Landsat-8 OLI and Terra+Aqua MODIS. At the sub-daily scale, the LFMC estimated using the GRG method from Himawari-8 AHI yielded reasonable accuracy (R2 = 0.61, rRMSE = 20.78%). When averaged to a daily scale, the accuracy of LFMC estimation based on the Himawari-8 AHI was lower (R2: 0.60–0.61, rRMSE = 25.38%–26.58%) than that based on the Landsat-8 OLI (R2: 0.68–0.79, rRMSE = 18.11%–25.89%) and Terra+Aqua MODIS (R2: 0.63–0.76, rRMSE = 19.73%–25.84%). However, after removing some heterogeneous measurements, the difference in the accuracy of LFMC estimates among these three data sources got smaller and improved (R2: 0.72–0.82, rRMSE = 17.96%–23.84%). Furthermore, the method proved its feasibility and applicability to identify fire danger conditions through two wildfire case studies: one in Queensland (Australia, 2019) and another in Xichang (China, 2020). These studies showed that the wildfires started when the Himawari-8 AHI-based sub-daily LFMC reached its daily minimum. Therefore, this study serves as a foundational step toward estimating sub-daily LFMC dynamics, an important yet overlooked factor in assessing sub-daily fire danger and behavior. [Display omitted] • The first sub-daily LFMC estimation from a geostationary satellite. • LFMC estimation relies on the GRG method and radiative transfer model inversion. • LFMC field measurements and two wildfire cases assessed the LFMC estimations. • Himawari-8 AHI-based LFMC estimation compared with that from the Landsat-8 OLI and MCD43A4. [ABSTRACT FROM AUTHOR]

Clouds play an essential role in the global energy budget, but the impact of anthropogenic aerosols on clouds is still poorly understood. We use 15‐min temporal resolution geostationary satellite data to study the temporal evolution of polluted cloud tracks detected in the European part of Russia. Previous analysis of polluted cloud tracks shows that cloud water response to aerosols is bidirectional. Here, we show that cloud water gradually increases in the afternoon for polluted clouds in some cases. Aerosol‐induced cloud thickening is much more likely in the case of thin unpolluted clouds. However, on average, there is less cloud water in the polluted clouds compared to the nearby unpolluted clouds. The temporal evolution of cloud thinning is not observed, and polluted clouds are thinner than the nearby unpolluted clouds since the early morning. The physical mechanisms leading to the observed changes and the influence of retrieval bias on the detected changes need further clarification. Our findings highlight the need to research the temporal evolution of cloud responses to aerosols to estimate the aerosol radiative forcing more accurately. Plain Language Summary: The global warming induced by anthropogenic greenhouse gases is partly compensated by the aerosol cooling effect, while the extent of aerosol cooling is relatively uncertain. Lately, the analysis of clouds polluted by isolated aerosol sources like industrial scale factories has provided new insights into aerosol influence on clouds and the resulting cooling effect. We use geostationary satellite data to study the temporal evolution of continental clouds polluted by aerosols emitted from heavy industrial sites. On average, polluted clouds are thinner than nearby unpolluted clouds, but no gradual thinning of polluted clouds is observed during the day. Some polluted clouds become thicker than the nearby unpolluted clouds in the afternoon. These results highlight the need for further research on the temporal evolution of cloud responses to aerosols. Key Points: We demonstrated that geostationary satellite data can be used to study the temporal evolution of pollution tracksIn some cases, polluted clouds gradually grow much thicker in the afternoonPolluted clouds are thinner on average compared to the nearby unpolluted clouds, but no gradual thinning is observed [ABSTRACT FROM AUTHOR]

This paper proposes a novel electric thruster configuration design optimization scheme to implement efficient station-keeping operations for geostationary satellites. In this work, the electric thrusters are equipped with 4-DOF robotic manipulators to provide flexible control thrust in space, which consumes much less propellant than the conventional station-keeping process using fixed-position electric thrusters. To effectively determine the firing right ascensions and firing durations of each electric thruster during the station-keeping process, a novel station-keeping control strategy is developed in view of the low-thrust feature of electric thrusters and the flexible pointing capacity of robotic manipulators. A low-thrust station-keeping period includes one two-day orbit determination period and six two-day control periods, where the initial orbital elements are determined in the orbit determination period, and the electric thrusters fire to compensate the perturbations in the control periods. Based on the control strategy, the electric thruster configuration design optimization problem is formulated to minimize the total propellant consumption via optimizing the robotic manipulator joint swivel angles and firing durations simultaneously, subject to several practical engineering constraints including the station-keeping accuracy, angular momentum dumping, collision between electric thruster and solar array, and electric thruster plume. Finally, the proposed electric thruster configuration design optimization scheme is applied to real-world station-keeping problems. The simulation results show that the geostationary satellites can be successfully maintained in an assigned station-keeping window using the robotic manipulators based low-thrust station-keeping control strategy. Moreover, the optimized electric thruster configuration with robotic manipulators can save 31.58% propellant consumption compared with that of the conventional configuration owing to the favorable flexibility of control thrust, which demonstrates the effectiveness and practicality of this work. [ABSTRACT FROM AUTHOR]

The Fengyun-4A (FY-4A) geostationary satellite carries the Lightning Mapping Imager that measures total lightning rate of convective systems from space at high spatial and temporal resolutions. In this study, the performance of FY-4A lightning data assimilation (LDA) on the forecast of non-typhoon oceanic mesoscale convective systems (MCSs) is investigated by using an LDA method implemented in the Weather Research and Forecasting-Four Dimensional Data Assimilation (WRF-FDDA). With the LDA scheme, three-dimensional graupel mixing ratio fields retrieved from the FY-4A lightning data and the corresponding latent heating rates are assimilated into the Weather Research and Forecasting model via nudging terms. Two oceanic MCS cases over the South China Sea were selected to perform the study. The subjective evaluation results demonstrate that most of the oceanic convective cells missed by the control experiments are recovered in the analysis period by assimilating FY-4A lightning data, due to the promoted updrafts by latent-heat nudging, the more accurate and faster simulations of the cold pools, and the associated gust-fronts at the observed lightning locations. The cold pools and gust-fronts generated during the analysis period helped to maintain the development of the MCSs, and reduced the morphology and displacement errors of the simulations in the short-term forecast periods. The quantitative evaluation indicates that the most effective periods of the LDA for simulation enhancement were at the analysis time and the nowcasting (0–2 h forecast) periods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Convective activities play an important role in tropical weather systems. To investigate the characteristic scales of convection, a method combining a principal component (PC) analysis with Fourier decomposition is applied to brightness temperature observations from Advanced Himawari-8 Imager (AHI). Characteristic scales of different modes in tropical convective systems are obtained. The explained variance reduces rapidly from the first to the 60th PC mode by two magnitudes; the horizontal scale decreases from over 2000 km to about 100 km, and the timescale changes from more than 4 days to around 5 h. By a detailed comparison of the first, 20th, 40th, and 60th PC modes, it is found that large-scale (over 2000 km in wavelength) convective activities usually have significant nocturnal enhancement, whereas meso-scale (about 100 km in wavelength) convective activities feature short period and fast change with more intense development in regions of active large-scale convection. This study may be of some importance for the choice of AHI data assimilation cycling interval, the horizontal resolution as well as data thinning for geostationary satellite observations. [ABSTRACT FROM AUTHOR]

As a vital parameter describing the Earth surface energy budget, surface all-wave net radiation ($R_{n}$) drives many physical and biological processes. Remote estimation of $R_{n}$ using satellite data is an effective approach to monitor the spatial and temporal dynamics of $R_{n}$. Accurate daily $R_{n}$ estimation typically depends on the spatio-temporal resolutions of satellite data. There are currently few high-spatial-resolution daily $R_{n}$ products from polar-orbiting satellite data, and they exhibit limited accuracy due to sparse diurnal observations. In addition, traditional estimation approaches typically require cloud mask and clear-sky albedo as inputs and ignore the length ratio of daytime (LRD), which may lead to large errors. To overcome these challenges and obtain $R_{n}$ data with improved spatial resolution and accuracy, an operational approach was proposed in this study to derive daily 1-km $R_{n}$ , which takes the advantages from a radiative transfer model, a machine learning algorithm, and multispectral and dense diurnal temporal information of geostationary satellite observations. An improved all-sky hybrid model (AHM) coupling radiative transfer simulations with a random forest (RF) model was first developed to estimate the shortwave net radiation ($R_{ns}$). Then, another RF model was developed to estimate the daily $R_{n}$ from $R_{ns}$ , incorporating the LRD, which is called extended hybrid model (EHM). Data from the Advanced Baseline Imager (ABI) onboard the new-generation Geostationary Operational Environmental Satellite (GOES)-16 with a 5-min temporal resolution and a 1-km spatial resolution were used to test the proposed method. Compared to traditional lookup table (LUT) algorithms, the results show that AHM not only makes the process of $R_{ns}$ estimation simple and efficient but also has high accuracy in estimating instantaneous all-sky $R_{ns}$. Benefiting from high spatio-temporal resolutions, our daily $R_{ns}$ estimates using GOSE-16 data exhibited superior performance compared to using the 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) and 1° Clouds and the Earth’s Radiant Energy System (CERES) product. Using accurate daily $R_{ns}$ estimates and LRD as inputs, the EHM model shows reasonably good results for estimating $R_{n}$ ($R^{2}$ , RMSE, and bias of 0.91, 20.95 W/m2, and −0.05 W/m2, respectively). Maps of 1-km $R_{ns}$ and $R_{n}$ exhibit similar spatial patterns to those from the 1° CERES product, but with substantially more spatial details. Overall, the proposed $R_{n}$ retrieval scheme can accurately estimate all-sky 1-km $R_{ns}$ and $R_{n}$ at mid- to low-latitudes and can be easily adapted and applied to other GOES- 16-like satellites, such as Himawari-8, Meteosat Third Generation (MTG), and Fenyun-4. This study demonstrates the advantages of estimating $R_{n}$ using geostationary satellites with improved accuracy and resolutions. [ABSTRACT FROM AUTHOR]

The problems on the attitude and orbit control of a space robot during modes of addition launching. rendezvous and checking the geostationary satellite state are considered. the developed methods. algorithms and simulation results are briefly presented. [ABSTRACT FROM AUTHOR]

Snow and cloud discrimination is a main factor contributing to errors in satellite-based snow cover. To address the error, satellite-based snow cover performs snow reclassification tests on the cloud pixels of the cloud mask, but the error still remains. Machine Learning (ML) has recently been applied to remote sensing to calculate satellite-based meteorological data, and its utility has been demonstrated. In this study, snow and cloud discrimination errors were analyzed for GK-2A/AMI snow cover, and ML models (Random Forest and Deep Neural Network) were applied to accurately distinguish snow and clouds. The ML-based snow reclassified was integrated with the GK-2A/AMI snow cover through post-processing. We used the S-NPP/VIIRS snow cover and ASOS in situ snow observation data, which are satellite-based snow cover and ground truth data, as validation data to evaluate whether the snow/cloud discrimination is improved. The ML-based integrated snow cover detected 33–53% more snow compared to the GK-2A/AMI snow cover. In terms of performance, the F1-score and overall accuracy of the GK-2A/AMI snow cover was 73.06% and 89.99%, respectively, and those of the integrated snow cover were 76.78–78.28% and 90.93–91.26%, respectively. [ABSTRACT FROM AUTHOR]

This paper extends a new temperature and emissivity separation (TES) algorithm for retrieving land surface temperature and emissivity (LST and LSE) to the Advanced Geosynchronous Radiation Imager (AGRI) onboard Fengyun-4A, China's newest geostationary meteorological satellite. The extended TES algorithm was named the AGRI TES algorithm. The AGRI TES algorithm employs a modified water vapor scaling (WVS) method and a recalibrated empirical function over vegetated surfaces. In situ validation and cross-validation are utilized to investigate the accuracy of the retrieved LST and LSE. LST validation using the collected field measurements showed that the mean bias and RMSE of AGRI TES LST are 0.58 and 2.93 K in the daytime and −0.30 K and 2.18 K at nighttime, respectively; the AGRI official LST is systematically underestimated. Compared with the MODIS LST and LSE products (MYD21), the average bias and RMSE of AGRI TES LST are −0.26 K and 1.65 K, respectively. The AGRI TES LSE outperforms the AGRI official LSE in terms of accuracy and spatial integrity. This study demonstrates the good performance of the AGRI TES algorithm for the retrieval of high-quality LST and LSE, and the potential of the AGRI TES algorithm in producing operational LST and LSE products. [ABSTRACT FROM AUTHOR]

Various methodologies have been developed for selecting a subset of channels from a hyperspectral infrared (IR) sounder for assimilation. The information entropy iterative method was considered optimal for channel selection. However, this method only considers the decrease in uncertainty in the atmospheric state caused by measurements at a single time, without considering the dynamic effect of measurements over a period of time; therefore, it might not be optimal for hyperspectral IR sounders onboard geosynchronous satellites that mainly aim to observe rapidly changing weather events. An alternative channel selection method is developed by adding an $M$ index, which reflects the Jacobian variance over time; the adjusted algorithm is ideal for the Geosynchronous Interferometric Infrared Sounder (GIIRS), which is the first high-spectral-resolution advanced IR sounder onboard a geostationary weather satellite. Comparisons between the conventional algorithm (information entropy iterative method) and the adjusted algorithm show that the channels selected from GIIRS by the adjusted algorithm will have larger brightness temperature diurnal variations and better information content than the conventional algorithm, based on the same background error covariance matrix, the observational error covariance matrix, and the channel blacklist. The adjusted algorithm is able to select the channels for monitoring atmospheric temporal variation while retaining the information content from the conventional method. The 1-D variational (1Dvar) retrieval experiment also verifies the superiority of this adjusted algorithm; it indicates that using the channel selected by the adjusted algorithm could enhance the water vapor profile retrieval accuracy, especially for the lower and middle troposphere atmosphere. [ABSTRACT FROM AUTHOR]