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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]

Polar‐orbiting satellites have significantly improved our understanding of the terrestrial carbon cycle, yet they are not designed to observe sub‐daily dynamics that can provide unique insight into carbon cycle processes. Geostationary satellites offer remote sensing capabilities at temporal resolutions of 5‐min, or even less. This study explores the use of geostationary satellite data acquired by the Geostationary Operational Environmental Satellite—R Series (GOES‐R) to estimate terrestrial gross primary productivity (GPP) and ecosystem respiration (RECO) using machine learning. We collected and processed data from 126 AmeriFlux eddy covariance towers in the Contiguous United States synchronized with imagery from the GOES‐R Advanced Baseline Imager (ABI) from 2017 to 2022 to develop ML models and assess their performance. Tree‐based ensemble regressions showed promising performance for predicting GPP (R2 of 0.70 ± 0.11 and RMSE of 4.04 ± 1.65 μmol m−2 s−1) and RECO (R2 of 0.77 ± 0.10 and RMSE of 0.90 ± 0.49 μmol m−2 s−1) on a half‐hourly time step using GOES‐R surface products and top‐of‐atmosphere observations. Our findings align with global efforts to utilize geostationary satellites to improve carbon flux estimation and provide insight into how to estimate terrestrial carbon dioxide fluxes in near‐real time. Plain Language Summary: Fighting climate change requires an understanding of how ecosystems absorb and release carbon dioxide. While most Earth‐orbiting satellites provide limited snapshots, this study explores how more frequent imagery—every 5 min—from geostationary satellites, also known as weather satellites, can be used to estimate ecosystem carbon dioxide flux. By combining this data with machine learning techniques, we successfully estimated carbon uptake and release at over 100 US sites at half‐hourly intervals. This paves the way for near‐real‐time global monitoring of carbon exchange, offering a powerful tool for scientists and policymakers tackling climate change. Key Points: Advanced Baseline Imager (ABI) observations can estimate sub‐daily ecosystem carbon uptake and respiration at 126 AmeriFlux sitesIntegration of top‐of‐atmosphere products enhances the accuracy of monitoring land surface functionsABI observations can fill eddy covariance data gaps: up to 1 week (high accuracy), 6 months (low accuracy) [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]

In this study, we propose a new approach to improve the accuracy of the horizontal atmospheric motion vector (AMV) in cloud-free skies and its forecasting. We adapted the optical flow of the convolutional neural network (CNN) framework model using two 10-min interval infrared images at water vapor channels (centered at 6.3, 7.0, and 7.3 μ m ) from the Korean geostationary satellite GEO-KOMPSAT-2A (GK2A). Since all pixels had seamless AMVs calculated by CNN (CNN AMVs), we could also predict AMVs using the linear regression method. The tracking performance of the CNN-based algorithm was validated using AMVs retrieved from GK2A (GK2A AMVs) by estimating the difference between those values and the ECMWF (European Centre for Medium-Range Weather Forecasts) Reanalysis v5 (ERA5) wind data over Korea in 2022. CNN AMVs showed similar or better root-mean-square vector differences (RMSVDs) than GK2A AMVs (12.33–12.86 vs. 15.89–19.96 m/s). The RMSVDs of the forecasted AMVs were 2.74, 2.95, 3.41, and 4.79 m/s at lead times of 10, 20, 30, and 60 min, respectively. Consequently, our method showed higher accuracy for tracking motion in the production of AMVs and succeeded in forecasting AMVs. We expect that such potential improvements in computational accuracy for operational GK2A AMVs will contribute to increased accuracy when forecasting meteorological phenomena related to wind. [ABSTRACT FROM AUTHOR]

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Abstract Polar‐orbiting satellites have significantly improved our understanding of the terrestrial carbon cycle, yet they are not designed to observe sub‐daily dynamics that can provide unique insight into carbon cycle processes. Geostationary satellites offer remote sensing capabilities at temporal resolutions of 5‐min, or even less. This study explores the use of geostationary satellite data acquired by the Geostationary Operational Environmental Satellite—R Series (GOES‐R) to estimate terrestrial gross primary productivity (GPP) and ecosystem respiration (RECO) using machine learning. We collected and processed data from 126 AmeriFlux eddy covariance towers in the Contiguous United States synchronized with imagery from the GOES‐R Advanced Baseline Imager (ABI) from 2017 to 2022 to develop ML models and assess their performance. Tree‐based ensemble regressions showed promising performance for predicting GPP (R2 of 0.70 ± 0.11 and RMSE of 4.04 ± 1.65 μmol m−2 s−1) and RECO (R2 of 0.77 ± 0.10 and RMSE of 0.90 ± 0.49 μmol m−2 s−1) on a half‐hourly time step using GOES‐R surface products and top‐of‐atmosphere observations. Our findings align with global efforts to utilize geostationary satellites to improve carbon flux estimation and provide insight into how to estimate terrestrial carbon dioxide fluxes in near‐real time.

Estimating downwelling surface longwave radiation (DSLR) under cloudy-sky conditions presents a significant challenge, the parameterization methods used to estimate cloudy-sky DSLR may yield disparate results for the same scenario. Hence, it is imperative to undertake a comparative and validation study of these methods to comprehend their applicability. This study compares and validates five parameterized schemes for estimating cloudy-sky DSLR using data from the Fengyun-4A (FY-4A) and Himawari-8 geostationary satellites. Additionally, an improved algorithm for cloudy-sky DSLR estimation is proposed, integrating the radiation effect of the entire cloud layer and a nonlinear parameterization algorithm. The verification results demonstrate that the nonlinear parameterization algorithm exhibits higher accuracy compared to those reliant on cloud-base temperature. Among these, the improved algorithm has high accuracy similar to the nonlinear algorithms in BSRN (Baseline Surface Radiation Network) and TPDC (National Tibetan Plateau Data Center) sites, its RMSE are 29.62 and 30.09 W/m2, respectively. Sensitivity analysis reveals that cloud type and cloud-base temperature exert a pronounced influence on DSLR estimation, particularly within parameterization algorithms based on cloud-base temperature, warranting thorough consideration. Furthermore, the influence of land cover type and surface elevation, especially in high-altitude regions with bare surfaces, should not be disregarded in DSLR estimation.

China’s second generation of geostationary meteorological satellites Fengyun-4 (FY-4) are equipped with the Advanced Geosynchronous Radiation Imager (AGRI) which has the advantages of large number of spectral bands and high temporal resolution. To fully leverage these features, a new snow cover (SC) mapping algorithm was proposed, which firstly derives SC map from a single scene image and then combines multi-temporal SC results within one day into a daily SC composite. The novelty of the proposed algorithm includes the utilization of small spatial and temporal divisions to avoid the use of complex kernel-driven models for the correction of bidirectional effects of surface reflectance and a novel framework to merge multi-temporal SC maps by weighting the solar illumination conditions. The SC results were validated against in-situ snow depth measurements, Landsat 8 derived SC data and MODIS SC products. Results indicate that merging multi-temporal SC data can reduce cloud obscuration with the average daily cloud coverage percentage decreasing from about 47.03% for single scene to 35.62% for merged one and slightly improve snow detection accuracy. The overall accuracy (OA) ranges from 95.00% to 96.19%, and the F-score (FS) varies from 77.78% to 87.14% when compared to different reference data, suggesting an overall good performance.

Observations of wind distribution in the eye of tropical cyclones (TCs) are still limited. In this study, a method to derive atmospheric motion vectors (AMVs) for TCs is developed, where selection from multiple local rotation speeds is made by considering continuity among neighboring grid points. The method is applied to 2.5‐min interval image sequences of three TCs, Lan (2017), Haishen (2020), and Nanmadol (2022), observed by the Himawari‐8 satellite. The results are compared with AMVs derived from research‐based 30‐s Himawari‐8 special observations conducted for Haishen and Nanmadol, as well as with in‐situ dropsonde observations conducted for Lan and Nanmadol. In these storms, the AMVs obtained from the 2.5‐min interval images in the eye are found to be in good agreement with the dropsonde observations. Examinations of AMVs in the eye reveal transient azimuthal wavenumber‐1 features in all three TCs. These features are consistent with algebraically growing wavenumber‐1 disturbances, which transport angular momentum inward and accelerate the eye rotation. In the case of Lan, the angular velocity in the eye increased by approximately 1.5 times within 1 hr. This short‐term increase is further examined. Visualization of low‐level vorticity in the eye and angular momentum budget analysis suggest that angular momentum transport associated with mesovortices played an important role in the increase of tangential wind and the homogenization of angular velocity in the eye of Lan. Plain Language Summary: Observations of winds in the eye of tropical cyclones (TCs) are still limited. In this study, a new method is developed to derive the winds by objectively tracking the clouds in the eye of TCs using geostationary meteorological satellite imagery. The method is applied to 2.5‐min interval image sequences of three TCs observed by the Himawari‐8 satellite. The estimated winds in the eye are found to be in good agreement with in‐situ dropsonde observations. Examinations of asymmetric motions in the eye reveal transient azimuthal wavenumber‐1 features in all three TCs. These features contribute to the inward transport of angular momentum and acceleration of eye rotation. In the eye of Typhoon Lan (2017), the rotation speed increased by about 1.5 times within an hour. The study further examines this short‐term acceleration and suggests that angular momentum transport associated with mesoscale vortices played an important role in the measured increase in rotation speed and the homogenization of the rotation in the eye. Key Points: A novel cloud‐tracking method for deriving atmospheric motion vectors specifically for tropical cyclones has been developedThe low‐level winds in the eye of tropical cyclones were estimated from the Himawari‐8 satellite with an error of approximately 1–2 m/sA rapid increase in angular velocity was observed in an eye when the angular momentum transport associated with mesovortices was focused [ABSTRACT FROM AUTHOR]

El Niño/Southern Oscillation (ENSO) is the main climate mode that drives the interannual variability in climate and consequently vegetation greenness. While widespread green‐up has been reported and examined in tropical America during El Niño, it remains unclear how vegetation in tropical Asia changes during the period. Here, we used four remote sensing‐based leaf area index (LAI) products to investigate changes in vegetation greenness during the 2015/16 El Niño in tropical Asia. We found a strong green‐up during the 2015/16 El Niño in tropical Asia, with its regional average LAI stronger than that of tropical America. The drivers for the green‐up vary across the region, with radiation being the main driver for continental tropical Asia, and temperature and soil water anomalies in the west and east parts of maritime tropical Asia, respectively. These findings provide important insights into the response of tropical Asia's vegetation to extreme climate anomalies. Plain Language Summary: El Niño is a climate pattern that is associated with warm and dry conditions in tropical forest regions. Significant climatic changes during El Niño thus affect vegetation greenness (i.e., growth, size of canopy, amount of leaves). While an increase in vegetation greenness has been reported in tropical America during El Niño, it remains unclear how vegetation in tropical Asia changes during the period. Here, we used satellite data to investigate changes in vegetation greenness during El Niño in 2015–2016 in tropical Asia. We found a strong increase in vegetation greenness in tropical Asia during this period. The cause of this increase in greenness varied across different parts of tropical Asia. In mainland tropical Asia, sunlight was the main driver, while in maritime Southeast Asia, temperature or soil moisture was the main driver. These findings help provide better understanding of how vegetation in tropical Asia responds to extreme climate events like El Niño. Key Points: Tropical Asia experienced strong green‐up during the 2015/16 El Niño, stronger than that of tropical AmericaIn continental tropical Asia, green‐up was mostly driven by anomalously high shortwave radiationIn maritime tropical Asia, green‐up was primarily driven either by anomalously warmer temperatures or drier soil moisture from the west to east [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]

The problems of autonomous attitude determination, guidance and control in the constellations of mini-observation satellites are considered. The synthesis of the guidance laws is based on explicit relations that link the image motion in the telescope focal plane with the satellite spatial motion relative to the Earth's surface. The developed methods and algorithms for scanning areal survey performed by mini-satellite constellations in sun-synchronous orbits as well as results of computer simulation are presented, that demonstrate their effectiveness. [ABSTRACT FROM AUTHOR]

Abstract 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.

This study investigates high-frequency mapping of downward shortwave radiation (DSR) at the Earth's surface using the advanced baseline imager (ABI) instrument mounted on Geostationary Operational Environmental Satellite—R Series (GOES-R). The existing GOES-R DSR product (DSRABI) offers hourly temporal resolution and spatial resolution of 0.25°. To enhance these resolutions, we explore machine learning (ML) for DSR estimation at the native temporal resolution of GOES-R Level-2 cloud and moisture imagery product (5 min) and its native spatial resolution of 2 km at nadir. We compared four common ML regression models through the leave-one-out cross-validation algorithm for robust model assessment against ground measurements from AmeriFlux and SURFRAD networks. Results show that gradient boosting regression (GBR) achieves the best performance (R2 = 0.916, RMSE = 88.05 W·m−2) with more efficient computation compared to long short-term memory, which exhibited similar performance. DSR estimates from the GBR model through the ABI live imaging of vegetated ecosystems workflow (DSRALIVE) outperform DSRABI across various temporal resolutions and sky conditions. DSRALIVE agreement with ground measurements at SURFRAD networks exhibits high accuracy at high temporal resolutions (5-min intervals) with R2 exceeding 0.85 and RMSE = 122 W·m−2. We conclude that GBR offers a promising approach for high-frequency DSR mapping from GOES-R, enabling improved applications for near-real-time monitoring of terrestrial carbon and water fluxes.

Typhoons' rapid variation makes effective hazard prevention difficult, which remains a challenging research topic requiring novel observation technology and analysis methodology. We developed an automatic methodology for mining the information on the rapid variation of typhoon cloud-top motion from high-spatial-temporal-resolution satellite cloud images, which has steps of estimating and decomposing the typhoon cloud-top motion, locating the typhoon cloud-top center, and analyzing the motion and center position variations. The Gaofen-4 satellite acquired 50-m-1-min-resolution cloud images of Typhoon Megi's (2016) landfall over mountainous Taiwan Island. The data provide an excellent chance to explore what such high-spatial-temporal-resolution successive observations can tell about the cloud-top motion variation of a typhoon landfall using the methodology. Before its landfall, Typhoon Megi's cloud-top center dramatically increased the northwestward migration speed from ∼22 to ∼58 km/h in only ∼2.5 h. It also presented small-scale oscillation along its migration path with a 1.6-to-2.4-km amplitude, a 31-to-33-km spatial period, and a 1.46-to-1.48-h temporal period. We averaged the motion speeds (velocity magnitudes) in each motion field to assess the field strength. The so-calculated cloud-top average rotation speed of Typhoon Megi decreased quickly during landfall. Meanwhile, its average divergence speed increased dramatically by ∼30% in a short period of ∼2.5 h, reaching ∼2 times the rotation speed. This result means that Megi's cloud-top motion rapidly changed from a typical rotation motion-dominated status to a divergence motion-dominated status. This rapid transformation from typical rotation motion dominance to divergence motion dominance implies intense upward warm and moist airflows that cause severe precipitation.

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.

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]

Abstract El Niño/Southern Oscillation (ENSO) is the main climate mode that drives the interannual variability in climate and consequently vegetation greenness. While widespread green‐up has been reported and examined in tropical America during El Niño, it remains unclear how vegetation in tropical Asia changes during the period. Here, we used four remote sensing‐based leaf area index (LAI) products to investigate changes in vegetation greenness during the 2015/16 El Niño in tropical Asia. We found a strong green‐up during the 2015/16 El Niño in tropical Asia, with its regional average LAI stronger than that of tropical America. The drivers for the green‐up vary across the region, with radiation being the main driver for continental tropical Asia, and temperature and soil water anomalies in the west and east parts of maritime tropical Asia, respectively. These findings provide important insights into the response of tropical Asia's vegetation to extreme climate anomalies.

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.

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 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.

This work evaluates the quality of the satellite-based GLobal radiation model version 1.2 (GL1.2) estimates for four cloud classes. The GL1.2 calculates the global solar irradiance at ground level using images from a single visible band channel (VIS) of the Geostationary Operational Environmental Satellite -- GOES (GOES-East). The model´s performance was assessed by comparing hourly mean GL1.2 values with groundbased hourly measurements from the Brazilian National Institute of Meteorology (INMET, 354 automatic weather stations), for the entire year 2017. A satellite-based cloud classifier was adopted to discriminate the datasets according to prevailing cloud conditions (cumulus, stratus, cirrus and multilayer), but the clear sky behaviour was presented. The results were analysed for the five Brazilian regions. In the first analysis, we selected days with a predominance for five stations. It was found that the diurnal cycle was well reproduced. Then the regional investigation for the cloud types reveals that the best results are found for the Center West region over multilayer cloud (mean bias error, MBEannual = -2 ± 91 Wm-2 and root mean squared error, RMSE = 91 Wm-2), while the worst ones are in the North over cumulus fields (MBEannual = 101.5 ± 136.4 Wm-2). When considering all cloud types, the MBEannual is lower than 5 Wm-2 for the Northeast, Southeast and South regions, but it reaches 101 Wm-2 in the North. It is noteworthy that winter has the highest MBE in all classes analysed in the North, as well as cirrus situations in other regions. Although the inhomogeneities of cumulus and the semitransparent cirrus clouds tend to propagate errors to the model, the quality of GL1.2 data has a high degree of agreement with the observations. Improvements including an updated monthly minimum reflectance (Rmin) and water vapour column (H2Ovapour), and a better spatial resolution of the Advanced Baseline Imager (ABI) of GOES-16 (ABI/GOES-16) VIS imagery will allow refinement, especially for cumulus clouds. [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]

Accurate estimation of gross primary production (GPP), the amount of carbon absorbed by plants via photosynthesis, is of great importance for understanding ecosystem functions, carbon cycling, and climate-carbon feedbacks. Remote sensing has been widely used to quantify GPP at regional to global scales. However, polar-orbiting satellites (e.g., Landsat, Sentinel, Terra, Aqua, Suomi NPP, JPSS, OCO-2) lack the capability to examine the diurnal cycles of GPP because they observe the Earth's surface at the same time of day. The Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), launched in June 2018, observes the land surface temperature (LST) at different times of day with high spatial resolution (70 m × 70 m) from the International Space Station (ISS). Here, we made use of ECOSTRESS data to predict instantaneous GPP with high spatial resolution for different times of day using a data-driven approach based on machine learning. The predictive GPP model used instantaneous ECOSTRESS LST observations along with the daily enhanced vegetation index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), land cover type from the National Land Cover Database (NCLD), and instantaneous meteorological data from the ERA5 reanalysis dataset. Our model estimated instantaneous GPP across 56 flux tower sites fairly well (R2 = 0.88, Root Mean Squared Error (RMSE) = 2.42 μmol CO2 m−2 s−1). The instantaneous GPP estimates driven by ECOSTRESS LST captured the diurnal variations of tower GPP for different biomes. We then produced multiple high resolution ECOSTRESS GPP maps for the central and northern California. We found distinct changes in GPP at different times of day (e.g., higher in late morning, peak around noon, approaching zero at dusk), and clear differences in productivity across landscapes (e.g., savannas, croplands, grasslands, and forests) for different times of day. ECOSTRESS GPP also captured the seasonal variations in the diurnal cycling of photosynthesis. This study demonstrates the feasibility of using ECOSTRESS data for producing instantaneous GPP (i.e., GPP for the acquisition time of the ECOSTRESS data) for different times of day. The ECOSTRESS GPP can shed light on how plant photosynthesis and water use vary over the course of the diurnal cycle and inform agricultural management and future improvement of terrestrial biosphere/land surface models.

Accurate estimation of gross primary production (GPP), the amount of carbon absorbed by plants via photosynthesis, is of great importance for understanding ecosystem functions, carbon cycling, and climate-carbon feedbacks. Remote sensing has been widely used to quantify GPP at regional to global scales. However, polar-orbiting satellites (e.g., Landsat, Sentinel, Terra, Aqua, Suomi NPP, JPSS, OCO-2) lack the capability to examine the diurnal cycles of GPP because they observe the Earth's surface at the same time of day. The Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), launched in June 2018, observes the land surface temperature (LST) at different times of day with high spatial resolution (70 m × 70 m) from the International Space Station (ISS). Here, we made use of ECOSTRESS data to predict instantaneous GPP with high spatial resolution for different times of day using a data-driven approach based on machine learning. The predictive GPP model used instantaneous ECOSTRESS LST observations along with the daily enhanced vegetation index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), land cover type from the National Land Cover Database (NCLD), and instantaneous meteorological data from the ERA5 reanalysis dataset. Our model estimated instantaneous GPP across 56 flux tower sites fairly well (R2 = 0.88, Root Mean Squared Error (RMSE) = 2.42 μmol CO2 m−2 s−1). The instantaneous GPP estimates driven by ECOSTRESS LST captured the diurnal variations of tower GPP for different biomes. We then produced multiple high resolution ECOSTRESS GPP maps for the central and northern California. We found distinct changes in GPP at different times of day (e.g., higher in late morning, peak around noon, approaching zero at dusk), and clear differences in productivity across landscapes (e.g., savannas, croplands, grasslands, and forests) for different times of day. ECOSTRESS GPP also captured the seasonal variations in the diurnal cycling of photosynthesis. This study demonstrates the feasibility of using ECOSTRESS data for producing instantaneous GPP (i.e., GPP for the acquisition time of the ECOSTRESS data) for different times of day. The ECOSTRESS GPP can shed light on how plant photosynthesis and water use vary over the course of the diurnal cycle and inform agricultural management and future improvement of terrestrial biosphere/land surface models.

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]

Snow plays a crucial role in the climate. In particular, snow controls the surface temperature and stabilizes the energy budget due to its spectral characteristics. However, snow can be affected by climate change. If the snow cover extent (SCE) diminishes due to global warming, the rate of introducing radiance to the surface increases because non-snow areas can absorb more solar radiance. Therefore, it is highly important to accurately detect snow-covered areas. In this paper, we develop an algorithm for deriving a daily snow cover map of East Asia using Geostationary-Korean Multi-Purpose SATellite-2A (GEO-KOMPSAT-2A, GK-2A) imagery. After processes for cloud masking and misclassification removal are conducted, a threshold with consideration for the characteristics of a geostationary satellite is applied to the normalized difference snow index (NDSI), and a daily snow cover map is generated utilizing a stacking process. In the case of the cloud mask, the angle-time variation and displacement by clouds proposed in this study were used to enhance the accuracy of cloud detection. For the quantitative validation, the F1 score was 0.89 for Landsat-8 and 0.79 for the interactive multisensor snow and ice mapping system (IMS). In addition, when the snow cover extent calculated from the IMS was compared, the correlation reached 0.91 from December 2020 to January 2021.

Geostationary meteorological satellites provide precipitation estimates with a high spatio-temporal resolution, which is important for near real-time precipitation monitoring. This study systematically evaluated geostationary orbit (GEO) satellite-based quantitative precipitation estimates (QPEs) from Chinese Fengyun-2 G (FY-2 G), Fengyun-4A (FY-4A), and South Korean Geo-KOMPSAT-2A (GK-2A) across Northeast Asia in 2020. Compared against ground-based rainfall gauges at a 6-hourly scale, FY-2 G provided the highest accuracy in the China region with a high correlation coefficient (R = 0.53) and a low bias (−0.26 mm) due to the ground calibration process in FY-2 G. Conversely, GK-2A provided more accurate precipitation estimates for South Korea and Japan stations. FY-4A QPE generally showed a large positive bias throughout different seasons, although it provided satisfactory R and categorical statistics. FY-based QPEs slightly overestimated summer precipitation, especially over South Korea and Japan region, while GK-2A tended to underestimate summer precipitation. All examined QPEs showed poor accuracy during the winter season due to the frozen particles and ice clouds. Intensity analysis revealed that FY-based QPEs tended to overestimate the occurrence of no rain and heavy rain cases, whereas GK-2A underestimated no rain and heavy rain cases and overestimated light rain occurrence. It is also found that all examined QPEs captured the temporal variation of precipitation during storm events, while FY-based products overestimated heavy precipitation peaks and GK-2A underestimated peak precipitation. The findings in the study provided valuable information to further improve current infrared precipitation retrieval algorithms.

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.

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.

Two geostationary satellites developed by the Korea Aerospace Research Institute and currently in operation are the GEO-KOMPSAT-2A (GK-2A) and the GEO-KOMPSAT-2B (GK-2B). The main instruments mounted on these satellites are the Advanced Meteorological Imager (AMI), the Geostationary Ocean Color Imager (GOCI-II) and the Geostationary Environment Monitoring Spectrometer (GEMS). This paper briefly introduced the GK-2A and GK-2B programs including measurement principles and elements of the instruments. Moreover, the data formats, operational products, and applications are summarized.

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]

Understanding bushfire–atmosphere interactions is essential for accurate prediction of fire behaviour, and for the safe and effective strategic management of fires to mitigate risk to people and property. Bushfires with feedbacks to thunderstorms represent the most extreme form of fire–atmosphere interaction, with potential to initiate tornadoes, lightning and hazardous winds causing dangerous fire behaviour and new ignitions many kilometres from the fire front. However, there is very little evidence that links quantitative fire behaviour with observed thunderstorm dynamics. Here we combine stochastic modelling of fire behaviour with satellite and mobile weather radar data of a bushfire thunderstorm in Queensland, Australia. The results show the coupling between fire behaviour and thunderstorm development in a conditionally unstable atmosphere. The process by which the coupling occurs raises questions as to the cause and effect relationship of the bushfire-initiated thunderstorms and associated fire behaviour. Recommendations for future research are made, highlighting the need for understanding links between modelled and observed fire behaviour dynamics and atmospheric thermodynamics. Bushfires can support the evolution of thunderstorms. Here, we combine a variety of observations of a bushfire thunderstorm with ensemble fire spread modelling and fire severity mapping. Results show the coupling between the intensity of the fire and its burning through forest canopy with thunderstorm updrafts. [ABSTRACT FROM AUTHOR]