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In this study, prominent dust source areas are identified along with their plume extent using high temporal frequency satellite observations. Hourly dust plume observations of the Dust Belt from geostationary‐orbit satellites are analyzed for the 2017‐12–2022‐11 period. To identify dust source areas and their extents, we back‐track plumes to their source, assessing source areas in terms of emission frequency, contribution, and plume extent patterns. This method advances over traditional source allocation techniques that rely on polar‐orbiting satellites based on a few daily passes and meteorological wind fields for backtracking. Our findings indicate that Boreal summer is the most intense season for most sources, except in the Southern Sahara, which experiences winterly winds. Our analysis also reveals significant contributions from regions within the Sahara that experience expansive but infrequent dust storms, highlighting the importance of considering both frequency and magnitude in understanding dust emissions. Plain Language Summary: This study focuses on the role of mineral dust, a significant climate constituent originating mainly from arid regions. Identifying where dust comes from and where it goes is essential for understanding how it affects the climate. To do so, we employ high temporal frequency satellite data to backtrack dust plumes to their sources, revealing key dust‐emitting regions and their seasonal variations. We also quantify each source area's frequency and magnitude of emissions and the spatial distribution of emanating dust plumes. Our findings show that summer is the peak season for most areas, except for the strongest dust sources, located in Southern Sahara, which peak in winter driven by seasonal winds. Additionally, some Saharan source areas exhibit severe storms but have been under‐emphasized because they were measured by the frequency of their storms. Key Points: High‐temporal‐resolution images from geostationary‐orbit satellites enable precise back‐tracking of dust source areasDust source areas are analyzed in terms of their emission frequency, contribution to the regional atmosphere, and corresponding plume extentSummer is the peak dust emission season for most sources in West Asia and Sahara and ephemeral lake beds are active during the dry season [ABSTRACT FROM AUTHOR]

The unsteady airflow will cause the distortion and delay change of the signal propagation path, resulting in the delay and distortion of the received satellite signal. This will lead to increased ranging and direction finding errors in the positioning system, which will affect the precision and accuracy of the positioning results. Therefore, a new multi-satellite anti-interference hybrid positioning method under the influence of unstable airflow is studied. Preprocess the unstable turbulence data at high altitude, and verify the turbulence data authenticity quickly. The flow field description equation is constructed based on the momentum conservation theorem. The multi-satellite motion equation is established by using the carrier phase observations and satellite antenna phase center coordinates, and the anti-interference hybrid positioning model is constructed. Global Navigation Satellite System (GNSS) technology is introduced to solve the multi-satellite hybrid anti-interference model by using the signals of multiple satellites to calculate the receiver's position, speed and time. The experimental results show that, for static targets, the research method has a small deviation in the positioning of the target N, S and E. When using the mixed positioning of near-earth satellite, medium altitude satellite and geosynchronous satellite, the mean square value of positioning error is always lower than 0.002 cm. For the dynamic target, the method is not disturbed by clutter, and the target is stably located. Moreover, the proposed method has lower localization delay and higher localization efficiency. [ABSTRACT FROM AUTHOR]

As a crucial element in the Earth's system, development of convective clouds is still insufficiently understood and simulated in both weather and climate models, particularly for small‐scale regional convective clouds. In this study, a series of convective clouds cases with relatively small scales are selected over south China, and the evolution characteristics of those convective clouds are investigated using high spatiotemporal resolution geostationary satellite data. Statistical results show that the shorter the life cycle or the smaller the area, the higher the proportion of convective clouds. Notably, approximately 79.23% of convective clouds have a life cycle of less than 3 hr, and 63.81% have an area of less than 500 km2 for selected cases. In addition, there are significant differences in the cloud characteristics and meteorological parameters during various convective cloud stages and durations. Nevertheless, the relative proportions of convective clouds at three identified stages remain relatively constant with almost no dependence on duration of convective clouds, which are 33.50%, 23.92%, and 42.58% for the developing, mature, and dissipation stages, respectively. In addition, we find that the cloud‐top cooling rate during the developing stage also affects the characteristics of the later stage of convective clouds. Quantitatively, the average cloud area and duration changed by 157.03 km2 and 0.17 hr when the cloud‐top cooling rate varies by 15 K/h. Plain Language Summary: Convective clouds have complex structures, develop rapidly, and pose a significant threat to aviation safety. The initiation and evolution of convective clouds are not well resolved, making the simulation of convective clouds highly challenging among current weather and climate models, particularly in south China, a hotpot of convective clouds. Evolution characteristics of convective clouds with relatively small spatial scales are investigated in this study based on the high spatiotemporal resolution Himawari‐8 data. It shows that convective clouds have significant diurnal variation, while with short‐term (duration less than 3 hr) and small‐scale (area less than 500 km2) convective clouds dominant. In addition, there are significant differences in cloud characteristics and meteorological conditions for convective clouds at various stages or with different durations. However, the duration proportion of convective clouds at different stages (developing, mature, and dissipation stages) remains basically the same relative to the total convective cloud life cycle, no matter how long the duration of convective cloud life cycle is. Key Points: Significant differences exist in cloud characteristics for convective clouds at various stages or with different durationsThe larger the cloud top cooling rate during the development stage, the smaller the maximum area and the shorter the cloud life cycleThe developing, mature, and dissipation stages account for 33.50%, 23.92%, and 42.58% of convective cloud duration, respectively [ABSTRACT FROM AUTHOR]

Based on the hourly precipitation data from the observation stations, the accuracy of hourly QPEs from Fengyun geostationary satellites FY-4A, FY-2 H and multi-satellite joint IMERG-Early precipitation products over the southeastern Tibetan Plateau were compared and assessed. And the relationship between the accuracy of various satellites and terrain were discussed. The results show that: (1) The QPEs of FY-4A, FY-2 H and IMERG-Early can reflect the temporal and spatial distribution characteristics of real precipitation; (2) The QPEs of FY-4A and FY-2 H overestimate the precipitation in the researched region, and the overestimation of FY-4A was more obvious, while IMERG-Early QPE underestimates the precipitation. IMERG-Early QPE showed the best overall accuracy performance. (3) All three satellite QPEs were less accurate in July and August. (4) The accuracy of the QPEs of FY-4A and FY −2 H were better than that of IMERG-Early QPE in heavy rainfall. (5) The accuracy of three satellite QPEs increase with the increase of altitude. The results can provide reference for selecting satellite QPEs for operational applications in the southeastern Tibetan Plateau. [ABSTRACT FROM AUTHOR]

The Advanced Geostationary Radiation Imager (AGRI) sensor on board the geostationary satellite Fengyun-4B (FY-4B) is capable of capturing particles in different phases in the atmospheric environment and acquiring aerosol observation data with high spatial and temporal resolution. To understand the quality of the Land Aerosol (LDA) product of AGRI and its application prospects, we conducted a comprehensive evaluation of the AGRI LDA AOD. Using the 550 nm AGRI LDA AOD (550 nm) of nearly 1 year (1 October 2022 to 30 September 2023) to compare with the Aerosol Robotic Network (AERONET), MODIS MAIAC, and Himawari-9/AHI AODs. Results show the erratic algorithmic performance of AGRI LDA AOD, the correlation coefficient (R), mean error (Bias), root mean square error (RMSE), and the percentage of data with errors falling within the expected error envelope of ± (0.05 + 0.15 × AOD A E R O N E T) (within EE15) of the LDA AOD dataset are 0.55, 0.328, 0.533, and 34%, respectively. The LDA AOD appears to be overestimated easily in the southern and western regions of China and performs poorly in the offshore areas, with an R of 0.43, a Bias of 0.334, a larger RMSE of 0.597, and a global climate observing system fraction (GCOSF) percentage of 15% compared to the inland areas (R = 0.60, Bias = 0.163, RMSE = 0.509, GCOSF = 17%). Future improvements should focus on surface reflectance calculation, water vapor attenuation, and more suitable aerosol model selection to improve the algorithm's accuracy. [ABSTRACT FROM AUTHOR]

The major link between satellite-derived vertical column densities (VCDs) of nitrogen dioxide (NO 2) and ground-level concentrations is theoretically the NO 2 mixing height (NMH). Various meteorological parameters have been used as a proxy for NMH in existing studies. This study developed a nested XGBoost machine learning model to convert VCDs of NO 2 into ground-level NO 2 concentrations across China using Geostationary Environmental Monitoring Spectrometer (GEMS) measurements. This nested model was designed to directly incorporate NMH into the methodological framework to estimate satellite-derived ground-level NO 2 concentrations. The inner machine learning model predicted the NMH from meteorological parameters, which were then input into the main XGBoost machine learning model to predict the ground-level NO 2 concentrations from its VCDs. The inclusion of NMH significantly enhanced the accuracy of ground-level NO 2 concentration estimates; i.e., the R2 values were improved from 0.73 to 0.93 in 10-fold cross-validation and from 0.88 to 0.99 in the fully trained model. Furthermore, NMH was identified as the second most important predictor variable, following the VCDs of NO 2. Subsequently, the satellite-derived ground-level NO 2 data were analyzed across subregions with varying geographic locations and urbanization levels. Highly populated areas typically experienced peak NO 2 concentrations during the early morning rush hour, whereas areas categorized as lightly populated observed a slight increase in NO 2 levels 1 or 2 h later, likely due to regional pollutant dispersion from urban sources. This study underscores the importance of incorporating NMH in estimating ground-level NO 2 from satellite column measurements and highlights the significant advantages of geostationary satellites in providing detailed air pollution information at an hourly resolution. [ABSTRACT FROM AUTHOR]

Cloud products from geostationary satellites are the main alternative to surface synoptic cloud observations (SYNOP), and have become the baseline products for the development and construction of the Quality Management System (QMS) of integrated meteorological observation in China. This study addresses the needs of the China Meteorological Administration (CMA) for such an operational reform, and it is carried out using real-time observations obtained from the Fengyun-2E (FY-2E) geostationary satellite in 2012 to derive cloud total amount and classification using two different methods. Compared to surface SYNOP observations, the cloud total amount estimated by FY-2E is generally significantly lower (about 30% lower on average). The cloud classification resulting from the two methods used in this study is also significantly different from the classification obtained from surface observations (difference between 22 and 32%). The difference is smaller for the classification method, which uses additional auxiliary temperature profiles. [ABSTRACT FROM AUTHOR]

In order to deepen the knowledge of the seasonal variation in total cloud cover (TCC) in Xinjiang, China (XJ), a typical arid region, and to broaden the understanding of the seasonal variation in cloud type (CLT) in the region, we used TCC and CLT datasets from the latest generation of the geostationary satellite Fengyun 4A (FY-4A) from 2018 to 2022 to investigate the seasonal variation characteristics of TCC and CLT in XJ. Meanwhile, to verify the accuracy of TCC from FY-4A, ground observation (GROB) TCC datasets from 105 national meteorological stations (NMSs) in XJ and TCC datasets from ERA5 during the same period were used. In addition, the correlation between TCC from FY-4A and meteorological factors from ERA5 was also analyzed in this study. The TCC from FY-4A, GROB, and ERA5 can all well reflect the significant seasonal variation in TCC in XJ, with the highest (lowest) mean TCC and a distribution pattern of high in the southwest (northwest) and low in the northeast (southeast) in spring (fall) in XJ. Although the mean TCC from FY-4A in all four seasons was lower than that from GROB, the two were comparable in spring (44.09% and 47.32%) and summer (42.88% and 43.17%), while there was a significant difference between the two in fall (27.86% and 40.19%) and winter (30.58% and 46.93%) for 105 NMSs in XJ. The TCC from FY-4A was lower (higher) than that from GROB in spring and summer at most NMSs in northern (southern) XJ, while the TCC from FY-4A was lower than that from GROB for the vast majority of NMSs in fall and winter, especially in northern XJ. The seasonal variation in the spatial distribution of different CLTs (clear, water-type, supercooled-type, mixed-type, ice-type, cirrus-type, and overlap-type) from FY-4A exhibited diverse variation characteristics. Water-type (supercooled-water-type) had a high-frequency center of over 30% in the Tarim Basin (Kunlun Mountains) during summer. Mixed-type (ice-type and cirrus-type) had the highest frequency in winter (spring), while overlap-type had the highest frequency in summer. The correlation between TCC and water vapor conditions (total column vertically integrated water vapor, specific humidity at 250 hPa, 500 hPa, and 700 hPa) was positive in XJ. [ABSTRACT FROM AUTHOR]

The Fengyun-4B (FY-4B) satellite is currently the primary operational geostationary meteorological satellite in China, replacing the previous FY-4A satellite. The advanced geostationary radiation imager (AGRI) aboard the FY-4B satellite provides an operational sea surface temperature (SST) product with a high observation frequency of 15 min. This paper conducts the first data quality assessment of operational SST products from the FY-4B/AGRI using quality-controlled measured SSTs from the in situ SST quality monitor dataset and foundation SSTs produced by the operational sea surface temperature and sea ice analysis (OSTIA) system from July 2023 to January 2024. The FY-4B/AGRI SST product provides a data quality level flag on a pixel-by-pixel basis. Accuracy evaluations are conducted on the FY-4B/AGRI SST product with different data quality levels. The results indicate that the FY-4B/AGRI operational SST generally has a negative mean bias compared to in situ SST and OSTIA SST, and that the accuracy of the FY-4B/AGRI SST, with an excellent quality level, can meet the needs of practical applications. The FY-4B/AGRI SST with an excellent quality level demonstrates a strong correlation with in situ SST and OSTIA SST, with a correlation coefficient R exceeding 0.99. Compared with in situ SST, the bias, root mean square error (RMSE), and unbiased RMSE (ubRMSE) of the FY-4B/AGRI SST with an excellent quality level are −0.19, 0.66, and 0.63 °C in daytime, and −0.15, 0.70, and 0.68 °C at night, respectively. Compared with OSTIA SST, the bias, RMSE, and ubRMSE of the FY-4B/AGRI SST with an excellent data quality level are −0.10, 0.64, and 0.63 °C in daytime, and −0.13, 0.68, and 0.67 °C at night. The FY-4B/AGRI SST tends to underestimate the sea water temperature in mid–low-latitude regions, while it tends to overestimate sea water temperature in high-latitude regions and near the edges of the full disk. The time-varying validation of FY-4B/AGRI SST accuracy shows weak fluctuations with a period of 3–4 months. Hourly accuracy verification shows that the difference between the FY-4B/AGRI SST and OSTIA SST reflects a diurnal effect. However, FY-4B/AGRI SST products need to be used with caution around midnight to avoid an abnormal accuracy. This paper also discusses the relationships between the FY-4B/AGRI SST and satellite zenith angle, water vapor content, wind speed, and in situ SST, which have an undeniable impact on the underestimation of the FY-4B/AGRI operational SST. The accuracy of the FY-4B/AGRI operational SST retrieval algorithm still needs to be further improved in the future. [ABSTRACT FROM AUTHOR]

In order to avoid harmful interference in the radio-frequency spectrum, geosynchronous (GEO) satellite operators coordinate their plans with one another by publicly describing their preferred orbital positions and frequency bands in an assignment process managed by the International Telecommunication Union (ITU), a specialized agency of the United Nations. But once assignments are determined, how well do satellite operators comply with them? This work describes a method for assessing GEO satellite operators' compliance with the physical component of the ITU's satellite network assignments in the geostationary belt using publicly available data. The positions of real, cataloged GEO satellites, measured in longitudinal degrees, are compared with data describing ITU satellite networks published by the Union's Space Services Department to determine whether satellites are operating near an active network assigned to one of their corresponding ITU administrations. Compliance for three satellites that perform relatively frequent longitudinal-shift maneuvers in GEO — Telesat's Nimiq 2 , Russia's Luch (Olymp) , and China's SJ-17 — is assessed over a multi-year study period and discussed. This work illustrates how historical compliance assessment can contribute to the identification and characterization of anomalous satellite behaviors, such as re-positioning the same satellite repeatedly to bring many ITU satellite networks into use, performing non-cooperative close approaches with nearby neighbors, and rendezvous operations, in the Nimiq , Luch , and SJ-17 examples, respectively. • Geosynchronous orbit is the only orbital regime with an existing slotting scheme. • Keeping to assigned orbital positions and frequencies prevents harmful interference. • Once assignments are determined, satellite operators do not always comply with them. • Publicly available data can be used to assess compliance with orbital assignments. • Patterns of violation to guidelines in GEO should inform new rules in LEO. [ABSTRACT FROM AUTHOR]

Through the application of cloud top temperature data and the extraction of supercooled cloud information in cloud-type data from the next-generation Himawari-8 geostationary satellite with high spatial–temporal resolution, a quantitative evaluation of the forecasting performance of the weather modification model named the Cloud and Precipitation Explicit Forecasting System (CPEFS) was conducted. The evaluation, based on selected forecast cases from 8 days in September and October 2018 initialized at 00 and 12 UTC every day, focused especially on the forecasting performance in supercooled clouds (vertical integrated supercooled liquid water, VISL > 0), including the comprehensive spatial distribution of cloud top temperature (CTT) and 3 h precipitation over 0.1 mm (R3 > 0.1). The results indicated that the forecasting performance for VISL > 0 was relatively good, with the Threat Score (TS) ranging from 0.46 to 0.67. The forecasts initialized at 12 UTC slightly outperformed the forecasts initialized at 00 UTC. Additionally, the corresponding spatial Anomaly Correlation Coefficient (ACC) of CTT between forecasts and observations was 0.23, and the TS for R3 > 0.1 reached as high as 0.87. For a mix of cold and warm cloud systems, there was a correlation between the forecasting performance of VISL > 0 and CTT. The trends in the TS for VISL > 0 and the ACC of CTT aligned with the forecast lead-time. [ABSTRACT FROM AUTHOR]

The paper presents the results of studying the dynamics of the magnetic field and electron fluxes of the Earth's outer radiation belt with an energy of >2 MeV according to the GOES-15 geostationary satellite during a fairly long period (October 16, 2016 to February 16, 2017) of moderate and weak magnetospheric activity caused by the arrival of a sequence of high-speed solar wind streams. The main variations in the electron flux in the geostationary orbit are caused by the movement, deceleration and acceleration of particles in the outer radiation belt of the Earth under the influence of geomagnetic activity. The results of a comparative analysis of variations in electron fluxes and components of the magnetospheric field testify to the predominant influence of the magnitude and structure of the magnetospheric field on the dynamics of relativistic electron fluxes in the outer radiation belt. Changes in the components of the magnetospheric magnetic field and in electron fluxes are results of a single process that occurs together with changes in the magnetosphere as a whole. [ABSTRACT FROM AUTHOR]

The sediment biogeochemistry of phosphorus (P) and biogenic silica (BSi) in the southern South China Sea shelf (SSCS) is inadequate understood. Here, we examine the spatial distributions of P species and BSi in surface sediments across the Sunda Shelf and explore their relationships with satellite data of sea surface chlorophyll-a (Chla), suspended particulate matter (SPM), particulate organic carbon, particulate inorganic carbon, primary production (PP), and phytoplankton functional types. The total sediment P (TSP), in the range from 283.94 to 579.94 mg/g dry sediment, was dominated by inorganic P with higher levels in stations outside the Lupar and Saribas estuaries. The TSP was composed of seven different P species including Ca-IP and Ca-OP (58%), Detr-P (15%), Exch-P (11%), Fe-IP (10%), Ref-P (4%) and Ads-IP (2%) based on a sequential extraction method. The concentrations of various sediment P species were found significantly correlated with satellite climatological SPM, diatom biomass, and monthly climatological pico-PP revealing the importance of biological production for the sediment P storage, whereas the sediment BSi concentration showed no correlations with any of the above satellite products. An elevated R2 value in the regression of BSi with the in-situ depth-integrated Chla points to the need of developing satellite algorithms with vertical profiles from space. Finally, we argue that future studies of the SSCS sediment biogeochemistry may be benefited from data of hyperspectral and geostationary satellites. [ABSTRACT FROM AUTHOR]

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Understanding of internal solitary wave (ISW) behavior has been limited due to sparse observations. We used high‐resolution Himawari‐8 satellite imagery and mooring observations to reveal the two‐dimensional (x–y) propagation process of ISWs in the South China Sea as they westward propagate onto the Dongsha plateau and encounter Dongsha Atoll. The 2D depiction of wave speed distribution, derived from detected wave crest positions every 10 min, shows the wave speeds range from 3 m s−1 to 1 m s−1 and have a tight correspondence to the local water depth. The correlation coefficient between the wave speeds and the Dubreil–Jacotin–Long (DJL) solutions is around 0.7, with a root mean squared value of 0.26 m s−1, and the representative available potential energy for this region is considered to be 130 MJ m−1. However, diffusions of wave speed in the ISW's lateral direction, particularly around abrupt topography, contribute to occurrences of outliers. Pairs of incident and reflected waves are well recognized east of Dongsha Atoll. The incident wave packet is known to be classified into a‐type and b‐type waves. The reflected waves associated with the b‐wave, identifiable as mode‐1 depression ISWs, are traced back to their generation site at depths of 100–200 m. In contrast, the reflected waves of the a‐wave remain elusive in shallower waters (<300 m), likely due to interference from their longer incident counterparts. The reflected wave, however, is slower and decelerates toward deeper water, deviating from the DJL prediction. These comprehensive observations can help refine models for improved accuracy. Plain Language Summary: Internal solitary waves (ISWs) in the South China Sea that propagate westward onto the Dongsha plateau and encounter Dongsha Atoll are traced. Wave speed varies from 3 m s−1 to 1 m s−1 and corresponds well to the local water depth. The variation of wave speed with depth shows an agreement with the solutions of a standard model (0.7 correlation coefficient), except in regions around abrupt topography. As the wave passes over a seamount, discrepancies in wave speed across the wave's lateral direction can contribute to occurrences of outliers. The incident wave and its reflected counterpart are well recognized semidiurnally to the east of Dongsha Atoll. However, when overlapping with the prior incident wave group, the reflected waves are unclear and difficult to distinguish. The reflected waves can be identified as waves causing isotherms to dip downward and creating a trough‐like depression in the water column. Their propagation speed is generally slower than its incident counterpart. The horizontal 2D depiction of wave speed distribution revisits the process from westward propagating ISWs to their reflected counterpart and improves understanding of reflected waves. Key Points: The spatial evolution of wave speeds with high spatiotemporal resolution mostly agrees with the Dubreil–Jacotin–Long modelAround abrupt topography, the wave's horizontal 2D distribution reveals the lateral diffusion of wave speeds along the wave crestInternal solitary waves propagating westward are observed to reflect off topography around the Dongsha Plateau by both satellite and mooring [ABSTRACT FROM AUTHOR]

Aerosols can cause brightening of stratocumulus clouds, thereby cooling the climate. Observations and models disagree on the magnitude of this cooling, partly because of the aerosol-induced liquid water path (LWP) adjustment, with climate models predicting an increase in the LWP and satellites observing a weak decrease in response to increasing aerosols. With higher-resolution global climate models, which allow the simulation of mesoscale circulations in which stratocumulus clouds are embedded, there is hope to start bridging this gap. In this study, we present boreal summertime simulations conducted with the ICOsahedral Non-hydrostatic (ICON) global storm-resolving model (GSRM). Compared to geostationary satellite data, ICON produces realistic cloud coverage in the stratocumulus regions; however, these clouds look cumuliform, and the sign of LWP adjustments disagrees with observations. We investigate this disagreement with a causal approach, which combines time series with knowledge of cloud processes, allowing us to diagnose the sources of observation–model discrepancies. The positive ICON LWP adjustment results from a superposition of processes, with an overestimated positive response due to (1) precipitation suppression, (2) a lack of wet scavenging, and (3) cloud deepening under a weak inversion, despite (4) small negative influences from cloud-top entrainment enhancement. We also find that precipitation suppression and entrainment enhancement occur at different intensities during the day and the night, implying that daytime satellite studies suffer from selection bias. This causal methodology can guide modelers on how to modify model parameterizations and setups to reconcile conflicting studies concerning the sign and magnitude of LWP adjustments across different data sources. [ABSTRACT FROM AUTHOR]

To address the increasing demand for diurnal information on trace gases and aerosols, a series of geostationary (GEO) satellite programs called GEO‐constellation have been initiated, with the launch of the Geostationary Environment Monitoring Spectrometer (GEMS) onboard Geostationary Korea Multi‐Purpose Satellite 2B (GK2B). To assess the sensor performance of GEMS in orbit, the current work suggests employing an inter‐calibration methodology involving the Advanced Meteorological Imager (AMI) aboard its twin satellite, GK2A. Twin satellites have a significant advantage in obtaining collocation data sets across diverse spatiotemporal, angular, and atmospheric conditions, enabling rigorous collocation criteria effectively reducing mismatch uncertainty. The results present robust correlation coefficients over 0.99, revealing the current calibration characteristics of the sensors. This research emphasizes the advantages of the GEO‐GEO inter‐calibration, particularly the capability of analyzing spatial and temporal dependencies. These findings confirm the mutual benefit of utilizing the sensors in similar configurations, highlighting their importance for future satellite monitoring endeavors. Plain Language Summary: Understanding diurnal changes in air pollutants is crucial for grasping how these substances move and disperse in the air, thereby aiding efforts to reduce pollution. In this regard, satellites have a distinct advantage in observation owing to their wide spatial coverage at regular time intervals. GEMS is one of the geostationary sensors providing such information for the Asia‐Pacific region, nearly 7–8 times a day. To evaluate the reliability of GEMS, this study proposes an inter‐calibration method by comparing GEMS observations with those of AMI aboard GK2A. These satellites have a unique advantage as they fly close to each other, observing the Earth with matched optical viewing paths. Scenes simultaneously observed by AMI and GEMS exhibit strong agreement, thus revealing the inherent observation characteristics of each sensor. These findings confirm the mutual benefit of utilizing the sensors in similar configurations for satellite monitoring during the operation. Key Points: Inter‐calibration based on ray‐matching between geostationary satellite sensors onboard twin satellitesImplementation of collocation process in the visible wavelengths and the application of weighted statistics for ensuring collocation stabilityThe novel advantages of the GEO‐GEO inter‐calibration approach for future applications [ABSTRACT FROM AUTHOR]

Accurate cloud detection is a crucial initial stage in optical satellite remote sensing. In this study, a daytime cloud mask model is proposed for the Advanced Geostationary Radiation Imager (AGRI) onboard the Fengyun 4A (FY-4A) satellite based on a deep learning approach. The model, named "Convolutional and Attention-based Cloud Mask Net (CACM-Net)", was trained using the 2021 dataset with CALIPSO data as the truth value. Two CACM-Net models were trained based on a satellite zenith angle (SZA) < 70° and >70°, respectively. The study evaluated the National Satellite Meteorological Center (NSMC) cloud mask product and compared it with the method established in this paper. The results indicate that CACM-Net outperforms the NSMC cloud mask product overall. Specifically, in the SZA < 70° subset, CACM-Net enhances accuracy, precision, and F1 score by 4.8%, 7.3%, and 3.6%, respectively, while reducing the false alarm rate (FAR) by approximately 7.3%. In the SZA > 70° section, improvements of 12.2%, 19.5%, and 8% in accuracy, precision, and F1 score, respectively, were observed, with a 19.5% reduction in FAR compared to NSMC. An independent validation dataset for January–June 2023 further validates the performance of CACM-Net. The results show improvements of 3.5%, 2.2%, and 2.8% in accuracy, precision, and F1 scores for SZA < 70° and 7.8%, 11.3%, and 4.8% for SZA > 70°, respectively, along with reductions in FAR. Cross-comparison with other satellite cloud mask products reveals high levels of agreement, with 88.6% and 86.3% matching results with the MODIS and Himawari-9 products, respectively. These results confirm the reliability of the CACM-Net cloud mask model, which can produce stable and high-quality FY-4A AGRI cloud mask results. [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]

The advancement of more precise remote sensing inversion technology for dust aerosols has long been a hot topic in the field of the atmospheric environment. In 2023, China experienced 18 dust-related weather events, predominantly in spring. These high-intensity and frequent dust events have attracted considerable attention. However, gridded observation data of dust intensity levels are not collected in current dust monitoring and forecasting operations. Based on the Himawari 9 geostationary satellite data, this study establishes a new method to identify spring dust events. This method integrates the brightness temperature difference method and the multiple infrared dust index, taking into account the response discrepancies of the multiple infrared dust index under various underlying surfaces. Furthermore, by obtaining dynamic background brightness temperature values eight times a day, threshold statistics are applied to analyze the correlation between the infrared difference dust index and ground-observed dust level, so as to establish a satellite-based near-surface dust intensity level identification algorithm. This algorithm aims to improve dust detection accuracy, and to provide more effective gridded observation support for dust forecasting and monitoring operations. The test results indicate that the algorithm can effectively identify the presence or absence of dust, with a misjudgment rate of less than 3%. With regard to dust intensity, the identification of blowing sand and floating dust aligns relatively well with ground-based observations, but notable uncertainties exist in determining a dust intensity of sand-storm level or above. Among these uncertainties, the differences between ground-based observations and satellite identification caused by non-grounded dust in the upper air, and the selection of dust identification thresholds, are two important error sources in the dust identification results of this study. [ABSTRACT FROM AUTHOR]

The cloud classification algorithm widely used in the International Satellite Cloud Climatology Project (ISCCP) tends to underestimate low clouds over the Tibetan Plateau (TP), often mistaking water clouds for high‐level clouds. To address this issue, we propose a new algorithm based on cloud‐top temperature and optical thickness, which we apply to TP using Advanced Himawari Imager (AHI) geostationary satellite data. Compared with Clouds and the Earth's Radiant Energy System cloud‐type products and ISCCP results obtained from AHI data, this new algorithm markedly improved low‐cloud detection accuracy and better aligned with cloud phase results. Validation with lidar cloud‐type products further confirmed the superiority of this new algorithm. Diurnal cloud variations over the TP show morning dominance shifting to afternoon high clouds and evening mid‐level clouds. Winter is dominated by high clouds, summer by mid‐level clouds, spring by daytime low clouds and nighttime high clouds, and autumn by low and mid‐level clouds. Plain Language Summary: The accurate identification of low clouds over the Tibetan Plateau (TP) is crucial for climate regulation, ecosystems, aviation safety, research, and modeling. However, satellite‐based methods often miss these clouds, misclassifying them as high‐level clouds. To remedy this, we developed a new algorithm using cloud‐top temperature and optical thickness, applied to Advanced Himawari Imager data. This significantly improves low‐cloud detection, better aligning with actual cloud phases. Simultaneously, we analyzed diurnal cloud variations over the TP with the new algorithm. Cloud types at different altitudes in the TP exhibit strong seasonality. The dominant cloud types in winter and summer are high and mid‐level, respectively. In spring, low clouds dominate during the day (2:00–10:00 UTC), transitioning to high clouds at night (10:00–18:00 UTC), with mid‐level clouds prevailing at other times. In autumn, low clouds dominate during the day, transitioning to mid‐level clouds at other times, with fewer occurrences of high clouds. Key Points: Employing cloud‐top temperature instead of pressure resolves classification‐phase inconsistencies for clouds in the Tibetan Plateau (TP)Lidar validation shows new algorithm's low cloud detection outperforms the conventional International Satellite Cloud Climatology Project algorithm for both TP and plainsThe study reveals significant diurnal and seasonal variations in low clouds over the TP [ABSTRACT FROM AUTHOR]

Ice clouds are a crucial component of the Earth's weather system, and their representation remains a principal challenge for current weather and climate models. Several past and future satellite missions were explicitly designed to provide observations offering new insights into cloud processes, but these specialized cloud sensors are limited in their spatial and temporal coverage. Geostationary satellites have been observing clouds for several decades and can ideally complement the sparse measurements from specialized cloud sensors. However, the geostationary observations that are continuously and globally available over the full observation record are restricted to a small number of wavelengths, which limits the information they can provide on clouds. The Chalmers Cloud Ice Climatology (CCIC) is a novel cloud-property dataset that aims to provide an improved climate record of ice hydrometeor concentrations by applying state-of-the-art machine-learning techniques to retrieve ice cloud properties from globally gridded, single-channel geostationary observations that are readily available from 1980 onwards. CCIC offers a novel perspective on the record of geostationary IR observations by providing spatially and temporally continuous retrievals of the vertically integrated and vertically resolved concentrations of frozen hydrometeors, typically referred to as ice water path (IWP) and ice water content (IWC). In addition to that, CCIC provides 2D and 3D cloud masks and a 3D cloud classification. A fully convolutional quantile regression neural network constitutes the core of the CCIC retrieval, providing probabilistic estimates of IWP and IWC. The network is trained against CloudSat retrievals using 3.5 years of global collocations. Assessed on a held-out test dataset, the CCIC-provided IWP and IWC estimates achieve correlations exceeding 0.7 and 0.6, respectively, and biases better than - 5 % and - 2 % demonstrating considerable skill in estimating both IWP and IWC. In addition, CCIC is extensively validated against both in situ and remote sensing measurements from two flight campaign series and a ground-based radar. The results of this independent validation confirm the ability of CCIC to retrieve IWP and IWC. CCIC thus ideally complements temporally and spatially more limited measurements from dedicated cloud sensors by providing spatially and temporally continuous estimates of ice cloud properties. The CCIC network and its associated software are made accessible to the scientific community. [ABSTRACT FROM AUTHOR]

Data fusion of aerosol optical depth (AOD) datasets from the second generation of the Geostationary Korea Multi-Purpose Satellite (GEO-KOMPSAT-2, GK-2) series was undertaken using both statistical and deep neural network (DNN)-based methods. The GK-2 mission includes an Advanced Meteorological Imager (AMI) aboard GK-2A and a Geostationary Environment Monitoring Spectrometer (GEMS) and Geostationary Ocean Color Imager II (GOCI-II) aboard GK-2B. The statistical fusion method, maximum likelihood estimation (MLE), corrected the bias of each aerosol product by assuming a Gaussian error distribution and accounted for pixel-level uncertainties by weighting the root-mean-square error of each AOD product for every pixel. A DNN-based fusion model was trained to target AErosol RObotic NETwork (AERONET) AOD values using fully connected hidden layers. The MLE and DNN AOD outperformed individual GEMS and AMI AOD datasets in East Asia (R = 0.888; RMSE = - 0.188; MBE = - 0.076; 60.6 % within EE for MLE AOD; R = 0.905; RMSE = 0.161; MBE = - 0.060; 65.6 % within EE for DNN AOD). The selection of AOD around the Korean Peninsula, which incorporates all aerosol products including GOCI-II, resulted in much better results (R = 0.911; RMSE = 0.113; MBE = - 0.047; 73.3 % within EE for MLE AOD; R = 0.912; RMSE = 0.102; MBE = - 0.028; 78.2 % within EE for DNN AOD). The DNN AOD effectively addressed the rapid increase in uncertainty at higher aerosol loadings. Overall, fusion AOD (particularly DNN AOD) showed improvements with less variance and a negative bias. Both fusion algorithms stabilized diurnal error variations and provided additional insights into hourly aerosol evolution. The application of aerosol fusion techniques to future geostationary satellite projects such as Tropospheric Emissions: Monitoring of Pollution (TEMPO), Sentinel-4, and Geostationary Extended Observations (GeoXO) may facilitate the production of high-quality global aerosol data. [ABSTRACT FROM AUTHOR]

• Inter-satellite links (ISL) establishment within libration point constellation for orbit determination (OD). • Analyzed the factors that affect the accuracy of establishing ISLs for OD within the constellation. • Research on the impact of ISLs building interval on OD performance. • Discusses the OD accuracy after establishing ISLs between the libration point constellation and BDS. Earth-Moon libration point navigation constellation has a merit of fully covering the cislunar space and providing navigation services with merely a small number of satellites. Furthermore, this type of constellation itself can achieve orbit determination by using only its Inter-satellite links (ISL) owing to the well-known gravitational asymmetry near the libration points. This paper adopts a representative four-satellite navigation constellation including three satellites located near the libration point L3, L4, L5, and an extra one in Distant Retrograde Orbit (DRO), and studies the orbit determination accuracy of the constellation under two conditions: establishing ISLs within itself (called Solely libration point satellites) and establishing additional ISLs with BeiDou Navigation Satellite System (BDS) (called Libration + BeiDou satellites). The simulation results indicate that, for the Solely libration point satellites scenario with the 1 m range error and solar radiation pressure (SRP) error with the level of 10% deviation between ball model and macro model, the final orbit accuracies for L3, L4, L5, and DRO satellites can respectively achieve 11.3 m, 12.7 m, 12.6 m, and 7.3 m. Reducing the link interval can significantly shorten the arc length to achieve final accuracy, whileas it has less impact on the final orbital accuracy. When the link interval is set as 240, 60, 10, and 2 min, the arc lengths at the final accuracy of better than 15 m are about 35, 29, 23, and 20 days, respectively. To balance the communication burden and the orbit determination arc length, the recommended ISL building interval is 10 min. Errors coming from range measurement model and dynamic model, in addition, are key factors in orbit determination, which can result in meters to tens of meters of orbital accuracy. For the Libration + BeiDou satellites scenario, the augmented ISLs to BDS can substantially improve constellation orbital accuracy and shorten the orbit determination arc length. When the DRO satellite attached to the Geostationary Earth Orbit (GEO), Inclined Geosynchronous Orbit (IGSO), and Medium Earth Orbit (MEO) satellites of BDS respectively, the improved accuracy are 37.1%, 61.2%, and 42.2%, while the shortened arc length are 20.1%, 28.3%, and 28.1%, respectively. The research findings presented in this paper can serve as a reference for the construction and assessment of navigation constellations in the Earth-Moon system. [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]

The precipitation estimation with high spatial and temporal resolution over Tibet is of great significance for monitoring the stability of the plateau ecosystem and early warning of natural disasters. In response to the problem of the unsatisfactory accuracy in traditional real-time precipitation inversion methods and quantitative accuracy estimation products in this region. This paper adopts deep-learning methods to explore the corresponding relationship between precipitation and multi-band observation data from geostationary satellite imagery. The U-High Resolution Network (U-HRNet) as backbone is fully exploited to generate multi-scale features and fused using attention gates to extract high-resolution spatial representations representing precipitation clouds, thereby achieving precipitation estimation of target clouds at different scales. In addition, in response to the characteristics of high precipitation intensity and drastic cloud cluster changes in summer convective weather within a short period of time, a multi-head attention mechanism is introduced to extract temporal information from time series inputs and fuse it with spatial features to improve the accuracy of real-time precipitation estimation. Compared with the operational precipitation products and baseline deep-learning models, the classification metrics such as Probability of detection (POD), False Alarm Ratio (FAR) and Critical success index (CSI) show that the proposed model (U-HRNet+) has better performance in test set and has high generalization ability for estimating short-term heavy rainfall over Tibet and its surrounding areas in summer. Therefore, the proposed model has the potential to serve as a more accurate and reliable satellite-based precipitation estimation product. [ABSTRACT FROM AUTHOR]

Space elevators are a promising technology for future space transportation. The primary component of a space elevator is a tether deployed from a geostationary orbit towards the Earth and into deep space. Payloads can be transported using a climber moving along the tether. However, obtaining a deeper comprehension of the precise dynamics of a space elevator is challenging. The tether could have various deformations such as elongation, bending, and torsion owing to its flexible nature and the low level of vibration damping in space. Therefore, understanding those effects on the tether is essential for the design concept of a space elevator. Reproducing the behavior of space tether systems on the ground is challenging, which is primarily due to the scale of the system and the difficulty of accurately replicating a space environment. Thus, high-fidelity numerical analysis methods are required to analyze the dynamic response of space elevators precisely. Previously proposed analysis models of space tether systems with climbers have focused on elongation, bending, and rigid body motion of the tether, and the effect of torsional deformation has been neglected. However, a tether can be twisted easily owing to its low torsional stiffness. This study established high-fidelity numerical simulation models for space elevators considering large deformations including the torsion of a tether. This study developed a flexible multibody model with a moving climber using the absolute nodal coordinate formulation with 14 degrees of freedom. Further, the gravitational perturbation caused by the non-sphericity of the Earth was considered. Using the constructed model, the motion and deformation of nonequatorial space elevators caused by the climber motion and the disturbance in the space environment were investigated. The obtained results indicate that gravitational perturbation can induce torsional deformation, particularly at higher anchor latitudes. • Nonlinear finite element method for numerical simulation model for noneuqatorial space elevators. • Elongation, bending, torsion, and rigid body rotation of a tether considered. • Flexible multibody model with moving climber that considered torsional deformation. • Gravitational perturbation caused by the non-sphericity of the Earth considered. • Model used to investigate motion and deformation of nonequatorial space elevators. [ABSTRACT FROM AUTHOR]

Specification of the observation‐error covariance matrix for data assimilation systems affects the observation information content retained by the analysis, particularly for observations known to have correlated observation errors (e.g., geostationary satellite and Doppler radar data). A widely adopted approach for estimating observation‐error covariance matrices uses observation‐minus‐background and observation‐minus‐analysis residuals, which are routinely produced by most data assimilation systems. Although this approach is known to produce biased and noisy estimates, due to sampling and misspecification errors, there has been no systematic study of sampling errors with this approach to date. Furthermore, the eigenspectrum of the estimated observation‐error covariance matrix is known to influence the analysis information content and numerical convergence of variational assimilation schemes. In this work, we provide new theorems for the sampling error and eigenvalues of the estimated observation‐error covariance matrices with this approach. We also conduct numerical experiments to illustrate our theoretical results. We find that this method produces large sampling errors if the true observation‐error standard deviation is large, while the other error characteristics, including the true background‐error standard deviation and observation‐ and background‐error correlation length‐scales, have a relatively small effect. We also find that the smallest eigenvalues of the estimated matrices may be smaller or larger than the true eigenvalues, depending on the assumed and true observation‐ and background‐error statistics. These results may provide insights for practical applications: for example, in deciding on appropriate sample sizes and choosing parameters for matrix reconditioning techniques. [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 report delves into the determination of standardized anomalies in various atmospheric parameters within Warri, Delta State. Focusing specifically on soil heat flux and net radiation, the investigation aims to uncover irregularities and variations in these critical parameters over a ten-year period (2009-2018). The need for such analysis arises from the significance of these atmospheric factors in understanding local climate patterns and their implications for the environment. To address this inquiry, comprehensive data sets were collected from the Nigerian Meteorological Agency (NiMet). The collected data underwent meticulous analysis, wherein the mean, standard deviation, and, most importantly, standardized anomalies were computed. The results of this study were presented graphically, showcasing the standardized anomalies plotted against the twelve months of each year. The graphical representation not only aids in visualizing the temporal variations but also serves as a platform for a detailed discussion of the findings. Through this analysis, the research offers a comprehensive understanding of how soil heat flux and net radiation deviate from their typical values, contributing to an anomaly of the atmospheric conditions in Warri, Delta State. [ABSTRACT FROM AUTHOR]

This research focuses on a critical aspect of the space environment, addressing the escalating issue of space debris and congestion in the geostationary orbit. The geostationary orbit is facing many satellites, leading to hazardous congestion levels and jeopardizing the limited resources available. Although organizations have established regulations for retiring satellites to graveyard orbits, a complete removal is not always achievable for numerous reasons. In response to this challenge, our study proposes a practical and cost-effective solution to mitigate debris accumulation in the region. In addition to the above, our research focuses on protecting the geostationary space environment, especially in unforeseen events involving inclined-operated satellites. We explore the implementation of an elevated eccentricity method, increasing the eccentricity of aging satellites and assessing its impact on their time in the geostationary and geostationary-protected regions. Our analysis encompasses short-term, medium-term, and long-term periods, enabling us to evaluate the effectiveness of this approach over different time frames. The study reveals a significant reduction in the time satellites spend in these regions as their eccentricity increases. Moderate eccentricity levels can reduce satellite residence time in these regions from 100.00% to 3.81%. This compelling evidence demonstrates the feasibility and effectiveness of adopting elevated eccentricity as a viable strategy to mitigate space debris in the regions. This proposed approach offers satellite operators a reliable and cost-effective solution, ensuring safe operations and protecting critical regions for aging GEO satellites. Accordingly, we contribute to space environment protection, securing the sustainability of the geostationary orbit. [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]

Summary: Recently, the Low Earth Orbit (LEO) satellite and Geostationary Earth Orbit (GEO) satellites share the same frequency to enhance spectrum efficiency in response to the scarcity of spectral resources. However, overlapping coverage areas emerge when LEO‐GEO and LEO‐LEO beams cover the same ground area in the GEO‐LEO co‐existing satellite system. Thus, the GEO‐LEO inter‐system interference and the LEO intra‐system interference are generated, which leads to more severe and complex interference. To solve the co‐channel interference problem without sacrificing the performance of the LEO system, a collaborative interference avoidance technology is proposed for the GEO‐LEO co‐existing satellite system. Firstly, the LEO satellite and user regrouping strategy (SURS) is employed to effectively avoid severe co‐channel interference by changing service satellites. Then, based on regrouping satellite and user pairs, a proximal policy optimization (PPO)‐based deep reinforcement learning (DRL) method is adopted to realize further continuous beam pointing optimization (BPO) of the LEO user antenna and reduce the co‐channel interference. Finally, simulation results verify that the proposed scheme can achieve the goal of interference mitigation and continuous service. [ABSTRACT FROM AUTHOR]

When investigating the potential effects of cloud changes on climate, the interday and intraday variabilities should be distinguished. Historically, studies have focused on the long-term cloud changes, and the intraday cycles of cloud properties have been mostly ignored partly owing to the limited availability of global datasets to study higher frequency variabilities. In this regard, DSCOVR's vantage point at the Lagrange L1 point overcomes the temporal limitations of polar orbiters as well as the limited spatial views of geostationary satellites, allowing characterization of the daytime variability of cloud properties using a single sensor. In previous analyses, we used DSCOVR's EPIC instrument to characterize the diurnal cycles of cloud height and cloud fraction; here, we expand on previous studies to additionally investigate the variability of cloud optical thickness. We observe a recurring diurnal pattern of cloud optical thicknesses for different latitudinal zones that reaches a maximum around noon regardless of the underlying surface. Once we separate the clouds based on their optical thickness into optically thin (0-3), intermediate (3-10), and thick (10-25), we find that these cloud classes follow different optical thickness diurnal cycles on the global scale. We further explore these differences by combining the evolution of cloud fraction and optical thickness of each group to obtain the diurnal evolution of cloud-fraction-weighted optical depths. [ABSTRACT FROM AUTHOR]

The single scattering albedo (SSA) of aerosol particles is one of the key variables that determine aerosol radiative forcing. Herein, an Algorithm for the retrieval of Single scattering albedo over Land (ASL) is proposed for application to full-disk data from the advanced Himawari imager (AHI) sensor flying on board the Himawari-8 satellite. In this algorithm, an atmospheric radiative transfer model known as the USM (the top of the atmosphere reflectance as the sum of Un-scattered, Single-scattered, and Multiple-scattered components) is used to calculate the SSA instead of predetermining the aerosol model; the USM is constrained by the surface bidirectional reflectance distribution function shape and aerosol optical depth (AOD) in the retrieval process. Combining two consecutive observations and a 2 * 2 pixel window, the optimal estimation algorithm is adopted to obtain the optimal solution for the aerosol SSA. These SSA results are evaluated by comparing with aerosol robotic network (AERONET) data. Linear regression shows that SSAASL = 0.60*SSSAERONET + 0.38, with a correlation coefficient (0.7284), mean absolute error (0.0319), mean bias error (0.00324), root mean square error (0.0427), and ~80.11% of the ASL SSA data within an uncertainty of ±0.05 of the AERONET data. A comparison of the ASL SSA products with collocated Himawari-8 SSA products (Version 03, officially released by the Japan Meteorological Agency (JMA), referred to herein as JMA SSA) shows that the accuracy of the ASL SSA is better than that of the JMA SSA products. For the SSA retrieval in large AODs (>0.4), the validation metrics vs. AERONET data are better. [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]

At mesoscale, trade wind clouds organize with various spatial arrangements, shaping their effect on Earth's energy budget. Representing their fine‐scale dynamics even at 1 km scale climate simulations remains challenging. However, geostationary satellites (GS) offer high‐resolution cloud observation for gaining insights into trade wind cumuli from long‐term records. To capture the observed organizational variability, this work proposes an integrated framework using a continuous followed by discrete self‐supervised deep learning approach, which exploits cloud optical depth from GS measurements. We aim to simplify the entire mesoscale cloud spectrum by reducing the image complexity in the feature space and meaningfully partitioning it into seven classes whose connection to environmental conditions is illustrated with reanalysis data. Our framework facilitates comparing human‐labeled mesoscale classes with machine‐identified ones, addressing uncertainties in both methods. It advances previous methods by exploring transitions between regimes, a challenge for physical simulations, and illustrates a case study of sugar‐to‐flower transitions. Plain Language Summary: Clouds are a fundamental player affecting our planet's energy balance, making their accurate representation crucial in climate models. One open question is how they organize on a scale of a few 100 km (mesoscale) in the tropical northern Atlantic region east of Barbados. Satellite observations can help to categorize these clouds, but previous methods had limitations in capturing the full range of cloud arrangements and transitions between different cloud forms. We have introduced a novel approach that utilizes machine learning and geostationary satellite data to address this issue. Our machine learning model autonomously learns to recognize various cloud patterns and distributions. We conducted a comparative analysis between the categories generated by the machine and those identified by human experts to understand the strengths and weaknesses of both methods. Additionally, we explore a case study where clouds undergo a transformation, changing from a structure resembling sugar to one resembling flowers. This particular transformation was found difficult to capture with physical simulation before. The clear signatures of the transition identified by our machine learning approach can help to better understand cloud evolution, which is crucial for improving climate models and predicting how cloud behavior may change in a changing climate. Key Points: Mesoscale cloud organization can be taxonomized by a two‐step deep learning approach in the feature space continuumComparing seven machine‐identified classes with humans' four recognized categories underlines the significance of uncertainty estimatesNew diagnostic is provided to analyze the temporal transition between regimes, as illustrated for human‐labeled sugar‐to‐flower regimes [ABSTRACT FROM AUTHOR]

The remote sensing community usually confuses the Spatial Resolution Distance (SRD) of satellite images with their Ground Sampling Distance (GSD). This misconception has been highlighted by assessments of Planet's CubeSat images conducted independently by NASA and ESA, which found that their SRD is about five times larger than their GSD. The discrepancies between different metrics to compute the SRD have contributed to the confusion between SRD and GSD. A recently developed spatial resolution metric computed in terms of the imaging sensor's Point Spread Function (PSF), named the Spatial Resolution Function (SRF), allows a comprehensive assessment of the SRD as a function of the resolving contrast generated in the image, when there are two-point sources in the scene. The SRF shows under what conditions the GSD is a good approximation to the SRD and provides a tool to assess the predictions of other metrics used to compute the SRD. We use the SRF to compute the SRD of images captured by Landsat 8 and Planet's SuperDove satellites. The PSF of these sensors is calculated by using the specifications and measurements provided by their operators. The SRD predictions of the SRF metric agree with NASA and ESA findings, showing that the average SRD of SuperDove and Landsat 8 images is 4.9 and 1.4 times larger than their GSD, respectively. The stability of the SRD of these images is assessed by computing the degradation (increase) of SRD resulting from an increase of atmospheric turbulence and spacecraft vibrations. We conclude that the confusion between SRD and GSD has led to the development of CubeSat images with a small GSD whose SRD is several times larger than their GSD. These CubeSat images have an intrinsically unstable SRD in the space environment, a property that explains the numerous radiometric inconsistencies reported by their users. [ABSTRACT FROM AUTHOR]

Aerosols exert a significant influence on the brightness temperature observed in the thermal infrared (IR) channels, yet the specific contributions of various aerosol types remain underexplored. This study integrated the Copernicus Atmosphere Monitoring Service (CAMS) atmospheric composition reanalysis data into the Radiative Transfer for TOVS (RTTOV) model to quantify the aerosol effects on brightness temperature (BT) simulations for the Advanced Himawari Imager (AHI) aboard the Himawari-8 geostationary satellite. Two distinct experiments were conducted: the aerosol-aware experiment (AER), which accounted for aerosol radiative effects, and the control experiment (CTL), in which aerosol radiative effects were omitted. The CTL experiment results reveal uniform negative bias (observation minus background (O-B)) across all six IR channels of the AHI, with a maximum deviation of approximately −1 K. Conversely, the AER experiment showed a pronounced reduction in innovation, which was especially notable in the 10.4 μm channel, where the bias decreased by 0.7 K. The study evaluated the radiative effects of eleven aerosol species, all of which demonstrated cooling effects in the AHI's six IR channels, with dust aerosols contributing the most significantly (approximately 86%). In scenarios dominated by dust, incorporating the radiative effect of dust aerosols could correct the brightness temperature bias by up to 2 K, underscoring the substantial enhancement in the BT simulation for the 10.4 μm channel during dust events. Jacobians were calculated to further examine the RTTOV simulations' sensitivity to aerosol presence. A clear temporal and spatial correlation between the dust concentration and BT simulation bias corroborated the critical role of the infrared channel data assimilation on geostationary satellites in capturing small-scale, rapidly developing pollution processes. [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]

This study introduces an advanced ensemble methodology employing lightweight neural network models for identifying severe convective clouds from FY-4B geostationary meteorological satellite imagery. We have constructed a FY-4B based severe convective cloud dataset by a combination of algorithms and expert judgment. Through the ablation study of a model ensembling combination of multiple specialized lightweight architectures—ENet, ESPNet, Fast-SCNN, ICNet, and MobileNetV2—the optimal EFNet (ENet- and Fast-SCNN-based network) not only achieves real-time processing capabilities but also ensures high accuracy in severe weather detection. EFNet consistently outperformed traditional, heavier models across several key performance indicators: achieving an accuracy of 0.9941, precision of 0.9391, recall of 0.9201, F1 score of 0.9295, and computing time of 18.65 s over the test dataset of 300 images (~0.06 s per 512 × 512 pic). ENet shows high precision but misses subtle clouds, while Fast-SCNN has high sensitivity but lower precision, leading to misclassifications. EFNet's ensemble approach balances these traits, enhancing overall predictive accuracy. The ensemble method of lightweight models effectively aggregates the diverse strengths of the individual models, optimizing both speed and predictive performance. [ABSTRACT FROM AUTHOR]

This paper is devoted to cloud and snow semantic segmentation using multispectral satellite imagery, received from a multizone scanning instrument (MSU-GS) installed on the Russian satellite Electro-L No. 2. For segmentation, some additional geographic information is used such as latitude, longitude, and altitude. The main difficulty of evaluating snow and cloud masks from MSU-GS data is absence of shortwave infrared (SWIR) spectral channels in the 1.3 - 1.6 μ m range, which are necessary for accurate snow and cloud distinction. This fact makes it impossible to apply any of existing algorithms for cloud and snow destinction used for other multispectral satellite imagery. The results of the presented work include a self-collected dataset, consisting of cloud masks from the meteorological satellites GOES-16 and Meteosat-10 L2 products, snow masks from Terra/MODIS products, considered to be the ground truth for multispectral imagery from Electro-L No. 2 with geographical information for each sample, as well as a trained Multi-Scale Attention Network (MANet), able to perform snow and cloud segmentation for multispectral data from Electro-L No. 2. The purpose of this study is to create a stable algorithm for MSU-GS imagery to conduct an operational snow and cloud coverage monitoring of the full Earth disk, that can not be evaluated by using multispectral data from other Russian satellites. The novelty of the work is the combining geographical and multispectral information with weighted sampling approach for hard samples, as well as using other satellite products for creating ground truth masks for needed satellite imagery. The developed algorithm was tested on various scenes during all seasons with F 1 and IoU metrics. We reach segmentation quality equal to F 1 = 0.7454 , F 1 = 0.8773 and IoU = 0.7398 , IoU = 0.7976 for snow and clouds respectively. The proposed neural network can be used at any time of year during daytime for processing Electro-L No. 2 multispectral data in real time. [ABSTRACT FROM AUTHOR]

Interplanetary Coronal Mass Ejections (ICMEs) are prominent drivers of space weather disturbances. The reported studies suggested that the planar ICME sheath and planar magnetic clouds (MCs) cause extreme geomagnetic storms. Here, we investigated the first two-step extreme geomagnetic storm (Sym - H ∼ - 231 nT) of 25th solar cycle. Our analysis demonstrates the transformation of ICME sheath into quasi-planar magnetic structures (PMS). The study corroborates our earlier reported finding that the less adiabatic expansion in possible quasi-PMS transformed ICME enhanced the strength of the southward magnetic field component. It contributes to the efficient transfer of plasma and energy in the Earth's magnetosphere to cause the observed extreme storm. We found that the magnetosphere stand-off distance reduced to < 6 R E (where R E stands for the Earth's radius), and it impacts geosynchronous satellites moving close to 6 R E . [ABSTRACT FROM AUTHOR]

Nitrogen oxide radicals (NOx≡NO+NO2) emitted by fuel combustion are important precursors of ozone and particulate matter pollution, and NO 2 itself is harmful to public health. The Geostationary Environment Monitoring Spectrometer (GEMS), launched in space in 2020, now provides hourly daytime observations of NO 2 columns over East Asia. This diurnal variation offers unique information on the emission and chemistry of NOx , but it needs to be carefully interpreted. Here we investigate the drivers of the diurnal variation in NO 2 observed by GEMS during winter and summer over Beijing and Seoul. We place the GEMS observations in the context of ground-based column observations (Pandora instruments) and GEOS-Chem chemical transport model simulations. We find good agreement between the diurnal variations in NO 2 columns in GEMS, Pandora, and GEOS-Chem, and we use GEOS-Chem to interpret these variations. NOx emissions are 4 times higher in the daytime than at night, driving an accumulation of NO 2 over the course of the day, offset by losses from chemistry and transport (horizontal flux divergence). For the urban core, where the Pandora instruments are located, we find that NO 2 in winter increases throughout the day due to high daytime emissions and increasing NO2/NOx ratio from entrainment of ozone, partly balanced by loss from transport and with a negligible role of chemistry. In summer, by contrast, chemical loss combined with transport drives a minimum in the NO 2 column at 13:00–14:00 local time (LT). Segregation of the GEMS data by wind speed further demonstrates the effect of transport, with NO 2 in winter accumulating throughout the day at low winds but flat at high winds. The effect of transport can be minimized in summer by spatially averaging observations over the broader metropolitan scale, under which conditions the diurnal variation in NO 2 reflects a dynamic balance between emission and chemical loss. [ABSTRACT FROM AUTHOR]

To enhance the accuracy of detecting objects in front of intelligent vehicles in urban road scenarios, this paper proposes a dual-layer voxel feature fusion augmentation network (DL-VFFA). It aims to address the issue of objects misrecognition caused by local occlusion or limited field of view for targets. The network employs a point cloud voxelization architecture, utilizing the Mahalanobis distance to associate similar point clouds within neighborhood voxel units. It integrates local and global information through weight sharing to extract boundary point information within each voxel unit. The relative position encoding of voxel features is computed using an improved attention Gaussian deviation matrix in point cloud space to focus on the relative positions of different voxel sequences within channels. During the fusion of point cloud and image features, learnable weight parameters are designed to decouple fine-grained regions, enabling two-layer feature fusion from voxel to voxel and from point cloud to image. Extensive experiments on the KITTI dataset demonstrate the significant performance of DL-VFFA. Compared to the baseline network Second, DL-VFFA performs better in medium- and high-difficulty scenarios. Furthermore, compared to the voxel fusion module in MVX-Net, the voxel feature fusion results in this paper are more accurate, effectively capturing fine-grained object features post-voxelization. Through ablative experiments, we conducted in-depth analyses of the three voxel fusion modules in DL-VFFA to enhance the performance of the baseline detector and achieved superior results. [ABSTRACT FROM AUTHOR]

The ECOSTRESS push-whisk thermal radiometer on the International Space Station provides the highest spatial resolution temperature retrievals over the ocean that are currently available. It is a precursor to the future TRISHNA (CNES/ISRO), SBG (NASA), and LSTM (ESA) 50 to 70 m scale missions. Radiance transfer simulations and triple collocations with in situ ocean observations and NOAA L2P geostationary satellite ocean temperature retrievals were used to characterize brightness temperature biases and their sources in ECOSTRESS Collection 1 (software Build 6) data for the period 12 January 2019 to 31 October 2022. Radiometric noise, non-uniformities in the focal plane array, and black body temperature dynamics were characterized in ocean scenes using L1A raw instrument data, L1B calibrated radiances, and L2 skin temperatures. The mean brightness temperature biases were −1.74, −1.45, and −1.77 K relative to radiance transfer simulations in the 8.78, 10.49, and 12.09 µm wavelength bands, respectively, and skin temperatures had a −1.07 K bias relative to in situ observations. Cross-track noise levels range from 60 to 600 mK and vary systematically along the focal plane array and as a function of wavelength band and scene temperature. Overall, radiometric uncertainty is most strongly influenced by cross-track noise levels and focal plane non-uniformity. Production of an ECOSTRESS sea surface temperature product that meets the requirements of the SST community will require calibration methods that reduce the biases, noise levels, and focal plane non-uniformities. [ABSTRACT FROM AUTHOR]

This paper presents an innovative approach aimed at enhancing satellite position determination accuracy within a geostationary equatorial orbit (GEO) by integrating a regional navigation satellite system (RNSS) with a global navigation satellite system (GNSS). In a GEO, incoming GNSS signals are typically constrained to a specific direction on the other side of the Earth, resulting in a significant dilution of precision (DOP) and, consequently, a significant radial error. By incorporating an RNSS, signals from more diverse directions are available, improving observability and enhancing navigation precision. Taking the quasi-zenith satellite system (QZSS) as a representative RNSS, this paper demonstrates the feasibility of receiving signals from GEO satellites across a substantial range. Link budget analyses were conducted using the precise side-lobe patterns of the QZSS, revealing that QZSS signals can be consistently observed across most arcs in a GEO. Two comprehensive simulations were conducted: a point solution and an extended Kalman filter-based orbit determination. The results affirm the anticipated improvement in navigation precision indicated by the DOP analysis. It is essential to note that whereas RNSS signals can be received from any longitude in a GEO, enhanced navigation precision relies on the distance from the satellite to the RNSS. Considering the availability of multiple RNSS options, the concept presented in this research can be adapted to any longitude within a GEO, thereby promoting stable, high-precision navigation. [ABSTRACT FROM AUTHOR]

The frequent occurrence of severe convective weather has certain adverse effects on the smart agriculture industry. To enhance the prediction of severe convective weather, the inversion model effectively fills radar reflectivity data gaps by leveraging geostationary satellite data, offering more comprehensive and accurate support for meteorological information in smart agriculture systems. Nevertheless, collaborative cross‐regional inversion driven by dispersed radar data faces challenges in efficiency, privacy, and model accuracy. To this end, we employ an U‐shaped residual network with an embedded light hybrid attention mechanism and utilize a federated averaging algorithm for efficient distributed training across multiple devices which could preserve the privacy of data from different locations, thereby improving inversion performance. In addition, to address the unbalanced nature of radar data, a weighted loss function is designed to enhance the model's sensitivity to high radar reflectivity. Experimental results demonstrate that the proposed model exhibits a certain level of improvement in evaluating radar reflectivity inversion performance across different thresholds compared to other models, thus substantiating the superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

Several observation types (e.g., geostationary satellite and Doppler radar observations) have recently been found to exhibit strong spatial error correlations. Including these error statistics in data assimilation for numerical weather prediction can improve analysis quality and forecast skill. Moreover, it allows for increases in the spatial density of observations assimilated, which is needed for the provision of information on appropriate scales for high‐resolution forecasting. However, introducing correlated error statistics may increase the computational complexity and parallel communication costs of matrix‐vector products involving observation precision matrices (inverse observation error covariance matrices). Without new approaches, we cannot take full advantage of new observation uncertainty estimates. We develop a new numerical approximation method based on a particular type of fast multipole method and a domain localization approach. The basic idea is to divide the observation domain into boxes and then separate calculations of matrix‐vector products according to the partition. These calculations can be done in parallel with very low communication overheads. The new method is easy to implement and parallelize, and it is applicable to a wide variety of observation precision matrices. We applied the new method to a simple variational data assimilation problem and found that the computational cost of the variational minimization was dramatically reduced while preserving analysis accuracy across a range of scales. The new method has the potential to be used as an efficient technique for practical applications where a large number of observations with mutual error correlations need to be assimilated quickly. Plain Language Summary: Weather forecasts are generated by running a computational model from a starting point that describes the current weather conditions. These conditions are estimated using a data assimilation process that combines observations of the weather with computational predictions from a previous forecast. In the data assimilation process, the statistical weighting given to the observations is determined by our knowledge of the observation error statistics. Spatial relationships between errors, or spatial error correlations, are often found in remotely sensed observations, such as those from satellites. The inclusion of spatially correlated observation error statistics in the data assimilation process can substantially increase the time required to complete the process. We develop a novel method that can significantly reduce the time taken to assimilate observations with mutual spatial error correlations. Moreover, the accuracy of data assimilation can be maintained with this new method. Key Points: The new method has the potential to be used in practice to assimilate a large number of observations with mutual spatial error correlationsThe new method allows fast computation of matrix‐vector products involving full observation precision matrices in data assimilationThe new method can preserve the analysis accuracy at all spatial scales [ABSTRACT FROM AUTHOR]