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The use of remote sensing method to accurately measure cloud properties and their spatiotemporal changes has been widely welcomed in many fields of atmospheric research. The Nanjing Joint Institute for Atmospheric Sciences (NJIAS) Himawari-8/9 Cloud Feature Dataset (HCFD) provides a comprehensive description of cloud features over the East Asia and west North Pacific regions for the 7 yr period from April 2016 to December 2022. Multiple cloud variables, such as cloud mask, phase/type, top height, optical thickness, and particle effective radius, as well as snow, dust and haze masks, were generated from the visible and infrared measurements of the Advanced Himawari Imager (AHI) onboard the Japanese geostationary satellites Himawari-8/9 using a series of cloud retrieval algorithms developed by Dr. Zhuge and his colleagues. Verifications with the Cloud-Aerosol Lidar with Orthogonal Polarization 1-km cloud layer product and the Moderate Resolution Imaging Spectroradiometer Level-2 cloud product (MYD06) demonstrates that the NJIAS HCFD gives higher skill scores than the Japanese Himawari-8/9 operational cloud product for all cloud variables except for the particle effective radius. The NJIAS HCFD even outperforms the MYD06 in the nighttime continental cloud detection and the infrared-only cloud-top phase determination. Then, two application examples are presented, to demonstrate the use of the NJIAS HCFD for climate and typhoon research. The NJIAS HCFD has been published at the Science Data Bank. [ABSTRACT FROM AUTHOR]

With the development of the Chinese Fengyun satellite series, Fengyun-2G (FY-2G) quantitative precipitation estimates (QPE) can provide real-time and high-quality precipitation data over East Asia. However, FY-2G QPE cannot offer precipitation information beyond the latitude band of 50°N due to the limitation of the observation coverage of the FY-2G-based satellite-borne sensor. To this end, a precipitation space reconstruction using the geographically weighted regression (GWR) coupled with a geographical differential analysis (GDA) (PSR2G) algorithm was developed, based on the land surface variables related to precipitation, including vegetational cover, land surface temperature, geographical location, and topographic characteristics. This study used the PSR2G-based reconstructed model to estimate the FY-2G QPE over Northeast China (the latitude band beyond 50°N) from December 2015 to November 2019 with a spatiotemporal resolution of 0.1°/month. The PSR2G-based reconstructed results were validated with the ground observations of 80 rain gauges, and also compared to the reconstructed results using random forest (RF) and GWR. The results show that the spatio-temporal pattern of PSR2G QPE is closer to ground observations than those of RF and GWR, which indicates that the PSR2G QPE is more competent to capture the spatio-temporal variation of rainfall over Northeast China than other two reconstruction methods. In addition, the reconstructed precipitation dataset using PSR2G has higher accuracy over study area than the FY-2G QPE below the band of 50°N. It suggested that PSR2G reconstruction precipitation strategies do not lose the precision of the original satellite precipitation data. [ABSTRACT FROM AUTHOR]

On 15 September 2015, a convective storm yielded heavy rainfalls that caused the strongest flash flood in the last 50 years in the South Negev Desert (Israel). None of the operational forecast models predicted the event, and thus, no warning was provided. We analyzed this event using satellite, radar, and numerical weather prediction model data. We generated cloud-free climatological values on a pixel basis using Temporal Fourier Analysis on a time series of MSG geostationary satellite data. The discrepancy between the measured and climatological values was used to detect "cloud-contaminated" pixels. This simple, robust, fast, and accurate method is valuable for the early detection of convection. The first clouds were detected 30 min before they were detected by the official MSG cloud mask, 4.5 h before the radar, and 10 h before the flood reached the main road. We used the "severe storms" RGB composite and the satellite-retrieved vertical profiles of cloud top temperature–particle's effective radius relations as indicators for the development of a severe convective storm. We also reran the model with different convective schemes, with much-improved results. Both the satellite and model-based analysis provided early warning for a very high probability of flooding a few hours before the actual flooding occurred. [ABSTRACT FROM AUTHOR]

The Geosynchronous Satellite Synthetic Aperture Radar (GEO SAR) operates at a high orbital altitude, resulting in an extended imaging time and substantial variations in slant range. Additionally, the GEO SAR satellite orbit experiences a bending effect, and the target's movement, caused by the Earth's rotation, is influenced by the Earth's curvature. The back-projection (BP) algorithm has been proven to be a highly effective technique for precise imaging with GEO SAR by processing these specific echo signals. However, this approach necessitates considerable computational resources. Existing BP algorithms, such as the fast BP algorithm, do not consider the "Stop-and-Go" error present in GEO SAR. Consequently, we developed a Precise Slant Range Model that considers the motion of both the satellite and targets. The model incorporates velocity and acceleration factors to accurately represent the signal transmission from transmission to reception. Additionally, we propose a Rapid Range History Construction Method to lessen the computational burden of generating the three-dimensional range history array. By utilizing the Precise Slant Range Model and the Rapid Range History Construction Method, and employing parallel processing through aperture segmentation, we propose an Accurate and Efficient BP imaging algorithm suitable for GEO SAR applications. To validate its effectiveness, simulations were conducted using the parameters of a GEO SAR system. The results indicated that the proposed algorithm enhances the imaging quality of GEO SAR, reduces the processing time, and achieves high-precision rapid imaging, thereby improving operational efficiency. [ABSTRACT FROM AUTHOR]

To study tropical cyclones and generate forecast applications using satellite observations, researchers often consolidate disparate sources of raw and ancillary data. Data consolidation involves obtaining, collocating, and intercalibrating data from different sensors and derived products; calculating environmental diagnostics from a homogeneous source; and standardizing these various products for a straightforward analysis. To alleviate preprocessing issues and provide a long-term, global digital dataset of tropical cyclone satellite observations, we construct the Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED). TC PRIMED contains tropical cyclone–centric 1) intercalibrated, multichannel, multisensor microwave brightness temperatures, 2) retrieved rainfall from NASA's Goddard Profiling Algorithm (GPROF), 3) nearly coincident geostationary satellite infrared brightness temperatures and derived metrics, 4) tropical cyclone position and intensity information, 5) ECMWF fifth-generation reanalysis fields and derived environmental diagnostics, and 6) precipitation radar observations from the TRMM and GPM Core Observatory satellites. TC PRIMED consists of over 176,000 overpasses of 2,101 storms from 1998 to 2019, providing researchers with an analysis-ready dataset to promote and support research into improving our understanding of the relationship between tropical cyclone convective and precipitation structure, intensity, and environment. Here, we briefly describe data sources and processing steps to create TC PRIMED. To demonstrate TC PRIMED's potential utility for studying important tropical cyclone processes and for application development, we present a shear-relative composite analysis of several multisensor satellite variables relative to the tropical cyclone lifetime maximum intensity. The composite analysis provides a simple example of how TC PRIMED can benefit future studies to advance our understanding of tropical cyclones and improve forecasts. [ABSTRACT FROM AUTHOR]

Rapid increases in the flash rate (FR) of a thunderstorm, so-called lightning jumps (LJs), have potential for nowcasting applications and to increase lead times for severe weather warnings. To date, there are some automated LJ algorithms that were developed and tuned for ground-based lightning locating systems. This study addresses the optimization of an automated LJ algorithm for the Geostationary Lightning Mapper (GLM) lightning observations from space. The widely used σ-LJ algorithm is used in its original form and in an adapted calculation including the footprint area of the storm cell (FRarea LJ algorithm). In addition, a new relative increase level (RIL) LJ algorithm is introduced. All algorithms are tested in different configurations, and detected LJs are verified against National Centers for Environmental Information severe weather reports. Overall, the FRarea algorithm with an activation FR threshold of 15 flashes per minute and a σ-level threshold of 1.0–1.5 as well as the RIL algorithm with FR threshold of 15 flashes per minute and RIL threshold of 1.1 are recommended. These algorithms scored the best critical success index (CSI) of ∼0.5, with a probability of detection of 0.6–0.7 and a false alarm ratio of ∼0.4. For daytime warm-season thunderstorms, the CSI can exceed 0.5, reaching 0.67 for storms observed during three consecutive days in April 2021. The CSI is generally lower at night and in winter. [ABSTRACT FROM AUTHOR]

CLAAS-3, the third edition of the Cloud property dAtAset using SEVIRI (Spinning Enhanced Visible and InfraRed Imager), was released in December 2022. It is based on observations from SEVIRI, on board geostationary satellites Meteosat-8, 9, 10 and 11, which are operated by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). CLAAS-3 was produced and released by the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF), which aims to provide high-quality satellite-based data records suitable for climate monitoring applications. Compared to previous CLAAS releases, CLAAS-3 is expanded in terms of both temporal extent and cloud properties included, and it is based on partly updated retrieval algorithms. The available data span the period from 2004 to present, covering Europe; Africa; the Atlantic Ocean; and parts of South America, the Middle East and the Indian Ocean. They include cloud fractional coverage, cloud-top height, phase (liquid or ice) and optical and microphysical properties (water path, optical thickness, effective radius and droplet number concentration), from instantaneous data (every 15 min) to monthly averages. In this study we present an extensive evaluation of CLAAS-3 cloud properties, based on independent reference data sets. These include satellite-based retrievals from active and passive sensors, ground-based observations and in situ measurements from flight campaigns. Overall results show very good agreement, with small biases attributable to different sensor characteristics, retrieval/sampling approaches and viewing/illumination conditions. These findings demonstrate the fitness of CLAAS-3 to support the intended applications, which include evaluation of climate models, cloud characterisation and process studies focusing especially on the diurnal cycle and cloud filtering for other applications. The CLAAS-3 data record is publicly available via the CM SAF website at 10.5676/EUM_SAF_CM/CLAAS/V003 (Meirink et al., 2022). [ABSTRACT FROM AUTHOR]

Turbulence is what we want to avoid the most during flight. Numerical weather prediction (NWP) model–based methods for diagnosing turbulence have offered valuable guidance for pilots. NWP-based turbulence diagnostics show high accuracy in detecting turbulence in general. However, there is still room for improvements such as capturing convectively induced turbulence. In such cases, observation data can be beneficial to correctly locate convective regions and help provide corresponding turbulence information. Geostationary satellite data are commonly used for upper-level turbulence detection by utilizing its water vapor band information. The Geostationary Operational Environmental Satellite (GOES)-16 carries the Advanced Baseline Imager (ABI), which enables us to observe further down into the atmosphere with improved spatial, temporal, and spectral resolutions. Its three water vapor bands allow us to observe different vertical parts of the atmosphere, and from its infrared window bands, convective activity can be inferred. Such multispectral information from ABI can be helpful in inferring turbulence intensity at different vertical levels. This study develops U-Net based machine learning models that take ABI imagery as inputs to estimate turbulence intensity at three vertical levels: 10–18, 18–24, and above 24 kft (1 kft ≈ 300 m). Among six different U-Net-based models, U-Net3+ model with a filter size of three showed the best performance against the pilot report (PIREP). Two case studies are presented to show the strengths and weaknesses of the U-Net3+ model. The results tend to be overestimated above 24 kft, but estimates of 10–18 and 18–24 kft agree well with the PIREP, especially near convective regions. Significance Statement: Turbulence is directly related to aviation safety as well as cost-effective aircraft operation. To avoid turbulence, turbulence diagnostics are calculated from numerical weather prediction (NWP) model outputs and are provided to pilots. The goal of this study is to develop a satellite data–driven machine learning model that estimates turbulence intensity in three different vertical layers to provide additional information along with the NWP-based turbulence diagnostics. Validation results against pilot reports show that the machine learning model performs comparable to NWP-based turbulence diagnostics. Furthermore, results with different channel selections reveal that using multiple water vapor channels can help extract additional information for estimating turbulence intensity at lower levels. [ABSTRACT FROM AUTHOR]

Based on the high-resolution data from April to October (the warm season) during the 2010 to 2020 timeframe provided by the FY-2F geostationary meteorological satellite, the classification and application evaluation of the eastward-moving southwest vortex cloud system affecting Zhejiang Province was conducted using cloud classification (CLC) and black body temperature (TBB) products. The results show that: (1) when the intensity of the eastward-moving southwest vortex is strong, the formed precipitation is predominantly regional convective precipitation. The cloud system in the center and southeast quadrant of the southwest vortex is dominated by cumulonimbus and dense cirrus clouds with convective precipitation, while the other quadrants are mainly composed of stratiform clouds, resulting in stable precipitation; (2) The original text is modified as follows: By using the TBB threshold method to identify stratiform and mixed cloud rainfall, we observed a deviation of one order of magnitude. This deviation is advantageous for moderate rain. However, the precipitation results from mixed clouds identified by the TBB threshold method are being overestimated; By means of the application of stratiform and mixed cloud rainfall identified by the TBB threshold method, an order of magnitude deviation was identified (3) The TBB can be consulted to estimate the precipitation, above which there is a large error. Moreover, the dispersion of precipitation produced by deep convective clouds is the largest, while the dispersion of precipitation produced by stratiform clouds is the smallest and has better predictability. Compared to CLC products, cloud type results based on TBB identification are better for convective cloud precipitation application. [ABSTRACT FROM AUTHOR]

The broad geographical coverage and high temporal and spatial resolution of geostationary satellite data provide an excellent opportunity to collect information on variables whose spatial distribution and temporal variability are not adequately represented by in situ networks. This study focuses on assessing the effectiveness of two geostationary satellite-based sunshine duration (SDU) datasets over Brazil, given the relevance of SDU to various fields, such as agriculture and the energy sector, to ensure reliable SDU data over the country. The analyzed datasets are the operational products provided by the Satellite Application Facility on Climate Monitoring (CMSAF) that uses data achieved with the Meteorological Satellite (Meteosat) series and by the Satellite and Meteorological Sensors Division of the National Institute for Space Research (DISSM–INPE) that employs Geostationary Operational Environmental Satellite (GOES) data. The analyzed period ranges from September 2013 to December 2017. The mean bias error (MBE), mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (r), and scatterplots between satellite products and in situ daily SDU measurements provided by the National Institute of Meteorology (INMET) were used to access the performance of the products. They were calculated on a monthly basis and grouped into climate regions. The statistical parameters exhibited a uniform spatial distribution, indicating homogeneity within a given region. Except for the tropical northeast oriental (TNO) region, there were no significant seasonal dependencies observed. The MBE values for both satellite products were generally low across most regions in Brazil, mainly between 0 and 1 h. The correlation coefficient (r) results indicated a strong agreement between the estimated values and the observed data, with an overall r value exceeding 0.8. Nevertheless, there were notable discrepancies in specific areas. The CMSAF product showed a tendency to overestimate observations in the TNO region, with the MBE consistently exceeding 1 h for all months, while the DISSM product exhibited a negative gradient of the MBE values in the west–east direction in the northern portion of Brazil. The scatterplots for the TNO region revealed that the underestimation pattern observed in the DISSM product was influenced by the sky condition, with more accurate estimations observed under cloudy skies. Additional analysis suggested that the biases observed might be attributed to the misrepresentation of clear-sky reflectance. In the case of the CMSAF product, the overestimation tendency observed in the TNO region appeared to be a result of systematic underestimation of the effective cloud albedo. The findings indicated that both satellite-based SDU products generally exhibited good agreement with the ground observations across Brazil, although their performance varied across different regions and seasons. The analyzed operational satellite products present a reliable source of data to several applications, which is an asset due to its high spatial resolution and low time latency. [ABSTRACT FROM AUTHOR]

In combination with observations from visible satellite channels, near-infrared channels can provide valuable additional cloud information, e.g. on cloud phase and particle sizes, which is also complementary to the information content of thermal infrared channels. Exploiting near-infrared channels for operational data assimilation and model evaluation requires a sufficiently fast and accurate forward operator. This study presents an extension to the method for fast satellite image synthesis (MFASIS) that allows for simulating reflectances of the 1.6 µm near-infrared channel based on a computationally efficient neural network with the same accuracy that has already been achieved for visible channels. For this purpose, it is important to better represent vertical variations in effective cloud particle radii, as well as mixed-phase clouds and molecular absorption in the idealized profiles used to train the neural network. A new approach employing a two-layer model of water, ice and mixed-phase clouds is described, and the relative importance of the different input parameters characterizing the idealized profiles is analysed. A comprehensive data set sampled from Integrated Forecasting System (IFS) forecasts together with different parameterizations of the effective water and ice particle radii is used for the development and evaluation of the method. Further evaluation uses a month of ICOsahedral Non-hydrostatic development based on version 2.6.1 (ICON-D2) hindcasts with effective radii directly determined by the two-moment microphysics scheme of the model. In all cases, the mean absolute reflectance error achieved is about 0.01 or smaller, which is an order of magnitude smaller than typical differences between reflectance observations and corresponding model values. The errors related to the imperfect training of the neural networks present only a small contribution to the total error, and evaluating the networks takes less than a microsecond per column on standard CPUs. The method is also applicable for many other visible and near-infrared channels with weak water vapour sensitivity. [ABSTRACT FROM AUTHOR]

To collect solar energy in outer space, Tethered Collecting Solar Power Satellite Systems have been proposed by several authors in the last years. A geostationary orbit would be the best location for a space-based solar power. However, a geostationary satellite occupies a single ring in the equatorial plane of the Earth. For this reason, out-of-plane configurations for solar panel systems have been studied by various authors to increase the number of slots over a particular longitude. In this paper, we assume a two-body tethered satellite system, where the microwave transmitting satellite is placed on a geostationary orbit and the tethered solar panel is deployed from the microwave transmitter. A tension control law is derived in conjunction with thruster forces by linearization of the dynamics of the tethered satellite system around an out-of-plane configuration. The stability analysis carried out in this paper shows that the linear motion is asymptotically stable. Results of numerical simulation show that the linear control strategy performs well for the nonlinear system, so that the solar panel achieves a nonplanar configuration. [ABSTRACT FROM AUTHOR]

The Weather Research Forecasting (WRF) V4.2 model is configured within a Lagrangian framework to quantify the impact of aerosols on evolving cloud fields. Simulations employing realistic meteorological boundary conditions are based on 10 case study days offering diverse meteorology during the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA). Cloud and aerosol retrievals in observations from aircraft measurements, ground-based Atmosphere Radiation Measurement (ARM) data at Graciosa Island in the Azores, and A-Train and geostationary satellites are in good agreement with the simulations. Higher aerosol concentration leads to suppressed drizzle and increased cloud water content. These changes lead to larger radiative cooling rates at cloud top, enhanced vertical velocity variance, and increased vertical and horizontal wind speed near the base of the lower-tropospheric inversion. As a result, marine cloud cell area expands, narrowing the gap between shallow clouds and increasing cloud optical thickness, liquid water content, and the top-of-atmosphere outgoing shortwave flux. While similar aerosol effects are observed in lightly to non-raining clouds, they tend to be smaller by comparison. These results show a strong link between cloud cell area expansion and the radiative adjustments caused by liquid water path and cloud fraction changes. These adjustments scale by 74 % and 51 %, respectively, relative to the Twomey effect. Given the limitations of traditional global climate model resolutions, addressing mesoscale cloud-state transitions at kilometer-scale resolutions or higher should be of utmost importance in accurately quantifying aerosol radiative forcing. [ABSTRACT FROM AUTHOR]

Vegetation water content (VWC) plays a key role in transpiration, plant mortality, and wildfire risk. Although land surface models now often contain plant hydraulics schemes, there are few direct VWC measurements to constrain these models at global scale. One proposed solution to this data gap is passive microwave remote sensing, which is sensitive to temporal changes in VWC. Here, we test that approach by using synthetic microwave observations to constrain VWC and surface soil moisture within the Climate Modeling Alliance Land model. We further investigate the possible utility of sub‐daily observations of VWC, which could be obtained through a satellite in geostationary orbit or combinations of multiple satellites. These high‐temporal‐resolution observations could allow for improved determination of ecosystem parameters, carbon and water fluxes, and subsurface hydraulics, relative to the currently available twice‐daily sun‐synchronous observational patterns. We find that incorporating observations at four different times in the diurnal cycle (such as could be available from two sun‐synchronous satellites) provides a significantly better constraint on water and carbon fluxes than twice‐daily observations do. For example, the root mean square error of projected evapotranspiration and gross primary productivity during drought periods was reduced by approximately 40%, when using four‐times‐daily relative to twice‐daily observations. Adding hourly observations of the entire diurnal cycle did not further improve the inferred parameters and fluxes. Our comparison of observational strategies may be informative in the design of future satellite missions to study plant hydraulics, as well as when using existing remotely sensed data to study vegetation water stress response. Plain Language Summary: The amount of water contained within the tissues of plants influences how much water plants transpire from the soil to the atmosphere and how much carbon they take in. However, it is difficult to estimate how much water is in plants around the world at any given time, due to the diversity of plants storing and releasing water in different ways. Certain earth‐orbiting satellites carry sensors that can indicate plant water content, but they provide only snapshots of data at two points in time per day due to their orbit shapes. Here, we simulated what information could be gleaned from different combinations of satellites in different orbits when they are combined with computer models of water flow in plants. We found that using data from two satellites in different orbits, instead of just one, could greatly increase the accuracy of plant water content estimates, almost as much as if we had a large fleet of satellites observing around the clock. Our work should be useful to scientists studying plant water content with existing data sets as well as those planning future satellite missions. Key Points: We demonstrate that ecohydrological parameters and variables can be inferred from microwave radiometry via model‐data fusionWe compare scenarios that use synthetic observations at different times of day, corresponding to current and proposed satellite orbitsFor inferring land surface variables, using observations from just four times of day proves to be as useful as using data from every hour [ABSTRACT FROM AUTHOR]

Positioning services based on GNSS (Global Navigation Satellite Systems) have been using MEO satellites designed to guarantee Earth global coverage for decades. This satellite distribution is sometimes complemented with satellites in Inclined Geosynchronous (IGSO) and Geostationary (GEO) Orbits to improve satellite visibility in particular service areas. During recent years, with the advancements and reduction in costs in the deployment of LEO (Low Earth Orbit) constellations, the opportunity of using LEO satellites for PNT (Positioning, Navigation, and Timing) solutions is being studied. This concept opens the possibility to provide high accuracy positioning overcoming the typical drawbacks of RTK (Real-Time Kinematics) or PPP (Precise Point Positioning), such as the need for ground infrastructure or long convergence times. The high velocity dynamics of the low orbits can help to cancel the effect of the ionosphere in the signals to be processed at the user level. Therefore, the introduction of LEO satellites together with the classical MEO GNSS constellations could be a solution to reduce the dependency on dense station networks. The size of the LEO constellations and the design of their orbits are key factors to improve the PPP solution. Moreover, both the accuracy and the convergence time of the PPP solution depend also on the quality of the on-board equipment of the satellite, especially on the quality of the atomic clock in terms of stability and noise, and on the quality of the orbit and clock corrections sent to the PPP users. GMV has decades of experience in both GNSS and LEO precise orbit determination (POD) fields and in high-accuracy GNSS applications for different market domains. With this experience, several analyses have been carried out to assess the achievable performance when introducing the processing of LEO signals for high accuracy positioning solutions, contributing to the overall GNSS community. The objective of this paper is to describe the analysis run by GMV with the use of synthetic data simulating GNSS and LEO signals, showing results and the associated assessment of the achievable performance. [ABSTRACT FROM AUTHOR]

A space-based augmentation system (SBAS) provides real-time GNSS correction signals via geostationary satellites for near-ground GNSS users. To use the SBAS correction for low Earth orbit (LEO) satellites, the correction, especially the ionosphere correction, must be adjusted for the LEO altitude. We apply modified SBAS data to LEO satellite onboard navigator to improve the positioning accuracy of a LEO satellite for possible real-time use. The onboard navigator requires high positioning reliability, and code pseudoranges, rather than phase pseudoranges, are used for the primary measurements. The Galileo NeQuick G model is used to determine the real-time conversion factor of the SBAS ionosphere correction for a LEO satellite. The GPS L1 data from GRACE satellite are combined with the SBAS data from the ground receiver. The onboard navigator combines the precise satellite dynamic model with an extended Kalman filter to improve positioning accuracy and stability. The kinematic positioning method, which uses the weighted least square method without the dynamic model, is also performed for comparison. The SBAS correction reduces the positioning error in both the kinematic positioning and the dynamic positioning. The positioning error reduction of the GPS and WAAS case over the GPS-only case is 25.2% for the kinematic method and 30.6% for the dynamic method. In the case of the dynamic method with the SBAS corrections, the positioning error remains smaller than that of the GPS-only dynamic method even after the satellite has left the SBAS service area. [ABSTRACT FROM AUTHOR]

Importance of continuous monitoring of rainfall from Indian geostationary satellites INSAT-3D/3DR over India and surrounding oceans is not only necessary to monitor the performance of the monsoon, but is extremely useful in disaster mitigation support system as well. It has been observed that rain products from INSAT-3D/3DR often show overestimation in certain areas with unrealistic instances of intense rain, which need a closer examination. With this objective, the present study is carried out, which suggests that a non-linear calibration equation for thermal infrared brightness temperatures (Tb) of INSAT-3D/3DR is more appropriate than routinely applied linear equation for rainfall estimation. The study elaborates a new calibration correction method and addresses the requirement of such calibration. To evaluate the performance of proposed method, Hydro-Estimator (HE) operational rainfall products from INSAT-3D/3DR are re-estimated from the newly calibrated Tbs during Indian summer monsoon (June- September), 2020. The re-estimated HE rain not only substantially reduces the instances of overestimation, but also show remarkable improvement in capturing rainfall distribution over India and surrounding ocean. Improvement in RMSE from 8.56 mm/hr to 3.23 mm/hr, when compared with the concurrent IMERG (Integrated Multi-satellitE Retrieval for Global Precipitation Mission (GPM)) rain, shows reliability of the new method. Furthermore, the reestimated HE rain compares better with in-situ rain gauges observations than the operational HE product with RMSE (bias) reduced from 85.43 mm/day (30.23 mm/day) to 40.02 mm/day (17.07 mm/ day), and correlation improved from 0.28 to 0.32. The significance of the new calibration method is also recognized in the rain monitoring over complex hilly terrain and coastal regions of India. [ABSTRACT FROM AUTHOR]

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

Northwest Mexico (NWM) is an extensive region with a humid and unstable environment leading to atmospheric convection during the North American monsoon. Occasionally, extreme events are associated with severe weather, including deep convection, heavy rainfall and frequent lightning. Based on the period from July through September 2009–2018, the present study investigates general characteristics of convection. Geostationary satellites were used to document three‐dimensional cloud cover structures over the mainland, Gulf of California (GOC) and Baja California Peninsula (BCP). The convection developed over the Sierra Madre Occidental (SMO) mountains with a distinct cloud top structure over the southern GOC. In contrast, reduced cloud cover was observed over the peninsula and eastern Pacific Ocean. An active (2014) and inactive (2010) season are examined in more detail; the former had a more favourable environmental setting to support above‐normal convection and rainfall with respect to the 10‐year average. A regional‐scale analysis of lightning was computed with data from a ground‐based sensor network, including date, time and location of more than 16 million records. Major findings include: (1) lightning enhancement occurred in conjunction with deep convection, west of the SMO from the afternoon through the evening, while the gulf entrance became a well‐defined hotspot close to the mainland coastal plains overnight; (2) most lightning‐related deaths were in specific areas near the coastal plains or within isolated regions of the SMO foothills; (3) high exposure is expected in areas with considerable lightning and population located within 100 km from the gulf coastline. These findings are valuable for monitoring monsoon thunderstorms and for improving the ability to provide timely warnings of severe weather. [ABSTRACT FROM AUTHOR]

The impact of aerosol number concentration on cloud albedo is a persistent source of spread in global climate predictions due to multi-scale, interactive atmospheric processes that remain difficult to quantify. We use 5 years of geostationary satellite and surface retrievals at the US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) eastern North Atlantic (ENA) site in the Azores to evaluate the representation of liquid cloud albedo susceptibility for overcast cloud scenes in the DOE Energy Exascale Earth System Model version 1 (E3SMv1) and provide possible reasons for model–observation discrepancies. The overall distribution of surface 0.2 % CCN concentration values is reasonably simulated, but simulated liquid water path (LWP) is lower than observed and layer mean droplet concentration (Nd) comparisons are highly variable depending on the Nd retrieval technique. E3SMv1's cloud albedo is greater than observed for given LWP and Nd values due to a lower cloud effective radius than observed. However, the simulated albedo response to Nd is suppressed due to a correlation between the solar zenith angle (SZA) and Nd created by the seasonal cycle that is not observed. Controlling for this effect by examining the cloud optical depth (COD) shows that E3SMv1's COD response to CCN concentration is greater than observed. For surface-based retrievals, this is only true after controlling for cloud adiabaticity because E3SMv1's adiabaticities are much lower than observed. Assuming a constant adiabaticity in surface retrievals as done in top-of-atmosphere (TOA) retrievals narrows the retrieved ln⁡Nd distribution, which increases the cloud albedo sensitivity to ln⁡Nd to match the TOA sensitivity. The greater sensitivity of COD to CCN is caused by a greater Twomey effect in which the sensitivity of Nd to CCN is greater than observed for TOA-retrieved Nd , and once model–observation cloud adiabaticity differences are removed, this is also true for surface-retrieved Nd. The LWP response to Nd in E3SMv1 is overall negative as observed. Despite reproducing the observed LWP– Nd relationship, observed clouds become much more adiabatic as Nd increases, while E3SMv1 clouds do not, associated with more heavily precipitating clouds that are partially but not completely caused by deeper clouds and weaker inversions in E3SMv1. These cloud property differences indicate that the negative LWP– Nd relationship is likely not caused by the same mechanisms in E3SMv1 and observations. The negative simulated LWP response also fails to mute the excessively strong Twomey effect, highlighting potentially important confounding factor effects that likely render the LWP– Nd relationship non-causal. Nd retrieval scales and assumptions, particularly related to cloud adiabaticity, contribute to substantial spreads in the model–observation comparisons, though enough consistency exists to suggest that aerosol activation, drizzle, and entrainment processes are critical areas to focus E3SMv1 development for improving the fidelity of aerosol–cloud interactions in E3SM. [ABSTRACT FROM AUTHOR]

The anvil clouds of tropical deep convection have large radiative effects in both the shortwave (SW) and longwave (LW) spectra with the average magnitudes of both over 100 Wm-2. Despite this, due to the opposite sign of these fluxes, the net average of anvil cloud radiative effect (CRE) over the tropics has been found to be neutral. Research into the response of anvil CRE to climate change has primarily focused on the feedbacks of anvil cloud height and anvil cloud area, in particular regarding the LW feedback. However, tropical deep convection over land has a strong diurnal cycle which may couple with the shortwave component of anvil cloud radiative effect. As this diurnal cycle is poorly represented in climate models it is vital to gain a better understanding of how its changes impact anvil CRE. To study the connection between deep convective cloud (DCC) lifecycle and CRE, we investigate the behaviour of both isolated and organised DCCs in a 4-month case study over sub-Saharan Africa (May–August 2016). Using a novel cloud tracking algorithm, we detect and track growing convective cores and their associated anvil clouds using geostationary satellite observations from Meteosat SEVIRI. Retrieved cloud properties and derived broadband radiative fluxes are provided by the CC4CL algorithm. By collecting the cloud properties of the tracked DCCs, we produce a dataset of anvil cloud properties along their lifetimes. While the majority of DCCs tracked in this dataset are isolated, with only a single core, the overall coverage of anvil clouds is dominated by those of clustered, multi-core anvils due to their larger areas and lifetimes. We find that the distribution of anvil cloud CRE of our tracked DCCs has a bimodal distribution. The interaction between the lifecycles of DCCs and the diurnal cycle of insolation results in a wide range of SW anvil CRE, while the LW component remains in a comparatively narrow range of values. The CRE of individual anvil clouds varies widely, with isolated DCCs tending to have large negative or positive CREs while larger, organised systems tend to have CRE closer to zero. Despite this, we find that the net anvil cloud CRE across all tracked DCCs is indeed neutral within our range of uncertainty (0.86 ± 0.91 Wm-2). Changes in the lifecycle of DCCs, such as shifts in the time of triggering, or the length of the dissipating phase, could have large impacts on the SW anvil CRE and lead to complex responses that are not considered by theories of LW anvil CRE feedbacks. [ABSTRACT FROM AUTHOR]

The Lightning Mapping Imager (LMI) onboard the Fengyun-4A (FY-4A) satellite is the first independently developed satellite-borne lightning imager in China. It enables continuous lightning detection in China and surrounding areas, regardless of weather conditions. The FY-4A LMI uses a Charge-Coupled Device (CCD) array for lightning detection, and the accuracy of lightning positioning is influenced by cloud top height (CTH). In this study, we proposed an ellipsoid CTH parallax correction (ECPC) model for lightning positioning applicable to FY-4A LMI. The model utilizes CTH data from the Advanced Geosynchronous Radiation Imager (AGRI) on FY-4A to correct the lightning positioning data. According to the model, when the CTH is 12 km, the maximum deviation in lightning positioning caused by CTH in Beijing is approximately 0.1177° in the east–west direction and 0.0530° in the north–south direction, corresponding to a horizontal deviation of 13.1558 km, which exceeds the size of a single ground detection unit of the geostationary satellite lightning imager. Therefore, it is necessary to be corrected. A comparison with data from the Beijing Broadband Lightning Network (BLNET) and radar data shows that the corrected LMI data exhibit spatial distribution that is closer to the simultaneous BLNET lightning positioning data. The coordinate differences between the two datasets are significantly reduced, indicating higher consistency with radar data. The correction algorithm decreases the LMI lightning location deviation caused by CTH, thereby improving the accuracy and reliability of satellite lightning positioning data. The proposed ECPC model can be used for the real-time correction of lightning data when CTH is obtained at the same time, and it can be also used for the post-correction of space-based lightning detection with other cloud top height data. [ABSTRACT FROM AUTHOR]

Forward radiative transfer (RT) models are essential for atmospheric applications such as remote sensing and weather and climate models, where computational efficiency becomes equally as important as accuracy for high-resolution hyperspectral measurements that need rigorous RT simulations for thousands of channels. This study introduces a fast and accurate RT model for the hyperspectral infrared (HIR) sounder based on principal component analysis (PCA) or machine learning (i.e., neural network, NN). The Geosynchronous Interferometric Infrared Sounder (GIIRS), the first HIR sounder onboard the geostationary Fengyun-4 satellites, is considered to be a candidate example for model development and validation. Our method uses either PCA or NN (PCA/NN) twice for the atmospheric transmittance and radiance, respectively, to reduce the number of independent but similar simulations to accelerate RT simulations; thereby, it is referred to as a multi-domain compression model. The first PCA/NN gives monochromatic gas transmittance in both spectral and atmospheric pressure domains for each gas independently. The second PCA/NN is performed in the traditional spectral radiance domain. Meanwhile, a new method is introduced to choose representative variables for the PCA/NN scheme developments. The model is three orders of magnitude faster than the standard line-by-line-based simulations with averaged brightness temperature difference (BTD) less than 0.1 K, and the compressions based on PCA or NN methods result in comparable efficiency and accuracy. Our fast model not only avoids an excessively complicated transmittance scheme by using PCA/NN but is also highly flexible for hyperspectral instruments with similar spectral ranges simply by updating the corresponding spectral response functions. [ABSTRACT FROM AUTHOR]

East Africa is particularly vulnerable to weather extremes, with severe weather linked to thousands of deaths per year. Improved forecasts of convective events in this region are urgently needed, from both nowcasting and numerical weather prediction models. Improving these forecasts requires further knowledge of convection in this region. This study aims to improve understanding of convective events in East Africa, based on a six‐year climatology of convective life cycles and the associated precipitation. Convective systems are identified as contiguous areas of cold cloud in geostationary satellite measurements over East Africa. A tracking algorithm is used to trace the evolution of the properties of these systems through time and space. Matching the systems to surface precipitation obtained from satellite microwave observations provides insight into how the life cycles of these systems relate to precipitation at the surface. Over the region as a whole, 59% of the accumulated precipitation can be attributed to the tracked convective systems. The majority (81%) of heavy precipitation events (≥$$ \ge $$10 mm·$$ \cdotp $$hr−1) are attributable to convective systems, while light rainfall events (<$$ < $$1 mm·$$ \cdotp $$hr−1) are not (1.8%). Most of the tracked convective systems have an area less than 400 km2 and last for less than an hour. However, the less frequent larger longer‐lived systems produce the majority of the regional accumulated precipitation. Composite life cycles of the tracked systems show rapid intensification and the heaviest precipitation initially, followed by a steady increase in area and weakening in intensity before the system decays. Finally, the Madden–Julian oscillation, which plays a key role in intraseasonal rainfall variability in the region, is also linked to the amount of rainfall due to convective systems through changes in the frequency and properties of these systems. [ABSTRACT FROM AUTHOR]

At 04:14:45 UT on 2022 January 15, a powerful eruption of the submarine Hunga Tonga–Hunga Ha'apai volcano occurred at about 30 km south of the Ha'apai Islands in the Kingdom of Tonga (at −20.55° N, −175.39° E). This eruption caused atmospheric waves that spread worldwide. In this study, we investigate the the total electron content (TEC) variation over China using the BeiDou Navigation Satellite System. The particularly interesting feature of the data set compared to other ground-based TEC data is the exclusive use of the BeiDou geostationary satellites, which monitor the TEC variations for fixed ionospheric piercing points and can provide more accurate calculations of the travelling speed of the disturbance. For comparison, atmospheric pressure records were examined, which show that the Lamb wave passed by the same stations four times with a constant speed of 310 m s−1. However, the TEC results show that the ionospheric disturbances passing over China four times with different speeds within four days after the eruption, two travelling along the short-path direction and two along the long-path direction. The primary front of the first short-path event travels with a speed of 340 m s−1, which is higher than the Lamb wave. The faster speed suggests that the primary front cannot be fully attributed to the Lamb wave, and further studies need to explore its mechanism. The second short-path and first long-path events travel with speeds of 301 and 310 m s−1, respectively, close to the speed of the Lamb wave, and they may be caused by upward energy leakage during the propagation of the Lamb wave. The second long-path event travels with a speed of 264 m s−1, possibly induced by the gravity waves. [ABSTRACT FROM AUTHOR]

Tropospheric vertical column densities (VCDs) of nitrogen dioxide (NO2) retrieved from sun-synchronous satellite instruments have provided abundant NO2 data for environmental studies, but such data are limited by retrieval uncertainties and insufficient temporal sampling (e.g., once a day). The Geostationary Environment Monitoring Spectrometer (GEMS) launched in February 2020 monitors NO2 at an unprecedented hourly resolution during the daytime. Here we present a research product for tropospheric NO2 VCDs, referred to as POMINO–GEMS (where POMINO is the Peking University OMI NO2 algorithm). We develop a hybrid retrieval method combining GEMS, TROPOMI (TROPOspheric Monitoring Instrument) and GEOS-CF (Global Earth Observing System Composition Forecast) data to generate hourly tropospheric NO2 slant column densities (SCDs). We then derive tropospheric NO2 air mass factors (AMFs) with explicit corrections for surface reflectance anisotropy and aerosol optical effects through parallelized pixel-by-pixel radiative transfer calculations. Prerequisite cloud parameters are retrieved with the O2 – O2 algorithm by using ancillary parameters consistent with those used in NO2 AMF calculations. The initial retrieval of POMINO–GEMS tropospheric NO2 VCDs for June–August 2021 exhibits strong hotspot signals over megacities and distinctive diurnal variations over polluted and clean areas. POMINO–GEMS NO2 VCDs agree with the POMINO–TROPOMI v1.2.2 product (R=0.98 ; NMB = 4.9 %) over East Asia, with slight differences associated with satellite viewing geometries and cloud and aerosol properties affecting the NO2 retrieval. POMINO–GEMS also shows good agreement with the following: OMNO2 (Ozone Monitoring Instrument (OMI) NO2 Standard Product) v4 (R=0.87 ; NMB = - 16.8 %); and GOME-2 (Global Ozone Monitoring Experiment-2) GDP (GOME Data Processor) 4.8 (R=0.83 ; NMB = - 1.5 %) NO2 products. POMINO–GEMS shows small biases against ground-based MAX-DOAS (multi-axis differential optical absorption spectroscopy) NO2 VCD data at nine sites (NMB = - 11.1 %), with modest or high correlation in diurnal variation at six urban and suburban sites (R from 0.60 to 0.96). The spatiotemporal variation in POMINO–GEMS correlates well with mobile car MAX-DOAS measurements in the Three Rivers source region on the Tibetan Plateau (R=0.81). Surface NO2 concentrations estimated from POMINO–GEMS VCDs are consistent with measurements from the Ministry of Ecology and Environment of China for spatiotemporal variation (R=0.78 ; NMB = - 26.3 %) and diurnal variation at all, urban, suburban and rural sites (R≥0.96). POMINO–GEMS data will be made freely available for users to study the spatiotemporal variations, sources and impacts of NO2. [ABSTRACT FROM AUTHOR]

By incorporating the high‐resolution satellite remote sensing sea surface temperature (SST) with low‐resolution sea level anomaly and sea surface salinity (SSS), this study explores the reconstructability of the three‐dimensional (3D) eddy structures via the "interior + surface quasigeostrophic" (isQG) method in the South Indian Ocean. We apply the incorporation of high‐resolution SST to improve the spatial resolution of the reconstruction. We also propose a correction scheme for density reconstruction within the mixed layer to offset the absence of mixed layer dynamics in the SQG framework. Comparison against the in situ observations demonstrates a satisfactory reconstructability for subsurface velocity and density anomalies. Statistically, the zonal velocity reconstructability outperforms its meridional counterpart and the corresponding velocity phase. The reconstructed shallow‐layer velocity exhibits a superior skill in eddy‐active regions, when compared with drogued drifter observations. Reconstructed subsurface velocities reproduce the spatial structures of eddy‐induced velocity anomaly along the GO‐SHIP observation transect, although present smaller magnitudes. Results demonstrate the potential applicability of the isQG method for reconstructing mesoscale eddies, particularly in the ocean at mid‐to‐high latitudes, where subsurface dynamics are strongly influenced by barotropic and the first baroclinic modes. With the upcoming high‐advanced satellite observations, the isQG framework is expected to achieve better subsurface estimations. Plain Language Summary: The South Indian Ocean (SIO) contains a significant number of mesoscale eddies that have been thoroughly studied for their horizontal structure, lifetime, and trajectory through satellite observations, particularly using the gridded sea level anomaly maps. Although the contributions of eddies to oceanic energy balance and material transports have become increasingly clear, understanding the three‐dimensional (3D) structures of smaller‐scale eddies in the SIO remains a challenge due to insufficient subsurface observations and the relatively low effective resolution of current satellite‐gridded sea surface height (SSH) products. This study investigates the potential of dynamically reconstructing the subsurface 3D structures of SIO eddies from satellite observations. We combine higher‐resolution sea surface temperature snapshots with the SSH map to enhance the effective resolution of the reconstructed subsurface density and velocity anomalies, which enables the reproduction of smaller‐scale structures associated with temperature fronts. We also propose a correction scheme to increase the reconstructability of the density anomaly within the mixed layer. Evaluations against multisource real observations suggest a satisfactory reconstructability of the interior + surface quasigeostrophic method for the eddy's 3D structures. This study would provide a new way to investigate the 3D structures of eddy's evolution. Key Points: The high‐resolution eddy's 3D structures are estimated from the satellite observations based on the interior + surface quasigeostrophic (isQG) methodIntroducing the higher‐resolution sea surface temperature observation to isQG method enhances the effective resolution of reconstructionA correction scheme in density reconstruction within the mixed layer is suggested to offset the absence of mixed layer dynamics [ABSTRACT FROM AUTHOR]

Unique antenna technology is needed for many satellite communication applications in a variety of communication systems, including military, amateur, and commercial ones. In marine communications, a mast-mounted structure mounted on a submarine is highly desired. For Network-Centric Warfare (NCW) and combined operations, the structure needs a variety of antennas to support the different functions based on the frequency coverage, such as UHF, IRNSS, GPS, GNSS, S-Band, and C-Band. Today, the structure has a variety of antenna systems, including RADAR and periscopes. The amount of area needed for the structure increases when single antenna technology is employed for a single application, but the amount of space on the structure that is really available decreases. There will be fewer antennas on the structure if the same antenna technology is applied to two or more applications. The space on the structure will be freed up as a result of the reduction in antennas and can be utilized for other crucial purposes. In addition, satellites must be used to operate on new submarines. The design of the antenna is a difficult issue for using satellites on submarines, and it must have distinctive qualities such as material selection, circular polarization, greater bandwidth, low VSWR, and a good axial ratio. For a variety of satellite communication applications, including the Indian Regional Navigation Satellite System, the Global Navigation Satellite System, and the Global Positioning System, a new design of a dual-wire helix antenna with a bow-tie technique is proposed in this study. The suggested antenna is constructed, experimentally tested, and modelled for a number of critical parameters, including gain, 3 dB beamwidth, VSWR, and axial ratio. To validate the antenna, simulation and experimental data are contrasted. [ABSTRACT FROM AUTHOR]

Differentiating between snow and clouds presents a formidable challenge in the context of mapping snow cover over the Qinghai–Tibetan Plateau (QTP). The frequent presence of cloudy conditions severely complicates the discrimination of snow cover from satellite imagery. To accurately monitor the spatiotemporal evolution of snow cover, it is imperative to address these challenges and enhance the segmentation schemes employed for snow cover assessment. In this study, we devised a pixel-wise classification algorithm based on Support Vector Machine (SVM) called the 3-D Orientation Gradient algorithm (3-D OG), which captures the variations of the gradient direction of snow and clouds in spatiotemporal dimensions based on geostationary satellite "Fengyun-4A" (FY-4A) multi-spectral and multi-temporal optical imagery. This algorithm assumes that the speed and direction of clouds and snow are different in the process of movement leading to their discrepancy of gradient characteristics in time and space. Therefore, in this algorithm, the gradient of the images in the spatiotemporal dimensions is calculated first, and then the movement angle and trend are obtained based on that. Finally, the feature space is composed of the multi-spectral image, gradient image, and movement feature maps, which are used as the input of the SVM. Our results demonstrate that the proposed algorithm can identify snow and clouds more accurately during snowfall by utilizing the FY-4A's high temporal resolution image. Weather station data, which was collected during snowstorms in the QTP, were used for evaluating the accuracy of our algorithm. It is demonstrated that the overall accuracy of snow cover segmentation by using the 3-D OG algorithm is improved by at least 12% and 10% as compared to snow products of Fengyun-2 and MODIS, respectively. Overall, the proposed algorithm has overcome the axial swing errors existing in Geostationary satellites and is successfully applied to cloud and snow segmentation in QTP. Furthermore, our study underscores that the visible and near-infrared bands of Fengyun-4A can be used for near real-time snow cover monitoring with high performance using the 3-D OG algorithm. [ABSTRACT FROM AUTHOR]

The Micro-Electro-Mechanical System (MEMS) LiDAR point cloud in autonomous vehicles has a large deflection range, which results in slow registration speed and poor applicability. To maximize speed, an improved Normal Distribution Transform (NDT) method that integrates point cloud density features has been proposed. First, the point cloud is reduced using a modified voxel filter and a pass-through filter. Next, the Intrinsic Shape Signature (ISS) algorithm is utilized to analyze the point cloud features and extract key points; the Four-Point Congruent Set (4PCS) algorithm is then employed to calculate the initial pose under the constraints of the key point set to complete the coarse registration. Finally, the self-adaptive segmentation model is constructed by using a K-D tree to obtain the density features of key points, and the NDT algorithm is combined with this model to form an SSM-NDT algorithm, which is used for fine registration. Each algorithm was compared on the autonomous vehicle dataset PandaSet and actual collected datasets. The results show that the novel method increases the speed by at least 60% and takes into account good registration accuracy and strong anti-interference. [ABSTRACT FROM AUTHOR]

Abstract Satellite remote sensing enables the study of atmospheric aerosols at large spatial scales, with geostationary platforms making this possible at sub‐daily frequencies. High‐temporal‐resolution aerosol observations can be made from geostationary data by using robust numerical inversion methods such as the widely‐used optimal estimation (OE) theory. This is the case of the instantaneous Aerosol and surfacE Retrieval Using Satellites in GEOstationary orbit (iAERUS‐GEO) algorithm, which successfully retrieves aerosol optical depth (AOD) maps from the Meteosat Second Generation weather satellite based on a simple implementation of the OE approach combined with the Levenberg–Marquardt method. However, the exact gain in inversion performances that can be obtained from the multiple and more advanced possibilities offered by OE is not well documented in the current literature. Against this background, this article presents the quantitative assessment of OE for the future improvement of the iAERUS‐GEO algorithm. To this end, we use a series of comprehensive experiments based on AOD maps retrieved by iAERUS‐GEO using different OE implementations, and ground‐based observations used as reference data. First, we assess the varying importance in the inversion process of satellite observations and a priori information according to the content of satellite aerosol information. Second, we quantify the gain of AOD estimation in log space versus linear space in terms of accuracy, AOD distribution and number of successful retrievals. Finally, we evaluate the accuracy improvement of simultaneous AOD and surface reflectance retrieval as a function of the regions covered by the Meteosat Earth's disk.

In the western United States, prolonged drought, warming climate, and historical fuel build-up have contributed to larger and more intense wildfires, as well as longer fire seasons. As these costly wildfires become more common, new tools and methods are essential for improving our understanding of the evolution of fires and how extreme weather conditions, including heatwaves, windstorms, droughts, and varying levels of active fire suppression, influence fire spread. Here we develop the GOES-Observed Fire Event Representation (GOFER) algorithm to derive the hourly fire progression of large wildfires and create a dataset of hourly fire perimeters, active fire lines, and fire spread rates. Using GOES-East and GOES-West geostationary satellite detections of active fires, we test the GOFER algorithm on 28 large wildfires in California from 2019-2021. The GOFER algorithm includes parameter optimizations for defining the burned-to-unburned boundary and correcting for the parallax effect from elevated terrain. We evaluate GOFER perimeters with using 12-hourly data from the VIIRS-derived Fire Event Data Suite (FEDS) and final fire perimeters from California's Fire and Resource Assessment Program (FRAP). Although the GOES imagery used to derive GOFER has coarser resolution (2 km at the equator), the final fire perimeters from GOFER correspond reasonably well with those obtained from FRAP, with a mean Intersection-over-Union (IoU) of 0.77, in comparison to 0.83 between FEDS and FRAP. GOFER fills a key temporal gap present in other fire tracking products that rely on low-earth- orbit imagery, where perimeters are available at 12-hour intervals or longer, or at ad hoc intervals from aircraft overflights. This is particularly relevant when a fire spreads rapidly, such as at maximum hourly spread rates of over 5 km/h. Our GOFER algorithm for deriving the hourly fire progression using GOES can be applied to large wildfires across North and South America and reveals considerable variability in rates of fire spread on diurnal time scales. The resulting GOFER dataset has a broad set of applications, including the development of predictive models for fire spread and improvement of atmospheric transport models for surface smoke estimates. [ABSTRACT FROM AUTHOR]

Predicting the evolution of burned area, smoke emissions, and energy release from wildfires is crucial to air quality forecasting and emergency response planning yet has long posed a significant scientific challenge. Here we compare predictions of burned area and fire radiative power from the coupled weather/fire‐spread model WRF‐Fire (Weather and Research Forecasting Tool with fire code), against simpler methods typically used in air quality forecasts. We choose the 2019 Williams Flats Fire as our test case due to a wealth of observations and ignite the fire on different days and under different configurations. Using a novel re‐gridding scheme, we compare WRF‐Fire's heat output to geostationary satellite data at 1‐hr temporal resolution. We also evaluate WRF‐Fire's time‐resolved burned area against high‐resolution imaging from the National Infrared Operations aircraft data. Results indicate that for this study, accounting for containment efforts in WRF‐Fire simulations makes the biggest difference in achieving accurate results for daily burned area predictions. When incorporating novel containment line inputs, fuel density increases, and fuel moisture observations into the model, the error in average daily burned area is 30% lower than persistence forecasting over a 5‐day forecast. Prescribed diurnal cycles and those resolved by WRF‐Fire simulations show a phase offset of at least an hour ahead of observations, likely indicating the need for dynamic fuel moisture schemes. This work shows that with proper configuration and input data, coupled weather/fire‐spread modeling has the potential to improve smoke emission forecasts. Plain Language Summary: Predicting wildfire growth and smoke behavior is an important and historically difficult task. Here we compare a coupled fire‐weather model to current practices used in air quality forecasting in their ability to predict daily wildfire spread and fire intensity for the 2019 Williams Flats Fire. We also evaluate the fire‐weather model against high‐resolution satellite and aircraft measurements in ways previously unexplored. We find that including fire‐fighting efforts in our model makes the biggest improvements toward accurate wildfire forecasting. When we account for fire‐fighting efforts, fuel moisture maps, and increased fuel loads, our coupled fire‐weather model significantly outperforms simpler methods with reasonable computational time. Despite this, modeled fire intensity tends to lead observations by at least an hour. Optimal configuration options and avenues for forecasting improvements are discussed. Key Points: Adding containment lines into our wildfire model made the biggest difference in improving accuracy of daily burned area predictionsAccounting for containment, fuel moisture content maps, and increased fuel density consistently increased prediction skill above persistenceModeled fire radiative power leads observations by at least an hour, but with similar accuracy to prescribed diurnal cycles [ABSTRACT FROM AUTHOR]

Insufficient image spatial resolution is a serious limitation in many practical scenarios, especially when acquiring images at a finer scale is infeasible or brings higher costs. This is inherent to remote sensing, including Sentinel-2 satellite images that are available free of charge at a high revisit frequency, but whose spatial resolution is limited to 10m ground sampling distance. The resolution can be increased with super-resolution algorithms, in particular when performed from multiple images captured at subsequent revisits of a satellite, taking advantage of information fusion that leads to enhanced reconstruction accuracy. One of the obstacles in multi-image super-resolution consists in the scarcity of real-world benchmarks—commonly, simulated data are exploited which do not fully reflect the operating conditions. In this paper, we introduce a new benchmark (named MuS2) for super-resolving multiple Sentinel-2 images, with WorldView-2 imagery used as the high-resolution reference. Within MuS2, we publish the first end-to-end evaluation procedure for this problem which we expect to help the researchers in advancing the state of the art in multi-image super-resolution. [ABSTRACT FROM AUTHOR]

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

The meteorological conditions required for the production of Terrestrial Gamma‐ray Flashes (TGFs) are not well understood. Particularly, the link between TGF production, meteorology, and weather severity is poorly characterized with most works focusing on only a small set of TGF events or isolated storms. This work is a further step toward understanding the general context of the meteorological conditions required for TGF production and if it differs from regular lightning production. We use TGFs observed from AGILE, ASIM, Fermi, and RHESSI to generate the largest catalog of TGFs with associated lightning sferics from either the World Wide Lightning Location Network (WWLLN) or Global Lightning Detection (GLD) combined with geostationary satellite images and meteorological conditions derived from ERA5 reanalysis data. In total we analyze 1582 TGF events and contextualize them in comparison to lightning flashes as characterized by ASIM. In our analysis we consider the proportion of TGFs and lightning coming from systems with overshooting tops as well as the Cloud Top Temperature (CTT) and the Convective Available Potential Energy (CAPE). Our results are consistent with previous studies, finding that TGFs observed from space come from primarily higher cloud tops than regular lightning flashes do. We find that CAPE and the proportion of cells with overshooting tops is similar for both TGF and lightning producing cells. It suggests that TGF observations from space are biased toward systems with higher cloud tops because the attenuation of the gamma‐rays from lower altitude TGFs reduce their intensity below the detection level of LEO instruments. Plain Language Summary: Terrestrial Gamma‐ray Flashes (TGFs) are bursts of high energy radiation associated with only some lightning flashes. Here we seek to determine if the systems which generate TGFs are significantly different from those that generate only lightning flashes. While the general trend of TGF observations indicate that observable TGFs come from systems with higher cloud tops than other lightning, relatively little is understood about how the weather severity impacts the production of TGFs. To explore this we use geostationary satellite images and meteorological data to investigate the systems which generate TGFs compared to those that do not. We find that observed TGFs come from systems with higher cloud tops but not significantly more severe weather systems and that regional differences dominate over seasonal differences. It suggests that TGF observations from space are biased toward systems with higher cloud tops, because lower altitude TGFs cannot be detected from the space observatories. Key Points: Global analysis of TGF cloud characteristics combining AGILE, ASIM, Fermi and RHESSITGF producing cells are higher than lightning producing cells but otherwise their weather severity is similarRegional effects on the meteorological conditions for TGF production are more significant than seasonal effects [ABSTRACT FROM AUTHOR]

In this paper, we propose an innovative spaceborne isoflux scanning digital phased array (ISDPA) design with two-stage digital beamforming (DBF) for geostationary satellites. To achieve isoflux scanning, a novel technique is presented to obtain an isoflux beam for the ISDPA equivalent element using a DBF completely shared subarray architecture and the differential evolution (DE) algorithm. By reutilizing the radiating elements of adjacent subarrays, the radiation aperture and element number are augmented, enhancing the degrees of optimization freedom. To validate the proposed design, a linear ISDPA with 16 DBF completely shared subarrays is optimized and analyzed using two sets of excitation coefficients in different DBF stages. The numerical results demonstrate that the proposed ISDPA can adaptively compensate for space loss variations during beam scanning for geostationary communications with low sidelobes better than −20 dB. [ABSTRACT FROM AUTHOR]

The high temporal resolution of thermal infrared imagery from the geostationary GOES-R satellites presents an opportunity to observe mountain snow and forest temperatures over the full diurnal cycle. However, the off-nadir views of these imagers may impact or bias surface temperature observations, especially when viewing a surface composed of both snow and forests. We used GOES-16 and -17 thermal infrared brightness temperature observations of a flat snow and forest-covered study site at Grand Mesa, Colorado, USA, to characterize how forest coverage and view angle impact these observations. These two geostationary satellites provided views of the study area from the southeast (134.1° azimuth, 33.5° elevation) and southwest (221.2° azimuth, 35.9° elevation) respectively. Coincident ground-based and airborne IR observations collected as part of the NASA SnowEx field campaign in February 2020 provided a rich dataset for comparison. Observations over the course of two cloud-free days spanned the entire study site. The surface temperature observations from each dataset were compared to find their relative differences, and how those differences may have varied over time or as a function of varying forest cover across the study area. GOES-16 and -17 surface brightness temperatures were found to match the diurnal cycle and temperature range within ~1 hour and ± 3 °C of ground-based observations. GOES-16 and -17 were both biased warmer than nadir-looking airborne IR and ASTER observations. The warm biases were higher at times when the sun-satellite phase angle was near its daily minimum, and the warm biases seen in GOES-16 were greater for pixels that contained more forest coverage. The observations suggest that a "thermal infrared shadow-hiding" effect may be occurring, where the geostationary satellites are preferentially seeing the warmer sunlit sides of trees at different times of day. These biases are important to understand for applications using GOES-R ABI for surface temperatures over areas with surface roughness features, such as forests, that could exhibit a thermal infrared shadow-hiding effect. [ABSTRACT FROM AUTHOR]

This paper studies the multiple geosynchronous spacecraft refueling problem (MGSRP) with multiple servicing spacecraft (Ssc) and fuel depots (FDs). In the mission scenario, multiple Ssc and FDs are parked in the geosynchronous Earth orbit (GEO) initially. Ssc start from FDs and maneuver to visit and refuel multiple GEO targets with known demands. These capacitated Ssc are expected to rendezvous with fuel-deficient GEO targets and FDs for the purpose of delivering the fuel stored in FDs to GEO targets. The objective is to find a set of Pareto-optimal solutions with minimum fuel cost and mission duration. The MGSRP is a much more complex variant of multidepot vehicle routing problems mixing discrete and continuous variables. A two-nested optimization model is built. We propose a new multiobjective hybrid particle swarm optimization to solve the outer-loop problem, and the design variables are the refueling sequence, task assignment, time distribution, and locations of FDs. In the inner-loop problem, branch and bound method is used to find the optimal decision variable for a given outer-loop solution. Finally, numerical simulations are presented to illustrate the effectiveness and validity of the proposed approach. [ABSTRACT FROM AUTHOR]

Fine spectral detection can basically solve the problem of low vertical resolution at the 183 GHz water-vapor absorption line, and it is expected to become one of the main methods for next-generation geostationary and polar-orbiting satellites. Here, using data from Microwave Humidity Sounder II (MWHS-II) onboard the Chinese Fengyun 3D (FY-3D) satellite in the Global/Regional Assimilation and Prediction System (GRAPES) Four-Dimensional Variational (4D-Var) system of the China Meteorological Administration (CMA), we explore the assimilation application of the water-vapor absorption line at 183.31 ± 1 GHz, 183.31 ± 3 GHz and 183.31 ± 7 GHz, as well as 183.31 ± 1.8 GHz and 183.31 ± 4.5 GHz, two added channels, to assess the impact of adding the 183.31 ± 1.8 GHz and 183.31 ± 4.5 GHz sampling channels on data assimilation and numerical weather prediction. Our findings reveal a significant increase in the specific-humidity increment, which in the middle–upper troposphere is numerically much larger than in the lower troposphere. Specifically, the assimilation of 183.31 ± 1.8 GHz observations, positioned near the center of the water-vapor absorption line, results in a pronounced adjustment compared with the 183.31 ± 4.5 GHz observations. And under the strong constraint of the numerical model, the Root Mean Square Error (RMSE) of the wind field diminishes more significantly (by an average of 2–4%) after assimilating the water-vapor observations at greater heights. [ABSTRACT FROM AUTHOR]

The Fengyun-4A (FY-4A) satellite is a new-generation geostationary meteorological satellite developed by China. The advanced geosynchronous radiation imager (AGRI), one of the key payloads onboard FY-4A, can monitor sea surface temperature (SST). This paper compares FY-4A/AGRI SST with in situ and Himawari-8/advanced Himawari imager (AHI) SST. The study area spans 30°E–180°E, 60°S–60°N, and the study period is from January 2019 to December 2021. The matching time window of the three data is 30 min, and the space window is 0.1°. The quality control criterion is to select all clear sky and well-distributed matchups within the study period, removing the influence of SST fronts. The results of the difference between FY-4A/AGRI and in situ SST show a bias of −0.12 °C, median of −0.05 °C, standard deviation (STD) of 0.76 °C, robust standard deviation (RSD) of 0.68 °C, and root mean square error (RMSE) of 0.77 °C for daytime and a bias of 0.00 °C, median of 0.05 °C, STD of 0.78 °C, RSD of 0.72 °C, and RMSE of 0.78 °C for nighttime. The results of the difference between FY-4A/AGRI SST and Himawari-8/AHI SST show a bias of 0.04 °C, median of 0.10 °C, STD of 0.78 °C, RSD of 0.70 °C, and RMSE of 0.78 °C for daytime and the bias of 0.30 °C, median of 0.34 °C, STD of 0.81 °C, RSD of 0.76 °C, and RMSE of 0.86 °C for nighttime. The three-way error analysis also indicates a relatively larger error of AGRI SST. Regarding timescale, the bias and STD of FY-4A/AGRI SST show no seasonal correlation, but FY-4A/AGRI SST has a noticeable bias jump in the study period. Regarding spatial scale, FY-4A/AGRI SST shows negative bias at the edge of the AGRI SST coverage in the Pacific region near 160°E longitude and positive bias in high latitudes of the southern hemisphere. The accuracy of FY-4A/AGRI SST depends on the satellite zenith angle and water vapor. Further research on the FY-4A/AGRI SST retrieval algorithm accounting for the variability of water vapor will be conducted. [ABSTRACT FROM AUTHOR]

The 48-h intensity forecasts for Hurricane Pamela (2021) from numerical weather prediction models, statistical–dynamical aids, and forecasters were a major forecast bust with Pamela making landfall as a minor rather than major hurricane. From the satellite presentation, Pamela exhibited a symmetric pattern referred to as central cold cover (CCC) in the subjective Dvorak intensity technique. Per the technique, the CCC pattern is accompanied by arrested development in intensity despite the seemingly favorable convective signature. To understand forecast uncertainty during occurrences, central cold cover frequency from 2011 to 2021 is documented. From these cases, composites of longwave infrared brightness temperatures from geostationary satellites for CCC cases are presented, and the surrounding tropical cyclone large-scale environment is quantified and compared with other tropical cyclones at similar latitudes and intensities. These composites show that central cold cover has a consistent presentation, but varies in the preceding hours for storms that eventually intensify or weaken. And, the synoptic-scale environment surrounding the tropical cyclone thermodynamically supports the vigorous deep convection associated with CCC. Finally, intensity forecast errors from numerical weather prediction models and statistical–dynamical aids are examined in comparison to similar tropical cyclones. This work shows that guidance struggles during CCC cases with intensity errors from these models being in the lowest percentiles of performance, particularly for 24- and 36-h forecasts. Significance Statement: The appearance of symmetric cold clouds near the center of developing tropical cyclones is most often associated with future intensification. This simple relationship is widely used by statistical tropical cyclone intensity forecast models. Here, we reexamine and confirm that one subjectively determined nighttime cold cyclone cloud pattern termed the "central cold cover" pattern in Vern Dvorak's seminal technique for estimating tropical cyclone intensity from infrared satellite images is indeed related to slow or arrested development, and represents a failure mode for these simple forecast models. [ABSTRACT FROM AUTHOR]

It remains challenging to accurately classify complicated clouds owing to the various types of clouds and their distribution on multiple layers. In this paper, multi-band radiation information from the geostationary satellite Himawari-8 and the cloud classification product of the polar orbit satellite CloudSat from June to September 2018 are investigated. Based on sample sets matched by two types of satellite data, a random forest (RF) algorithm was applied to train a model, and a retrieval method was developed for cloud classification. With the use of this method, the sample sets were inverted and classified as clear sky, low clouds, middle clouds, thin cirrus, thick cirrus, multi-layer clouds and deep convection (cumulonimbus) clouds. The results indicate that the average accuracy for all cloud types during the day is 88.4%, and misclassifications mainly occur between low and middle clouds, thick cirrus clouds and cumulonimbus clouds. The average accuracy is 79.1% at night, with more misclassifications occurring between middle clouds, multi-layer clouds and cumulonimbus clouds. Moreover, Typhoon Muifa from 2022 was selected as a sample case, and the cloud type (CLT) product of an FY-4A satellite was used to examine the classification method. In the cloud system of Typhoon Muifa, a cumulonimbus area classified using the method corresponded well with a mesoscale convective system (MCS). Compared to the FY-4A CLT product, the classifications of ice-type (thick cirrus) and multi-layer clouds are effective, and the location, shape and size of these two varieties of cloud are similar. [ABSTRACT FROM AUTHOR]

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

Obtaining high spatiotemporal resolution surface currents with wide coverage is of great importance for understanding marine physical and biogeochemical processes, as well as practical applications. This study utilizes the sequential hourly images from the Himawari‐8 geostationary satellite to map the semidiurnal M2 surface tidal current pattern over the shallow shelf (<100 m) of the northern South China Sea. The comparison between the satellite‐derived tidal currents and the High‐Frequency Radar observations shows good agreement. The satellite‐derived tidal currents are also fairly comparable to the prediction of the regional tidal model from Oregon State University. The detided daily mean surface flows provide an apparent representation of the spatial pattern of the Guangdong Coastal Current (GCC) and its synoptic variability, well consistent with the simultaneous sea surface temperature and salinity satellite observations. The observed surface flows are compared to the Copernicus Marine Environment Monitoring Service daily model currents, and the model findings are in good agreement with the observations, indicating the sensitivity of the GCC to rapid changes of northeasterly winds. This study highlights the potential of the Himawari‐8 geostationary satellite in measuring sea surface currents over a broad, shallow continental shelf. Plain Language Summary: This study used hourly images from the Himawari‐8 geostationary satellite to map the semidiurnal M2 surface tidal current pattern in the shallow shelf (<100 m) of the Northern South China Sea. The satellite‐derived results are compared to high‐frequency radar observations and a regional tidal model from Oregon State University and are found to be in good agreement. The satellite observations also provide a representation of the spatial pattern of the Guangdong Coastal Current (GCC) and its synoptic variability, which are well consistent with sea surface temperature and salinity observations. The observed surface flows are also in good agreement with the Copernicus Marine Environment Monitoring Service daily model currents, showing the sensitivity of the GCC to rapid changes of northeasterly winds. This study demonstrates the usefulness of the Himawari‐8 geostationary satellite in measuring surface currents over a broad, shallow continental shelf. Key Points: Semidiurnal M2 tidal currents are derived from the Himawari‐8 geostationary imageryThe satellite‐derived mean flows reveal the spatial pattern of a strong, broad Guangdong Coastal Current and its synoptic scale variabilitySatellite‐derived tidal and mean currents are used for evaluating the model outputs [ABSTRACT FROM AUTHOR]

Recently, megaconstellations with a massive number of low Earth orbit (LEO) satellites are being considered as a possible solution for providing global coverage due to relatively low latency and high throughput compared to geosynchronous orbit (GEO) satellites. However, as the number of satellites and operators participating in the LEO constellation increases, inter-satellite interference will become more severe, which may yield marginal improvement or even decrement in network throughput. In this article, we introduce the concept of satellite clusters that can enhance network performance through satellites’ cooperative transmissions. The characteristics, formation types, and transmission schemes for the satellite clusters are highlighted. Simulation results evaluate the impact of clustering from coverage and capacity perspectives, showing that when the number of satellites is large, the performance of clustered networks outperforms the unclustered ones. The viable network architectures of the satellite cluster are proposed based on the 3rd Generation Partnership Project (3GPP) standard. Finally, the future applications of clustered satellite networks are discussed. [ABSTRACT FROM AUTHOR]

Dissolved oxygen (DO) concentration is a widely used and effective indicator for assessing water quality and pollution in aquatic environments. Continuous and large-scale inversion of water environments using remote sensing imagery has become a hot topic in water environmental research. Remote sensing technology has been extensively applied in water quality monitoring, but its limited sampling frequency necessitates the development of a high-frequency dynamic water quality monitoring model. In this study, we utilized Lake Chaohu as a case study. Firstly, we constructed a dynamic water quality inversion model for monitoring DO concentrations using machine learning methods, with Himawari-8 (H8) satellite imagery as input data and DO concentrations in Lake Chaohu as output data. Secondly, the developed DO concentration inversion model was employed to estimate the overall grid-based DO concentration in the Lake Chaohu region for the years 2019 to 2021. Lastly, Pearson correlation analysis and significance tests were performed to examine the correlation and significance between the estimated grid-based DO concentration and the ERA5 reanalysis dataset. The results demonstrate that the Random Forest (RF) model performs best in DO concentration inversion, with a high R2 score of 0.84, and low RMSE and MAE values of 0.69 and 0.54, respectively. Compared to other models, the RF model improves average performance with a 38% increase in R2, 13% decrease in RMSE, and 33% decrease in MAE. The model accurately predicts DO concentrations. Furthermore, the inversion results reveal seasonal differences in DO concentrations in Lake Chaohu from 2019 to 2021, with higher concentrations in spring and winter, and lower concentrations in summer and autumn. The average DO concentrations in the northwest, central-south, and northeast regions of Lake Chaohu are 10.12 mg/L, 9.98 mg/L, and 9.96 mg/L, respectively, with higher concentrations in the northwest region. Pearson correlation analysis indicates a significant correlation (p < 0.01) between DO concentrations and temperature, surface pressure, latent heat flux from the atmosphere to the surface, and latent heat flux from the surface to the atmosphere, with correlation coefficients of −0.615, 0.583, −0.480, and 0.444, respectively. The results verify the feasibility of using synchronous satellites for real-time inversion of DO concentrations, providing a more efficient, economical, and accurate means for real-time monitoring of DO concentrations. This study has practical value in improving the efficiency and accuracy of water environmental monitoring. [ABSTRACT FROM AUTHOR]

The accurate attitude estimation of target ships plays a vital role in ensuring the safety of marine transportation, especially for tugs. A Light Detection and Ranging (LiDAR) system can generate 3D point clouds to describe the target ship's geometric features that possess attitude information. In this work, the authors put forward a new attitude-estimation framework that first extracts the geometric features (i.e., the board-side plane of a ship) using point clouds from shipborne LiDAR and then computes the attitude that is of interest (i.e., yaw and roll in this paper). To extract the board-side plane accurately on a moving ship with sparse point clouds, an improved Random Sample Consensus (RANSAC) algorithm with a pre-processing normal vector-based filter was designed to exclude noise points. A real water-pool experiment and two numerical tests were carried out to demonstrate the accuracy and general applicability of the attitude estimation of target ships brought by the improved RANSAC and estimation framework. The experimental results show that the average mean absolute errors of the angle and angular-rate estimation are 0.4879 deg and 4.2197 deg/s, respectively, which are 92.93% and 75.36% more accurate than the estimation based on standard RANSAC. [ABSTRACT FROM AUTHOR]

A large fraction of the uncertainty around future global warming is due to the cooling effect of aerosol-liquid cloud interactions, and in particular to the elusive sign of liquid water path (LWP) adjustments to aerosol perturbations. To quantify this adjustment, we propose a causal approach that combines physical knowledge in the form of a causal graph with geostationary satellite observations of stratocumulus clouds. This allows us to remove confounding influences from large-scale meteorology and to disentangle counteracting physical processes (cloud-top entrainment enhancement and precipitation suppression due to aerosol perturbations) on different timescales. This results in weak LWP adjustments that are time-dependent (first positive then negative) and meteorological regime-dependent. More importantly, the causal approach reveals that failing to account for covariations of cloud droplet sizes and cloud depth, which are, respectively, a mediator and a confounder of entrainment and precipitation influences, leads to an overly negative aerosol-induced LWP response. This would result in an underestimation of the cooling influence of aerosol-cloud interactions. [ABSTRACT FROM AUTHOR]