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Understanding and attributing changes to water quality is essential to the study and management of coastal ecosystems and the ecological functions they sustain (e.g., primary productivity, predation, and submerged aquatic vegetation growth). However, describing patterns of water clarity—a key aspect of water quality—over meaningful scales in space and time is challenged by high spatial and temporal variability due to natural and anthropogenic processes. Regionally tuned satellite algorithms can provide a more complete understanding of coastal water clarity changes and drivers. In this study, we used open‐access satellite data and low‐cost in situ methods to improve estimates of water clarity in an optically complex coastal water body. Specifically, we created a remote sensing water clarity product by compiling Landsat‐8 and Sentinel‐2 reflectance data with long‐term Secchi depth measurements at 12 sites over 8 years in a shallow turbid coastal lagoon system in Virginia, USA. Our satellite‐based model explained ∼33% of the variation in in situ water clarity. Our approach increases the spatiotemporal coverage of in situ water clarity data and improves estimates from bio‐optical algorithms that overpredicted water clarity. This could lead to a better understanding of water clarity changes and drivers to better predict how water quality will change in the future. Plain Language Summary: Water quality affects coastal ocean ecosystems and their ecological functions, including primary productivity, predation, and aquatic vegetation growth. An important component of water quality, water clarity, has historically been measured by tools such as Secchi disks. They are black and white painted disks that are lowered into the water column to estimate the depth at which they are no longer visible from the surface. They are a reliable, repeatable, and low‐cost tool; thus, they have contributed to extensive long‐term records of overall water quality. However, they produce gaps in space and time. Here, we created a remote sensing product to fill these gaps using open‐access high resolution satellite data and low‐cost in situ methods. Our product allows for the retrieval of water clarity data across an entire water body and at times field measurements are not available. This could lead to a better understanding of water clarity changes and drivers to better predict how water quality will change in the future. Key Points: Coupled satellite estimates with in situ observations increased the spatiotemporal coverage of water clarity estimation in a coastal ocean lagoon systemDemonstrated an accessible approach for modifying bio‐optical algorithms in optically complex coastal watersModel improved water clarity estimates, reduced biases, and decreased errors associated with Landsat‐8/Sentinel‐2 differences [ABSTRACT FROM AUTHOR]

Global gravity models from satellite altimetry missions have widely contributed to the improvement of the knowledge over the oceans. However, few models are available with a validated accuracy model, as the most recent models V21 to V32 from Scripps Institution of Oceanography (SIO). The aim of this paper is to propose a method to estimate the accuracy of any free air gravity anomaly model derived from satellite altimetry from validated marine data sets. The main idea is to correlate the error of the model to the mean roughness of the free air gravity anomaly on precise marine measurement areas. The relationship obtained is then generalized to each point of the model. This error grid is eventually combined with the grid provided by the satellite models, reflecting the errors related to the technology of the different altimetry satellites used. The method has been successfully applied to successive versions of SIO gravity anomaly models (from V18 to V31) to produce continuous accuracy grids over the North Atlantic Ocean. Validation of the results from independent marine data shows that the proposed approach allows to strongly improve the estimation of the used models' accuracy which converges, whatever the tested versions, toward a minimal value of 0.9 mGal over flat areas. Plain Language Summary: Satellite altimetry missions allow to build models of the gravity anomaly over the oceans. However, it is very difficult to assess for the accuracy of such models, many sources of error being involved between data acquisition and the model itself. Comparing a model to marine data is a good way to estimate its accuracy over the marine survey, but generalizing this information to the whole model seems challenging. We have proposed to relate the error to the roughness of the model, which is a good indicator of the part of signal the model may miss. However, we also discovered that this method cannot be applied everywhere, especially where the error of the model relies on specificity of the satellite data acquisition: coastal zones and ocean mesoscale high variability areas. Combining our error grid with the one associated to some models eventually appeared as a good solution to assess for the accuracy of the model. Key Points: Comparing marine data to gravity anomaly model derived from satellite altimetry is a good mean to estimate the accuracy of the latterGeneralization to any grid point can be performed using the relation between model roughness and accuracyThis method cannot account for all sources of error, especially those related to altimetry measurement [ABSTRACT FROM AUTHOR]

As the volume of data collected at monitored volcanoes continues to expand, researchers will need quick, reliable, and automated methods of inverting those data into useful models of the underlying magma systems. Recently adapted from other fields for use in volcanology, the Ensemble Kalman Filter (EnKF) is one such inversion technique that has been used to produce several successful forecasts and hind‐casts of volcanic unrest, correlating geodetic deformation with mechanical stresses around the magma reservoir. However, given the similarity in which changes to a reservoir's size and pressure are expressed at the surface, the filter can have trouble fully resolving magmatic conditions. In this study, we therefore test several different published variations of the EnKF workflow to produce an optimal configuration for use in future forecasting efforts. By generating synthetic observations of ground deformation under known conditions and then assimilating them through different implementations of the EnKF, we find that many variants favored in other fields underperform for this specific application. We conclude that correlations between model parameters that develop within the EnKF's Monte Carlo ensemble distort the filter's ability to correctly update the model state, causing the filter to systematically favor changes in some parameters over others and ultimately converge to a partially inaccurate solution. This effect can be somewhat mitigated by interrupting these parameter correlations, and the filter remains sensitive to many aspects of the magma system regardless. However, further research and novel approaches will be needed to truly optimize the EnKF for use in volcanology. Plain Language Summary: One of the largest goals of volcanology is building the ability to forecast future eruptions, minimizing the risk to populations on or near active volcanoes. While fields such as atmospheric sciences and physical oceanography have developed a wide variety of computational techniques for forecasting complex physical processes, such techniques have only recently been adapted for volcanology. We test the effectiveness of different variations of the Ensemble Kalman Filter (EnKF), an approach that repeatedly updates a set of physics‐based simulations to best fit incoming observations and extrapolate the system trajectory. In particular, we simulate observations of how the ground surface moves in response to an expanding underground magma reservoir, and then pass those data through several versions of the EnKF to see how closely the original conditions are reproduced. Different magma reservoir conditions produce similar observations at the surface, and we find that on a computational level this ambiguity negatively impacts the EnKF's ability to precisely constrain the size and pressure state of the chamber. Regardless, the filter remains sensitive to key aspects of the magma system, including its mechanical stability, and any computational distortions can be somewhat reduced by using versions that introduce varying degrees of randomness into the algorithm. Key Points: Non‐uniqueness in geodetic deformation inhibits data assimilation performance on a computational levelIntroducing randomness and breaking parameter correlations can reduce distortions and improve inversion outcomesWhile unable to precisely resolve magma reservoir size and shape, the filter remains sensitive to overall system stability [ABSTRACT FROM AUTHOR]

Model resolution is a key factor that impacts the model bias in simulating ocean dynamics. How would model resolution influence model bias is an important question. In this study, we set up two sets of experiments with identical settings except the grid resolution, one with a minimum spacing of 2 km, referred to as the submesoscale permitting (SP) model, and the other with a minimum spacing of 6 km, referred to as the mesoscale resolving (MR) model, to investigate the impact of horizontal resolution on simulated dynamic features in the South China Sea. Results show that the SP outperforms the MR model in simulating both barotropic and baroclinic tides, upper ocean circulation, sea surface height, and temperature in the South China Sea, and in simulating the evolution of the eddy pair east of Indo‐China Peninsula in summer and the Kuroshio loop current in the Luzon Strait in winter. The SP model also performs better in simulating thermal structure within the ocean and the winter mixed layer depth. Additionally, the SP model can capture more vertical motion features. The superiority of the SP model lies in its ability to better resolve terrain features that have a significant impact on ocean dynamical processes, and to resolve more physical processes. As a result, the SP model relies less on the uncertain parameterized processes. Our study has important implications for studies on the modeling of physical oceanography in the South China Sea, as well as for the reduction of climate model biases. Plain Language Summary: The spatial resolution of numerical models significantly influences the model performance. The question is, how can model bias be reduced by increasing model resolution. By examining results from a numerical model with different spatial resolutions, involving some important ocean dynamical processes in the South China Sea, we addressed this question. This is done through two sets of numerical experiments with different horizontal resolutions but otherwise identical settings. Results show that the higher resolution model (∼2 km) outperforms the lower resolution model (∼6 km) in all dynamical and thermal features in the South China Sea. Analysis shows that this is mainly related to the fact that higher resolution models can better resolve the topography and more physical processes. Key Points: Two sets of models with different horizontal resolutions simulate key ocean dynamics processes in the South China SeaThe higher resolution model outperforms the lower resolution model in simulating all dynamical and thermal features of interestPreliminary analysis suggests that higher resolution models can better resolve topography as well as more physical processes [ABSTRACT FROM AUTHOR]

First‐year ice (FYI) and multi‐year ice (MYI) are the two most common ice types in the Arctic. In this article, the classification of FYI and MYI over the Arctic region in the winter of 2019/2020 and 2020/2021 is investigated by combining the data of the scatterometer on Chinese‐French Oceanography Satellite (CFOSCAT) and the Advanced Microwave Scanning Radiometer‐2 (AMSR‐2) based on the Tree Augmented Naive Bayes (TAN) classifier. The CFOSCAT/AMSR ice type classification results are validated by using Ocean and Sea Ice Satellite Application Facility (OSI SAF) sea ice type products, Canadian Ice Service ice charts, and synthetic‐aperture radar data. The results showed that the overall MYI extent change trend retrieved from CFOSCAT/AMSR was consistent with the OSI SAF product with a correlation coefficient of 0.89 for winter of 2019/2020 and 0.88 for 2020/2021. In addition, CFOSCAT/AMSR identified a slightly larger MYI extent than OSI SAF and the average deviation between them is 10.1% for 2019/2020 while 8.3% for 2020/2021. Besides, CFOSCAT/AMSR can identify more MYI pixels when the MYI concentration is relatively low in the Western Arctic region. We also used CFOSCAT data only to retrieve ice type and found that the active and passive microwave data fusion could capture more MYI pixels located near the boundary of MYI and FYI main body, and the introduction of AMSR‐2 data in ice type classification could reduce the error caused by the abnormal values of CFOSCAT parameters. Key Points: Sea ice type in the Arctic was classified by combining the CFOSCAT and AMSR‐2 data for the winter of 2019/2020 and 2020/2021The CFOSCAT/AMSR‐2 classification detects a daily average MYI extent of 2.26 × 106 km2 in 2019/2020 and 2.04 × 106 km2 in 2020/2021The average difference of MYI extent between CFOSCAT/AMSR and OSI SAF is 0.25 × 106 km2 in 2019/2020 and 0.14 × 106 km2 in 2020/2021 [ABSTRACT FROM AUTHOR]

This article is composed of three independent commentaries about the state of Integrated, Coordinated, Open, Networked (ICON) principles (Goldman et al., 2021, https://doi.org/10.1002/essoar.10508554.1) in the AGU section paleoclimatology and paleoceanography (P&P), and a discussion on the opportunities and challenges of adopting them. Each commentary focuses on a different topic: (Section 2) Global collaboration, technology transfer and application, reproducibility, and data sharing and infrastructure; (Section 3) Local knowledge, global gain: improving interactions within the scientific community and with locals, indigenous communities, stakeholders, and the public; (Section 4) Field, experimental, remote sensing, and real‐time data research and application. P&P projects can better include ICON principles by directly incorporating them into research proposals. A promising way to overcome the challenges of interdisciplinarity and integration is to foster networking, which will advance our research discipline through the application of ICON. Plain Language Summary: Paleoceanography and Paleoclimatology seeks to reveal past changes in the oceans and the climate, to help us better understand how Earth systems work. Traditionally, it has a strong focus on international networks and cooperation and we have accomplished many international projects. However, we are still facing some major challenges to achieve equity among researchers and science questions. In this article we discuss the current state, issues and solutions from three different perspectives: (a) data structure problems and data sharing issues, (b) relationships between research groups with different levels of knowledge, cultural aspects and their interaction with local communities in fieldwork, and (c) utilization of data, instrument sharing, promotion of networking, and effective communication with the public to make our research as inclusive and visible as possible. Actions aimed at ICON could be further promoted by research programs, governments, and scientists via integration of cooperation strategies into research projects. Key Points: Open science and global collaboration increase networking opportunities, data availability, and quality of scientific outcomesICON principles support inclusivity and diversity, and ultimately resulting in increased and quicker uptake of scientific outcomesICON practices are well established in large‐scale international collaborations, but coordinated data and sampling standards are needed [ABSTRACT FROM AUTHOR]

The China‐France Oceanography Satellite (CFOSAT) was successfully launched on October 29, 2018, which carries two innovative payloads, that is, the surface waves investigation and monitoring (SWIM) instrument and the rotating fan‐beam scatterometer (RFSCAT). CFOSAT scatterometer (CSCAT) is dedicated to the monitoring of sea surface wind vectors and climate studies, but also for the applications over land and polar regions. CSCAT employs a "rotating fan‐beam" design rather than "pencil‐beam" approach and "fixed fan‐beam" approach adopted by the scatterometers in orbit before. In this paper, the data processing approach of CSCAT is described. The geolocation reference mapping model is employed in CSCAT slice geolocation. In noise processing, CSCAT adopts the method of weighted least squares polynomial surface fitting to conduct noise correction factor estimation adaptively. The attitude cube algorithm used for X integral factor computation is developed to obtain precise backscatter coefficients based on the radar equation. Because of the interdependency of the CSCAT data processing and the hardware configuration and onboard signal processing of the instrument, the instrument module configuration and signal processing design are also analyzed and presented comprehensively. Especially, the influence and compensation of Doppler frequency are introduced. The backscatter coefficient accuracy is estimated on the Amazon forest, and the results show that the backscatter coefficient accuracy of CSCAT on 25 × 25 km grids is less than 0.5 dB except that the near end and far end of the beam are slightly higher. The statistical results show the wind retrieval results meet the CFOSAT mission requirements. Key Points: The data preprocessing principle and algorithm of China‐France Oceanography Satellite (CFOSAT) rotating fan‐beam scatterometer are summarized comprehensivelyThe CFOSAT rotating fan‐beam scatterometer design and signal processing are summarized comprehensivelyThe backscatter coefficient measurement accuracy of CFOSAT are provided [ABSTRACT FROM AUTHOR]

The rotating fan‐beam architecture wind scatterometer Chinese‐French Oceanography Satellite (CSCAT) onboard the Chinese‐French Oceanography Satellite leads to varying geometries and varying wind retrieval performance across the swath. To assess this, the wind vector cells (WVCs) are classified into three groups: outer, sweet, and nadir. The sweet swath WVCs contain the most diverse geometries, which give the best wind retrieval performance, whereas on the opposite the outer swath WVCs have the least diverse geometries, which makes the wind retrieval and ambiguity removal more challenging. NWP Ocean Calibration (NOC) is used to calibrate the radar backscatter prior to the wind inversion. It computes the difference between measured backscatter and simulated backscatter from collocated NWP winds through the geophysical model function. The difference is applied as a correction. An improved NOC method, NOC as a function of incidence angle and azimuth angle (NOCant) is introduced, which takes the orientation of the antenna into account. This method is compared with the widely used NOC as a function of incidence angle only (NOCinc). It shows a better fit with the geophysical model function, except for the outer swath WVCs which have limited geometry diversity. It also corrects the asymmetric wind direction bias distribution across the swath and reduced the nadir swath relative wind direction bias which is caused by the poor azimuth diversity. In conclusion, the rotating fan‐beam architecture of CSCAT leads to unique and varying data characteristics across the swath. Overall, the performance of the proposed NOCant correction is better than NOCinc and improves the wind statistics. Key Points: The unique rotating fan‐beam architecture leads to varying geometries across the swath as well as varying wind retrieval performanceA new NWP Ocean Calibration (NOC) is developed, and it results in improved wind statistics over previous techniques [ABSTRACT FROM AUTHOR]