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We thank the authors (hereafter referred to as CCC) for providing us an opportunity to clarify some points of our original paper. CCC list in their abstract three “key points”that we respond to in this Reply. The first comment is the central one and the most developed. It deals mainly with discussion of features of methods of spectral analysis, mainly SSA. We have quoted the sub‐parts of that comment as items 1a to 1k. The replies to comments/key points 2 and 3 are shorter. We disagree with most of the comments by CCC and explain why. We conclude that we have successfully countered CCC's criticism and shown many of their points to be unsubstantiated. The main problem seems to reside in differences concerning the literature on Singular Spectral Analysis and our use of it. Much of our response to the comments can be found in textbooks and review papers on SSA and time series analysis; we quote extensively, both in our original paper and in this response to CCC, Golyandina and Zhigljavsky (2013).Key Points: We find compelling results showing a significant co‐variability between sunspot numbers and global temperatureWe do not have a mechanism to explain that forcing: it remains to be found. We do not discuss further the parts of CCC on mechanismsThe trends of temperature and sunspots (first SSA component) are similar; the second component results in an opposite trend in the last 30 years [ABSTRACT FROM AUTHOR]

We thank the authors (hereafter referred to as CCC) for providing us an opportunity to clarify some points of our original paper. CCC list in their abstract three "key points"that we respond to in this Reply. The first comment is the central one and the most developed. It deals mainly with discussion of features of methods of spectral analysis, mainly SSA. We have quoted the sub‐parts of that comment as items 1a to 1k. The replies to comments/key points 2 and 3 are shorter. We disagree with most of the comments by CCC and explain why. We conclude that we have successfully countered CCC's criticism and shown many of their points to be unsubstantiated. The main problem seems to reside in differences concerning the literature on Singular Spectral Analysis and our use of it. Much of our response to the comments can be found in textbooks and review papers on SSA and time series analysis; we quote extensively, both in our original paper and in this response to CCC, Golyandina and Zhigljavsky (2013). Key Points: We find compelling results showing a significant co‐variability between sunspot numbers and global temperature.We do not have a mechanism to explain that forcing: it remains to be found. We do not discuss further the parts of CCC on mechanisms.The trends of temperature and sunspots (first SSA component) are similar; the second component results in an opposite trend in the last 30 years. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new ambitious project led by AGU in collaboration with Wiley and Curvenote and a team of geoscientists, computer and data scientists. The project, aptly called Notebooks Now!, aims to develop an end‐to‐end workflow for the preparation, submission, peer‐review and publication of Earth and space science papers, based on computational notebooks, specifically Jupyter Notebooks or R Markdown. The idea, first proposed by Dr. Peter Fox, Editor in Chief of Earth and Space Science from 2019 to early 2021, is to revolutionize the way data‐ and computation‐rich scientific research is performed and published. The vision is to establish the computational notebook as the centerpiece of the scientific work, around which the manuscript is then written. In this model, the notebook is the primary publication, accompanied by a static "traditional" paper as the enduring archive. Advantages of this model include: ease of editing of data and code during manuscript writing and revision; open access to the same by reviewers and readers; prompt reusability and repurposing of research outputs by other researchers; access to sophisticated computational tools by the global community; "multiplier effect" of the research. The model thus fulfills the findable, accessible, interoperable, and reusable principles of scientific publishing, and commits to Open Science. This is part of AGU's strategic initiative to accelerate openly accessible scientific contributions to sustainable development. Plain Language Summary: Transparency and reproducibility are the fundamental principles of publication of scientific research. In compliance with these principles, data and methods described in papers published in AGU's journals must be findable, accessible, interoperable, and reusable (FAIR). This is especially important for data‐rich and computationally‐intensive studies. Notebooks Now! is a project based on computational notebooks as the primary publication output, that will fulfill the requirement for FAIR. The project will also advance AGU's commitment to Open Science, the global effort to increase participation and access to scientific research. Here we report on the current progress of the project. Key Points: Notebooks Now! is a workflow for the submission and publication of computational notebooks as the primary element of the scientific recordThe model fulfills the findable, accessible, interoperable, and reusable principles of scientific publicationThis project commits to Open Science, and is part of AGU's contribution to openly accessible sustainable development [ABSTRACT FROM AUTHOR]

Earthquake nowcasting has been proposed as a means of tracking the change in large earthquake potential in a seismically active area. The method was developed using observable seismic data, in which probabilities of future large earthquakes can be computed using Receiver Operating Characteristic methods. Furthermore, analysis of the Shannon information content of the earthquake catalogs has been used to show that there is information contained in the catalogs, and that it can vary in time. So an important question remains, where does the information originate? In this paper, we examine this question using stochastic simulations of earthquake catalogs. Our catalog simulations are computed using an Earthquake Rescaled Aftershock Seismicity ("ERAS") stochastic model. This model is similar in many ways to other stochastic seismicity simulations, but has the advantage that the model has only 2 free parameters to be set, one for the aftershock (Omori‐Utsu) time decay, and one for the aftershock spatial migration away from the epicenter. Generating a simulation catalog and fitting the two parameters to the observed catalog such as California takes only a few minutes of wall clock time. While clustering can arise from random, Poisson statistics, we show that significant information in the simulation catalogs arises from the "non‐Poisson" power‐law aftershock clustering, implying that the practice of de‐clustering observed catalogs may remove information that would otherwise be useful in forecasting and nowcasting. We also show that the nowcasting method provides similar results with the ERAS model as it does with observed seismicity. Plain Language Summary: Earthquake nowcasting was proposed as a means of tracking the change in the potential for large earthquakes in a seismically active area, using the record of small earthquakes. The method was developed using observed seismic data, in which probabilities of future large earthquakes can be computed using machine learning methods that were originally developed with the advent of radar in the 1940s. These methods are now being used in the development of machine learning and artificial intelligence models in a variety of applications. In recent times, methods to simulate earthquakes using the observed statistical laws of earthquake seismicity have been developed. One of the advantages of these stochastic models is that it can be used to analyze the various assumptions that are inherent in the analysis of seismic catalogs of earthquakes. In this paper, we analyze the importance of the space‐time clustering that is often observed in earthquake seismicity. We find that the clustering is the origin of information that makes the earthquake nowcasting methods possible. We also find that a common practice of "aftershock de‐clustering", often used in the analysis of these catalogs, removes information about future large earthquakes. Key Points: Earthquake nowcasting tracks the change in the potential for large earthquakes, using information contained in seismic catalogsWe analyze the information contained in the space‐time clustering that is observed in earthquake seismicityWe find that "aftershock de‐clustering" of catalogs removes information about future large earthquakes that the nowcasting method uses [ABSTRACT FROM AUTHOR]

This paper describes a catalog of simultaneous observations of the Earth's magnetosheath by ESA's Cluster and NASA's MMS missions. The catalog is built from a visual inspection of summary plots provided by the two missions complemented by an analysis of high‐resolution magnetic field data. The catalog includes 117 events when Cluster 4 and MMS 4 crossed simultaneously the magnetosheath between January–April, 2017–2021. The dynamical and turbulent features of the magnetosheath are strongly influenced by θBn, the angle between the interplanetary magnetic field (IMF) and the shock normal direction. To facilitate such investigations, we also determine the bow shock geometry for each event based on two different approaches: (a) a minimum variance analysis of in‐situ magnetic field measurements, and (b) a geometrical approach which considers a bow shock model parameterized by OMNI data. A description of spacecraft trajectory during each event is also provided. Additional data describe the relative distances between Cluster 4 and MMS 4, a classification of each event as either quasi‐parallel or quasi‐perpendicular, and the distribution of events per magnetospheric flank. The time intervals for the Cluster ‐ MMS conjunctions are included in the catalog, as well as all associated figures and tables discussed in this paper are made available through an independent online data repository, and can be freely downloaded and used by any interested researcher. Key Points: We use in‐situ measurements from Cluster 4 and MMS 4 to build a catalog/database of simultaneous observations of the Earth's magnetosheathEmpirical models and minimum variance analysis of the magnetic field allow for an estimation of the bow‐shock orientation for each eventWe also provide details on the relative spacecraft position and an analysis of the bow‐shock geometry [ABSTRACT FROM AUTHOR]

The Editors and Staff of Earth and Space Science thank the reviewers whose selfless work has significantly contributed to the publication process of papers highlighting the best research in geophysics, planetary, and space science in 2023. Peer‐reviewing is a demanding and thankless job. It is however an essential component of the scientific process, requiring the highest standards of integrity and rigor. Reviewers check data and procedures and test reproducibility of methods and results; they share their expertise to verify that the interpretations and conclusions of a paper are consistent with assumptions and existing knowledge. Without this essential work it would not be possible to trust in the scientific process. Publication of papers in a multidisciplinary journal such as Earth and Space Science, that highlights methods, instruments, data and algorithms, relies directly on the expertise of its reviewers to verify and vouch for the quality of the papers that are published. We are indebted to all our reviewers and are delighted to acknowledge them publicly in this Editorial. Plain Language Summary: The Editors and Staff of Earth and Space Science acknowledge the importance of hundreds of peer reviewers who contributed to the scientific rigor of the papers published in the journal. The Editors wish to publicly recognize the 926 reviewers who gave selflessly of their time and expertise in 2023. Key Points: The editors thank the 2023 peer reviewers [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and machine learning (ML) pose a challenge for achieving science that is both reproducible and replicable. The challenge is compounded in supervised models that depend on manually labeled training data, as they introduce additional decision‐making and processes that require thorough documentation and reporting. We address these limitations by providing an approach to hand labeling training data for supervised ML that integrates quantitative content analysis (QCA)—a method from social science research. The QCA approach provides a rigorous and well‐documented hand labeling procedure to improve the replicability and reproducibility of supervised ML applications in Earth systems science (ESS), as well as the ability to evaluate them. Specifically, the approach requires (a) the articulation and documentation of the exact decision‐making process used for assigning hand labels in a "codebook" and (b) an empirical evaluation of the reliability" of the hand labelers. In this paper, we outline the contributions of QCA to the field, along with an overview of the general approach. We then provide a case study to further demonstrate how this framework has and can be applied when developing supervised ML models for applications in ESS. With this approach, we provide an actionable path forward for addressing ethical considerations and goals outlined by recent AGU work on ML ethics in ESS. Plain Language Summary: Artificial intelligence and machine learning can make it hard to do science in a way that can be repeated. This can mean redoing a study in the exact same way to see if you can get the same or similar results (reproducibility) or trying to use the same study design on a new problem to see if the results are the same or similar (replicability). These types of scientific repetitions is important for developing robust knowledge, but is hard to do with certain types of machine learning that rely on data that were categorized by researchers. The researchers have to make decisions and categorize their data, which the machine learning algorithm then uses as a guide to make its own decisions. Generally, there is not enough information shared by the researchers about how these decisions were made to repeat the science or evaluate how good it is. In this paper, we provide a way to address these shortcomings. The approach and example we offer illustrates how to (a) create a rulebook that can be shared for how to make decisions and (b) quantitatively measure how consistent the researchers are at using that rulebook to make their decisions. Key Points: We provide a rigorous hand labeling procedure to improve the replicability and reproducibility of supervised machine learning (ML)Our case study and step‐by‐step guide clearly outline how the procedure can be appliedThe procedure is an actionable path forward for addressing ethical considerations and goals for ML development in Earth systems science [ABSTRACT FROM AUTHOR]

The Mars Reconnaissance Orbiter and its Context Camera (CTX) have acquired more than 100,000 separate panchromatic images that capture nearly the entire surface of Mars at ∼5–6 m/pixel. This paper describes a data processing workflow used to generate the first contiguous global mosaic of CTX data, which represents a large improvement in spatial resolution over existing 100 m/pixel contiguous global mosaics. We describe the overarching strategy for the mosaic's construction, which was to maximize the scientific utility of a continuous mosaic that is 5.7 trillion pixels in size. The pipeline used for data processing prioritized traceability and reproducibility of the final mosaic, such that the provenance of all pixels is reported, equipping scientists with information to differentiate mosaic artifacts from surface landforms and to incorporate critical image metadata into their analyses. The CTX data set synthesized into a global CTX mosaic facilitates ready analysis and provides a new capability in transitioning global studies of Mars from high‐resolution investigations of individual images to systematic studies of the entire Martian surface at outcrop‐resolving quality without regard to image boundaries. Plain Language Summary: We generated a global mosaic of Mars using Context Camera satellite imagery at 5.0 m/pixel, a substantial improvement in spatial resolution over the previous global mosaic at 100 m/pixel. This paper describes a new technique for merging overlapping images together in a way that preserves all information from each component image and produces a map of all image boundaries that is included with the mosaic. This makes scientific analyses that are facilitated by the mosaic fully traceable and reproducible. With this mosaic, scientists can now readily analyze landforms without the overhead of downloading individual images and perform complete global analyses of Mars at a resolution capable of seeing rock outcrops on the surface. Key Points: A global mosaic of Mars at 5.0 m/pixel has been created from 86,571 Context Camera images that are co‐registered to each other and seam‐correctedNon‐destructive image processing was used to preserve all image metadata and map seamsThe new mosaic will facilitate systematic studies of Mars at 5.0 m/pixel spatial resolution [ABSTRACT FROM AUTHOR]

The challenges of Reproducibility and Replicability (R & R) in computer science experiments have become a focus of attention in the last decade, as efforts to adhere to good research practices have increased. However, experiments using Deep Learning (DL) remain difficult to reproduce due to the complexity of the techniques used. Challenges such as estimating poverty indicators (e.g., wealth index levels) from remote sensing imagery, requiring the use of huge volumes of data across different geographic locations, would be impossible without the use of DL technology. To test the reproducibility of DL experiments, we report a review of the reproducibility of three DL experiments which analyze visual indicators from satellite and street imagery. For each experiment, we identify the challenges found in the data sets, methods and workflows used. As a result of this assessment we propose a checklist incorporating relevant FAIR principles to screen an experiment for its reproducibility. Based on the lessons learned from this study, we recommend a set of actions aimed to improve the reproducibility of such experiments and reduce the likelihood of wasted effort. We believe that the target audience is broad, from researchers seeking to reproduce an experiment, authors reporting an experiment, or reviewers seeking to assess the work of others. Plain Language Summary: This paper aims to help researchers understand the challenges of reproducing Deep Learning (DL) publications, mitigate reproducibility gaps, and make their own work more reproducible. We build on the work of others and add recommendations organized by (a) the quality of the data set (and associated metadata), (b) the DL methodology, (c) the implementation methodology, and the infrastructure used. To our knowledge, this is the first initiative of its kind to address the problem of reproducibility in remote sensing imagery and DL problems for real‐world tasks. We hope this paper lowers the barrier to entry for the DL community to improve research. Following the lifecycle mantra: reproduce!, then replicate! With the goal of improving reproducibility! Key Points: We discuss the reproducibility challenges faced in research by Deep Learning approaches using Big DataWe provide advice for pre‐screening papers (before experiments) to avoid poorly invested effortWe present a recipe with a set of mitigation strategies to address common errors users (researchers, authors, reviewers) may encounter [ABSTRACT FROM AUTHOR]

Large horizontal winds and shears across the mesopause region have been observed by sounding rockets and satellites but with limited simultaneous temperature profiles to date. This feature is critical to the diagnosis of the insitu dynamic conditions. Theories suggest this feature may be related to variations of the atmosphere static stability, Brunt‐Väisälä frequency square (N2), and the wave activity in the mesosphere and lower thermosphere (MLT) that cannot be resolved by the models. Since 2020, the Na Doppler lidar at Utah State University has attained several hundred hours of wind and temperature profiles in the MLT up to ∼110 km altitude. This paper focuses on the variations of these large winds and shears (>40 m/s/km), along with their potential relation to the activities of the various scales of gravity waves in the static state upper mesosphere, measured coordinately by the Advanced Mesospheric Temperature Mapper operating alongside the lidar. The investigation reveals that larger shears tend to occur in the meridional direction than the zonal direction and are observed more frequently in winter. The shears are comparable in both directions in summer and more large shears were observed in zonal direction. In addition, small shears occur when the medium‐scale waves are completely or partially blocked by the mean wind, though no convincing evidence relates small‐scale wave activities with these large winds and shears. Furthermore, small and decreasing N2 in the upper mesopause region are associated with insignificant magnitude of the shears. Plain Language Summary: Throughout the year, extremely large horizontal wind shears in the atmosphere occur in the mesopause region near or above 105 km altitude around the globe. Theoretical and numerical simulations suggest their formation is closely related to the atmospheric gravity waves generated in the troposphere, but experimental observations at these altitudes are difficult to obtain. In this paper, an advanced wind and temperature lidar and a temperature mapper are utilized at Utah State University to investigate this topic comprehensively. The coordinated campaigns by the two instruments are capable of observing these large shears and the gravity wave behaviors in the mesopause region simultaneously, providing critical information to understand this dramatic atmospheric feature. Key Points: The large horizontal winds and the associated shears occur more frequently in the meridional wind than the zonal windThe very large shears occur more in the winter than summerThe large shears are highly correlated with medium scale gravity waves with large horizontal phase speed and long horizontal wavelength [ABSTRACT FROM AUTHOR]

Satellite infrared detectors cannot penetrate clouds, especially precipitating clouds. Improving precipitation estimation accuracy based on infrared brightness temperature has always been important but challenging. In this paper, based on the infrared brightness temperature of the Advanced Geosynchronous Radiation Imager (AGRI) onboard China's Feng‐Yun 4A satellite, we develop and evaluate a new precipitation estimation method. First, using static data, physical characteristics of clouds, cloud image texture features, temporal motion features, and AGRI infrared channel brightness temperature, we construct features for a machine learning model. Then, we develop precipitation estimation methods. Precipitation is estimated in two steps: classification and regression. We employ a random forest classification model to identify whether there is precipitation in a given field of view. If there is precipitation, a multi‐model ensemble regression learning method is used to estimate the areas with this precipitation. The ensemble learning method uses convex optimization to integrate prediction results based on the optimization of hyperparameters of five basic models (i.e., those of random forest, XGBoost, LightGBM, decision tree, and extra tree models). Furthermore, two regression stacking ensemble models—the Least Absolute Shrinkage and Selection Operator (herein referred to as Stacking1‐LASSO) and K‐nearest neighbor (herein referred to as Stacking2‐KNN)—are used to predict the results of the aforementioned basic models. The results of basic models are used as inputs of these two stacking models. Finally, based on the Integrated Multi‐satellitE Retrievals for GPM (IMERG) precipitation product and rain gauge precipitation data, we conduct precipitation estimation experiments and evaluate our methods. The results show that ensemble learning models have greater accuracy in estimating precipitation than the basic models. When using IMERG precipitation as the target precipitation, ensemble learning models can estimate the central area of heavy precipitation during typhoons Ampil and Maria. The ensemble learning estimation effect is better than that of Stacking2‐KNN. Moreover, when rain gauge data is used as the target precipitation, ensemble learning can also estimate the center of heavy precipitation and with good consistency with recorded satellite brightness temperature data. Key Points: A convex optimization weighted and stacked ensemble learning method for satellite data estimation of precipitation is developedNear real‐time precipitation estimates can be obtained throughout the day using only geographic information and Advanced Geosynchronous Radiation Imager infrared brightness temperature dataThe proposed ensemble learning method can estimate the central areas of typhoons and short‐term heavy rainfall [ABSTRACT FROM AUTHOR]

In situ spacecraft observations are critical to our study and understanding of the various phenomena that couple mass, momentum, and energy throughout near‐Earth space and beyond. However, on‐orbit telemetry constraints can severely limit the capability of spacecraft to transmit high‐cadence data, and missions are often only able to telemeter a small percentage of their captured data at full rate. This presents a programmatic need to prioritize intervals with the highest probability of enabling the mission's science goals. Larger missions such as the Magnetospheric Multiscale mission (MMS) aim to solve this problem with a Scientist‐In‐The‐Loop (SITL), where a domain expert flags intervals of time with potentially interesting data for high‐cadence data downlink and subsequent study. Although suitable for some missions, the SITL solution is not always feasible, especially for low‐cost missions such as CubeSats and NanoSats. This manuscript presents a generalizable method for the detection of anomalous data points in spacecraft observations, enabling rapid data prioritization without substantial computational overhead or the need for additional infrastructure on the ground. Specifically, Principal Components Analysis and One‐Class Support Vector Machines are used to generate an alternative representation of the data and provide an indication, for each point, of the data's potential for scientific utility. The technique's performance and generalizability is demonstrated through application to intervals of observations, including magnetic field data and plasma moments, from the CASSIOPE e‐POP/Swarm‐Echo and MMS missions. Plain Language Summary: Measurements captured by spacecraft are necessary to our understanding the space environment near Earth and throughout our solar system. However, spacecraft can often only transmit a small portion of the data they capture back to Earth. This means that many spacecraft must prioritize intervals of data that have the highest probability of helping to further our understanding of these environments. Some missions utilize humans, on Earth, to help select these scientifically important intervals. This solution, called the Scientist‐In‐The‐Loop, can be too expensive or programmatically complex for many small missions to implement. This manuscript presents a technique for the detection of anomalous events in spaceflight measurements using statistical analysis and machine learning. These detected anomalies can be used to prioritize data that has a high probability of scientific relevance. Further, the proposed technique is highly generalizable and computationally lightweight, making it suitable for a variety of missions. Several case studies from multiple existing missions will be analyzed throughout this paper. Key Points: Spacecraft often cannot transmit all measurements to Earth at full cadence due to telemetry bandwidth limitationsMany missions must implement complex data prioritization schemes to ensure only the highest‐priority data is transmitted at high cadenceThe proposed data prioritization technique is highly generic, compatible with inexpensive hardware, and suitable for low‐cost missions [ABSTRACT FROM AUTHOR]

We evaluate a decadal ozone profile record derived from the Suomi National Polar‐orbiting Partnership (SNPP) Ozone Mapping and Profiler Suite (OMPS) Limb Profiler (LP) satellite instrument. In 2023, the OMPS LP data were re‐processed with the new version 2.6 retrieval algorithm that combines measurements from the ultraviolet (UV) and visible (VIS) parts of the spectra. It employs the second order Tikhonov regularization to retrieve a single vertical ozone profile between 12.5 km (or cloud tops) and 57.5 km with a vertical resolution of about 1.9–2.5 km between 20 and 55 km. The algorithm uses radiances measured at six UV ozone‐sensitive wavelengths (295, 302, 306, 312, 317, and 322 nm) paired with 353 nm, and one VIS wavelength at 606 nm combined with 510 and 675 nm to form a triplet. Each wavelength pair or triplet is used over a limited range of tangent altitudes where the sensitivity to ozone change is strongest. A new aerosol correction scheme was implemented based on a gamma‐function particle size distribution. In addition, numerous calibration changes that affected ozone retrievals were applied to measured LP radiances, including updates in altitude registration, radiometric calibration, stray light, and spectral registration. The key version 2.6 enhancement is the improved stability of the LP ozone record confirmed by the reduction in relative drifts between LP ozone and correlative measurements, linked previously to a drift in the version 2.5 LP altitude registration. Plain Language Summary: The Montreal Protocol protects the Earth's ozone layer by regulating the production and usage of ozone‐depleting substances. As a result of this international treaty, we expect stratospheric ozone to increase over time. A series of OMPS Limb Profilers (LP) was designed to ensure continuous spaceborne capabilities for detecting changes in the stratospheric ozone distribution over several decades. The ozone record from the first OMPS LP on board the Suomi NPP mission spans more than 12 years, from April 2012 to the present. A statistically significant positive drift in the previous version 2.5 of LP ozone data, linked to a drift in the LP altitude registration, has compromised its fitness to accurately detect ozone trends. In this paper, we introduce the new version 2.6 OMPS LP ozone data set with improved stability. We found a substantial reduction in relative drifts between LP ozone and correlative measurements. Therefore, the version 2.6 LP ozone can be used with higher confidence for monitoring and quantifying stratospheric ozone recovery. Key Points: The ozone record from the first OMPS Limb Profiler that spans more than 12 years has been reprocessed with the new version 2.6 algorithmThe key v2.6 improvement is the reduction in relative drift that was linked previously to a drift in the v2.5 LP altitude registrationThe study demonstrates the importance of continuous improvements in calibration and retrieval to ensure accuracy and stability of LP ozone [ABSTRACT FROM AUTHOR]

The Macau Science Satellites (MSS‐1) mission, consisting of twin low earth orbit satellites (Satellite A and B), is the first low‐inclination and high‐precision geomagnetic surveying satellite project in China. Among the multiple scientific payloads equipped with the scientific satellites, MSS‐1 carries a GNSS radio occultation (RO) receiver on Satellite A, and aims to observe the Earth's ionosphere and monitor the space weather especially in the South Atlantic Anomaly (SAA) area. This paper focuses on the ionospheric data of MSS‐1 obtained during the first 3 months after its launch, and assesses the RO products as well as initial scintillation measurements. Results show that MSS‐1 RO data have very good agreement with contemporary COSMIC‐2 and ionosonde observations. The scintillation amplitude indices S4 are comparable with that of COSMIC‐2 and well reflect the occurrence rates and distributions of equatorial plasma bubble and sporadic E (Es) in June solstice season. Special attentions are paid to the SAA longitude sector and inspire explorations on the seasonal variations and local‐time dependences of ionospheric irregularities. Key Points: The MSS‐1 ionospheric radio occultation products are comparable with COSMIC‐2 and ionosondesScintillation characteristics in the June solstice season are well reflected based on MSS‐1 high‐rate measurements [ABSTRACT FROM AUTHOR]

Among several hydrological processes, river flow is an essential parameter that is vital for different water resources engineering activities. Although several methodologies have been adopted over the literature for modeling river flow, the limitation still exists in modeling the river flow time series curve. In this research, a functional quantile autoregressive of order one model was developed to characterize the entire conditional distribution of the river flow time series curve. Based on the functional principal component analysis, the regression parameter function was estimated using a multivariate quantile regression framework. For this purpose, hourly scale river flow collected from three rivers in Australia (Mary River, Lockyer Valley, and Albert River) were used to evaluate the finite‐sample performance of the proposed methodology. A series of Monte‐Carlo experiments and historical data sets were examined at three stations. Further, uncertainty analysis was adopted for the methodology evaluation. Compared with the existing methods, the proposed model provides more robust forecasts for outlying observations, non‐Gaussian and heavy‐tailed error distribution, and heteroskedasticity. Also, the proposed model has the merit of predicting the intervals of future realizations of river flow time series at the central and non‐central locations. The results confirmed the potential for predicting the river flow time series curve with a high level of accuracy in comparison with the benchmark existing functional time series methods. Plain Language Summary: This paper proposes a functional quantile autoregressive model of order one, which is used to predict the entire distribution of the realizations of river flow time series curve. The proposed model allows modeling the conditional quantiles of the response variable as a function of its past values of it. The proposed method for historical river flow curves is an excellent alternative to existing mean regression methods at the 0.5 quantile level (median regression). Also, as an advantage over existing methods, it offers a more thorough explanation of the connection among previous and future realizations of river flow curves at various quantile levels, providing a more extensive understanding of the relationship. Moreover, this feature of the proposed method allows for the effortless generation of pointwise prediction intervals for future realizations of river flow curves. The numerical results obtained by Monte Carlo experiments and empirical data analyses exhibit that, compared with existing methods, the proposed method produces competitive or even better forecasting results. The results also indicate that the future realizations of the river flow measurements are well covered by the prediction intervals constructed by the proposed method. Key Points: Predicting the mean and extreme values of the river flow curve is important for various applications in water resources managementThe FQAR(1) allows predicting the entire distribution of future realizations of the river flow curve as a function of its past values of itNumerical results based on river flow measurements collected from the Australia Continent confirmed the potential of the FQAR(1) [ABSTRACT FROM AUTHOR]

This study provided a comprehensive understanding of the source process of the 1819 M 7.7 Kachchh Indian earthquake using physics‐based dynamic rupture modeling and strong ground motion simulations. We successfully simulated the spontaneous dynamic rupture along a curved non‐planar fault using the 3‐D curved‐grid finite‐difference method (CGFDM). The estimated earthquake magnitude is around 7.6, consistent with previous estimations. Our simulations accurately replicated macroscopic rupture patterns and surface deformation, showing agreement with observed data along the Allah Bund fault (ABF) with a maximum displacement ∼5.5 m at the Earth's surface. The maximum modeled coseismic slip on the fault was approximately 7.5 m. Notably, the ABF exhibited characteristics of a weak barrier (leaky barrier) at the bending part, allowing the rupture to propagate further. Despite limitations in surface deformation calculations, the modeled values aligned with the trend of surface fault slip, with a slight deviation in the epicenter toward the east compared to earlier studies. We observed a homogeneous principal stress oriented N25°E, consistent with the present day Indian plate motion. The estimated horizontal peak ground velocities (PGVh) and the maximum value of Intensity X+ aligns well with observations. Furthermore, conducting thorough case studies on significant earthquakes and potential seismic scenarios in stable continental regions is crucial. Such studies play a vital role in validating and improving dynamic rupture models. When combined with statistical methods, this research holds great promise for advancing seismic hazard assessments, earthquake engineering, and strategies for disaster management. Plain Language Summary: This paper is centered around the simulation of the dynamic rupture of the 1819 M 7.7 Kachchh earthquake in India. We have successfully replicated the earthquake's behavior using a three‐dimensional simulation method. The study's results demonstrate the significant influence of the local tectonic setting and non‐planar fault structure on earthquake generation and rupture progression. Although slight discrepancies exist between the simulation results and actual observations, the simulations capture significant trends and reproduce macroscopic rupture patterns. The estimated magnitude of the earthquake aligns well with previous studies. The study highlights the role of fault bending and its impact on surface deformation, contributing to a better understanding of seismic hazards and providing insights for seismic hazard assessments and earthquake source characterization. This work is valuable for comprehending earthquake sources, particularly from earthquake perspectives in the SCR region and other areas. Key Points: Dynamic rupture simulations of the 1819 Kachchh M 7.7 earthquake replicate co‐seismic fault slip, enhance our knowledge of the earthquake sourceWeak barrier characteristics observed at the bending part of the fault enabled further rupturing, influenced by SH and the nucleation pointContributes to understanding earthquake hazards, enhancing seismic assessment, engineering, and disaster management strategies [ABSTRACT FROM AUTHOR]

The Terminal Tracking Camera (TTCam) imaging system on the NASA Lucy Discovery mission consists of a pair of cameras that are being used mainly as a navigation and target acquisition system for the mission's asteroid encounters. However, a secondary science‐focused function of the TTCam system is to provide wide‐angle broadband images over a large range of phase angles around close approach during each asteroid flyby. The scientific data acquired by TTCam can be used for shape modeling and topographic and geologic analyses. This paper describes the pre‐flight and initial in‐flight calibration and characterization of the TTCams, including the development of a radiometric calibration pipeline to convert raw TTCam images into radiance and radiance factor (I/F) images, along with their uncertainties. Details are also provided here on the specific calibration algorithms, the origin and archived location of the required ancillary calibration files, and the archived sources of the raw calibration and flight data used in this analysis. Key Points: We detail the pre‐flight and initial in‐flight characterization and calibration of the NASA Lucy mission's Terminal Tracking Camera systemPre‐flight results primarily include sensor and system characterization (gain, dark current, linearity, flat field, bad pixels, and radiometry estimates)We describe the calibration pipeline as well as initial in‐flight optical assessment and refined radiometry results from Earth, Moon and star field imaging [ABSTRACT FROM AUTHOR]

High spectral resolution infrared sounders are an integral component of the global observing system and are used in a broad range of applications. This is enabled by their high accuracies which are ensured by rigorous calibration/validation activities. One of these activities is the post‐launch intercomparison with other high spectral resolution infrared sounders using simultaneous nadir overpasses (SNOs). This paper introduces a novel application of the previously developed SNO methodology by including time difference histogram symmetrization and a spatial sampling uncertainty. Where possible, radiometric measurement uncertainties are included and propagated through the statistics. Comparisons of Atmospheric Infrared Sounder (AIRS), METOP‐A/B/C Infrared Atmospheric Sounding Interferometer (IASI), and Cross‐track Infrared Sounder (CrIS) from 1 October 2019, to 1 October 2020, are analyzed. Results show AIRS and IASI differences as well as CrIS and AIRS differences are generally less than 0.4 K across the spectrum, and CrIS and IASI differences are generally less than 0.25 K. Comparison of the Suomi National Polar‐Orbiting Partnership and NOAA‐20 CrIS instruments via IASI and AIRS shows differences are generally less than 0.1 K across all bands and that the two CrIS instruments statistically agree within their radiometric uncertainties except for the narrow 2,370 cm−1 region where artifacts due to brightness temperature conversion are prone to occur. Plain Language Summary: One of the primary sources of information used to monitor Earth's weather patterns are satellite based meteorological instruments that measure radiation in the infrared region with high spectral resolution. Agencies worldwide use data from these types of instruments to provide input into weather prediction models. The long term data sets are also very valuable for multi‐decadal climate studies. To ensure the value of these data, the instruments need to be properly calibrated using external references. This paper details and expands upon the previously established methodology of one commonly used calibration technique—called SNOs. This method compares nadir‐viewing data from different instruments measuring radiation coming from the same mass of atmosphere in time and space. Introduced in this paper are two additions to the methodology: (a) a symmetrization of the time differences between when the instruments measure the specific air mass, and (b) an uncertainty estimate on the comparison based on the spatial coincidence of the measurements. Results from this methodology demonstration show how well the current suite of satellite‐based instrumentation measuring infrared radiation at high spectral resolution agree with each other. Key Points: Simultaneous nadir overpass comparisons include time difference symmetrization, spatial sampling uncertainty, and Cross‐track Infrared Sounder (CrIS) radiometric uncertaintyDifferences between Suomi National Polar‐Orbiting Partnership (SNPP) CrIS and NOAA‐20 CrIS are generally less than 0.1 K and within combined radiometric measurement uncertaintiesDifferences between Atmospheric Infrared Sounder (AIRS) and CrIS or AIRS and Infrared Atmospheric Sounding Interferometer (IASI) generally less than 0.4 K, differences between CrIS and IASI generally less than 0.25 K [ABSTRACT FROM AUTHOR]

High‐precision observation of significant wave height (SWH) is crucial for marine research. The Surface Waves Investigation and Monitoring (SWIM) aboard the China France Oceanography Satellite (CFOSAT) provides the ocean wave spectrum that allows for the calculation of the off‐nadir SWH parameters, but there exists a certain bias with the in‐situ SWH values. To improve the accuracy of the SWH calculation bias from the off‐nadir 6°, 8°, 10° wave spectra and the whole combined spectrum, this paper establishes a spatio‐temporal hybrid model that combines convolutional neural network (CNN) and long short‐term memory network (LSTM). Additionally, to further correct bias exhibited under high sea state, we introduce a bias correction module based on deep neural network (DNN) to adjust the SWIM off‐nadir SWH greater than 4 m. The experimental results demonstrate a significant enhancement in the accuracy of corrected SWIM off‐nadir SWH, and the best calibration result is 10° with 0.267 m root mean square error (RMSE), and 0.979 correlation coefficient (R) compared with the ERA5 value. We conducted a comprehensive study and analysis on the performance of the proposed model under different wave heights, extreme sea states, and wind and swell regions. Meanwhile, the buoy and altimeters are leveraged to render further evaluation the RMSE of the corrected SWH is less than 0.5 m. Plain Language Summary: The Surface Waves Investigation and Monitoring (SWIM) on the China France Oceanography Satellite (CFOSAT) provides wave data support for ocean research. However, the significant wave height (SWH) parameter calculated from the SWIM off‐nadir spectra has a certain bias from the real value due to the influence of speckle noise. In order to improve the accuracy of the SWIM off‐nadir SWH parameter, we developed a CNN‐LSTM model and introduced a high sea state bias correction network to improve the accuracy of SWH and achieve the purpose of correction. The analysis evaluated the correction performance across various sea conditions, comparing the corrected SWH data with measurements from altimeters and buoys. The findings demonstrated that the root mean square error of the corrected SWH was consistently below 0.5 m, with a correlation coefficient exceeding 0.940. This underscores the effectiveness of our model in enhancing the precision of nadir SWH data bias from SWIM instruments. Key Points: A model of convolutional and long short‐term memory neural network is developed to calibrate the surface waves investigation and monitoring (SWIM) off‐nadir significant wave heightThe corrected SWIM off‐nadir significant wave height achieves comparable accuracy with the Jason‐3, HY‐2B, and buoysThe proposed model performs well in the calibration of the wind wave region and the swell region [ABSTRACT FROM AUTHOR]

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

This study examines the accumulation, distribution, and mobility of Potentially Toxic Elements (PTEs) in the sediments of a low‐lying coastal drainage network (Ravenna, Italy). The aim is to understand the geochemical processes occurring between drainage water and canal bed sediments and assess factors affecting and driving PTE distribution and enrichment in these environments. A geochemical database resulting from the analysis of 203 drainage sediment samples was analyzed using Principal Component Analysis and compared to undisturbed near‐surface sediment samples from the same depth and depositional environment. The results reveal PTEs exceeding national regulation limits. Distance from the sea, electrical conductivity of drainage water, and fertilizer use were identified as the main driving factors. The primary mechanisms for PTE precipitation (As, Co, Mo) and subsequent enrichment in the sediments is attributed to the absorption on Fe‐ and Mn‐oxyhydroxides (HFO and HMO), particularly in high salinity areas near the coast. While Cu, Zn, Pb, Cr, and V also have affinity for HFO and HMO, their adsorption efficiency decreases due to the competition with salt‐derived cations during ongoing salinization processes. Anthropogenic sources, including agriculture, hunting activities, traffic dust, and railways, contribute to the local abundance of other elements (Cr, Ni, Cu, Zn, Pb, and Sn). This paper's significant progress lies in assessing the concurrent interactions of chemical and physical processes that drive PTE distribution and accumulation in reclaimed low‐lying coastal plains. The findings are significant for assessing PTE accumulation risks and sediment toxicity in coastal areas affected by water salinization, drainage, and subsidence, providing valuable information to water management institutions globally. Plain Language Summary: This study investigates the presence of harmful substances called Potentially Toxic Elements (PTEs) in the sediment of the drainage canals in Ravenna's coastal area (Northern Italy). Researchers want to understand how these substances accumulate and spread in the sediment and what factors influence their distribution. They collected sediment samples from the bottom of the drainage canals and compared them to natural sediment samples. The results show that the PTE levels exceed the national and international limits. The distance from the sea, the water salinity, and the fertilizers are found to be the main factors affecting the distribution of PTEs. The researchers also discover that some PTEs (Arsenic, Cobalt, and Molybdenum) are absorbed by certain minerals in the sediment, especially in areas with high salinity close to the coast. However, other harmful elements, like copper, zinc, lead, chromium, and vanadium are not as strongly absorbed due to competition with salt‐related substances. The abundance of other elements like chromium, nickel, copper, zinc, lead, and tin come from human activities like agriculture, hunting, traffic, and railways. These findings are important for understanding the risks associated with these substances in the sediment, particularly in low‐lying coastal areas that have been reclaimed for human use. Key Points: First study on Potentially Toxic Elements (PTEs) enrichment in drainage canal sediments of reclaimed low‐lying coastal areaKey roles of (i) Fe‐ and Mn‐oxyhydroxides in PTE enrichments and (ii) salt‐derived cations in adsorption efficiencyDistance from the sea, salinity of drainage water, and use of fertilizers are the main factors affecting the distribution and enrichment of PTEs [ABSTRACT FROM AUTHOR]

On 18 June 2023, comprehensive observations were conducted to an altitude‐triggered lightning flash. Upward positive leader (UPL) and downward negative leader (DNL) in a bi‐directional development system were detected simultaneously by a high‐speed camera, together with the coordinated measurements of magnetic field and very‐high‐frequency (VHF) emissions. High‐speed images reveal, for the first time, the enhancement of the UPL's propagation speed by DNL in the bi‐directional leader system. Concretely, the upward positive leader initially originates from a suspended wire tip and propagates at a two dimensional (2D) speed of 5.32 × 104 m/s, and after about 6.3 ms, its propagation speed was enhanced to 1.12 × 105 m/s when the stepped DNL started advancing at an average speed of 1.44 × 105 m/s. Additionally, based on the evolution of channel luminosity and the variations of magnetic radiation, it is found that there is a consistency in luminosity variation between the ascending channel and the descending channel in the bi‐directional leader system, and the amplitude of the magnetic field increases when the negative discharges start at the bottom wire end with intensive VHF emissions. Those facts indicate that the DNL has an effect, may be a positive one, on the UPL's development in the early stage of altitude‐triggered lightning. Plain Language Summary: The proposal and verification of the bi‐directional leader development shed light on the understanding of lightning initiation and extension. However, the study on the initiation and propagation of bi‐directional leader is limited as a result of the destitution of close and detailed observations due to its random occurrence time and position. Therefore, the comprehensive observation on the discharges in the initial stage of the altitude‐triggered lightning through simultaneous optical images, and magnetic field and very‐high‐frequency emissions measurements at close range is of great value for distinctly clarifying the evolution of the bi‐directional leader's propagation manner and the associated electromagnetic radiation. In this paper, we analyzed the initiation process and propagation manner of the bi‐directional leader in an altitude‐triggered lightning flash using high‐speed imaging and concurrent measurements of the associated magnetic radiation. It aims to understand the initiation and propagation process of the bi‐directional leader and revealing the evolution of discharges in the early stage of the altitude‐triggered lightning. Key Points: Bi‐directional leader's initiation and propagation in altitude‐triggered lightning was documented by coordinated observationsPropagation speed of upward positive leader (UPL) is enhanced as fast as the DNL's propagation in the bi‐directional leader systemDownward negative leader has a possible positive effect on the UPL's development in the early stage of altitude‐triggered lightning [ABSTRACT FROM AUTHOR]

Far infrared (FIR) emission from the Earth's polar regions has become an area of increasing scientific interest and value. FIR emission is important for understanding Earth's radiative balance and improving global climate models, especially in rapidly changing Arctic conditions. Far‐infrared emission from Earth is not currently being monitored from space, except as part of broadband emission channels of Earth radiation budget measurements like those from the CERES project, and only limited measurements in the FIR spectrum exist. The Absolute Radiance Interferometer (ARI), developed as a prototype of the infrared spectrometer for CLARREO at the University of Wisconsin‐Madison, Space Science and Engineering Center, measures absolute spectrally resolved infrared (IR) radiance from 200 to 2,000 cm−1 (or 5–50 μm) at 0.5 cm−1 resolution with high accuracy (<0.1 K 3‐sigma brightness temperature at scene temperature). This instrument was taken into the field in Madison, Wisconsin, USA, during the winters of 2021 and 2022, where the weather can reach polar‐like conditions to measure high spectral resolution radiances of various sample types. Sample materials included water, snow, ice, evergreen leaves, dry grass, and sand, all characteristic of high latitude regions. Radiances collected from both a sky view and the sample view in clear‐sky conditions were used to retrieve FIR emissivity. This paper describes the ARI instrument configuration and capability for ground‐based measurements in the FIR region, and documents retrieved emissivities of various analyzed samples. The retrieved emissivity results are publicly available, and comparisons are made to simulated emissivity estimates. Key Points: Absolute Radiance Interferometer used to obtain high spectral resolution far infrared radiation measurements in semi‐controlled environmentsRadiation measurements of the sky and samples, including snow, water, ice, sand, and vegetation, allowed for retrieval of sample emissivityThe retrieved far infrared emissivity spectra and radiance measurements are available for model intercomparisons and other uses [ABSTRACT FROM AUTHOR]

Accurate rainfall measurement with a precise spatial and temporal resolution is essential for making informed decisions during disasters and conducting scientific studies, particularly in regions characterized by intricate terrain and limited coverage of automated weather stations. Retrieval of precipitation with satellite is currently the most effective means to obtain precipitation over large‐scale areas. The key to enhancing the accuracy of precipitation estimation and forecasting in regions with complex terrain lies in effectively integrating satellite data with topographic information. This paper introduces a deep learning approach called AttUnet_R_SFT that utilizes high temporal, spatial, and spectral resolution data obtained from the Fengyun 4A satellite, and incorporates the Deep Spatial Feature Transform (SFT) layer to incorporate geographical data for estimating half‐hourly precipitation in northeastern China. We assess it by compared to operational near‐real‐time satellite precipitation products demonstrated to be successful in estimating precipitation and baseline deep learning models. According to the experimental findings, the AttUnet_R_SFT model outperforms practical precipitation products and baseline deep learning models in both identifying and estimating precipitation. The main enhancement of the model performance is shown in the windward slope of the Greater Khingan Mountains as a result of the successful incorporation of geographical data. Hence, the suggested framework holds the capability to function as a superior and dependable satellite‐derived precipitation estimation solution in regions characterized by intricate terrain and infrequent rainfall. The findings of this study indicate that the utilization of deep learning algorithms for satellite precipitation estimation shows potential as a fruitful avenue for further research. Plain Language Summary: A deep learning model named AttUnet_R_SFT is proposed, which use high temporal, spatial and spectral resolution data from the Fengyun 4A satellite, and combines with the Deep Spatial Feature Transform (SFT) layer to input geographic information for half‐hourly precipitation estimation in the complex terrain region represented by northeast China. The model can provide a reference for improving the performance of precipitation estimation in areas with complex topography. Key Points: A deep‐learning model is proposed to effectively fuse satellite multispectral data of the Fengyun 4A satellite, with topographic informationHalf‐hourly precipitation is estimated with higher temporal resolution, which is closer to the operational needs of weather forecastingAs precipitation in the study area is a non‐high‐frequency event, data enhancement is attempted to use and obtain effective results [ABSTRACT FROM AUTHOR]

Extracting volatiles from lunar regolith for analysis or utilization is one of the most important aspects of future lunar exploration. However, the low thermal conductivity of lunar regolith poses a challenge. Here, we conduct simulations to analyze the heat and mass transfer processes within the sample inside the oven. We identify three main factors affecting oven heat‐up rate: water ice content (WIC) in the regolith, oven diameter, and power supply. Taking these factors into account, we devise an oven design and apply it to three case studies: (a) assessing water ice and isotopic composition in Permanently Shadowed Regions, akin to Chang'e‐7 mini‐fly probe missions; (b) measuring noble gases, as Chang'e‐7 and Luna‐27 landers; and (c) large‐scale in‐situ resources utilization (ISRU). The simulation results indicate that water ice can be extracted using sufficiently high heating power without issues. However, the complete extraction of noble gases is challenging and may require alternative heating methods. For ISRU purposes, large ovens can be subdivided into smaller ones by adding internal structures, for example, honeycomb, to improve the heat‐up rate by at least 1.5 times. Additionally, we find that the oven can serve as a scientific payload for WIC measurement using the heating curve. A flowchart of this new WIC measurement method is provided, offering an alternative method to mass spectrometry or spectroscopy measurements. Key Points: The paper presents a realistic heating oven design for in‐situ extraction of volatiles from lunar regolithThree factors controlling the oven's heat‐up rate are water ice content (WIC) in regolith, oven diameter, and power supplyThe oven can serve as a scientific payload for measuring the WIC in regolith based on the heating curve [ABSTRACT FROM AUTHOR]

Inversion of magnetic basement interfaces in basins is essential for interpreting potential field data and studying geothermal resource distribution, as well as basin formation and evolution. This paper introduces a novel method for inverting magnetic basement interfaces using a random forest regression (RFR) algorithm that combines potential field processing and machine learning techniques. The method creates magnetic base interface models and corresponding magnetic anomaly data through the random midpoint displacement method and magnetic interface finite element forward simulation. These anomalies are then processed using techniques such as directional transformations, analytical continuation, spatial derivatives, and fractional transformations. Feature attributes are extracted, and Gini importance is utilized to measure the contributions of feature factors, identify effective features, and enhance algorithm efficiency. The validity and practicality of the method are demonstrated through the analysis of both idealized and noisy models. The proposed machine learning‐based approach is more intelligent, efficient, and accurately represents the relief of magnetic base interfaces. When applied to magnetic survey data in the Datong Basin, it produced a reliable basin base model that aligns with known structural information, paving the way for further research in magnetic interface inversion. Key Points: A machine learning method based on random forest regression algorithm is used to recover the relief of the magnetic basement interface by using high‐precision magnetic dataApplying potential field processing and transformation to feature extraction for machine learningThe proposed technique intelligently and efficiently restores the 3D relief of the magnetic basement interface, and it has been successfully implemented in the Datong Basin's Tianzhen‐Yanggao sag [ABSTRACT FROM AUTHOR]

The Global Precipitation Measurement (GPM) Mission Core Observatory satellite launched in 2014 as a joint mission between National Aeronautics and Space Administration (NASA) and JAXA. Global Precipitation Measurement (GPM) has, since that time, provided continuous, valuable dual‐frequency radar and passive microwave radiometer observations. Here, we introduce a gridded data set of collocated GPM Core Observatory observational products merged with a reanalysis‐derived Atmospheric river (AR) data set in the North Atlantic and North Pacific sectors. The three data sets that are merged and gridded are: (a) the NASA Goddard Profiling (GPROF) precipitation product, which uses GPM passive microwave radiometer observations to derive surface precipitation rates, (b) a water vapor data product derived from the GPM Core Observatory radiometer, provided by Remote Sensing Systems (RSS), and (c) the Mattingly et al. (2018, https://doi.org/10.1029/2018jd028714) AR data set that is specifically tuned to the high‐latitude regions. This novel merged data set spans from May 2014 to December 2022 with plans to update annually through 2026 at minimum. This gridded product combines RSS passive water vapor and precipitation estimates with coincident AR detection. This data product benefits the scientific community by providing (a) user‐friendly gridded satellite data compared to standard satellite data sets, while maintaining high temporal resolution, and (b) coincident satellite observations to assess the link between ARs and precipitation. Plain Language Summary: This paper summarizes the creation of a new, gridded data set containing precipitation, atmospheric water vapor content, and atmospheric river (AR) information from May 2014 to December 2022. The precipitation and water vapor data are both remotely‐sensed data products using the Global Precipitation Measurement Core Observatory Microwave Imager (GMI). The AR algorithm detection is more sensitive to moisture transport than global algorithms and is thus better suited to colder and drier environments. We temporally match and interpolate all data to a common grid and provide examples of the merged data to illustrate the seasonality and impacts of AR presence on precipitation. Lastly, we demonstrate the utility of this data set by investigating the assumptions used to estimate precipitation from GMI measurements. The data set confirms that ARs are associated with warmer temperatures, higher water vapor content, and more intense precipitation rates. Key Points: A new data set is presented that collocates satellite‐derived precipitation rates and water vapor with reanalysis‐derived Atmospheric rivers (ARs)Higher precipitation rates and water vapor content are coincident with AR events in the high‐latitude regionsAn example application illustrates warmer, more moist conditions during precipitating AR events that are present in the GPROF database [ABSTRACT FROM AUTHOR]

Since the tropopause was first identified, the quality and resolution of weather balloons has dramatically improved. NOAA Earth System Research Laboratories (ESRL) have provided high resolution and very high quality ozonesondes from eight locations: Fiji; American Samoa; Greenland; Antarctica; and several locations in USA (Hawai'i, Colorado, California and Alabama). These locations collectively cover polar regions, mid‐latitudes and tropics. Using this publicly archived data set, we studied the variability of the tropopause for all eight locations for one complete year (2016). Along with the standard estimates of the tropopause provided by NOAA ESRL, we developed four alternative tropopause definitions each based on changes in one of the following: (a) molar density; (b) temperature lapse rates; (c) water vapor content; (d) ozone content. These old and new tropopause definitions appear to hold over all eight locations—for all seasons and from the tropics to the poles. The cohesiveness between all five of these independent tropopause definitions is remarkable, although the NOAA ESRL estimates sometimes identify higher tropopause onsets than the other estimates. Therefore, each tropopause definition could potentially be used as proxies for other tropopause definitions. However, it also confirms that the troposphere/tropopause transition is a multi‐faceted physical and chemical phenomenon associated with more than just temperature changes. Finally, these high‐resolution results suggest that the original term "tropopause" might be a misnomer since they suggest increases in temperature lapse rate variability, rather than the "pausing" implied by lower resolution data or by lapse rates that are averaged over large distances. Plain Language Summary: In the lower atmosphere ("the troposphere"), the temperature decreases with height. This is why it is colder on mountaintops. Since 1902, it has been known that higher up in the atmosphere, in the region near where commercial airplanes fly, temperatures remain relatively constant with height. This region is called "the tropopause." In 1957, the World Meteorological Organization (WMO) defined the start of the tropopause in terms of a set of rules, that were complex, but designed to be applicable for all latitudes and seasons. However, these rules are often hard to implement and can give ambiguous results. So, other definitions have been developed for specific regions, such as the tropics, that are more straightforward and easier to interpret. In this paper, we have developed four new tropopause definitions that can be used with modern high resolution "ozonesondes" (weather balloons that also measure ozone). These four independent definitions describe different aspects of the atmosphere but each give very similar estimates of the tropopause. This reveals that the tropopause involves many different but synchronous changes in the atmosphere. It also shows that the name "tropopause" is confusing because it is a part of the atmosphere involving lots of changes instead of "pausing." Key Points: Four new tropopause definitions have been developed for use with high‐resolution ozonesondes that work for all latitudes and seasonsAll these definitions yield almost identical tropopause estimates yet are derived from independent physical or chemical metricsSurprisingly, the "tropopause" marks a region with increased lapse rate variability over short altitudes as opposed to long‐term averages [ABSTRACT FROM AUTHOR]

Vertical profiles of nitric oxide (NO) concentration are derived between 120 and 250 km using updated NO emission rates measured by Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument on the NASA Thermosphere Ionosphere Mesosphere Energetics and Dynamics satellite. The Naval Research Laboratory Mass Spectrometer Incoherent Scatter Radar (MSIS) 2.1 model is used to provide the required parameters of temperature, atomic oxygen number density, and molecule oxygen number density needed to derive the NO concentrations using a non‐local thermodynamic equilibrium (non‐LTE) model. The SABER NO concentration shows a significant correlation with solar activity with larger peak NO concentrations and higher altitude extent during solar maximum years compared to those during the solar minimum years. The SABER NO agrees well with the MSIS 2.1 NO at altitudes above 120 km for all latitudes, while the pronounced SABER‐MSIS NO discrepancy below 120 km is likely due to the temperature underestimation by MSIS 2.1. A detailed error analysis is presented and considers systematic and random errors in all the terms in the non‐LTE model used to derive the NO concentration. Random error in MSIS 2.1 temperature and atomic oxygen dominates the uncertainty in single NO profiles above 120 km. We estimated a systematic error up to ∼36% between 120 and 250 km during solar maximum years. Plain Language Summary: Nitric oxide (NO) is a minor constituent of the Earth's thermosphere and regulates the energy budget by emitting infrared radiation that cools the region. Understanding the distribution of the NO concentration is important in understanding the energy redistribution in the Earth's upper atmosphere. In this paper, a new NO concentration data set between 120 and 250 km from January 2002 to the present day is derived from the latest Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) NO emission measurements and Mass Spectrometer Incoherent Scatter Radar (MSIS) 2.1 empirical model of temperature and O, and O2 number density. The derived SABER NO concentration varies with solar activity. The SABER NO agrees well with the empirical MSIS 2.1 NO between 120 and 250 km. Below 120 km, the temperature may be warmer than predicted by MSIS 2.1, leading to a substantial difference between SABER NO and MSIS 2.1 NO. A comprehensive error analysis considering systematic and random errors in all the terms in the model used to derive the NO concentration from SABER data shows that the systematic uncertainty of the SABER NO density is up to 36% above 120 km. Key Points: The nitric oxide (NO) 5.3 μm infrared emission rates derived from Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) measurements have been updatedVertical profiles of NO concentration, spanning 20+ years, are derived from the SABER NO emission rates, and Mass Spectrometer Incoherent Scatter Radar (MSIS) 2.1 T, n(O), and n(O2)The SABER NO concentrations exhibit good agreement (±30%) with those in the MSIS 2.1 empirical model between 120 and 250 km [ABSTRACT FROM AUTHOR]

Economic growth, air pollution, and forest fires in some states in the United States have increased the concentration of particulate matter with a diameter less than or equal to 2.5 μm (PM2.5). Although previous studies have tried to observe PM2.5 both spatially and temporally using aerosol remote sensing and geostatistical estimation, they were limited in accuracy by coarse resolution. In this paper, the performance of machine learning models on predicting PM2.5 is assessed with linear regression (LR), decision tree (DT), gradient boosting regression (GBR), AdaBoost regression (ABR), XGBoost (XGB), k‐nearest neighbors (K‐NN), long short‐term memory (LSTM), random forest (RF), and support vector machine (SVM) using PM2.5 station data from 2017 to 2021. To compare the accuracy of all the nine machine learning models, the coefficient of determination (R2), root mean square error (RMSE), Nash‐Sutcliffe efficiency (NSE), root mean square error ratio (RSR), and percent bias (PBIAS) were evaluated. Among all nine models, the RF (100 decision trees with a max depth of 20) and support vector regression (SVR; nonlinear kernel, degree 3 polynomial) models were the best for predicting PM2.5 concentrations. Additionally, comparison of the PM2.5 performance metrics displayed that the models had better predictive behavior in the western United States than that in the eastern United States. Key Points: We present the prediction of PM2.5 concentrations over the United States using various machine learning (ML) algorithmsWe show ML as a new approach for analyzing large data sets due to the computational speed and easy implementation for massive amounts of dataThe study is important for improving our understanding of the differences among ML algorithms for Earth Science research [ABSTRACT FROM AUTHOR]

Crater mapping using neural networks and other automated methods has increased recently with automated Crater Detection Algorithms (CDAs) applied to planetary bodies throughout the solar system. A recent publication by Benedix et al. (2020, https://doi.org/10.1029/2019ea001005) showed high performance at small scales compared to similar automated CDAs but with a net positive diameter bias in many crater candidates. I compare the publicly available catalogs from Benedix et al. (2020, https://doi.org/10.1029/2019ea001005) and Lee and Hogan (2021, https://doi.org/10.1016/j.cageo.2020.104645) and show that the reported performance is sensitive to the metrics used to test the catalogs. I show how the more permissive comparison methods indicate a higher CDA performance by allowing worse candidate craters to match ground‐truth craters. I show that the Benedix et al. (2020, https://doi.org/10.1029/2019ea001005) catalog has a substantial performance loss with increasing latitude and identify an image projection issue that might cause this loss. Finally, I suggest future applications of neural networks in generating large scientific datasets be validated using secondary networks with independent data sources or training methods. Plain Language Summary: I have compared two computer programs that use neural networks to identify craters on solar system bodies. The crater catalogs created by these programs have been previously compared against an existing human‐made catalog to measure their performance, but each comparison used a different method making it challenging to understand the difference between the catalogs. In this paper, I use the comparison methods from these two independent papers, discuss where the catalogs agree and disagree, and suggest why they disagree. I emphasize the need for more accurate and consistent methods to validate automatic crater‐mapping programs, especially when it is impractical for humans to check the craters found by automated methods. Key Points: The apparent performance of automatic crater detection algorithms is sensitive to the choice of metric used to gauge the performanceFeature Detection Algorithms trained to find spatial features should be used with appropriately projected images where possibleAs neural networks extend crater detection beyond practical human limits, validating their results with a independent networks is critical [ABSTRACT FROM AUTHOR]

Currently, the opportunistic method to estimate rainfall using commercial microwave links (CMLs) has been shown as an efficient way to complement traditional instruments in terms of spatial‐temporal resolution and coverage. In this paper, we collected data from 26 CMLs in Jiangyin City, Jiangsu Province, and conducted experiments on rainfall field reconstruction and nowcasting. First, the raw CML data were processed to invert the path‐averaged rainfall intensity. Second, the algorithms of inverse distance weighting (IDW) and ordinary kriging (OK) interpolation were employed to reconstruct the rainfall field. Then a 10‐min prediction of the rainfall field was achieved using a nowcasting model based on the long short‐term memory neural network and a setup window was introduced to improve the prediction performance of the first few minutes. The reconstruction results show that the average correlation coefficient (ACC) and the average root mean square error (ARMSE) between the IDW‐based results and daily cumulative rainfall from rain gauges (RGs) are 0.89 and 8.69 mm, respectively, while the ACC and ARMSE between the OK‐based results and RG data are 0.89 and 9.13 mm, respectively. The nowcasting results show that the ACC between the prediction results with a 5‐min setup window and the IDW‐retrieved rainfall fields can reach 0.91 at the first minute and gradually decrease to 0.20 within 10 min. Furthermore, the model has better nowcasting performance for stratiform precipitation and mixed precipitation compared to convective precipitation. Plain Language Summary: Accurate and real‐time rainfall monitoring and forecasting are of great significance for disaster prevention and control, agriculture, meteorological research, and related fields. However, traditional rainfall measurement methods are insufficient for meeting the demands of comprehensive precipitation observations because of poor spatial‐temporal resolution and limited coverage. Currently, measuring rainfall using additional microwave attenuation caused by raindrops has been shown as an efficient way to complement traditional instruments. Based on commercial microwave link (CML) rainfall measurement technology, this paper carries out rainfall field reconstruction and nowcasting experiments in Jiangyin City, China. The results show the CML network enables accurate rainfall field reconstruction and nowcasting. Key Points: Based on the commercial microwave link (CML) rainfall monitoring network in Jiangyin, China, accurate rainfall inversion by CMLs is achievedThe two‐dimensional rainfall fields are accurately reconstructed using the inverse distance weighting and ordinary kriging interpolation algorithmsA long short‐term memory‐based rainfall field nowcasting model is proposed to achieve 10‐min continuous predictions of rainfall fields [ABSTRACT FROM AUTHOR]

In network real‐time kinematic positioning of multi‐reference station, the spatial and temporal distribution of tropospheric delay is affected by both horizontal and elevation. The traditional modeling strategy of regional troposphere takes more consideration of the horizontal factor, and the incomplete consideration of the elevation factor will lead to the problem of reduced modeling accuracy, especially in the face of the scene with large regional height deviation. Based on the traditional linear interpolation method (LIM), a simple and effective height‐deviation compensation‐based linear interpolation method (HCLIM) for regional tropospheric is proposed. The modeling accuracy of troposphere and the positioning accuracy of user RTK in large height deviation region are significantly improved. The method was verified based on six experimental subnets with large height deviations from a provincial continuously operating GNSS reference stations network in central China. The results showed that: For GPS satellite modeling, compared with the traditional LIM method, the average modeling accuracy improvement rate of HCLIM method is (84.5%, 75.5%, 59.3%, 26.7%) in the elevation angle range of (10–30°/30–40°/40–50°/50–90°). For BDS satellite, the average modeling accuracy improvement rate of HCLIM method in the above four elevation angles is (83.3%, 70%, 50%, 23.5%). For the positioning performance of user RTK, The horizontal positioning accuracy and RTK fixing rate were similar under the two methods, while HCLIM method showed only slight improvement. However, in the U direction, LIM method showed obvious systematic bias, while HCLIM method showed consistent positioning accuracy, which was improved to 82.8% compared with LIM method. Plain Language Summary: In GNSS relative positioning, the positioning performance of user largely depends on the extent to which atmospheric deviations (ionospheric delay or tropospheric delay) are eliminated or weakened. Compared with the ionosphere, the temporal and spatial characteristics of troposphere are significantly different. It is part of the lower atmosphere, extends from the Earth's surface to an altitude of about 20 km, and contains most of the atmospheric water vapor. The delay of satellite signals through the troposphere is affected by both horizontal and elevation. The traditional tropospheric regional modeling strategy only uses plane coordinates for interpolation, ignoring the influence of elevation factors or the consideration of height‐deviation is not perfect. When the height difference between the reference station and the rover station is large, the modeling accuracy will obviously be reduced. The large modeling residual may bring systematic deviation to the user coordinates, especially the elevation direction component. In this paper, we propose a regional tropospheric linear interpolation method (HCLIM) considering height deviation compensation. This method is simple and effective, The modeling accuracy of troposphere and the positioning accuracy of user RTK in large height difference region are significantly improved. Key Points: A simple and effective height‐deviation compensation‐based linear interpolation method (HCLIM) for regional tropospheric is proposedThe HCLIM method greatly improves the tropospheric modeling accuracy of each elevation angle range in the large height difference site areaThe HCLIM method significantly improves the systematic bias in elevation direction of user RTK positioning due to modeling residuals [ABSTRACT FROM AUTHOR]

SABER (Sounding of the Atmosphere using Broadband Emission Radiometry) is a 10‐channel infrared radiometer that is one of four instruments on the NASA TIMED (Thermosphere‐Ionosphere‐Mesosphere Energetics and Dynamics) satellite mission to study the structure, energetics, chemistry, and dynamics of the Earth's mesosphere and lower thermosphere. Each of the ten SABER channels has a unique filter over its detector. The filter passes infrared radiation within a defined spectral region that optimizes the ability to derive temperature and constituent concentrations from the infrared radiance measurements. The TIMED spacecraft was launched into a 625 km circular polar orbit (74.1° inclination) via a Boeing Delta II rocket from Vandenberg Air Force Base on 7 December 2001. SABER continues to operate nominally and collect data routinely as it has for over 21 years. Nearly 2,200 peer‐reviewed journal articles have been published worldwide using SABER data. A list of these articles is included in Supporting Information S1 accompanying this paper. This paper presents a detailed technical description of the SABER instrument including major subsystems of the instrument and technical performance parameters. This paper comprehensively describes the instrument and its components and provides final instrument design and performance parameters. The motivation for this paper is to document this information permanently for future reference. The Space Dynamics Laboratory (SDL) of Utah State University designed, fabricated, and calibrated the SABER instrument in close collaboration with NASA Langley Research Center, Hampton University, and Global Atmospheric Technologies and Science (GATS). Plain Language Summary: Earth's mesosphere and lower thermosphere (MLT), approximately 50–180 km in altitude or 30 to 110 miles high, was the least explored region of the atmosphere 30 years ago. The MLT is a critical region of Earth's atmosphere as it is the boundary or interface between the space environment and the lower atmosphere. Today the region is referred to as part of the "geospace" environment. To examine the MLT in more detail, NASA developed the Thermosphere‐Ionosphere‐Mesosphere Energetics and Dynamics or TIMED satellite mission beginning in 1996. Launch of the TIMED satellite occurred in December 2001. One of the four instruments carried by the TIMED satellite is named SABER (Sounding of the Atmosphere using Broadband Emission Radiometry). SABER continues to provide exceptional scientific results and is still routinely collecting data more than 21 years after launch. This paper provides a technical description of the SABER instrument. Key Points: Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) is an infrared limb sounding instrument observing the Earth's mesosphere and lower thermosphere continuously for over two decadesSABER was developed through a partnership involving NASA Langley, Space Dynamics Laboratory, Hampton University, and Global Atmospheric Technologies and ScienceSABER is still operational with no reduction in capability and has yielded nearly 2,200 peer‐reviewed journal articles by worldwide authors [ABSTRACT FROM AUTHOR]

With the help of the analytical expression of the vertical gravity gradient (VGG) generated by a rectangular prism, an analytical algorithm for predicting seafloor topography using the VGG data has been studied. Nevertheless, ship sounding data are an essential constraint in solving the seafloor topography. This paper combines ship sounding data with VGG anomaly to predict the seafloor topography. The main research contents include the following: Using the ship soundings and VGG data in the study area, the observation equations about sea depth are established, and the stability of the equations are studied; furthermore, considering the influence of seafloor topography outside the study area on the observation equations, these effects are divided into boundary effects and far‐field effects, and different processing methods are proposed. Finally, the method is tested on the East Pacific Rise, only using VGG anomaly and adding the mean value to fix the boundary region, the RMS error of the results is 108.8 m; SIO's model is added to the boundary region and the seven maximum absolute errors are replaced by ship sounding data, the RMS error of the results can reach 94.2 m and the accuracy improvement is 13.42%. Plain Language Summary: The topographic fluctuation can be composed of many different rectangular cylinders, and the vertical gravity gradient generated by a rectangular cylinder on the sea level has an analytical expression. In this paper, the observation equations are established by using this analytical expression to predict the seafloor topography, and different methods are studied to control or weaken the influence of boundary effects and far‐field effects. This study will provide a new idea for the refinement of seafloor topography in the future. Key Points: A new method for predicting seafloor topography from vertical gravity gradient and ship soundings, and the effectiveness of the algorithm is verified by numerical simulationThe sources of interference errors are refined as "boundary effects" and "far‐field effects," and different processing methods are proposed; the algorithm is applied to the test area and the RMS error of prediction results can reach 94.2 m [ABSTRACT FROM AUTHOR]

Deep generative models have recently become popular in heliophysics for their capacity to fill in gaps in solar observational data sets, thereby helping mitigating the data scarcity issue faced in space weather forecasting. A particular type of deep generative models, called conditional Generative Adversarial Networks (cGAN), has been used since a few years in the context of image‐to‐image (I2I) translation on solar observations. These algorithms have however hyperparameters whose values might influence the quality of the synthetic image. In this work, we use magnetograms produced by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) and EUV images from the Atmospheric Imaging Assembly (AIA) for the problem of generating Artificial Intelligence (AI) synthetic magnetograms from multiple SDO/AIA channels using cGAN, and more precisely the Pix2PixCC algorithm. We perform a systematic study of the most important hyperparameters to investigate which hyperparameter might generate magnetograms of highest quality with respect to the Structural Similarity Index. We propose a structured way to perform training with various hyperparameter values, and provide diagnostic and visualization tools of the generated versus targeted image. Our results shows that when using a larger number of filters in the convolution blocks of the cGAN, the fine details in the generated magnetogram are better reconstructed. Adding several input channels besides the 304 Å channel does not improve the quality of generated magnetogram, but the hyperparameters controlling the relative importance of the different loss functions in the optimization process have an influence on the quality of the results. Plain Language Summary: Performance of space weather forecasting methods relies on the availability of data to be ingested in physical models, which is scarce in space weather as compared to terrestrial weather. Since a few years, deep learning methods have produced algorithms capable of performing image‐to‐image‐translation that are used in solar physics to mitigate this scarcity issue. In this work, we consider a deep generative model called the Pix2PixCC algorithm, which is based on conditional Generative Adversarial Networks. One needs to fix some hyperparameters in the Pix2PixCC algorithm, such as for example, the number of filters used to build the features that characterize the information contained in the data. Our aim in this paper is to make an extensive study of these hyperparameters. We propose a structured way to perform training with various hyperparameter values and provide diagnostic and visualization tools of the generated versus real image. Our results show that a higher number of filters will improve the quality of the synthetic magnetogram and that the respective contribution of each loss function in the global objective function to be minimized also has an influence on the quality of the result. Key Points: We study an image to image translation algorithm that generates solar vector magnetograms from multichannel extreme ultraviolet observationsWe provide insight into the architecture and optimization of the Pix2PixCC algorithm, a conditional Generative Adversarial NetworkOur experimental design highlights relevant hyperparameters, and our interactive tool analyses the quality of the generated magnetograms [ABSTRACT FROM AUTHOR]

The reduction of non‐gravitational accelerations through an onboard accelerometer is a vital part of accurately estimating gravity fields for Gravity Recovery and Climate Experiment (GRACE) Follow‐On (GRACE‐FO). Current accelerometer quality, specifically a defect on the GRACE‐D accelerometer, necessitates the use of extensive calibration techniques to optimally utilize the accelerometer measurements. This paper describes these techniques and correction algorithms as they are used for the official GRACE‐FO accelerometer products, which rest on the information present in the partially functional GRACE‐D accelerometer. First, transplanted data from GRACE‐C is used to remove common effects between the spacecraft. Then, GRACE‐D data is utilized to compute a correction to the transplanted data to account for solar radiation pressure (SRP) and albedo using a parity argument and to account for non‐common drag and thruster leaks in the attitude control system. The SRP and albedo term predominantly improves C3,0 while the drag and leaky thruster term improves the gravity field across the entire spectrum. Finally, some comments on alternative data usage, initial modifications to the algorithms to account for other spacecraft operational modes, and future outlook are provided. Key Points: GRACE‐D accelerometer data, while noisy, is calibrated to recover useable signals and optimize science dataUpdated accelerometer calibrations improve gravity field recovery [ABSTRACT FROM AUTHOR]

The seismometer onboard InSight NASA Mars mission discovered a seismically active planet. We focused on the strongest event named S1222a (4 May 2022, Mw ∼ 4.7), which was recorded by the Very Broad Band sensors and associated channel ELYSE and is located 37.2° away from InSight. We use two different methods based on a point source approach for an elastic, horizontally layered medium to retrieve source parameters of S1222a. In the first case, the seismic moment tensor inversion of high‐frequency seismogram data is calculated using a matrix method for the direct waves. The process includes the generation of records in displacement using the frequency‐wavenumber integration technique. A method of inversion of the moment tensor of direct P‐ and S‐waves, less sensitive to path effects than reflected and transformed waves, is presented, which significantly increases the accuracy and reliability of the method. In the second case, tensors were calculated using common low‐frequency full‐waveform inversion and the tests to verify the plausibility of this solution obtained from the single station calculation were performed and the uncertainty estimations for inversions can be useful in future research. Plain Language Summary: The paper presents the seismic moment tensor solution and the focal mechanism for the largest event S1222a (4 May 2022, magnitude Mw4.7) recorded at one seismic station on Mars. We consider two different basic approaches to address the problem of the unavoidable inaccuracy of seismic waves modeling: focusing only on direct waves and using low‐frequency full‐waveform inversion. The moment tensor and focal mechanism of the S1222a event were obtained, as well as the optimal depth of a source. Despite the range of possible, it is encouraging that independent studies based on different methodologies, and using different structural models, point to reasonable solutions. The estimates of the focal mechanism when the single‐station inversion is calculated are taken into account and the stability tests to verify our solutions were performed. The importance of this study lies in expanding the possibilities of how to calculate this kind of tasks. Key Points: We consider two different methods based on a point source approach to retrieve source parameters of S1222aA method of inversion of the moment tensor of direct waves, less sensitive to path effects than reflected and transformed waves, is presentedWe obtained the results of seismic tensor solution and time‐independent focal mechanism [ABSTRACT FROM AUTHOR]

The NW‐SE oriented Middle Niger Basin, which is a Campano‐Maastrichtian inland basin has been the subject of debate among geoscientists regarding its origin and development. This paper aims to unravel the basin's origin and evolution by using high‐resolution magnetic, gravity, and topographical data. The integration of aeromagnetic and gravity data provides a means to assess the influence of igneous intrusions during the basin's evolution. The study presents a new perspective on the origin of the basin, suggesting a rift origin where orthogonal extension played a crucial role in its evolution. The research also investigates the relationship between intrusive rocks and the formation of the basin, finding these rocks are solely located within the basement. The magnetic and gravity maps reveal anomalies associated with intrusive structures within the Precambrian crystalline basement. This is a common feature shared with rifted basins on a global scale. Plain Language Summary: The Middle Niger Basin, which spans about 350 km from Dekina in the SE to Shegwa in the NW, is unique among Nigerian basins due to the absence of a universally accepted description of its evolution within the geoscientific community. Magnetic data analysis reveals circular and elliptical anomalies that correspond to intrusive rocks. These anomalies are further enhanced by 2‐D forward modeling and gravity data inversion. Additionally, the SRTM data identifies lineaments that border the rifted basin, supporting the interpretations from the magnetic and gravity data. Our study suggests that fractures facilitated igneous intrusions, shaping the basin's evolution and providing new insights into its tectonic history. The new findings from this research provide valuable insights into the evolution of the Middle Niger Basin and may serve as a useful guide for future exploration initiatives. Key Points: The axis of the Middle Niger Basin is marked by structural discontinuitiesIntrusions in the basement of the basin were constrained by gravity inversionEvolution of the Middle Niger Basin is primarily controlled by crustal extension [ABSTRACT FROM AUTHOR]

Severe Typhoon Koinu passed south of Hong Kong on 8 and 9 October 2023, triggering the issuance of the Increasing Gale or Storm Signal No. 9, the second highest tropical cyclone (TC) warning signal in Hong Kong. Koinu was a difficult case for TC warning service due to its compact size and rather erratic movement over the coastal waters of Guangdong. To monitor Koinu's movement and wind structure, the Hong Kong Observatory utilized various observational platforms, including meteorological aircraft, ocean radar, and synthetic aperture radar on polar orbiting satellites. The paper presents major observations derived from these measurements. The aircraft probe and dropsonde data suggested boundary layer inflow, warm core structure, eyewall updraft, and high turbulence in the eyewall of the typhoon. The weather radar observations indicated the occurrence of a waterspout in the vicinity of the typhoon. Additionally, the study highlights the forecasting performance of the AI‐based Pangu‐Weather model, which could outperform the conventional global numerical weather prediction models in forecasting TC track in the region. The documentation of these observations aims to provide valuable references for weather forecasters and stimulate further research on forecasting this type of tropical cyclone. Key Points: Observations from meteorological aircraft, ocean radar, and synthetic aperture radar show the structure of Severe Typhoon KoinuWeather radar observations suggest the occurrence of a waterspout in the vicinity of KoinuAI‐based Pangu‐Weather model could outperform the conventional global numerical weather prediction models in forecasting Koinu's track [ABSTRACT FROM AUTHOR]

Tsunami simulations require powerful computational resources to be performed efficiently. Although the modern graphics processing units (GPUs) allow the acceleration of this kind of simulations, they can still last many minutes or even hours for simulations which have to deal with very high spatial resolutions or simulation times. In this paper, we propose a method to predict the alert or inundation level of a tsunami generated by an earthquake using deep learning methods. In particular, we train multilayer perceptron (MLP) neural networks for predicting the alert level due to a tsunami at given coastal locations. Ensemble methods are used to improve the predictions of the neural networks. Tsunamis caused by ruptures of several fault segments at different time instants, application to real events, probabilistic forecasting and comparison with other machine learning algorithms are also addressed. Results on realistic scenarios confirm that good accuracies are obtained. The inference times of the trained networks and ensembles are also very low, lasting less than one second to predict the results of thousands of simulations. The proposed method could be used in a tsunami early warning system along with the application of scaling laws. Plain Language Summary: In tsunami early warning systems (TEWS) it is crucial to perform fast predictions of the risk of a tsunami in coastal areas in order to take appropriate measures before the arrival of the tsunami. Currently, tsunami simulations may take many minutes or even hours despite using modern computational resources. In this work we propose a method based on deep learning techniques to predict the alert level of a tsunami generated by an earthquake at given coastal locations in a fraction of a second. This method could be used in TEWS along with techniques to estimate the values of some parameters of the earthquake. Application to real events and probabilistic predictions are also addressed. Key Points: A deep learning method to predict different tsunami alert levels is proposedTsunamis caused by the rupture of multiple fault segments at different time instants are supportedApplication to real events with probabilistic predictions are addressed [ABSTRACT FROM AUTHOR]

A major advance in global bathymetric observation occurred in 2018 with the launch of NASA's ICESat‐2 satellite, carrying a green‐wavelength, photon‐counting lidar, the Advanced Topographic Laser Altimeter System (ATLAS). Although bathymetric measurement was not initially a design goal for the mission, pre‐ and post‐launch studies revealed ATLAS's notable bathymetric mapping capability. ICESat‐2 bathymetry has been used to support a wide range of coastal and nearshore science objectives. However, analysis of ICESat‐2 bathymetry in numerous locations around the world revealed instances of missing or clipped bathymetry in areas where bathymetric measurement should be feasible. These missing data were due to the ATLAS receiver algorithms not being optimized for bathymetry capture. To address this, two updates have been made to ICESat‐2's receiver algorithm parameters with the goal of increasing the area for which ICESat‐2 can provide bathymetry. This paper details the parameter changes and presents the results of a two‐phased study designed to investigate ICESat‐2's bathymetry enhancements at both local and global scales. The results of both phases confirm that the new parameters achieved the intended goal of increasing the amount of bathymetry provided by ICESat‐2. The site‐specific phase demonstrates the ability to fill critical bathymetric data gaps in open ocean and coastal settings. The global analysis shows that the area of potential bathymetry approximately doubled, with 6.1 million km2 of new area in which bathymetric measurements may be feasible. These enhancements are anticipated to facilitate a range of science objectives and close the gap between ICESat‐2 bathymetry and offshore sonar data. Key Points: Recent updates to ICESat‐2's receiver algorithm parameters have boosted the bathymetric measurement capabilitiesThe new updates allow ICESat‐2 to consistently measure bathymetry up to 41 m deepThe updates allow ICESat‐2 to potentially measure over 6.1 million km2 of new bathymetry in both coastal and ocean settings [ABSTRACT FROM AUTHOR]

Radio occultation (RO) by the Earth's atmosphere of the transmitted signals of the Global Navigation Satellite Systems' satellites has improved numerical weather prediction, has benefited atmospheric process studies, and benchmarked climate change by its strong traceability to the international definition of the second. Until now, research with RO has been isolated to the few centers that actually process the data and impracticable elsewhere because of limitations on data formats, organization and volume of the data, download bandwidth, and the lack of a database that can be queried before downloading data. We announce the availability of RO data in the Registry of Open Data of Amazon Web Services (AWS). RO data are freely available for download for the user and free to process and store for the manager of the repository. RO data as processed by the COSMIC Program Office at the University Corporation for Atmospheric Research, by the Radio Occultation Meteorology Satellite Application Facility (ROM SAF), by EUMETSAT, and by the NASA Jet Propulsion Laboratory are available beginning with calibrated (excess) phase, extending to bending angle and refractivity and to retrievals of pressure, temperature and water vapor. The same RO repository also contains a metadata database that permits simple querying, filtering, and download of data using a Python application programming interface provided by this same project. Four tutorial demonstrations are provided as jupyter notebooks to provide representative samples of code to perform common research activities with RO data, including database querying and analysis, inter‐center comparison of RO retrieval performance, and atmospheric process studies. Plain Language Summary: Radio occultation is a space‐based technique used to profile pressure, temperature, and water vapor in the Earth's atmosphere from the surface to the top of the stratosphere. It has unusual properties compared to other methods for measuring the atmosphere from space, particularly its powerful calibration and its astonishing vertical resolution, both made possible because it is based on a measurement of timing rather than radiance. This paper announces the availability of all Earth radio occultation (RO) data ever obtained (with a few small exceptions) from multiple data providers in the Registry of Open Data of Amazon Web Services. This repository of RO data is formulated to greatly ease access and manipulation of RO data to users anywhere in the world. It comes with tutorials on how the data can be accessed, downloaded, and analyzed in the form of jupyter notebooks. Key Points: A repository of all historical Earth Radio occultation (RO) data of the atmosphere is now freely accessible in the Amazon Web Services Registry of Open DataData are organized to enable common RO, atmospheric science, and climate science research by users anywhereRO sounding data are easily queried, subset, and downloaded through a Python package available by PyPI [ABSTRACT FROM AUTHOR]

The Green's function approach is well‐established and widely used for modeling the surface mass loading displacements. Global mean Green's functions (MGFs) are commonly applied without considering local variations of the crustal structure. Derived from the modified layered Earth structure, the local Green's functions (LGFs) are theoretically beneficial to generate more accurate deformation, since they consider interior information of the local crust. This paper analyzed the differences among MGFs from Gutenberg–Bullen A model and two sets of LGFs derived from modified PREM Earth models consolidating with two crust models TEA12 and CRUST1.0, hereafter called PREMTEA and PREMCRU, respectively. Utilizing MGFs and two sets of LGFs, we modeled the corresponding 3D atmospheric loading displacements for 984 ITRF2014 stations and compared them with the ITRF2014 residuals. The results show that LGFs from PREMTEA and PREMCRU perform well in further promoting scatter reduction for ∼72%, ∼56%, and ∼85% of stations for the Up, East and North components, respectively. The improvements for the North components are significant (up to 3.6%). In particular, stations in the east coastal areas of North America and the west edge of Greenland exhibit further promoting scatter reduction for the East components (up to ∼2.5%), while those located in west coastline of North America show better performance for the North components. Nevertheless, there are significant anomalies in northern Europe for PREMCRU, the mutation margin of which should be carefully considered when using a resolution higher than 1°. In the area around station MORP (358.31°W, 55.21°N, sited at coastline of Britain), we suggest using PREMTEA model. Plain Language Summary: In any location worldwide, we employ a consistent set of numbers, known as load Green's functions that characterize the elastic properties of the Earth, to model the deformation caused by mass transfer on Earth's surface, which is referred to as surface mass loading effects. Most research and data products adopt same set of global mean Green's functions for any location on Earth, which neglects the differences in elastic properties of various regions of the Earth, leading to modeling errors to some extent. This study aims to optimize the modeling using local load Green's functions, with the objective of obtaining results that are more theoretically valuable. "Local" means that the Green's functions for each site are derived from an Earth model that takes into account the local crustal structure. The modeled atmospheric loading displacements exhibit enhanced alignment with GNSS position time series. This, in turn, further elucidates that deformations can be more attributed to atmospheric loading. Moreover, the model shows improved performance in the horizontal components when compared to the vertical components. Key Points: Local load Green's Functions enable a more precise modeling of loading displacements than global mean Green's FunctionsThe modeled atmospheric loading displacements based on Local Green's Functions can improve scatter reduction of GNSS position time seriesTwo sets of local load Green's Functions, based on CRUST1.0 model and TEA12 model, are compared. We recommend TEA12 for European region [ABSTRACT FROM AUTHOR]

The determination of reference points for telescopes is crucial for obtaining the local‐tie vector for a multi‐technology co‐located station. Traditional methods suffer from labor‐intensive fieldwork, long execution cycles, and systematic errors due to the incident angle of the total station's laser beam on the prism. In this paper, we propose an algorithm aligning the prism pointing vector with the prism‐to‐total station vector and introduce a pan‐tilt platform and the Tianma VGOS telescope for sub‐millimeter unmanned reference point monitoring. Our results show that the prototype system based on the pan‐tilt achieves a formal error of ±100 μm (1σ) in reference point positions with just 2.5 hr of automated monitoring. The consistency of reference point positions is comparable to the local control network accuracy. Single‐direction observations may introduce a deviation of ±1 mm in the reference point compared to multiple direction observations. Preliminary experiments on the Tianma VGOS telescope have also shown that this system can efficiently automate the monitoring of prism scatter points (one measurement every minute) and achieve sub‐millimeter horizontal precision in reference point positions within a single day. Key Points: A unique algorithm that aligns the prism pointing vector with the prism‐to‐total station vector was derivedAn automatic sub‐millimeter unmanned reference point monitoring prototype is designed and applied to the VGOS telescopeReference point coordinates solved by data sets from multiple directions are evaluated [ABSTRACT FROM AUTHOR]

We present a feasibility study for passive sounding of Uranian icy moons using Uranian Kilometric Radio (UKR) emissions in the 100–900 kHz band. We provide a summary description of the observation geometry, the UKR characteristics, and estimate the sensitivity for an instrument analogous to the Cassini Radio Plasma Wave Science (RPWS) but with a modified receiver digitizer and signal processing chain. We show that the concept has the potential to directly and unambiguously detect cold oceans within Uranian satellites and provide strong constraints on the interior structure in the presence of warm or no oceans. As part of a geophysical payload, the concept could therefore have a key role in the detection of oceans within the Uranian satellites. The main limitation of the concept is coherence losses attributed to the extended source size of the UKR and dependence on the illumination geometry. These factors represent constraints on the tour design of a future Uranus mission in terms of flyby altitudes and encounter timing. Plain Language Summary: The large moons of Uranus are hypothesized to have subsurface oceans beneath their icy crusts. This paper analyzes the possibility to use natural radio emissions originating from Uranian auroras to probe for these oceans. Cold ice is transparent to radio waves allowing reflections from liquid water to be readily observed. Monitoring the radio noise patterns from Uranus and searching for the reflections could constitute a direct way to detect the subsurface oceans. Key Points: Passive Sounding of the Uranian moons is a promising concept and no insurmountable obstacles have been identifiedIn the presence of NH3 rich oceans, the direct detection of the ice‐ocean interface is feasibleThe method works best for the outer moons and low ocean temperatures, making it complementary to magnetic induction measurements [ABSTRACT FROM AUTHOR]

This study proposes a novel method, which is a U‐shaped convolutional neural network that combines non‐local attention mechanisms, Res2net residual modules, and terrain information (UNR‐Net). The original U‐Net method and the linear regression (LR) method are conducted as benchmarks. Generally, the UNR‐Net has demonstrated promise in performing a 10× downscaling for daily 2‐m temperature over North China with lead times of 1–7 days and shows superiority to the U‐Net and LR methods. To be specific, U‐Net and UNR‐Net demonstrate higher Nash‐Sutcliffe Efficiency coefficient values compared to LR by 0.052 and 0.077, respectively. The corresponding improvements in pattern correlation coefficient are 0.013 and 0.016, while the root mean square error values are higher by 0.22 and 0.338, respectively. Additionally, the structural similarity index metric is higher by 0.033 and lower by 0.015. Furthermore, regions with significant errors are primarily distributed in complex terrain areas such as the Taihang Mountains, where UNR‐Net exhibits noticeable improvements. In addition, the 12 components‐based error decomposition method is proposed to analyze the error source of different models. Generally, the smallest errors are observed during the summer season and the sequence error component is proven to be the main source error of 2‐m temperature forecasts. Furthermore, UNR‐Net consistently demonstrates the lowest errors among all 12 error components. Therefore, combining the numerical weather prediction model and deep learning method is very promising in downscaling temperature forecasts and can be applied to routine forecasting of other atmospheric variables in the future. Plain Language Summary: This research proposes a new method for downscaling using deep learning. The method uses a specific type of neural network called UNR‐Net, which combines attention mechanisms, residual modules, and terrain information. The performance of UNR‐Net is compared to two other methods: U‐Net and LR. In the study, UNR‐Net shows promise in performing a 10× downscaling of the daily 2‐m temperature in North China. The UNR‐Net demonstrates the best overall performance among all the comprehensive indicators (NSE, pattern correlation coefficient, root mean square error, and structural similarity index metric). Errors in the predictions are mainly found in complex terrain areas like the Taihang Mountains, but UNR‐Net shows noticeable improvements in these regions. The study also proposes a 12 components‐based error decomposition method to analyze the error sources of different models. All in all, it is found that the smallest errors are observed during the summer season and the main source error is the sequence error component. Additionally, when considering lead times of 1–7 days, UNR‐Net consistently shows the lowest errors among all 12 error components. Based on these findings, combining numerical weather prediction models with deep learning methods holds great promise for generating high‐resolution temperature forecasts. Key Points: This paper presents a novel deep learning downscaling method, UNR‐Net, capable of downscaling daily 2‐m temperature by a factor of 10The overall performance of the UNR‐Net method surpasses the U‐Net method and linear regression methodThe 12 components‐based error decomposition method is proposed to analyze the error source of different models [ABSTRACT FROM AUTHOR]

The first Joint Workshop on Representation Uncertainty in the Earth Sciences was held in March 2021. This brought together the Earth observation, data assimilation and forecast verification and post‐processing communities, alongside metrologists to discuss the definition and quantification of representation uncertainty within the Earth sciences. The aim of the workshop was to facilitate cross‐disciplinary discussion, establishing where existing methodologies could be shared and to foster future collaboration. A key outcome of the workshop was a working definition of representation uncertainty applicable across all the Earth science communities, which is presented in this white paper. The cross‐disciplinary discussions at the workshop highlighted the need for scientists to work with metrologists to establish a common vocabulary for uncertainties, accessible to Earth science applications. Further recommendations included regular workshops to discuss progress in defining and quantifying representation uncertainty and awareness of cross‐disciplinary funding opportunities to further address representation uncertainty issues. Plain Language Summary: This paper describes a workshop which brought together experts from different Earth science disciplines to discuss and attempt to define the term "representation uncertainty". We make observations of the Earth using satellites, ground based instruments (such as weather stations), air and sea‐borne sensors. These observations are used in their own right, and also by computer models to generate weather forecasts. The observations themselves are imperfect and we quantify these imperfections using the term "uncertainty". In this paper we discuss the uncertainty that occurs when we compare two different sets of observations, two different models, observations and models, or where there are differences in underlying assumptions. As well as the uncertainties inherent in models and observations, there is also an uncertainty due to the fact that the two things being compared are not representing a phenomenon in exactly the same way. For example, a satellite observation may represent an average value over a few hundred square meters, while an instrument on the surface measures only at a single point (typically one‐square‐meter or less), and the model represents an area of several square kilometres. Understanding those differences is essential to be able to properly combine different sets of observations, and observations with models. Key Points: Representation uncertainty can be defined as occurring at a comparison interface between two quantitiesA common definition of representation uncertainty can be applied across the Earth Science communityCollaboration between scientists and metrologists is recommended to define an accessible and relevant vocabulary for uncertainty analysis [ABSTRACT FROM AUTHOR]