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Aerosol–cloud interactions play a crucial role in shaping Earth's climate and hydrological cycle. Observing these interactions with high precision and accuracy is of the utmost importance for improving climate models and predicting Earth's climate. Over the past few decades, lidar techniques have emerged as powerful tools for investigating aerosol–cloud interactions due to their ability to provide detailed vertical profiles of aerosol particles and clouds with high spatial and temporal resolutions. This review paper provides an overview of recent advancements in the study of ACI using lidar techniques. The paper begins with a description of the different cloud microphysical processes that are affected by the presence of aerosol, and with an outline of lidar remote sensing application in characterizing aerosol particles and clouds. The subsequent sections delve into the key findings and insights gained from lidar-based studies of aerosol–cloud interactions. This includes investigations into the role of aerosol particles in cloud formation, evolution, and microphysical properties. Finally, the review concludes with an outlook on future research. By reporting the latest findings and methodologies, this review aims to provide valuable insights for researchers engaged in climate science and atmospheric research. [ABSTRACT FROM AUTHOR]

This study analyzed 1,636 top papers in the subject category of meteorology and atmospheric sciences about eleven years from 2011 to 2021, which included 1,636 highly cited papers and 24 hot papers in the field belonged to 20 Web of Science categories and 14 research areas. All top papers, written in English, were from 13,878 authors, 2,913 organizations, and 124 countries or territories, and published in 72 journals in the field. The top five journals are the Nature Climate Change (15.9% of the studied paper), Atmospheric Chemistry and Physics (12.1%), Journal of Climate (7.0%), Bulletin of the American Meteorological Society (6.7%), and Journal of Geophysical Research Atmospheres (4.6%), each published more than 76 papers. Top five countries were the USA, England, PR China, Germany, and France. Furthermore, top five organizations of National Oceanic and Atmospheric Administration (NOAA), National Center for Atmospheric Research (NCAR), National Aeronautics and Space Administration (NASA), Chinese Academy of Sciences, and University Colorado were popular based on contribution of articles more than 134 papers each. All keywords were separated into eight clusters for different research topic. Visualizations offer exploratory information on the current state in a scientific field or discipline, as well as can indicate possible developments in the future. [ABSTRACT FROM AUTHOR]

The GRACE (Gravity Recovery and Climate Experiment) satellite, operational from its launch in 2002 until October 2017, documented critical changes in global terrestrial water storage, significantly advancing the fields of gravity field analysis, hydrological variations, and environmental monitoring. This study investigates the thematic evolution of 2,966 SCIE-indexed publications on GRACE satellite applications from 2002 to 2022, utilizing co-occurrence network mapping and complex network analysis. Our results indicate a steady growth in GRACE satellite application research, with early publications (2002-2004) achieving an average citation rate exceeding 100, underscoring their substantial impact. Keyword clustering identified four primary research themes: Hydrology and Water Resources, Atmospheric and Environmental Science, Geophysical and Temporal Analysis, and Polar and Glaciological Studies. Over time, these themes have expanded and deepened, transitioning from initial focuses on gravity field computations to hydrological applications, sea level studies, and more specific investigations into water resources, climate change, and advancements in satellite geodesy. Recent research has particularly concentrated on groundwater and water storage issues. Geographically, the United States leads in publication output, impact, and international collaborations, with China following in publication count and network centrality, albeit with potential for improved impact. European countries demonstrate significant influence and centrality within the cooperation network. The evolution index of the international cooperation network shows a significant increase in connectivity from 2002 to 2022, with closer cooperation between countries. There is notable growth in both the number and intensity of collaborations, as evidenced by the rising average degree and average weighted degree. Overall, these indices underscore the evolution towards a more connected and globally integrated research network in GRACE satellite research. Looking forward, technological advancements, enhanced international collaboration, and integration with other data sources will drive future research in GRACE satellite application research, with important implications for addressing global environmental and climate challenges. [ABSTRACT FROM AUTHOR]

The accurate prediction of heavy precipitation in convective environments is crucial because such events, often occurring in Italy during the summer and fall seasons, can be a threat for people and properties. In this paper, we analyse the impact of satellite-derived surface-rainfall-rate data assimilation on the Weather Research and Forecasting (WRF) model's precipitation prediction, considering 15 days in summer 2022 and 17 days in fall 2022, where moderate to intense precipitation was observed over Italy. A 3DVar realised at CNR-ISAC (National Research Council of Italy, Institute of Atmospheric Sciences and Climate) is used to assimilate two different satellite-derived rain rate products, both exploiting geostationary (GEO), infrared (IR), and low-Earth-orbit (LEO) microwave (MW) measurements: One is based on an artificial neural network (NN), and the other one is the operational P-IN-SEVIRI-PMW product (H60), delivered in near-real time by the EUMETSAT HSAF (Satellite Application Facility in Support of Operational Hydrology and Water Management). The forecast is verified in two periods: the hours from 1 to 4 (1–4 h phase) and the hours from 3 to 6 (3–6 h phase) after the assimilation. The results show that the rain rate assimilation improves the precipitation forecast in both seasons and for both forecast phases, even if the improvement in the 3–6 h phase is found mainly in summer. The assimilation of H60 produces a high number of false alarms, which has a negative impact on the forecast, especially for intense events (30 mm/3 h). The assimilation of the NN rain rate gives more balanced predictions, improving the control forecast without significantly increasing false alarms. [ABSTRACT FROM AUTHOR]

The land use, land-use change and forestry (LULUCF) sector, as a source and a sink of greenhouse gas (GHG) emissions, is critical for achieving carbon neutrality. Many academic journals have published papers on land use carbon emission or sink (LUCES), but LUCES reviews are relatively rare, which poses great challenges in accurately understanding the research progress and future prospects. This work analyzes the research characteristics, hotspots and future perspectives of LUCES research by using a bibliometric analysis (such as DDA, VOSviewer, CiteSpace software) and a review based on the data (6115 scientific papers) during 1991–2023 from the Web of Science (WoS) platform. We found that (1) over the past 33 years, it first presented a steady growth, then fluctuating growth, and finally a rapid growth trend in the yearly number of publications in LUCES research. The USA (17.31%), China (14.96%), and the UK (7.37%) occupy a dominant position in this research field. (2) The related LUCES research is interdisciplinary, which mainly cover science and technology, meteorology and atmospheric sciences, geology, and environmental sciences and ecology disciplines. (3) The research hotspot analysis on LUCES shows that these articles mostly covered the follow three aspects: ecosystem services, climate change, and carbon neutrality. (4) A review of the past LUCES literature suggests that it is mainly focused on exploring the forefront issues in terms of the definition and boundaries, evaluation method and influencing factors, etc. This work suggests that further research could explore the main scientific problems on quantification of land-based carbon neutrality, quantitative analysis of the impact mechanisms, as well as interdisciplinary research and collaborative governance needed for carbon neutrality. [ABSTRACT FROM AUTHOR]

This paper describes a neural network cloud masking scheme from PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar) multi-angle polarimetric measurements. The algorithm has been trained on synthetic measurements and has been applied to the processing of 1 year of PARASOL data. Comparisons of the retrieved cloud fraction with MODIS (Moderate Resolution Imaging Spectroradiometer) products show overall agreement in spatial and temporal patterns, but the PARASOL neural network (PARASOL-NN) retrieves lower cloud fractions. Comparisons with a goodness-of-fit mask from aerosol retrievals suggest that the NN cloud mask flags fewer clear pixels as cloudy than MODIS (∼ 3 % of the clear pixels versus ∼ 15 % by MODIS). On the other hand the NN classifies more pixels incorrectly as clear than MODIS (∼ 20 % by NN, versus ∼ 15 % by MODIS). Additionally, the NN and MODIS cloud mask have been applied to the aerosol retrievals from PARASOL using the Remote Sensing of Trace Gas and Aerosol Products (RemoTAP) algorithm. Validation with AERONET shows that the NN cloud mask performs comparably with MODIS in screening residual cloud contamination in retrieved aerosol properties. Our study demonstrates that cloud masking from multi-angle polarimeter (MAP) aerosol retrievals can be performed based on the MAP measurements themselves, making the retrievals independent of the availability of a cloud imager. [ABSTRACT FROM AUTHOR]

At present, a variety of global remote sensing surface downwelling longwave radiation (SDL) products are used for atmospheric science research; however, there are few studies on the quantitative evaluation of differences among different SDL products. In order to evaluate the differences among different SDL products quantitatively, we have selected three commonly used SDL products—Clouds and the Earth's Radiant Energy System-Synoptic Radiative Fluxes and Clouds (CERES-SYN), the European Centre for Medium Range Weather Forecasts-Surface Radiation Budget (ECMWF-SRB) and the Global Energy and Water Exchanges Project-Surface Radiation Budget (GEWEX-SRB)—to comprehensively study in this paper. The results show that there are significant differences among the three SDL products in some areas, such as in the Arctic, the Antarctic, the Sahara, the Tibet Plateau, and Greenland. The maximum absolute root mean square error (RMSEab) in these areas is greater than 20 Wm−2, the maximum relative root mean square error (RMSEre) is greater than 20%, the maximum and minimum absolute mean bias error (MBEab) are about 20 Wm−2 and −20 Wm−2, respectively, and the maximum and minimum relative mean bias error (MBEre) are about 10% and −10%, respectively. Among the three SDL products, the difference between the ECMWF-SRB and GEWEX-SRB is the most significant. In addition, this paper also analyzed the differences among different SDL products based on three aspects. Firstly, the differences among the three SDL products show that there is significant seasonality, and the differences among different months may vary greatly. However, the differences are not sensitive to years. Secondly, there are some differences in cloud-forcing radiative fluxes (CFRFs) of different SDL products, which is also an important factor affecting the difference between different SDL products. Finally, in the process of converting high temporal resolution SDL products into monthly SDL products, data processing also affects the difference between different SDL products. [ABSTRACT FROM AUTHOR]

From a passive satellite remote sensing point of view, the richest set of information on aerosol properties can be obtained from instruments that measure both intensity and polarization of backscattered sunlight at multiple wavelengths and multiple viewing angles for one ground pixel. However, it is challenging to exploit this information at a global scale because complex algorithms are needed with many fit parameters (aerosol and land/ocean reflection), based on online radiative transfer models. So far, two such algorithms have demonstrated this capability at a global scale: the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm and the Remote sensing of Trace gas and Aerosol Products (RemoTAP) algorithm. In this paper, we present a detailed comparison of the most recent versions of RemoTAP and GRASP. We evaluate both algorithms for synthetic observations, for real PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar) observations against AERONET (Aerosol Robotic Network) for common pixels, and for global PARASOL retrievals for the year 2008. For the aerosol optical depth (AOD) over land, both algorithms show a root mean square error (RMSE) of 0.10 (at 550 nm). For single scattering albedo (SSA), both algorithms show a good performance in terms of RMSE (0.04), but RemoTAP has a smaller bias (0.002) compared to GRASP (0.021). For the Ångström exponent (AE), GRASP has a smaller RMSE (0.367) than RemoTAP (0.387), mainly caused by a small overestimate of AE at low values (large particles). Over ocean both algorithms perform very well. For AOD, RemoTAP has an RMSE of 0.057 and GRASP an even smaller RMSE of 0.047. For AE, the RMSEs of RemoTAP and GRASP are 0.285 and 0.224, respectively. Based on the AERONET comparison, we conclude that both algorithms show very similar overall performance, where both algorithms have stronger and weaker points. For the global data products, we find a root mean square difference (RMSD) between RemoTAP and GRASP AOD of 0.12 and 0.038 over land and ocean, respectively. The largest differences occur over the biomass burning region in equatorial Africa. The global mean values are virtually unbiased with respect to each other. For AE the RMSD between RemoTAP and GRASP is 0.33 over land and 0.23 over ocean. For SSA, we find much better agreement over land (bias = - 0.01, RMSD = 0.043 for retrievals with AOD > 0.2) than over ocean (bias = 0.053, RMSD = 0.074). As expected, the differences increase towards low AOD, over both land and ocean. We also compared the GRASP and RemoTAP AOD and AE products against MODIS. For AOD over land, the agreement of either GRASP or RemoTAP with MODIS is worse than the agreement between the two PARASOL algorithms themselves. Over ocean, the agreement is very similar among the three products for AOD. For AE, the agreement between GRASP and RemoTAP is much better than the agreement of both products with MODIS. The agreement of the latest product versions with each other and with AERONET improved significantly compared to the previous version of the global products of GRASP and RemoTAP. The results demonstrate that the dedicated effort in algorithm development for multi-angle polarimetric (MAP) aerosol retrievals still leads to substantial improvement of the resulting aerosol products, and this is still an ongoing process. [ABSTRACT FROM AUTHOR]

A source apportionment scheme based on gas and aerosol phase reactive tracers has been implemented in the chemical-transport model FARM, to efficiently estimate contributions of different sources to ambient concentrations. The on-line scheme deals with all the main processes that the chemical species undergo in the model, to enhance consistency with the calculation of bulk concentrations. The fate of precursors through gas-phase chemical reactions is followed by an efficient solver that determines their incremental reactivity, while the contributions to the secondary particulate species from their gaseous precursors is determined by assuming the thermodynamic equilibrium between the two phases. The paper details the new employed methodologies and illustrates the application of the apportionment scheme (based on 6 source sectors) to PM10 and O3, simulated on three domains of different dimensions in Italy, all sharing the same horizontal resolution and a common region (Lombardy). Spatial patterns of results show, on average, a relevant contribution of heating on PM10 concentration in January, with local hotspots dominated by road traffic. Contributions appear consistent in the three simulated domains, apart from the boundary conditions, influenced by the dimension of the domain. Hourly series of contributions to O3 concentrations in July at three selected sites show the dominance of boundary conditions, underlining the large scale of O3 formation. Finally, for PM10 components, the resulting sectorial contributions are compared with the impacts computed via the brute force method, showing that results are similar for elemental carbon and sulfate, while they are different for nitrate and ammonium, due to a different allocation of contributions and impacts between the methods. Each approach responds in principle to a different purpose, and their combined use provides possibly a wide set of information useful for addressing the different air quality management needs. [ABSTRACT FROM AUTHOR]

Trajectory ensemble receptor models (TERMs) were widely used to determine the likely source locations and apportionment of air pollutants. This paper describes the development and applications of the Trajectory-ensemble Potential Source Apportionment Web application (TraPSA-Web), a comprehensive toolkit for likely atmospheric pollutant source location apportionments using TERMs and back trajectories generated with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The TERMs integrated within the TraPSA-web include Concentration Field Analysis (CFA), Concentration Weighted Trajectory (CWT), single-site and multiple-site Potential Source Contribution Function (PSCF), and Simplified Quantitative Transport Bias Analysis (SQBA). TraPSA-Web is designed as a web application with a user-friendly modern graphical user interface (GUI), which largely enhances the accessibility to the users. TraPSA-Web will provide the air quality research community with a sophisticated toolkit for (1) easy management of the research project and datasets, (2) efficient automatization for HYSPLIT configurations, calculations, and result aggregations, (3) flexible configurations for the research scenarios and TERM parameters, and (4) interactive visualizations for the pollutant pattern analysis and TERM result mapping. [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]

Uncrewed aerial vehicles (UAVs) have become widely used in a range of atmospheric science research applications. Because of their small size, flexible range of motion, adaptability, and low cost, multirotor UAVs are especially well-suited for probing the lower atmosphere. However, their use so far has been limited to conditions outside of clouds, first because of the difficulty of flying beyond visual line of sight and second because of the challenge of flying in icing conditions in supercooled clouds. Here, we present two UAVs for cloud microphysical research: one UAV (the measurement UAV) equipped with a Portable Optical Particle Spectrometer (POPS) and meteorological sensors to probe the aerosol and meteorological properties in the boundary layer and one UAV (the seeding UAV) equipped with seeding flares to produce a plume of particles that can nucleate ice in supercooled clouds. A propeller heating mechanism on both UAVs allows for operating in supercooled clouds with icing conditions. These UAVs are an integral part of the CLOUDLAB project in which glaciogenic cloud seeding of supercooled low stratus clouds is utilized for studying aerosol–cloud interactions and ice crystal formation and growth. In this paper, we first show validations of the POPS on board the measurement UAV, demonstrating that the rotor turbulence has a small effect on measured particle number concentrations. We then exemplify the applicability for profiling the planetary boundary layer, as well as for sampling and characterizing aerosol plumes, in this case, the seeding plume. We also present a new method for filtering out high-concentration data to ensure good data quality of POPS. We explain the different flight patterns that are possible for both UAVs, namely horizontal or vertical leg patterns or hovering, with an extensive and flexible parameter space for designing the flight patterns according to our scientific goals. Finally, we show two examples of seeding experiments: first characterizing an out-of-cloud seeding plume with the measurement UAV flying horizontal transects through the plume and, second, characterizing an in-cloud seeding plume with downstream measurements from a POPS and a holographic imager mounted on a tethered balloon. Particle number concentrations and particle number size distributions of the seeding plume from the experiments reveal that we can successfully produce and measure the seeding plume, both in-cloud (with accompanying elevated ice crystal number concentrations) and out-of-cloud. The methods presented here will be useful for probing the lower atmosphere, for characterizing aerosol plumes, and for deepening our cloud microphysical understanding through cloud seeding experiments, all of which have the potential to benefit the atmospheric science community. [ABSTRACT FROM AUTHOR]

Educators can enrich their teaching with best practices, share resources, and contribute to the growing atmospheric science education research community by reading and participating in the scholarship of teaching and learning in atmospheric science. This body of scholarship has grown, particularly over the past 15 years, and is now a sizable literature base that documents and exemplifies numerous teaching innovations in undergraduate atmospheric science education. This literature base benefits the entire atmospheric science community because graduates of atmospheric science programs are better prepared to enter the workforce. This literature base has not yet been examined, however, to see how well the evidence supports education practices in the atmospheric science education literature. In this study, we characterized that evidence and show that the majority of papers we reviewed share education innovations with anecdotal or correlational evidence of effectiveness. While providing useful practitioner knowledge and preliminary evidence of the effectiveness of numerous innovative teaching practices, opportunities exist for increasing readers' confidence that the innovations caused the learning gains. Additional studies would also help move conclusions toward generalizability across academic institutions and student populations. We make recommendations for advancing atmospheric science education research and encourage atmospheric science educators to actively use the growing body of education literature as well as contribute to advancing atmospheric science education research. [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]

Climate change is one of the biggest threats to human living and health in the 21st century. Whilst a large number of papers have been published addressing the health impact of climate change, there is a lack of comprehensive bibliometric analysis in the crosscutting field. This study evaluated the global scientific output of research in the field of climate change and health between 1990 and 2020, based on the Web of Science Core Collection database. Research themes were identified using a social network analysis technique based on author keywords. Research trends were assessed by the change in overall publication number and the percentage of publications in each research theme. Articles were further categorized by the availability of funding and author affiliation to compare the difference between developed and developing countries. Results showed that the research output in the field of climate change and health has increased dramatically in the past 30 years, mainly dominated by researchers in developed countries. The percentage of research receiving funding was found to be the lowest in those published by developing countries only and the highest in those published by the collaboration of developed and developing countries. A total of nine major research themes was identified. Research related to 'risk assessment and adaptation', 'sustainable development' and 'infectious diseases' were relatively underfunded. A significant research trend was observed between 2006 and 2020, with increased attention on research themes related to 'risk assessment and adaptation', 'sustainable development', 'extreme events' and 'air pollution', and reduced attention on research themes related to 'ocean', 'infectious disease' and 'phenology'. The shift of the research trend was mainly driven by research in developed countries. Suggestions, recommendations and future priorities identified by experts in the field of atmospheric sciences, epidemiology, public health, climate change, environmental sciences, and policy development are also provided to guide future research. It is important to shift our focus from single health aspects to an integrated system (such as One Health framework, which considers environmental health, animal health and human health as a whole), with future research focusing more on the systemic impact of climate change in order to achieve better, more effective and efficient risk governance. More funding should be mobilized to support the research capacity building in developing countries and to support climate change adaptation strategies for sustainable development. [ABSTRACT FROM AUTHOR]

Paul Jozef Crutzen was a pioneer in the atmospheric sciences, a kind-hearted and humorous person with empathy for the private lives of his colleagues and students, and a man who upheld the highest scientific standards for himself and for others. He made fundamental scientific contributions to a wide range of scientific topics in all parts of the atmosphere, from the mesosphere to the stratosphere and from the stratosphere to the troposphere. In particular, he was the first to describe the NOx -driven ozone depletion cycle in the stratosphere, he was among the first to develop the idea of chemical formation of ozone in the troposphere, he provided key concepts to explain the "ozone hole", and he made fundamental discoveries about the effects of biomass burning on the troposphere. Understanding and addressing the causes of anthropogenic air pollution and climate change were the driving motivations for his scientific work. In his work, he did not shy away from challenge and provocation. His work on smoke from fires after a potential nuclear war inspired new research on a concept now known as "nuclear winter". He also initiated the reopening of the debate on "geoengineering" – a concept now referred to as "climate intervention". He also brought the term "Anthropocene" to the popular debate. Moreover, he had a strong influence on atmospheric science through his educational role; there is a very large number of outstanding scientists who started their career with scientific work with Paul. In 2000, Paul was among the founders of the journal Atmospheric Chemistry and Physics, which was unique at the time in providing public discussion of published preprints as well as what we now call "open access" to published articles. Paul's work on human impacts on atmosphere and climate has had a profound impact on the environmental policies of many countries for decades. In the future, his work will continue to be a guide for generations of scientists and environmental policymakers to come. [ABSTRACT FROM AUTHOR]

The continued interest in air pollution and stratospheric ozone variability has motivated the development of a Geostationary Environmental Monitoring Spectrometer (GEMS) for hourly ozone monitoring. This paper provides the atmospheric science community with the world's first assessment of GEMS total column ozone (TCO) retrieval performance and diurnal ozone variation. The algorithm used for GEMS is a more advanced version of its predecessor, the Total Ozone Mapping Spectrometer (TOMS) V8, that incorporates several improvements, including a new lookup table, a simple Lambertian-equivalent reflectivity model, and a spectral dependence correction. The GEMS algorithm also uses the optimal estimation method (OEM) to make error analysis more accessible and robust. The estimated retrieval errors range from 1.5 to 2 DU in September and 2 DU in December, with a constant degree of freedom of the signal (DFS) of 1 in September and a variable DFS of 1.25 to 1.4 in December throughout the day, depending on solar zenith angle (SZA). To assess the performance of the GEMS algorithm, the hourly GEMS total ozone was compared with ground-based measurements from Pandora instruments and other satellite platforms from TROPOMI (TROPOspheric Monitoring Instrument) and OMPS (Ozone Mapping and Profiler Suite Nadir Mapper). GEMS has a high correlation of 0.97 and small RMSE values compared to Pandora TCO at Busan and Seoul in South Korea. It is notable that despite exhibiting seasonal dependence in the mean bias of GEMS with Pandora, GEMS is capable of observing daily variations in ozone that are highly consistent with Pandora measurements, with a bias of approximately 1 %. The comparison of GEMS TCO data with TROPOMI and OMPS TCO data shows a high correlation of 0.99 and low RMSE compared to TROPOMI and OMPS TCO data, but the data have a negative bias of -2.38 % and -2.17 %, with standard deviations of 1.33 % and 1.57 %, respectively. Similar to OMPS, the influence of SO 2 from volcanic eruptions is not properly removed in some regions, leading to GEMS overestimating TCO in those areas. The mean biases of GEMS TCO data with TROPOMI and OMPS TCO are within ±1 % at low latitudes but become negative at midlatitudes, with an increasingly negative dependence on latitude. Furthermore, this dependence becomes more prominent from summer to winter. The empirical correction applied to the GEMS irradiance data improves the dependence of the mean bias on season and latitude, but a consistent bias still remains, and a marginal positive trend was observed in December. Therefore, further investigation into correction methods is needed. The results are a meaningful scientific advance by providing the first validated, hourly UV ozone retrievals from a satellite in geostationary orbit. This experience can be used to advance research with future geostationary environmental satellite missions, including the incoming TEMPO (Tropospheric Emissions: Monitoring of Pollution) and Sentinel-4. [ABSTRACT FROM AUTHOR]

Deep learning has recently gained attention in the atmospheric and oceanic sciences for its potential to improve the accuracy of numerical simulations or to reduce computational costs. Super‐resolution is one such technique for high‐resolution inference from low‐resolution data. This paper proposes a new scheme, called four‐dimensional super‐resolution data assimilation (4D‐SRDA). This framework calculates the time evolution of a system from low‐resolution simulations using a physics‐based model, while a trained neural network simultaneously performs data assimilation and spatio‐temporal super‐resolution. The use of low‐resolution simulations without ensemble members reduces the computational cost of obtaining inferences at high spatio‐temporal resolution. In 4D‐SRDA, physics‐based simulations and neural‐network inferences are performed alternately, possibly causing a domain shift, that is, a statistical difference between the training and test data, especially in offline training. Domain shifts can reduce the accuracy of inference. To mitigate this risk, we developed super‐resolution mixup (SR‐mixup)–a data augmentation method for domain generalization. SR‐mixup creates a linear combination of randomly sampled inputs, resulting in synthetic data with a different distribution from the original data. The proposed methods were validated using an idealized barotropic ocean jet with supervised learning. The results suggest that the combination of 4D‐SRDA and SR‐mixup is effective for robust inference cycles. This study highlights the potential of super‐resolution and domain‐generalization techniques, in the field of data assimilation, especially for the integration of physics‐based and data‐driven models. Plain Language Summary: One challenge in the Earth sciences is the simulation of atmospheric and oceanic processes with high accuracy or at high spatio‐temporal resolution. The neural network, a model developed in the field of artificial intelligence, has recently gained attention for its potential to learn the relationship between any variables from a sufficient amount of data. For example, a neural network can be trained to make high‐resolution images from low‐resolution images. This process is called super‐resolution. We propose the incorporation of a neural network for super‐resolution into an atmospheric or ocean model that performs numerical simulations at low resolution. Since the atmospheric or ocean model is of low resolution, the proposed framework is computationally efficient while still allowing for high‐resolution results with the aid of the neural network. Furthermore, to make the inference accurate, observation data are fused with atmospheric or ocean predictions in the neural network. In real‐world applications, it is important to make predictions robust to noise from various factors. Accordingly, we developed a new technique where certain noise is added during the training of the neural network. The approach proposed here can efficiently compute high‐resolution predictions of the atmosphere and ocean without compromising robustness. Key Points: We propose a neural network‐based scheme for data assimilation and spatio‐temporal super‐resolution using physics‐based simulationsA data‐augmentation technique was developed to improve the robustness of inference in the fusion of data‐driven and physics‐based modelsA test with idealized ocean jets showed that the proposed approach efficiently infers high‐resolution results with ensuring the robustness [ABSTRACT FROM AUTHOR]

Spherical harmonics provide a smooth, orthogonal, and symmetry-adapted basis to expand functions on a sphere, and they are used routinely in physical and theoretical chemistry as well as in different fields of science and technology, from geology and atmospheric sciences to signal processing and computer graphics. More recently, they have become a key component of rotationally equivariant models in geometric machine learning, including applications to atomic-scale modeling of molecules and materials. We present an elegant and efficient algorithm for the evaluation of the real-valued spherical harmonics. Our construction features many of the desirable properties of existing schemes and allows us to compute Cartesian derivatives in a numerically stable and computationally efficient manner. To facilitate usage, we implement this algorithm in sphericart, a fast C++ library that also provides C bindings, a Python API, and a PyTorch implementation that includes a GPU kernel. [ABSTRACT FROM AUTHOR]

Geoffrey Norris, a respected researcher in the field of palynology, made significant contributions to various areas of study throughout his career. He conducted important research on freshwater dinoflagellate cysts in Minnesota and Neogene dinoflagellates of the Atlantic coastal plain. Norris also collaborated with researchers in Germany and Venezuela, where he conducted fieldwork and contributed to the Venezuelan oil industry. Later in his career, Norris worked as a consultant for the petroleum industry and became involved in environmental issues, particularly concerning rising sea levels and harmful algal blooms. He mentored numerous students and associates, many of whom went on to successful careers. Norris is remembered for his warmth, kindness, and infectious laugh. [Extracted from the article]

Modeled geospatial Lagrangian trajectories are widely used in Earth Science, including in oceanography, atmospheric science and marine biology. The typically large size of these data sets makes them arduous to analyze, and their underlying pathways challenging to identify. Here, we show that we can use a machine learning unsupervised k‐means++ clustering method combined with expert aggregation of clusters to identify the pathways of the Labrador Current from a large set of modeled Lagrangian trajectories. The presented method requires simple pre‐processing of the data, including a Cartesian correction on longitudes and a principal component analysis reduction. The clustering is performed in a kernelized space and uses a larger number of clusters than the number of expected pathways. To identify the main pathways, similar clusters are grouped into pathway categories by experts in the circulation of the region of interest. We find that the Labrador Current mainly follows a westward‐flowing and an eastward retroflecting pathway (20% and 50% of the flow, respectively) that compensate each other through time in a see‐saw behavior. These pathways experience a strong variability (representing through time 4%–42% and 24%–73% of the flow, respectively). Two thirds of the retroflection occurs at the tip of the Grand Banks, and one quarter at Flemish Cap. The westward pathway is mostly fed by the on‐shelf branch of the Labrador Current, and the eastward pathway by the shelf‐break branch. Among the pathways of secondary importance, we identify a previously unreported one that feeds the subtropics across the Gulf Stream. Plain Language Summary: Lagrangian trajectories, in which parcels of a fluid or objects are tracked as they move, are widely used in Earth Science, including in oceanography, atmospheric science and marine biology. They typically come in very large data sets containing chaotic trajectories, from which it is difficult to identify the main pathways of the flow. Here, we use a machine learning based algorithm, more specifically an unsupervised clustering algorithm, to identify the main pathways of the Labrador Current in the North Atlantic based on a large set of Lagrangian trajectories obtained from an ocean model. This study shows the power of such a method to help analyze this type of data, and provides a detailed description of the method so it can be used by a broad community on various applications. We find that, when it reached the Grand Banks of Newfoundland, most of the Labrador Current flows either westward toward the Slope Sea or eastward toward the North Atlantic Ocean, in a see‐saw behavior. We also identify a previously unknown minor pathway that brings Labrador Current waters south of the Gulf Stream front. Key Points: Unsupervised clustering followed by expert aggregation can identify the main pathways in geospatial Lagrangian trajectoriesThe clusters provide information on the properties, origin and time‐scale of the pathwaysAt the tip of the Grand Banks, the Labrador Current breaks into an east‐west see‐saw system [ABSTRACT FROM AUTHOR]

The study of precipitation is one of the most intriguing areas in atmospheric sciences, with significant implications for our daily lives and climate change projections. This paper explores the estimation of rainfall trends in South American regions using convolutional neural networks (CNNs). The study focuses on the application of Cloud-Net, a CNNbased model with a format similar to an autoencoder, to obtain qualitative estimates of precipitation patterns. The employed loss functions, Categorical Cross Entropy and Categorical Focal Loss, address the challenges of classifying minority categories in unbalanced data. Regional analysis was conducted, identifying days with high rainfall intensity and the predominant intensities in 25 regions. The CNN model's performance was compared with the XGBoost algorithm, showing excellent results for extreme rainfall categories and challenging intermediate categories. Furthermore, a comparison was made with Quantitative Precipitation Estimation (QPE) data and ground measurements from rain gauges. While the CNN model provided a valuable qualitative estimate of precipitation trends, achieving precise quantitative estimation would require an extensive data set of in-situ measurements. Overall, this research demonstrates the potential of CNNs for estimating rainfall trends and understanding precipitation patterns in South American regions. The findings offer valuable insights for further applications in meteorology and environmental studies. [ABSTRACT FROM AUTHOR]

The article focuses on particulate matter (PM) research and management in Serbia. It discusses the impact of PM on human health, its various sizes and origins, including natural and anthropogenic sources, and the importance of improving outdoor and indoor air quality. It also highlights the WeBIOPATR conference platform, which aims to bridge the gap between research communities and air quality practitioners, facilitating dialogue and the incorporation of research into practice.

The present study focused on the research publications of women faculty in the field of physics, Astrophysics, Astronomy and Atmospheric Science in India. To collect the data we have covered the women faculty of Central Universities, State Universities, IITs, NITs, IISER's, NISER's, research institutes, laboratories and some selected deemed universities. The first fact we found was that the number of women were very less as compare to male staff. To see the publication productivity of women authors we tried to analyze the year-wise productivity, authorship pattern, preferred journals, place and designation and the research papers received highest citations for the period of five years from 2011- 2015. Year 2014 found to be the most productive year and in authorship pattern, women authors preferred to publish their research in collaboration. Highest number of research papers was published in Journal of Applied Physics. [ABSTRACT FROM AUTHOR]

The Journal of the Meteorological Society of Japan (JMSJ) is a well-established journal in the field of meteorology and related sciences. The editorial board is committed to continuously improving the journal for authors, readers, and reviewers. In 2024, the JMSJ will publish its 102nd volume and will discontinue the note service for submissions. The journal has recently implemented minor reforms in the technical editing process. The JMSJ Award for 2023 was presented to authors who conducted novel research on important topics. The most accessed papers in 2023 included topics such as geostationary meteorological satellites and reanalysis data. The journal also organized three special editions on various topics. JMSJ authors are encouraged to use J- STAGE Data for archiving datasets related to their papers. The journal expresses gratitude to the meteorological research community for their support and looks forward to continued success in 2024. [Extracted from the article]

Paul J. Crutzen was a pioneer in atmospheric sciences. At the same time, he was a kind-hearted, humorous person with empathy for the private lives of his colleagues and students, but also with the highest scientific standards for himself and for others. He made fundamental scientific contributions to a wide range of scientific topics in all parts of the atmosphere, from the mesosphere to the stratosphere and to the troposphere near the ground. In particular, he was the first to describe the NOx -driven ozone depletion cycle in the stratosphere, he developed the first mechanisms for the chemical formation of ozone in the troposphere, he provided key ideas to explain the "ozone hole", and he made fundamental discoveries about the effects of biomass burning on the troposphere. Understanding and addressing the causes of man-made air pollution and climate change was the driving motivation for his scientific work. In his work he did not shy away from challenge and provocation. He pioneered the concept now known as "nuclear winter" and initiated the reopening of the debate on geoengineering. He also brought the term "Anthropocene" to the popular debate. In 2000, Paul was among the founders of the journal "Atmospheric Chemistry and Physics", which was unique at the time in providing public discussion of published preprints, and also what we now call "open access" to published articles. Paul's work on human impacts on atmosphere and climate has had a profound impact on the environmental policies of many countries for decades. In the future, his work will continue to be a guide for generations of scientists and environmental policy makers to come. [ABSTRACT FROM AUTHOR]

Copyright of Revista Espanola de Antropologia Americana is the property of Universidad Complutense de Madrid and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper analyses how the current loss modelling framework that was developed in the 1990s to respond to Hurricane Andrew market crisis falls short in dealing with today's complexity. In effect, beyond reflecting and supporting the current understanding and knowledge of risks, data and models are used in the assessment of situations that have not been experienced yet. To address this question, we considered the (re)insurance market's current body of knowledge on natural hazard loss modelling, the fruit of over 30 years of research conducted by (re)insurers, brokers, modelling firms, and other private companies and academics in the atmospheric sciences, geosciences, civil engineering studies, and data sciences among others. Our study shows that to successfully manage the complexity of the interactions between natural elements and the customer ecosystem, it is essential that both private companies in the insurance sector and academia continue working together to co-build and share common data collection and modelling. This paper (i) proves the need to conduct an in-depth review of the existing loss modelling framework and (ii) makes it clear that only a transdisciplinary effort will be up to the challenge of building global loss models. These two factors are essential to capture the interactions and increasing complexity of the three risk drivers – exposure, hazard, and vulnerability – thus enabling insurers to anticipate and be equipped to face the far-ranging impacts of climate change and other natural events. [ABSTRACT FROM AUTHOR]

This paper describes a neural network cloud masking scheme from PARASOL (Polarisation and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar) Multi-Angle Polarimetric measurements. The algorithm has been trained on synthetic measurements and has been applied to the processing of one year of PARASOL data. Comparisons of the retrieved cloud fraction with MODIS (Moderate Resolution Imaging Spectroradiometer) products show overall agreement in spatial and temporal patterns but the PARASOL-NN retrieves lower cloud fractions. Comparisons with a goodness-of-fit mask from aerosol retrievals suggest that the NN cloud mask flags less clear pixels as cloudy than MODIS (∼ 3% of the clear pixels, versus ∼ 15% by MODIS). On the other hand the NN classifies more pixels incorrectly as clear than MODIS (∼ 19% by NN, versus ∼ 15% by MODIS). Additionally, the NN and MODIS cloud mask have been applied to the aerosol retrievals from PARASOL using the Remote Sensing of Trace Gas and Aerosol Products (RemoTAP) algorithm. Validation with AERONET shows that the NN cloud mask performs comparably with MODIS in screening residual cloud contamination in retrieved aerosol properties. Our study demonstrates that cloud masking from MAP aerosol retrievals can be performed based on the MAP measurements themselves, making the retrievals independent of the availability of a cloud imager. [ABSTRACT FROM AUTHOR]

Copyright of Advances in Earth Science (1001-8166) is the property of Advances in Earth Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Although clusters made of polycyclic aromatic hydrocarbon and water monomers are relevant objects in both atmospheric and astrophysical science, little is known about their energetic and structural properties. In this work, we perform global explorations of the potential energy landscapes of neutral clusters made of two pyrene units and one to ten water molecules using a density-functional-based tight-binding (DFTB) potential followed by local optimizations at the density-functional theory level. We discuss the binding energies with respect to various dissociation channels. It shows that cohesion energies of the water clusters interacting with a pyrene dimer are larger than those of the pure water clusters, reaching for the largest clusters an asymptotic limit similar to that of pure water clusters and that, although the hexamer and octamer can be considered magic numbers for isolated water clusters, it is not the case anymore when they are interacting with a pyrene dimer. Ionization potentials are also computed by making use of the configuration interaction extension of DFTB, and we show that in cations, the charge is mostly carried by the pyrene molecules. [ABSTRACT FROM AUTHOR]

The Vadose Zone Journal has announced the recipients of the 2023 Editor's Citation for Excellence. These awards recognize the dedication and professional commitment of volunteer reviewers and editors who have contributed to the high standard and quality of papers published in the journal. The recipients were nominated based on their thorough, competent, and timely reviews or editing of manuscripts. Each recipient will receive a certificate of appreciation, an ASA-CSSA-SSSA gift certificate, and recognition in CSA News. The recipients include Dr. Dianna Bagnall, Dr. Josh Heitman, Dr. Abou Najm, Dr. Jirka Šimůnek, Dr. Chao Zhang, Dr. Jan W. Hopmans, Dr. Ty Ferre, Dr. Andres Patrignani, Dr. Valérie Pot, Prof. Dr. Andrea Schnepf, and Dr. Briana Wyatt. [Extracted from the article]

Convective Quasi‐Equilibrium (CQE) is often adopted as a useful closure assumption to summarize the effects of unresolved convection on large‐scale thermodynamics, while existing efforts to observationally validate CQE largely rely on specific spatial domains or sites rather than the source of CQE constraints—deep convection. This study employs a Lagrangian framework to investigate leading temperature perturbation patterns near deep convection, of which the centers are located by use of an ensemble of satellite measurements. Temperature perturbations near deep convection with high peak precipitation are rapidly adjusted toward the CQE structure within the [−2, 1] hours centered on peak precipitation. The top 1% precipitating deep convection constrains neighboring free‐tropospheric leading perturbations up to 9°. Notable CQE validity beyond a 1° radius is observed when peak precipitation exceeds the 93rd percentile. These findings suggest that only a small fraction of deep convection with extreme precipitation shapes tropical free‐tropospheric temperature patterns dominantly. Plain Language Summary: Convective Quasi‐Equilibrium (CQE) is a concept in atmospheric science that explains a state where the influence of deep convection (cumulonimbus clouds) and large‐scale atmospheric forces is balanced, causing certain thermodynamic properties to adjust toward specific reference profiles. Previous studies have focused on how temperature changes relate to the CQE structure but in specific regions or sites while this study aims areas near deep convection—supposedly the source of CQE constraints. Using a unique framework with data from multiple satellites, we track the evolution of temperature patterns near deep convection and find temperatures near deep convection with extreme rainfall are adjusted toward the CQE structure rapidly within 3 hr of maximum rainfall. However, only the deep convection with top 7% extreme rainfall can effectively affect nearby temperature pattern beyond 1°, with the top 1% influencing up to an 9° radius. These findings highlight the dominant impact of a small fraction of deep convection, particularly those with extreme rainfall, on nearby temperature perturbation patterns. Key Points: Tropical temperature perturbations near extreme deep convection rapidly conform to convective quasi equilibrium in a three‐hour windowOnly the top 7% precipitating deep convection can modulate hourly tropical temperature patterns beyond a 1° radiusTop 1% precipitating deep convection constrains nearby temperature perturbations up to an 9° radius during peak precipitation [ABSTRACT FROM AUTHOR]

Climate extremes are on the rise. Impacts of extreme climate and weather events on ecosystem services and ultimately human well‐being can be partially attenuated by the organismic, structural, and functional diversity of the affected land surface. However, the ongoing transformation of terrestrial ecosystems through intensified exploitation and management may put this buffering capacity at risk. Here, we summarize the evidence that reductions in biodiversity can destabilize the functioning of ecosystems facing climate extremes. We then explore if impaired ecosystem functioning could, in turn, exacerbate climate extremes. We argue that only a comprehensive approach, incorporating both ecological and hydrometeorological perspectives, enables us to understand and predict the entire feedback system between altered biodiversity and climate extremes. This ambition, however, requires a reformulation of current research priorities to emphasize the bidirectional effects that link ecology and atmospheric processes. Plain Language Summary: Climate extremes are increasing and impacting both nature and people. We hypothesize that intact ecosystems, particularly via their biodiversity, can mitigate the impacts of climate extremes. What happens when biodiversity decreases? Could this loss make the effects of climate extremes even worse or change how these events occur? We explore these two questions and summarize the current state of knowledge. We conclude that targeted research efforts at the interface of ecology and atmospheric sciences are needed to answer these questions conclusively. Key Points: Mounting evidence suggests that an ecosystem's capacity to buffer the impacts of climate extremes depends on its biodiversityNumerous mechanisms suggest that a reduction in biodiversity could exacerbate climate extremesA series of research gaps need to be addressed to understand the full feedback between biodiversity change and climate extremes [ABSTRACT FROM AUTHOR]

To tackle the current pressing atmospheric science issues, as well as those in the future, a robust scientific community is necessary in all regions across the globe. Unfortunately, this does not yet exist. There are many geographical areas that are still underrepresented in the atmospheric science community, many of which are in the Global South. There are also larger gaps in the understanding of atmospheric composition, processes, and impacts in these regions. In this opinion, we focus on two geographical areas in the Global South to discuss some common challenges and constraints, with a focus on our strengths in atmospheric science research. It is these strengths, we believe, that highlight the critical role of Global South researchers in the future of atmospheric science research. [ABSTRACT FROM AUTHOR]

Applications of machine learning (ML) in atmospheric science have been rapidly growing. To facilitate the development of ML models for tropical cyclone (TC) research, this binary dataset contains a specific customization of the National Center for Environmental Prediction (NCEP)/final analysis (FNL) data, in which key environmental conditions relevant to TC formation are extracted for a range of lead times (0–72 hours) during 1999–2023. The dataset is designed as multi-channel images centered on TC formation locations, with a positive and negative directory structure that can be readily read from any ML applications or common data interface. With its standard structure, this dataset provides users with a unique opportunity to conduct ML application research on TC formation as well as related predictability at different forecast lead times. [ABSTRACT FROM AUTHOR]

Warm-sector heavy rainfall along the south China coast poses significant forecasting challenges due to its localized nature and prolonged duration. To improve the prediction of such high-impact weather events, high-resolution numerical weather prediction (NWP) models are increasingly used to more accurately represent topographic effects. However, as these models' grid spacing approaches the scale of convective processes, they enter a "gray zone", where the models struggle to fully resolve the turbulent eddies within the atmospheric boundary layer, necessitating partial parameterization. The appropriateness of applying convection parameterization (CP) schemes within this gray zone remains controversial. To address this, scale-aware CP schemes have been developed to improve the representation of convective transport. Among these, the multi-scale Kain–Fritsch (MSKF) scheme enhances the traditional Kain–Fritsch (KF) scheme, incorporating modifications that facilitate its effective application at spatial resolutions as high as 2 km. In recent years, there has been an increase in the application of machine learning (ML) models across various domains of atmospheric sciences, including efforts to replace conventional physical parameterizations with ML models. This work introduces a multi-output bidirectional long short-term memory (Bi-LSTM) model intended to replace the scale-aware MSKF CP scheme. This multi-output Bi-LSTM model is capable of simultaneously predicting the convection trigger while also modeling the associated convective tendencies and precipitation rates with a high performance. Data for training and testing the model are generated using the Weather Research and Forecast (WRF) model over south China at a horizontal resolution of 5 km. Furthermore, this work evaluates the performance of the WRF model coupled with the ML-based CP scheme against simulations with the traditional MSKF scheme. The results demonstrate that the Bi-LSTM model can achieve high accuracy, indicating the promising potential of ML models to substitute the MSKF scheme in the gray zone. [ABSTRACT FROM AUTHOR]

The acquisition and dissemination of essential information for understanding global biogeochemical interactions between the atmosphere and ecosystems and how climate–ecosystem feedback loops may change atmospheric composition in the future comprise a fundamental prerequisite for societal resilience in the face of climate change. In particular, the detection of trends and seasonality in the abundance of greenhouse gases and short-lived climate-active atmospheric constituents is an important aspect of climate science. Therefore, easy and fast access to reliable, long-term, and high-quality observational environmental data is recognised as fundamental to research and the development of environmental forecasting and assessment services. In our opinion article, we discuss the potential role that environmental research infrastructures in Europe (ENVRI RIs) can play in the context of an integrated global observation system. In particular, we focus on the role of the atmosphere-centred research infrastructures ACTRIS (Aerosol, Clouds and Trace Gases Research Infrastructure), IAGOS (In-service Aircraft for a Global Observing System), and ICOS (Integrated Carbon Observation System), also referred to as ATMO-RIs, with their capabilities for standardised collection and provision of long-term and high-quality observational data, complemented by rich metadata. The ATMO-RIs provide data through open access and offer data interoperability across different research fields including all fields of environmental sciences and beyond. As a result of these capabilities in data collection and provision, we elaborate on the novel research opportunities in atmospheric sciences which arise from the combination of open-access and interoperable observational data, tools, and technologies offered by data-intensive science and the emerging collaboration platform ENVRI-Hub, hosted by the European Open Science Cloud (EOSC). [ABSTRACT FROM AUTHOR]

The use of remote sensing methods to accurately measure cloud properties and their spatiotemporal changes has been widely welcomed in many fields of atmospheric research. The Nanjing Joint Institute for Atmospheric Sciences (NJIAS) Himawari-8/9 Cloud Feature Dataset (HCFD) provides a comprehensive description of cloud features over the East Asia and west North Pacific regions for the 7-year period from April 2016 to December 2022. Multiple cloud variables, such as cloud mask, phase/type, top height, optical thickness, and particle effective radius, as well as snow, dust, and haze masks, were generated from the visible and infrared measurements of the Advanced Himawari Imager (AHI) on board the Japanese geostationary satellites Himawari-8 and Himawari-9 using a series of recently developed cloud retrieval algorithms. Verifications with the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) 1 km cloud layer product and the Moderate Resolution Imaging Spectroradiometer (MODIS) Level-2 cloud product (MYD06) demonstrate that the NJIAS HCFD gives higher skill scores than the Japanese Himawari-8/9 operational cloud product for all cloud variables except for cloud particle effective radius. The NJIAS HCFD even outperforms the MYD06 in nighttime cloud detection; cloud-top height, pressure, and temperature estimation; and infrared-only cloud-top phase determination. All evaluations are performed at the nominal 2 km scale, not including the effects of sub-pixel cloudiness or very thin cirrus. Two examples are presented to demonstrate applications of the NJIAS HCFD for climate and typhoon research. The NJIAS HCFD has been published in the Science Data Bank (https://doi.org/10.57760/sciencedb.09950 , Zhuge 2023a; https://doi.org/10.57760/sciencedb.09953 , Zhuge 2023b; https://doi.org/10.57760/sciencedb.09954 , Zhuge 2023c; https://doi.org/10.57760/sciencedb.10158 , Zhuge 2023d; https://doi.org/10.57760/sciencedb.09945 , Zhuge 2023e). [ABSTRACT FROM AUTHOR]

The ocean general circulation model (OGCM) is an essential tool for researching oceanography and atmospheric science. The LASG/IAP climate system ocean model version 3 (LICOM3) is a parallel version of the OGCM. Our goal is to implement and optimize a GPU version of LICOM3 based on compute unified device architecture (CUDA) called LICOM3-CUDA. Considering the characteristics of LICOM3 and CUDA, we design and implement some pivotal optimization methods, including redesigning the numerical algorithms of complicated functions, decoupling data dependency, avoiding memory write conflicts, and optimizing communication. In this paper, we selected two experiments, including 1 ∘ (small-scale) and 0.1 ∘ (large-scale) resolutions to evaluate the performance of LICOM3-CUDA. Under the experimental environment of two Intel Xeon Gold 6148 CPUs and four NVIDIA Quadro GV100s, the LICOM3-CUDA (1 ∘ ) achieves a simulation speed of 114.3 simulation-year-per-day (SYPD). Compare with the performance of LICOM3, the LICOM3-CUDA can run much faster with 6.5 times, and the compute-intensive module achieves over 70 × speedup. In addition, the energy consumption for the simulation year is reduced by 41.3%. As for high-resolution and large-scale simulation, the number of GPUs increased from 96 to 1536 as well as the LICOM3-CUDA (0.1 ∘ ) time consumption decreased from 3261 to 720 seconds with approximately 4.5 × of speedup. [ABSTRACT FROM AUTHOR]

The underestimation of cloud fraction, especially the low stratus cloud (LSC) fraction over the eastern oceans, remains a problem in most atmospheric global climate models. This study investigated potential improvements through perturbing nine moist physical parameters, using uniform sampling and Latin hypercube sampling methods, and quantified the parametric uncertainty and effects of non‐linear interaction between parameters on the tropical cloud fraction in the Grid‐Point Atmospheric Model of the Institute of Atmospheric Physics/State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, version 2. Results showed that the uncertainty ranges of the tropical total cloud fraction and LSC associated with multiple‐parameter perturbation were larger than those from any single‐parameter perturbation. The total cloud fraction was significantly improved with multiple‐parameter perturbation and the LSC also increased notably when using parameter values optimized for the total cloud fraction because of the indirect parametric effect on lower‐tropospheric stability. Non‐linear effects between parameters on simulating LSC were much stronger than those on simulating the total cloud fraction. Furthermore, the non‐linear interaction reduced large values of total cloud fraction but had a strong incremental effect on large values of LSC over the southeast Pacific. The findings demonstrate the feasibility of improving the simulation of LSC over the eastern oceans by tuning moist parameters, and provide further insight into the non‐linear effects between parameters on the simulation of cloud fraction. Plain Language Summary: Clouds play a crucial role in Earth's climate system by modulating radiation budgets. This paper reports the results of a study aimed at better understanding the moist parametric uncertainty and non‐linear effects on the tropical cloud fraction in climate model simulations. This is important because underestimation of the tropical cloud fraction—particularly over marine stratus regions—remains a problem for climate models. Determining whether the biases can be reduced by tuning parameters may provide a solution. Our results demonstrate the feasibility of improving the simulation of total cloud fraction, and even marine stratus, through parameter tuning. Furthermore, our results highlight the different non‐linear effects on cloud over different areas, which should motivate further studies on these non‐linear effects within other important physical processes or phenomena in climate simulations. Key Points: Two methods are used to quantify the parametric uncertainty and non‐linear effects between parameters on tropical cloud fractionThe total cloud fraction is improved and the marine stratus also increases because of the indirect parametric effect on lower‐tropospheric stabilityThe effects of non‐linear interaction between parameters on the marine stratus are stronger than those on the total cloud fraction [ABSTRACT FROM AUTHOR]

Recovering additional historical weather observations from known archival sources will improve the understanding of how the climate is changing and enable detailed examination of unusual events within the historical record. The UK National Meteorological Archive recently scanned more than 66,000 paper sheets containing 5.28 million hand‐written monthly rainfall observations taken across the UK and Ireland between 1677 and 1960. Only a small fraction of these observations were previously digitally available for climate scientists to analyse. More than 16,000 volunteer citizen scientists completed the transcription of these sheets of observations during early 2020 using the RainfallRescue.org website, built using the Zooniverse platform. A total of 3.34 million observations from more than 6000 locations have so far been quality controlled and made openly available. This has increased the total number of monthly rainfall observations that are available for this time period and region by a factor of six. The newly rescued observations will enable longer and much improved reconstructions of past variations in rainfall across the British and Irish Isles, including for periods of significant flooding and drought. Specifically, this data should allow the official gridded monthly rainfall reconstructions for the UK to be extended back to 1836, and even earlier for some regions. [ABSTRACT FROM AUTHOR]

Micropulse differential absorption lidars (MPD) for water vapor, temperature, and aerosol profiling have been developed, demonstrated, and are addressing the needs of the atmospheric science community for low-cost ground-based networkable instruments capable of long-term monitoring of the lower troposphere. The MPD instruments use a diode-laser-based (DLB) architecture that can easily be adapted for a wide range of applications. In this study, a DLB direct-detection Doppler lidar based on the current MPD architecture is modeled to better understand the efficacy of the instrument for vertical wind velocity measurements, with the long-term goal of incorporating these measurements into the current network of MPD instruments. The direct-detection Doppler lidar is based on a double-edge receiver that utilizes two Fabry–Pérot interferometers and a vertical velocity retrieval that requires the ancillary measurement of the backscatter ratio, which is the ratio of the total backscatter coefficient to the molecular backscatter coefficient. The modeling in this paper accounts for the major sources of error. It indicates that the vertical velocity can be retrieved with an error of less than 0.56 m s−1 below 4 km with a 150-m range resolution and an averaging time of 5 min. Significance Statement: Monitoring the temperature, relative humidity, and winds in the lower atmosphere is important for improving weather forecasting, particularly for severe weather such as thunderstorms. Cost-effective micropulse differential absorption lidar (MPD) instrumentation for continuous temperature and humidity monitoring has been developed and demonstrated, and its effects on weather forecasting are currently being evaluated. The modeling study described in this paper studies the feasibility of using a similar cost-effective MPD instrument architecture for monitoring vertical wind velocity in the lower atmosphere. Modeling indicates that wind velocities can be measured with less than 0.56 m s−1 accuracy and demonstrates the feasibility of adding vertical wind velocity measurements to the MPD instruments. [ABSTRACT FROM AUTHOR]

Keywords: History EN History 827 833 7 12/12/22 20221101 NES 221101 In discussing the early history of the National Weather Service (NWS) and its predecessor agencies, [24] make several critical errors and confound the historical record surrounding Cleveland Abbe, one of the key figures in the history of the nation's weather service. In August 1888, for example, Abbe responded to a letter from journalist and meteorological kite experimenter William A. Eddy, who wrote to Abbe to fact-check an article he had written about the history of the weather service. Cleveland Abbe Papers, General Correspondence, 1850 - 1916, Manuscript Division, Library of Congress, Box 8. 2 Abbe, C., 1892: The modern weather bureau. In his letter, [11] erroneously suggested that Abbe had approached Chief Signal Officer Gen. Albert J. Myer prior to the creation of the service "with a proposition to include weather reports with information forwarded from signal stations" and that the weather service had been organized by Abbe. [Extracted from the article]

The article focuses on a warning made by volcano risk experts Mike Cassidy and Lara Mani in a paper in the August 2022 issue of "Nature" regarding the possibility of a volcanic eruption in the 21st century that would result to a large amount of destruction. Topics discussed include their concern of the world's preparation for such event, prediction and monitoring of volcanic eruptions, and the need for an increase in research into volcano geoengineering.

Paul Crutzen received his doctorate in meteorology from the University of Stockholm in 1968 and was awarded the Nobel Prize in Chemistry in 1995. In addition to chemistry and atmospheric science, however, the breadth of his accomplishments has also been recognized by biologists, Earth system scientists, and geologists. This tribute provides some insight into Crutzen's career and how it contributed to so many scientific disciplines. In addition, we offer a road map showing how these diverse contributions were woven together over the course of more than five decades of research. The citation for the 1995 Nobel Prize reads that it was given for "work in atmospheric chemistry, particularly concerning the formation and decomposition of ozone." The inclusion of the wording "formation ... of ozone" applies only to him among the three laureates (Crutzen, Mario Molina, and F. Sherwood Rowland). His research on tropospheric chemistry led to seminal studies of tropical biomass burning, which eventually evolved into the concept later known as "nuclear winter," a topic in the forefront of far-ranging popular discussions in the 1980s. Last, Crutzen's proposal for the emergence of the "Anthropocene" as a new geological epoch that would terminate the 11,700-yr-old Holocene is considered by the Earth system science community to be the most pronounced trademark of his remarkable career. Crutzen also received American Meteorological Society's Battan Award for his coauthorship of Atmosphere, Climate, and Change, recognized by the organization as the best book for general audiences. In the later years of his career, as a member of the Pontifical Academy of Sciences, Crutzen was a key player in the formulation of Laudato Si', Pope Francis's encyclical on climate change, which was released in advance of the Conference of Parties (COP 21) meeting that announced the formulation of the Paris Climate Accords in 2015. [ABSTRACT FROM AUTHOR]