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The number of studies investigating the effects of ocean acidification on marine organisms and communities increases every year. Results are not easily comparable since the carbonate chemistry and ancillary data are not always reported in similar units and scales and are not calculated using similar sets of constants. To facilitate data comparison, a data compilation hosted by the PANGAEA Data Publisher was initiated in 2008 and is updated on a regular basis (10.1594/PANGAEA.962556; Ocean Acidification International Coordination Centre (OA-ICC), 2023). By November 2023, a total of 1501 datasets (over 25 million data points) from 1554 papers had been archived. To easily filter and access relevant biological response data from this compilation, a user-friendly portal (https://oa-icc.ipsl.fr , last access: 9 November 2023) was launched in 2018. Here, we present an update of this data compilation since its second description by Yang et al. (2016) and provide an overview of the OA-ICC portal for ocean acidification biological response data, launched in 2018. Most of the study sites from which data have been archived are in the North Atlantic Ocean, North Pacific Ocean, South Pacific Ocean, and Mediterranean Sea, while polar oceans are still relatively poorly represented. Mollusca and Cnidaria are still the best-represented taxonomic groups. The biological processes most reported in the datasets are growth and morphology. Other variables that can potentially be affected by ocean acidification and are often reported include calcification/dissolution, primary production/photosynthesis, and biomass/abundance. The majority of the compiled datasets have considered ocean acidification as a single stressor, but their relative contribution has decreased from 68 % before 2015 to 57 % today, showing a clear tendency towards more data archived from multifactorial studies. [ABSTRACT FROM AUTHOR]

ISPRS Student Consortium (ISPRS SC) has played a significant role in bringing students and young researchers closer to the field of remote sensing and photogrammetry in the evolving landscape of geospatial sciences. Innovative strategies and events have been employed by the ISPRS SC to address contemporary challenges and harness emerging opportunities in the field of photogrammetry and remote sensing. Through an analysis of the global contributions of ISPRS SC, ranging from research initiatives to the online webinar series, this paper underscores the consortium's commitment to fostering collaboration, innovation, and knowledge exchange among young scholars and professionals worldwide. The paper highlights key thematic areas addressed by ISPRS SC such as advancements in remote sensing technologies, geospatial data analysis, and applications in diverse domains including environmental monitoring, agriculture, urban planning, and disaster management. Moreover, in this paper, we emphasize the role of the consortium in nurturing future leaders in geospatial sciences through ISPRS SC mentorship programs, networking opportunities, and capacity-building initiatives. By highlighting the initiatives led by the ISPRS Student Consortium, this paper underscores the key role of youth-driven innovation for positive change and shaping the future of geospatial sciences on a global scale. [ABSTRACT FROM AUTHOR]

Earth observation (EO) applications offer significant potential for addressing challenges and driving sustainable development in the Philippines. Despite the positive effect EO services can bring, its utilization encounter barriers that includes the lack of awareness of its benefits. The Philippine Space Agency (PhilSA) initiated the Space Information Infrastructure Capacity Building and Training Program (SIICaP) that aims to promote awareness of EO applications through free and inclusive online webinar platforms. This paper highlights the various webinars conducted by SIICaP showing their relevance in raising awareness of EO data, services, and programs. The analysis of evaluation results from the webinar participants further illustrates the program's reach and success. By showcasing SIICaP, this paper contributes to efforts in developing capacity building programs that aim to have the same objectives of SIICaP. [ABSTRACT FROM AUTHOR]

Analyzing night lights images allows identifying and monitoring affected areas during conflicts, as an overall decrease in brightness indicates abandoned or destroyed buildings, curfews, or power shortages. While detecting decrease in lights intensity is an established technique to identify critical areas during conflicts, the causes of appearing bright lights have not been extensively studied. An increase in brightness may be related to extreme events, such as fires resulting from bombing. This paper shows several examples supporting this new use of these images, validated using high-resolution optical earth observation data and ancillary information sources in the frame of the conflict in Ukraine which has been ongoing since February 2022. [ABSTRACT FROM AUTHOR]

In 3D building models, a large number of texture maps with different sizes increase the number of model data loading and drawing batches, which greatly reduces the drawing efficiency of the model. Therefore, this paper proposes a texture set mapping method based on vertex importance. Firstly, based on the 2D space boxing algorithm, the texture maps are merged and a series of Mipmap texture maps are generated, and then the vertex curvature, texture variability and location information of each vertex are calculated, normalized, and weighted to get the importance of each vertex, and then finally, different Mipmap-level textures are remapped according to the importance of the vertices. The experiment proves that the algorithm in this paper can reduce the amount of texture data on the one hand, and avoid the rendering pressure brought by the still large amount of data after merging on the other hand, so as to improve the rendering efficiency of the model. [ABSTRACT FROM AUTHOR]

The Land Use/Cover Area frame Survey (LUCAS) of the European Union (EU) presents a rich resource for detailed understanding of land cover and use, making it invaluable for Earth observation (EO) applications. This paper discusses the recent enhancements and improvements in the LUCAS Copernicus module, particularly the data collection process of 2022, its protocol simplifications, and geometry definitions compared to the 2018 survey and data. With approximately 150 000 polygons collected in 2022, an increase from 60 000 in 2018, the LUCAS Copernicus 2022 data provide a unique and comprehensive in situ dataset for EO applications. The protocol simplification also facilitates a faster and more efficient data collection process. In 2022, there were 137 966 polygons generated out of the original 149 408 LUCAS Copernicus points, which means that 92.3 % of the points were actually surveyed. The data have 82 land cover classes for the Copernicus module that map to 88 classes up to the LUCAS level-3 legend. For land use the data have 40 classes, along with 18 classes of land use types. The dataset is available for download (product IDentification – PID: http://data.europa.eu/89h/e3fe3cd0-44db-470e-8769-172a8b9e8874 ;). The paper elaborates further on the implications of these enhancements and the need for continuous harmonization to ensure semantic consistency and temporal usability of data across different periods. Moreover, it calls for additional studies exploring the potential of the collected data, especially in the context of remote sensing and computer vision. It ends with a discussion of future data usage and dissemination strategies. [ABSTRACT FROM AUTHOR]

This paper delves into the multifaceted experiences and insights garnered from organizing a short-term training programme aimed at professionals, students, academics, and climate enthusiasts. The programme, named CLImbing for CLImate GEOspatial School (CLIGEOS-2024), was a collaborative effort by the International Society of Photogrammetry and Remote Sensing (ISPRS) and ISPRS Student Consortium (ISPRS-SC) alongside Center for space science and geomatics studies (CSSGS) Pashchimanchal campus, Institute of Engineering, Tribhuvan University, Nepal, Universita' Degli Studi Di Udine, Italy and LJ University, India aimed at addressing environmental challenges, particularly in hill and mountain regions. Despite facing logistical and communication hurdles due to the geographical dispersion of organizing institutions, the event successfully brought together participants with backgrounds in GIS, remote sensing, agriculture, and forestry. The programme was structured to promote participatory learning and project-based learning methodologies, incorporating classroom teaching, hands-on activities, fieldwork, data collection, short projects, quizzes, and evaluation mechanisms. Through a meticulously planned sequence, participants engaged in theoretical lectures, practical sessions on drone technology, industry expert presentations, and a mountain trek to observe climate change phenomena first-hand. Despite challenges related to infrastructure, including limited internet connectivity and access to data, the programme served as a catalyst for raising awareness about climate change and promoting sustainable practices. Key takeaways highlighted the importance of spatial data analysis, interdisciplinary collaboration, and hands-on learning in advancing solutions for sustainable development. The paper concludes with lessons learned from the programme, providing insights for future initiatives aimed at capacity building and knowledge dissemination. [ABSTRACT FROM AUTHOR]

The paper investigates multidisciplinary education and specialization at the master's level, analyzes assessment methods, and proposes a technique for evaluating geospatial technology education. It emphasises is on pedagogical approaches, and highlights the importance of capacity building to meet the growing demand for skilled professionals in spatial information management. The research provides guidelines for improving course content, structure, and teaching methodologies, aiming to enhance the quality and relevance of geospatial education for both students and stakeholders. This paper is based on online data gathered from 115 universities across the globe, who are imparting geospatial education, campus-based and e-learning, also a survey was conducted of two thousand students. It supported in analysis and suggestion to develop a specific robust technique for assessment of the geospatial technology education and provides the corrective measures to the users of the technique. It proposed a framework aimed at enhancing the course content, organization, and teaching methods substantially. Further an attempt was made to adopt AHP model for investigating the statistical approach. It was aimed to know the actual values and significance of each parameter used for analysis of the study. In order to prepare a common guideline for improving the course structure, course content and teaching methodology, ANFIS architecture (Artificial Neural Fuzzy-Inference System) was adopted. Spatial information represented in the form of thematic maps in relation with the respective attribute data in Geographical Information System (GIS) environment is now used by all spectrum of the society, be it government organizations, industry, and academia. The result of the study has designed the criteria and common guidelines, which may be implemented, would able to help and curtail the challenges of the geospatial education as global vision for Local Action. [ABSTRACT FROM AUTHOR]

This paper presents a collaborative effort to develop localized solar photovoltaic (PV) power output (PPV) forecasting models for the Philippines using geospatial data. It underlines the importance of solar energy in the country and discusses the opportunities and challenges associated with PPV forecasting. Project SINAG, a two-year research project, aimed to develop solar PV output forecasting models through a collaborative approach with academic institutions, solar energy industries, and government agencies. Actual PPV data from 43 solar PV installations were analyzed alongside meteorological data from the PAGASA weather bureau, ERA5, AHI-8, and FY- 4A. These datasets were filtered based on a one-year period to ensure quality. The study employed SARIMAX, LSTM, and XGBoost models individually and in hybrid models to develop the forecasting models. Model performance was evaluated using root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). In a case study in Baguio City, the SARIMAX model exhibited strong seasonal dependence, providing more accurate forecasts in dry seasons than in wet seasons. Additionally, the forecasting accuracy of each model (SARIMAX, LSTM, and XGBoost) varied based on the month and location of the installation, emphasizing the need for local and season-based PPV forecasting models. Despite implementation challenges, such as collaboration arrangements, bureaucratic barriers, and budget constraints, the project produced thirteen research publications and provided data for three student theses. This paper also demonstrated diverse engagements and contributions that emphasize the significance of collaborative research in conducting nationwide-scale data-driven projects. [ABSTRACT FROM AUTHOR]

Terrain services have increasingly wide applications in urban planning, 3D gaming animation, and other fields. Map websites typically provide terrain services to users in the form of terrain shaded relief tiles. Map tile technology offers a new solution for large-scale concurrent user access. Although terrain shaded relief tiles can effectively represent changes in terrain relief, users cannot adjust the visualized raster images for visualization schemes, making data analysis and applications more challenging. The emergence of Terrain-RGB technology has effectively addressed this limitation. However, currently, internet maps mostly provide multi-level map services to users in the form of web map tile service (WMTS). Terrain-RGB images produced by simple encoding tools may exhibit grid lines when rendered on tiles with a small grid spacing, affecting the usability of Terrain-RGB products. Therefore, this paper proposes a technical approach for producing global multi-level Terrain-RGB products using digital elevation model (DEM), focusing on addressing the issues of gridlines and splicing traces from different scenes DEM data, offering a new solution for map websites to release analyzable terrain services. [ABSTRACT FROM AUTHOR]

The rapid development of urbanization presents challenges and requirements for multi-class object detection in urban scenes. Accurately identifying buildings, vehicles, and trees in urban scenes can optimize urban planning, traffic management, monitoring environmental conditions, and promote the development of smart cities. Traditional target detection methods perform poorly in complex urban environments, while deep learning technology achieves accurate target recognition and positioning by automatically extracting high-level semantic features. In this study, we chose to use the YOLOv5s algorithm for multi-class target detection in urban scenes. YOLOv5s is a lightweight deep learning model with small storage space and efficient detection speed. In this paper, the Potsdam area data published by ISPRS is used to make the label data of buildings, vehicles and trees. The YOLOv5s algorithm is used to iteratively train the model. The results show that the mAP value detected by the YOLOv5s model can reach 82.83%. The experimental results show that the algorithm shows higher accuracy than SSD and Faster R-CNN in tree detection. Although it has a slight decline in building and vehicle detection, considering the factors such as detection accuracy, speed, and model size, the YOLOv5s algorithm has a better recognition and detection effect for the detection of multi-class targets in urban scenes. [ABSTRACT FROM AUTHOR]

Offshore wind farm (OWF)'s impact on marine ecological environment has attracted widespread attention since its rapid development. The paper uses high-resolution satellite image data to extract the distribution of offshore wind farms (OWFs) in China based on deep learning method. Chlorophyll-a (CHL-a) concentration and sea surface temperature (SST) were used as indicators to analyze the impact of OWFs' development. We can then draw the following conclusions: 1) From 2016 to 2022, the number of OWFs in China continues to increase, the growth rate reachs 28% in 2022. 2) The construction of OWFs presents a development trend from near sea to far sea and from shallow sea to deep sea. From 2016 to 2022, the new OWFs are mainly concentrated in sea areas with 5-50 meters water depth, accounting for 84.7 % of the total increase in OWFs. 3) The distribution density of OWFs is negatively correlated with CHL-a concentration and SST, the more OWFs, the greater the impact on CHL-a and SST. In one word, our work analyzed the development trend of OWFs in China and evaluated the impact of OWFs' expansion on marine ecological environment and climate. [ABSTRACT FROM AUTHOR]

Precise georeferencing of remote sensing data is usually implemented in a post-processing fashion and is a crucial step for Earth observation applications such as change detection, natural hazard management, and ground target tracking. It is particularly important for small satellites intending to perform temperature monitoring and wildfire detection on a global scale wherein the precise location of fire is to be communicated. The cost-efficient navigating systems on board such satellites are often not capable of providing accurate geolocation information directly due to space and power limitations. Therefore, it is very important to have a globally applicable georeferencing refinement framework that is robust against illuminational and time-relevant scene changes. In this paper, we propose a georeferencing framework for thermal infrared images that consists of ensemble matching of deep learning-based land cover predictions to archival, well-georeferenced land cover maps. We verify the proposed framework on the georeferencing of single-band Landsat thermal imagery. Experimental results show the efficiency and practicality of the method with 72% of the test images geolocated within 1-pixel accuracy with no trajectory information available. [ABSTRACT FROM AUTHOR]

This paper reduces uncertainty in the quantification of offshore wind speed and turbulence intensity. A range of industry standard relationships, including those from International Electrotechnical Commission (IEC) and International Organization for Standardization (ISO), are compared with an extensive set of met mast data collected offshore northwestern Europe over recent decades. Analysis initially focused on over 1000 independent storm peak events identified within the 10 min mean wind speed data. Time series and coherent vertical profiles were subjected to detailed scrutiny and analysis, considering wind speeds at various averaging periods, turbulence intensity and gust factors. Most peak events were associated with neutral atmospheric conditions, so they were well represented by the ISO Frøya profile, with a shear close to the IEC power law exponent of α= 0.11. A new pragmatic approximation of atmospheric stability in terms of relative shear is outlined, bringing together key elements of the IEC and ISO standards. New empirical relationships to quantify offshore wind ambient turbulence intensity are described. A simple generalised form of gust factor relationship is adopted, with a coefficient that varies with averaging period. Distinct recommendations are made for estimating peak 10 min mean winds from peaks in 1 or 3 h mean winds. Finally, a simplified workflow for the estimation of extreme offshore winds is used to show how IEC and ISO relationships can be effectively combined. [ABSTRACT FROM AUTHOR]

Array Interferometric Synthetic Aperture Radar (ArrayInSAR) point cloud is a point cloud obtained by three-dimensional imaging of two-dimensional Synthetic Aperture Radar (SAR) images using array interferometric synthetic aperture radar technology, which can eliminate the phenomenon of layover in two-dimensional images and provide new data support for intelligent mapping. However, due to the influence of system thermal noise, baseline error and environment interference, there are noise points in the ArrayInSAR point cloud with uneven distribution and large elevation error, which bring a lot of gross errors to the 3D terrain results. To solve the problem that discrete points in ArrayInSAR point cloud data seriously affect the quality of three-dimensional data, this paper proposes an ArrayInSAR point cloud outlier noise removal method based on hybrid filtering. According to the distribution pattern of noise points, the outlier noise is divided into discrete outlier noise points and clusters of outlier noise. Firstly, the adaptive segmentation of point cloud dispersion coefficient within the k-neighborhood is used to remove discrete outlier noise points, and then the adaptive threshold segmentation algorithm based on the number of in-class points after K-means clustering is used to remove the cluster outlier noise of ArrayInSAR point cloud data. In order to verify the effectiveness, the proposed method is compared with other classical outlier noise removal methods. The experimental results show that the method proposed in this paper can effectively remove outlier noise in ArrayInSAR point cloud data and improve the quality of ArrayInSAR point clouds. [ABSTRACT FROM AUTHOR]

Extreme cold winter temperatures in Europe have huge societal impacts. Being able to simulate worst-case scenarios for such events for present and future climates is hence crucial for short- and long-term adaptation. In this paper, we are interested in persistent cold events, whose probability will decrease with climate change. Large ensembles of simulations allow us to better analyse the mechanisms and characteristics of such events but can require significant computational resources. Rather than simulating very large ensembles of normal climate trajectories, rare-event algorithms allow the sampling of the tail of distributions more efficiently. Such algorithms have been applied to simulate extreme heat waves. They have emphasized the role of atmospheric circulation in such extremes. The goal of this study is to evaluate the dynamics of extreme cold spells simulated by a rare-event algorithm. We focus on cold winter temperatures that have occurred in France from 1950 to 2021. We investigate winter mean temperatures (December, January and February) and identify a record-shattering event in 1963. We find that although the frequency of extreme cold spells decreases with time, their intensity is stationary. We apply a stochastic weather generator (SWG) approach with importance sampling to simulate the coldest winters that could occur in a factual and counterfactual climate. We thus simulate ensembles of the worst winter cold spells that are consistent with reanalysis data. We find that a few simulations reach colder temperatures than the historical record-shattering event of 1963. This shows that present-day conditions can trigger winters as cold as that record in spite of global warming. The atmospheric circulation that prevails during those events is analysed and compared to the observed circulation during the record-breaking events, showing no main change in the mechanisms leading to this type of extreme event. [ABSTRACT FROM AUTHOR]

Ocean alkalinity enhancement (OAE) and direct ocean removal (DOR) are emerging as promising technologies for enacting negative emissions. The long equilibration timescales, potential for premature subduction of surface water parcels, and extensive horizontal transport and dilution of added alkalinity make direct experimental measurement of induced CO2 uptake challenging. Therefore, the challenge of measurement, reporting, and verification (MRV) will rely to a great extent on general circulation models, parameterized and constrained by experimental measurements. A number of recent studies have assessed the efficiency of OAE using different model setups and different metrics. Some models use prescribed atmospheric CO2 levels, while others use fully coupled Earth system models. The former ignores atmospheric feedback effects, while the latter explicitly models them. In this paper it is shown that, even for very small OAE deployments, which do not substantially change atmospheric pCO2 , the change in oceanic CO2 inventories differs significantly between these methods due to atmospheric feedback causing some ocean CO2 off-gassing. An analogous off-gassing occurs during direct air capture (DAC). Due to these feedback effects, care must be taken to compute the correct metrics when assessing OAE efficiency with respect to determining negative emissions credits, as opposed to determining the effect on global temperatures. This paper examines the commonly used metrics of OAE efficiency, their exact physical meanings, the assumptions inherent in their use, and the relationship between them. It is shown that the efficiency metric η(t) , used in prescribed p CO 2atm simulations, equals the equivalent schedule of a gradual DAC removal and storage in a fully coupled system. [ABSTRACT FROM AUTHOR]

This paper describes the methods that are used in the SAPRC mechanism generation system, MechGen, to estimate rate constants and derive mechanisms for gas-phase reactions of volatile organic compounds (VOCs) in the lower atmosphere. Versions of this system have been used for over 20 years in the development of the SAPRC mechanisms for air quality models, but this is the first complete documentation of the scientific basis for the chemical mechanisms it derives. MechGen can be used to derive explicit gas-phase mechanisms for most compounds with C, H, O, or N atoms. Included are reactions of organic compounds with hydroxy (OH) and nitrate (NO3) radicals, O3, and O3P; photolysis or unimolecular reactions; and the reactions of the radicals they form in the presence of O2 and oxides of nitrogen (NOx) at lower-atmospheric temperatures and pressures. Measured or theoretically calculated rate constants and branching ratios are used when data are available, but in most cases rate constants and branching ratios are estimated using various structure–reactivity or other estimation methods. Types of reactions include initial reactions of organics with atmospheric oxidants or by photolysis; unimolecular and bimolecular reactions of carbon-centered, alkoxy, and peroxy radicals; and those of Criegee and other intermediates that are formed. This paper documents the methods, assignments, and estimates currently used to derive these reactions and provides examples of MechGen predictions. Many of the estimation methods discussed here have not been published previously, and others have not been used previously in developing comprehensive mechanisms. Our knowledge of atmospheric reactions of organic compounds rapidly and continuously evolves, and therefore mechanism generation systems such as MechGen also need to evolve to continue to represent the current state of the science. This paper points out areas where MechGen may need to be modified when the system is next updated. This paper concludes with a summary of the major areas of uncertainty where further experimental, theoretical, or mechanism development research is most needed to improve predictions of atmospheric reaction mechanisms of volatile organic compounds. [ABSTRACT FROM AUTHOR]

During the Water Vapor Lidar Network Assimilation (WaLiNeAs) campaign, eight lidars specifically designed to measure water vapor mixing ratio (WVMR) profiles were deployed on the western Mediterranean coast. The main objectives were to investigate the water vapor content during case studies of heavy-precipitation events in the coastal western Mediterranean and assess the impact of high spatiotemporal WVMR data on numerical weather prediction forecasts by means of state-of-the-art assimilation techniques. Given the increasing occurrence of extreme events due to climate change, WaLiNeAs is the first program in Europe to provide network-like, simultaneous and continuous water vapor profile measurements over a period of 3–4 months. This paper focuses on the WVMR profiling datasets obtained from three of the lidars run by the French part of the WaLiNeAs team. These three lidars were deployed in the cities of Coursan, Le Grau-du-Roi and Cannes. This measurement setup enabled monitoring of the water vapor content of the lower troposphere over periods of 3 months in fall and winter 2022, with some interruptions, and 4 months in summer 2023. The lidars measured the WVMR profiles from the surface up to approximately 6–10 km at nighttime and 1–2 km during daytime. They had a vertical resolution of 100 m and a time resolution between 15 and 30 min, and they were selected to meet the needs of weather forecasting with an uncertainty lower than 0.4 g kg−1. The paper presents details about the instruments, the experimental strategy and the datasets provided. The final dataset (10.25326/537; Chazette et al., 2023) is divided into two sub-datasets: the first with a time resolution of 15 min, which contains a total of 26 423 WVMR vertical profiles, and the second with a time resolution of 30 min to improve the signal-to-noise ratio and signal altitude range. [ABSTRACT FROM AUTHOR]

Floods pose significant risks to cultural heritage (CH), yet post-disaster damage data on CH remain lacking. In this paper, we address this gap by focusing on the ex post assessment of flood-induced damage to CH. The method involves the identification of damaged assets and a field survey to assess loss in tangible value (LTV) and loss in intangible value (LIV). The potential contributing factors, e.g. water depth and river slope, are analysed through geospatial analysis. Ex post damage data on CH are compared with the outcome of an ex ante analysis based on available methods to verify the quality of exposure data and possible limitations. The method is applied to the 15–16 September 2022 flood event that occurred in the Marche region (Italy). The survey involved 14 CH in 4 municipalities and 3 catchments. Results highlight the inadequacy of existing exposure data for ex ante damage assessment and the importance of building characteristics. However, ex post data confirm that religious architectures are likely to suffer the highest LTV and LIV. The ex post damage analysis provided a semi-quantitative evaluation of both LTV and LIV in relation to flood characteristics. Notably, significant correlations between LTV and flood depth, as well as with the slope of the riverbed (a proxy for river flow velocity), were found. LIV correlates well to flood depth and river slope albeit with lower R2 and larger RMSE, highlighting that intangible impact analysis requires more effort than hazard characterization. Further research should increase the availability of ex post damage data on CH to form the basis for damage model validation and development of empirical vulnerability functions. [ABSTRACT FROM AUTHOR]

This paper presents the results of the 2022 Groundwater Time Series Modelling Challenge, where 15 teams from different institutes applied various data-driven models to simulate hydraulic-head time series at four monitoring wells. Three of the wells were located in Europe and one was located in the USA in different hydrogeological settings in temperate, continental, or subarctic climates. Participants were provided with approximately 15 years of measured heads at (almost) regular time intervals and daily measurements of weather data starting some 10 years prior to the first head measurements and extending around 5 years after the last head measurement. The participants were asked to simulate the measured heads (the calibration period), to provide a prediction for around 5 years after the last measurement (the validation period for which weather data were provided but not head measurements), and to include an uncertainty estimate. Three different groups of models were identified among the submissions: lumped-parameter models (three teams), machine learning models (four teams), and deep learning models (eight teams). Lumped-parameter models apply relatively simple response functions with few parameters, while the artificial intelligence models used models of varying complexity, generally with more parameters and more input, including input engineered from the provided data (e.g. multi-day averages). The models were evaluated on their performance in simulating the heads in the calibration period and in predicting the heads in the validation period. Different metrics were used to assess performance, including metrics for average relative fit, average absolute fit, fit of extreme (high or low) heads, and the coverage of the uncertainty interval. For all wells, reasonable performance was obtained by at least one team from each of the three groups. However, the performance was not consistent across submissions within each group, which implies that the application of each method to individual sites requires significant effort and experience. In particular, estimates of the uncertainty interval varied widely between teams, although some teams submitted confidence intervals rather than prediction intervals. There was not one team, let alone one method, that performed best for all wells and all performance metrics. Four of the main takeaways from the model comparison are as follows: (1) lumped-parameter models generally performed as well as artificial intelligence models, which means they capture the fundamental behaviour of the system with only a few parameters. (2) Artificial intelligence models were able to simulate extremes beyond the observed conditions, which is contrary to some persistent beliefs about these methods. (3) No overfitting was observed in any of the models, including in the models with many parameters, as performance in the validation period was generally only a bit lower than in the calibration period, which is evidence of appropriate application of the different models. (4) The presented simulations are the combined results of the applied method and the choices made by the modeller(s), which was especially visible in the performance range of the deep learning methods; underperformance does not necessarily reflect deficiencies of any of the models. In conclusion, the challenge was a successful initiative to compare different models and learn from each other. Future challenges are needed to investigate, for example, the performance of models in more variable climatic settings to simulate head series with significant gaps or to estimate the effect of drought periods. [ABSTRACT FROM AUTHOR]

Water – Global Assessment and Prognosis (WaterGAP) is a modeling approach for quantifying water resources and water use for all land areas of the Earth that has served science and society since 1996. In this paper, the refinements, new algorithms, and new data of the most recent model version v2.2e are described, together with a thorough evaluation of the simulated water use, streamflow, and terrestrial water storage anomaly against observation data. WaterGAP v2.2e improves the handling of inland sinks and now excludes not only large but also small human-made reservoirs when simulating naturalized conditions. The reservoir and non-irrigation water use data were updated. In addition, the model was calibrated against an updated and extended data set of streamflow observations at 1509 gauging stations. The modifications resulted in a small decrease in the estimated global renewable water resources. The model can now be started using prescribed water storages and other conditions, facilitating data assimilation and near-real-time monitoring and forecast simulations. For specific applications, the model can consider the output of a glacier model, approximate the effect of rising CO2 concentrations on evapotranspiration, or calculate the water temperature in rivers. In the paper, the publicly available standard model output is described, and caveats of the model version are provided alongside the description of the model setup in the ISIMIP3 framework. [ABSTRACT FROM AUTHOR]

This paper describes the discipline of geodesy and geoinformatics with a focus on university studies and career opportunities. It is shown how the disciplines contributes to the solutions of major societal challenges such as sustainable development, mobility and global change. Then, the structure of German universities is shortly explained, followed by an overview of where in the country the discipline can be studied. Finally, the example of Leibniz University Hannover, one of the prime centres of excellence in the field, is described in more detail. We thus hope to encourage young talents to consider this exciting field of studies for their future career. [ABSTRACT FROM AUTHOR]

Annually, a considerable quantity of forest is burned on a global scale. Therefore, it is essential to obtain precise and fast information regarding the size of burned regions in order to effectively monitor the adverse consequences of wildfires. The objective of this investigation is to indicate the effectiveness and usefulness of a deep learning (DL) architecture, such as Convolutional Neural Networks (CNNs), in the mapping of areas affected by fire, employing an eXplainable artificial intelligence (XAI) algorithm known as SHapley Additive exPlanations (SHAP) with accuracy evaluation criteria. Furthermore, this paper presents the evaluation of the Çanakkale-Kizilkeçili village wildfire. The research investigated the impacts of a variety of spectral indices, including the normalized burn index (NBR), differentiated normalized burn index (dNBR), Green-Red Vegetation Index (GRVI), simple ratio vegetation index (RVI), and normalized difference vegetation index (NDVI). At the end of the training process, the model achieved a training accuracy of approximately 0.99, with model loss values converging to approximately 0.1. The findings of the burned area identification analysis indicate that by incorporating spectral indices as supplementary information, the CNN model achieved a high level of accuracy, with an overall accuracy of 98.88% and a Kappa Coefficient of 0.98. Additionally, the SHAP technique was employed to gain insights into the output of the models. The feature importances of the spectral bands were determined through the SHAP analysis of the CNN model. Hence, the significance of the auxiliary data generated by the NBR, dNBR, and NDVI indices was identified as being the highest among the original bands and auxiliary data employed in this investigation. [ABSTRACT FROM AUTHOR]

The Philippine Space Agency (PhilSA) implemented the PhilSA Integrated Network for Space Enabled Actions towards Sustainability (PINAS) to establish an active network of institutions and citizen scientists that will work together to utilize space data and information in support of the sustainable development agenda. The project was conceptualized to address the inherent challenge in gathering feedback, disseminating space data and information, and conducting capacity building initiatives in an archipelagic nation. The project will be conducted in three (3) phases, Phase 1: Network Building, Phase 2: Capacity Building, and Phase 3: Build Up and Expansion. This paper highlights the initial activities and preliminary assessment of the PINAS Project. The results of the initial activities illustrate the potential of the project in optimizing data products and its distribution processes, and in strengthening local capacity to use space data and information. This contributes to the development of effective capacity building initiatives and efficient space data optimization and data-sharing workflows in the country. [ABSTRACT FROM AUTHOR]

Geomatics, also known as geospatial technology and geomatics engineering, analyzes spatially referenced geographical information. Geomatics is a scientific term. It consists of tools and techniques. Technology is advancing, and modern Geomatics has the utility of maps by analyzing it with many coverages, each with interrelated themes combined to give meaningful answers for decision-makers. A multidisciplinary approach is essential to analyze spatial and nonspatial parameters together for decision-making at the local and global levels. Spatial information represented in the form of thematic maps about the respective attribute data, the system called, Geographical Information System (GIS), has been more popular during the last decades in government and private sectors. The decision support system can take input of data from these sources and analyze multiple parameters together to plan and manage available resources. This training program was intended for women scientists, researchers, academia, and professionals who were engaged and using spatial information, and who wanted to add and upgrade their skills. It was anticipated that a proportion of the trainee participants would come from research and academia. This training program helped participants generate interest in the use of GIS technology in their respective research and professional domains. This paper elaborately describes the process, and structure of the short-term capacity-building training program, and its usefulness for young scientists and professionals, specifically a target group is 'women in science' who have not been able to remain in the continue its use and are interested in building a career in geospatial technology field. [ABSTRACT FROM AUTHOR]

Air quality management is crucial in the Philippines due to its profound implications for public health, ecosystems, and sustainable development. Project AiRMoVE, short for the Ambient Air Remote Sensing, Modeling, and Visualization Environment project, aims to enhance air quality management through engineering solutions and geospatial technologies such as remote sensing (RS), geographic information systems (GIS) and numerical modeling. Implemented by the University of the Philippines Training Center for Applied Geodesy and Photogrammetry (UP TCAGP), Project AiRMoVE collaborates with key stakeholders to identify attainment and non-attainment areas in the National Capital Region (NCR) for air quality monitoring. This paper provides an overview of Project AiRMoVE's objectives, methodologies, and outcomes, emphasizing the significance of RS and GIS in revolutionizing air quality monitoring and management. It underscores a systematic approach adopted in enhancing government capacity for air quality management -- a combination of workshops, technical trainings, and stakeholders' forums. [ABSTRACT FROM AUTHOR]

Immersive technologies are becoming a powerful tool for educators across multiple disciplines including geospatial science. They offer new ways to engage and educate geospatial students, removing barriers that may exist in traditional teaching methods. Especially because the limitations of 2D screens are often exceeded by the complexity of modern data sets. Examples of challenges that educators often encounter are explaining theoretical concepts in the classroom, providing alternate scenarios, preparing students for physical labs, and limited / restricted access to physical sites. Immersive technologies can be a great resource to support on-site lectures and enhance remote learning, a necessity in today's educational panorama. Immersive technologies typically include virtual, augmented, and mixed reality. Each method offers different pedagogical advantages and poses different challenges. Before any implementation, these advantages and challenges must be examined and understood to maximize the benefit of immersive methods to the students and mitigate potential drawbacks that could hinder learning outcomes. This paper provides a review of the different immersive methods, presenting examples of their application in geospatial education with lessons learned and recommendations for future work. The case examples include a variety of different instruments and tasks such as the simulation of GNSS, differential leveling, total station operations, and airborne LiDAR data collection. [ABSTRACT FROM AUTHOR]

Like in most bachelor engineering programs, the education of geomatics students typically begins with an extensive theoretical introduction covering fundamental subjects. The general objective of the bachelor program is to cultivate geomatics engineers with robust hands-on knowledge. Nevertheless, the progression to a stage where students can independently engage in exercises with minimal supervision is a long process. Although field work-based practices are dominant in this field, they often entail activities limited to testing, experimenting, and the application of basic theories. While this approach is efficient in imparting routine knowledge, it may lead to the fragmentation of acquired knowledge, thereby lacking a comprehensive understanding of the broader significance and context. Students often encounter more complex challenges when commencing work on their bachelor thesis, leading to a steep learning curve within a constrained timeframe. This late exposure to real-world tasks can be a limiting factor, hindering a smoother transition from education to professional work. This paper provides a case study of project-based learning (PjBL) within a geomatics bachelor program. Specifically, it discusses a digital heritage preservation project where students documented an old Norwegian log house throughout the autumn semester of 2023. The outcomes of this learning approach are analyzed with the help of the key features of the PjBL methodology, focusing on students' learning outcomes and their assessment measures. [ABSTRACT FROM AUTHOR]

The rivers of High-mountain Asia provide freshwater to around 1.9 billion people. However, precipitation, the main driver of river flow, is still poorly understood due to limited in situ measurements in this area. Existing tools to interpolate these measurements or downscale and bias-correct precipitation models have several limitations. To overcome these challenges, this paper uses a probabilistic machine learning approach called multi-fidelity Gaussian processes (MFGPs) to downscale the fifth ECMWF climate reanalysis (ERA5). The method is first validated by downscaling ERA5 precipitation data over data-rich Europe and then data-sparse upper Beas and Sutlej river basins in the Himalayas. We find that MFGPs are simpler to implement and more applicable to smaller datasets than other state-of-the-art machine learning methods. MFGPs are also able to quantify and narrow the uncertainty associated with the precipitation estimates, which is especially needed over ungauged areas and can be used to estimate the likelihood of extreme events that lead to floods or droughts. Over the upper Beas and Sutlej river basins, the precipitation estimates from the MFGP model are similar to or more accurate than available gridded precipitation products (APHRODITE, TRMM, CRU TS, and bias-corrected WRF). The MFGP model and APHRODITE annual mean precipitation estimates generally agree with each other for this region, with the MFGP model predicting slightly higher average precipitation and variance. However, more significant spatial deviations between the MFGP model and APHRODITE over this region appear during the summer monsoon. The MFGP model also presents a more effective resolution, generating more structure at finer spatial scales than ERA5 and APHRODITE. MFGP precipitation estimates for the upper Beas and Sutlej basins between 1980 and 2012 at a 0.0625° resolution (approx. 7 km) are jointly published with this paper. [ABSTRACT FROM AUTHOR]

This paper introduces the Ocean Derived Column Optical Depth (ODCOD) algorithm. ODCOD is now being used to retrieve full-column optical depths from the 532 nm measurements acquired by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) spacecraft. ODCOD uses the lidar integrated attenuated backscatter from the ocean surface, together with collocated wind speed estimates from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), to estimate the full-column optical depths of particulates (i.e., clouds and aerosols) in the Earth's atmosphere. Unlike CALIOP's standard retrievals, which estimate optical depths only when particulate layers are detected, ODCOD retrievals deliver a comprehensive estimate that accounts for attenuation by all particulates present within the lidar profiles. This paper describes the ODCOD algorithm, develops random uncertainty estimates, and characterizes the systematic differences between ODCOD optical depths and those reported by previously validated data sets. This paper presents performance assessments of ODCOD cloud-free profiles to compare the ODCOD aerosol optical depth (AOD) retrievals to collocated measurements made by the airborne High Spectral Resolution Lidar (HSRL) instruments flown by NASA Langley Research Center (LaRC), to daytime estimates derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), and to daytime and nighttime retrievals using the Synergized Optical Depth of Aerosols (SODA) algorithm. ODCOD AODs are biased high relative to LaRC HSRL AODs by 0.009 ± 0.043 (median ± median absolute deviation), with a correlation coefficient of 0.724, and biased low relative to MODIS by 0.009 ± 0.041, with a correlation coefficient of 0.834. Relative to SODA, which derives AOD from a combination of CALIOP and CloudSat ocean surface measurements, ODCOD is biased high in the daytime by 0.004 ± 0.035 and higher at night by 0.027 ± 0.034, with correlation coefficients of 0.887 and 0.891, respectively. Because ODCOD estimates are independent from the standard CALIOP optical depth retrievals, they offer potential for future advances in the CALIPSO data record, both in validating CALIOP's standard estimates and as a potential total column constraint to improve extinction coefficient retrievals. [ABSTRACT FROM AUTHOR]

Rapid urban growth makes green space management crucial to improve citizens' well-being. Urban olive trees characterize the Italian landscapes and their culture. This study explores different methodologies for urban tree assessment in this context, using an iPhone 14 Pro Max. These included: 1) its integrated Light Detection and Ranging (LiDAR) sensor using the Recon3D app, 2) its camera with Structure from Motion (SfM) techniques, and 3) its camera for generating 3D models using Neural Radiance Fields (NeRF). Additionally, a professional Mobile Laser Scanner (MLS), was used for comparison. Total height (H), canopy base height (CBH) and canopy volume (CV) measurements were extracted using both CloudCompare and allometric formulas. The main aim of this paper is to compare the 3D models of olive trees obtained from low-cost sensors with those generated from the MLS, which is a more accurate device but comes with significantly higher costs. The results, in terms of RMSE (iPhone LiDAR - H: 0.46 m, CBH: 0.12 m, CV: 15.66 m3; iPhone-SfM - H: 0.95 m, CBH: 0.19 m, CV: 25.85 m3; iPhone-NeRF - H: 1.26 m, CBH: 0.31 m, CV: 33.79 m3), bias and volume differences, reveal that the smartphone, in all the methodologies, tends to underestimate measurements as the size of the trees increases. This is due to the higher MLS range of acquisition. Despite these limitations, low-cost solutions like smartphone-based methods can be a viable alternative given their economic accessibility. [ABSTRACT FROM AUTHOR]

Relief mapping through dense tropical forest is a challenge, which can be met by processing radar images. Various global digital elevation models (DEMs) are available, either free or paid, the most widely used methods for producing them at global scale being photogrammetry and short wavelength radar interferometry. However, the resulting DEMs do not directly represent the ground surface but rather the canopy or something in between. Since most users need terrain models for a variety of applications in geoscience, water management etc. but use such canopy models for lack of anything better, it was relevant to assess the consequences of this inappropriate but unavoidable choice. This paper presents a study carried out in the Brazilian Amazon, where an airborne P-band radar interferometric DEM was used as a reference based on its accuracy assessed in previous studies. A well-established DEM (SRTM) and newer ones (Copernicus and Fabdem), as well as a DEM obtained from aerial X-bands radar data, were compared to the reference according to different criteria related to elevation (accuracy and a typical application, i.e., dam filling simulation) and to slope at different scales (accuracy and a typical application, i.e., terrain classification). The results highlighted the risks of using short wavelengths to represent the terrain and emphasized the importance of slope in different resolution scales. [ABSTRACT FROM AUTHOR]

Due to the influence of nonlinear radiation distortion and geometric deformation, achieving multimodal image matching remains a challenging task. To address these issues, this paper proposes a method called radiation invariant phase correlation (RIPC) to simultaneously estimate the rotation, scale, and displacement changes of multimodal image pairs. Firstly, based on the local structure characteristics of the image itself, we harness the nonlinear invariance of kernel canonical correlation analysis to devise the multimodal local self-correlation (MLSC) descriptor. This descriptor is resilient to nonlinear radiative differences, as well as local rotation and scale variations. Subsequently, we incorporate the log-polar coordinate transformation to capture the overall rotation and scale changes in the image, enabling independent representation of these factors on the Cartesian coordinate system. Finally, drawing upon the continuity of displacement estimation, as well as rotation and scale estimation, we construct a five-dimensional descriptor tailored for phase correlation. Extensive experiments conducted on five open-source datasets demonstrate that our proposed method surpasses state-of-the-art (SOTA) techniques in matching performance. Furthermore, our RIPC method achieves matching accuracy within 2-pixel threshold, which underscores its effectiveness in multimodal remote sensing image matching. [ABSTRACT FROM AUTHOR]

Brazil is the largest producer of oranges in the world and the automatic detection of fruits has been a challenging task in the context of remote sensing, due to variations in fruit appearance, changes in lighting and occlusions of foliage and neighboring fruits. In this sense, this paper focus on the detection of oranges in multispectral images, with different spectral bands and exposures, using a convolutional neural network (CNN) known as YOU ONLY LOOK ONCE (YOLO). The results indicate that, after 300 epochs, the model demonstrated an accuracy of 81.5% and an approximate recovery rate of 85%. Shutter speeds 1/640s and 1/250s are not suitable for detection due to low light and overexposure, respectively. Intermediate values may be more suitable for identifying a larger number of fruits. [ABSTRACT FROM AUTHOR]

This paper reports research that is part of a project to combat deforestation in the Brazilian Amazon rainforest by developing an online system designed to forecast deforestation risk over the short term, spanning 2 to 4 weeks. This online platform aims to empower stakeholders with timely data, facilitating proactive conservation and intervention strategies to safeguard the Amazon rainforest. We built a multitemporal database that compiles weekly deforestation alerts from the DETER project, forming our analysis's backbone. Utilizing the XGBOOST regression algorithm, we have crafted a predictive model that identifies areas within the Amazon at imminent risk of more intensive deforestation. Preliminary results reveal an RMSE of 0.29 for predicting areas under deforestation risk, as validated against early alert data from 2020 to 2023. Our work advances environmental monitoring by focusing on a spatial resolution of 25 km × 25 km, providing accessible, near real-time information on deforestation risks. [ABSTRACT FROM AUTHOR]

Semantic segmentation is essential in the field of remote sensing because it is used for various applications such as environmental monitoring and land cover classification. Recent advancements aim to collectively classify data from diverse sensors and epochs to improve predictive accuracy. With the availability of vast Satellite Image Time Series (SITS) data, supervised deep learning methods, such as Transformer models, become viable options. This paper introduces the Temporal Vision Transformer(ViT), designed to extract features from SITS. These features, capturing the temporal patterns of land cover classes, are integrated with features derived from aerial imagery to improve land cover classification. Drawing inspiration from the success of transformers in Natural language processing (NLP), Temporal ViT concurrently extracts spatial and temporal information from SITS data using tailored positional encoding strategies. The proposed approach fosters comprehensive feature learning across both domains, facilitating seamless integration of encoded data from SITS into aerial images. Furthermore, a training strategy is proposed that supports the Temporal ViT to focus on classes with a changing appearance over the year. Extensive experiments carried out in this work indicate the enhanced classification performance of Temporal ViT compared to existing state-of-the-art techniques for multi-modal land cover classification. Our model achieves a 3.8% increase in the mean IoU compared to the network solely relying on aerial images. [ABSTRACT FROM AUTHOR]

The era of Artificial Intelligence 'AI' with all the benefits brought along, has raised new and additional challenges to the ongoing efforts of assessing, defining, formulating, and implementing the quality aspects of geospatial remote sensing data. Developed practices using artificial intelligence leveraged techniques such as image interpretation, classification, thematic mapping, and even image quality enhancement, necessitating by that the reassessment and redevelopment of some of the related emerging quality aspects. Moreover, technology also made the generation of false images and false data possible, this matter constrained and increased precaution and doubtfulness, altogether making some practices based on that data almost halt to further notice. This paper presents the collaborative research work to assess and clarify the quality aspects that arose with the advent and implementation of AI and associated technologies; the concerns and issues that can accompany the generation of false satellite and aerial images including the generated geospatial data out of which, how the new emerged quality aspects fit into the currently existing methods through the lifecycle of remote sensing data production and usage, and consequently how the quality dimensions are affected and should be further developed and improved to tackle the changes and innovations. Also, lame a bit on investigating how to accommodate the new challenges in standards, and practical procedures and raise the awareness to users, the level of dependency on improved and enhanced satellite images when it comes to data collection interpretation and classification, and finally define the research gaps, future expected challenges and thus enclose suggestions and recommendations in that respect. [ABSTRACT FROM AUTHOR]

This paper investigates fault detection in offshore wind permanent-magnet synchronous generators (PMSGs) for demagnetization and eccentricity faults (both static and dynamic) at various severity levels. The study utilizes a high-speed PMSG model on the National Renewable Energy Laboratory (NREL) 5 MW reference offshore wind turbine at the rated wind speed to simulate healthy and faulty conditions. An unsupervised convolutional autoencoder (CAE) model, trained on simulated signals from the generator in its healthy state, serves for anomaly detection. The main aim of the paper is to evaluate the possibility of fault detection by means of high-resolution electrical and electromagnetic signals, given that the typically low-resolution standard measurements used in supervisory control and data acquisition (SCADA) systems of wind turbines often impede the early detection of incipient failures. Signals analyzed include three-phase currents, induced shaft voltage, electromagnetic torque, and magnetic flux (air gap and stray) from different directions and positions. The performance of CAE models is compared across time and frequency domains. Results show that in the time domain, stator three-phase currents effectively detect faults. In the frequency domain, stray flux measurements, positioned at the top, bottom, and sides of the outside of the stator housing, demonstrate superior performance in fault detection and sensitivity to fault severity levels. In particular, radial components of stray flux can successfully distinguish between eccentricity and demagnetization. [ABSTRACT FROM AUTHOR]

Peatland restoration and rehabilitation action has become more widely acknowledged as a necessary response to mitigating climate change risks and improving global carbon storage. Peatland ecosystems require restoration time spans of the order of decades and, thus, cannot be dependent upon the shorter-term monitoring often carried out in research projects. Hydrological assessments using geospatial tools provide the basis for planning restoration works as well as analysing associated environmental influences. "Restoration" encompasses applications to pre-restoration and post-restoration scenarios for both bogs and fens, across a range of environmental impact fields. The aim of this scoping review is to identify, describe, and categorize current process-based modelling uses in peatlands in order to investigate the applicability and appropriateness of ecohydrological and/or hydrological models for northern peatland restoration. Two literature searches were conducted using the entire Web of Science database in September 2022 and August 2023. Of the final 211 papers included in the review, models and their applications were categorized according to this review's research interests in seven distinct categories aggregating the papers' research themes and model outputs. Restoration site context was added by identifying 229 unique study site locations from the full database, which were catalogued and analysed against raster data for the Köppen–Geiger climate classification scheme. A majority of northern peatland sites were in temperate oceanic zones or humid continental zones that experienced snow. Over one in five models from the full database of papers were unnamed and likely intended for single use. Key themes emerging from topics covered by papers in the database included the following: modelling restoration development from a bog growth perspective, the prioritization of modelling greenhouse gas (GHG) emissions dynamics as a part of policymaking, the importance of spatial connectivity within or alongside process-based models to represent heterogeneous systems, and the increased prevalence of remote sensing and machine learning techniques to predict restoration progress with little physical site intervention. Models are presented according to their application to peatlands or broader ecosystem and organized from most to least complex. This review provides valuable context for the application of ecohydrological models in determining strategies for peatland restoration and evaluating post-intervention development over time. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has played an increasing role in the hydrological sciences. In particular, Long Short-Term Memory (LSTM) networks are popular for rainfall–runoff modeling. A large majority of studies that use this type of model do not follow best practices, and there is one mistake in particular that is common: training deep learning models on small, homogeneous data sets, typically data from only a single hydrological basin. In this position paper, we show that LSTM rainfall–runoff models are best when trained with data from a large number of basins. [ABSTRACT FROM AUTHOR]

The Northeast Greenland Ice Stream (NEGIS) is experiencing sustained accelerated mass loss due to the combined effects of atmospheric and ocean warming. The Nioghalvfjerdsfjorden glacier (NG) and Zachariae Isstrøm (ZI) are major contributors to the mass balance in NEGIS, draining 12% of the Greenland Ice Sheet (GrIS). Precise measurements of the ice flow velocities of NG and ZI are crucial for estimating the mass balance in NEGIS and predicting future sea level rise. However, the study of early NEGIS is limited by the lack of ice flow velocity before 1985. We proposed a systematic process for orthorectification on CORONA KH-4A imagery, which proved to be efficient and accurate, yielding 25 m precision. Using a hierarchical network densification approach based on ARGON KH-5 and CORONA KH-4A imagery, we successfully generated the ice flow velocity map for NG and ZI from 1963 to 1967. The results showed different changes in ice flow velocity over a 57-year period for NG and ZI. The average ice flow velocity near the grounding line increased by 12.4% for NG and 81.4% for ZI from 1963 to 2020. This paper filled a gap in historical ice flow velocity data for NEGIS and provided early, and valuable information for studying the long-term motion of ice flow. It can also improve the prediction accuracy of future changes in mass balance in this region. [ABSTRACT FROM AUTHOR]

Structural connectivity is an important factor in preserving coral diversity. It maintains the stability and adaptability of coral reef ecosystems by facilitating ecological flow, species migration, and gene exchange between coral communities. However, there has always been a lack of consistent solutions for accurate structural connectivity describing and quantifying, which has hindered the understanding of the complex ecological processes in coral reefs. Based on this, this paper proposes a framework that uses advanced remote sensing and deep learning technologies to assess coral structural connectivity. Specifically, accurate coral patches are firstly identified through image segmentation techniques. And the structural connectivity is quantified by assessing the connectivity patterns between and within these coral patches. Furthermore, Tetiaroa Island in the South Pacific is used as a case study to validate the effectiveness and accuracy of the framework in assessing coral structural connectivity. The experimental results demonstrate that the framework proposed in this paper provides a powerful tool for understanding the internal ecological processes and external spatial patterns of coral reef ecosystems, thereby promoting scientific understanding and effective management of coral reef conservation. [ABSTRACT FROM AUTHOR]

Climate change has already significantly impacted infrastructures built on permafrost, with thaw settlement as the most frequently reported issue. By characterizing thaw settlement tests, an improved understanding of the thaw settlement properties of permafrost sediment can be obtained. Thaw settlement tests involve thawing permafrost samples under an initial load, followed by applying additional load to the thawed sample to characterize its volume change behaviour upon thawing. In the absence of a standardized procedure for conducting thaw settlement tests, characterizing thaw settlement properties has been done using various methods in the existing literature; however, to date, these data have not been broadly compared. This is in part because they had not previously been compiled in a single dataset. This study presents a comprehensive dataset of thaw settlement test results, digitized from published papers and reports. The data are standardized and stored in an open-source repository. Aggregating the data enabled a cross-comparison of thaw settlement properties for different soil types. This was achieved by constructing an idealized stress-strain curve for each test and deriving thaw settlement parameters from the curves. These parameters were then used to compare the thaw settlement behaviour of fine-grained, coarse-grained, and highly organic permafrost samples. Additionally, the compiled data was used to evaluate the effectiveness of various empirical tools developed to predict thaw strain from index properties. The predicted thaw strain values were compared with the measured thaw strains to determine which tool provided the most accurate and reliable predictions for each soil type. The results suggest that a correlation developed by Nixon and Ladanyi (1987) for estimating thaw strain based on frozen bulk density shows the smallest deviation from actual values and exhibits the least bias in its predictions. This dataset is expected to enhance the understanding of thaw settlement and improve its estimation. Used alone or in conjunction with localized data, it can contribute to developing new empirical tools for predicting thaw strain from index properties. Additionally, this dataset aids enhanced characterization of stress-strain behaviour upon thawing, facilitating future efforts in the numerical modelling of the thaw settlement process. The dataset is accessible at https://doi.org/10.5281/zenodo.14538524 , and the results presented in this paper are based on version 1.2.0 of the dataset (Mohammadi and Hayley, 2024c). [ABSTRACT FROM AUTHOR]

Additive manufacturing has enabled rapid prototyping of components with minimum investment in specific fabrication infrastructure. These tools allow a fast iteration from design to functional prototypes within days or even hours. Such prototyping technologies exist in many fields, from 3-dimensional mechanical components, or printed electric circuit boards (PCBs) for electrical connectivity, to mention two. In the case of nuclear magnetic resonance (NMR) spectroscopy one needs the combination of both fields, we need to fabricate three-dimensional electrically conductive tracks as coils that are wrapped around a sample container. Fabricating such structures is difficult (e.g. 6 axes micro-milling) or simply not possible with conventional methods. In this paper, we modified an additive manufacturing method that is based on the extrusion of conductive ink to fastprototype solenoidal coil designs for NMR. These NMR coils, need to be as close to the sample as possible and by their shape have specific inductive values. The performance of the designs was first investigated using EM-field simulations, and circuit simulations. The coil found to have optimal parameters for NMR was fabricated by extrusion printing and its performance was tested in a 1.05 T imaging magnet. The objective is to demonstrate reproducible rapid prototyping of complicated designs with high precision that as a side effect hardly produces material waste during production. [ABSTRACT FROM AUTHOR]

The Aerosol Layer Height (ALH), from the Sentinel-5P/TROPOMI L2__AER_LH product, is based on an optimal estimation (OE) approach, fitting cloud-free measurements to synthetic reflectances in the strongest oxygen absorption band, provided by a neural network trained with high resolution simulated reflectances. The ALH has been continuously improved since its release in 2019, focusing especially on (bright) land surfaces, over which the ALH product showed underestimated aerosol layer heights (biased towards the surface). This paper describes the latest updates of the ALH product, that includes first the introduction of the Directional Lambertian-Equivalent Reflectance (DLER) climatology to improve the surface albedo characterisation over land. Second, the paper describes a further improvement, adding the surface albedo in the feature vector of the OE inversion, using the DLER as prior information. Using this approach, the retrievals over land largely match the retrievals over ocean, which have shown a good comparison with validation data since its release, most notably with CALIOP weighted extinction heights. The albedo is fitted for both land and ocean surfaces, but the implementation is different over land and ocean because of the large range of land surface albedos. Over land, the a priori surface albedo values are relaxed so the fitting procedure can incorporate the albedo effects in the retrieval over land. Over ocean, the retrievals are optimised by tuning the a priori error settings. The current implementation improves retrievals over land with about 1.5 times more converged results, and decreases land-ocean contrasts in the aerosol layer height retrievals. The average difference between CALIOP weighted extinction height decreased for selected cases from about −1.9 km to −0.9 km over land and from around −0.8 km to +0.1 km over ocean. [ABSTRACT FROM AUTHOR]

Western disturbances (WDs) are synoptic-scale weather systems embedded within the subtropical westerly jet. Manifesting as upper-level troughs often associated with a lower-tropospheric low over western or northern India, they share some dynamical features with extratropical cyclones. WDs are most common during the boreal winter (December to March), during which they bring the majority of precipitation – both rain and snow – to the western Himalaya, as well as to surrounding areas of north India, Pakistan, and the Tibetan Plateau. WDs are also associated with weather hazards such as heavy snowfall, hailstorms, fog, cloudbursts, avalanches, frost, and cold waves. In this paper, we review recent developments in understanding WDs and their impacts. Over the last decade, recent studies have collectively made use of novel data, novel analysis techniques such as tracking algorithms, and the increasing availability of high-resolution weather and climate models. This review is separated into six main sections – structure and thermodynamics, precipitation and impacts, teleconnections, modelling experiments, forecasting at a range of scales, and paleoclimate and climate change – each motivated with a brief discussion of the accomplishments and limitations of previous research. A number of step changes in understanding are synthesised. Use of new modelling frameworks and tracking algorithms has significantly improved knowledge of WD structure and variability, and a more frequentist approach can now be taken. Improved observation systems have helped quantification of water security over the western Himalaya. Convection-permitting models have improved our understanding of how WDs interact with the Himalaya to trigger natural hazards. Improvements in paleoclimate and future climate modelling experiments have helped to explain how WDs and their impacts over the Himalaya respond to large-scale natural and anthropogenic forcings. We end by summarising unresolved questions and outlining key future WD research topics. [ABSTRACT FROM AUTHOR]

The West African monsoon (WAM) system is a critical climatic phenomenon with significant socio-economic impacts on millions of people. Despite many advancements in numerical weather and climate models, accurately representing the WAM remains a challenge due to its intricate dynamics and inherent uncertainties. Building upon our previous work utilizing the ICON (icosahedral nonhydrostatic) numerical model to construct statistical surrogate models for quantities of interest (QoIs) characterizing the WAM, this paper focuses on the optimization of the three uncertain model parameters of entrainment rate, fall speed of ice, and soil moisture evaporation fraction through innovative multi-objective optimization (MOO) techniques. The problem is approached in two distinct ways: (1) optimization of 15 designated QoIs, such as the latitude and magnitude of the African rain belt or African easterly jet, using existing surrogate models and (2) optimization of twelve 2D meteorological output fields, such as precipitation, cloud cover, and pressure, using new surrogate models that employ principal component analysis. The objectives subject to minimization in the MOO process are defined as the difference between the surrogate model and reference data for each QoI or output field. As reference data, Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM IMERG) mission are used for precipitation, and the ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts are used for all other quantities. The multi-objective optimization problems are tackled through two strategies: (1) assignment of weights with uncertainties to the objectives based on expert opinion and (2) variation in these weights in order to assess their influence on the optimal values of the uncertain model parameters. Results show that the ICON model is already generally well-tuned for the WAM system. However, a lower entrainment parameter would lead to a more accurate simulation of accumulated precipitation, averaged 2 m dew point temperature, and mean sea level pressure over the considered domain (15° W to 15° E, 0 to 25° N). An improvement in 2D output fields instead of QoIs is barely possible with the considered parameters, which confirms meaningful default values of the model parameters for the region. Nevertheless, optimal model parameters strongly depend on the assigned weights for the objectives. To further enhance the accuracy of climate simulations and potentially improve weather predictions, it is crucial to prioritize the refinement of the overall physical models, including the reduction in inherent structural errors, rather than solely adjusting the uncertain parameters in existing model parametrizations. Nevertheless, our methodology demonstrates the potential of integrating statistical and expert-driven approaches to assess and improve the simulation accuracy of the WAM. The findings underscore the importance of considering uncertainties in MOO and the need for a holistic understanding of the WAM's dynamics to enhance prediction skills. [ABSTRACT FROM AUTHOR]

This work presents the first step in the development of the VISION toolkit, a set of Python tools that allows easy, efficient, and more meaningful comparison between global atmospheric models and observational data. Whilst observational data and modelling capabilities are expanding in parallel, there are still barriers preventing these two data sources from being used in synergy. This arises from differences in spatial and temporal sampling between models and observational platforms: observational data from a research aircraft, for example, are sampled on specified flight trajectories at very high temporal resolution. Proper comparison with model data requires generating, storing, and handling a large number of highly temporally resolved model files, resulting in a process which is data-, labour-, and time-intensive. In this paper we focus on comparison between model data and in situ observations (from aircraft, ships, buoys, sondes, etc.). A standalone code, In-Situ Observations Simulator, or ISO_simulator for short, is described here: this software reads modelled variables and observational data files and outputs model data interpolated in space and time to match observations. These model data are then written to NetCDF files that can be efficiently archived due to their small sizes and directly compared to observations. This method achieves a large reduction in the size of model data being produced for comparison with flight and other in situ data. By interpolating global gridded hourly files onto observation locations, we reduce data output for a typical climate resolution run, from ∼3 Gb per model variable per month to ∼15 Mb per model variable per month (a 200-times reduction in data volume). The VISION toolkit is relatively fast to run and can be automated to process large volumes of data at once, allowing efficient data analysis over a large number of years. Although this code was initially tested within the Unified Model (UM) framework, which is shared by the UK Earth System Model (UKESM), it was written as a flexible tool and it can be extended to work with other models. [ABSTRACT FROM AUTHOR]

Calibrations of optical particle spectrometers (OPSs) are non-trivial and conventionally involve aerosolisation techniques, which are challenging for larger particles. In this paper, we present a new technique for OPS calibration that involves mounting a static fibre within the instrument sample area, measuring the scattering cross section (SCS), and then comparing the SCS with a calculated value. In addition, we present a case for the use of generalised Lorenz–Mie theory (GLMT) simulations to account for deviations in both minor- and major-axis beam intensity, which has a significant effect on particles that are large compared with the beam waist, in addition to reducing the need for a "top-hat" spatial intensity profile. The described technique is OPS independent and could be applied to a field calibration tool that could be used to verify the calibration of instruments before they are deployed. In addition to this, the proposed calibration technique would be suited for applications involving the mass production of low-cost OPSs. [ABSTRACT FROM AUTHOR]