166 research outputs found
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Trimming a rigid-wing airborne wind system for coordinated circular flights
Full text linkAirborne wind energy systems (AWESs) are tethered flying devices used for electricity generation. During the power-generation phase, the aerial component usually flies in a circular or figure-of-eight pattern. This paper examines the control surface movements required for circular flights in rigid-wing AWESs. In the absence of gravity, steady trim with equilibrium solutions can be achieved if the orbit plane is normal to the wind. The radius depends on how much the aircraft leans into the turn: leaning in reduces the radius and is statically stable, while leaning out achieves a larger radius but is unstable. For the latter case, artificial stabilisation can be done by cross-feeding the pitch and roll responses to the aileron. For circular trajectories that are not normal to the wind (i.e. experiencing out-of-plane wind), energy needs to be added to the system through the periodic forcing of a control surface. The correct timing of the forcing will excite the orbit's natural frequency, enabling full control of the circle centre and orientation for navigation in the 3D space. This can be done even in the presence of gravity, which is discussed in the second half of this paper. The aileron is the most effective control effector for forcing. Although the trimming method presented in this paper is only suitable for theoretical studies, it provides insights into the flight dynamics of rigid-wing AWESs and lays the groundwork for future flight control developments.</p
A microwave scattering database of oriented ice and snow particles: supporting habit-dependent growth models and radar applications (McRadar 1.0.0)
Full text linkThe optical properties of atmospheric hydrometeors are a crucial component of any forward operator. These forward operators are essential data assimilation, and atmospheric model evaluations. Recent advances in microphysical modelling, such as Lagrangian super-particle models with habit prediction for ice particles, allow for the continuous evolution of particle properties in contrast to fixed hydrometer classes with fixed properties. This increasing complexity demands scattering databases capable of handling a wide range of particle properties. The discrete dipole approximation (DDA) is one of the most accurate and widely used methods for computing the scattering properties of irregular ice particles. However, its high computational cost typically limits either the diversity of particle shapes or the range of environmental parameters (e.g., frequency, temperature) represented in existing databases, constraining their applicability to models with highly variable microphysics. In this study, we present a new DDA-based database of optical properties at 5.6, 9.6, 35.6, and 94 GHz, specifically designed to accommodate the broad range of ice crystal morphologies predicted by habit-evolving schemes. The database contains 2627 individual ice crystals, including dendrites, plates, and columns, as well as 450 aggregates with varying degrees of riming and crystal types. The data are organized in three levels: Level 0 provides raw scattering matrices at individual orientations for a full range of scattering angles; level 1a summarizes Mueller and Amplitude matrix elements relevant for radar applications (at forward and backward scattering angles); and level 1b offers lookup tables of scattering properties that are relevant for polarimetric radars assuming azimuthally random orientations of the particles. These data allow for flexible treatment of the canting angle of the particles. The lookup tables are directly accessible via the McRadar simulator and can also be interfaced with other forward operators. The new database allows for a more consistent and realistic treatment of evolving ice particle properties in atmospheric models, improving the interpretation of radar observations and model-observation integration.</p
Measuring exposure of agriculture to observed temperature change
Full text linkWe used available FAO statistics as input metrics to compute simple indicators of exposure of agriculture at regional and global levels to temperature change thresholds, ΔT > 1.5 °C and ΔT > 2.0 °C, relative the 1951–1980 climatology. Since 1995 and with respect to ΔT > 1.5 °C, results show that exposure of rural populations increased globally 14 times (from 64 to 920 million people); exposed agricultural land area grew five-fold (from 350 to 2000 million hectare, Mha). The exposed harvested area of soybean increased globally 90 times (0.5 to 45 Mha); rice 78 times (0.5 to 39 Mha); maize 38 times (2 to 76 Mha) and wheat 5 times (22 to 110 Mha). Finally, exposure of livestock grew six-fold for dairy cows and 20-fold for chicken broilers. Among regions, Europe had the largest exposure to ΔT > 1.5 °C, with more than 80 % of its rural population, cropland area, dairy cattle, maize and wheat harvested areas exposed in 2024. Exposure indicators for Central Asia, Western Asia and Northern Africa were above 65 % for cropland area and wheat harvested area. The computed exposure indicators were found to increase nearly exponentially over the period 1961–2024 across all regions, highlighting the urgency of implementing appropriate agricultural adaptation strategies to avert possible negative impacts in coming decades. The data is available at https://doi.org/10.5281/zenodo.12665841 (Tubiello, 2025)
A Manually Labeled Contrail Dataset from MSG/SEVIRI
Full text linkContrails — thin ice clouds formed by aircraft — are a major contributor to aviation-induced climate forcing, yet their observational characterization remains limited. We present a manually labeled contrail dataset derived from observations of the Meteosat Second Generation (MSG) SEVIRI instrument over Europe and the North Atlantic, comprising 140 scenes of 256 × 256 pixels. Each scene was independently annotated by three labelers, with ground truth established via majority consensus. To provide additional context, the dataset includes outputs from two satellite retrievals: CiPS (Cirrus Properties from SEVIRI) and ProPS (Probabilistic Cloud Top Phase retrieval), offering information on cloud cover and cloud top phase, cirrus probability, ice optical thickness, and ice cloud top height. These complementary variables enable detailed investigations, such as factors influencing contrail visibility. The dataset supports analyses of contrail detection, contrail characteristics, cloud-contrail interactions, and environmental conditions affecting detection. By providing high-quality labeled data with auxiliary cloud information, this resource facilitates the development and evaluation of contrail studies, contributes to improved understanding of aviation-related cloud effects, and informs strategies for climate impact mitigation. The full dataset is available under: https://doi.org/10.5281/zenodo.17669444
Gulf of St. Lawrence and Estuary Dataset (GOSLED): A 20-year compilation of quality-controlled biogeochemical observations (2003–2023)
Full text linkThis paper presents the Gulf of St. Lawrence and Estuary Dataset (GOSLED), a quality-controlled compilation of biogeochemical observations collected during 21 research cruises in the St. Lawrence Estuary, Gulf of St. Lawrence, and Saguenay Fjord between 2003 and 2023. This dataset integrates hydrographic measurements and a broad suite of discrete biogeochemical variables into a single, standardized compilation suitable for reuse, synthesis, and long-term analysis. GOSLED includes discrete measurements of dissolved oxygen, carbonate-system parameters, macronutrients, dissolved organic carbon, selected biogeochemical gases, stable isotope ratios of carbon and water, and transient and deliberate tracers. Data were compiled from multiple independent research cruises and laboratory archives (2003–2020), including contributions from the Marine Environmental Observation, Prediction and Response Network (MEOPAR) – Réseau Quëbec maritime (RQM) Gulf of St. Lawrence Tracer Release Experiment (TReX; 2021-2023), RQM Odyssée Saint Laurent program (2018-2023), and the Fisheries and Oceans Canada (DFO) Atlantic Zone Monitoring Program (AZMP; fall 2022). Sampling was conducted predominantly during the ice-free season, resulting in limited winter coverage across much of the system. All data were harmonized and processed following primary quality-control procedures adapted from GLODAP and CODAP-NA standards. Secondary crossover analysis was not possible due to a lack of deep-water sampling. This paper documents the data provenance, quality-control procedures, known limitations, and recommended considerations for dataset usage. GOSLED is archived at the Canadian Integrated Ocean Observing System - St. Lawrence Global Observatory (CIOOS-SLGO) and is publicly accessible at https://doi.org/10.26071/d6f3fdfc-788d-48ff (Nesbitt et al., 2026)
Ten years of hydrometeorological observations at 10-minute resolution and its application in machine learning hydrological models
Full text linkAccurate urban flash flood forecasting relies on well-spatialized rainfall data distribution. This study introduces and utilizes the TTI-HydroMet dataset, a publicly available and unique collection for the Tamanduateí River Watershed, in Sao Paulo (Brazil). The dataset includes rainfall measurements from 23 rain gauge stations, stage observations from a hydrological gauge near the outlet, and quantitative precipitation estimates at 1-km radar resolution, accumulated in 10-minute precipitation fields over 10 years. The weather radar data presents missing values for only 0.3 % of timestamps during rainfall events observed by rain gauges. The Spearman correlation coefficient between weather radar and rain gauges varies from 0.675 (full period) to 0.949 (a specific event). It was used to assess the predictive capacity of Machine Learning (ML) hydrological models trained on accumulated rainfall data from rain gauges and estimated by a weather radar. Using an advanced cross-validation framework, two representative algorithms (LinearSVR and XGBRegressor) were tested across different rainfall source configurations and showed strong performance at lead times up to 120 minutes. The Nash–Sutcliffe Efficiency index ranges from 0.781 to 0.996. The statistically comparable performance of ML models driven by radar and rain gauge rainfall indicates that radar-based ML approaches can represent a viable alternative for short-term stage forecasting in regions lacking rain gauge networks
Monitoring of Surface Deformations in Geothermal Areas Using the InSAR Method: A Case Study from Denizli, Türkiye
Full text linkThis study examines surface deformations in the Sarayköy geothermal field (Denizli, Türkiye) between 2020 and 2025 using Interferometric Synthetic Aperture Radar (InSAR) techniques. Sentinel-1 ascending and descending datasets were processed with the Small Baseline Subset (SBAS) method, and tropospheric corrections were applied to improve measurement reliability. By decomposing line-of-sight displacements into horizontal and vertical components, both the direction and intensity of ground movements were identified. The results reveal localized vertical subsidence at rates reaching up to −0.7 m/year, indicating significant ground instability in areas of intensive geothermal production. Time-series analyses further highlight seasonal deformation patterns, linked to extraction and reinjection cycles of geothermal fluids. These cycles influence not only the magnitude of subsidence but also the partial recovery of ground levels, underlining the sensitivity of the subsurface system to operational practices. From an applied perspective, such deformation poses risks to critical infrastructure, including pipelines, wells, and energy transmission facilities, as well as to surrounding communities. Continuous InSAR-based monitoring provides a cost-effective and scalable tool for identifying hazardous zones, improving reservoir management, and guiding mitigation strategies. Beyond local safety, these results contribute to broader discussions on sustainable geothermal energy production and national energy policy by demonstrating the need to balance energy generation with environmental and geotechnical stability. Integrating geodetic observations with operational data and geomechanical modeling will further enhance predictive capacity, supporting long-term resilience of Türkiye’s geothermal sector
Atmospheric nitrogen deposition fluxes into coastal wetlands and their impacts on ecosystem carbon sequestration in East Asia
Full text linkCoastal wetlands serve as critical sinks for both carbon and nitrogen within regional ecosystems, playing an essential role in mitigating atmospheric greenhouse gases and nutrient enrichment. This study integrates high-resolution wetland type data, ship emission inventories, and regional nitrogen deposition simulations to quantify nitrogen inputs to East Asian coastal wetlands from the perspective of source–sink coupling. Firstly, atmospheric nitrogen deposition fluxes in coastal wetland areas of East Asia were simulated and evaluated using an air quality model. Nitrogen deposition fluxes were spatially coupled with classified wetland maps. Net primary productivity (NPP) was estimated using a modified light-use efficiency model, incorporating solar radiation and the fraction of photosynthetically active radiation (FPAR) from remote sensing. Carbon sequestration and oxygen release were then quantified using stoichiometric relationships based on NPP. The results indicate that total nitrogen deposition across East Asian coastal wetlands follows a general gradient of “high in the south, low in the north” and “strong in urban-industrial clusters, weak in remote coastal zones.” On average, ship emissions contribute 10.13 % and 15.22 % to NO3--N and NH4+-N deposition, respectively, while their contribution to gaseous NH3-N is negligible. Among wetland types, salt marshes receive the highest nitrogen input per unit area (654.99 mg NO3--N m−2 yr−1), although tidal flats dominate total regional nitrogen input due to their extensive spatial coverage. Dry and wet deposition exhibit significant seasonal variation: wet deposition consistently prevails during the spring and summer months due to frequent rainfall, while dry deposition becomes increasingly prominent in autumn and winter. For instance, in the Korean Peninsula (KP), the wet-dry gap in nitrate deposition reaches 0.17 g N m−2 yr−1, while the Yangtze River Delta (YRD) exhibits relatively balanced ammonium inputs (dry-wet difference of only 0.05 g N m−2 yr−1). Carbon sequestration capacity shows strong spatial and temporal coupling with nitrogen deposition. Mangrove forests exhibit the highest annual NPP (∼ 776.16 g C m−2 yr−1 in summer), supported by high FPAR and solar radiation (1749.29 MJ m−2), followed by salt marshes and tidal flats. Seasonal patterns reveal a summer peak in carbon uptake across all wetland types, with mangrove NPP in summer being two times higher than winter values. Nitrogen deposition primarily enhances carbon sequestration during warm seasons; for instance, in the mangroves of the Pearl River Delta (PRD), nitrogen inputs increase summer carbon sequestration by 6.85 g C m−2, while the effect is negligible in winter (<0.06 %) or in nitrogen-saturated regions. These findings provide a scientific foundation for understanding how coastal ecosystems respond to anthropogenic activities and long-range nitrogen transport. Furthermore, the results serve as an important reference for wetland conservation, nitrogen cycle management, and the development of regional carbon neutrality strategies.</p
Global atmospheric hydrogen chemistry and source-sink budget equilibrium simulation with the EMAC v2.55 model
Full text linkoai:publications.copernicus.org:gmd128460In this study, we use an earth system model with detailed atmospheric chemistry (EMAC v2.55.2) to undertake simulations of hydrogen (H2) atmospheric dynamics. Extensive global equilibrium simulations were performed with a horizontal resolution of 1.9°. The results of this simulation are compared with observational data from 56 stations in the National Oceanic and Atmospheric Administration (NOAA) Global Monitoring Laboratory (GML) Carbon Cycle Cooperative Global Air Sampling Network. We introduced H2 sources and sinks, the latter inclusive of a soil uptake scheme, that accounts for bacterial consumption. The model thus accounts for detailed H2 and methane (CH4) flux boundary conditions. Results from the EMAC model are accurate and predict the magnitude, amplitude and interhemispheric seasonality of the annual H2 cycle at most observational stations. Time series comparison of EMAC and observational data produces Pearson correlation coefficients (r) in excess of 0.9 at eight remote stations located in polar regions and on high mid-latitude islands. A further 23 stations yielded correlation coefficients between 0.7–0.9, predominantly located in remote marine stations across all latitudes and also in polar regions. The quality of model predictions (r<0.5, 9 stations) is reduced in anthropogenically highly polluted stations in east Asia and the Mediterranean region and stations impacted by peat fire emissions in Indonesia, as local and incidental emissions are difficult to capture. Our H2 budget corroborates bottom-up estimates in the literature in terms of source and sink strengths and overall atmospheric burden. By simulating hydroxyl radicals (OH) in the atmosphere leading to a CH4 lifetime in agreement with observationally constrained estimates, we show that the EMAC model is a capable tool for undertaking high accuracy simulation of H2 at global scale. Future research applications could target the impact of potentially significant natural and anthropogenic H2 sources on air quality and climate, reducing uncertainties in the H2 soil sink and impacts of H2 release on the future oxidising capacity of the atmosphere.</p
In Situ Real-Time Determination of SO2 Photochemical Oxidation in Nanoscale Sea Salt Aerosols based on Dark-Field Microscopy
Full text linkHeterogeneous reaction processes of aerosols play an important role in air quality and climate change. However, the lack of in-situ measurements of single-nanoparticle reactions results in large uncertainties in modeling the nanoparticle reaction kinetics. The study introduces a method to quantify reaction rates of single-nanoparticles using hygroscopic growth factors (GFs) and the Zdanovskii-Stokes-Robinson (ZSR) rule. Planar waveguide dark-field microscopy was employed to monitor sodium chloride (NaCl) aerosol GFs under ultraviolet (UV) irradiation and SO2 exposure in real time. The results revealed a first-order reaction rate constant of 0.6523 h⁻¹ for 100 nm NaCl aerosols. Moreover, the reaction rate constant exhibits a non-monotonic size dependence on particle diameter-increasing in the 50–200 nm range and decreasing for particle sizes larger than 200 nm. This reflects a competitive interplay between the surface curvature effect at small particle sizes and specific surface area effect at larger sizes, which is further validated by a combined analysis based on transition state theory and the double-film mass transfer approach. Subsequently, sodium octyl sulfate (SOS) was introduced to form binary NaCl-based nanoaerosols, where the organic coating content was systematically varied under constant surface curvature to modulate the specific surface area. An increase in organic volume fraction reduces the effective specific surface area and suppresses heterogeneous reaction rates, accompanied by a pronounced nonlinear transition from partial to complete coating. This further confirms the experimentally observed size-dependent nonlinearity in reaction rates and offers new insights into nanoscale sulfate formation, improving atmospheric chemical models and pollution-climate assessments