Search Results (2,636)

Large language models (LLMs) offer new possibilities for natural-language interaction with geospatial analysis systems, but their use in remote sensing instrument data analysis remains limited by weak execution control, poor reproducibility, and limited integration with domain-specific computation. The paper presents an agent for Incoherent Doppler wind LiDAR (ICDL) data analysis, named ICDL-Agent, a tool-augmented LLM framework for remote sensing instrument workflows. The system maps conversational user requests to executable analysis pipelines for wind retrieval, uncertainty estimation, visualization, and higher-level diagnostics through structured planning over a registry of domain-specific tools. To improve execution reliability, the system combines schema-constrained workflow generation, shared-state reuse of intermediate scientific products, and validation with bounded repair. In addition to supporting routine LiDAR processing, the framework can generate new tools when required and adapt to related analytical tasks through domain-aware guidance and procedural documentation. We evaluate the system on multiple atmospheric wind-observation datasets in China and show that it faithfully reproduces the refined Doppler wind-retrieval pipeline, achieving representative R²/MAE values of 0.52/3.73 m/s against ERA5 and 0.80/2.31 m/s against radiosonde observations, while supporting downstream analyses such as profile comparison, climatological interpretation, and gravity-wave diagnostics. More broadly, this study demonstrates how constrained LLM orchestration can support LiDAR researchers, remote-sensing instrument teams, and geospatial analysts seeking transparent, reproducible, and automated scientific data-processing workflows. Full article

This study aims to characterize rainfall-triggered phenomena, including floods, landslides, and urban flooding, defined as damaging hydrogeological events (DHEs), through the integration of the scientific literature and historical documentary sources, and to analyze their rainfall-triggering conditions. The analysis focuses on a sector of the Ionian coast of Calabria (southern Italy) in the period 1925–2025. The identified DHEs were organized into 463 damage records (DRs), enabling a municipal-scale analysis at monthly temporal resolutions. To characterize the rainfall conditions associated with DHEs, we identified a rainfall indicator (R), defined as the ratio between the monthly rainfall observed during a DHE and the corresponding long-term climatological average rainfall. Results show that DHEs occur more frequently during autumn (46%) and winter (41%) and are mainly associated with moderate (1< R < 2) to strong rainfall anomalies (R > 3). Summer events, although limited in number, are often (43%) associated with very strong rainfall anomalies (R > 3). Spatial analysis highlights a heterogeneous distribution of DHEs in the study area, with some municipalities showing a greater occurrence of multiple phenomena. Landslides are the most frequent phenomenon, occurring in 29% of cases in combination with other processes and across a wide range of precipitation conditions. Floods are most often (over 60%) associated with moderate to strong anomalies, while urban flooding exhibits intermediate behavior. Stronger-rainfall-anomaly conditions are generally associated with DHE impacts with wider spatial extents. The study suggests that the proposed indicator may provide a useful framework for the first-order characterization of rainfall conditions associated with DHEs in contexts characterized by the limited availability of long-term data or in similar climatic areas. Full article

Continuous streamflow monitoring is fundamental for water management in high-mountain Andean basins. Traditionally, this process relies on empirical regressions, although artificial intelligence (AI) has recently emerged as a robust alternative. However, extreme geomorphological dynamics compromise classical hydraulic methods, while AI models frequently lack physical validation. In this context, this study compares the performance of Artificial Neural Networks against traditional methods to reduce uncertainty in stage–discharge rating curves. The methodology, applied to a nested basin scheme in Loja, Ecuador, contrasted traditional exponential fits with a Multilayer Perceptron optimized using the Levenberg–Marquardt algorithm. The analysis included the evaluation of uncertainty bands and a sub-hourly spatial validation based on the principle of mass conservation. Results evidence that AI refines statistical accuracy (NSE > 0.95) and effectively adapts to bed non-linearity; nevertheless, cross-validation revealed a high susceptibility to algorithmic overfitting. It is concluded that while AI offers superior analytical flexibility for interpolating non-linear dynamics, traditional methods remain more robust for extreme flood extrapolation. Furthermore, while AI reduces computational complexity, it entails a higher “data cost” requiring denser field gauging campaigns. Operational viability requires rigorous dynamic uncertainty controls and spatial water balance validation. Full article

This article presents the results of a field study conducted in 2022 on groundwater outflows located at the edge of the Kashubian Lake District and the Reda-Łeba Proglacial Stream Valley in northern Poland. The recharge of numerous springs was found to occur from the first aquifer, locally supported by a deeper aquifer connected to the first one near the bowl of Lubowidzkie Lake. Groundwater drainage occurs by gravity. It is relatively abundant for young glacial areas and averages 82 dm³·s⁻¹, making the springs capable of acting as a drinking water reservoir. This assessment is based on major ions and nutrients only; microbiological and trace-organic/metal analyses are required before any drinking-water designation. Spring water is important in the lake’s supply, accounting for 18.0% of the total inflow to the basin. The hydrochemical characteristics of these waters keep the lake in ecological balance. The waters from the springs are characterized by little variation in chemical composition, with the Ca-HCO₃ hydrochemical type. They represent young infiltration waters associated with direct recharge from precipitation (the average age of the water is 60 years). Currently, low nitrate and chloride suggest limited agricultural and urban influence, but phosphate levels and observed human activities warrant caution. Forest management is gradually developing in its catchment, which may result in a reduction of the spring yield and a deterioration of their quality in the future. This may result in a disturbance of the hydrological balance of structures hydraulically connected to spring recharge and to groundwater inflow (river, lake). Although the springs studied are local hydrological phenomena, their functioning and the need for protection are closely linked to global challenges in the field of sustainable development. This primarily concerns the protection of groundwater-dependent ecosystems and, more broadly, water security and increased resilience to climate change. Full article

Todos Santos Bay, Mexico, features several wave-focusing areas driven by its complex bathymetry, making it an ideal real-world test case for wave energy converter (WEC) park optimization. This study quantifies the influence of submarine cable costs and bathymetry-dependent mooring costs on the proposed park layout (hereafter the star-layout) and the levelized cost of energy (LCOE) of a 10-device WEC park, using a multi-state operational wave climatology of $N=179$ representative sea states from a 2008–2018 SNL-SWAN hindcast (covering 97.20% of the annual time). A binary genetic algorithm combined with K-means clustering analysis was used to minimize LCOE under three cost scenarios: baseline, cable-only, and cable plus bathymetry-dependent mooring. Both infrastructure cost components contribute substantially: cable costs add 52.2% to the baseline LCOE, and bathymetry-dependent mooring costs add a further 16.0% at this site, with cable approximately three times more impactful. These quantitative magnitudes are conditioned on the moderate depth-gradient setting of Todos Santos Bay; the qualitative cost-component hierarchy is expected to generalize, but the relative weights will depend on the bathymetric and wave-climate characteristics of each candidate site. The mooring contribution is nontrivial both economically and spatially (the centroid of the park shifts by approximately 151 m between the cable-only and cable-plus-depth scenarios). K-means clustering identified 2–4 layout families per scenario (K $=4\to 3\to 2$ as cost components are added), indicating that infrastructure constraints reduce the viable solution space. These results support the central hypothesis of this work: WEC park optimization studies that adopt flat-bathymetry simplifications, the prevailing assumption in much of the prior literature, risk substantial underestimation of LCOE at sites with nontrivial depth variation. We recommend that bathymetry-dependent mooring costs be included alongside cable costs in any early-stage techno-economic assessment of WEC parks at sites with complex bathymetry. Full article

Mars climatology is a growing interest domain for planetary research and for operational missions. In the last three decades, Martian General Circulation Models have been developed to support the interpretation of spacecraft and telescopic observations and for the advancement of theoretical understanding of the climate. They have been designed to represent key processes, such as dust cycle, seasonal CO₂ condensation, and interaction between boundary layer and surface. At the same time, new observations from orbiters and landers have enhanced the diagnostics, but several uncertainties in the parameterization, especially in dust representation and turbulent mixing, require further improvements. This review represents a synthesis of the state of the art of existing global and regional models, comparing numerical and physical approaches, identifying the main challenges for the next years, with particular attention to the needs of operational missions and machine learning techniques. Full article

This study tackles the middle atmosphere phenomenon known as Mesospheric Inversion Layers (MILs). Reinterpreting Envisat’s GOMOS instrument limb-sounding temperature profiles which we compared to the MSIS-2.0 climatological model, we studied 340,000 resolute temperature profiles, detecting 44,000 (13%) MILs in this dataset. We have shown that MILs are a worldwide phenomenon, concentrated around the tropics and in the Winter Hemisphere’s mid-latitude region (between $30\%$ and $50\%$ of profiles are MILs in those areas). MILs follow a correlation law ($R^2=0.5$ on pure data, $R^2=0.97$ on binned-mean data) between the log-amplitude of its peak and its altitude. Median altitudes are about 70 km worldwide, but the median amplitude reached by equatorial MILs is typically higher (14.5 K compared to the others at 12.5 K). Lastly, equatorial MILs (but not mid-latitude MILs) are correlated with high-difference estimated tide temperature gradient contributions. Results suggest that the MIL is a common phenomenon with statistically consistent characteristics. Seasonal occurrence hinted that there is probably a class of MILs favoured by planetary waves at the edge of the polar vortex, while the equatorial type of inversions seems to occur when the atmospheric tide model flattens the temperature gradient around 70 km. Full article

Tunisia is characterized by high precipitation variability, which results in frequent extreme floods and droughts. This study aims to develop long-term forecasting models for total and daily maximum annual precipitation by incorporating information related to climate variability. These models use low-frequency climate oscillation indices as predictors. A linear functional model for scalar response is developed for this purpose. The model based on functional data analysis is also compared to a linear regression model. The station under study is located in north-eastern Tunisia. The association between precipitation and four climate indices is evaluated: the North Atlantic Oscillation (NAO), the Pacific Decadal Oscillation (PDO), the Mediterranean Oscillation (MO) and the Western Mediterranean Oscillation (WeMO) climate indices. The results show that both linear and functional regression provide good and comparable results, likely due to the limited length of the data series. NAO, PDO and MO are the best indices to forecast total annual precipitation with an RMSE between 3.564% and 4.151% of the average precipitation, while MO seems to be the best index to forecast daily maximum annual precipitation achieving slightly higher RMSE between 11.174% and 11.916% of the average maximum precipitation. These results suggest that total precipitation at the study station is controlled by large-scale climatic processes operating over the Atlantic, Pacific, and Mediterranean regions, whereas the few most extreme precipitation events are primarily driven by regional climatic phenomena occurring at the Mediterranean scale. The results may have practical applications to improve disaster response preparedness and water resource management. Full article

Extratropical cyclones (ETCs) are primary drivers of mid-latitude weather variability, yet most climatologies rely on single-level tracking, leaving their vertical structure poorly characterised. Because the vertical extent of a cyclone reflects its degree of baroclinic coupling and the tropospheric layer in which it resides is closely linked to the dominant physical processes governing its formation and impacts, a multi-level perspective is essential. Using the STACKER 4D tracking algorithm and ERA5 reanalysis (2000–2023), this study provides a comprehensive climatology of shallow ETCs (2–3 pressure levels) across 12 levels (925–100 hPa) over the Southern Hemisphere (14° S–78° S). A total of 21,701 shallow systems were detected, representing 42% of all multi-level ETCs. Classification into three subfamilies, shallow low (SL, 925–600 hPa; 43%), shallow mid (SM, 500–250 hPa; 35%), and shallow upper (SU, 200–100 hPa; 22%), suggests a possible linkage with different physical mechanisms: surface baroclinic instability for SL, upper-level potential vorticity forcing for SM, and tropopause-level dynamics for SU. SM and SU systems, jointly accounting for 57% of shallow events, are unlikely to be detected by conventional single-level-based tracking methods. Three-level systems (S3) exhibit higher vorticity, longer lifetimes, and greater interaction with the UTLS region than two-level systems (S2), with implications for stratosphere–troposphere exchange. Maximum cyclone density is concentrated between 30–40° S and 50–60° S. Full article

Accurate precipitation forecasting is essential for water resource management, flood early-warning systems, and agriculture, but remains difficult because of the nonlinear and highly variable spatiotemporal nature of rainfall. This paper compares four deep learning architectures—a standalone LSTM, a standalone CNN, a hybrid CNN–LSTM, and a Transformer encoder—against three classical baselines (persistence, day-of-year climatology, and per-grid-point ARIMA) for daily precipitation forecasting over Washington State at lead times of one to four days. A 40-year ERA5 dataset (1985–2024) of near-surface air temperature, mean sea-level pressure, and total precipitation is split into training (1985–2012), validation (2013–2015), and test (2016–2024) periods, with the test years held out completely. Each (model, horizon) is trained with three random seeds and evaluated in physical units (mm/day). On the held-out test period, the hybrid CNN–LSTM achieves the lowest RMSE at every horizon $h\ge 2$, with $R^2=0.576\pm 0.007$ and RMSE $=15.08\pm 0.07$ mm/day at $h=4$. Diebold–Mariano tests, paired t-tests, and bootstrap 95% confidence intervals confirm that the CNN–LSTM advantage over the LSTM is statistically significant at horizons 2–4 (but not at $h=1$), while CNN–LSTM is significantly better than every classical baseline and the Transformer at every horizon. The headline result is reproduced under a rolling-origin temporal cross-validation across three non-overlapping splits ($R^2\in [0.576,0.590]$). Practically, the sub-millisecond inference cost of the CNN–LSTM makes it directly deployable in operational forecasting pipelines used for flood early-warning, irrigation scheduling, and reservoir management, where even modest improvements in 3–4-day-ahead RMSE translate into measurable risk reduction and improved decision lead time for water managers and emergency planners. Full article

In his lectures from November 1980 to March 1981, Deleuze describes the immanent and compositional nature of Spinoza’s philosophy expressed in the content, the method, and the form of his writings. Spinoza himself uses in the Ethics and the TP the images of the climatologist studying the weather and the geometric drawing of lines and surfaces for his technical, artisanal, and neutral approach to the affects and political life. His ontology is characterized by the absence of hierarchical order and by nature as the principle and source of diversity. This approach is reminiscent of art, which also orders the chaos of human existence and makes it productive in a free and immeasurable way. Deleuze conceives of Spinoza’s ontology as a practical philosophy, leading him to the examples and the analysis of paintings (and, vice versa, from the art of painting to Spinoza’s philosophy), to which he dedicates his subsequent lectures from March to June 1981. In this article I reflect on the link between Deleuze’s lectures on Spinoza and on painting, and therefore also between Spinoza’s compositional thought itself and painting. Full article

The Northern Sea Route (NSR) is becoming increasingly accessible as Arctic sea ice declines, motivating data-driven forecasts of seasonal navigability. We compiled a 13-year (2013–2025) monthly dataset of AMSR2 sea ice concentration (SIC) and ERA5 atmospheric reanalysis variables over the NSR corridor (68–80° N, 30–180° E) and benchmarked a hierarchy of forecasting models for 1-, 3-, and 6-month lead times. Baselines (climatology, persistence, anomaly persistence, SARIMA, ridge regression) were compared with compact deep learning architectures (LSTM, Transformer; 10,000–70,000 parameters) trained on 12-month sequences with anomaly targets and five-seed ensembles. Three findings emerge. First, the seasonal cycle explains 98.0% of the monthly SIC variance, so climatology alone yields RMSE = 4.56% and three-class navigability accuracy of 87.5%. Second, SARIMA, the compact LSTM ensemble, random forest, and MLP_small all yield small positive skill scores over climatology: SARIMA achieves the lowest 1-month RMSE (3.98%, skill score +0.239), while the compact LSTM ensemble shows positive skill at all horizons (mean skill score +0.038); however, the bootstrap confidence intervals overlap and these differences are not statistically distinguishable from climatology. Third, all skilful models converge to identical classification metrics (accuracy 0.875, macro-F1 0.78, κ = 0.76); McNemar tests and overlapping bootstrap confidence intervals show no statistically significant differences. Permutation importance confirms that AMSR2 ice-state features dominate, whereas the high raw correlations of ERA5 radiation variables collapse after detrending. These results indicate that compact statistical and deep learning models are equivalent for NSR seasonal navigability prediction and that honest baseline comparison is essential when seasonal cycles dominate. Full article

A novel double-criterion early stopping pruning strategy is introduced in this study. The proposed approach enables the structural optimization of a hybrid filtering framework that integrates FeedForward Neural Networks with an adaptive Extended Kalman Filter, by simultaneously monitoring the validation error and the trace of the error covariance matrix. Unlike classical pruning methods, which are applied after the completion of the training process and aggressively remove network neurons, the proposed scheme exploits the learning procedure, achieving a more selective reduction of 2% to 13%, balancing effectively between strong generalization performance and computationally efficient training. The proposed framework is evaluated on wind speed forecasts obtained from a numerical weather prediction model, within a time-varying window scheme, demonstrating promising improvements. Key statistical indices, such as the Mean Absolute Error and the Root Mean Square Error, were significantly reduced, with reductions ranging from approximately 65% to 80% and 60% to 78%, respectively. These findings suggest that the proposed methodology offers a robust and accurate framework for time series forecasting in operational settings. Full article

Accurate multi-day sea surface temperature (SST) prediction at sub-mesoscale resolution is challenging due to nonlinear ocean dynamics, heterogeneous multi-source observations, and error accumulation during autoregressive rollout. This paper proposes a hybrid Mamba–ConvLSTM framework that combines recurrent local spatiotemporal encoding with selective state-space long-range spatial modeling. The ConvLSTM branch captures local spatial patterns and short-range temporal dependencies through convolutional gating, while the Mamba branch captures long-range spatial dependencies across each frame through cross-direction window scanning and maintains temporal coherence via persistent hidden states across successive time steps. A physically informed preprocessing stage aligns 0.083° reanalysis variables to the 0.05° OSTIA target grid via a Grow-and-Cut strategy and extracts gradient-based advection and diffusion proxy features under boundary-aware finite differencing. During autoregressive rollout, auxiliary variables are held at their last observed values and physical proxies are recomputed from the predicted SST, following a clearly specified protocol. Experiments on a South China Sea benchmark compare the proposed model against nine baselines—including persistence, daily climatology, ConvLSTM, PredRNN, ConvGRU, TCTN, PANN, Swin-UNet, and ViT-ST—under an identical data-split, normalization, and rollout protocol. Evaluation with RMSE, MAE, SSIM, $R^2$, and anomaly correlation coefficient (ACC) shows that the proposed model achieves a 10-day average RMSE of 0.512 °C, outperforming the strongest learning-based baseline ViT-ST by 5.0% and the persistence forecast by 21.0%. Ablation studies, sensitivity analyses, seasonal evaluation, and statistical significance testing verify the contribution of each component and the robustness of the results. Full article

Rain-on-snow (ROS) is a hydrometeorological phenomenon in which liquid precipitation falls onto an existing snowpack, augmenting runoff through the combined effects of rainfall and accelerated snowmelt. Anthropogenic climate change is progressively shifting the rain-to-snow partitioning of precipitation and altering land-surface conditions across mid- to high-latitude mountainous regions, thereby heightening flood potential. Most previous work, however, has addressed ROS at regional scales and over historical periods; hemispheric-scale assessments of future ROS dynamics and their implications for flood hazard and societal exposure remain scarce. Here we apply 10 bias-corrected CMIP6 models together with ERA5-Land reanalysis data to project changes in ROS days across the Northern Hemisphere under four Shared Socioeconomic Pathway (SSP) scenarios. ROS days are coupled with flood frequency analysis to quantify changes in ROS flood occurrence, and gridded population and Gross Domestic Product (GDP) data are integrated to evaluate future population-socioeconomic exposure. Under low-to-medium emission scenarios, ROS days increase substantially over historical hotspots, whereas under high-emission scenarios they decline at mid- to high latitudes yet expand into previously unaffected high-latitude and inland cold regions. ROS flood days respond nonlinearly to ROS frequency because progressive snow water equivalent loss limits runoff generation, causing ROS floods to decrease in some mountainous areas even as ROS events become more frequent. Population-socioeconomic exposure exhibits a corresponding polarization: it declines in mid-latitude regions where snow cover is disappearing but rises sharply at high latitudes, with high-emission pathways accelerating the northward migration of disaster risk. These findings bridge critical gaps in large-scale ROS climatology and shed light on future changes in ROS-induced hydrological extremes. Besides, the findings facilitate the creation of regionally focused adaptation strategies and provide useful references for integrating climate model projections with remote sensing observations to improve future monitoring and risk assessment of ROS-related floods. Full article