Search Results (339)

Modern web mapping technologies implement web standards that make the visualization of geoscience data on the web possible using various methods, offering a high degree of customizability for creating web maps. In meteorology, synoptic surface weather charts serve as crucial products to communicate observed surface weather at a point in time. To convey such information, these maps implement complex symbology, such as a multi-element surface station model symbol to indicate station data, isobars, and special line symbology to visualize weather fronts. Synoptic messages (SYNOP standard numerical code by WMO) are periodic meteorological reports of weather observations, exchanged by national meteorological services around the globe. This study focuses on visualizing surface weather data decoded from SYNOP reports. The paper introduces an open-source JavaScript module, which handles data decoding and dynamic symbol generation, using a WMO-compliant method for creating station model vector symbols for observational GeoJSON data on the client-side, in an interactive web mapping environment. Its output is compatible with popular, open-source web mapping libraries. It runs Python in the browser with Pyodide and makes use of the Web Workers API for parallelization, speeding up the decoding and visualization process without blocking the user interface thread. The developed module intends to help with easy representation of surface weather observations on web maps used in meteorology, which can also be implemented in a dynamically updated server–client architecture. The code is presented with a ready-to-use wrapper for Leaflet. Full article

Spring frosts pose a major threat to tea production, causing severe damage to tender spring buds and substantial economic losses. To support timely frost protection measures, this study develops an interpretable machine learning framework for next-day frost forecasting in a tea plantation in Danyang, eastern China. Leveraging nine years (2008–2016) of multi-source data—including high-resolution on-site meteorological observations and daily records from surrounding regional stations—we engineered a comprehensive set of predictive features capturing local microclimatic, regional synoptic, and short-term temporal dynamics. A two-stage feature selection approach, combining Spearman correlation screening with SHAP-based importance ranking, identified an optimal subset of 14 robust predictors. Among eight benchmarked models, XGBoost achieved the best performance on a chronologically held-out test set, yielding a CSI of 0.736, accuracy of 91.0%, F1-Score of 0.848 and AUC-ROC of 0.968. Ablation experiments demonstrated the added value of data integration: model performance improved from a CSI of 0.617 (using only local data) to 0.736 (with full multi-source inputs). SHAP interpretability analysis further revealed that the model’s predictions align with established frost formation physics, highlighting key drivers such as nocturnal cooling rate and regional humidity. This work demonstrates that integrating multi-scale meteorological data with interpretable machine learning offers a reliable, transparent, and operationally viable tool for frost risk management—providing actionable insights to enhance resilience in precision horticulture for perennial crops like tea. Full article

Egypt generally experiences a hot and arid climate, with rainfall primarily confined to the northern coast during winter season. However, on 31 May 2025, Alexandria experienced an unusual late-spring convective storm that was associated with heavy rainfall, strong winds, intense lightning, and localized hail. This rare event caused temporary disruptions to urban life and underscored the growing vulnerability of coastal cities to short-duration, high-intensity precipitation events occurring outside the climatological rainy season. This study investigates the atmospheric mechanisms underlying this event through a comprehensive synoptic and dynamic analysis of pressure systems, wind fields, and temperature structures extending from the surface to the 200 hPa level. Particular emphasis is placed on the role of moisture convergence and upper-level dynamical forcing in triggering the rapid development of deep convection. Furthermore, the influence of anomalous large-scale circulation patterns on storm initiation and intensification is systematically examined. Improved understanding of these processes provides valuable insight into off-season convective activity over the southeastern Mediterranean and enhances forecasting capability, risk assessment, and early warning strategies for similar extreme events in the region. Furthermore, the influence of anomalous large-scale circulation patterns on storm initiation and intensification is quantitatively assessed to clarify their contribution to the event’s development. A deeper understanding of these processes offers critical insight into the mechanisms governing off-season convective activity over the southeastern Mediterranean and strengthens forecasting skill, risk assessment frameworks, and early warning systems for comparable extreme events in the region. Full article

Mediterranean cyclones over North Africa were analyzed using CyTRACK and ERA5 reanalysis data for the period 2015–2025. Cyclones were classified by minimum sea level pressure into Very Deep, Deep, Moderate, and Weak categories, and their structural characteristics—including spatial extent, lifetime, and associated synoptic-scale systems—were examined. The relationship between cyclone activity and monthly precipitation was assessed for Morocco, Algeria, Tunisia, Libya, and Egypt, revealing substantial spatial variability in rainfall response. Egypt exhibited the strongest correspondence between cyclone frequency and precipitation, while other countries showed weaker or inconsistent associations, highlighting the role of cyclone intensity and moisture availability in driving regional hydroclimatic impacts. This intensity-resolved, region-specific analysis provides a comprehensive view of Mediterranean cyclone behavior and its influence on rainfall extremes, offering a valuable framework for improved forecasting, risk assessment, and climate resilience planning in the southern Mediterranean. Full article

Atmospheric Rivers (ARs) are filamentary moisture pathways responsible for a large fraction of extreme precipitation and often occur as interacting filament bundles within the same synoptic regime. Existing diagnostics typically analyze ARs in isolation, despite the frequent coexistence and interaction of multiple filaments. We introduce a topological framework for AR analysis based on framed braids and framed braidoids, which encodes both the geometric interaction of AR centroids and the internal evolution of moisture transport. In this approach, AR filaments are represented as strands whose time-ordered crossings form braid words, while moisture-based framing captures internal intensification or weakening along each filament. Applying this framework to reanalysis-derived Atmospheric River track data, we construct braid and framed braid representations over sliding time windows and analyze a strongly interacting multi-filament AR episode in the North Pacific. The results show that braid-based indicators capture structural reorganizations and moisture intensification episodes that are not apparent from centroid geometry or IVT magnitude alone, offering a complementary structural perspective on atmospheric moisture transport. Full article

In July 2024, a severe rainfall event struck Sichuan Province, Southwest China, triggering deadly landslides and causing significant societal impacts. This study investigates the spatiotemporal characteristics and underlying mechanisms of the event using high-resolution surface observations, radar reflectivity, and ERA5 reanalysis data. The rainfall exhibited distinct mesoscale organization, with two primary precipitation centers identified: subregion A located within the plateau-lain transitional zone of the western Sichuan Basin, and subregion B situated over the Chengdu Plain. Synoptic-scale analysis indicated that the rainfall developed under favorable large-scale atmospheric conditions, including a mid-tropospheric trough, a pronounced low-level jet, and a well-defined Southwest Vortex (SWV), which is a dominant lower-tropospheric circulation system in this region. The evolution of rainfall was closely tied to the initiation and subsequent eastward progression of the SWV. The rainfall-producing mesoscale convective system (MCS) first formed over subregion A at approximately 2300 BST (UTC + 8) on 19 July. Vorticity budget diagnostics revealed that vertical advection and low-level convergence significantly contributed to vortex intensification during this initial phase, closely associated with the orographic lifting of low-level airflow. Convective activity in subregion B commenced roughly four hours later, coinciding with the eastward propagation of the SWV, during which horizontal vorticity advection became the primary mechanism sustaining the vortex. After 1400 BST on 20 July, the SWV weakened significantly, leading to the dissipation of the MCS and the cessation of rainfall. Full article

Soil salinization is a widespread form of land degradation that severely constrains agricultural productivity and ecosystem stability. Efficient and transferable monitoring methods are therefore essential for large-scale salinization assessment. Remote sensing provides timely and synoptic observations, while the integration of multi-source datasets offers complementary spectral and spatial information. In this study, we developed a cross-platform spectral index specifically for soda saline–alkali (carbonate/bicarbonate-dominated) soils by integrating laboratory spectra and hyperspectral satellite observations through a collaborative, cross-dataset spectral feature selection framework. Dual-band spectral indices were constructed from transformed reflectance spectra, and a stepwise coupled correlation analysis was applied to identify representative candidates that consistently exhibited strong associations with log-transformed soil electrical conductivity (logEC) across datasets. An optimal central-wavelength analysis was then performed to determine a stable and transferable band pair. The study was conducted in the Songnen Plain of Northeast China using laboratory-measured soil spectra and Ziyuan-1 02D Advanced Hyperspectral Imager data, and the proposed index was further validated using Landsat-8 and Sentinel-2 Multispectral data. Results show that the proposed Difference Index based on Square Root Reflectance at 520 nm and 900 nm (DISRR520900) exhibited consistent relationships with logEC (R = 0.60 for hyperspectral satellite data and R = 0.82 for laboratory spectral data), outperforming commonly used salinity indices in terms of cross-sensor stability. The spatial distribution of soil salinization derived from DISRR520900 is highly consistent with true-color imagery, and multi-source data fusion further improves mapping continuity and spatial coverage. It should be noted that the proposed index is primarily applicable to bare or sparsely vegetated soil surfaces in soda saline–alkali regions. Under dense vegetation cover, substantial crop residue, or wet surface conditions, additional masking or correction may be required. These results demonstrate that DISRR520900 provides a stable cross-sensor solution for large-scale soil salinization mapping within comparable soil chemical contexts. Full article

The 2025 fire season in Portugal was marked by large fires, underscoring the vulnerability of the forested areas to fire. The study analyzes the main meteorological conditions during a critical period of fire activity and addresses the following question: Why can the northeast (NE) weather pattern be so critical for fire danger in Portugal? Fire severity in the Arouca wildfire, the largest fire of the period, was estimated using a methodology that integrates foundation vision models with computer vision algorithms. ECMWF analyses and convection-permitting Meso-NH simulations are used to examine large-scale circulation and the mesoscale environment, respectively. Synoptic-scale analysis revealed the Azores anticyclone centered slightly northwest of the Iberian Peninsula (IP), with its eastern sector directly affecting the northern IP under north/northeast winds. The hectometric-scale simulation demonstrated that orographically enhanced wind gusts over the northern Portuguese mountains substantially intensified near-surface fire-weather conditions when the winds were nearly easterly. Furthermore, strong low-level winds and atmospheric stability constrained vertical plume growth, favoring horizontal smoke transport. In addition, the study highlights that Arouca’s fire had 88% of its area affected with moderate to high severity. Overall, the results demonstrate that the interaction between large-scale NE circulation and local orography plays a decisive role in amplifying fire danger in northern Portugal, emphasizing the need for high-resolution atmospheric modeling to identify fire-prone regions under specific synoptic patterns. Full article

This study investigates the scaling properties of rainfall in Tunis over temporal scales ranging from 5 min to 2.5 years using high-resolution rain gauge data from three recording stations. We employ the Universal Multifractal (UM) framework to characterize scaling properties across multiple temporal regimes. The UM model was selected over alternative multifractal approaches because of its parsimonious three-parameter formulation (C₁, α, H). It explicitly accounts for non-conservative processes through the Fractionally Integrated Flux (FIF) extension and includes established bias correction methods for highly intermittent signals. This framework has demonstrated universality across diverse climatic conditions and enables direct comparison with existing rainfall studies in Mediterranean environments. Spectral analysis reveals three distinct scaling regimes: micro-scale (5 min–2 h 40 min), meso-scale (2 h 40 min–7 days), and synoptic scale (>7 days). The non-conservative nature of the micro-scale regime is addressed through a multifractal fractionally integrated flux model. A key challenge in applying UM analysis to rainfall data is the prevalence of low and zero rain rates (>98% zeros in our dataset). This extreme intermittency introduces significant bias in parameter estimation. Existing correction methods require either continuous rain sequences—scarce in semi-arid climates—or are limited to moderate intermittency levels. We propose an empirical correction method that extends the existing semi-empirical approach by explicitly linking the percentage of zero values to biased UM parameters through empirical relationships applicable to sequences with as few as 50% rainy observations. This advancement enables reliable parameter estimation from highly intermittent datasets. In such conditions, traditional event-by-event analysis yields insufficient samples (only five continuous events longer than 2 h 40 min over 2.5 years in Tunis). The corrected estimates (α = 1.63, C₁ = 0.16 for micro-scales) demonstrate strong consistency with continuous rainfall events and align well with high-resolution studies, validating our approach for extreme intermittency conditions characteristic of Mediterranean semi-arid climates. Full article

Low-level jets (LLJs) are important mesoscale features in the Arctic and are highly relevant for the atmospheric transport of heat, moisture, and air pollutants, as well as for wind energy and aircraft operations. In this paper, LLJs at the Tiksi observatory in the Laptev Sea region are investigated during the period 2014–2020 using simulations performed with the regional climate model CCLM with a 5 km resolution. The main synoptic weather patterns for LLJs at Tiksi were identified using a self-organizing map (SOM) analysis. LLJs occurred in about 55% of all profiles with an average height of about 400 m and an average speed of about 13 m/s. About 60% of the LLJs had core speeds larger than 10 m/s (strong jets). The occurrence frequency for all jets showed a pronounced seasonal cycle with more and stronger LLJs during winter. The turbulent kinetic energy in the lower ABL was four times as large for LLJs than for situations without LLJs, which underlines the impact of LLJs on turbulent processes in the ABL. The mean duration of LLJ events (duration of at least 6 h) was almost 24 h and the 90th percentile was about two days. About 70% of the LLJ events were associated with downslope winds of the local mountain ridge and had a longer duration of about three days for the 90th percentile. Full article

Twin wildfires burned over 9500 ha in Seferihisar, İzmir, western Türkiye, on 29—30 June 2025 under extreme fire weather conditions. This study reconstructs the spatiotemporal progression of the fires and examines the drivers of contrasting behaviors and burn severity. Multi-source datasets—Sentinel-2 imagery, VIIRS/MODIS thermal detections, MTG images and thermal detections, aerial photos, and ground data—were integrated to delineate progression polygons and compute rate of spread (ROS), fuel consumption (FC), and fire-line intensity (FI). Kuyucak fire showed rapid early growth, burning 3554 ha in 2.5 h (mean ROS of 5.0 km h⁻¹; mean FI of 37,789 kW m⁻¹), driven by strong northeasterly winds of 40–50 km h⁻¹, steep terrain, dense Pinus brutia fuels, and very low dead fine-fuel moisture (<6%). Kavakdere fire advanced more slowly (mean ROS of 1.6 km h⁻¹) across open grassland and cropland, yielding lower FC and FI. Synoptic analysis revealed a strong pressure-gradient-induced northeasterly wind regime linked to a mid-tropospheric geopotential height dipole between Central Europe and the Eastern Mediterranean, while WRF simulations indicated a dry boundary layer and enhanced low-level winds during peak spread. Sentinel-2 dNBR burn severity mapping showed substantial spatial variability tied to fuel and topography contrasts. Findings demonstrate how twin ignitions under similar weather conditions can produce divergent outcomes, underscoring the need for terrain- and fuel-aware strategies during extreme Mediterranean fire outbreaks. Full article

The increasing occurrence of unfavorable meteorological conditions under global warming has significantly impacted urban atmospheric environments, particularly ozone (O₃) and fine particulate matter (PM_2.5) pollution in densely populated cities. Using nationwide air quality observations and reanalysis data from 2013 to 2022, we assessed the variations in three typical unfavorable meteorological conditions—heatwave (HW), atmospheric stagnation (AS), and temperature inversion (TI)—in Eastern China and their influences on air pollution, as well as the large-scale synoptic drivers behind them. Results indicate that HW and AS events have increased substantially by 9.61 and 1.72 days/decade, leading to remarkable rises in O₃ and PM_2.5 concentrations. Compound events (e.g., HW + AS and HW + TI) exhibit even stronger synergistic impacts, raising O₃ and PM_2.5 concentrations by more than 57.34% and 46.76%, respectively, compared to individual events. In addition, by applying the T-mode Principal Component Analysis (T-PCA), this study identified typical synoptic patterns favorable for such conditions and air pollution events. Synoptic patterns such as the northward displacement of Western Pacific Subtropical High (WPSH) were identified as critical large-scale drivers. These findings highlight linkages between unfavorable meteorological conditions and air quality, providing scientific support for air-quality management and pollution control in Eastern China. Full article

Accurate precipitation mapping is essential for effective disaster management; however, individual radar, satellite, and numerical weather prediction products often struggle in the topographically complex terrain of South Korea. This study proposes a high-resolution (~500 m) daily precipitation fusion framework that integrates Korea Meteorological Administration (KMA) radar, Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for GPM (IMERG), and Local Data Assimilation and Prediction System (LDAPS) data. The framework employs a Random Forest model augmented with a monthly Empirical Cumulative Distribution Function (ECDF) correction. Auxiliary predictors are incorporated to enhance physical interpretability and stability, including terrain attributes to represent orographic effects, land-cover information to account for surface-related modulation of precipitation, and seasonal cyclic signals to capture regime-dependent variability. These predictors complement dynamic precipitation inputs and enable the model to effectively capture nonlinear spatiotemporal patterns, resulting in improved performance relative to individual radar, IMERG, and LDAPS products. Evaluation against Automated Synoptic Observing System (ASOS) observations yielded a correlation coefficient of 0.935 and a mean absolute error of 3.304 mm day⁻¹ in a Leave-One-Year-Out (LOYO) validation for 2024. Regional analyses further indicate substantial performance gains in complex mountainous areas, including the Yeongdong–Yeongseo region, where the proposed framework markedly reduces estimation errors under challenging winter conditions. Overall, the results demonstrate the potential of the proposed fusion framework to provide robust, high-resolution precipitation estimates in regions characterized by strong topographic and seasonal heterogeneity, supporting applications related to hazard analysis and hydrometeorological assessment. Full article

Synoptic assessment has gained prominence in higher education as a way to bridge fragmented curricula by enabling students to synthesize knowledge across modules. However, structural integration through assessment does not automatically produce authentic learning. Drawing on theoretical analysis and three reflective case studies from UK undergraduate programmes, this paper offers a critical practitioner perspective on how synoptic assessment and authentic learning intersect in practice. We argue that integration and authenticity represent distinct pedagogical imperatives that require deliberate alignment. Through comparative analysis of successful, partially successful, and unsuccessful implementations of assessment strategies, we demonstrate that authentic learning emerges not from integration per se, but from intentional design embedding real-world relevance, developmental scaffolding, clear purpose, and student agency. Our case studies reveal that without such intentionality, synoptic assessments risk becoming structurally coherent but pedagogically hollow exercises that fail to engage students meaningfully. Key challenges include inconsistent staff understanding, inadequate contextual framing, and insufficient attention to progressive capability development. We propose practical design principles grounded in practitioner experience: embedding authenticity through professional relevance, scaffolding complexity appropriately, enabling open-ended student responses, and establishing strong programme-level leadership with authority over assessment strategy. The core contribution of the paper is to articulate these design principles for embedding authenticity within synoptic assessment at programme level, particularly in increasingly modularised and flexible curricula, such as those designed to enable lifelong learning. By positioning integration as necessary but insufficient for authentic learning, we advance critical understanding of assessment reform and address emerging tensions between programme coherence and increasingly modularized curricula serving diverse learner pathways. Full article

The Canadian Prairies are a region of critical importance to continental hydroclimate and agriculture, exhibiting high sensitivity to variability in atmospheric moisture transport. This study investigates the seasonal and interannual variability of integrated moisture flux over the Canadian Prairie region (96° W–114° W, 49° N–53° N) using the National Centers for Environmental Prediction (NCEP) Reanalysis dataset from 1979 to 2023. We employ a combination of composite analysis and Empirical Orthogonal Function (EOF) analysis to identify the dominant modes of variability and their associated large-scale synoptic drivers. Our results confirm a strong seasonal reversal: winter moisture flux is predominantly zonal (westerly), contributing an average of 90% to total inbound flux, while summer flux is primarily meridional (southerly), contributing a dominant 72.6%. Composite analysis of extreme moisture years reveals that anomalously high-moisture winters are associated with an intensified Aleutian Low and a strengthened pressure gradient off the North American west coast, facilitating enhanced westerly flow. Conversely, a strengthened continental high-pressure system characterizes anomalously low-moisture winters. During summer, high-moisture years are driven by an enhanced southerly component of the flow, likely linked to a strengthened Great Plains Low-Level Jet (GPLLJ). The first EOF mode for winter explains 43% of the variance in eastward flux and is characterized by a pattern consistent with the El Niño Southern Oscillation (ENSO) teleconnection pattern. These findings underscore the control of Pacific-centric circulation patterns on Prairie hydroclimate in winter and have significant implications for predicting seasonal water availability. Full article