Search Results (48)

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

Flooding episodes caused by a heavy rainfall event have become more frequent, especially during the rainfall season in Botswana, which poses some socio-economic and environmental risks. This study investigates the capability of the Weather Research and Forecasting (WRF) model in simulating a heavy rainfall event that occurred on 26 December 2023 in Mahalapye District, Botswana. This event is one among many that have negatively impacted the lives and infrastructures in Botswana. The WRF model was configured using the tropical-suite physics schemes, i.e., (Rapid Radiative Transfer Model, Yonsei University planetary boundary layer scheme, Unified Noah land surface model, New Tiedtke, Weather Research and Forecasting Single-Moment six-class) on a two-way nested domain (9 km and 3 km grid spacing) and was initialized with the GFS dataset. Gauged station data was used for verification alongside synoptic charts generated using ECMWF ERA5 dataset. The results show that the WRF model simulation using the tropical-suite physics schemes is able to reproduce the spatial and temporal patterns of the observed rainfall but with some notable biases. Performance metrics, including RMSE, correlation coefficient, and KGE, showed moderate to good agreement, highlighting the model’s sensitivity to physical parameterization and resolution. The results of this study conclude that the WRF model demonstrates promising potential in forecasting extreme rainfall events in Botswana, but more sensitivity tests to different parameterization schemes are needed in order to integrate the model into the early warning systems to enhance disaster preparedness and response. Full article

Recent studies have reported a rapid increase in short-duration, high-intensity rainfall over the Seoul Metropolitan Area (SMA), primarily associated with mesoscale convective systems (MCSs), highlighting the need for high-resolution and multi-platform observations for accurate forecasting. To address this challenge, the Korea Meteorological Administration (KMA) established the Korean Precipitation Observation Program (KPOP), an intensive observation network integrating radar, wind lidar, wind profiler, and storm tracker measurements. This study introduces the design and implementation of the KPOP network and evaluates its observational and forecasting value through a heavy rainfall event that occurred on 17 July 2024. Wind lidar data and weather charts reveal that a strong low-level southwesterly jet and enhanced moisture transport from the Yellow Sea played a key role in sustaining a quasi-stationary, line-shaped rainband over the metropolitan region, leading to extreme short-duration rainfall exceeding 100 mm h⁻¹. To investigate the impact of KPOP observations on numerical prediction, preliminary data assimilation experiments were conducted using the Korean Integrated Model-Regional Data Assimilation and Prediction System (KIM-RDAPS) with WRF-3DVAR. The results demonstrate that assimilating wind lidar observations most effectively improved the representation of low-level moisture convergence and spatial structure of the rainband, leading to more accurate simulation of rainfall intensity and timing compared to experiments assimilating storm tracker data alone. These findings confirm that intensive, high-resolution wind observations are critical for improving initial analyses and enhancing the predictability of extreme rainfall events in densely urbanized regions such as the SMA. Full article

The oxidation of sulfide minerals in the presence of oxygen and water, facilitated by microbes, is the principal cause of acid mine drainage (AMD). Static testing for the quantitative assessment of the acidic potential and acid-neutralizing capacity of mineral samples has been thoroughly investigated; the extent of its accuracy remains uncertain. This study involved 329 ore samples from 34 drill holes from abandoned mining sites and conducted laboratory static tests and mineralogical analysis. Static testing and mineralogical characterization identified a significant positive correlation between total sulfur and net acid generation (NAG), confirming that sulfide oxidation is the dominant mechanism for acid production. Furthermore, the strong positive correlation between calcium content and acid-neutralizing capacity (ANC) demonstrates that the buffering capacity stems mainly from carbonate dissolution, with negligible contribution from silicate weathering. The effectiveness of a detailed acid-generating potential discrimination chart was also assessed. Through the examination of acid drainage samples and groundwater from the research area, with their stable isotope and Deuterium excess (D-excess) properties, hydrochemical classifications were established, and sources of acid drainage were evaluated. This comprehensive method pinpoints the main “acid-generating sources” in the abandoned mining sites, elucidating the geochemical origins of acid drainage in the research area. It offers a case study and analytical framework for employing static test findings from abandoned mining sites to evaluate acid-generating potential in those areas. Full article

The Jucar is a perennial river with a high sedimentary load which has transferred sediment to the continental shelf in the form of a deltaic lobe since pre-historic times. The aim of this study is to analyze the changes that have occurred in the submerged delta of the Jucar since the nineteenth century. With this aim in mind, five nautical charts were georeferenced, covering the period from 1893 to the present day, from which Digital Elevation Models were generated and compared using Geographic Information Systems. The results indicate that the large-scale contributions of the nineteenth century caused the submerged delta to grow during the cold, dry period of the Little Ice Age. In the mid-twentieth century, the flow and solid load of the river were reduced by the construction of dams, leading to the stabilization of the delta. The bursting of the Tous Dam in 1982 and the ensuing ordinary floods that occurred until its reconstruction, led to large amounts of sediment that counteracted the anthropic action generated by the sediment trap of the dams. The climate of the twenty-first century, characterized by frequent extreme weather events, has allowed the deltaic lobe to continue to grow until the present day since these events increased sediment input to the shelf. Coastal erosion is also observed. Full article

Deep learning models for atmospheric pattern recognition require spatially consistent training labels that align precisely with input meteorological fields. This study introduces an automatic cold front detection method using the ERA5 reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF) at 850 hPa, specifically designed to generate physically consistent labels for machine learning applications. The approach combines the Thermal Front Parameter (TFP) with temperature advection (AdvT), applying optimized thresholds (TFP < 5 × 10⁻¹¹ K m⁻²; AdvT < −1 × 10⁻⁴ K s⁻¹), morphological filtering, and polynomial smoothing. Comparison against 1426 manual charts from 2009 revealed systematic spatial displacement, with mean offsets of ~502 km. Although pixel-level overlap was low, with Intersection over Union (IoU) = 0.013 and Dice coefficient (Dice) = 0.034, spatial concordance exceeded 99%, confirming both methods identify the same synoptic systems. The automatic method detects 58% more fronts over the South Atlantic and 44% fewer over the Andes compared to manual charts. Seasonal variability shows maximum activity in austral winter (31.3%) and minimum in summer (20.1%). This is the first automatic front detection system calibrated for South America that maintains direct correspondence between training labels and reanalysis input fields, addressing the spatial misalignment problem that limits deep learning applications in atmospheric sciences. Full article

The growing demand for equitable and efficient transportation solutions has positioned autonomous vehicles (AVs) as a transformative technology with significant potential for rural areas. This literature review examines the challenges and opportunities associated with AV deployment in rural environments, characterized by sparse infrastructure, diverse road conditions, and aging populations. Using a systematic analysis of field tests, simulation-based studies, and survey research, key obstacles are identified, including limited lane markings, unpaved roads, digital connectivity gaps, and user acceptance issues. The results highlight the critical role of advancements in sensor technology, localization methods, and edge computing in addressing these barriers. Additionally, strategic infrastructure modifications, such as enhanced road signage and reliable communication systems, are essential for AV integration. This paper emphasizes the need for tailored AV solutions to meet the specific requirements of rural settings, including adaptability to adverse weather conditions and mixed traffic environments. Insights into public perception reveal the importance of trust-building initiatives and community engagement to foster widespread acceptance. The findings provide actionable recommendations for policymakers, industry leaders, and infrastructure operators, focusing on scalable deployment strategies, policy adaptations, and sustainable solutions. By addressing these challenges, AVs enhance mobility, safety, and accessibility, transforming rural transportation networks into more equitable and efficient systems. This review serves as a foundational reference for future research, charting pathways for the integration of AVs in rural contexts. Full article

Amidst the increasingly escalating global concern regarding climate change, adopting a low-carbon approach has become crucial for charting the future developmental trajectory of urban areas. It also offers a novel angle for cities to avoid high-temperature risks. This paper estimates carbon emissions in Wuhan City from both direct and indirect aspects. Then, the ANN (artificial neural network)–CA (Cellular Automata) model is employed to establish three distinct development scenarios (Ecological Priority, Tight Growth, and Natural Growth) to predict future urban expansion. Additionally, the WRF (Weather Research and Forecasting Model)—UCM (Urban Canopy Model) model is used to investigate the thermal environmental impacts of varying urban development scenarios. This study uses a low-carbon perspective to explore how cities can develop scientifically sound urban strategies to meet climate change challenges and achieve sustainable development goals. The conclusions are as follows: (1) The net carbon emission for Wuhan in 2022 is estimated to be approximately 20.8353 million tonnes. Should the city maintain an average annual emission reduction rate of 10%, the carbon sink capacity of Wuhan would need to be enhanced by 382,200 tonnes by 2060. (2) In the absence of anthropogenic influence, there is a propensity for the urban construction zone of Wuhan to expand primarily towards the southeast and western sectors. (3) The Ecological Priority (EP) and Tight Growth (TG) scenarios are effective in alleviating the urban thermal environment, achieving a reduction of 0.88% and 2.48%, respectively, in the urban heat island index during afternoon hours. In contrast, the Natural Growth (NG) scenario results in a degradation of the urban thermal environment, with a significant increase of over 4% in the urban heat island index during the morning and evening periods. (4) An overabundance of urban green spaces and water bodies could exacerbate the urban heat island effect during the early morning and at night. The findings of this study enhance the comprehension of the climatic implications associated with various urban development paradigms and are instrumental in delineating future trajectories for low-carbon sustainable urban development models. Full article

This study is aimed to improve the intelligence level, efficiency, and accuracy of ship safety and security systems by contributing to the development of marine weather forecasting. The accurate and prompt recognition of weather fax charts is very important for navigation safety. This study employed many artificial intelligent (AI) methods including a vectorization approach and target recognition algorithm to automatically detect the severe weather information from Japanese and US weather charts. This enabled the expansion of an existing auto-response marine forecasting system’s applications toward north Pacific and Atlantic Oceans, thus enhancing decision-making capabilities and response measures for sailing ships at actual sea. The OpenCV image processing method and YOLOv5s/YOLO8vn algorithm were utilized to make template matches and locate warning symbols and weather reports from surface weather charts. After these improvements, the average accuracy of the model significantly increased from 0.920 to 0.928, and the detection rate of a single image reached a maximum of 1.2 ms. Additionally, OCR technology was applied to retract texts from weather reports and highlighted the marine areas where dense fog and great wind conditions are likely to occur. Finally, the field tests confirmed that this auto and intelligent system could assist the navigator within 2–3 min and thus greatly enhance the navigation safety in specific areas in the sailing routes with minor text-based communication costs. Full article

Representative weather data are fundamental to characterizing a place and determining ideal design approaches. This is particularly important for large countries like Brazil, whose extension and geographical position contribute to defining diverse climatic conditions along the territory. In this context, this study intends to characterize the Brazilian territory based on a 15-year weather record (2008–2022), providing a climatic assessment based on a climatic and bioclimatic profile for the whole country. The climate analysis was focused on temperature, humidity, precipitation, and solar radiation, followed by a bioclimatic analysis guided by the Givoni chart and the natural ventilation potential assessment. In both situations, the results were analyzed using three resolutions: country-level, administrative division, and bioclimatic zones. This study also identified representative locations for the Brazilian bioclimatic zones for a building-centered analysis based on the thermal and energy performance of a single-family house with different envelope configurations. The results proved that most Brazilian territories increased above 0.4 °C in the dry bulb temperature and reduced relative humidity. The precipitation had the highest reduction, reaching more than 50% for some locations. The warmer and drier conditions impacted also the Köppen–Geiger classification, with an increase in the number of Semi-Arid and Arid locations. The bioclimatic study showed that ventilation is the primary strategy for the Brazilian territory, as confirmed by the natural ventilation potential results, followed by passive heating strategies during the year’s coldest months. Finally, building performance simulation underlined that, in colder climates, indoor thermal comfort conditions and air-conditioning demands are less affected by solar absorptance for constructions with low U-values, while in warmer climates, low solar absorptance with intermediary U-values is recommended. Full article

Recent advancements in computational technologies have enhanced the importance of meteorological modeling, driven by an increased reliance on weather-dependent systems. This research implemented a lightning data assimilation technique to improve short-term weather forecasts in South America, potentially refining initialization methods used in meteorological operation centers. The main goal was to implement and enhance a data assimilation algorithm integrating lightning data into the WRF model, assessing its impact on forecast accuracy. Focusing on southern Brazil, a region with extensive observational infrastructure and frequent meteorological activity, this research utilized several data sources: precipitation data from the National Institute of Meteorology (INMET), lightning data from the Brazilian Lightning Detection Network (BrasilDAT), GOES-16 satellite images, synoptic weather charts from the National Institute for Space Research (INPE), and initial conditions from the GFS model. Employing the WRF-ARW model version 3.9.1.1 and WRFDA system version 3.9.1 with 3DVAR methodology, the study conducted three experimental setups during two meteorological events to evaluate the assimilation algorithm. These included a control (CTRL) without assimilation, a lightning data assimilation (LIGHT), and an adaptive humidity threshold assimilation (ALIGHT). Results showed that the lightning data assimilation system enhanced forecasts for large-scale systems, especially with humidity threshold adjustments. While it improved squall line timing and positioning, it had mixed effects when convection was thermally driven. The lightning data assimilation methodology represents a significant contribution to the field, indicating that using such alternative data can markedly improve short-term forecasts, benefiting various societal sectors. Full article

Thin ice with a thickness of less than half a meter produces strong salt and heat fluxes which affect deep water circulation and weather in the polar oceans. The identification of thin ice areas is essential for ship navigation. We have developed thin ice detection algorithms for the AMSR2 and FY-3C MWRI radiometer data over the Arctic Ocean. Thin ice (<20 cm) is detected based on the classification of the H-polarization 89–36-GHz gradient ratio (GR8936H) and the 36-GHz polarization ratio (PR36) signatures with a linear discriminant analysis (LDA) and thick ice restoration with GR3610H. The brightness temperature ($T_B$) data are corrected for the atmospheric effects following an EUMETSAT OSI SAF correction method in sea ice concentration retrieval algorithms. The thin ice detection algorithms were trained and validated using MODIS ice thickness charts covering the Barents and Kara Seas. Thin ice detection is applied to swath $T_B$ datasets and the swath charts are compiled into a daily thin ice chart using 10 km pixel size for AMSR2 and 20 km for MWRI. On average, the likelihood of misclassifying thick ice as thin in the ATIDA2 daily charts is 7.0% and 42% for reverse misclassification. For the MWRI chart, these accuracy figures are 4% and 53%. A comparison of the MWRI chart to the AMSR2 chart showed a very high match (98%) for the thick ice class with SIC > 90% but only a 53% match for the thin ice class. These accuracy disagreements are due to the much coarser resolution of MWRI, which gives larger spatial averaging of $T_B$ signatures, and thus, less detection of thin ice. The comparison of the AMSR2 and MWRI charts with the SMOS sea ice thickness chart showed a rough match in the thin ice versus thick ice classification. The AMSR2 and MWRI daily thin ice charts aim to complement SAR data for various sea ice classification tasks. Full article

In view of the problem that the exchange effectiveness is calculated according to a fixed value or only considering the influence of outdoor air parameters when analyzing the suitability of total heat recovery for plate heat recovery equipment in air-conditioned spaces without humidity control, the indoor humidity calculation model and moisture balance equation were established in this research to predict relative indoor humidity. Moreover, the relationship between total heat recovery, effective heat recovery, and the reduction in outdoor air heating load was analyzed using a psychrometric chart of the outdoor air treatment process. Referring to the standard for weather data of building energy efficiency in the Ningbo region, 6 typical days were taken as the calculation conditions. The moisture balance differential equation was solved using MATLAB software to obtain numerical solutions for the hourly indoor air humidity ratio, relative humidity, exchange effectiveness, and effective heat recovery when adopting an air-to-air total heat exchanger in an air-conditioned room of an office, classroom, or commercial building in the winter. The results indicate that, under the calculation conditions, the relative indoor humidity of commercial buildings is relatively high, making it unsuitable for a total heat exchanger. The relative humidity of indoor air in offices and classrooms can be maintained above 30%, and the total exchange effectiveness of a total heat exchanger is between 45% and 100%. The effective total heat recovery was calculated as sensible heat recovery under most calculation conditions. Full article

This paper aims to analyze the weather conditions in Porto Velho (Rondonia, Brazil, Western Amazon) and the influence of air masses on the climatic elements between 2017 and 2018, using rhythmic analysis. Climatic data were obtained through the official weather station, tabulated and statistically organized, and processed in R Studio programming language. The monitoring of air masses occurred through the synoptic charts of the Navy Hydrography Center. The results were analyzed by dry–rainy transition season, rainy season, wet–dry transition season, and dry season. Thus, the results point out that the Tropical Continental mass (mTc) acted up to 62.9%, responsible for the low precipitation index in October 2017. Although the mass has characteristics of warm and unstable weather, it is even lower than the action of the mEc. In January 2018, there was an 85.5% prevalence of the Continental Equatorial Mass (mEc), added to the action of the South Atlantic Convergence Zone (ZCAS), which contributed to an accumulated rainfall of 443 mm/month. In April 2018, the mEC acted with 56.7%, reaching 35.5% in August. Another highlight was the performance of the Tropical Atlantic mass (mTa) (27.4%) and mTc (19.4%), both of which had a crucial role in the dry season, followed by the Polar Atlantic mass (mPa) (17.7%), that contributed to the phenomenon of “coldness” in the region. Therefore, the mEc is extremely important in the control of the relative humidity of the air and the precipitations, while the mTc is a dissipator of winds that, at times, inhibits the performance of the mEc. Full article

Sailing vessel navigators always want to receive state-of-the-art prompt and accurate marine weather-forecasting services. However, the weather-routing services by private sectors are expensive. Further, forecasting results from public institutes are usually free, and they are not in real-time or numerical modes, so they are not quite suitable for small-size or offshore vessels. In this study, an intelligent system was constructed for delivering sea forecasting at specific areas according to the navigator’s order. The system can automatically obtain web-based forecasting charts issued from multi-source meteorological agencies and convert the regional information into numerical text at requested points. During this step, several intelligent algorithms, like the OpenCV digital image processing algorithm and the YOLO wind vector deep learning recognition method, were applied. By applying this state-of-the-art system, navigators on board do not need to download different institutional graphics (usually with large stream bytes) to explore the future states of the sea surface in a specific area in the sailing route but can obtain the multi-source text forecasting information just by sending the area coordinates to a designated email address. The field tests confirmed that this auto-intelligent system could assist the navigator within a few minutes and thus greatly enhance the navigation safety with minor text-based communication costs. It is expected that by improving the efficiency of marine services and bringing in more artificial intelligence technology, maritime security would be more sustainable. Full article