Meteorological Service Research Articles

Bibliometric Analysis of Weather Radar Research from 1945 to 2024: Formations, Developments, and Trends.

In the development of meteorological detection technology and services, weather radar undoubtedly plays a pivotal role, especially in the monitoring and early warning of severe convective weather events, where it serves an irreplaceable function. This research delves into the landscape of weather radar research from 1945 to 2024, employing scientometric methods to investigate 13,981 publications from the Web of Science (WoS) core collection database. This study aims to unravel, for the first time, the foundational structures shaping the knowledge domain of weather radar over an 80-year period, exploring general features, collaboration, co-citation, and keyword co-occurrence. Key findings reveal a significant surge in both publications and citations post-1990, peaking in 2022 with 1083 publications and 13832 citations, signaling sustained growth and interest in the field after a period of stagnation. The United States, China, and European countries emerge as key drivers of weather radar research, with robust international collaboration playing a pivotal role in the field's rapid evolution. Analysis uncovers 30 distinct co-citation clusters, showcasing the progression of weather radar knowledge structures. Notably, deep learning emerges as a dynamic cluster, garnering attention and yielding substantial outcomes in contemporary research efforts. Over eight decades, the focus of weather radar investigations has transitioned from hardware and software enhancements to Artificial Intelligence (AI) technology integration and multifunctional applications across diverse scenarios. This study identifies four key areas for future research: leveraging AI technology, advancing all-weather observation techniques, enhancing system refinement, and fostering networked collaborative observation technologies. This research endeavors to support academics by offering an in-depth comprehension of the progression of weather radar research. The findings can be a valuable resource for scholars in efficiently locating pertinent publications and journals. Furthermore, policymakers can rely on the insights gleaned from this study as a well-organized reference point.

Проверка сообщений о преступлениях, связанных с лесными пожарами

The investigation of crimes associated with forest fires is a critical endeavor aimed at determining the causative factors preceding the commencement of criminal proceedings. Key aspects under scrutiny encompass the origin of the forest fire, extent of its propagation, magnitude of material damage incurred, employed technical means and methodologies for fire suppression, and identification of individuals present within the boundaries of the forested area where ignition occurred. The investigative protocol entails a comprehensive array of measures including on-site inspection of the forest fire locus, testimonies elicited from eyewitnesses and lessees of forested parcels, procurement and scrutiny of documents sourced from meteorological services, territorial forest protection authorities, and commissioning of judicial expertise. Additionally, the judicious utilization of unmanned aerial vehicles (UAVs) in fire scene reconnaissance is advocated for its intrinsic safety benefits to operational personnel. Moreover, the significance of satellite-derived imagery depicting pre- and post-fire forested terrain conditions is underscored for facilitating causal inference regarding forest fire occurrences. In conclusion, the investigation of crimes associated with forest fires embodies a symbiotic interplay between scientific inquiry and legal scrutiny, wherein the convergence of empirical evidence and jurisprudential deliberation serves as the linchpin of investigative integrity and judicial recourse. Moving forward, continued investment in research and technological development is imperative to augment the efficacy and expediency of forest fire investigations, thereby fortifying societal resilience against the perils of environmental degradation and criminal malfeasance.

Evaluation of Gridded Rainfall Products in Three West African Basins

In recent years, accessing rainfall data from ground observation networks maintained by national meteorological services in West Africa has become increasingly challenging. This is primarily due to high acquisition costs and the often sparse distribution of rainfall gauges across the region, which limits their use in hydrological studies and related research. At the same time, the rising availability of precipitation products derived from satellite/earth observations, reanalysis datasets, and in situ measurements presents exciting prospects for hydrological applications. Nonetheless, these datasets constitute indirect measurements, necessitating rigorous validation against ground-based rainfall data. This study comprehensively assesses twenty-three gridded rainfall products, including sixteen from satellites, six from reanalysis data, and one from in situ measurements, across the Senegal, Gambia, and Casamance River basins. Performance evaluation is conducted across distinct climatic zones, both pre- and post-resampling against observed rainfall data gathered from forty-nine rainfall stations over a six-year period (2003–2008). Evaluation criteria include the Kling–Gupta Efficiency (KGE) and Percentage of Bias (PBIAS) metrics, assessed at daily, monthly, and seasonal time steps. The results reveal distinct performance levels among the evaluated rainfall products. RFE, ARC2, and CPC notably yield the highest KGE scores at the daily time step, while GPCP, CHIRP, CHIRPS, RFE, MSWEP, ARC2, CPC, TAMSAT, and CMORPHCRT demonstrate superior performance at the monthly time step. During the rainy season, these products generally exhibit robustness. However, rainfall estimates derived from reanalysis datasets (ERA5, EWEMBI, MERRA2, PGF, WFDEICRU, and WFDEIGPCC) perform poorly in the studied basins. Based on the PBIAS metric, most products tend to underestimate precipitation values, while only PERSIANN and PERSIANNCCS lead to significant overestimations. Spatially, optimal performance of the products is observed in the Casamance basin and the Sudanian and Sahelian climatic zones within the Gambia and Senegal basins. Conversely, in the Guinean zone of the Gambia and Senegal Rivers, the rainfall products displayed the poorest performance.

Technoeconomic Analysis of a Wind Power Generation System and Wind Resource Mapping Using GIS Tools: The Case of Twelve Locations in the Commune of Evodoula, Cameroon

This paper performs a technoeconomic analysis of the wind power potential and evaluates the cost of wind power generation in Evodoula, comparing two methods, the conventional method and the uncertainty method based on a comparative spatiotemporal approach using the geographic information system (GIS) software tool. This study is based on satellite wind data measured at 10 m above ground level (AGL) over a 40-year period (1980-2019), by the meteorological service NASA (National Aeronautics and Space Administration)/Goddard Space Flight Center (GSFC). The main objectives are to obtain an appropriate design for a proven optimal location and to assess the viability of wind power at Evodoula. For this type of study, there is little literature available. The optimization of an onshore wind farm and deployment in the wind energy development interest area (ZIDEE) is carried out to obtain a minimum and parsimonious discounted cost production unitary (CPU) of the electricity produced. The results showed that in Okok, a location with a large energy deficit, an onshore wind farm with an electricity generation capacity of 12.5 MW with 5 NORDEX N100-2.5MW wind turbines would have a total energy production (TEP) of about 64.0254825 TWh and a selling price of electricity that would be 0.0034 CAD$/kWh, which is very low compared to the utility price (about 98% cheaper). The total cost of the wind farm would be about $11 million, with a net present cost of about $218 million. The annual profit generated by the wind farm would be over $6 billion. The return on investment (ROI) of the project is estimated at 2880.882%. The constructed onshore wind farm would avoid CO2 emission at over 11 MtCO2,eq/year as the energy generated is from the atmosphere. The wind farm would realize an average annual cash flow estimated at nearly $30 million after 20 years of operation. These savings would allow the installation of CO2 capture systems in conventional power plants. In addition, the analysis of uncertainties and risks was identified and quantified to estimate the confidence levels of the project development results. The risks have been assessed, and we recommend that the total uncertainty of the project is around 15%. The energy values in P75 and P90 are 10.08% and 19.22%, respectively, lower than the energy value in P50 of 11.60 GWh/An. Finally, the main policy recommendations for an inclusive design process are highlighted. The contribution of this study is to assist policy makers in making appropriate decisions in the development and implementation of energy and environmental policy in Cameroon and in many continental areas.

REFORMS IN THE FIELD OF METROLOGY AND ITS IMPORTANCE TODAY

The meteorological service of the state administration bodies of the republic will be introduced (it used to consist of the state meteorological service and the metrological services of legal entities). They are organized in necessary cases to ensure the uniformity of measurements and to carry out metrological control. This article describes the work and reforms carried out in the system for the further development and improvement of work in the field of metrology in our country.

Improving Weather Forecasts for Sailing Events Using a Combination of a Numerical Forecast Model and Machine Learning Postprocessing

Accurate predictions of wind and other weather phenomena are essential for making informed strategic and tactical decisions in sailing. Sailors worldwide utilize current state-of-the-art forecasts, yet such forecasts are often insufficient because they do not offer the high temporal and geographic resolution required by sailors. This paper examines wind forecasting in competitive sailing and demonstrates that traditional wind forecasts can be improved for sailing events by using an integration of traditional numerical modeling and machine learning (ML) methods. Our primary objective is to provide practical and more precise wind forecasts that will give sailors a competitive edge. As a case study, we demonstrate the capabilities of our proposed methods to improve wind forecasting at Lake Kinneret, a popular sailing site. The lake wind pattern is highly influenced by the area’s topographic features and is characterized by unique local and mesoscale phenomena at different times of the day. In this research, we simulate the Kinneret wind during the summers of 2015–2021 in up to one-kilometer resolution using the Weather Research and Forecasting (WRF) atmospheric model. The results are used as input for convolutional neural network (CNN) and multilayer perceptron (MLP) ML models to postprocess and improve the WRF model accuracy. These advanced ML models are trained using training datasets based on the WRF data as well as real data measured by the meteorological service, and subsequently, a validation process of the trained ML model is performed on unseen datasets against site-specific meteorological service observations. Through our experimental analysis, we demonstrate the limitations of the WRF model. It uncovers notable biases in wind direction and velocity, particularly a persistent northern bias in direction and an overestimation of wind strength. Despite its inherent limitations, this study demonstrates that the integration of ML models can potentially improve wind forecasting due to the remarkable prediction accuracy rate achieved by the CNN model, surpassing 95%, while achieving partial success for the MLP model. Furthermore, a successful CNN-based preliminary forecast was effectively generated, suggesting its potential contribution to the future development of a user-friendly tool for sailors.

Ocorrência de ondas de frio no Rio Grande do Sul no final do século XVIII e no século XIX e a produção do conhecimento climatológico

The state of Rio Grande do Sul experienced intense cold at the end of the 15th century and heavy snowfalls in the 19th century, specifically in 1858, 1862, 1867, 1870, 1871, 1873, 1875, 1879 and 1885. Due to the social problems caused by these severe weather events, the need arose to create a meteorological service. There were several initiatives in the 19th century, most notably the service organized by the Commissão de Obras do Porto de Rio Grande, the Public Works station in Porto Alegre and the Escola de Artes e Officio station in Pelotas.

Time Series Analysis of the Impact of Meteorological Conditions and Air Quality on the Number of Medical Visits for Hypertension in Haikou City, China

Meteorological conditions and air quality are important environmental factors in the occurrence and development of cardiovascular diseases (CVDs) such as hypertension. The aim of this study was to take Haikou City, located on the tropical edge, as the research area and to analyze the exposure–response relationship and lag effect between its meteorological conditions, air quality, and the number of hypertensive patients. Using the data from the hypertension outpatient department of Hainan Provincial People’s Hospital from 2016 to 2018, together with meteorological data and air quality data, a distributed lag nonlinear model based on the nested generalized addition model of meteorological element base variables was established. The results showed that the impact of temperature on the risk of hypertension was mainly due to the cold effect, which was associated with high risk, with a lag of 1–10 days. When the temperature dropped to 10 °C, the cumulative effect on the risk of hypertension of relative risk (RR) reached its highest value on the day the low temperature occurred (RR was 2.30 and the 95% confidence interval was 1.723~3.061), passing the test with a significance level of 0.05. This result indicated that efforts should be made to strengthen the prevention of hypertension under low-temperature conditions and the prediction and early warning of disease risks. The impact of the air-quality effect (the environmental Air Quality Index was selected as an indicator) on the risk of hypertension was mainly characterized by a low air-quality effect, with a lag effect of 0–8 days. When the risk reached approximately 124, the RR was highest (RR was 1.63 and the 95% confidence interval was 1.104~2.408), passing the test with a significance level of 0.05. The research results can provide technical support for conducting medical meteorological forecasting, early warning, and services for hypertension. A joint work and research mechanism among multiple departments such as meteorology and medical health should be established to improve the level of medical and health care, optimize the allocation of social resources, and develop targeted prevention and control strategies to reduce the health and economic burden of hypertension.

Nurturing partnerships to support data access for impact forecasts and warnings: Theoretical integration and synthesis

This paper presents a synthesis and theoretical integration of findings from a research project that explored the data needs and sources for implementing impact forecasts and warnings for hydrometeorological hazards. Impact forecasts and warnings (IFW) have received global attention in recent years as they offer a novel way of improving the communication of hazards and risks. The fundamental idea behind IFWs is to enable warning services to meaningfully communicate the anticipated outcomes, consequences, or impacts of the hazard interacting with society or the environment by incorporating knowledge about the underlying and dynamic exposure and vulnerability of people and assets. One key question for IFW implementation is about data needs and sources to inform IFWs.Using the Grounded Theory Methodology, we address the question “How can partnerships and collaboration better facilitate the collection, creation, and access to hazard, impact, vulnerability, and exposure data for IFWs?” Our findings point to partnerships and collaboration as a necessary strategy for implementing IFWs. Implementation requires accessing various types and sources of hazard, impact, vulnerability, and exposure data to assess and communicate the potential impacts of hydrometeorological hazards. Partnerships and collaboration facilitate the sharing of and access to required data and knowledge. Based on our findings, we provide recommendations to increase interagency communication and partnerships for IFWs and disaster risk reduction, such as making cohabitation arrangements between agencies, running joint training scenarios, and encouraging meteorological services and emergency responders to co-define tailored warning thresholds.

Predictive modeling of severe weather impact on individuals and populations using Machine Learning

In this work, Machine Learning (ML) techniques are used to develop tools capable of accurately predicting the impact of severe weather events. We use readily accessible predictors, including daily meteorological data, basic demographics, geographic and terrain features, along with the number of daily meteorological incidents reported to the emergency services. The model was built using disaggregated data from the 947 municipalities from the region of Catalonia between January 1, 2015 and June 30, 2021. Catalonia’s region is situated in the northeastern part of Spain along the Mediterranean Basin, and frequently experiences storms with strong winds and intense rainfall. In 2020, such weather events resulted in 64 injuries and damages amounting to approximately 70 million USD. The ML-based model presented in this study shows a predictive capacity for extreme weather risk superior to that of the daily meteorological warning system of the Meteorological Service of Catalonia. In addition, the model has been used to estimate how urbanization modifies the extreme weather impact. Given the expected increase in the intensity, frequency, and duration of extreme weather events in the context of global warming, the methodology presented in this work could be helpful in developing tools to assist emergency service managers and policy makers in making rapid and effective decisions.

How to provide actionable information on weather and climate impacts?–A summary of strategic, methodological, and technical perspectives

Climate change will result in more intense and more frequent weather and climate events that will continue to cause fatalities, economic damages and other adverse societal impacts worldwide. To mitigate these consequences and to support better informed decisions and improved actions and responses, many National Meteorological and Hydrological Services (NMHSs) are discussing how to provide services on weather and climate impacts as part of their operational routines. The authors outline how a risk framework can support the development of these services by NMHSs. In addition to the hazard information, a risk perspective considers the propensity for a given hazard to inflict adverse consequences on society and environment, and attempts to quantify the uncertainties involved. The relevant strategic, methodological and technical steps are summarized and recommendations for the development of impact-related services are provided. Specifically, we propose that NMHSs adopt an integrated risk framework that incorporates a hazard-exposure-vulnerability model into operational services. Such a framework integrates all existing forecast and impact services, including the underlying impact models, and allows for flexible future extensions driven by the evolving collaboration with partners, stakeholders and users. Thereby, this paper attempts to unify existing work streams on impact-related services from different spatial and temporal scales (weather, climate) and disciplines (hydrology, meteorology, economics, social sciences) and to propose a harmonized approach that can create synergies within and across NMHSs to further develop and enhance risk-based services.

Parallel prediction of dengue cases with different risks in Mexico using an artificial neural network model considering meteorological data.

In 2022, Mexico registered an increase in dengue cases compared to the previous year. On the other hand, the amount of precipitation reported annually was slightly less than the previous year. Similarly, the minimum-mean-maximum temperatures recorded annually were below the previous year. In the literature, it is possible to find studies focused on the spread of dengue only for some specific regions of Mexico. However, given the increase in the number of cases during 2022 in regions not considered by previously published works, this study covers cases reported in all states of the country. On the other hand, determining a relationship between the dynamics of dengue cases and climatic factors through a computational model can provide relevant information on the transmission of the virus. A multiple-learning computational approach was developed to simulate the number of the different risks of dengue cases according to the classification reported per epidemiological week by considering climatic factors in Mexico. For the development of the model, the data were obtained from the reports published in the Epidemiological Panorama of Dengue in Mexico and in the National Meteorological Service. The classification of non-severe dengue, dengue with warning signs, and severe dengue were modeled in parallel through an artificial neural network model. Five variables were considered to train the model: the monthly average of the minimum, mean, and maximum temperatures, the precipitation, and the number of the epidemiological week. The selection of variables in this work is focused on the spread of the different risks of dengue once the mosquito begins transmitting the virus. Therefore, temperature and precipitation were chosen as climatic factors due to the close relationship between the density of adult mosquitoes and the incidence of the disease. The Levenberg-Marquardt algorithm was applied to fit the coefficients during the learning process. In the results, the ANN model simulated the classification of the different risks of dengue with the following precisions (R2): 0.9684, 0.9721, and 0.8001 for non-severe dengue, with alarm signs and severe, respectively. Applying a correlation matrix and a sensitivity analysis of the ANN model coefficients, both the average minimum temperature and precipitation were relevant to predict the number of dengue cases. Finally, the information discovered in this work can support the decision-making of the Ministry of Health to avoid a syndemic between the increase in dengue cases and other seasonal diseases.

Climatological reference stations: Definitions and requirements

AbstractGround‐based stations are one of many observing systems contributing to the generation of data to evaluate climate trends and variations locally and globally. Networks of stations are made of various numbers of observing sites, equipped with different typologies and varieties of instruments, differently managed and maintained. Among such networks a limited number of stations are required as a reference, to provide top quality traceable measurements and as the top level of a tiered approach; it is these stations which are here designated as Climatological Reference Stations (CRS). At present, there is no agreed definition of the key instrumental and technical features of a CRS, nor are there defined reference measurement procedures. This leads to the situation of a multitude of approaches among different National Meteorological and Hydrological Services (NMHSs), research institutes and other agencies that reduces the comparability of results in space and in time. The lack of CRSs, moreover, is a major contributor to the huge efforts required to harmonize data and detect biases locally, regionally and globally, as in their absence there is a need to fall back on other stations which are more likely to have such biases. This article reports on the outcomes of the work of a group of experts, nominated by the World Meteorological Organization (WMO) Commission for Climatology (CCI), tasked to promote a standardized and agreed concept about the definitions, specifications and technical characteristics of climate reference data and stations for ground‐based networks, with the addition of a practical example of climatological reference station installed in the framework of an European funded project, with the purpose of comparing and confirming the prescribed characteristics and guidance, with respect to practical field use and available instrumentation. The adoption of a unique definition and prescription on the technical setup, measurement procedures and uncertainty evaluation will substantially progress towards a common approach in detecting climate trends. This will improve data comparability in space and time and allow a more robust understanding of climate evolution locally and globally.

Small-Scale Diffusion Dryer on an Optical Particle Counter for High-Humidity Aerosol Measurements with an Uncrewed Aircraft System

Abstract The negative effects of relative humidity to measurements of particulate matter (PM) due to hygroscopic growth are often not inherently handled by low-cost optical particle counters (OPCs). This study presents a new approach in constructing a miniaturized diffusion dryer, for use with an OPC mounted on an uncrewed aircraft system (UAS), namely, the DJI S900 (weight of 7.5 kg and flight endurance of 20 min) for short-term measurements under humid conditions. In this work, an OPC of type N3 (Alphasense) was employed alongside the dryer, with experiments both in the laboratory and outdoors. Evaluation of the dryer’s performance in a fog tank showed effective drying from almost saturated air to 41% relative humidity for 35 min, which is longer than the endurance of the UAS, and therefore sufficient. Changes in the flow rate through the OPC-N3 with the dryer showed a 17% reduction compared to an absent dryer, but the measured PM values remained unaffected. Airborne measurements were taken from four hovering flights near a governmental air pollution station (Mannheim-Nord, Germany) under humid conditions (88%–93%) where the system gave agreeable concentrations when the dryer was in place, but significantly overestimated all PM types without it. At a rural area near the Boundary Layer Field Site Falkenberg (Lindenberg, Germany), operated by the German Meteorological Service (DWD), vertical profiles inside a low-altitude cloud showed sharp increase in concentrations when the UAS entered the cloud layer, demonstrating its capability to accurately detect the layer base.

Incorporating indigenous knowledge systems-based climate services in anticipatory action in Zimbabwe: an ex-ante assessment

IntroductionThe success of anticipatory action (AA) in climate change related disaster risk reduction depends largely on the ability to accurately predict adverse weather events and to subsequently take appropriate and timely action. The current seasonal climate forecasts obtained through national and global forecasting centers such as the Zimbabwe Meteorological Services Department (MSD) and the European Center for Medium-Range Weather Forecasts (ECMWF) have been found to have several limitations including limited skill in intra-seasonal variability which is vital in most processes, lack of accessibility, difficult interpretability and applicability to local communities. As a result, calls for the integration of Indigenous Knowledge Systems (IKS)-based climate services in seasonal climate forecasting to bolster the efficacy and accuracy of forecasts are increasing.MethodologyThe study employed a mixed methods approach combining a survey of 200 household heads, document reviews and key informant interviews with IKS specialists, AA community of practice members and disaster risk reduction specialists.ResultsThis article presents the findings of a study carried out in four semi-arid districts of Zimbabwe (Mbire, Matobo, Binga, and Mudzi) to assess the feasibility of integrating IKS into the drought AA mechanism at national, district, and ward level. We find that 82% of the surveyed households used indigenous knowledge systems for seasonal climate forecasting and extreme weather forecasting combined with scientific forecasts. Respondents demonstrated preference for triangulated forecasting to increase reliability. Both the meteorological approaches to seasonal climate forecasting and the IKS based forecasting had different strengths and weaknesses.DiscussionBased on the found gaps in current seasonal forecasting techniques, local applicability, and limited quantitative analysis in IKS, this study provides a guideline on how scientific and IKS forecasting can be triangulated for leveraging forecasting information in Zimbabwe. The use of IKS may be of utmost relevance to agencies and entities seeking the achievement of drought risk reduction through AA programmes.

Impact of Temperature and Atmospheric Pressure on Hospitalizations of Patients Presenting With Acute Coronary Syndrome.

This study aims to investigatethe impact of temperature and atmospheric pressure on hospitalizations of patients with acute coronary syndrome (ACS). This is a retrospective, observational, analytical study conducted in a single center, University Hospital Center "Mother Teresa," Tirana, Albania, in the period January-December 2018. This study included 1,165 patients with ACS, who performed urgent coronary angiography, from January 2018 to December 2018. Patients were diagnosed withACS based on clinical and examination findings. The data were collected retrospectively using patient files. Baseline demographic, clinical, and procedural characteristics were collected. Data on atmospheric parameters, measured at the weather monitoring station, were obtained from the National Meteorological Service database. Measurements from the meteorological service provided values ​​for each parameter: average daily temperature and atmospheric pressure in each country district. Atmospheric data measurements were taken for the day under review. The number of inhabitants for the respective districts is taken from the National Institute of Statistics (INSTAT). The study involved 1,165 patients, with a mean age of 63.1 years, ranging from 27 years to 89 years old. The majority of patients (78.6%) were male, while 21.4% were female. A statistically significant relationship was observed between seasonal changes in temperature and atmospheric pressure concerning the number of cases with ACS; the autumn season prevails with 27.9% of the total cases, followed by the spring season with 25.6%, the summer season with 24.2%, and winter season with 22.3% (p = 0.04). Additionally, significant changes in the average monthly values ​​of temperature and atmospheric pressure were accompanied by a statistically significant increase in the number of cases as occurred in March-April and October-November (p ≤ 0.05). Most cases in the cold period (November-March) occurred on days with a change in temperature or atmospheric pressure with a statistically significant value of p < 0.05. An important relationship between seasonal, monthly, and daily changes in temperature and atmospheric pressure concerning the frequency of cases with ACS was observed.

The importance of crowdsourced observations for urban climate services

AbstractCrowdsourced observation networks are typically much more dense than those maintained by National Meteorological Services, and sample a much wider range of local climates. This offers an opportunity to build observed climatologies that are more representative of lived experience, particularly in cities. This study provides a worked example to show their potential for improving operational climate services, and to identify the challenges to realizing that potential. To demonstrate the concept, data from personal weather stations, obtained through citizen science, are used to build an observed record of daily maximum temperatures in 2020 in Manchester (UK). This record is compared to the standard baseline used in a current climate service, showing a substantial increase in the estimated heat hazard. If such potential benefits are to be realized in a climate service, it will be necessary to first build an alternative observed baseline of decadal length and at national or international scale. This requires further work to acquire, quality‐control, exposure‐control and map the crowdsourced observations.

A Comparative Study on the Estimation of Wind Speed and Wind Power Density Using Statistical Distribution Approaches and Artificial Neural Network-Based Hybrid Techniques in Çanakkale, Türkiye

In recent years, wind energy has become remarkably popular among renewable energy sources due to its low installation costs and easy maintenance. Having high energy potential is of great importance in the selection of regions where wind energy investments will be made. In this study, the wind power potential in Çanakkale Province, located in the northwest of Türkiye, is examined, and the wind speed is estimated using hourly and daily data over a one-year period. The data, including 12 different meteorological parameters, were taken from the Turkish State Meteorological Service. The two-parameter Weibull and Rayleigh distributions, which are the most widely preferred models in wind energy studies, are employed to estimate the wind power potential using hourly wind speed data. The graphical method is implemented to calculate the shape (k) and scale (c) parameters of the Weibull distribution function. Daily average wind speed estimation is performed with artificial neural network–genetic algorithm (ANN-GA) and ANN–particle swarm optimization (ANN-PSO) hybrid approaches. The proposed hybrid ANN-GA and ANN-PSO algorithms provide correlation coefficient values of 0.94839 and 0.94042, respectively, indicating that the predicted and measured wind speed values are notably close. Statistical error indices reveal that the ANN-GA model outperforms the ANN-PSO model.

A charge emitter for use in evaluating aircraft rainfall enhancement

Charging of droplets is known to influence their properties and interactions, such as through modifying their evaporation, collisions or stability. Introducing additional charge into a droplet system, such as a natural fog or cloud may therefore provide a route to modify its behaviour. As aircraft are already used in rainfall enhancement activities by some national meteorological services, adapting their existing delivery systems to include charge release provides a convenient method with which to pursue this. Rainfall enhancement aircraft typically carry an array of under-wing tubes into which cloud seeding agents (e.g. salt or silver iodide) are contained in flares which are loaded pre-flight. Our approach for a charge release system is to construct a stand-alone device able to fit within an existing flare housing, to provide controlled release of ions by corona emission. The “flare emitter” device is battery powered and able to release either polarity of corona ions. It contains a programmable microcontroller (Arduino Nano) able to operate in a fixed sequence and record data to an internal memory card at 10Hz. Monitoring systems for the corona emission voltage and current are included, as well as environmental sensors for pressure, temperature and relative humidity.

Urban heat islands and the transformation of Singapore

An urban heat island is defined as an urban area that experiences warmer temperatures than its surroundings. This study examines how Singapore’s planning efforts established after the mid-20th century have affected the thermal environment of the city in association with land transformation, using historical temperature data available from the Meteorological Service of Singapore and some historical studies. Singapore’s planners have carefully regulated the growth of its downtown while promoting expansion in other parts of the city-state. These effects of planning have also unconsciously shaped the location and outline of Singapore’s urban heat island. As a result, new urban heat peaks were found around the centres of newly constructed large-scale new towns compared to industrial areas. This study provides lessons for land planning in mitigating a city’s urban heat island effects.