Meteorological Stations Research Articles

Heat Adaptation Benefits for Vulnerable groups In Africa (HABVIA): a study protocol for a controlled clinical heat adaptation trial

BackgroundTemperatures across Africa are expected to rise at up to twice the rate of mean global temperatures, posing significant health threats to vulnerable communities. Prolonged exposure to high day- and night-time temperatures has been implicated in a myriad of adverse health outcomes. The built environment and inadequate housing can exacerbate these consequences, prompting the need to evaluate heat adaptation interventions as a sustainable adaptation strategy for low-income and informal settlement dwellers. The Heat Adaptation Benefits for Vulnerable groups In Africa (HABVIA) study aims to assess the impact of passive cooling interventions in homes on several key physiologic and mental health outcomes, as well as building internal thermal conditions.MethodsHABVIA is a 3-year prospective controlled study to identify, implement and assess heat adaptation solutions in four low-income communities in one urban and one rural site in Ghana and South Africa, respectively. In each site, N=240 participants (N=60 per site) will be assigned to intervention or control groups. The intervention is focused on lowering the nighttime temperature of the home environment. Health and biometric data will be collected through a combination of physiological measurements, questionnaires, and biochemical measures taken at 3 time points during the hot season. Clinical outcomes include objective sleep behaviour, core body temperature, physical activity, blood pressure, blood glucose, anthropometrics, and body composition. Indoor and outdoor environmental data will be collected continuously using fixed indoor sensors and automatic weather stations. Housing and community characteristics, and socio-economic information will be collected. Quantitative comparisons will be made between intervention and control conditions using generalised linear mixed models. Qualitative data from consultive workshops will be used to assess the acceptability and feasibility of the adaptations.DiscussionRobust evaluation of the environmental and health outcomes of heat adaptations are limited for Africa, despite high climate vulnerability. HABVIA will address some of these gaps by assessing low-cost passive cooling interventions to promote heat resilience and improve health outcomes, providing real-world evidence for the feasibility of readily implementable and scalable adaptations in local contexts.Trial registrationPan African Clinical Trials Registry (PACTR) PACTR202401521630856, version 1. Retrospectively registered on January 12, 2024.

Predicting individual's decision to enter the water at a high-energy recreational surf beach in France.

To predict beachgoer decision to enter the water at a high-energy surf beach, in southwest France. We built a unique multidisciplinary database combining data collected by an on-site beachgoers survey, weather stations, marine buoys and tidal reconstruction. Human, weather and meteocean factors were considered as potentially predictive of beachgoer behaviour. We employed a logistic regression analysis to predict beachgoers' decision to enter the water on any given day at a high-energy recreational beach. We demonstrated that both environmental and human factors influence a beachgoer's decision to enter the water. Daily mean wave height and daily mean insolation duration were significant predictors at the p<0.001 level, while age, place of residence and self-confidence in swimming out of a rip current were significant at the p<0.05 level or higher. Beachgoers were more likely to enter the water on sunny days with lower waves. Younger individuals, those living outside the Landes département, and those who declared themselves to be 'confident' or 'uncertain' about their ability to swim out of a rip current expressed a higher propensity to enter the water. Our model has an accuracy, F-Score, precision and recall of 71%, 73%, 86%, 79%, respectively. Beachgoer exposure on any given day can ultimately be predicted by coupling our model with beach attendance models. This would allow for the design of rescue and preventive operations on days with high expected exposure. While models based solely on environmental factors can be used to forecast beach risks, incorporating human factors into the model provides valuable insight for crafting prevention messages. In this regard, lifeguards could engage more actively with beach users to deliver appropriate safety messages.

Climatic factors driving influenza transmission in Sahelian area: A twelve-year retrospective study in Niger (2010-2021).

The relationship between influenza transmission and climate has many public health implications, particularly on the occurrence of epidemics and disease severity. Environmental factors such as temperature, wind and humidity can influence transmission, particularly in this time of climate change. This study aims to use statistical modelling to decipher the impact of climate factors on influenza transmission in Niger. The reference center of respiratory disease (CERMES) collected samples from patients with acute respiratory illness in eight sentinel sites over a period of twelve years. Detection of respiratory virus was conducted on each sample using molecular approaches. Meteorological parameters were recorded on a weekly basis at the National Meteorological Station in Niamey. Climatic and virological data were plotted over the weeks of the years. A multivariate approach was used to identify clusters of weeks with homogeneous climatic conditions, independent of the season. The impact of the predictor variables was determined using generalized additive modelling (GAM). During this study, 9836 suspected influenza cases were PCR tested, of which 982 (9.98%) were confirmed positive for either influenza A or B. 631 (64.25%) of the influenza A/B positive cases were detected during the low temperature periods (December to February). Using clustering analysis, six distinct periods can be identified, with the most favorable conditions for influenza occurring in conjunction with dry, cold and windy weather patterns. Of greater importance, however, are the conditions that predominate in the weeks preceding the detection of clinical cases. The final GAM model accounts for 77% of the variability in the occurrence of influenza cases, indicating that the epidemic can be anticipated weeks before clinical detection in dispensaries using wind and minimum temperature as indicators. Clustering and GAM models can be considered as an efficient and simple approach to analyze the impact of climatic conditions on the transmission of infectious diseases.

Seasonal Occurrence and Severity of Early Blight of Potato Caused by Alternaria solani (L.) in Relation to Weather Conditions

The potato ranks third in global significance as a human food crop, trailing only rice and wheat. However, the potato plant is susceptible to fungal diseases, including Early blight, Alternaria solani, which can lead to significant yield losses in potato crops. The present investigation was conducted for the management of early blight of potato by using bioagents and newer fungicides. The experiment was conducted at experimental fields near Shri Venkateshwara University, Amroha, UP. A survey was conducted, and the daily weather data were collected from the weather station at Shri Venkateshwara University. The crop was regularly observed for the first appearance of the disease. Disease severity was recorded using a score chart consisting of a 0-9 scale. Fifty leaves were randomly selected from the field for measurement of disease severity. The results revealed that the early blight of potato disease occurred every year at Shri Venkateshwara University, Rajabpur, Amroha, UP. The first appearance of early blight was observed on 12th December 2023 with the value of 0.17 per cent at the maximum temperature 23.69, minimum temperature 2.15, and mean temperature 12.92, relative humidity 92.26%, minimum relative humidity 37.26%, mean 64.76 and 0.00 sun shine hours and maximum severity of 31.28 per cent was noted on 12. January 2024, after 32 days of the first appearance of disease at the maximum temperature 23.470C and minimum temperature 5.54, Mean 14.51 0C, relative humidity maximum 95.38 % and minimum 65.37 %, mean 80.38% and sunshine hours 6.12, evaporation 1.25 and rainfall 0.00. It was clear that the infection rate increased suddenly from 17.12.23 to 25.12.2023 and from 28.12.2023 to 11.1.2024, which might be due to the occurrence of rainfall, resulting in high relative humidity and low sunshine hours, which are optimum for the pathogens' growth. The results also showed that during second year 2024-25, the first appearance of early blight of potato A. solani was observed on 17th December, 2024 with the value of 0.95 per cent at the 24.670C maximum temperature and 7.58 minimum temperature, mean 16.13 per cent maximum relative humidity was 90.38 per cent, 52.57 per cent minimum and mean 71.48 relative humidity and 5.26 sun shine hours and 2.25 evaporation and the maximum disease severity of 28.54 per cent on 15 January, 2025 after 33 days first appearance of disease at maximum and minimum temperature 21.540C - 4.280C and mean 12.91 0C, maximum, minimum and mean relative humidity 94.57% – 69.54% and 82.06 % sun shine hours 5.98 and evaporation 1.39 respectively. It is clear that during 2024-25, cloud covered continuously at frequent intervals of date, but light rainfall occurred on 12th and 15th January, considering favourable conditions for the development of the disease, resulting in increased infection of the potato crop A. solani.

The wind niche: The thermal and hydric effects of wind speed on terrestrial organisms.

Wind can significantly influence heat and water exchange between organisms and their environment, yet microclimatic variation in wind is often overlooked in models forecasting the effects of environmental change on organismal performance. Accounting for the effects of wind may become even more critical given the anticipated changes in wind speed across the planet as climates continue to warm. In this study, we first assessed how wind speed varies across the planet and how wind speed may change under climate warming at macroclimatic scales. We also used microclimatic data to assess how wind speed changes temporally throughout the day and year as well as the relationship between wind speed, temperature, and standard deviation in each environmental variable using data from weather stations in North America. Finally, we used a suite of biophysical simulations to understand how wind speed (and its interactions with other environmental variables and organismal traits) affects the temperatures and rates of water loss that plants and animals experience at a microclimatic scale. We found substantial latitudinal variation in wind speed and the change in wind speed under climate change, demonstrating that temperate regions are predicted to experience simultaneous warming and reductions in wind speed. From the microclimatic data, we also found that wind speed is positively associated with temperature and temperature variability, indicating that the effects of wind speed may become more challenging to predict under future warming scenarios. The biophysical simulations demonstrated that convective and evaporative cooling from wind interacts strongly with organismal traits (such as body size, solar absorptance, and conductance) and the heating effects of solar radiation to shape heat and water fluxes in terrestrial plants and animals. In many cases, the effect of wind (or its interaction with other variables) was comparable to the effects of air temperature or solar radiation. Understanding these effects will be important for predicting the ecological impacts of climate change and for explaining clinal variation in traits that have evolved across a range of thermal environments.

Influence of Weather Parameters on Seasonal Incidence of Pod Boring Weevil, Apion clavipes Gerst (Apionidae: Coleoptera) on Pigeonpea [Cajanus cajan (L.) Millsp.] under West Bengal Conditions

Pigeon pea is a high protein legume cultivated by marginal farmers in the Indian sub-continent. It exposes a wide variety of biotic stresses especially insect pests. Pod boring weevil, Apion clavipes Gerst is a major concern in eastern India which causes direct damages to economic parts such as flowers and pods. Pod boring weevil, Apion clavipes Gerst infests pigeonpea during vegetative, flowering, pod formation and pod maturity stages. However, there is not much information on seasonal incidence of pod boring weevil, A. clavipes Gerst on this particular host, which is very much essential for forecasting and forewarning of pest incidence and development of Integrated Pest Management module. The seasonal incidence of pod boring weevil was studied in two consecutive years during 2016-17 and 2017-18. The grub and weevil populations in shoots, flower raceme and pods were recorded and obtained weather data from automatic weather station of BCKV, West Bengal and subjected to correlation and regression analysis. The experimental results revealed that pest population started during 43rd SMW (4th week of October) and pest population attained the maximum population of 45.00 grub/plant during 4th SMW (4th week of January). Pest population was observed till 11th SMW (3rd week of March). Seasonal incidence of pod boring weevil negatively correlated with maximum relative humidity, minimum relative humidity and rainfall, whereas, positively correlated with maximum temperature, minimum temperature and sunshine hours.

The Impact of Climate Variability and Trends on Drought Over Benin: A Statistical and Conceptual Approach

ABSTRACTThis study assessed drought trends and variability in Benin and their relationship with climate modes such as El Niño–Southern Oscillation (ENSO), the Atlantic Interhemispheric Sea Surface Temperature Gradient (AISSTG), and the Pacific Decadal Oscillation (PDO). Drought intensity, duration and frequency were analysed using the Standardised Precipitation Index (SPI) from monthly precipitation data at six weather stations (Bohicon, Cotonou, Kandi, Natitingou, Parakou and Savè) for 1970–2015. Nonparametric tests showed that all stations had random, independent, homogeneous and stationary 12‐month SPI series, except for Kandi and Parakou, which exhibited autocorrelation and a significant positive trend (p &gt; 0.05). No significant trends were observed at other stations. Significant correlations were found between the 12‐month SPI and both the Multivariate ENSO Index (MEI, r = −0.35) and the PDO index (r = −0.30). Principal component analysis (PCA) revealed wet years in Benin during negative MEI and PDO phases. Droughts of higher intensity and duration were more frequent (54%) during El Niño/Neutral and warm PDO phases, whereas wet years (56%) occurred during La Niña and cool PDO phases. Extreme drought events (SPI &lt; −2) were more common (89%) from 1970 to 1995, coinciding with high positive MEI and PDO values, whereas intense wet years (SPI &gt; 1.5) occurred more often (64%) from 1996 to 2010. Harmonic and spectral analyses identified dominant dry/wet frequencies of 5–7.5 and 2–4.5 years. Stations nearest the coast (Cotonou, Bohicon) and in northern Benin (Kandi) experienced more frequent extreme events (14–16 events). These findings highlight the significant influence of climate variability on interannual and decadal precipitation patterns in Benin.

Eye2Sky – a network of all-sky imager and meteorological measurement stations for high resolution nowcasting of solar irradiance

Eye2Sky – a network of all-sky imager and meteorological measurement stations for high resolution nowcasting of solar irradiance

Seasonal regulation of vehicle traffic on the roads of the timber industry complex

The paper touches upon one of the issues of seasonal weight restrictions on the movement of vehicles, which are established for roads of regional and local importance in the Arkhangelsk region, which negatively affect the functioning of timber transport logistics for periods of spring and autumn thaws. The relevance lies on the urgent need to introduce differentiated deadlines for weight restrictions instead of prescriptively assigned uniform deadlines for different regions. The novelty of the study lies on the fact that in the mathematical processing of empirical data on the course of air temperature, a qualitatively new approach was founded on statistical dynamics, the main tool of which is cross-spectral analysis of air temperature series. The basis for the study was an array of long-term data on daily air temperatures with a 50-year retrospective and a volume of 1800 units/year, from the archives of the Northern Administration for Hydrometeorology and Environmental Monitoring. The data was grouped into 3?50 dynamic series, with 365 daily temperature values in each series. Next, an analysis of the statistical quality of the empirical data was carried out, after which the time series were processed by cross-spectral analysis. As a result, the ability to objectively, with a high degree of accuracy and reliability, determine the dates of temperature inversions, as well as shifts in air temperature graphs in various natural and climatic conditions relatively to the accepted base weather station, was confirmed. This, in turn, serves as a starting point for the next stage of the study – correct timing of seasonal freezing and thawing of similar soils and using them as a basis for introducing and removing weight restrictions on the movement of vehicles. The authors reasonably believe that the use of this methods is also possible in the agro-industrial complex of the Russian Federation, for example, when solving problems related to the long-term variation of air temperature and requiring high reliability of the results.

Emerging Human Fascioliasis in India: Review of Case Reports, Climate Change Impact, and Geo-Historical Correlation Defining Areas and Seasons of High Infection Risk

The trematodes Fasciola hepatica and F. gigantica are transmitted by lymnaeid snails and cause fascioliasis in livestock and humans. Human infection is emerging in southern and southeastern Asia. In India, the number of case reports has increased since 1993. This multidisciplinary study analyzes the epidemiological scenario of human infection. The study reviews the total of 55 fascioliasis patients, their characteristics, and geographical distribution. Causes underlying this emergence are assessed by analyzing (i) the climate change suffered by India based on 40-year-data from meteorological stations, and (ii) the geographical fascioliasis hotspots according to archeological–historical records about thousands of years of pack animal movements. The review suggests frequent misdiagnosis of the wide lowland-distributed F. gigantica with F. hepatica and emphasizes the need to obtain anamnesic information about the locality of residence and the infection source. Prevalence appears to be higher in females and in the 30–40-year age group. The time elapsed between symptom onset and diagnosis varied from 10 days to 5 years (mean 9.2 months). Infection was diagnosed by egg finding (in 12 cases), adult finding (28), serology (3), and clinics and image techniques (12). Climate diagrams and the Wb-bs forecast index show higher temperatures favoring the warm condition-preferring main snail vector Radix luteola and a precipitation increase due to fewer rainy days but more days of extreme rainfall, leading to increasing surface water availability and favoring fascioliasis transmission. Climate trends indicate a risk of future increasing fascioliasis emergence, including a seasonal infection risk from June–July to October–November. Geographical zones of high human infection risk defined by archeological–historical analyses concern: (i) the Indo-Gangetic Plains and corridors used by the old Grand Trunk Road and Daksinapatha Road, (ii) northern mountainous areas by connections with the Silk Road and Tea-Horse Road, and (iii) the hinterlands of western and eastern seaport cities involved in the past Maritime Silk Road. Routes and nodes are illustrated, all transhumant–nomadic–pastoralist groups are detailed, and livestock prevalences per state are given. A baseline defining areas and seasons of high infection risk is established for the first time in India. This is henceforth expected to be helpful for physicians, prevention measures, control initiatives, and recommendations for health administration officers.

A Pattern‐Based Machine Learning Model for Imputing Missing Records in Coastal Wind Observation Networks

ABSTRACTPromoting green energy is essential for environmental sustainability, with wind energy playing a crucial role in this effort. While the Taiwan Strait has long been developed as a prime wind farm location, the search for new sites has led the government to focus on northern Taiwan, where the Northeast Monsoon prevails during winter. Since 2022, new meteorological stations have been established to monitor wind potential in this region. However, missing wind data from these stations can undermine the accuracy of wind assessments. To address this, we develop an imputation model using the Weighted K‐Nearest Neighbors (WKNN) algorithm. This study focuses on seven meteorological stations near National Taiwan Ocean University (NTOU), located along the northeastern coast of Taiwan, including six on Taiwan proper and one on a nearby offshore islet, each recording wind speed and direction hourly. Complete data points, where all stations have recorded data simultaneously, are compiled into a reference database. When data from a particular station is missing, several complete data points from the database are used to estimate the missing values through weighted averaging. Calibration, validation, and testing procedures confirm that the model reliably estimates missing data, even when only four of the seven stations are operational.

Plant Responses to Anomalous Heat and Drought Events in the Sonoran Desert.

A shift to greater aridification in dry regions of the world is ongoing and rapidly increasing in intensity, including in the biodiverse Sonoran Desert of the Southwest United States and northern Mexico. In addition to experiencing over two decades of drought, the Sonoran Desert is facing anomalous heat events that are increasing in frequency, evidenced in a record hot and dry period from 2020 to 2021. This article evaluates the impacts of the 2020-2021 region-wide heat and drought event at three scales: (1) a landscape level assessment of ecosystem stress across the entirety of the Sonoran Desert based on precipitation and temperature data from meteorological stations and a satellite-derived vegetation health index (VHI), (2) assessments of stress on iconic columnar cacti and succulent trees, and (3) mechanistic plant responses to extreme heat and drought, and secondary biotic stressors from insect attacks. 2020 was the hottest and driest year since 1980 across the Sonoran Desert region, and vegetation health, determined from VHI, was also near its lowest point. Field-based assessments of columnar cacti across the Sonoran Desert revealed high levels of acute plant stress, including cactus scorching, defined by rapid onset of discolored photosynthetic tissue that leads to permanent photosynthetic dysfunction and increased plant mortality. Tissue scorching corresponded with a three-fold increase in mortality of giant cactus species across the region relative to background levels following 2020-2021. Likewise, repeated plant health surveys show a persistent legacy of the 2020-2021 anomaly, resulting in a marked reduction in the current health and survival of the iconic giant saguaro (Carnegiea gigantea) in the northern Sonoran Desert. This multi-scale assessment of previously anomalous heat and drought events on succulent desert plants shows landscape-wide impacts that could fundamentally reshape populations of these keystone species and the communities that depend on them.

Internet of things meteorological station for climate monitoring and crop optimization in Carabayllo-Perú

In the agricultural sector, monitoring environmental variables such as temperature, humidity, and atmospheric pressure is crucial for efficient and sustainable agriculture. However, conventional monitoring systems are expensive and need more autonomy, making their implementation difficult in small- and medium-scale agricultural operations. This study presents the design, implementation, and evaluation Internet of things (IoT)-based autonomous for watch remote critical climate variables in the Carabayllo region, Peru. The system uses a data acquisition, processing, and transmission architecture based on the ESP32 microcontroller, DHT22 sensors for measure climatic aspects, BMP180 for detection barometric, and the ThingSpeak cloud platform for data storage and visualization. Results show that the proposed system achieves accuracy comparable to commercial weather stations, making it accessible to small farmers. The implementation demonstrated the system’s ability to detect feasible local microclimates to monitor and predict weather patterns for proper crop growth. This approach enables farmers to monitor conditions in real time, receive early alerts on adverse weather events, and optimize agricultural practices such as irrigation and fertilization. The study concludes that the proposed IoT weather station represents a viable and cost-effective solution to improve agricultural decision-making in developing regions, potentially contributing to increasing crops.

A Case Study of a Hailstorm in the Shanghai Region: Leveraging Multisource Observational Data and a Novel Single-Polarization X-Band Array Weather Radar

This study investigates a severe summer convective hailstorm that occurred in Shanghai on 18 August 2019, using multisource meteorological datasets, with a particular focus on the innovative application of a single-polarization X-band array weather radar (AWR). Radiosonde data revealed high convective available potential energy and unstable atmospheric indices, while wind profiler radars (WPRs) showed initial easterly moisture transport near the ground and strong southwesterly flow aloft, both contributing significantly to intense convection. Ground-based automatic meteorological stations (AMSs) recorded abrupt temperature drops of approximately 10 °C and wind speed increases exceeding 20 m s−1, which aligned closely with the rapid expansion of the hailstorm. In addition, an integrated analysis of data from AWR, WPRs, and AMSs enabled detailed tracking of the storm’s evolution, providing deeper insights into the interplay between moisture transport and dynamic lifting. The AWR’s unique ability to capture divergence and vorticity fields at different altitudes revealed low-level convergence coupled with high-level divergence and cyclonic rotation, sustaining convective updrafts. This study underscores the value of high-resolution AWR data in capturing short-lived, intense precipitation processes, thereby enhancing our understanding of wind field structures and storm development. These findings highlight the comprehensive application of AWR data and the potential of this new high-spatiotemporal-resolution radar for investigating the mechanisms of short-lived severe convective processes.

Influence of Sub‐Cloud Evaporation on Precipitation Isotopes: Insights From Hourly‐Scale Meteorological Assessment in a Large Lake in the East Asian Monsoon Region

ABSTRACTSub‐cloud evaporation is a critical aspect of the hydrological cycle, reducing surface precipitation totals and altering the stable isotopic composition as raindrops fall from the cloud base towards the surface. However, isotopic modelling of sub‐cloud evaporation in humid climates and its implications for hydrological processes remain poorly understood, posing challenges for regional water resource management and ecological conservation. In this study, a comprehensive assessment was conducted to understand the influence of sub‐cloud evaporation on precipitation isotopes in Poyang Lake, the largest freshwater lake in China. Using 4‐year hourly meteorological observations from 11 national meteorological stations, we found that there was significant sub‐cloud evaporation during the precipitation process in humid regions. The remaining fraction of evaporated raindrops varied between 81% and 95%, with the lowest values occurring in September and the highest in February. The monthly average ∆δ2H, ∆δ18O and ∆d‐excess values ranged from 2.9‰ to 7.0‰, 0.7‰ to 1.8‰, and −7.7‰ to −2.8‰, respectively, and the sub‐cloud evaporation effect during the rainy season was more intense than that during the dry season. By modifying the sub‐cloud evaporation effect, precipitation isotopes monitored at the surface and estimated at the cloud base were confirmed to exhibit consistent temporal patterns on both monthly and daily scales. Sensitivity analysis revealed that precipitation isotopic changes were more sensitive to fluctuations in relative humidity and precipitation intensity under varying meteorological scenarios. The underrepresentation of low‐intensity precipitation events was found to lead to a statistical underestimation of precipitation isotopic changes, and when the low‐intensity events (≤ 1.0 mm/h) were excluded, the average ∆δ18O and ∆d‐excess values shifted from 1.25‰ and −5.25‰ to 0.63‰ and −2.72‰, respectively. These findings contribute to a better understanding of hydrological cycle processes in Poyang Lake and other regions with similar humid climate characteristics, especially for the interpretation of regional paleohydrological records and ecohydrological mechanisms using stable isotopes.

The diurnal cycle and event-scale precipitation characteristics in Galápagos at different altitudes during ENSO 2022-2024

An understanding of sub-hourly precipitation variability in the Galapagos Islands is crucial for water resource management and effective biodiversity conservation. This study compares the diurnal cycle and event-scale precipitation characteristics (ESPC), such as mean and maximum intensity, duration and rainfall accumulation at different altitudes during El Niño-Southern Oscillation (ENSO) 2022-2024 on Santa Cruz Island. The La Niña phase was analyzed from April 2022 to January 2023 and the El Niño phase from June 2023 to April 2024. Precipitation data, recorded every 10 minutes, was collected from a recently established network of automatic weather stations, which were strategically positioned at three windward and two leeward sites. The results suggest that the diurnal cycle was influenced by altitude, with a maximum variability between morning and afternoon, regardless of ENSO phase. During La Niña, ESPC exhibited similarities at intermediate altitudes at both windward and leeward sides. However, rainfall events at the island’s summit were less intense and of longer duration. During El Niño, the highest intensities were observed along the coast and at intermediate altitudes of both windward and leeward locations. In contrast, at the top of the island, rainfall events were less intense and more prolonged. At all altitudes, more than half of the rainfall events corresponded to garúa events, and at the top of the island, almost all events were of this type. At this altitude, the contribution of garúa events to the total rainfall accumulation was 80% and 85% for La Niña and El Niño, respectively. This study provides a detailed analysis of how sub-hourly precipitation varies significantly at different altitudes on the windward and leeward sides as a function of ENSO phases, providing valuable baseline information for future studies in this unique and fragile ecosystem.

Trend Analysis of Extreme Precipitation Indices and Climate Oscillations Over the Yucatan Peninsula for the Period 1980–2010

ABSTRACTUsing daily precipitation data from 69 weather stations across the Yucatan Peninsula (YP), we analysed trends in extreme precipitation over 30 years at annual and seasonal scales. The analysis included total precipitation (PRCPTOT), intensity indices (R95p, R99p, SDII and Rx1day), frequency indices (R10mm, R20mm and R30mm), and persistence indices (consecutive dry days [CDD] and consecutive wet days [CWD]). Characterising rainfall distribution is crucial, as southeastern Mexico's YP lacks surface water bodies and relies solely on rainfall to recharge its aquifer. Our findings reveal significant spatial and temporal variability in precipitation across the region. Yucatan and northern Campeche exhibit positive trends in total precipitation and extreme rainfall, while Quintana Roo and southern Campeche show negative trends. Notably, Yucatan experiences more intense rainfall during spring and summer, whereas Quintana Roo shows a marked reduction in winter precipitation. In terms of persistence indices, the CDD index shows a significant positive trend, indicating an extension of dry periods in the region, especially in Quintana Roo. Conversely, the CWD index shows a negative trend, highlighting that rainfall is concentrated over fewer days each year. This study also examines the influence of four climate oscillations on YP rainfall. We found that La Niña particularly affects both winter and summer precipitation. Moreover, the positive phase of the North Atlantic Oscillation (NAO) increases the frequency of intense rainfall events in Yucatan during winter. These results highlight the complexity of regional climate dynamics. Additionally, we analysed intensity–duration–frequency (IDF) curves for three tropical cyclones that impacted the YP in 2020. These events caused flooding, infrastructure damage, and crop losses. Some extreme rainfall associated with these cyclones exceeded the 100‐year return period, emphasising the urgent need for adaptive strategies to address changing precipitation patterns and mitigate the worst impacts of such events.

From mountains to data: low-cost weather stations in Kyrgyzstan’s challenging terrain

Kyrgyzstan, a landlocked nation in Central Asia, is characterized by its rugged mountainous terrain, which covers approximately 90% of its land area. This unique geography poses specific challenges related to climate vulnerability. To address these challenges, we propose a comprehensive approach that involves gathering meteorological data and making it accessible to decision-makers. By leveraging LoRaWAN communication technology, which efficiently transmits sparse and low-speed data over long distances while minimizing power consumption, we can enhance climate resilience. The Internet Society Kyrgyz Chapter, in collaboration with the International Centre for Theoretical Physics (ICTP) and the Central Asia Institute for Applied Geosciences (CAIAG), has initiated the installation of meteorological sensors and disaster mitigation devices, including river water level sensors, terrain moisture sensors, and tilt detectors. These sensors collect critical data, which are stored within the country on an ad hoc server. Stakeholders can access these data according to their specific requirements. This paper outlines the criteria for selecting the deployed equipment and provides details on the installation process at pilot sites, along with the challenges encountered during project execution.

Determination of Onset of the Farming Season of Maize in Uyo Local Government Area, Akwa Ibom State

The study was conducted to determine the Onset of Farming Season in Uyo Local Government Area. The study analyzed the annual rainfall trend in Uyo from 2004 - 2023, investigated the trend in temperature within the study period, determined the onset of rain in the study area as well as the cessation date of rain in the study area. Daily rainfall and monthly temperature data were collected from the University of Uyo weather station from 2004 – 2023 for the study. The data were analyzed using descriptive and inferential statistics. Results showed that mean annual minimum rainfall was 3095.3 mm obtained in 2023 while annual maximum rainfall amount was 4594.82 mm obtained in 2022. The mean annual rainfall in the area within the study period was 3868.91 ± 428.96 mm. The variability of annual and mean rainfall within this study period was 11.09 %. The annual trend of rainfall showed an increasing trend at an annual rate of 41.53 with R2 of 32.81 % while the annual trend of temperature showed a decreasing trend at an annual rate of 0.045 and R2 of 19.19 %. The results also showed that rainfall commences in Uyo between 62 and 112 Julian days with a mean of 77.6 ± 12.9 Julian days. The coefficient of variability was 16.6 % which showed that yearly onset period differs moderately in Uyo. Similarly, rainfall ceases in the area between 264 and 355 Julian days with mean of 309.6 ± 34.2 Julian days for the individual years between 2004 and 2023. The coefficient of variability was 11.0 % which shows that yearly retreat periods in Uyo differ slightly. The minimum length of growing season in Uyo was between 173 and 293 Julian days with a mean of 234.5 ± 35.1 Julian days. This implies that the majority of the period had long rain which suggests that farmers can successfully grow a second short maize crop. It was recommended among others that the farming calendar for maize should be reviewed to identify new planting dates for farmers in every new season.

Impact of El Niño - Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) on Air Temperature in Bengkulu City

Bengkulu City has experienced rising air temperatures due to climate variability events, particularly ENSO and IOD. This study analyzes the relationship between ENSO, IOD, and air temperature in Bengkulu over the past 20 years (2004-2023) using data from Meteorological and Climatology stations, as well as ONI and DMI indices from NOAA. Pearson and multiple correlation analyses show a temperature increase of 0.08-0.1°C per year. ENSO has a stronger influence than IOD, especially on maximum temperature ( = 0.28-0.38). To strengthen the analysis, multiple linear regression was applied, revealing that ONI had a statistically significant positive effect on average air temperature, while DMI showed a weaker and insignificant influence ( = 0.10-0.11). A phase-based composite analysis revealed that average temperatures peaked during El Niño combined with Positive IOD phases, highlighting their synergistic warming effect, with maximum temperature reaching 35.9°C (February 2019), and the lowest minimum temperature recorded at 18°C (September 2019). The temperature increase during El Niño poses risks such as prolonged dry seasons, increased drought, and disruption of coastal ecosystems. Therefore, adaptation measures such as early warning systems and water resource management must be integrated into regional planning, particularly in agriculture and health sectors in Bengkulu.