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Tchamda, André Rodrigue, Merlain Boris Djousse K., Anselme Maffo Koumetio, et al. "Low-cost Wireless Personal Weather Station for Rural Farmers." Transactions on Networks and Communications 9, no. 1 (2021): 01–10. http://dx.doi.org/10.14738/tnc.91.9647.
Testo completoAbstract (sommario):
This document presents the design of a prototype of a low-cost personal weather station suitable for farmers in rural areas who are or may not be engaged in rudimentary agriculture. This prototype measure several weather data: temperature, relative humidity, wind speed, wind direction, rainfall. For further data analysis, these are transmitted for recording to a remote server via wireless communication. The server offers data extraction possibilities in multiple file formats. A prototyping of the personal weather station is designed and commissioned. An extract of the results over two days is presented in the results section of this document
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Lascano, Robert J., Timothy S. Goebel, Dennis C. Gitz III, and John E. Stout. "Evaluation of a Wireless Solar Powered Personal Weather Station." Agricultural Sciences 15, no. 01 (2024): 36–53. http://dx.doi.org/10.4236/as.2024.151003.
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Hahn, Claudia, Irene Garcia-Marti, Jacqueline Sugier, et al. "Observations from Personal Weather Stations—EUMETNET Interests and Experience." Climate 10, no. 12 (2022): 192. http://dx.doi.org/10.3390/cli10120192.
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The number of people owning a private weather station (PWS) and sharing their meteorological measurements online is growing worldwide. This leads to an unprecedented high density of weather observations, which could help monitor and understand small-scale weather phenomena. However, good data quality cannot be assured and thorough quality control is crucial before the data can be utilized. Nevertheless, this type of data can potentially be used to supplement conventional weather station networks operated by National Meteorological & Hydrological Services (NMHS), since the demand for high-resolution meteorological applications is growing. This is why EUMETNET, a community of European NMHS, decided to enhance knowledge exchange about PWS between NMHSs. Within these efforts, we have collected information about the current interest in PWS across NMHSs and their experiences so far. In addition, this paper provides an overview about the data quality challenges of PWS data, the developed quality control (QC) approaches and openly available QC tools. Some NMHS experimented with PWS data, others have already incorporated PWS measurements into their operational workflows. The growing number of studies with promising results and the ongoing development of quality control procedures and software packages increases the interest in PWS data and their usage for specific applications.
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Giazzi, Marco, Gianandrea Peressutti, Luca Cerri, et al. "Meteonetwork: An Open Crowdsourced Weather Data System." Atmosphere 13, no. 6 (2022): 928. http://dx.doi.org/10.3390/atmos13060928.
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Citizen science has shown great potential for bringing large groups of people closer to science, thanks in part to cooperation with universities and research centers. In this context, amateur weather networks played a major role in the last few decades thanks to a constant growth in technology. An example is given by the Meteonetwork association, born in 2002, and mainly composed by atmospheric science enthusiasts, who built up in time a huge weather station network in Italy. In recent years, they have enlarged their horizons over Europe, displaying real time observations and daily maps coming from both personal weather stations and official networks. This study described how Meteonetwork has set up an open crowdsourced weather data system, how data are validated, and which products are generated and freely accessible for scientists and stakeholders for their own purposes. Two concrete use cases were described as examples: the Weatherness Project, which selects a subnet of Meteonetwork data for biometeorological and health purposes and the data assimilation process implemented to improve the initial conditions into the WRF meteorological model for daily weather forecasts.
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Zavattaro, Staci M., and Kelly A. Stevens. "Broadcast Meteorologists and Personal Branding: An Exploratory Study after a Hurricane Crisis." Weather, Climate, and Society 14, no. 2 (2022): 611–20. http://dx.doi.org/10.1175/wcas-d-21-0139.1.
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Abstract Television station and on-air talent marketing and branding has been studied with increasing attention because there is recognition that the people are part of an overall brand strategy. In this paper, we focus on broadcast meteorologists and their views of their personal brands and how those work to engage audiences. With Hurricane Dorian in 2019 as the background major weather event, the paper focuses on how on-air meteorologists develop their brand identities. From these interviews, we find 1) personal branding to build trust is paramount, 2) social media are game changers for personal branding, and 3) station branding can influence personal branding. Our findings shed light on the tension some on-air meteorologists experience when seeing themselves as a commodity while also trying to build trust as an expert crisis communicator. Significance Statement The purpose of our study is to examine how on-air meteorologists understand the role that personal branding plays—if any at all—in helping them deliver information to viewers. In previous research, Daniels and Loggins noted that, as the landscape for communicating lifesaving information changes, understanding how on-air meteorologists understand their roles and personal identities becomes paramount. If weather is a product, the people delivering the information become part of the product and overall brand strategy, according to Daniels and Loggins. Our exploratory study indicates that personal branding poses some opportunities and challenges for on-air meteorologists, who sometimes see an internal conflict between station branding strategies and their roles as scientists.
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Bárdossy, András, Jochen Seidel, and Abbas El Hachem. "The use of personal weather station observations to improve precipitation estimation and interpolation." Hydrology and Earth System Sciences 25, no. 2 (2021): 583–601. http://dx.doi.org/10.5194/hess-25-583-2021.
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Abstract. The number of personal weather stations (PWSs) with data available through the internet is increasing gradually in many parts of the world. The purpose of this study is to investigate the applicability of these data for the spatial interpolation of precipitation using a novel approach based on indicator correlations and rank statistics. Due to unknown errors and biases of the observations, rainfall amounts from the PWS network are not considered directly. Instead, it is assumed that the temporal order of the ranking of these data is correct. The crucial step is to find the stations which fulfil this condition. This is done in two steps – first, by selecting the locations using the time series of indicators of high precipitation amounts. Then, the remaining stations are then checked for whether they fit into the spatial pattern of the other stations. Thus, it is assumed that the quantiles of the empirical distribution functions are accurate. These quantiles are then transformed to precipitation amounts by a quantile mapping using the distribution functions which were interpolated from the information from the German National Weather Service (Deutscher Wetterdienst – DWD) data only. The suggested procedure was tested for the state of Baden-Württemberg in Germany. A detailed cross validation of the interpolation was carried out for aggregated precipitation amount of 1, 3, 6, 12 and 24 h. For each of these temporal aggregations, nearly 200 intense events were evaluated, and the improvement of the interpolation was quantified. The results show that the filtering of observations from PWSs is necessary as the interpolation error after the filtering and data transformation decreases significantly. The biggest improvement is achieved for the shortest temporal aggregations.
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Gibbons, John, Kristina Collins, David Kazdan, and Nathaniel Frissell. "Grape Version 1: First prototype of the low-cost personal space weather station receiver." HardwareX 11 (April 2022): e00289. http://dx.doi.org/10.1016/j.ohx.2022.e00289.
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Hewage, Pradeep, Ardhendu Behera, Marcello Trovati, et al. "Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station." Soft Computing 24, no. 21 (2020): 16453–82. http://dx.doi.org/10.1007/s00500-020-04954-0.
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Abstract Non-predictive or inaccurate weather forecasting can severely impact the community of users such as farmers. Numerical weather prediction models run in major weather forecasting centers with several supercomputers to solve simultaneous complex nonlinear mathematical equations. Such models provide the medium-range weather forecasts, i.e., every 6 h up to 18 h with grid length of 10–20 km. However, farmers often depend on more detailed short-to medium-range forecasts with higher-resolution regional forecasting models. Therefore, this research aims to address this by developing and evaluating a lightweight and novel weather forecasting system, which consists of one or more local weather stations and state-of-the-art machine learning techniques for weather forecasting using time-series data from these weather stations. To this end, the system explores the state-of-the-art temporal convolutional network (TCN) and long short-term memory (LSTM) networks. Our experimental results show that the proposed model using TCN produces better forecasting compared to the LSTM and other classic machine learning approaches. The proposed model can be used as an efficient localized weather forecasting tool for the community of users, and it could be run on a stand-alone personal computer.
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Tiransri, Loudry Achmad, Favian Dewanta, and Hilal Hudan Nuha. "Analisis Quality of Service Modul NRF24L01 pada Sistem Stasiun Cuaca Lokal." TELKA - Telekomunikasi Elektronika Komputasi dan Kontrol 9, no. 1 (2023): 44–51. http://dx.doi.org/10.15575/telka.v9n1.44-51.
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Internet of Things sering dimanfaatkan untuk menggantikan pekerjaan manusia secara otomatis, salah satu contohnya adalah sistem stasiun cuaca lokal. Stasiun cuaca lokal sering kali digunakan untuk melakukan pengamatan dan pendeteksian cuaca di kawasan rawan bencana yang belum terjangkau oleh jaringan internet. Sehingga pembuatan sistem stasiun cuaca lokal sering menggunakan modul pengiriman data secara nirkabel sebagai hub yang menghubungkan gateway jaringan dengan perangkat yang berada jauh di lapangan. Salah satu modul pengiriman secara nirkabel tersebut adalah modul NRF24L01 yang merupakan modul Wireless Personal Area Networks (WPAN) dengan daya kecil tetapi memiliki kecepatan pengiriman data yang tinggi. Sehingga pada penelitian ini penulis mengimplementasikan modul NRF24L01 pada sistem stasiun cuaca lokal dan melakukan analisis Quality of Service (QoS) dari komunikasi tersebut. Eksperimen dilakukan dengan cara pengiriman informasi dari transmitter menuju receiver yang bergerak bebas pada jarak tertentu dan kondisi kanal tertentu yang merepresentasikan LOS dan non-LOS. Selanjutnya, perhitungan quality of service (QoS) dari komunikasi antar modul NRF24L01 tersebut dilakukan dengan menggunakan metode Best Effort Service, dengan nilai packet loss sebesar 21,67% pada jarak pengiriman efektif yang kurang dari 300 meter dan kanal yang bersifat LOS, serta nilai packet loss sebesar 4,55% pada jarak pengiriman efektif yang kurang dari 70 meter dan kanal yang bersifat non-LOS. The Internet of Things is often used to replace human work automatically; one example is a local weather station system. Local weather stations often observe and detect weather in potential disaster areas the internet network has not reached. So, creating a local weather station system often uses wireless modules as a hub that connects the network gateway with devices far in the field. One of the wireless modules is the NRF24L01 module, a Wireless Personal Area Networks (WPAN) module with a small power but high data-sending speed. So in this study, the author implements the NRF24L01 module on the local weather station system and conducts a Quality of Service (QOS) analysis of the communication. The experiment was carried out by sending information from the transmitter to the receiver that moved freely at a certain distance and certain canal conditions representing LOS and Non-LOS. Furthermore, the calculation of the Quality of Service (QOS) of communication between the NRF24L01 module is carried out using the Best Effort Service method, with a loss packet value of 21.67% at an effective transmission distance of fewer than 300 meters and a LOS channel, and packet value Loss of 4.55% at an effective transmission distance of fewer than 70 meters and a non-LOS channel.
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Mandement, Marc, and Olivier Caumont. "Contribution of personal weather stations to the observation of deep-convection features near the ground." Natural Hazards and Earth System Sciences 20, no. 1 (2020): 299–322. http://dx.doi.org/10.5194/nhess-20-299-2020.
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Abstract. The lack of observations near the surface is often cited as a limiting factor in the observation and prediction of deep convection. Recently, networks of personal weather stations (PWSs) measuring pressure, temperature and humidity in near-real time have been rapidly developing. Even if they suffer from quality issues, their high temporal resolution and their higher spatial density than standard weather station (SWS) networks have aroused interest in using them to observe deep convection. In this study, the PWS contribution to the observation of deep-convection features near the ground is evaluated. Four cases of deep convection in 2018 over France were considered using data from Netatmo, a PWS manufacturer. A fully automatic PWS processing algorithm, including PWS quality control, was developed. After processing, the mean number of observations available increased by a factor of 134 in mean sea level pressure (MSLP), of 11 in temperature and of 14 in relative humidity over the areas of study. Near-surface SWS analyses and analyses comprising standard and personal weather stations (SPWSs) were built. The usefulness of crowdsourced data was proven both objectively and subjectively for deep-convection observation. Objective validations of SWS and SPWS analyses by leave-one-out cross validation (LOOCV) were performed using SWSs as the validation dataset. Over the four cases, LOOCV root-mean-square errors (RMSEs) decreased for all parameters in SPWS analyses compared to SWS analyses. RMSEs decreased by 73 % to 77 % in MSLP, 12 % to 23 % in temperature and 17 % to 21 % in relative humidity. Subjectively, fine-scale structures showed up in SPWS analyses, while being partly, or not at all, visible in SWS observations only. MSLP jumps accompanying squall lines or individual cells were observed as well as wake lows at the rear of these lines. Temperature drops and humidity rises accompanying most of the storms were observed sooner and at a finer resolution in SPWS analyses than in SWS analyses. The virtual potential temperature was spatialized at an unprecedented spatial resolution. This provided the opportunity for observing cold-pool propagation and secondary convective initiation over areas with high virtual potential temperatures, i.e. favourable locations for near-surface parcel lifting.
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Sasirekha, S., and S. Swamynathan. "Fuzzy Rule Based Environment Monitoring System for Weather Controlled Laboratories using Arduino." International Journal of Intelligent Information Technologies 13, no. 1 (2017): 50–66. http://dx.doi.org/10.4018/ijiit.2017010103.
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Weather controlled laboratories such as blood banks, plasma centers, biomedical, research, pharmacy and healthcare always require a portable, low cost and web-based centralized wireless monitoring system. However, it has become more stringent to monitor various weather controlling devices of these laboratories in order to reduce the risk of non-compliance with accreditation requirements. In literature, it is inferred that the majority of existing event detection approaches relies only on precise value to specify event thresholds, but those values cannot adequately handle the imprecise sensor reading. Therefore, in this work, one of the soft computing technologies, fuzzy logic is adopted to demonstrate that fuzzy rule based value significantly improves the accuracy of event detection. The prototype built is tested in a physical laboratory as a personal mobile weather station for monitoring and analyzing the environmental conditions using fuzzy simulated values.
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Mohegh, Arash, Ronnen Levinson, Haider Taha, et al. "Observational Evidence of Neighborhood Scale Reductions in Air Temperature Associated with Increases in Roof Albedo." Climate 6, no. 4 (2018): 98. http://dx.doi.org/10.3390/cli6040098.
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The effects of neighborhood-scale land use and land cover (LULC) properties on observed air temperatures are investigated in two regions within Los Angeles County: Central Los Angeles and the San Fernando Valley (SFV). LULC properties of particular interest in this study are albedo and tree fraction. High spatial density meteorological observations are obtained from 76 personal weather-stations. Observed air temperatures were then related to the spatial mean of each LULC parameter within a 500 m radius “neighborhood” of each weather station, using robust regression for each hour of July 2015. For the neighborhoods under investigation, increases in roof albedo are associated with decreases in air temperature, with the strongest sensitivities occurring in the afternoon. Air temperatures at 14:00–15:00 local daylight time are reduced by 0.31 °C and 0.49 °C per 1 MW increase in daily average solar power reflected from roofs per neighborhood in SFV and Central Los Angeles, respectively. Per 0.10 increase in neighborhood average albedo, daily average air temperatures were reduced by 0.25 °C and 1.84 °C. While roof albedo effects on air temperature seem to exceed tree fraction effects during the day in these two regions, increases in tree fraction are associated with reduced air temperatures at night.
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Overeem, Aart, Hidde Leijnse, Gerard van der Schrier, Else van den Besselaar, Irene Garcia-Marti, and Lotte Wilhelmina de Vos. "Merging with crowdsourced rain gauge data improves pan-European radar precipitation estimates." Hydrology and Earth System Sciences 28, no. 3 (2024): 649–68. http://dx.doi.org/10.5194/hess-28-649-2024.
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Abstract. Ground-based radar precipitation products typically need adjustment with rain gauge accumulations to achieve a reasonable accuracy. This is certainly the case for the pan-European radar precipitation products. The density of (near) real-time rain gauge accumulations from official networks is often relatively low. Crowdsourced rain gauge networks have a much higher density than conventional ones and are a potentially interesting (complementary) source to merge with radar precipitation accumulations. Here, a 1-year personal weather station (PWS) rain gauge dataset of ∼ 5 min accumulations is obtained from the private company Netatmo over the period 1 September 2019–31 August 2020, which is subjected to quality control using neighbouring PWSs and, after aggregating to 1 h accumulations, using unadjusted radar data. The PWS 1 h gauge accumulations are employed to spatially adjust OPERA radar accumulations, covering 78 % of geographical Europe. The performance of the merged dataset is evaluated against daily and disaggregated 1 h gauge accumulations from weather stations in the European Climate Assessment & Dataset (ECA&D). Results are contrasted to those from an unadjusted OPERA-based radar dataset and from EURADCLIM. The severe average underestimation for daily precipitation of ∼ 28 % from the unadjusted radar dataset diminishes to ∼ 3 % for the merged radar–PWS dataset. A station-based spatial verification shows that the relative bias in 1 h precipitation is still quite variable and suggests stronger underestimations for colder climates. A dedicated evaluation with scatter density plots reveals that the performance is indeed less good for lower temperatures, which points to limitations in observing solid precipitation by PWS gauges. The outcome of this study confirms the potential of crowdsourcing to improve radar precipitation products in (near) real time.
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Danilovich, Margaret, Aura Espinoza, and Christie Norrick. "Weather, Built Environment, or Personal Factors: Predictors of Walking by Independent Living Residents With Frailty." Innovation in Aging 5, Supplement_1 (2021): 447–48. http://dx.doi.org/10.1093/geroni/igab046.1734.
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Abstract Environmental factors influence older adult physical activity. However, the evidence about which factors lead to increased physical activity is mixed and few have studied how these factors affect those with frailty or living in retirement communities. This study investigated how environmental and weather factors influence physical activity among pre-frail and frail older adults residing in independent living retirement communities. We used ActivPal accelerometers to measure 7-day step counts among (n=108) pre-frail and frail residents in 9 independent living residences in metropolitan Chicago. We conducted environmental audits using the MAPS Abbreviated tool and collected National Weather Service Station data (temperature, precipitation, and daylight minutes) during the ActivPal periods. Participants were on average 80.0 years, 74% female, and average daily step count was 3,450 (range 151 - 11,663). Four buildings were in suburban areas and 5 in urban areas and four were private-pay residences while 5 offered subsidized rent. ANOVA results showed private-pay buildings had higher total MAPS scores than subsidized buildings (p=0.001), and urban buildings had higher total MAPS scores than suburban buildings (p < 0.000). Mean step differences were non-significant between different building types: (mean steps = 3,317 private-pay, 3,629 subsidized, 3,536 urban, 3,350 suburban). Pearson product-moment correlations showed a positive association between steps and MAPS positive streetscape features (p=0.011). Multiple regression analysis showed higher temperature days, precipitation, and more minutes of daylight were associated with higher step counts (p=.04). Given the dramatic variation in individual step counts, future research should investigate personal factors that contribute to activity among independent living residents.
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Marquès, Eva, Valéry Masson, Philippe Naveau, Olivier Mestre, Vincent Dubreuil, and Yves Richard. "Urban Heat Island Estimation from Crowdsensing Thermometers Embedded in Personal Cars." Bulletin of the American Meteorological Society 103, no. 4 (2022): E1098—E1113. http://dx.doi.org/10.1175/bams-d-21-0174.1.
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Abstract An ever-growing portion of the global population lives in urban areas. Cities are expanding quickly and consequently, the urban heat island effect has become a major health concern to maintain city dwellers’ thermal comfort. For this reason, city planners want to access urban meteorological databases in local areas where specific attention is needed. With the growth of connected devices, it is possible to collect unusual but massive temperature measurements from people’s activities. In this article, we study temperatures measured by thermometers embedded in everyday personal cars. To assess the quality of such opportunistic data, we first detect factors deteriorating the measurement. After preprocessing, the measurement error is then estimated thanks to two weather station networks providing a local-scale reference through the cities of Dijon and Rennes, France. The overall aggregation of private car temperature measurements allows us to estimate very precisely the urban heat island at a 200-m resolution. We detect the cooling effect of parks in Rennes and Paris urban areas. In Barcelona and Dijon, we observe the impact of regional environments and the orographic effect on the urban heat island. With our method, similar maps can be made accessible to every interested city in western Europe to target critical areas and support urban planning decisions.
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Collins, Kristina, John Gibbons, Nathaniel Frissell, et al. "Crowdsourced Doppler measurements of time standard stations demonstrating ionospheric variability." Earth System Science Data 15, no. 3 (2023): 1403–18. http://dx.doi.org/10.5194/essd-15-1403-2023.
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Abstract. Ionospheric variability produces measurable effects in Doppler shift of HF (high-frequency, 3–30 MHz) skywave signals. These effects are straightforward to measure with low-cost equipment and are conducive to citizen science campaigns. The low-cost Personal Space Weather Station (PSWS) network is a modular network of community-maintained, open-source receivers, which measure Doppler shift in the precise carrier signals of time standard stations. The primary goal of this paper is to explain the types of measurements this instrument can make and some of its use cases, demonstrating its role as the building block for a large-scale ionospheric and HF propagation measurement network which complements existing professional networks. Here, data from the PSWS network are presented for a period of time spanning late 2019 to early 2022. Software tools for the visualization and analysis of this living dataset are also discussed and provided. These tools are robust to data interruptions and to the addition, removal or modification of stations, allowing both short- and long-term visualization at higher density and faster cadence than other methods. These data may be used to supplement observations made with other geospace instruments in event-based analyses, e.g., traveling ionospheric disturbances and solar flares, and to assess the accuracy of the bottomside estimates of ionospheric models by comparing the oblique paths obtained by ionospheric ray tracers with those obtained by these receivers. The data are archived at https://doi.org/10.5281/zenodo.6622111 (Collins, 2022).
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Chougule, Prof Sadhana. "Air Quality Monitoring with Speech Announcements." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (2023): 340–45. http://dx.doi.org/10.22214/ijraset.2023.56651.
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Abstract: In this project, we design and develop an advanced weather station system using an ESP32 microcontroller, temperature sensor, humidity sensor, and air quality sensor. The primary objectives of this project are data collection, visualization, and auditory feedback. The ESP32 microcontroller acts as the central processing unit and connects to the internet via Wi-Fi to transmit real-time sensor data to an IoT platform. This platform stores and manages the data, making it accessible from anywhere with an internet connection. Users can monitor temperature, humidity, and air quality remotely through a userfriendly web interface Additionally, the project incorporates a display screen that provides local, real-time weather information for on-site users. This display enhances the accessibility of weather data, even in the absence of internet connectivity. To ensure inclusivity and accessibility, the system employs a speech-to-text algorithm implemented in Python. At predefined intervals, the system announces the sensor data via a speaker using text-to-speech (TTS) technology. This feature aids visually impaired individuals in accessing weather information in an auditory format. The combination of sensor integration, IoT connectivity, data visualization, and text-to-speech capabilities creates a comprehensive and user-friendly weather monitoring system suitable for both personal and public use. This project demonstrates the potential of IoT technologies to improve data accessibility and inclusivity in weather monitoring applications. IQ test continue to be one of most reliable tools to measure intelligence skills of the human. The Intelligence Quotient (IQ) tests and the corresponding psychometric explanations dominate both the scientific and popular views about human intelligence. Though the IQ tests have been in currency for long, there exists a gap in what they are believed to measure and what they do. While the IQ tests index the quality of cognitive functioning in selected domains of mental repertoire, the applied settings often inflate their predictive value leading to an interpretive gap.
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Dąbrowska, Anna, Monika Kobus, Piotr Sowiński, Łukasz Starzak, and Bartosz Pękosławski. "Integration of Active Clothing with a Personal Cooling System within the NGIoT Architecture for the Improved Comfort of Construction Workers." Applied Sciences 14, no. 2 (2024): 586. http://dx.doi.org/10.3390/app14020586.
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Intense physical activity and high ambient temperature cause construction workers to be exposed to an increased risk of overheating, especially in the summer season. Personal cooling systems have great potential to support workers’ thermoregulation and reduce this risk. In particular, solutions based on the thermoelectric effect can provide high cooling effectiveness and ergonomics at the same time. In this paper, a newly developed active clothing solution with flexible thermoelectric modules intended for outdoor activities is presented. The active clothing was subjected to utility tests on a treadmill under laboratory conditions with the participation of potential end users. A comparison of results from cooled and uncooled places indicated a reduction in local skin temperature of as much as 2.7 °C. Moreover, a gradual decrease in temperature in the uncooled place during the experiment was observed. Based on the positive results from this evaluation, the personal cooling system was integrated into active clothing within the ASSIST-IoT NGIoT reference architecture. This allows contextual and personalized adjustment of the cooling power to be provided using AI techniques and, additionally, by using data from a weather station and a smartwatch. Training procedures and models for the AI system are proposed, with special attention paid to the privacy aspect.
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Bedrina, T., A. Parodi, A. Quarati, and A. Clematis. "ICT approaches to integrating institutional and non-institutional data services for better understanding of hydro-meteorological phenomena." Natural Hazards and Earth System Sciences 12, no. 6 (2012): 1961–68. http://dx.doi.org/10.5194/nhess-12-1961-2012.
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Abstract. It is widely recognised that an effective exploitation of Information and Communication Technologies (ICT) is an enabling factor to achieve major advancements in Hydro-Meteorological Research (HMR). Recently, a lot of attention has been devoted to the use of ICT in HMR activities, e.g. in order to facilitate data exchange and integration, to improve computational capabilities and consequently model resolution and quality. Nowadays, ICT technologies have demonstrated that it is possible to extend monitoring networks by integrating sensors and other sources of data managed by volunteer's communities. These networks are constituted by peers that span a wide portion of the territory in many countries. The peers are "location aware" in the sense that they provide information strictly related with their geospatial location. The coverage of these networks, in general, is not uniform and the location of peers may follow random distribution. The ICT features used to set up the network are lightweight and user friendly, thus, permitting the peers to join the network without the necessity of specialised ICT knowledge. In this perspective it is of increasing interest for HMR activities to elaborate of Personal Weather Station (PWS) networks, capable to provide almost real-time, location aware, weather data. Moreover, different big players of the web arena are now providing world-wide backbones, suitable to present on detailed map location aware information, obtained by mashing up data from different sources. This is the case, for example, with Google Earth and Google Maps. This paper presents the design of a mashup application aimed at aggregating, refining and visualizing near real-time hydro-meteorological datasets. In particular, we focused on the integration of instant precipitation depths, registered either by widespread semi-professional weather stations and official ones. This sort of information has high importance and usefulness in decision support systems and Civil Protection applications. As a significant case study, we analysed the rainfall data observed during the severe flash-flood event of 4 November 2011 over Liguria region, Italy. The joint use of official observation network with PWS networks and meteorological radar allowed for the making of evident finger-like convection structure.
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Naik, Akshaya V. "A Cross Sectional Study of Occupational Hazards among Petrol Pump Attendants of Panjim City in Goa." International Journal of Preventive, Curative & Community Medicine 06, no. 01 (2020): 15–19. http://dx.doi.org/10.24321/2454.325x.202002.
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Introduction: Occupational hazards are the risks to life or functioning of an individual that is inherently associated with his occupation or working environment which can have deleterious effect on the individual’s health and his working efficiency. Petrol pump attendants are one such category of workers who are constantly exposed to various hazards at their workplace. Methodology: A cross sectional study was conducted from November- December 2018 among 7 petrol pumps located in the Panjim city. Census method was used for sampling. Ethical approval was obtained from the Institutional Ethics Committee. Data was collected using pre-designed semi structured questionnaire. Data was collected using Epicollect 5 mobile app and analyzed using SPSS version 22. Result: Musculoskeletal pain was the most common health problem perceived by the study participants (49.3%) followed by frequent headaches (26.7%), eye irritation (22.7%) and stuffy nose (18.7%). Fuel spillage over skin (68%), extreme weather conditions (37.3%), fuel spillage in the eyes (32%) were the common hazards reported by the participants. None of the study participant was seen wearing goggles, mask or gloves for their personal safety. 73% of the study participants felt that the Personal Protective Equipment (PPE) should be made available for their safety. 22.7% of the study participants had high blood pressure (>140/90mmHg) at the time of interview. Conclusion: This study highlights the most common illnesses experienced by the study subjects at their work place. Provision of PPE to every worker working on the petrol station with periodic medical checkups is required.
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Li, Rumeng, Qinghong Zhang, Juanzhen Sun, Yun Chen, Lili Ding, and Tian Wang. "Smartphone pressure data: quality control and impact on atmospheric analysis." Atmospheric Measurement Techniques 14, no. 2 (2021): 785–801. http://dx.doi.org/10.5194/amt-14-785-2021.
Testo completoAbstract (sommario):
Abstract. Smartphones are increasingly being equipped with atmospheric measurement sensors providing huge auxiliary resources for global observations. Although China has the highest number of cell phone users, there is little research on whether these measurements provide useful information for atmospheric research. Here, for the first time, we present the global spatial and temporal variation in smartphone pressure measurements collected in 2016 from the Moji Weather app. The data have an irregular spatiotemporal distribution with a high density in urban areas, a maximum in summer and two daily peaks corresponding to rush hours. With the dense dataset, we have developed a new bias-correction method based on a machine-learning approach without requiring users' personal information, which is shown to reduce the bias of pressure observation substantially. The potential application of the high-density smartphone data in cities is illustrated by a case study of a hailstorm that occurred in Beijing in which high-resolution gridded pressure analysis is produced. It is shown that the dense smartphone pressure analysis during the storm can provide detailed information about fine-scale convective structure and decrease errors from an analysis based on surface meteorological-station measurements. This study demonstrates the potential value of smartphone data and suggests some future research needs for their use in atmospheric science.
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Hartin, Janet S., David W. Fujino, Lorence R. Oki, S. Karrie Reid, Charles A. Ingels, and Darren Haver. "Water Requirements of Landscape Plants Studies Conducted by the University of California Researchers." HortTechnology 28, no. 4 (2018): 422–26. http://dx.doi.org/10.21273/horttech04037-18.
Testo completoAbstract (sommario):
University of California (UC) researchers have been involved in research and extension pertaining to measuring evapotranspiration (ET) rates and determining the minimum irrigation requirements of landscape plants for more than 30 years. Early work included the design and implementation of the California Irrigation Management Information System (CIMIS) weather station network and determining crop coefficients for warm and cool season turfgrasses based on historical ET and CIMIS data. Other researchers determined the minimum irrigation requirements for several species of established landscape trees, shrubs, and groundcovers in diverse climate zones throughout the state. In addition, the Water Use Classification of Landscape Species (WUCOLS) system was developed by UC personnel in the early 1990s which, to date, has classified more than 3500 landscape species into very low, low, moderate, and high water-use categories based on observation and personal experience by industry experts and UC personnel. Future work in the area of landscape water use and conservation will include updating WUCOLS as more data from replicated trials become available. New research at UC Riverside aims to improve irrigation efficiency (IE) through precision irrigation using smart controllers, remote sensing, and geospatial analysis under controlled conditions. Irrigation training and certification for public and private landscape managers must remain a priority because, even with advanced smart controller technologies, water savings will not occur with poorly designed and functioning irrigation systems.
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Butler, Michael K. "Personal weather stations and sharing weather data via the Internet." Weather 74, no. 1 (2018): 22–29. http://dx.doi.org/10.1002/wea.3206.
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Mamadiyarov, Marat, Nurlybek Abdimutalip, Saken Kurbaniyazov, and Gulshat Shalabayeva. "TRAINING STUDENTS IN THE METHODOLOGY OF CONDUCTING ECOLOGICAL AND CLIMATOLOGICAL RESEARCH IN THE PROCESS OF LEARNING." 3i intellect idea innovation - интеллект идея инновация 4 (2023): 122–32. http://dx.doi.org/10.52269/22266070_2023_4_122.
Testo completoAbstract (sommario):
This article presents the research works of fourth-year students of the educational program 6B01514-Geography on acquiring the necessary competencies and skills during the learning process by developing a project with an analysis of local climatological features and environmental factors using the Sokol-1M small weather station. The objective of this paper is to shape students' understanding and knowledge regarding the distinct characteristics and primary features of ecological and climatological factors in the region under study. It also emphasizes teaching analytical, synthetic, and summarizing skills, problem identification, and comparison, all within the context of developing students' cognitive abilities during their learning process.Taking into account current educational needs, the concept of teaching aidshas become firmly established in the modern educator's mindset. Thus, the traditional teaching model, where professors or lecturers, responsible for the development of the course curriculum, play a central role, is being replaced by an approach that prioritizes student learning and practical skills. Upon concluding their study of the topic, students presented the project on the climatic characteristics of any given area and substantiated their findings based on the knowledge gained during their practical training on the subject. An analysis of the importance of the project development method leads to the conclusion that students' project work contributes to the activation of cognitive activities, the development of creative abilities, the cultivation of professional competencies, the fostering of independence, the enhancement of research skills, and the development of personal qualities.
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de Vos, L. W., A. M. Droste, M. J. Zander, et al. "Hydrometeorological Monitoring Using Opportunistic Sensing Networks in the Amsterdam Metropolitan Area." Bulletin of the American Meteorological Society 101, no. 2 (2020): E167—E185. http://dx.doi.org/10.1175/bams-d-19-0091.1.
Testo completoAbstract (sommario):
Abstract The ongoing urbanization and climate change urges further understanding and monitoring of weather in cities. Two case studies during a 17-day period over the Amsterdam metropolitan area, the Netherlands, are used to illustrate the potential and limitations of hydrometeorological monitoring using nontraditional and opportunistic sensors. We employ three types of opportunistic sensing networks to monitor six important environmental variables: 1) air temperature estimates from smartphone batteries and personal weather stations, 2) rainfall from commercial microwave links and personal weather stations, 3) solar radiation from smartphones, 4) wind speed from personal weather stations, 5) air pressure from smartphones and personal weather stations, and 6) humidity from personal weather stations. These observations are compared to dedicated, traditional observations where possible, although such networks are typically sparse in urban areas. First, we show that the passage of a front can be successfully monitored using data from several types of nontraditional sensors in a complementary fashion. Also, we demonstrate the added value of opportunistic measurements in quantifying the urban heat island (UHI) effect during a hot episode. The UHI can be clearly determined from personal weather stations, though UHI values tend to be high compared to records from a traditional network. Overall, this study illustrates the enormous potential for hydrometeorological monitoring in urban areas using nontraditional and opportunistic sensing networks.
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Ma, Xinwei, Yurui Yin, Yuchuan Jin, Mingjia He, and Minqing Zhu. "Short-Term Prediction of Bike-Sharing Demand Using Multi-Source Data: A Spatial-Temporal Graph Attentional LSTM Approach." Applied Sciences 12, no. 3 (2022): 1161. http://dx.doi.org/10.3390/app12031161.
Testo completoAbstract (sommario):
As a convenient, economical, and eco-friendly travel mode, bike-sharing greatly improved urban mobility. However, it is often very difficult to achieve a balanced utilization of shared bikes due to the asymmetric spatio-temporal user demand distribution and the insufficient numbers of shared bikes, docks, or parking areas. If we can predict the short-run bike-sharing demand, it will help operating agencies rebalance bike-sharing systems in a timely and efficient way. Compared to the statistical methods, deep learning methods can automatically learn the relationship between the inputs and outputs, requiring less assumptions and achieving higher accuracy. This study proposes a Spatial-Temporal Graph Attentional Long Short-Term Memory (STGA-LSTM) neural network framework to predict short-run bike-sharing demand at a station level using multi-source data sets. These data sets include historical bike-sharing trip data, historical weather data, users’ personal information, and land-use data. The proposed model can extract spatio-temporal information of bike-sharing systems and predict the short-term bike-sharing rental and return demand. We use a Graph Convolutional Network (GCN) to mine spatial information and adopt a Long Short-Term Memory (LSTM) network to mine temporal information. The attention mechanism is focused on both temporal and spatial dimensions to enhance the ability of learning temporal information in LSTM and spatial information in GCN. Results indicate that the proposed model is the most accurate compared with several baseline models, the attention mechanism can help improve the model performance, and models that include exogenous variables perform better than the models that only consider historical trip data. The proposed short-term prediction model can be used to help bike-sharing users better choose routes and to help operators implement dynamic redistribution strategies.
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Gaumont, Damien F. "Thermal comfort during emergency transport by ambulance services." International Paramedic Practice 12, no. 3 (2022): 65–72. http://dx.doi.org/10.12968/ippr.2022.12.3.65.
Testo completoAbstract (sommario):
Background: Cold is an unpleasant sensation and is linked to increased anxiety, pain and risk of hypothermia. A question was developed to test the authors' hypothesis that patients would feel cold while being transported to an Irish emergency department (ED) by the Ambulance Service (NAS). Method: A survey of a convenience sample of 96 respondents was undertaken, for 77 consecutive hours in March 2020 at University Hospital Limerick's ED. During that period, the NAS transported 163 patients to the ED. A seven-point Likert satisfaction rating scale was used to assess perception. Taking this in combination with a visual assessment of personal insulation and the vehicle's patient compartment heater status, this project endeavoured to determine the passengers' thermal comfort. Results: Fifty-three per cent of the sample (n=51) were women, 22% (n=21) were escorts and 78% (n=75) were patients. Forty-two patients (56%) were aged >65 years. Of those interviewed, 66.6% (n=64) rated 0 (neutral/comfortable/I didn't notice) at the beginning of their journey and 67.7% (n=65) rated 0 at the end of their journey. Thirteen reported finding the ambulance cold at the beginning of their journey, and five reported still being cold at the end of it. In contrast, 15 respondents found the ambulance warm. The vehicle's patient compartment heater was switched on in 80% (n=69) of the 86 journeys. Transportation had a median time of 27.5 minutes (range: 3–90 minutes). The recorded air temperature at the nearest weather station ranged between −0.8°C and 10.1°C, significantly lower than the average for the time of the year. Conclusions: During this relatively cold week, two-thirds of passengers rated their thermal comfort in the ambulance as comfortable, 15 were warm beyond comfortable and very few felt cold.
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Vos, Lotte Wilhelmina, Hidde Leijnse, Aart Overeem, and Remko Uijlenhoet. "Quality Control for Crowdsourced Personal Weather Stations to Enable Operational Rainfall Monitoring." Geophysical Research Letters 46, no. 15 (2019): 8820–29. http://dx.doi.org/10.1029/2019gl083731.
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Demortier, Alan, Marc Mandement, Vivien Pourret, and Olivier Caumont. "Assimilation of surface pressure observations from personal weather stations in AROME-France." Natural Hazards and Earth System Sciences 24, no. 3 (2024): 907–27. http://dx.doi.org/10.5194/nhess-24-907-2024.
Testo completoAbstract (sommario):
Abstract. Spatially dense surface pressure observations from personal weather stations (PWSs) are able to describe pressure patterns at the surface, such as those associated with convective events, in more detail than with standard weather stations (SWSs) only. In this study, the benefit of assimilating PWS observations with the 3DVar and the 3DEnVar data assimilation schemes of the AROME-France model is evaluated over a 1-month period and during a heavy precipitation event in the South of France. Observations of surface pressure from PWSs are bias-corrected, quality-controlled, and thinned with a spacing equal to the horizontal dimension of an AROME-France grid cell. Over France, almost half of the 55 187 available PWS observations are assimilated, which is 129 times more than the number of assimilated SWS observations. Despite the limited advantages found from their assimilation with the 3DVar assimilation scheme, the 3DEnVar assimilation scheme shows systematic improvement and reduces by 10.3 % the root-mean-square deviation in surface pressure between 1 h model forecasts and SWS observations over France. Significant improvement is observed over the first 9 h of the forecasts in mean sea level pressure. Finally, when PWS observations are assimilated with the 3DEnVar assimilation scheme, a surface pressure anomaly generated by a mesoscale convective system – observed by PWSs and not visible without them – is successfully assimilated. In that case, the forecasts of location and temporal evolution of the mesoscale convective system as well as rainfall are closer to the observations when PWS observations are assimilated.
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Calovi, Martina, Weiming Hu, Guido Cervone, and Luca Delle Monache. "NAM-NMM Temperature Downscaling Using Personal Weather Stations to Study Urban Heat Hazards." GeoHazards 2, no. 3 (2021): 257–76. http://dx.doi.org/10.3390/geohazards2030014.
Testo completoAbstract (sommario):
Rising temperatures worldwide pose an existential threat to people, properties, and the environment. Urban areas are particularly vulnerable to temperature increases due to the heat island effect, which amplifies local heating. Throughout the world, several megacities experience summer temperatures that stress human survival. Generating very high-resolution temperature forecasts is a fundamental problem to mitigate the effects of urban warming. This paper uses the Analog Ensemble technique to downscale existing temperature forecast from a low resolution to a much higher resolution using private weather stations. A new downscaling approach, based on the reuse of the Analog Ensemble (AnEn) indices, resulted by the combination of days and Forecast Lead Time (FLT)s, is proposed. Specifically, temperature forecasts from the NAM-NMM Numerical Weather Prediction model at 12 km are downscaled using 83 Private Weather Stations data over Manhattan, New York City, New York. Forecasts for 84 h are generated, hourly for the first 36 h, and every three hours thereafter. The results are dense forecasts that capture the spatial variability of ambient conditions. The uncertainty associated with using non-vetted data is addressed.
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Barbano, Francesco, Erika Brattich, Carlo Cintolesi, et al. "Performance evaluation of MeteoTracker mobile sensor for outdoor applications." Atmospheric Measurement Techniques 17, no. 10 (2024): 3255–78. http://dx.doi.org/10.5194/amt-17-3255-2024.
Testo completoAbstract (sommario):
Abstract. The morphological complexity of urban environments results in a high spatial and temporal variability of the urban microclimate. The consequent demand for high-resolution atmospheric data remains a challenge for atmospheric research and operational application. The recent widespread availability and increasing adoption of low-cost mobile sensing offer the opportunity to integrate observations from conventional monitoring networks with microclimatic and air pollution data at a finer spatial and temporal scale. So far, the relatively low quality of the measurements and outdoor performance compared to conventional instrumentation has discouraged the full deployment of mobile sensors for routine monitoring. The present study addresses the performance of a commercial mobile sensor, the MeteoTracker (IoTopon Srl), recently launched on the market to quantify the microclimatic characteristics of the outdoor environment. The sensor follows the philosophy of the Internet of Things technology, being low cost, having an automatic data flow via personal smartphones and online data sharing, supporting user-friendly software, and having the potential to be deployed in large quantities. In this paper, the outdoor performance is evaluated through tests aimed at quantifying (i) the intra-sensor variability under similar atmospheric conditions and (ii) the outdoor accuracy compared to a reference weather station under sub-optimal (in a fixed location) and optimal (mobile) sensor usage. Data-driven corrections are developed and successfully applied to improve the MeteoTracker data quality. In particular, a recursive method for the simultaneous improvement of relative humidity, dew point, and humidex index proves to be crucial for increasing the data quality. The results mark an intra-sensor variability of approximately ± 0.5 °C for air temperature and ± 1.2 % for the corrected relative humidity, both of which are within the declared sensor accuracy. The sensor captures the same atmospheric variability as the reference sensor during both fixed and mobile tests, showing positive biases (overestimation) for both variables. Through the mobile test, the outdoor accuracy is observed to be between ± 0.3 to ± 0.5 °C for air temperature and between ± 3 % and ± 5 % for the relative humidity, ranking the MeteoTracker in the real accuracy range of similar commercial sensors from the literature and making it a valid solution for atmospheric monitoring.
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López Lorente, Javier, Xueqin Liu, and David John Morrow. "Worldwide evaluation and correction of irradiance measurements from personal weather stations under all-sky conditions." Solar Energy 207 (September 2020): 925–36. http://dx.doi.org/10.1016/j.solener.2020.06.073.
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Caumont, Olivier, Marc Mandement, François Bouttier, et al. "The heavy precipitation event of 14–15 October 2018 in the Aude catchment: a meteorological study based on operational numerical weather prediction systems and standard and personal observations." Natural Hazards and Earth System Sciences 21, no. 3 (2021): 1135–57. http://dx.doi.org/10.5194/nhess-21-1135-2021.
Testo completoAbstract (sommario):
Abstract. The case of the heavy precipitation event on 14 and 15 October 2018 which has led to severe flash flooding in the Aude watershed in south-western France is studied from a meteorological point of view using deterministic and probabilistic numerical weather prediction systems, as well as a unique combination of observations from both standard and personal weather stations. This case features typical characteristics of Mediterranean heavy precipitation events such as its classic synoptic situation and its quasi-stationary convective precipitation that regenerates continuously, as well as some peculiarities such as the presence of a former hurricane and a pre-existing cold air mass close to the ground. Mediterranean Sea surface temperature and soil moisture anomalies are briefly reviewed, as they are known to play a role in this type of hydrometeorological events. A study of rainfall forecasts shows that the event had limited predictability, in particular given the small size of the watersheds involved. It is shown that the stationarity of precipitation, whose estimation benefits from data from personal stations, is linked to the presence near the ground of a trough and a strong potential virtual temperature gradient, the stationarity of both of which is highlighted by a combination of observations from standard and personal stations. The forecast that comes closest to the rainfall observations contains the warmest, wettest, and fastest low-level jet and also simulates near the ground a trough and a marked boundary between cold air in the west and warm air in the east, both of which are stationary.
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Vuckovic, Milena, and Johanna Schmidt. "On the Importance of Data Quality Assessment of Crowdsourced Meteorological Data." Sustainability 15, no. 8 (2023): 6941. http://dx.doi.org/10.3390/su15086941.
Testo completoAbstract (sommario):
This study reflects on the quality aspects of urban meteorological time series obtained by crowdsourcing, specifically the air temperature and humidity data originating from personal weather stations (PWS) and the related implications for empirical and numerical research. A number of year-long hourly-based PWS data were obtained and compared to the data from the authoritative weather stations for selected areas in the city of Vienna, Austria. The results revealed a substantial amount of erroneous occurrences, ranging from singular and sequential data gaps to prevalent faulty signals in the recorded PWS data. These erroneous signals were more prominent in humidity time series data. If not treated correctly, such datasets may be a source of substantial errors that may drive inaccurate inferences from the modelling results and could further critically misinform future mitigation measures aimed at alleviating pressures related to climate change and urbanization.
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Chen, Zhenwei, Yang Lu, Zusong Li, and Xiancun Zhou. "Task Grid-Based Urban Environmental Information Release Mechanism for Mobile Crowd Sensing." Journal of Electrical and Computer Engineering 2022 (September 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/1738660.
Testo completoAbstract (sommario):
With the increased awareness of environmental protection, people have higher requirements for the accuracy of environmental information of surrounding life. The current monitoring of urban environmental information mainly comes from local environmental weather stations. Although the monitoring equipment of environmental weather stations is better than personal monitoring equipment, the monitoring equipment of weather monitoring stations is too expensive and only suitable for large-scale coarse-grained monitoring. Because the environmental information of a city is affected by factors such as landforms, buildings, rivers, factories, population density, and traffic flow, there are great differences in the environmental information of different areas in a city. Therefore, this study proposes a method that can be used for small-scale and fine-grained environmental information monitoring: the task grid-based urban environmental information release mechanism for mobile crowd sensing (MCS). Through this mechanism, the monitoring area is divided into different task grids according to the characteristics of the area, and the environmental information is sensed by mobile crowd sensing. For the sensing data, through an efficient data fusion algorithm designed in this study, the sensing information is fused to obtain the fine-grained environmental information of different task grids in the area. Through the use of this mechanism, differentiated environmental information can be provided to users in different areas of the city. In a simulation, this mechanism showed higher information accuracy than traditional information release methods. Thus, the mechanism is scientific and has good application value.
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Malozyomov, Boris V., Nikita V. Martyushev, Svetlana N. Sorokova, Egor A. Efremenkov, Denis V. Valuev, and Mengxu Qi. "Analysis of a Predictive Mathematical Model of Weather Changes Based on Neural Networks." Mathematics 12, no. 3 (2024): 480. http://dx.doi.org/10.3390/math12030480.
Testo completoAbstract (sommario):
In this paper, we investigate mathematical models of meteorological forecasting based on the work of neural networks, which allow us to calculate presumptive meteorological parameters of the desired location on the basis of previous meteorological data. A new method of grouping neural networks to obtain a more accurate output result is proposed. An algorithm is presented, based on which the most accurate meteorological forecast was obtained based on the results of the study. This algorithm can be used in a wide range of situations, such as obtaining data for the operation of equipment in a given location and studying meteorological parameters of the location. To build this model, we used data obtained from personal weather stations of the Weather Underground company and the US National Digital Forecast Database (NDFD). Also, a Google remote learning machine was used to compare the results with existing products on the market. The algorithm for building the forecast model covered several locations across the US in order to compare its performance in different weather zones. Different methods of training the machine to produce the most effective weather forecast result were also considered.
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de Vos, Lotte, Hidde Leijnse, Aart Overeem, and Remko Uijlenhoet. "The potential of urban rainfall monitoring with crowdsourced automatic weather stations in Amsterdam." Hydrology and Earth System Sciences 21, no. 2 (2017): 765–77. http://dx.doi.org/10.5194/hess-21-765-2017.
Testo completoAbstract (sommario):
Abstract. The high density of built-up areas and resulting imperviousness of the land surface makes urban areas vulnerable to extreme rainfall, which can lead to considerable damage. In order to design and manage cities to be able to deal with the growing number of extreme rainfall events, rainfall data are required at higher temporal and spatial resolutions than those needed for rural catchments. However, the density of operational rainfall monitoring networks managed by local or national authorities is typically low in urban areas. A growing number of automatic personal weather stations (PWSs) link rainfall measurements to online platforms. Here, we examine the potential of such crowdsourced datasets for obtaining the desired resolution and quality of rainfall measurements for the capital of the Netherlands. Data from 63 stations in Amsterdam (∼ 575 km2) that measure rainfall over at least 4 months in a 17-month period are evaluated. In addition, a detailed assessment is made of three Netatmo stations, the largest contributor to this dataset, in an experimental setup. The sensor performance in the experimental setup and the density of the PWS network are promising. However, features in the online platforms, like rounding and thresholds, cause changes from the original time series, resulting in considerable errors in the datasets obtained. These errors are especially large during low-intensity rainfall, although they can be reduced by accumulating rainfall over longer intervals. Accumulation improves the correlation coefficient with gauge-adjusted radar data from 0.48 at 5 min intervals to 0.60 at hourly intervals. Spatial rainfall correlation functions derived from PWS data show much more small-scale variability than those based on gauge-adjusted radar data and those found in similar research using dedicated rain gauge networks. This can largely be attributed to the noise in the PWS data resulting from both the measurement setup and the processes occurring in the data transfer to the online PWS platform. A double mass comparison with gauge-adjusted radar data shows that the median of the stations resembles the rainfall reference better than the real-time (unadjusted) radar product. Averaging nearby raw PWS measurements further improves the match with gauge-adjusted radar data in that area. These results confirm that the growing number of internet-connected PWSs could successfully be used for urban rainfall monitoring.
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Herdt, Alexandria, Robert Brown, Ian Scott-Fleming, et al. "Outdoor Thermal Comfort during Anomalous Heat at the 2015 Pan American Games in Toronto, Canada." Atmosphere 9, no. 8 (2018): 321. http://dx.doi.org/10.3390/atmos9080321.
Testo completoAbstract (sommario):
Mass sporting events in the summertime are influenced by underlying weather patterns, with high temperatures posing a risk for spectators and athletes alike. To better understand weather variations in the Greater Toronto Area (GTA) during the Pan American Games in 2015 (PA15 Games), Environment and Climate Change Canada deployed a mesoscale monitoring network system of 53 weather stations. Spatial maps across the GTA demonstrate large variations by heat metric (e.g., maximum temperature, humidex, and wet bulb globe temperature), identifying Hamilton, Ontario as an area of elevated heat and humidity, and hence risk for heat-related illness. A case study of the Hamilton Soccer Center examined on-site thermal comfort during a heat event and PA15 Soccer Games, demonstrating that athletes and spectators were faced with thermal discomfort and a heightened risk of heat-related illness. Results are corroborated by First Aid and emergency response data during the events, as well as insight from personal experiences and Twitter feed. Integrating these results provides new information on potential benefits to society from utilizing mesonet systems during large-scale sporting events. Results further improve our understanding of intra-urban heat variability and heat-health burden. The benefits of utilizing more comprehensive modeling approaches for human heat stress that coincide with fine-scale weather information are discussed.
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Barbazyuk, E., and P. Velmovsky. "Long-term changes in qualitative composition of the Buzuluk Pine Forest avifauna (Orenburg Province area) based on retrospective and present-time observations." Proceedings of the Zoological Institute RAS 328, no. 1 (2024): 3–19. http://dx.doi.org/10.31610/trudyzin/2024.328.1.3.
Testo completoAbstract (sommario):
We conducted a comparative analysis of qualitative composition of the Buzuluk Pine Forest avifauna, mainly based on data collected by ornithologist E.P. Knorre for the period 1928–1941, and modern observations done between 2000 and the present. Retrospective data were obtained from the now-defunct Buzuluk Bor Reserve with an area of 10 503 hectares (now Borovoye-Opytnoe Forestry in the Orenburg Province). Present-time personal avifaunistic observations (using standard methods with referring to literature sources) were carried out on an area of 49 000 hectares, both in Borovoye-Opytnoe and in several adjacent forestries that were part of the Buzuluk Bor National Park within the Orenburg Province. Comparing the historical and current bird lists, we used a relative assessment of changes in the composition of various bird groups based on the observed and expected frequencies according to the χ2 test. Long-term data on average annual temperatures and precipitation from a nearby state weather station were also used. As a result, it is shown that by now the list of birds of E.P. Knorre decreased from 153 to 109 species (28.8%). When comparing by orders, the water birds were found to be the most affected group. The number of species in orders Podicipediformes, Ciconiiformes, Gruiformes decreased by 80–100%, and it dropped by more than 50% in orders Anseriformes and Charadriiformes. There was also a slightly smaller decrease in the number of species in orders Falconiformes and Strigiformes – by about 40%. An assessment of the relative changes in various bird groups from Knorre’s list based on expected and observed frequencies showed significant differences in the portion of avifauna associated with water. It decreased between 23.5% and 9% in the group ‘Limnophiles’ (χ2 = 11.7, df = 1, p = 0.0006) and between 24.8% and 9% in the group of orders represented by waterfowl and waterbird species (χ2 = 13.4, df = 1, p = 0.0002). The change in the portion of birds of prey was statistically non-significant. The same portion assessment method did not statistically confirm the decrease in the rest ecological and order groups. Comparison of groups by occurrence status found no significant changes in the portions of all groups of birds in the past and present, but the largest reduction was recorded in the group of migrating species. The portion of migrants decreased from 8.7% to 3.0% (χ2 = 3.2, df = 1, p = 0.07, Yates corrected χ2 = 2.2, p = 0.1). A dramatic decline in group of bird species associated with wetlands was suggested to be due, among other factors, to the progressive desiccation of the Buzuluk Pine Forest, as E. Knorre himself wrote in his report. The ongoing aridization of the climate, as shown by the graph of average annual air temperatures, has been taking place since at least the 20s of the twentieth century and led to the drying-up of wetlands, and also to migration of groundwater to deeper horizons. The emerging trend towards a reduction in the portion of migrants could be explained by the fact that a number of migratory bird species, mainly ducks and waders, disappeared in the study area, and several other migrants began breeding species. In addition, large-scale fires and logging led to formation of burned wasteland areas inside the forest at the beginning of the twentieth century and also probably contributed to fragmentation and desiccation of this biome.
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Breen, Michael, Catherine Seppanen, Vlad Isakov, et al. "Development of TracMyAir Smartphone Application for Modeling Exposures to Ambient PM2.5 and Ozone." International Journal of Environmental Research and Public Health 16, no. 18 (2019): 3468. http://dx.doi.org/10.3390/ijerph16183468.
Testo completoAbstract (sommario):
Air pollution epidemiology studies of ambient fine particulate matter (PM2.5) and ozone (O3) often use outdoor concentrations as exposure surrogates. Failure to account for the variability of the indoor infiltration of ambient PM2.5 and O3, and time indoors, can induce exposure errors. We developed an exposure model called TracMyAir, which is an iPhone application (“app”) that determines seven tiers of individual-level exposure metrics in real-time for ambient PM2.5 and O3 using outdoor concentrations, weather, home building characteristics, time-locations, and time-activities. We linked a mechanistic air exchange rate (AER) model, a mass-balance PM2.5 and O3 building infiltration model, and an inhaled ventilation model to determine outdoor concentrations (Tier 1), residential AER (Tier 2), infiltration factors (Tier 3), indoor concentrations (Tier 4), personal exposure factors (Tier 5), personal exposures (Tier 6), and inhaled doses (Tier 7). Using the application in central North Carolina, we demonstrated its ability to automatically obtain real-time input data from the nearest air monitors and weather stations, and predict the exposure metrics. A sensitivity analysis showed that the modeled exposure metrics can vary substantially with changes in seasonal indoor-outdoor temperature differences, daily home operating conditions (i.e., opening windows and operating air cleaners), and time spent outdoors. The capability of TracMyAir could help reduce uncertainty of ambient PM2.5 and O3 exposure metrics used in epidemiology studies.
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Mizzell, Hope, Mark Malsick, and Wes Tyler. "The Historic South Carolina Rainfall and Major Floods of October 1-5, 2015." Journal of South Carolina Water Resources, no. 3 (June 1, 2016): 3–7. http://dx.doi.org/10.34068/jscwr.03.01.
Testo completoAbstract (sommario):
A record setting and historic rainfall event occurred October 1-5, 2015, producing widespread and significant flooding across much of South Carolina. The rainfall resulted from several atmospheric and hydrometeorological factors. The record rainfall triggered flash floods and riverine flooding that resulted in emergency evacuations, travel disruptions, personal property damage, business losses, bridge collapses, dam failures and tragic loss of life. Precipitation records were broken from the midlands to the coast, with totals ranging from 10 to over 26 inches of rain. Sixteen National Weather Service Cooperative Weather Stations set new 24-hour rainfall records for October. The amount of rainfall during the event at various locations and for various durations (6-, 24-, 48-, 72-, 96- hours) had a statistical probability of occurrence of 0.1% or 1 in 1,000 chance of happening in any given year, according to the National Oceanic and Atmospheric Administration (NOAA) Atlas 14. Streams and creeks swelled out of their banks with at least 17 U.S. Geological Survey (USGS) stream gages reaching record peaks. The event was the worst flooding most residents had ever experienced. This report will provide a synoptic and chronological overview of how the historic rain and flooding unfolded with documentation of the meteorological and hydrological records. A comprehensive interactive journal of the event is available on-line at http://www.dnr.sc.gov/flood2015.
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Benjamin, Kit, Zhiwen Luo, and Xiaoxue Wang. "Crowdsourcing Urban Air Temperature Data for Estimating Urban Heat Island and Building Heating/Cooling Load in London." Energies 14, no. 16 (2021): 5208. http://dx.doi.org/10.3390/en14165208.
Testo completoAbstract (sommario):
Urban heat island (UHI) effects significantly impact building energy. Traditional UHI investigation methods are often incapable of providing the high spatial density of observations required to distinguish small-scale temperature differences in the UHI. Crowdsourcing offers a solution. Building cooling/heating load in 2018 has been estimated in London, UK, using crowdsourced data from over 1300 Netatmo personal weather stations. The local climate zone (LCZ) scheme was used to classify the different urban environments of London (UK). Inter-LCZ temperature differences are found to be generally consistent with LCZ temperature definitions. Analysis of cooling degree hours in July shows LCZ 2 (the densest urban LCZ in London) had the highest cooling demand, with a total of 1550 cooling degree hours. The suburban related LCZs 5 and 6 and rural LCZs B and D all had about 80% of the demand of LCZ 2. In December, the rural LCZs A, B and D had the greatest heating demand, with all recording around 5750 heating degree hours. Urban LCZs 2, 5 and 6 had 91%, 86% and 95% of the heating demand of LCZ D, respectively. This study has highlighted both advantages and issues with using crowdsourced data for urban climate and building energy research.
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Çalışkan, Cüneyt, Aysun Algan, Hüseyin Koçak, Burcu Küçük Biçer, Meltem Şengelen, and Banu Çakir. "Preparations for Severe Winter Conditions by Emergency Health Personnel in Turkey." Disaster Medicine and Public Health Preparedness 8, no. 2 (2014): 170–73. http://dx.doi.org/10.1017/dmp.2014.28.
Testo completoAbstract (sommario):
AbstractObjectiveEmergency and core ambulance personnel work under all environmental conditions, including severe weather condtions. We evaluated emergency medical personnel in Çanakkale, Turkey, for their degree of preparedness.MethodsA descriptive study was conducted in Çanakkale, Turkey, within 112 emergency service units and their 17 district stations. Surveys were developed to measure the level of preparedness for serious winter conditions that individual workers made for themselves, their homes, and their cars.ResultsOf the 167 survey participants, the mean age was 29.8 ± 7.9 years; 52.7% were women; more than half (54.75%) were emergency medical technicians; and 53.3% were married. Only 10.4% of those who heated their homes with natural gas had carbon monoxide detectors. Scores relating to household and individual preparation for severe winter conditions increased by participants’ age (P < .003), being married (P < .000) and working in the city center (P < .021); and for men whose cars were equipped with tow ropes, extra clothing, and snow tires (P < .05). Absenteeism was higher for central-city personnel than district workers because they were less prepared for harsh winter conditions (P = .016).ConclusionMany of the surveyed emergency health personel demonstrated insufficient preparations for serious winter conditions. To increase the safety and efficiency of emergency medical personnel, educational training programs should be rountinely conducted. (Disaster Med Public Health Preparedness. 2014;0:1-4)
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Fencl, Martin, Roberto Nebuloni, Jafet C. M. Andersson, et al. "Data formats and standards for opportunistic rainfall sensors." Open Research Europe 3 (October 10, 2023): 169. http://dx.doi.org/10.12688/openreseurope.16068.1.
Testo completoAbstract (sommario):
Opportunistic sensors are increasingly used for rainfall measurement. However, their raw data are collected by a variety of systems that are often not primarily intended for rainfall monitoring, resulting in a plethora of different data formats and a lack of common standards. This hinders the sharing of opportunistic sensing (OS) data, their automated processing, and, at the end, their practical usage and integration into standard observation systems. This paper summarises the experiences of the more than 100 members of the OpenSense Cost Action involved in the OS of rainfall. We review the current practice of collecting and storing precipitation OS data and corresponding metadata, and propose new common guidelines describing the requirements on data and metadata collection, harmonising naming conventions, and defining human-readable and machine readable file formats for data and metadata storage. We focus on three sensors identified by the OpenSense community as prominent representatives of the OS of precipitation: Commercial microwave links (CML): fixed point-to-point radio links mainly used as backhauling connections in telecommunication networks Satellite microwave links (SML): radio links between geostationary Earth orbit (GEO) satellites and ground user terminals. Personal weather stations (PWS): non-professional meteorological sensors owned by citizens. The conventions presented in this paper are primarily designed for storing, handling, and sharing historical time series and do not consider specific requirements for using OS data in real time for operational purposes. The conventions are already now accepted by the ever growing OpenSense community and represent an important step towards automated processing of OS raw data and community development of joint OS software packages.
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Fencl, Martin, Roberto Nebuloni, Jafet C. M. Andersson, et al. "Data formats and standards for opportunistic rainfall sensors." Open Research Europe 3 (February 13, 2024): 169. http://dx.doi.org/10.12688/openreseurope.16068.2.
Testo completoAbstract (sommario):
Opportunistic sensors are increasingly used for rainfall measurement. However, their raw data are collected by a variety of systems that are often not primarily intended for rainfall monitoring, resulting in a plethora of different data formats and a lack of common standards. This hinders the sharing of opportunistic sensing (OS) data, their automated processing, and, at the end, their practical usage and integration into standard observation systems. This paper summarises the experiences of the more than 100 members of the OpenSense Cost Action involved in the OS of rainfall. We review the current practice of collecting and storing precipitation OS data and corresponding metadata, and propose new common guidelines describing the requirements on data and metadata collection, harmonising naming conventions, and defining human-readable and machine readable file formats for data and metadata storage. We focus on three sensors identified by the OpenSense community as prominent representatives of the OS of precipitation: Commercial microwave links (CML): fixed point-to-point radio links mainly used as backhauling connections in telecommunication networks Satellite microwave links (SML): radio links between geostationary Earth orbit (GEO) satellites and ground user terminals. Personal weather stations (PWS): non-professional meteorological sensors owned by citizens. The conventions presented in this paper are primarily designed for storing, handling, and sharing historical time series and do not consider specific requirements for using OS data in real time for operational purposes. The conventions are already now accepted by the ever growing OpenSense community and represent an important step towards automated processing of OS raw data and community development of joint OS software packages.
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Azhari, Ferian, Nawanto Budi Sukoco, and Khoirol Imam Fatoni. "Studi Karakteristik Parameter Meteorologi dan Gelombang untuk Operasi Amfibi di Perairan Singkawang Kalimantan Barat." Jurnal Chart Datum 6, no. 1 (2021): 1–9. http://dx.doi.org/10.37875/chartdatum.v6i1.169.
Testo completoAbstract (sommario):
Operasi amfibi merupakan Operasi Gabungan TNI yang mengintegrasikan berbagai jenis kekuatan yaitu kapal, pesawat udara dan pasukan pendarat dalam suatu serangan terhadap pantai musuh dan/atau berpotensi dikuasai musuh dan bersifat kelautan. Oleh karena itu, dalam melaksanakan operasi amfibi dibutuhkan data kelautan untuk menunjang operasi tempur laut yaitu data tentang laut dan lingkungannya atau data Hidro-oseanografi sebagai produk Pusat Hidrografi dan Oseanografi TNI-AL (Pushidrosal). Untuk mendukung pemenuhan data-data tersebut, Pushidrosal telah mendirikan stasiun meteorologi permanen sistem telemetri dengan menggunakan peralatan Automatic Weather Station (AWS) yang mampu mengirim data online melalui sinyal GPRS, yang didirikan di daerah operasi dan latihan TNI-AL yang salah satunya didirikan di Singkawang Kalimantan Barat. Dengan adanya stasiun meteorologi permanen yang menggunakan sistem telemetri ini, Pushidrosal dapat mengumpulkan data meteorologi di daerah operasi dan latihan secara realtime dan termonitor langsung dari kantor Pushidrosal di Jakarta.
 Data meteorologi yang didapatkan dari stasiun meteorologi antara lain berupa data temparatur/suhu udara, lembab nisbi, tekanan udara, radiasi matahari, curah hujan dan kondisi angin. Data-data tersebut merupakan parameter-parameter meteorologi atau biasa disebut parameter cuaca. Dengan melakukan pengamatan terhadap parameter-parameter tersebut tentunya akan diperoleh informasi cuaca di daerah pengamatan. Selain mendapatkan informasi cuaca dari pengamatan parameter-parameter tersebut, dengan melaksanakan pengolahan dari hasil pengukuran kondisi angin dapat dihitung tinggi gelombang perairan daerah penelitian. Kebutuhan data cuaca sebagai bagian dari Data Cumemu (Cuaca Medan Musuh) dalam tahapan perencanaan operasi tempur laut, menjadi tanggung jawab TNI AL khususnya Pushidrosal.
 Pengolahan data hasil pengukuran stasiun meteorologi Pushidrosal di Singkawang akan memperlihatkan karakteristik cuaca berupa temparatur/suhu udara, lembab nisbi, tekanan udara, radiasi matahari, curah hujan dan kondisi angin di daerah Perairan Singkawang. Pengolahan dan perhitungan data kondisi angin akan menunjukkan kondisi tinggi dan periode gelombang di Perairan Singkawang. Data karakteristik cuaca, kondisi tinggi dan periode gelombang sangat dibutuhkan dalam membuat suatu perencanaan operasi dan latihan pendaratan amfibi, agar operasi dan latihan dapat berjalan dengan baik serta keselamatan personil dan material yang terpenuhi.
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Mandement, Marc, and Olivier Caumont. "A numerical study to investigate the roles of former Hurricane Leslie, orography and evaporative cooling in the 2018 Aude heavy-precipitation event." Weather and Climate Dynamics 2, no. 3 (2021): 795–818. http://dx.doi.org/10.5194/wcd-2-795-2021.
Testo completoAbstract (sommario):
Abstract. In south-eastern France, the Mediterranean coast is regularly affected by heavy-precipitation events. On 14–15 October 2018, in the Aude department, a back-building quasi-stationary mesoscale convective system produced up to about 300 mm of rain in 11 h. At synoptic scale, the former Hurricane Leslie was involved in the formation of a Mediterranean surface low that channelled conditionally unstable air towards the coast. At mesoscale, convective cells focused west of a decaying cold front that became quasi-stationary and downwind of the terrain. To investigate the roles of the moisture provided by Leslie, orography and evaporative cooling among the physical processes that led to the location and intensity of the observed rainfall, numerical simulations are run at 1 km and 500 m horizontal grid spacing and evaluated with independent near-surface analyses including novel crowd-sourced observations of personal weather stations. Simulations show that, in a first part of the event, low-level conditionally unstable air parcels found inside strong updraughts mainly originated from areas east of the Balearic Islands, over the Mediterranean Sea, whereas in a second part, an increasing number originated from Leslie's remnants. Air masses from areas east of the Balearic Islands appeared as the first supplier of moisture over the entire event. Still, Leslie contributed to substantially moistening mid-levels over the Aude department, diminishing evaporation processes. Thus, the evaporative cooling over the Aude department did not play any substantial role in the stationarity of the quasi-stationary front. Regarding lifting mechanisms, the advection of conditionally unstable air by a low-level jet towards the quasi-stationary front, confined to altitudes below 2 km, reactivated convection along and downwind of the front. Most of the air parcels found inside strong updraughts near the location of the maximum rainfall were lifted above the quasi-stationary front. Downwind of the Albera Massif, mountains bordering the Mediterranean Sea, cells formed by orographic lifting were maintained by low-level leeward convergence, mountain lee waves and a favourable directional wind shear; when terrain is flattened, rainfall is substantially reduced. The location of the exceptional precipitation was primarily driven by the location of the quasi-stationary front and secondarily by the location of convective bands downwind of orography.
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Kelling, Steve. "Technology Developments for Biodiversity Monitoring and Conservation." Biodiversity Information Science and Standards 2 (May 22, 2018): e25833. http://dx.doi.org/10.3897/biss.2.25833.
Testo completoAbstract (sommario):
Over the next 5 years major advances in the development and application of numerous technologies related to computing, mobile phones, artificial intelligence (AI), and augmented reality (AR) will have a dramatic impact in biodiversity monitoring and conservation. Over a 2-week period several of us had the opportunity to meet with multiple technology experts in the Silicon Valley, California, USA to discuss trends in technology innovation, and how they could be applied to conservation science and ecology research. Here we briefly highlight some of the key points of these meetings with respect to AI and Deep Learning. Computing: Investment and rapid growth in AI and Deep Learning technologies are transforming how machines can perceive the environment. Much of this change is due to increased processing speeds of Graphics Processing Units (GPUs), which is now a billion-dollar industry. Machine learning applications, such as convolutional neural networks (CNNs) run more efficiently on GPUs and are being applied to analyze visual imagery and sounds in real time. Rapid advances in CNNs that use both supervised and unsupervised learning to train the models is improving accuracy. By taking a Deep Learning approach where the base layers of the model are built upon datasets of known images and sounds (supervised learning) and later layers relying on unclassified images or sounds (unsupervised learning), dramatically improve the flexibility of CNNs in perceiving novel stimuli. The potential to have autonomous sensors gathering biodiversity data in the same way personal weather stations gather atmospheric information is close at hand. Mobile Phones: The phone is the most widely used information appliance in the world. No device is on the near horizon to challenge this platform, for several key reasons. First, network access is ubiquitous in many parts of the world. Second, batteries are improving by about 20% annually, allowing for more functionality. Third, app development is a growing industry with significant investment in specializing apps for machine-learning. While GPUs are already running on phones for video streaming, there is much optimism that reduced or approximate Deep Learning models will operate on phones. These models are already working in the lab, with the biggest hurdle being power consumption and developing energy efficient applications and algorithms to run complicated AI processes will be important. It is just a matter of time before industry will have AI functionality on phones. These rapid improvements in computing and mobile phone technologies have huge implications for biodiversity monitoring, conservation science, and understanding ecological systems. Computing: AI processing of video imagery or acoustic streams create the potential to deploy autonomous sensors in the environment that will be able to detect and classify organisms to species. Further, AI processing of Earth spectral imagery has the potential to provide finer grade classification of habitats, which is essential in developing fine scale models of species distributions over broad spatial and temporal extents. Mobile Phones: increased computing functionality and more efficient batteries will allow applications to be developed that will improve an individual’s perception of the world. Already AI functionality of Merlin improves a birder’s ability to accurately identify a bird. Linking this functionality to sensor devices like specialized glasses, binoculars, or listening devises will help an individual detect and classify objects in the environment. In conclusion, computing technology is advancing at a rapid rate and soon autonomous sensors placed strategically in the environment will augment the species occurrence data gathered by humans. The mobile phone in everyone’s pocket should be thought of strategically, in how to connect people to the environment and improve their ability to gather meaningful biodiversity information.
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Hassani, Amirhossein, Gabriela Sousa Santos, Philipp Schneider, and Núria Castell. "Interpolation, Satellite-Based Machine Learning, or Meteorological Simulation? A Comparison Analysis for Spatio-temporal Mapping of Mesoscale Urban Air Temperature." Environmental Modeling & Assessment, November 14, 2023. http://dx.doi.org/10.1007/s10666-023-09943-9.
Testo completoAbstract (sommario):
AbstractFine-resolution spatio-temporal maps of near-surface urban air temperature (Ta) provide crucial data inputs for sustainable urban decision-making, personal heat exposure, and climate-relevant epidemiological studies. The recent availability of IoT weather station data allows for high-resolution urban Ta mapping using approaches such as interpolation techniques or machine learning (ML). This study is aimed at executing these approaches and traditional numerical modeling within a practical and operational framework and evaluate their practicality and efficiency in cases where data availability, computational constraints, or specialized expertise pose challenges. We employ Netatmo crowd-sourced weather station data and three geospatial mapping approaches: (1) Ordinary Kriging, (2) statistical ML model (using predictors primarily derived from Earth Observation Data), and (3) weather research and forecasting model (WRF) to predict/map daily Ta at nearly 1-km spatial resolution in Warsaw (Poland) for June–September and compare the predictions against observations from 5 meteorological reference stations. The results reveal that ML can serve as a viable alternative approach to traditional kriging and numerical simulation, characterized by reduced complexity and higher computational speeds within the domain of urban meteorological studies (overall RMSE = 1.06 °C and R2 = 0.94, compared to ground-based meteorological stations). The results have implications for identifying the urban regions vulnerable to overheating and evidence-based urban management in response to climate change. Due to the open-sourced nature of the applied predictors and input parsimony, the ML method can be easily replicated for other EU cities.
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Fatoni, Arrizal R., Ahmad Bahtiar, Nurzaka Faridatussafura, Ady Hermanto, and M. Ferdaus N. Aulady. "The application of PPE at the Juanda meteorological station (BMKG)." Public Health Risk Assesment Journal 1, no. 2 (2024). http://dx.doi.org/10.61511/phraj.v1i2.2024.316.
Testo completoAbstract (sommario):
The application of Personal Protective Equipment (PPE) at the Meteorology, Climatology, and Geophysics Agency (BMKG) is an important step in maintaining employees’ safety and health in their work environment. Even though BMKG is not involved in heavy industrial activities, there are still potential hazards and risks that need to be addressed in carrying out meteorological, climatological, and geophysical tasks. This study aims to describe the importance of implementing PPE in the BMKG environment, especially the Aviation Meteorological Station, where we took a case study at the Juanda Meteorological Station. First, we identify potential hazards that may be faced by BMKG employees, such as radiation risk, potential for hazardous gas explosions or fires, extreme weather conditions, and risks of physical injury. In the face of these risks, the use of proper PPE can help protect employees from hazardous exposures, and gas explosions and prevent injury. Next, we discuss the factors that need to be considered in implementing PPE at the BMKG. This includes a comprehensive risk assessment, identification of appropriate PPE for specific hazards, training of employees on the use and care of PPE, and regular monitoring and maintenance of the PPE used. Therefore, by implementing the Occupational Health and Safety Management System, in this case, the application of PPE, BMKG can ensure that employees have a safe and healthy work environment, reduce the risk of work accidents, and comply with applicable regulations and standards related to work safety.