Showing papers on "Weather station published in 2019"

Suitability of gridded climate datasets for use in environmental epidemiology.

Abstract: Epidemiologic analyses of the health effects of meteorological exposures typically rely on observations from the nearest weather station to assess exposure for geographically diverse populations. Gridded climate datasets (GCD) provide spatially resolved weather data that may offer improved exposure estimates, but have not been systematically validated for use in epidemiologic evaluations. As a validation, we linearly regressed daily weather estimates from two GCDs, PRISM and Daymet, to observations from a sample of weather stations across the conterminous United States and compared spatially resolved, population-weighted county average temperatures and heat indices from PRISM to single-pixel PRISM values at the weather stations to identify differences. We found that both Daymet and PRISM accurately estimate ambient temperature and mean heat index at sampled weather stations, but PRISM outperforms Daymet for assessments of humidity and maximum daily heat index. Moreover, spatially-resolved exposure estimates differ from point-based assessments, but with substantial inter-county heterogeneity. We conclude that GCDs offer a potentially useful approach to exposure assessment of meteorological variables that may, in some locations, reduce exposure measurement error, as well as permit assessment of populations distributed far from weather stations.

Detecting local environmental change: the role of experience in shaping risk judgments about global warming

Abstract: Anthropogenic climate change is increasing the frequency and severity of extreme weather events (e.g. flooding, heat waves, and wildfires). As a result, it is often reasoned that as more individuals experience unusual weather patterns that are consistent with changing climate conditions, the more their concern about global warming will increase, and the more motivated they will become to respond and address the problem effectively. Social science research evaluating the relationships between personal experiences with and risk perceptions of climate change, however, show mixed results. Here, we analyze a representative statewide survey of Floridians and compare their risk perceptions of five-year trends in climate change with local weather station data from the five years preceding the survey. The results show that Floridians are unable to detect five-year increases in temperature, but some can detect changes in precipitation. Despite an inability to detect the correct direction of change, responde...

Ensemble Machine Learning-Based Wind Forecasting to Combine NWP Output With Data From Weather Station

Abstract: Wind generation resources are the fastest growing energy resources throughout the world. An accurate wind forecasting is critical to the integration of a large amount of wind generation units into the grid operations. This paper presents an ensemble machine learning-based method to forecast wind power production, which uses both the wind generation forecasted by a numerical weather prediction (NWP) model and the meteorological observation data from weather stations. In this way, it takes advantage of the atmosphere models while capturing spatial and temporal correlation between meteorological observations at different locations. Three machine learning algorithms (artificial neural network, support vector regression, Gaussian process) are proposed to synthesize the meteorological data and the prediction from NWP. An ensemble forecast is then created by blending the results derived from three algorithms through a Bayesian model average. The performance of this ensemble forecast has been validated by the 2-year operational data collected at Electricity Reliability Council of Texas.

Assessment of different gridded weather data for soybean yield simulations in Brazil

Abstract: A high-density, well-distributed, and consistent historical weather data series is of major importance for agricultural planning and climatic risk evaluation. A possible option for regions where weather station network is irregular is the use of gridded weather data (GWD), which can be downloaded online from different sources. Based on that, the aim of this study was to assess the suitability of two GWD, AgMERRA and XAVIER, by comparing them with measured weather data (MWD) for estimating soybean yield in Brazil. The GWD and MWD were obtained for 24 locations across Brazil, considering the period between 1980 and 2010. These data were used to estimate soybean yield with DSSAT-CROPGRO-Soybean model. The comparison of MWD with GWD resulted in a good agreement between climate variables, except for solar radiation. The crop simulations with GWD and MWD resulted in a good agreement for vegetative and reproductive phases. Soybean potential yield (Yp) simulated with AgMERRA and XAVIER had a high correlation (r > 0.88) when compared to the estimates with MWD, with the RMSE of about 400 kg ha−1. For attainable yield (Ya), estimates with XAVIER resulted in a RMSE of 700 kg ha−1 against 864 kg ha−1 from AgMERRA, both compared to the simulations using MWD. Even with these differences in Ya simulations, both GWD can be considered suitable for simulating soybean growth, development, and yield in Brazil; however, with XAVIER GWD presenting a better performance for weather and crop variables assessed.

Forecasting wildlife die-offs from extreme heat events

Abstract: Extreme heat events pose increasing challenges to biodiversity conservation worldwide, yet our ability to predict the time, place and magnitude of their impacts on wildlife is limited. Extreme heat events in Australia are known to kill thousands of flying-foxes (Pteropus spp.), and such die-offs are expected to become more frequent and widespread in the future under anthropogenic climate change. There is a growing need for predicting when and where such heat-related die-offs would occur, to facilitate short-term wildlife management and conservation actions. In this study, we used gridded hourly air temperature forecasts [Australian Community Climate and Earth-System Simulator (ACCESS-R) Numerical Weather Prediction (NWP) model] from the Australian Bureau of Meteorology to predict flying-fox heat-related mortality based on an empirically determined threshold of 42.0°C. We tested the accuracy and precision of this model using a twofold evaluation of the ACCESS-R NWP forecast air temperature during a recorded extreme heat event with in situ air temperature measurements and interpolated weather station data. While our results showed a slight discrepancy between the modelled and measured air temperatures, there was no significant difference in the forecast's accuracy to predict die-offs during an extreme heat event and the overall summer period. We evaluated the accuracy of mortality predictions based on different air temperature thresholds (38.0, 40.0, 42.0 and 44.0°C). Our results revealed a significant probability of flying-fox mortality occurrence when forecast air temperature was ≥42.0°C, while the 24- and 48-h forecasts accurately predicted 77 and 73% of the die-offs, respectively. Thus, the use of 42.0°C forecast air temperature from the ACCESS-R NWP model can predict flying-fox mortality reliably at the landscape scale. In principle, the forecaster can be used for any species with known thermal tolerance data and is therefore a promising new tool for prioritizing adaptation actions that aim to conserve biodiversity in the face of climate change.

Boosting a Weather Monitoring System in Low Income Economies Using Open and Non-Conventional Systems: Data Quality Analysis.

Abstract: In low-income and developing countries, inadequate weather monitoring systems adversely affect the capacity of managing natural resources and related risks. Low-cost and IoT devices combined with a large diffusion of mobile connection and open technologies offer a possible solution to this problem. This research quantitatively evaluates the data quality of a non-conventional, low-cost and fully open system. The proposed novel solution was tested for a duration of 8 months, and the collected observations were compared with a nearby authoritative weather station. The experimental weather station is based in Arduino and transmits data through the 2G General Packet Radio Service (GPRS) to the istSOS which is a software to set-up a web service to collect, share and manage observations from sensor networks using the Sensor Observation Service (SOS) standard of the Open Geospatial Consortium (OGC). The results demonstrated that this accessible solution produces data of appropriate quality for natural resource and risk management.

The influence of weather conditions on adult transit ridership in the sub-tropics

Abstract: This paper investigates the influence of local weather conditions on adult transit ridership across three transit modes. Drawing on smart card data and half hourly weather station records for a 12 month period, analysis reveals that weather imposes an effect on adult transit ridership and that its influence varies by mode. Ferry ridership is found to be more sensitive to changes in weather compared to either bus or train ridership. Findings also reveal that weather’s influence on ridership varies across the course of a day. During morning and evening peak hours, weather is shown to exert a weaker effect than other periods throughout the day. We argue that our findings are important in their capacity to contribute to a new evidence base with the potential to inform the (re)design of more weather-resilient transit systems by shedding new light on the weather–transit ridership relationship.

Combining Weather Stations for Electric Load Forecasting

Abstract: Weather is a key factor affecting electricity demand. Many load forecasting models rely on weather variables. Weather stations provide point measurements of weather conditions in a service area. Since the load is spread geographically, a single weather station may not sufficiently explain the variations of the load over a vast area. Therefore, a proper combination of multiple weather stations plays a vital role in load forecasting. This paper answers the question: given a number of weather stations, how should they be combined for load forecasting? Simple averaging has been a commonly used and effective method in the literature. In this paper, we compared the performance of seven alternative methods with simple averaging as the benchmark using the data of the Global Energy Forecasting Competition 2012. The results demonstrate that some of the methods outperform the benchmark in combining weather stations. In addition, averaging the forecasts from these methods outperforms most individual methods.

Cloud Based Weather Station using IoT Devices

Abstract: This paper proposes a smart system cloud based weather station. The system uses Raspberry Pi, for collecting and observing weather data. The storing and processing of the obtained weather data is done in cloud to predicting the effect of this weather change. The system is designed to effectively monitor the ambient weather conditions such as temperature, humidity, wind speed, pressure, and rainfall etc. The objective is to design a system which is low cost, requires less maintenance, and involved minimal manual intervention. The system is built using commodity hardware Raspberry Pi, various sensors and uses WiFi as a communication medium which makes the system consume very low power and low cost of building. Smaller Raspberry Pi Zero W boards are used to collect the sensor's data and send it to the base station Raspberry Pi 3 board. The Raspberry Pi 3 then further transmits the data over WiFi to the cloud database and this data is further used to train new Machine Learning model deployed in the cloud for prediction of the effect and to observe and study various weather patterns and trends. The users can access the weather data and insights remotely, and in real time through a web application that is built using the Django Framework, and is deployed in the cloud.

Comparison of temperature-mortality associations estimated with different exposure metrics.

Abstract: Studies of the short-term association between ambient temperature and mortality often use temperature observations from a single monitoring station, frequently located at the nearest airport, to represent the exposure of individuals living across large areas. Population-weighted temperature estimates constructed from gridded meteorological data may offer an opportunity to improve exposure assessment in locations where station observations do not fully capture the average exposure of the population of interest. Methods We compared the association between daily mean temperature and mortality in each of 113 United States counties using (1) temperature observations from a single weather station and (2) population-weighted temperature estimates constructed from a gridded meteorological dataset. We used distributed lag nonlinear models to estimate the 21-day cumulative association between temperature and mortality in each county, 1987-2006, adjusting for seasonal and long-term trends, day of week, and holidays. Results In the majority (73.4%) of counties, the relative risk of death on extremely hot days (99th percentile of weather station temperature) versus the minimum mortality temperature was larger when generated from the population-weighted estimates. In contrast, relative risks on extremely cold days (first percentile of weather station temperature) were often larger when generated from the weather station observations. In most counties, the difference in associations estimated from the two temperature metrics was small. Conclusions In a large, multi-site analysis, temperature-mortality associations were largely similar when estimated from weather station observations versus population-weighted temperature estimates. However, spatially refined exposure data may be more appropriate for analyses seeking to elucidate local health effects.

Is there a need to integrate human thermal models with weather forecasts to predict thermal stress

Abstract: More and more people will experience thermal stress in the future as the global temperature is increasing at an alarming rate and the risk for extreme weather events is growing. The increased exposure to extreme weather events poses a challenge for societies around the world. This literature review investigates the feasibility of making advanced human thermal models in connection with meteorological data publicly available for more versatile practices and a wider population. By providing society and individuals with personalized heat and cold stress warnings, coping advice and educational purposes, the risks of thermal stress can effectively be reduced. One interesting approach is to use weather station data as input for the wet bulb globe temperature heat stress index, human heat balance models, and wind chill index to assess heat and cold stress. This review explores the advantages and challenges of this approach for the ongoing EU project ClimApp where more advanced models may provide society with warnings on an individual basis for different thermal environments such as tropical heat or polar cold. The biggest challenges identified are properly assessing mean radiant temperature, microclimate weather data availability, integration and continuity of different thermal models, and further model validation for vulnerable groups.

Spatial modeling of mosquito vectors for rift valley fever virus in northern Senegal: Integrating satellite-derived meteorological estimates in population dynamics models

Abstract: Mosquitoes are vectors of major pathogen agents worldwide. Population dynamics models are useful tools to understand and predict mosquito abundances in space and time. To be used as forecasting tools over large areas, such models could benefit from integrating remote sensing data that describe the meteorological and environmental conditions driving mosquito population dynamics. The main objective of this study is to assess a process-based modeling framework for mosquito population dynamics using satellite-derived meteorological estimates as input variables. A generic weather-driven model of mosquito population dynamics was applied to Rift Valley fever vector species in northern Senegal, with rainfall, temperature, and humidity as inputs. The model outputs using meteorological data from ground weather station vs satellite-based estimates are compared, using longitudinal mosquito trapping data for validation at local scale in three different ecosystems. Model predictions were consistent with field entomological data on adult abundance, with a better fit between predicted and observed abundances for the Sahelian Ferlo ecosystem, and for the models using in-situ weather data as input. Based on satellite-derived rainfall and temperature data, dynamic maps of three potential Rift Valley fever vector species were then produced at regional scale on a weekly basis. When direct weather measurements are sparse, these resulting maps should be used to support policy-makers in optimizing surveillance and control interventions of Rift Valley fever in Senegal.

An IoT-based Smart Garden with Weather Station System

Abstract: Internet of Things (IoT) consists of devices that connect to the internet and communicate with each other. It enables these devices to collect and exchange data with a consumer. This paper presents an IoT-based Smart Garden with Weather Station system, which can be used to monitor the growth of plant every day and predict the probability for raining. Why this IoT-based device is been created? Many people are interested in growing the plants are always forget on watering the plants. Hence, in this study, the device is equipped with a water pump, where it can be monitored and controlled by using a smartphone. In addition, the devices also consist of four main sensors, which are Barometric Pressure, DHT11 Temperature, and Humidity Sensor, Soil Moisture Sensor and Light intensity module sensor. The Soil and Light Intensity sensor used to measure the value by percentages. Besides, two actuators, which are the water pump and LED light can be used remotely or by using a button on the devices. The LED is purposely to replicate the sunlight and make the plant grow faster. This IoT-based Smart Garden with Weather Station System can record the data and send the result to user through the smartphone application named as Blynk apps. This research is beneficial, and the system can be easily managed by all users such as researcher or farmer, and children.

Weather Based Strawberry Yield Forecasts at Field Scale Using Statistical and Machine Learning Models

Abstract: Strawberry is a high value and labor-intensive specialty crop in California. The three major fruit production areas on the Central Coast complement each other in producing fruits almost throughout the year. Forecasting strawberry yield with some lead time can help growers plan for required and often limited human resources and aid in making strategic business decisions. The objectives of this paper were to investigate the correlation among various weather parameters related with strawberry yield at the field level and to evaluate yield forecasts using the predictive principal component regression (PPCR) and two machine-learning techniques: (a) a single layer neural network (NN) and (b) generic random forest (RF). The meteorological parameters were a combination of the sensor data measured in the strawberry field, meteorological data obtained from the nearest weather station, and calculated agroclimatic indices such as chill hours. The correlation analysis showed that all of the parameters were significantly correlated with strawberry yield and provided the potential to develop weekly yield forecast models. In general, the machine learning technique showed better skills in predicting strawberry yields when compared to the principal component regression. More specifically, the NN provided the most skills in forecasting strawberry yield. While observations of one growing season are capable of forecasting crop yield with reasonable skills, more efforts are needed to validate this approach in various fields in the region.

Statistical Analysis of Wind Energy Potential in Uzbekistan’s Bukhara Region Using Weibull Distribution

Abstract: With the development of small business in Uzbekistan’s rural areas, there is a shortage of electric power, as well as power outages for these consumers. The use of renewable energy sources is one way to solve this problem, and this has been little studied in Bukhara oblast. A preliminary study of this problem shows that the region has the necessary capacity of renewable energy sources. This paper presents the results of a study of wind speed and wind energy potential in Bukhara oblast located in southwestern Uzbekistan. The data of wind speed and direction measured at the weather station at Bukhara’s international airport, taken at an altitude of 10 meters, were analyzed on the basis of the two-parameter Weibull distribution function. The main parameters k and c of the Weibull distribution function were determined using the empirical method. Wind speed and direction were statistically analyzed in MatLab and a graph (wind rose) was plotted. Wind energy potentials at different altitudes were also evaluated.

Estimating Adaptive Setpoint Temperatures Using Weather Stations

Abstract: Reducing both the energy consumption and CO2 emissions of buildings is nowadays one of the main objectives of society. The use of heating and cooling equipment is among the main causes of energy consumption. Therefore, reducing their consumption guarantees such a goal. In this context, the use of adaptive setpoint temperatures allows such energy consumption to be significantly decreased. However, having reliable data from an external temperature probe is not always possible due to various factors. This research studies the estimation of such temperatures without using external temperature probes. For this purpose, a methodology which consists of collecting data from 10 weather stations of Galicia is carried out, and prediction models (multivariable linear regression (MLR) and multilayer perceptron (MLP)) are applied based on two approaches: (1) using both the setpoint temperature and the mean daily external temperature from the previous day; and (2) using the mean daily external temperature from the previous 7 days. Both prediction models provide adequate performances for approach 1, obtaining accurate results between 1 month (MLR) and 5 months (MLP). However, for approach 2, only the MLP obtained accurate results from the 6th month. This research ensures the continuity of using adaptive setpoint temperatures even in case of possible measurement errors or failures of the external temperature probes.

Market Expectations About Climate Change

Abstract: An emerging literature examines how agents update their beliefs about climate change. Most studies have relied on indirect belief measures or opinion polls. We analyze a direct measure: prices of financial products whose payouts are tied to future weather outcomes. We compare these market expectations to climate model output for the years 2002 to 2018 as well as observed weather station data across eight cities in the US. All datasets show statistically significant and comparable warming trends. Nonparametric estimates suggest that trends in weather markets follow climate model predictions and are not based on shorter-term variation in observed weather station data. When money is at stake, agents are accurately anticipating warming trends in line with the scientific consensus of climate models.

Internet of Things Based Weather-Soil Sensor Station for Precision Agriculture

Abstract: This paper presents the design, development, and testing of a customizable and cost-effective Weather-Soil Sensor Station (W-SSS) for use in Precision Agriculture based on high accuracy sensors, wireless communication, cloud data storage, and computation technology. The W-SSSs operated from July 25, 2018, to September 15, 2018, using an off-grid power system, Arduino microcontroller, and Wi-Fi connection to the cloud. Sensor data quality was evaluated using several statistical techniques. The data obtained from the weather-soil stations illustrate the differences in weather and soil conditions both relative to the local weather station as well as those within a field. Knowledge of these differences would allow a farmer to vary planting densities relative to soil conditions, irrigation control, as well as pest/herbicide management.

Updated climate database and impacts on WEPP model predictions

Abstract: CLImate GENerator (CLIGEN) (v5.3), a stochastic weather generator, is widely used in conjunction with the Water Erosion Prediction Project (WEPP) model for runoff and soil loss predictions. CLIGEN generates daily estimates of weather based on long-term observed weather station data statistics. For the United States, the original CLIGEN database released with WEPP in 1995 was derived using inconsistent periods of climate records through 1992 that could lead to significant variations in runoff and soil loss predictions on spatial and temporal scales. To achieve more reliable estimates of runoff and soil loss, an updated climate database was derived from a consistent 40 years of recent climate records of 1974 to 2013 in the United States. The objectives of this study were to (1) examine the spatial patterns in trends of differences in precipitation and maximum and minimum temperatures between the two databases, and (2) evaluate the impacts on WEPP-predicted mean annual runoff and soil loss, from the original to the updated databases. For runoff and soil loss estimates, WEPP simulations were conducted under a tilled fallow condition for 1,600 CLIGEN locations in the contiguous United States for a slope profile of 22.1 m length, 9% slope gradient, and silt loam soil. Comparison of precipitation and maximum and minimum temperatures between the original and updated databases showed variations in spatial patterns both annually and seasonally. Annual precipitation and minimum temperature generally increased across most of the country while maximum temperature increased in the western half of the United States and parts of the Northeast. Seasonally, increases in precipitation are evident in the Midwest in spring, fall, and winter, the Northwest in spring, and the Southeast in fall. Maximum daily temperature has increased in the western half of United States and parts of the Northeast in the winter, fall, and spring, whereas minimum daily temperature has increased in all seasons across the United States. Changes in WEPP-simulated mean annual runoff and soil loss from the use of the original to the updated CLIGEN database showed increases in runoff and soil loss in most of the United States. However, some stations showed either increases or decreases in runoff and/or soil loss with the updated database primarily because of differences in monthly precipitation and intensity values in the two databases. Understanding the impacts of the use of the updated database on runoff and soil loss from this study will help in making informed decisions for conservation planning and management when utilizing the WEPP erosion model.

A smart weather station based on sensor technology

Abstract: In this paper a new approach to utilize technology in a practical and meaningful manner within a smart weather station system is presented. This system is primary intended for use in agriculture and meteorological stations, but its application is not limited here. Weather parameters observing plays an important role in human live, so the observing, collecting and storing of information about the temporal dynamics of weather changes is very important. The primary goal is to design a low cost smart system for storing data obtained by measuring various physical parameters in the atmosphere without human involvement. Realized system use Internet of Things technology to storage measured results, and allows the user to access the results anytime and anywhere. In this research Internet of Things is used as technology for storing measured data, because this technology is an advanced and efficient solution for connecting the things to the Internet and to connect the entire world of things in a network. The proposed smart weather station system is based on the following steps: direct environment sensing, measuring and storing data and then allowing user to customize the settings. This research will present the design and implementation of a practical smart weather station system, which can be further extended. The system is based on: group of embedded sensors, Peripheral Interface Microcontroller (PIC) microcontroller as a core and server system and wireless internet using Global System for Mobile Telecommunications (GSM) module with General Packet Radio Service (GPRS) as a communication protocol.

Using climate change models to assess the probability of weather extremes events: a local scale study based on the generalized extreme value distribution

Abstract: Regional climate models (e.g. Eta) nested to global climate models (e.g. HadGEM2-ES and MIROC5) have been used to assess potential impacts of climate change at regional scales. This study used the generalized extreme value distribution (GEV) to evaluate the ability of two nested models (Eta-HadGEM2-ES and Eta-MIROC5) to assess the probability of daily extremes of air temperature and precipitation in the location of Campinas, state of Sao Paulo, Brazil. Within a control run (1961-2005), correction factors based on the GEV parameters have been proposed to approach the distributions generated from the models to those built from the weather station of Campinas. Both models were also used to estimate the probability of daily extremes of air temperature (maximum and minimum) and precipitation for the 2041-2070 period. Two concentration paths of greenhouse gases (RCP 4.5 and 8.5) have been considered. Although both models project changes to warmer conditions, the responses of Eta-Hadgem2-ES to both RCPs are significantly larger than that of Eta-Miroc5. While Eta-Hadgem2-ES suggests the location of Campinas will be free from agronomic frost events, Eta-Miroc5 indicates that air temperature values equal to or lower than 5 and 2 °C are expected to present a cumulative probabilityof ~0.20 and ~0.05, respectively (RCP 8.5). Moreover, while the Eta-Miroc5 projected a reduction in the extreme-precipitation amounts, the Eta-Hadgem2-ES projected implausible large daily precipitation amounts. The Eta-Miroc5 performed better than the Eta-Hadgem2-ES for assessing the probability of air temperature and precipitation in Campinas. This latter statement holds particularly true for daily-extreme precipitation data.

Performance sensitivity of a wind farm power curve model to different signals of the input layer of ANNs: Case studies in the Canary Islands

Abstract: Improving the estimation of the power output of a wind farm enables greater integration of this type of energy source in electrical systems. The development of accurate models that represent the real operation of a wind farm is one way to attain this objective. A wind farm power curve model is proposed in this paper which is developed using artificial neural networks, and a study is undertaken of the influence on model performance when parameters such as the meteorological conditions (wind speed and direction) of areas other than the wind farm location are added as signals of the input layer of the neural network. Using such information could be of interest, either to study possible improvements that could be obtained in the performance of the original model, which uses exclusively the meteorological conditions of the area where the wind farm is located, or simply because no reliable meteorological data for the area of the wind farm are available. In the study developed it is deduced that the incorporation of meteorological data from an additional weather station other than that of the wind farm site can improve by up to 17.6% the performance of the original model.