Showing papers on "Weather station published in 2008"

The effects of weather on daily mood: A multilevel approach.

Abstract: The present study examines the effects of six weather parameters (temperature, wind power, sunlight, precipitation, air pressure, and photoperiod) on mood (positive affect, negative affect, and tiredness). Data were gathered from an online diary study (N = 1,233), linked to weather station data, and analyzed by means of multilevel analysis. Multivariate and univariate analyses enabled distinction between unique and shared effects. The results revealed main effects of temperature, wind power, and sunlight on negative affect. Sunlight had a main effect on tiredness and mediated the effects of precipitation and air pressure on tiredness. In terms of explained variance, however, the average effect of weather on mood was only small, though significant random variation was found across individuals, especially regarding the effect of photoperiod. However, these individual differences in weather sensitivity could not be explained by the Five Factor Model personality traits, gender, or age.

One Day Ahead Prediction of Wind Speed and Direction

Abstract: This paper presents a new technique for predicting wind speed and direction. This technique is based on using a linear time-series-based model relating the predicted interval to its corresponding one- and two-year old data. The accuracy of the model for predicting wind speeds and directions up to 24 h ahead have been investigated using two sets of data recorded during winter and summer season at Madison weather station. Generated results are compared with their corresponding values when using the persistent model. The presented results validate the effectiveness and accuracy of the proposed prediction model for wind speed and direction.

Wind energy potential of Gökçeada Island in Turkey

Abstract: The main objective of the present study is to estimate wind power potential of Gokceada Island in the Northern Aegean Sea in Turkey using the wind data collected at four different locations. Wind data collected over a period of 3 years at Ugurlu and Cinaralti stations and a period of 10 years at Aydincik and National Weather Station. In this regard, wind data collected at 10 and 30 m of height above ground, were extrapolated to 50 m which had been chosen as the wind turbine hub height, using power law. Extrapolated wind data of four stations were represented by Weibull probability density functions to find the wind speed distribution curves. Two Weibull parameters of the wind speed distribution function, shape parameter k (dimensionless) and scale parameter c (m/s) were calculated by the developed Fortran programme on monthly and yearly basis to find the wind profiles. Annual wind speed distributions throughout the Gokceada Island were also obtained using the calculated Weibull probability density function parameters. The suitability of the distributions is judged by the discrepancies between the observed and calculated values of the monthly average wind speed. The results show the general availability of wind energy potential across Gokceada Island.

Linking beach recreation to weather conditions: a case study in Zandvoort, Netherlands.

Abstract: Beach recreation is one of the most weather-sensitive leisure activities. However, there is a lack of scientific knowledge about how the different weather/climate variables influence beach visitation levels, and the role of other factors such as the hour of the day or the day of the week. This study, carried out during the summer of 2006, uses webcams in combination with real-time weather data as an innovative approach to study the relationship between weather and beach use in Zandvoort, a seaside town in The Netherlands. Over a period of 6 weeks, images were taken hourly and for every day, and then compared to the specific weather conditions from a nearby weather station to assess the relationship between beach visitation and atmospheric conditions. Precipitation has an overriding effect over other weather variables while high temperatures lead to higher beach visitation. These results indicate that webcam-based research is a promising field that can provide important information for coastal planning and climate change research.

Composite Predictor Maps of Extraordinary Weather Events in the Sacramento, California, Region*

Abstract: Extraordinary weather events in the Sacramento, California, region are examined using a simple compositing technique. The extraordinary events identified are uncommon and the worst of their kind, but not necessarily severe. While the criteria outlined herein are drawn from Sacramento weather station data, the identified events are extraordinary elsewhere over much, if not all, of California’s Central Valley. Several types of extraordinary events are highlighted, including the hardest freezes, heaviest prolonged rain events, longest-duration fog, and worst heat waves (onset and end) in a 21-yr period. Bootstrap resampling establishes the statistical significance of features on the composite maps. The composite maps with statistically significant features highlighted allow a forecaster to search for key features in forecast maps that coexist with or that precede an extraordinary weather event. Local- and regional-scale extraordinary events have larger-scale signatures that can be traced back in tim...

Short-term wind speed forecasting model for wind farm based on wavelet decomposition

Abstract: Accurate wind speed forecasting of wind farms can relieve or avoid the disadvantageous impact to the electric network. Wind speed forecasting therefore is necessary for power system because of the intermittence nature of wind. A lot of studies have been performed to develop the precision of wind speed prediction. In this paper, wavelet theory is described to decompose highly nonlinear wind speed time series into several approximate stationary time series. In terms of decomposed time series, different ARMA models are established respectively, and then, last forecasting results can be obtained by hybrid method. In order to test this approach, actual wind speed data from a weather station was used to establish forecasting model. The results indicate that wavelet theory is a useful tool in wind speed forecasting and possesses certain actual value.

Spatial estimation of mean temperature and precipitation in areas of scarce meteorological information

Abstract: In regions of complex relief and scarce meteorological information it becomes difficult to implement techniques and models of numerical interpolation to elaborate reliable maps of climatic variables essential for the study of natural resources using the new tools of the geographic information systems. This paper presents a method for estimating annual and monthly mean values of temperature and precipitation, taking elements from simple interpolation methods and complementing them with some characteristics of more sophisticated methods. To determine temperature, simple linear regression equations were generated associating temperature with altitude of weather stations in the study region, which had been previously subdivided in accordance with humidity conditions and then applying such equations to the area’s digital elevation model to obtain temperatures. The estimation of precipitation was based on the graphic method through the analysis of the meteorological systems that affect the regions of the study area throughout the year and considering the influence of mountain ridges on the movement of prevailing winds. Weather stations with data in nearby regions were analyzed according to their position in the landscape, exposure to humid winds, and false color associated with vegetation types. Weather station sites were used to reference the amount of rainfall; interpolation was attained using analogies with satellite images of false color to which a model of digital elevation was incorporated to find similar conditions within the study area.

Neural Networks for Short Term Wind Speed Prediction

Abstract: Predicting short term wind speed is essential in order to prevent systems in-action from the effects of strong winds. It also helps in using wind energy as an alternative source of energy, mainly for Electrical power generation. Wind speed prediction has applications in Military and civilian fields for air traffic control, rocket launch, ship navigation etc. The wind speed in near future depends on the values of other meteorological variables, such as atmospheric pressure, moisture content, humidity, rainfall etc. The values of these parameters are obtained from a nearest weather station and are used to train various forms of neural networks. The trained model of neural networks is validated using a similar set of data. The model is then used to predict the wind speed, using the same meteorological information. This paper reports an Artificial Neural Network model for short term wind speed prediction, which uses back propagation algorithm. Keywords—Short term wind speed prediction, Neural networks, Back propagation.

Combining observations and model data for short-term storm forecasting

Abstract: This paper describes the use of a machine learning data fusion methodology to support development of an automated short-term thunderstorm forecasting system for aviation users. Information on current environmental conditions is combined with observations of current storms and derived indications of the onset of rapid change. Predictor data include satellite radiances and rates of change, satellite-derived cloud type, ground weather station measurements, land surface and climatology data, numerical weather prediction model fields, and radar-derived storm intensity and morphology. The machine learning methodology creates an ensemble of decision trees that can serve as a forecast logic to provide both deterministic and probabilistic estimates of thunderstorm intensity. It also provides evaluation of predictor importance, facilitating selection of a minimal skillful set of predictor variables and providing a tool to help determine what weather regimes may require specialized forecast logic. This work is sponsored by the Federal Aviation Administration's Aviation Weather Research Program. Its aim is to contribute to the development of the Consolidated Storm Prediction for Aviation (CoSPA) system, which is being developed in collaboration with the MIT Lincoln Laboratory and the NOAA Earth System Research Laboratory's Global Systems Division. CoSPA is scheduled to become part of the NextGen Initial Operating Capability by 2012.

Site evaluations of the summit of Co. Chajnantor for infrared observations

Abstract: Because of the high transparency in infrared wavelength, Co. Chajnantor (5,650m altitude) at Atacama, Chile, is one of the most promising sites for infrared astronomy in the world. For evaluating the site condition quantitatively we carried out weather and cloud emissivity monitoring campaign from April 2006 to April 2007. The ground-level condition such as wind direction, wind speed, air temperature, and humidity was monitored by a weather station installed at the summit. Cloud emissivity was estimated by mid-infrared sky images taken by a whole-sky infrared camera every five minutes for 24 hours a day, every day. Results are summarized as followings. 1) The weather condition at the summit is slightly harsher than the condition at the Pampa la Bola plateau. Maximum speed of the wind is 35m/s, and minimum temperature is about -10 degree. 2) Fraction of "clear+usable" weather (which is defined as the cloud emissivity < 10%)" is 82% in a year. The fraction decrease to 40-50% on Bolivian winter season, and increases to over 90% from April to July. This is comparable or even better than the other astronomical sites.

Wind Resource Assessment for the Kingdom of Bahrain

Abstract: The geographical distribution of wind speed (the wind atlas) for the kingdom of Bahrain is presented, based on measured data and on calculations undertaken using WAsP,. The data used were recorded by the Meteorological Directorate at a weather station situated at Bahrain International Airport, taken on an hourly basis for a period of time extended for ten years. These data indicate an annual mean wind speed of 4.6 m/s at 10 m height and mean Weibull scale and shape parameters C and k of 5.2 m/s and 1.9 respectively. At a typical wind turbine hub height of sixty metres, these values are extrapolated to 6.9 m/s, 7.8 m/s and 1.8 respectively, which suggests that the area has a good wind resource. The wind atlas shows that several locations in the less populated central and southern regions of the main island of the archipelago of Bahrain are potentially suitable for wind energy production.

Regional analysis and calibration for the South of Portugal of a simple ev apotranspiration model for use in an autonomous landscape irrigation controller

Abstract: This work is part of the development of an automatic and autonomous landscape irrigation controller which uses a simpler method for computing ETo than the FAO Penman-Monteith method (FAO-PM) in order to reduce the number of sensors required and lower the system cost. It is also intended to select a simple equation, since one of the requirements of this controller is that it should use an ETo formula with a reduced number of programming instructions due to its limited capacity. The controller to be developed should be autonomous and inexpensive, enabling it to be installed in small green areas such as small parks and gardens, having as main users the municipalities. Thus a regional analysis was made about the suitability of 6 methods for computing reference evapotranspiration (Hargreaves, Hargreaves-Samani, Jensen-Haise, Makkink, Priestley-Taylor and Turc) based only on the weather parameters temperature and solar radiation in the Alentejo. For this analysis a network of automatic weather stations was used, providing the meteorological data with a daily time-step for the period from 2003 to 2007. Results show that after the calibration, for each station, these methods present a good correlation with the ETo values calculated by the FAO-PM method. The best results were obtained by the Jensen-Haise method, and the worst results were computed by the Priestley-Taylor, Makkink, and Hargreaves- Samani methods. A considerable variation exists in the adjustment parameters from one weather station to another, so one can not use a single set of medium parameters to calibrate these functions for the entire region. Thus, it can be concluded that the Jensen-Haise is the best method for the Alentejo conditions, and it should be calibrated for each meteorological station. The results obtained by the Hargreaves-Samani method, based only on temperatures, are similar to other 5 methods and is the only one that does not need calibration, which indicates that this method can be considered for the elimination of the radiation sensor.

Short term wind speed prediction using support vector machine model

Abstract: Wind speed prediction in short term is required to asses the effect of wind on different objects in action in free space, like rockets, navigating ships and planes, guided missiles satellites in launch etc. Forecasting also helps in usage of wind energy as an alternative source of energy in Electrical power generation plants. The wind speed depends on the values of other atmospheric variables, such as pressure, moisture content, humidity, rainfall etc. This paper reports a Support Vector Machine model for short term wind speed prediction. The model uses the values of these parameters, obtained from a nearest weather station, as input data. The trained model is validated using a part of data. The model is then used to predict the wind speed, using the same meteorological information.

The Oklahoma Dispersion Model: Using the Gaussian Plume Model as an Operational Management Tool for Determining Near-Surface Dispersion Conditions across Oklahoma

Abstract: The Oklahoma Dispersion Model (ODM) represents a current innovative application of the classic Gaussian plume model in an operational setting. Utilizing a statewide mesoscale automated weather station network (the Oklahoma Mesonet) for current weather conditions and 60-h gridded Nested Grid Model (NGM) model output statistics (MOS) forecasts for future conditions, the ODM is an Internet-based management tool that can be used to qualitatively assess current and future atmospheric dispersion conditions across Oklahoma for near-surface releases of gases and small particulates. The ODM is designed to qualitatively assess concentrations at ground level near the plume centerline at downwind distances of up to 4000 m. The Gaussian plume model is used in conjunction with rural Briggs sigma-y and sigma-z coefficients to estimate horizontal and vertical dispersion. Pasquill stability class is calculated in two ways: for current conditions, Oklahoma Mesonet weather data are used in conjunction with algorith...

Active weather station system

Abstract: An active weather system includes a main weather station unit, a humidifier unit and a remote sensor. The main weather station unit has a main housing with a humidity sensor that detects a level of humidity in an area surrounding the main weather station unit. The humidifier unit includes a housing with a nest for removably mounting the main housing. The humidifier unit also includes a water tank and a humidifier. The humidifier vaporizes water from the water tank when the humidity sensor detects a level of humidity lower than a preset level. Electrical contacts in the nest of the humidifier housing and the main weather station electrically connect the units when the main housing is mounted in the nest. The remote sensor may be placed away from the main unit, such as outdoors, to sense temperature and/or humidity in its environment. The remote sensor includes a transmitter, which sends the temperature and/or humidity information it detects to the main weather station unit.

Estimation of Daily Solar Radiation in China

Abstract: By analyzing daily observed routine meteorological variables such as sunshine-hours,maximum temperature,minimum temperature and solar radiation from 23 stations in China,models were established for assessment of daily solar radiation.The models include two independent variables i.e.percentage of sunshine and diurnal temperature range.Except for Lhasa weather station with a high elevation located in plateau,multiple correlation coefficients of all models range from 0.80 to 0.93.Through analysis on the modeled results,we found that it is necessary to use seasonal models to estimate daily solar radiation,especially for spring and summer.It could come to a conclusion that the models developed in this paper are effective and feasible in the assessment of daily solar radiation in whole China.

Viticultural agroclimatic cartography and zoning at mesoscale level using terrain information, remotely sensed data and weather station measurements. Case study of Bordeaux winegrowing area

Abstract: Climate is a key variable for grapevine development and berry ripening processes. At mesoscale level, climate spatial variations are often determined empirically, as weather station networks are generally not dense enough to account for local climate variations. In this study, climate spatial variations of Bordeaux winegrowing area were assessed by means of solar radiation cartography using satellite sensing and Digital Elevation Model (DEM) information, daily temperature interpolation using weather station and terrain information, spatialized rainfall using rain gauge data and kriging techniques. Temperature and solar radiation data were used to generate evapotranspiration maps at daily time step. Spatialized data was used to characterize the production potential of several zones of Bordeaux winegrowing areas, according to their agroclimatic characteristics. Temperature differences within Bordeaux vineyards induce considerable discrepancies in vine phenology, as is shown by means of a degree.day model. Solar radiation data and potential evapotranspiration are mostly governed by terrain characteristics (slope and aspect). Rainfall data spatial patterns indicate that the north-western part of Bordeaux vineyards is recurrently drier and the south-western receives higher rainfall amounts during the grapevine growing season. However, spatial distribution of summer rainfall events changes considerably from one year to another. The results of this study offer useful information to adapt grapevine cultivars and vineyard management to local climate.

Modeling Rail Temperature with Real-Time Weather Data

Abstract: Railroad safety is a top concern for the railroad industry. Preventing track buckling is important to infrastructure integrity and operation safety. To prevent heat-related track buckling, many railroads commonly impose slow orders during hot weather which is perceived to increase risks of track buckling. The deficiency of this practice is that trains are often slowed unnecessarily, while in some circumstances, when buckling risk is high, trains may not be slowed. The difficulty in arriving at an optimum decision for slow orders lies in the quantification of track-buckling risk. Track buckling is influenced by numerous factors, among which rail temperature is a critical factor. Unfortunately, rail temperatures are not easily obtainable. Decisions for slow orders are often based on an arbitrary ambient temperature limit. The limit is set on the basis of a simple assumption that the rail temperature will be 30°F to 35°F above the ambient temperature. This assumption is widely adopted even though rail temperature is not linearly related to the ambient temperature. It is therefore important to quantify rail temperatures accurately for reference in the slow order decision-making process and more importantly for assessing derailment risks that ambient temperature may hinder. This paper presents a model for predicting rail temperatures based on real-time meteorological forecast data. The model was developed by modeling the heat transfer process of the rail exposed to the sun. In the development of such a model, an experimental station was instrumented, composed of a portable weather station and a short segment of rail track with temperature sensors installed on rails. The model has proven to be able to predict the maximum rail temperature within a few degrees and within 30 min of the actual time when the maximum rail temperature occurs during the day. The model has been validated for three locations where real-time weather data and rail temperature were collected.

The climate of łeba

Abstract: Quickly developing tourist infrastructure of Łeba, including the needs of the city as a health resort and a perfect place for holiday at any time of the year, requires an elaboration of the comprehensive climatic characteristics. Taking into consideration the needs of the typical holiday makers and people with respiratory, circulatory and rheumatic diseases or metabolic disorders, elaboration of an appropriate description of the local climatic conditions in one of the most often visited spa on the Polish sea coast is more and more crucial. Daily meteorological data from the IMGW weather station in Łeba for the period 1986-2005 were the basis for this paper. The climatological analysis, besides the characteristics of the air temperature and precipitation, includes the distribution of other principal meteorological elements (pressure, wind, sunshine hours, cloudiness, relative humidity) and the frequency of weather phenomena (fogs, storms). Key words: Łeba, climate, sea coast

Spatially explicit seasonal forecasting using fuzzy spatiotemporal clustering of long-term daily rainfall and temperature data

Abstract: A major limitation of statistical forecasts for specific weather station sites is that they are not spatial in the true sense And while spatial predictions have been studied, their results have indicated a lack of seasonality Global Circulation Models (GCMs) are spatial, but their spatial resolution is rather coarse Here we propose spatially explicit seasonal forecasting, based on the Fuzzy Classification of long-term (40 years) daily rainfall and temperature data to create climate memberships over time and location Data were obtained from weather stations across south-east Australia, covering sub-tropical to arid climate zones Class memberships were used to produce seasonal predictions using correlations with climate drivers and a regression rules approach Therefore, this model includes both local climate feedback and the continental drivers The developed seasonal forecasting model predicts rainfall and temperature reasonably accurately The final 6-month forecast for average maximum temperature and rainfall produced relative errors of 089 and 056 and Pearson correlation coefficients of 083 and 082, respectively

Site prospection at san pedro mártir

Abstract: Seeing and weather observations were conducted at 5 sites within the boundaries of the area reserved for astronomy at the Sierra de San Pedro Martir National Park (SPM), for at least 15 nights at each one of these. Weather variables were measured using a Davis Weather Station and a Metek Ultrasonic Anemometer. Seeing information was collected with a NOAO RoboDIMM unit. Seeing and weather results were compared to those being delivered at the same time by the instrumentation of the Thirty Meter Telescope Project at the Observatorio Astronomico Nacional (OAN) at SPM. Seeing differences are small in most cases. We recommend a long term campaign at the easily accessible site Llano Alto 1, where we found that seeing may be slightly better.

Drivers' opinions about road weather conditions and road weather information. Interviews during winter conditions

Abstract: The objective of this study was to produce information for developing the information on road weather conditions. This was done by determining how drivers experience different road weather conditions and how their impressions relate to the Road Weather Information Service forecasts, whether the drivers had received information on the road weather conditions and whether they had made any changes in their behaviour or travel plans based on this information. The material for this study was gathered via interviews at service stations (24%) and on the roadside (76%). In total, slightly over 300 drivers answered. 180 of the interviews were carried out in poor or hazardous road weather conditions. Information on the speed of the drivers was available for the roadside interviews. The forecast class by the Road Weather Information Service was poor or hazardous during 61% of the time the interviews took place. About 75% of respondents rated the road weather conditions to be poor or hazardous. Snowfall was most often mentioned as a factor affecting the current road weather conditions. The drivers' estimations of the road surface skidding level did not correspond to the information from the road weather stations. About half of the drivers rated the road surface as very slippery or slippery and about half as non-skidding or mostly non-skidding. The opinions of the drivers did not depend on whether the road surface was skidding or non-skidding according to the road weather station. 62% of drivers had received or looked for information on weather and road conditions before the start of their trip and/or during the trip. The most common sources of information were radio and TV. The share of information received via the Internet was notably higher than in previous studies. In the future, the drivers wished to receive information also through mobile services in addition to the traditional information sources. The information on road weather conditions corresponded well to the drivers' own experiences. Drivers that were less experienced, had driven for a long time before the interview and were on a trip they did not do frequently, were more likely to have acquired information on weather and road conditions than other drivers. In general, those who had looked for or received information on the current weather and road conditions rated the conditions worse, the road surface more slippery and the accident risk higher than those that had not received this information. In some of the survey locations those drivers that had received information on road weather conditions drove more slowly than other drivers. Every fifth respondent stated that they had changed or considered changing the travel plans for their current trip because of the road weather conditions either before the trip or during the trip. The change mentioned most often was allocating more time to the trip. The road weather condition information was most often stated to lead to increasing the distance to the preceding vehicle, focusing attention to the road surface, avoiding overtaking and lowering travel speed. The information affecting behaviour the most efficiently was warnings affecting main roads shown on a map at province level as well as verbal descriptions of the weather and road conditions. Also individual and focused information, e.g. information on road maintenance, was supported. This report may be found at http://alk.tiehallinto.fi/julkaisut/pdf2/3201096-v-kuljett_kasityk_kelista.pdf

Pocket weather station

Abstract: A pocket weather station with integral climate measurement sensors for the calculation and prediction of local weather conditions. The pocket weather station includes a housing having a key ring, a circuit board mounted within the housing and including a processor and a memory module, a input keys connected to the circuit board and interfacing with the processor, a display device connected to the circuit board and interfacing with the processor, and a weather sensor for measuring a plurality of ambient conditions at a current location of the pocket weather station. The weather sensor interfaces with the processor and memory module to record data regarding the measured ambient conditions and displays one or more of the measurements of the plurality of ambient conditions on the display device. The pocket weather station is also capable of forecasting weather for a given location and takes into account local variations in weather patterns.

Regression Models for Forecasting Fog and Poor Visibility at Donmuang Airport in Winter

Abstract: A method has been developed for predicting visibility over Donmuang Airport at 07:00 a.m. in winter from surface meteorological observations at 01:00 a.m. and 05:00 a.m. using multiple linear regression. Data from the Thai Meteorological Department weather station at Bangkok International Airport and The Royal Thai Air Force in December, January and February were used. For each month two models were found: one containing all the available surface observations, and one omitting the insignificant observations. The model forecast consists of the probabilities of fog, poor visibility, and good visibility, respectively.