Showing papers on "Weather station published in 2006"

Development of scale‐free climate data for Western Canada for use in resource management

Abstract: Applying climate data in resource management requires matching the spatial scale of the climate and resource databases. Interpolating climate data in mountainous regions is difficult. In this study, we present methodology to generate scalefree climate data through the combination of interpolation techniques and elevation adjustments. We apply it to monthly temperature and precipitation normals for 1961–90 produced by the Parameter-elevation Regressions on Independent Slopes Model (PRISM) for British Columbia, Yukon Territories, the Alaska Panhandle, and parts of Alberta and the United States. Equations were developed to calculate biologically relevant climate variables including various degree-days, number of frost-free days, frost-free period, and snowfall from monthly temperature and precipitation data. Estimates of climate variables were validated using an independent dataset from weather stations that were not included in the development of the model. Weather station records generally agreed well with estimated climate variables and showed significant improvements over original PRISM climate data. A stand-alone MS Windows application was developed to perform all calculations and to integrate future climate predictions from various global circulation models. We demonstrate the use of this application by showing how climate change may affect lodgepole pine seed planning zones for reforestation in British Columbia. Copyright  2006 Royal Meteorological Society.

Calibrating a real-time traffic crash-prediction model using archived weather and ITS traffic data

Abstract: Growing concern over traffic safety has led to research efforts directed towards predicting freeway crashes in Advanced Traffic Management and Information Systems (ATMIS) environment. This paper aims at developing a crash-likelihood prediction model using real-time traffic-flow variables (measured through series of underground sensors) and rain data (collected at weather stations) potentially associated with crash occurrence. Archived loop detector and rain data and historical crash data have been used to calibrate the model. This model can be implemented using an online loop and rain data to identify high crash potential in real-time. Principal component analysis (PCA) and logistic regression (LR) have been used to estimate a weather model that determines a rain index based on the rain readings at the weather station in the proximity of the freeway. A matched case-control logit model has also been used to model the crash potential based on traffic loop data and the rain index. The 5-min average occupancy and standard deviation of volume observed at the downstream station, and the 5-min coefficient of variation in speed at the station closest to the crash, all during 5-10 min prior to the crash occurrence along with the rain index have been found to affect the crash occurrence most significantly.

One-hour-ahead wind speed prediction using a Bayesian methodology

Abstract: The contribution of wind power in market-driven power systems together with the uncertain nature of the wind resource have led to many research efforts on methodologies to predict future wind speed/power production. Applications such as the operational balancing market in the UK would benefit from accurate one-hour-ahead forecasts of the available power from all generators, wind being no exception. This paper focuses on one-hour-ahead wind speed prediction using a Bayesian approach to characterise the wind resource. To test the approach, two years of wind speed data from a weather station were modelled as an autoregressive process. In this paper, the methodology used is described together with the model employed and prediction results are presented and compared to the persistence method. The results obtained indicate that Bayesian inferencing can be a useful tool in wind speed/power prediction, particularly due to the flexibility inherent to the methodology.

Analysis of Italian Wine Quality Using Freely Available Meteorological Information

Abstract: Meteorological conditions strongly affect viticultural activity, modifying grapevine (Vitis vinifera) responses and determining the quality and quantity of production. The analysis of meteorological information can provide viticulturists with operational and forecasting tools for improving the management of vineyards. Meteorological information is presently available on Internet sites with different spatial and temporal scales, allowing easy access and overcoming the necessity of installing costly weather station networks. The present research was performed for the purpose of analyzing the relationship between meteorological information freely available on Internet and the average quality (defined by vintage ratings) of Italian wine. Temperature and precipitation data were analyzed. The presence of teleconnections and their effect on quality was investigated by considering 500 hPa geopotential height, sea surface temperature, and meteorological indices such as North Atlantic Oscillation and Southern Oscillation. Results highlight strong relationships between meteorological conditions and wine quality. Higher-quality wines were obtained in the years characterized by a reduction in rainfall and high temperature patterns. Teleconnections were also detected in different periods of the growing season, thus allowing for the development of wine-quality forecasting tools. Results could aid in the evaluation of operations concerning the analysis and forecasting of wine quality.

Spatial Variations in Weather Conditions and Wine Prices in Bordeaux

Abstract: The purpose of this paper is to study the impact of weather conditions on prices of Bordeaux wines. Unlike previous studies (based on data from the main weather station in Merignac), we use climatological variables from many local stations. Two models are compared: one where prices are related to Merignac weather conditions, and one where prices are related to local conditions (weather variables measured in the station the nearest to the château). Although a (non-nested) test suggests that the model based on local data is better, the two specifi cations lead to very similar results. This is reassuring news for researchers interested in the relationship between weather and prices, but who do not have access to spatial variations in climate. (JEL Classification: Q19)

Potential Impacts of Climate Change on Agroclimatic Indicators in Iran

Abstract: The climate model, United Kingdom Meteorological Organization model (UKMO) and multivariate statistics, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were employed to determine the climate diversity and agroclimatic indicators in future climate change. Monthly weather data from 1968 to 2000 at 36 weather stations in Iran were used to generate climate change scenarios for years 2025 and 2050. The UKMO model predicted a temperature rise of 2.7°C and a rainfall decrease of 12% by 2050. By 2050, length of the growth period is predicted to increase by 16 days, length of the dry period will increase by 22 days because of a delay in the first freezing day and an advance in the last freezing day, and the subsequent increase in temperature and decrease in rainfall. Cluster analysis of weather station data shows that 10 currently defined agroenvironment zones will be reduced to 8 by 2025 and to 7 by 2050. Climate change will decrease geographic differences in temperature and precipitati...

A case study in resort climatology of Phoenix, Arizona, USA

Abstract: Tourists often use weather data as a factor for determining vacation timing and location. Accuracy and perceptions of weather information may impact these decisions. This study: (a) examines air temperature and dew points from seven exclusive resorts in the Phoenix metropolitan area and compares them with official National Weather Service data for the same period, and (b) utilizes a comfort model called OUTCOMES-OUTdoor COMfort Expert System-in a seasonal appraisal of two resorts, one mesic and one xeric, compared with the urban Sky Harbor International Airport first-order weather station site in the central urban area of Phoenix, Arizona, USA (lat. 33.43 degrees N; long. 112.02 degrees W; elevation at 335 m). Temperature and humidity recording devices were placed within or immediately adjacent to common-use areas of the resorts, the prime recreational sites used by guests on most resort properties. Recorded data were compared with that of the official weather information from the airport station, a station most accessible to potential tourists through media and Web sites, to assess predicted weather for vacation planning. For the most part, Sky Harbor's recorded air temperatures and often dew points were higher than those recorded at the resorts. We extrapolate our findings to a year-round estimate of human outdoor comfort for weather-station sites typical of resort landscapes and the Sky Harbor location using the OUTCOMES model to refine ideas on timing of comfortable conditions at resorts on a diurnal and seasonal basis.

Socotra`s Annual Weather Pattern

Abstract: Specific form of weather pattern in Socotra archipelago is described. Great attention to character of monsoons in the semiarid conditions is given. Pictures show records of temperature, humidity, wind direction, velocity of the wind and sunshine from automacial weather station run by Czech research team.

Influence of Malaysian Climate on the Efficiency of Polycrystalline Solar Cells

Abstract: The performance of a solar panel is measured in terms of its efficiency at turning sunlight into electricity. This energy is highly influenced by weather variation. This research tried to find out the relationship between solar efficiency and Malaysian climate. The output current and voltage of the solar panel had been collected 24 hours non-stop within a week. These data were used to get the efficiency of solar panel. At the same time, the climate parameters had been recorded using weather station. The correlation and regression test had been done to get the mathematical relationship between these climate parameters and the solar cells efficiency. Correlation test results showed that the solar irradiance and temperature had great influenced while humidity and wind speed had low influenced on the efficiency of polycrystalline solar cells. Regression test result showed that there are a polynomial relationship between efficiency and all climate parameters.

Weather station apparatus

Abstract: Weather station apparatus has a base station (100) and a remote indicator (200). The base station (100) has an MCU (151) for evaluating weather information relating to an atmospheric parameter, which a sensor (143) senses for subsequent processing by the MCU (151). An RF transmitter (160) is also included for transmitting a wireless signal representing the weather information evaluated by the MCU (151). The indicator (200) has an RF receiver (260) for receiving the wireless signal from the transmitter (160), a control circuit (251) for subsequently processing the wireless signal, and a color-changing light source (230) controlled by the control circuit (251) for illumination to indicate the weather information represented by the wireless signal in a color that is dependent upon the value of the weather information.

Development of the Wind Power Forecasting System, KIER Forecaster

Abstract: In this paper, the first forecasting system of wind power generation, KIER Forecaster is presented. KIER Forecaster has been constructed based on statistical models and was trained with wind speed data observed at Gosan Weather Station nearby Walryong Site. Due to short period of measurements at Walryong Site for training the model, Gosan wind data were substituted and transplanted to Walryong Site by using Measure-Correlate-Predict(MCP) technique. The results of One to Three-hour advanced forecasting models are consistent with the measurement at Walryong site. In particular, the multiple regression model by classification of wind speed pattern, which has been developed in this work, shows the best performance comparing with neural network and auto-regressive models.

Footprint Analysis to Assess the Conditioning of Temperature and Humidity Measurements in a Weather Station Vicinity

Abstract: Equations for estimating the influence of upwind fetch on air temperature and humidity measurements are applied to assess fetch impacts on agricultural weather stations. The equations are derived from flux and scalar footprint models and may serve as a first approximation of the amount of impact (F = 0 to 1) that upwind fetch distance has on measured air temperature or humidity at height z above the surface. This information is useful for judging adequacy of green fetch upwind of a weather station and for judging the amount of impact that a dry surface in the vicinity of a weather station has on measurements. The equations were applied to various fetch lengths of clipped grass and dry, bare soil over a range of wind speed. Results indicate that F increases more-or-less logarithmetically with fetch distance for both surface conditions and show F to increase with decreasing wind speed for a specified fetch distance. Results indicate that the 100:1 fetch distance:measurement height ruleof-thumb applies to unstable boundary layer conditions (positive Bowen ratio), but may underestimate the fetch requirement for neutral and stable conditions. Values for F for short fetch lengths, for example 5 and 10 m, show the fallacy of locating a weather station over a small area of grass or other vegetation, but surrounded by dry, poorly vegetated further upwind. Essentially none of the sensor signal (T and e measurement) is conditioned by the grass when wind speed is greater than 2 m s -1 .

A Study on Development of a Forecasting Model of Wind Power Generation for Walryong Site

Abstract: In this paper, a forecasting model of wind speed at Walryong Site, Jeju Island is presented, which has been developed and evaluated as a first step toward establishing Korea Forecasting Model of Wind Power Generation. The forecasting model is constructed based on neural network and is trained with wind speed data observed at Cosan Weather Station located near by Walryong Site. Due to short period of measurements at Walryong Site for training statistical model Gosan Weather Station's long-term data are substituted and then transplanted to Walryong Site by using Measure-Correlate-Predict technique. One to three-hour advance forecasting of wind speed show good agreements with the monitoring data of Walryong site with the correlation factors 0.96 and 0.88, respectively.

Spatial variations in climate and Bordeaux wine prices

Abstract: The purpose of this paper is to study the impact of climate conditions on Bordeaux wine prices. Unlike previous studies (based on data from the main weather station in Merignac), we use climatological variables from many local stations. Two models are compared: one where prices are related to Merignac weather conditions, and one where prices are related to local conditions (weather variables measured in the station the nearest to the château). Although a non-nested test suggests that the model based on local weather data is the preferred one, regressions of the two speciÞcations lead to very similar results. This is reassuring news for researchers interested in the relationship between climate and wine prices, but who do not have access to small-scale spatial variations in climate.

A Method of Improving Cloud Predictions for Real-Time Weather Forecasting and Visualization

Abstract: Indirect or passive observations using satellite remote sensing in the visible, infrared and microwave spectra provide global coverage of the thermal states of the cloud tops or the ground surface. The commonly employed temperature profile matching techniques using satellite data and numerical weather prediction models are only relatively successful in estimating the cloud top height (CTH) for optically dense middle and high clouds (cloud tops at heights generally greater than two kilometers). Therefore, accurate predictions of low-level CTH present a formidable challenge to the forecasting and nowcasting community. In this study, we present an approach to estimating low-level CTH by combining the above-cloud information extracted from the satellite imagery and the below-cloud information obtained from weather station measurements. Assumed ranges of brightness temperature and CTH are used to process the cloudy pixels for visualization and classification purposes. Our study indicates that the CTH evaluated using satellite data confirms the presence of low-level clouds in the range 400-1000 m. Accurate estimates of the boundary layer CTH can provide better low-level cloud products (e.g., fog or clouds formed by fog lifting) for improved weather forecasting and applications in the research community.

System and method for indicating predicted weather using sounds and/or music

Abstract: A weather station predicts weather based on atmospheric pressure readings and plays music and/or nature sounds based on the predicted weather.

Radiation powered battery-free energy-burst source for wireless weather stations and home-climate systems

Abstract: The power of a complete picture of energy-weather information can be used for novel energy saving algorithms in home-climate systems. Exploiting the human biological clock, we recently proposed to optimize climate systems by allowing for a correlation between inside and outside temperatures, while preserving maximal comfort. Following our earlier disclosure on a Gas Energy Observatory, we here disclose a detailed description of a battery-free wireless weather station for reliable, long-term and maintenance-free measurements. It is solar powered. Combined with energy storage in high-voltage capacitors using recently introduced low-cost step-up and step-down DC-DC converters, a versatile energy burst-source is created. Energies of a few J per day suffice for measurement, data-collection and wireless data transmission in bursts to a central data-processing device inside a nearby home. Provided as a high-volume consumer product, residential weather data can be gathered over the internet for creating a climate observation system with unprecedented areal coverage and spatial resolution at no additional cost—serving modern climate research and studies on global warming.

Emergency Preparedness and Tourism: Both Win with Help of the Amateur Meteorologist

Abstract: Weather is an important aspect of tourism. Ample snow and cold are needed for cross country skiing while beachgoers prefer the other extreme. In both cases individuals seek information about the weather in order to prepare for a recreation experience. The amateur weather hobbyist can serve that need. These individuals establish a home weather station in the backyard. The opportunity then arises, how can this weather data get to the end user – the tourist? This paper examines the potential of using the Internet to link all of these amateur weather stations in order to provide a more complete picture of our atmosphere. In addition to the interest by tourists, emergency preparedness agencies gain valuable information from this data.

A Weather Station for SensorScope

Abstract: The SensorScope project is a collaboration between environmental scientists and hardware/software engineers at EPFL. It will consist of two large-scale outdoor sensor networking deployments, the first on the EPFL campus, and the second on an alpine glacier. The aim of these deployments is to measure key environmental quantities at high spatial resolution, for the purpose of modeling and understanding energy exchanges and balances at the earth/atmosphere boundary. The goal of this demo is to show SWeaS (the SensorScope Weather Station), a complete weather sensing unit that we have developed in the scope of this project. SWeaS is the core component of SensorScope and will be installed at over one hundred locations in each of the two deployments. In comparison to previous weather boards designed for mote-class nodes, the design of SWeaS considers the entire chain of requirements for a scientific atmospheric measurement campaign, including packaging, energy autonomy, sensor placement and a diverse set of sensors.

Modeling and Pricing Rain Risk

Abstract: In this paper we price a precipitation option based on empirical weather data from Germany using different pricing methods, among them Burn Analysis, Index Value Simulation and Daily Simulation. For that purpose we develop a daily precipitation model. Moreover, a decorrelation analysis is proposed to assess the spatial basis risk that is inherent to rainfall derivatives. The models are applied to precipitation data in Brandenburg, Germany. Based on simplifying assumptions of the production function, we quantify and compare the risk exposure of grain producers with and without rainfall insurance. It turns out that a considerable risk remains with producers who are remotely located from the weather station. Another finding is that significant differences may occur between the pricing methods. We identify the strengths and weaknesses of the pricing methods and give some recommendations for their applications. Our results are relevant for producers as well as for potential sellers of weather derivatives.

Weather Station Website for Pace University Environmental Center

Abstract: This project developed a fully functional website for the Davis Vantage Pro Wireless Weather Station located in the Pace University Environmental Center. The primary goal is to upload local weather information automatically to a website every 15 minutes. This is accomplished by using Virtual Weather Station by Ambient Weather, used by Pace University, to populate a configuration file housed on the web server. The configuration file is loaded to the database weather table currently on the web server using a Data Transformation Service (DTS) job. A SQL Agent job runs in the background every fifteen minutes to initiate the DTS job.

Fifty years of ocean observations in the Pacific Northeast

Abstract: Ocean Station Papa, at 50°N, 145°W in the Alaska Gyre (Figure 1), started as a weather station in the 1940s. In 1956, oceanographers began collecting a suite of standard measurements from the cool subarctic waters at Ocean Station Papa (OSP), including temperature, salinity, oxygen, and plankton.Three years later, a series of sampling stations was added along the 1400-kilometer ‘Line P’ from the Canadian coast to OSRto aid in understanding ocean variability. From 1956 to 1981, weather ships made the transit to and from OSP every six weeks, resulting in high temporal resolution sampling. The weather ship era ended in 1981 when satellites began providing better data for forecasting ocean weather. Since then, Canadian research vessels have continued to sample along Line P two to five times each year. In this recent era, transport of carbon within the ocean has become a major research focus.

Analysis of Weather Data Collected From Two Locations in a Small Urban Community

Abstract: The heat island effect is a well known feature in the microclimate of urban areas, and is considered to be the difference between the urban area and its surroundings. While this study only employs two instruments, the authors are not aware of any studies which examine the differences in temperature between an instrument inside a town the size of Sedalia and its surroundings by collecting hourly information. We attempt to infer here the impact of Sedalia, Missouri, the State Fair Community College campus, and the state fairgrounds on the temperature patterns for a small region of westcentral Missouri. The two stations, one on the grounds of State Fair Community College and the other at the Sedalia Airport were used. Temperature, precipitation, cloudiness, and wind information were gathered hourly between 1 February and 31 March, 2005. The weather station at the regional airport was located 11 km (7 miles) northeast of the campus instrument. Our results indicate that the city has no discernable impact on the distribution of monthly precipitation totals. We found a distinct difference between the local surface temperatures as recorded by each instrument. For the Sedalia area, the temperature differences between the town center and the outside location were approximately 2 – 6F (1.0 – 3.3 C) warmer, typically, than the surrounding environment, as inferred by these instruments. This difference was as much as 11 F (6C) when comparing hourly temperature information. Additionally, the difference was larger for clear days and days during which there was little wind.

Project-Based Learning, Surface Energy Balance, and Establishment of a New Undergraduate Weather Station

Abstract: Through collaborative and individual projects in two upper level courses, undergraduate students established a new surface environmental observation station (Austin College Weather Station). In addition to standard meteorological observations, the Austin College Weather Station detects radiation and soil measurements. These additional measurements are used to calculate the local surface energy balance, an important indicator of local climate system interactions. The weather station projects provided excellent opportunities for students to participate actively in the scientific process. The first phase involved establishment of the weather station. In this semester-long collaborative project, students engaged in all aspects of scientific field research, including planning, testing, implementation, data collection, analysis, and evaluation. They became experts on two weather station instruments. The second phase involved calibration and validation of the Austin College Weather Station. These 7-week individual projects required student research proposals, research papers, and peer review. Student learning outcomes included both scientific content and scientific process. Many innovative assessment tools were utilized, including proposal writing, peer review, group meetings, research presentations, research papers, and a faculty review panel. These courses both received strong marks from students for promoting critical thinking and teaching effectiveness. Perhaps most importantly, students had fun participating in these research projects with real-world applications.

Use of a statewide mesoscale automated weather station network for real-time operational assessment of near-surface dispersion conditions

Abstract: Developed in the late 1990s, the Oklahoma Dispersion Model (ODM) is a Web-based management tool that can be used to assess current and future atmospheric dispersion conditions for near-surface releases of gases and small particulates (diameters less than 20 microns). The ODM constitutes a current innovative application of the classic Gaussian plume model in an operational setting. Using a statewide mesoscale automated weather station network (the Oklahoma Mesonet) to assess current weather conditions, the ODM generates statewide maps which depict current atmospheric dispersion conditions (dilution of plume) as well as transport conditions (direction of plume movement). For future conditions, the ODM utilizes 60-hour NGM MOS forecasts to create similar output. With such products, one can better assess appropriate times for conducting operations which involve the near-surface release of gases and small particulates so as to minimize downwind concentrations at sensitive non-target areas. The Oklahoma Dispersion Model has been used largely as a management tool in the agriculture and natural resources arena in conjunction with such operations as prescribed burning (smoke), pesticide application, and dispersion of odors associated with animal agriculture. However, it has had unforeseen uses such as in the debris burning necessitated by the Oklahoma City F5 tornado of 3 May 1999.