Showing papers on "Weather station published in 2010"

Review of the use of Numerical Weather Prediction (NWP) Models for wind energy assessment

Abstract: Wind energy resource assessment applications require accurate wind measurements. Most of the published studies used data from existing weather station network operated by meteorological departments. Due to relatively high cost of weather stations the resolution of the weather station network is coarse for wind energy applications. Typically, meteorological departments install weather stations at specific locations such as airports, ports and areas with high density population. Typically, these locations are avoided during wind farms siting. According to WMO regulations, weather stations provide measurements for different weather elements at specific altitudes such as 2 m for air temperature and 10 m for wind measurements. For wind energy resource assessment applications, minimum of one year of wind measurements is required to build wind climatology for a certain site. Therefore data collected from a certain site cannot be used before one year of operation. Due to these limitations, wind energy resource assessment application needs to use data from different sources. Recently, wind assessment studies were conducted using data generated by Numerical Weather Prediction models. This paper reviews the use of the Numerical Weather Prediction data for wind energy resource assessment. It gives a general overview of NWP models and how they overcome the limitations in the classical wind measurements.

Linking Inuit knowledge and meteorological station observations to understand changing wind patterns at Clyde River, Nunavut

Abstract: Connecting indigenous and scientific observations and knowledge has received much attention in the Arctic, not least in the area of climate change. On some levels, this connection can be established relatively easily, linking observations of similar phenomena or of various effects stemming from the same cause. Closer examinations of specific environmental parameters, however, can lead to far more complex and difficult attempts to make those connections. In this paper we examine observations of wind at Clyde River, Nunavut, Canada. For Inuit, many activities are governed by environmental conditions. Wind, in particular, is identified by Inuit as one of the most important environmental variables, playing a key role in driving sea ice, ocean, and weather conditions that can either enable or constrain hunting, travel, or other important activities. Inuit observe wind patterns closely, and through many means, as a result of their close connection to the land and sea. Inuit in many parts of Nunavut are reporting changes in wind patterns in recent years. At Clyde River, a community on the eastern coast of Baffin Island, Inuit have observed that at least three key aspects of wind have changed over the last few decades: wind variability, wind speed, and wind direction. At the same time, wind observations are also available from an operational weather station located at Clyde River. An analysis of this information shows little change in wind parameters since the mid-1970s. Though the station data and Inuit observations correspond in some instances, overall, there is limited agreement. Although the differences in the two perspectives may point to possible biases that may exist from both sources—the weather station data may not be representative of the region, Inuit observations or explanations may be inaccurate, or the instrumental and Inuit observations may not be of the same phenomena—they also raise interesting questions about methods for observing wind and the nature of Arctic winds.

Assessment of wind energy potential locations in Oman using data from existing weather stations

Abstract: This paper analyzes five years hourly wind data from twenty-nine weather stations to identify the potential location for wind energy applications in Oman. Different criteria including theoretical wind power output, vertical profile, turbulence and peak demand fitness were considered to identify the potential locations. Air density and roughness length, which play an important role in the calculation of the wind power density potential, were derived for each station site. Due to the seasonal power demand, a seasonal approach was also introduced to identify the wind potential on different seasons. Finally, a scoring approach was introduced in order to classify the potential sites based on the different factors mentioned above. It is concluded that Qayroon Hyriti, Thumrait, Masirah and Rah Alhad have high wind power potential and that Qayroon Hyriti is the most suitable site for wind power generation.

Satellite Microwave Remote Sensing of Daily Land Surface Air Temperature Minima and Maxima From AMSR-E

Abstract: We present an approach to retrieve daily minimum and maximum 2-m height air temperatures from 18.7, and 23.8 GHz H and V polarized brightness temperature from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) during the snow free season. The approach accounts, with minimal ancillary data, for vertically integrated atmospheric water vapor, and variable surface emissivity due to open water and vegetation. Retrieved temperatures were evaluated using Northern Hemisphere weather stations and independent satellite-based air temperatures from the Atmosphere Infrared Sounder and Advanced Microwave Sounding Unit (AIRS/AMSU; hereafter AIRS) sensors on Aqua. The retrieved temperatures are within 1.0 - 3.5 K of surface weather station measurements for vegetated locations, but uncertainty can exceed 4 K for desert and sparsely vegetated regions, mainly due to site to site biases. The AIRS and AMSR-E temperature retrievals generally agree more closely with one another than with weather stations and are generally within 1.0-2.8 K over vegetated regions, but with less agreement ( > 4 K ) over desert and mountainous regions. Additional useful information produced by our approach includes open water fraction, vegetation optical depth and atmospheric water vapor. The results of this study provide inputs for land surface models and a new approach for monitoring of land surface air temperatures with well quantified accuracy and precision.

Effects of the resolution of soil dataset and precipitation dataset on SWAT2005 streamflow calibration parameters and simulation accuracy

Abstract: The resultant calibration parameter values and simulation accuracy of hydrologic models such as the 2005 Soil and Water Assessment Tool (SWAT2005) depend on how well spatial input parameters describe the characteristics of the study area. The objectives of this study were to (1) investigate the effect of soils dataset resolution (State Soil Geographic Database and Soil Survey Geographic Database) on SWAT2005 streamflow simulation performance and calibration parameters using four precipitation datasets and (2) determine the best combination of soil and precipitation datasets for the Cobb Creek, Lake Creek, and Willow Creek subwatersheds within the Fort Cobb Reservoir Experimental watershed, Oklahoma. SWAT2005 was calibrated and validated for streamflow for the three subwatersheds using the State Soil Geographic Database and the Soil Survey Geographic Database for each of the four available precipitation datasets with different spatial resolutions. The four sources of rainfall data included the National Weather Service's network of Cooperative Observer Program weather stations, statewide Oklahoma Mesonet, USDA Agricultural Research Service's weather station network (MICRONET), and National Weather Service Next Generation Radar (NEXRAD) precipitation estimates. The model performance was assessed using the Nash-Sutcliffe efficiency coefficient and percent bias statistics. During both the calibration and validation periods, there were no significant differences in the model monthly performance statistics between the higher resolution Soil Survey Geographic Database and the lower resolution State Soil Geographic Database across subwatersheds, irrespective of the rainfall dataset used. However, the model performed better when the NEXRAD and MICRONET precipitation datasets were used. There were slight to large differences in the resultant calibration parameter values depending on the calibration parameter, the precipitation data used, and the subwatershed. Large differences in the simulated surface runoff and deep aquifer recharge due to soils dataset resolution could lead to significant differences in the simulated water quality components such as sediments and nutrients. This is important because significant differences in simulated sediments and/or nutrients could lead to significantly different outcomes in terms of the impacts of a given conservation practice for studies like the Conservation Effects Assessment Project. Due to the lack of measured data to validate the simulated water balance components, it was recommended to use both the fine and coarse resolution soil datasets in combination with the finer spatial resolution precipitation datasets and the simulated water balance components of interest reported as a range.

Integrated Building Heating, Cooling and Ventilation Control

Abstract: Current research studies show that building heating, cooling and ventilation energy consumption account for nearly 40% of the total building energy use in the U.S. The potential for saving energy through building control systems varies from 5% to 20% based on recent market surveys. In addition, building control affects environmental performances such as thermal, visual, air quality, etc., and occupancy such as working productivity and comfort. Building control has been proven to be important both in design and operation stages. Building control design and operation need consistent and reliable static and dynamic information from multiple resources. Static information includes building geometry, construction and HVAC equipment. Dynamic information includes zone environmental performance, occupancy and outside weather information during operation.. At the same time, model-based predicted control can help to optimize energy use while maintaining indoor set-point temperature when occupied. Unfortunately, several issues in the current approach of building control design and operation impede achieving this goal. These issues include: a) dynamic information data such as real-time on-site weather (e.g., temperature, wind speed and solar radiation) and occupancy (number of occupants and occupancy duration in the space) are not readily available; b) a comprehensive building energy model is not fully integrated into advanced control for accuracy and robustness; c) real-time implementation of indoor air temperature control are rare. This dissertation aims to investigate and solve these issues based on an integrated building control approach. This dissertation introduces and illustrates a method for integrated building heating, cooling and ventilation control to reduce energy consumption and maintain indoor temperature set-point, based on the prediction of occupant behavior patterns and weather conditions. Advanced machine learning methods including Adaptive Gaussian Process, Hidden Markov Model, Episode Discovery and Semi-Markov Model are modified and implemented into this dissertation. A nonlinear Model Predictive Control (NMPC) is designed and implemented in real-time based on Dynamic Programming. The experiment test-bed is setup in the Solar Decathlon House (2005), with over 100 sensor points measuring indoor environmental parameters such as temperature, relative humidity, CO2, lighting, motion and acoustics, and power consumption for electrical plugs, HVAC and lighting. The outdoor environmental parameters, such as temperature, relative humidity, CO2, global horizontal solar radiation and wind speed, are measured by the on-site weather station. The designed controller is implemented through LabVIEW. The experiments are carried out for two continuous months in the heating season and for a week in cooling season. The results show that there is a 26% measured energy reduction in the heating season compared with the scheduled temperature set-points, and 17.8% energy reduction in the cooling season. Further simulation-based results show that with tighter building facade, the cooling energy reduction could reach 20%. Overall, the heating, cooling and ventilation energy reduction could reach nearly 50% based on this integrated control approach for the entire heating/cooling testing periods compared to the conventional scheduled temperature set-point.

Wind power potential and characteristic analysis of Chiang Mai, Thailand

Abstract: An investigation of wind characteristics and wind energy potential at Chiang Mai Province, Thailand was studied. Wind data taken from the weather station at Chiang Mai International Airport between 2001–2006 was analyzed in order to obtain the potential energy generated by a Vertical Axis Wind Turbine (VAWT). It was found that the yearly average wind velocity was 5.7 meters per second with a standard deviation value of 2.2 meters per second. The analysis assumed that wind blew in the S.W. to N.E. direction. Two parameters for the local wind, the shape parameter (k) and scale parameter (c) were obtained at 2.928 and 6.381 meters per second, respectively. The estimated power that could be generated by a Vertical Axis Wind Turbine was 183.09 W/m2 at 30 meters above ground level. This particular site corresponds to class 1 wind power. This level of power density may be adequate for non-connected electrical and mechanical applications, such as battery charging and water pumping.

Determining temperature lapse rates over mountain slopes using vertically weighted regression: a case study from the Pyrenees

Abstract: This study took place in the Pyrenees Range, in the northeastern Iberian Peninsula. The Pyrenees extend longitudinally, separating the Iberian Peninsula from the rest of Europe, and high peaks around 3000 m arise from deep valleys. As a mountain range it creates a barrier to advection, in this case from the north and south, and typical meteorological phenomena of mountainous areas occur within it (inversions, Foehn effect, extreme wind-chill, snow storms). Thus, two specific valleys in Catalonia were considered, Val d'Aran and Cerdanya. In both valleys automatic weather stations (AWSs) are available at similar heights. Although these valleys are only 100 km apart, they have different climates. However, the main reason for developing the study was that Numerical Weather Prediction (NWP) has problems when forecasting temperatures in complex terrain areas, mainly in the valley floor in winter season. Firstly, different equations based on a multilinear regression were obtained for each weather station. Multilinear regression was considered in this case as the most suitable downscaling method and data used were provided by the AWSs and MM5 (PSU/NCAR mesoscale model) numerical weather prediction model outputs. These equations were obtained to set up a Geographically Weighted Regression (GWR) method, although this one was modified and changed to a Vertically Weighted Regression (VWR) in order to create vertical temperature profiles. Copyright © 2009 Royal Meteorological Society

Injury Severity of Multivehicle Crash in Rainy Weather

Abstract: As part of the Wisconsin road weather safety initiative, the objective of this study is to microscopically assess rainy weather effect on the severities of multi-vehicle involved crashes on Wisconsin interstate highways utilizing a polychotomous response model, sequential logistic regression. Weather related factors considered in this study included estimated rainfall intensity for 15 minutes prior to a crash occurrence, water film depth, temperature, wind speed/direction, stopping sight distance and car-following distance at the crash moment. For each crash observation, weather station data around the crash location were interpolated using the inverse squared distance method. Non-weather factors such as road geometries, traffic conditions, collision manners, vehicle types, and driver and temporal attributes were also considered. The sequential logistic regression was tested with forward and backward formats for the polychotomous outcomes of multi-vehicle crash severity. The best format to predict the multi-vehicle crash severities in rainy weather was selected by combining measures of model performance for goodness of fit, parameter significance, and prediction accuracies. In conclusion, the backward sequential logistic regression model produced the best results for predicting crash severities in rainy weather where water film depth, the number of traffic lanes, tangent roadway section, peak traffic hours, at-fault driver's action at the crash moment, standard deviation of 5-minute traffic volume and safety belt usage were found to be statistically significant. These findings can be used to determine the probabilities of multi-vehicle crash severity in rainy weather and provide quantitative support on improving road weather safety via weather warning systems, highway facility improvements, and traffic management.

Using estimated on-site ambient temperature has uncertain benefit when estimating postmortem interval.

Abstract: The forensic entomologist uses weather station data as part of the calculation when estimating the postmortem interval (PMI). To reduce the potential inaccuracies of this method caused by the distance between the crime scene and the meteorological station, temperature correlation data from the site of the corpse may be used. This experiment simulated the impact of retrospective weather data correction using linear regression between seven stations and sites in three climatic exposure groups during three different seasons as part of the accumulated degree days calculation for three necrophagous species (Diptera: Calliphoridae). No consistent benefit in the use of correlation or the original data from the meteorological stations was observed. In nine cases out of 12, the data from the weather station network limited the risk of a deviation from reality. The forensic entomologist should be cautious when using this correlation model.

National Drought Insurance for Malawi

Abstract: Malawi has experienced several catastrophic droughts over the past few decades. The impact of these shocks has been far reaching, and the resulting macroeconomic instability has been a major constraint to growth and poverty reduction in Malawi. This paper describes a weather risk management tool that has been developed to help the government manage the financial impact of drought-related national maize production shortfalls. The instrument is an index-based weather derivative contract designed to transfer the financial risk of severe and catastrophic national drought that adversely impacts the government's budget to the international risk markets. Because rainfall and maize yields are highly correlated, changes in rainfall -- its timing, cumulative amount, and distribution -- can act as an accurate proxy for maize losses. An index has been constructed using rainfall data from 23 weather stations throughout Malawi and uses daily rainfall as an input to predict maize yields and therefore production throughout the country. The index picks up the well documented historical drought events in 2005, 1995, 1994, and 1992 and a weather derivative contract based on such an index would have triggered timely cash payouts to the government in those years. This innovative risk management instrument was pioneered in 2008/2009 by the Government of Malawi, with the assistance of the World Bank, and was a first for a sovereign entity in Africa. Several piloting seasons will be necessary to understand the scope and limitations of such contracts, and their role in the government's strategy, contingency planning, and operational drought response framework.

Vamos passear na floresta! O conforto térmico em fragmentos florestais urbanos

Abstract: This study investigated the role of urban forest fragments in thermal comfort. For that purpose, five different locations in the city of Campinas (Brazil) were monitored during the summer, autumn and winter of 2009. Microclimatic data were obtained with the use of a portable weather station that measured air temperature, air humidity, radiation, wind speed and globe temperature. In addition, structured interviews and field observations were carried out to evaluate the thermal comfort conditions and the occupants’ perceptions of their environment. The PMV and PET indices were calculated by the RayMan 1.2 software and compared with the actual votes obtained through the interviews. The results indicate that urban forest fragments improve thermal comfort. The percentage of subjects reported as thermally neutral varied between indices: 72.4% were found comfortable, 63.3% were in the PET limit of 18-23 o C, and 39,8% were in the PMV range of -0.5 to +0.5. Occupants perceived those places as comfortable, linking this to nature, but they also observed some problems regarding conservation and occupation. The specific forest microclimate, fresh air, and the perception of clean air were also cited by the population and can be linked to environmental comfort.

Multimodel SuperEnsemble technique for quantitative precipitation forecasts in Piemonte region

Abstract: . The Multimodel SuperEnsemble technique is a powerful post-processing method for the estimation of weather forecast parameters reducing direct model output errors. It has been applied to real time NWP, TRMM-SSM/I based multi-analysis, Seasonal Climate Forecasts and Hurricane Forecasts. The novelty of this approach lies in the methodology, which differs from ensemble analysis techniques used elsewhere. Several model outputs are put together with adequate weights to obtain a combined estimation of meteorological parameters. Weights are calculated by least-square minimization of the difference between the model and the observed field during a so-called training period. Although it can be applied successfully on the continuous parameters like temperature, humidity, wind speed and mean sea level pressure, the Multimodel SuperEnsemble gives good results also when applied on the precipitation, a parameter quite difficult to handle with standard post-processing methods. Here we present our methodology for the Multimodel precipitation forecasts, involving a new accurate statistical method for bias correction and a wide spectrum of results over Piemonte very dense non-GTS weather station network.

Summer hydroclimatology of an extensive low-gradient wetland: Polar Bear Pass, Bathurst Island, Nunavut, Canada.

Abstract: Polar Bear Pass (PBP) (75°40′N, 98°30′W) is considered a critical wetland area for migratory birds, caribou and muskox. Little is known of its climatology and hydrology. Here we evaluate both the short-term and long-term summer climatic record for this wetland. A 10 m high automatic weather station (AWS) was established here 27 years ago, and in 2007 this centrally located AWS was supplemented by three more weather stations placed across the wetland pass. The long-term climate record here indicates little significant departure when compared to the long-term climate means (1971–2005) at Resolute Bay, a government weather station lying 90 km to the southwest (74°43′N, 94°59′W). Exceptions exist for July minimum air temperature (PBP > Resolute) and number of days in June, July and August < 0°C (PBP < Resolute). Climate variability from year to year remains the norm. Radiation receipt, air temperature, humidity and wind speed vary little across the wetland pass, while terrain-modified fluxes do. The precipitation regime is similar to Resolute Bay but local site conditions modify the amounts. In 2007, July evaporation levels were twice as high as that of 2008; more akin to Low Arctic sites. As yet, no clear trend in long-term climatic signals can be established.

Impacts of Climate Change in Degree Days and Energy Demand in Coastal California

Abstract: An analysis of 1970―2005 observed summer daily maximum and minimum temperatures in two California air basins showed concurrent daytime coastal cooling and inland warming. To study the impacts of these results on energy consumption, summer cooling degree day (CDD) and winter heating degree day (HDD) trends were analyzed via these temperatures. The 2 m level air temperatures consisted of data from 159 locations in California, each with daily minimum and maximum values. Primary data sources included Cooperative Weather Station Network sites, first order National Weather Service stations, and military weather stations. An analysis of the CDD and HDD data has been undertaken for California, in general, and the San Francisco Bay Area and South Coast Air Basin, in particular, as the source of data for an analysis of energy-demand trends. Regional climate fluctuations have considerable effects on surface temperatures, which in turn affect CDD and HDD values. An asymmetric increase in summer CDD values between coastal and inland regions of California was found during the last 35 years, while winter HDD values showed decreases in most of California. In general, coastal areas experienced decreases of CDD, while inland regions experienced increases. The summer asymmetric increases in CDD is attributed to intensified sea breeze flows, which suggests increases in cold marine air intrusions over coastal land masses due to an increased regional sea breeze potential, which ventilates coastal areas, helps reduce maximum temperatures, and contributes to CDD decreases. An analysis of energy demands in the two air basins supports these climatological findings. .

Verification of Virtual Climatic Data in MEPDG Using the LTPP Database

Abstract: The Enhanced Integrated Climatic Model (EICM) integrated in the Mechanistic-Empirical Pavement Design Guide (MEPDG) allows users to select adjacent weather stations to generate a virtual weather station (VWS), whose data are further used to predict environmental impact on pavement performance. It is essential that the derived virtual data be accurate and representative of the actual climatic conditions. To evaluate the accuracy of the MEPDG generated VWS data, climatic data from corresponding automated weather stations (AWS) in the long-term pavement performance (LTPP) database are obtained to conduct the comparison analysis. It is observed that most VWS climatic data estimate the actual weather data reasonably well. However, in some cases, significant differences are observed. The potential factors resulting in the discrepancies are investigated. MEPDG analyses are conducted to quantify the climate data differences on predicted pavement performance. Finally, the paper presents recommendations when using the MEPDG software to generate VWS so as to help establish more accurate key climatic inputs.

A new portable micro weather station

Abstract: We present a novel and practical micro weather station, which can sense temperature, relative humidity, pressure and anemometer, and is portable in small size and possesses high precisions. The micro weather station comprises multi-sensor chip, anemometer, measurement system, display system and power management system. Based on MEMS technology, multi-sensor chip integrated temperature, relative humidity and pressure is developed and manufactured. A drag force wind sensor using the torque of cantilever to measure the velocity of wind is developed. The wind direction can be measured by perpendicularly encapsulating the two wind sensor. Compared with those processes used in other types of micro weather station, the processes we used were very simple and compatible. All the results exhibit outstanding performances of our micro weather station.

Influence of weather conditions and spatial variability on glacier surface melt in Chilean Patagonia

Abstract: In order to clarify how differences in weather conditions affect the surface heat balance of a large maritime glacier, meteorological observations were carried out in the ablation area of Glaciar Exploradores in the Chilean Patagonia during the austral summer of 2006/2007. Under cloudy/rainy weather, when the air temperature and wind speed were high due to advection, the average melting heat was 18.8 MJ m−2 day−1 and the turbulent heat fluxes contributed 35% of the total melt. During clear weather, the average melting heat was 16.9 MJ m−2 day−1 and 13% of the total was the turbulent heat fluxes. A decrease in air temperature due to the development of the glacier boundary layer on clear days will lead to an overestimation of the melt using the air temperature at a weather station outside of the glacier.

Meteorological parameters effects on solar energy power generation

Abstract: As Turkey lies near the sunny belt between 36 and 42°N latitudes, most of the locations in Turkey receive abundant solar energy. The yearly average solar radiation is 3.6 kWh/m2 day, and the total yearly radiation period is approximately 2610 h. Meteorological data such as solar radiation, ambient temperature, relative humidity, wind speed, air pressure and sunshine duration are accepted as dependable and widely variable renewable energy resources. These data play a very important role in photovoltaic systems. In this study, permanent resistive load directly fed from photovoltaic panel which produce DC electrical energy. Experiments were done during 23 month period from 2008 to 2010. Permanent resistive load currents and voltages measured with power analyzer continuously during the day. At the same time meteorological parameters like outdoor temperature, air pressure, humidity, wind speed and solar radiation etc. measured and recorded with digital weather station. These measurement results compared with the graphics at the same time bases. Photovoltaic panel output power calculated with current and voltage measurements. A mathematical equation found with curve fitting method from power graphics to examine dependencies for meteorological parameters. Thus correlation between photovoltaic performance and meteorological conditions is examined for Istanbul-Goztepe.

A procedure for automated quality control and homogenization of historical daily temperature and precipitation data (APACH): part 1: quality control and application to the Argentine weather service stations

Abstract: The present paper describes the quality-control component of an automatic procedure (APACH: A Procedure for Automated Quality Control and Homogenization of Weather Station Data) developed to control quality and homogenize the historical daily temperature and precipitation data from meteorological stations. The quality-control method is based on a set of decision-tree algorithms analyzing separately precipitation and minimum and maximum temperature. All our tests are non-parametric and therefore are potentially useful in regions or countries presenting different climates as those observed in Argentina. The method is applied to the 1959-2005 historical daily database of the Argentine National Weather Service. Our results are coherent with the history of the Weather Service and more specifically with the history of implementation of systematized quality control processes. In temperature, our method detects a larger number of suspect values before 1967 (when there was no quality control) and after 1997 (when only real-time quality control had been applied). In precipitation, the detection of error in extreme precipitations is complex, but our method clearly detected a strong decrease in the number of potential outliers after 1976 when the National Weather Service was militarized, and the network was strongly reduced, focusing more on airport weather stations. Also in precipitation, we analyze in detail the long dry sequences and are able to identify potential long erroneous sequences. This is important for the use of the data for hydrological or agricultural impact studies. Finally, all the data are flagged with codes representing the path followed by the record in our decision-tree algorithms. While each code is associated to one of the categories (“Useful”, “Need-Check”, “Doubtful” or “Suspect”), the final user is free to redefine such category-assignment.

Estimation of Fire Danger in Hawai'i Using Limited Weather Data and Simulation

Abstract: The presence of fire in Hawai'i has increased with introduction of nonnative grasses. Fire danger estimation using the National Fire Danger Rating System (NFDRS) typically requires 5 to 10 yr of data to determine percentile weather values and fire activity. The U.S. Army Pōhakuloa Training Area in Hawai'i is located in the interface zone between windward and leeward weather conditions and needed to develop fire danger values but did not have sufficient weather or fire occurrence data. Use of simulation to estimate fire danger (expressed as fire risk) for areas with limited weather data was investigated. Influence of spatial resolution of weather information on fire risk was examined by comparing fire risk calculated using one or three weather stations and gridded weather predictions from the Mesoscale Spectral Model. Predicted gridded temperature was positively correlated with observed temperature; predicted and observed relative humidity were not significantly correlated. Simulated fire risk differed between weather data percentiles and between weather data resolutions. Predicted risk estimated from gridded weather data agreed more closely with observed risk estimated from weather data observed at all three remote automated weather stations. Correlation between simulated fire risk and the NFDRS Ignition Component was statistically significant for the single weather station simulations. Correlations between risk and the Ignition Component were not statistically significant for the three station and gridded weather data scenarios, which illustrates the difference between fire danger determined at broad spatial scales and fire risk resolved at finer spatial scales. Fire spread simulation modeling to estimate fire risk in areas with limited historical weather and fire occurrence data can provide finer-scale information than the NFDRS, which is better suited to larger, homogeneous areas with more complete fire and weather data. Values for the NFDRS Burning Index were determined and incorporated into the wildland fire management plan for Pōhakuloa Training Area.

Climate change data for Austria and the period 2008-2040 with one day and km2 resolution

Abstract: We have developed climate change data for Austria and the period from 2008 to 2040 with temporal and spatial resolution of one day and one km2 based on historical daily weather station data from 1975 to 2007. Daily data from 34 weather stations have been processed to 60 spatial climate clusters with homogeneous climates relating to mean annual precipitation sums and mean annual temperatures from the period 1961-1990. We have performed regression model analysis to compute a set of daily climate change data for each climate cluster. The integral parts of our regression models are i) the extrapolation of the observed linear temperature trend from 1975 to 2007 using an average national trend of ~0.05 °C per year derived from a homogenized dataset, and ii) the repeated bootstrapping of temperature residuals and of observations for solar radiation, precipitation, relative humidity, and wind to ensure consistent spatial and temporal correlations. The repeated bootstrapping procedure has been performed for all weather parameters based on the observed climate variabilities from the period 1975-2007. To account for a wider range of precipitation patterns, we have also developed precipitation scenarios including higher or lower annual precipitation sums as well as unchanged annual precipitation sums with seasonal redistribution. These precipitation scenarios constitute together with the bootstrapped scenarios of temperature, solar radiation, relative humidity and wind our climate change spectrum for Austria until 2040. These climate change data are freely available and we invite users to apply and comment on our high resolution climate change data.

Prediction of the ground-level refractive index structure parameter from the measurement of atmospheric conditions

Abstract: Evaluation of the methods developed by Bendersky, Kopeika, and Blaunstein1 to predict the refractive index structure parameter from the direct measurement of macroscopic atmospheric conditions were investigated. Measurements of ground-level temperature, relative humidity, wind speed, solar flux, and aerosol loading taken by the University of Central Florida weather station were compared against concurrent measurements of the refractive index structure parameter made by Scintec SLS-20 scintillometers positioned near the weather station. Wind measurements were obtained by three, three-axis sonic anemometers (capable of resolving a three-dimensional wind vector) positioned at heights of 1, 1.5, and 2.5 meters above the ground. Temperature measurements were taken at ground level, and at heights of 1 and 1.5 meters. Data were collected for two days atop Antelope Peak, NV. Collection times covered both daytime and nighttime measurements.

Climate Characteristics across the Austrian Forest Estate from 1960 to 2008

Abstract: Summary Process modelling of forest growth for any purpose requires precise climatic data. This data is rarely available for the exact site being studied, so climate parameters are often taken from a nearby weather station or downscaled from broad-scale gridded datasets. This paper presents a third option, based on a detailed climate interpolation developed at the University of Natural Resources and Applied Sciences (BOKU) in Vienna. DAYMET gives precise daily climate data for 2224 forest inventory sites in Austria. The interpolation compares well with other datasets at the National scale, and provides more precise information at any specific site. Marked regional differences are apparent within Austria for both temperature and precipitation trends. Modelling applications often require precise climate inputs, and downscaled data from broad grids or the use of data from the nearest climate station may not be adequate. Interpolated datasets such as DAYMET can provide both an accurate representation of broad-scale averages and precise point data for model inputs.

Analysis of forest potential fire environment based on GIS and RS

Abstract: Forest fires cause a significant damage for public property by destroying a large tract of forest. Forest fire risk assessment, which based on an integrated index, becomes an important tool for forest fires management. The integrated index includes the information about fuel, topography and weather condition which constitute potential fire environment together. The fuel and weather condition are essential for forest fire occurrence, so the main potential fire environment parameters in the process of the forest fire risk assessment are temperature, fuel moisture content and vegetation status. The environment parameters data for traditional forest fire risk assessment were always obtained from the weather station, but these data are kind of point data. We must interpolate these point data into two-dimension continuous data, but existing interpolating methods produce larger error which we cannot accept if the number of the weather stations is very sparse in study area. Otherwise, not only the current environment status affects the assessment result but the cumulative effect of potential fire environment over longer period before fire event also contributes to the current potential fire environment, which has not been discussed in detail. RS and GIS technology, which can provide time series of continuous data and advanced data processing methods, becomes a viable avenue for providing accurate potential fire environment parameters data for forest fire risk assessment. In this paper, Land Surface Temperature (LST), Fuel Moisture Content (FMC) and Normalized Difference Vegetation Index (NDVI) were used to indicate the potential fire environment. We analyzed the cumulative effect of potential fire environment over one-month period before each of the typical historical forest fires occurrence from the year of 2000 to 2006. The analysis showed that the cumulative effect of the potential fire environment plays positive role on the fire occurrence, especially the cumulative effect of LST. 73% of the Accumulated Land Surface Temperature Departure (ALSTD) is plus over one-month period. Therefore, the variation character of potential fire environment parameters before forest fire occurrence will provide much useful reference information for forest fire risk assessment.