Relationship Between Pavement Temperature and Weather Data: Wisconsin Field Study to Verify Superpave Algorithm

1998 ◽  
Vol 1609 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Peter J. Bosscher ◽  
Hussain U. Bahia ◽  
Suwitho Thomas ◽  
Jeffrey S. Russell
Keyword(s):  
Standard Deviation ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Test Site ◽  
Temperature Data ◽  
Pavement Design ◽  
Asphalt Binders ◽  
Weather Data ◽  
Air Temperatures ◽  
Pavement Temperature

Six test sections were constructed on US-53 in Trempealeau County by using different performance-graded asphalt binders to validate the Superpave pavement temperature algorithm and the binder specification limits. Field instrumentation was installed in two of the test sections to monitor the thermal behavior of the pavement as affected by weather. The instrumentation was used specifically to monitor the temperature of the test sections as a function of time and depth from the pavement surface. A meteorological station was assembled at the test site to monitor weather conditions, including air temperature. Details of the instrumentation systems used and analysis of the data collected during the first 22 months of the project are presented. The analysis was focused on development of a statistical model for estimation of low and high pavement temperatures from meteorological data. The model was compared to the Superpave recommended model and to the more recent model recommended by the Long-Term Pavement Performance (LTPP) program. The temperature data analysis indicates a strong agreement between the new model and the LTPP model for the estimation of low pavement design temperature. However, the analysis indicates that the LTPP and Superpave models underestimate the high pavement design temperature at air temperatures higher than 30°C. The temperature data analyses also indicate that there are significant differences between the standard deviation of air temperatures and the standard deviation of the pavement temperatures. These differences raise some questions about the accuracy of the reliability estimates used in the current Superpave recommendations.

Medical Emergencies During a Half Marathon Race – The Influence of Weather

2019 ◽  
Vol 40 (05) ◽  
pp. 312-316 ◽  
Author(s):  
Eric Carlström ◽  
Mats Borjesson ◽  
Gunnar Palm ◽  
Amir Khorram-Manesh ◽  
Fredrik Lindberg ◽  
...  
Keyword(s):  
Meteorological Data ◽  
Weather Conditions ◽  
Daily Maximum ◽  
Equivalent Temperature ◽  
Physiological Equivalent Temperature ◽  
Marathon Race ◽  
Air Temperatures ◽  
Medical Emergencies ◽  
Pet Index ◽  
Maximum Air Temperature

AbstractThe aim was to analyze the influence of weather conditions on medical emergencies in a half-marathon, specifically by evaluating its relation to the number of non-finishers, ambulance-required assistances, and collapses in need of ambulance as well as looking at the location of such emergencies on the race course. Seven years of data from the world’s largest half marathon were used. Meteorological data were obtained from a nearby weather station, and the Physiological Equivalent Temperature (PET) index was used as a measure of general weather conditions. Of the 315,919 race starters, 104 runners out of the 140 ambulance-required assistances needed ambulance services due to collapses. Maximum air temperature and PET significantly co-variated with ambulance-required assistances, collapses, and non-finishers (R2=0.65–0.92; p=0.001–0.03). When air temperatures vary between 15–29°C, an increase of 1°C results in an increase of 2.5 (0.008/1000) ambulance-required assistances, 2.5 (0.008/1000) collapses (needing ambulance services), and 107 (0.34/1000) non-finishers. The results also indicate that when the daily maximum PET varies between 18–35°C, an increase of 1°C PET results in an increase of 1.8 collapses (0.006/1000) needing ambulance services and 66 non-finishers (0.21/1000).

scholarly journals Effects of Variable Weather Conditions on Baled Proportion of Varied Amounts of Harvestable Cereal Straw, Based on Simulations

2021 ◽  
Vol 13 (16) ◽  
pp. 9449
Author(s):  
Alfredo de Toro ◽  
Carina Gunnarsson ◽  
Nils Jonsson ◽  
Martin Sundberg
Keyword(s):  
Standard Deviation ◽  
Moisture Content ◽  
Annual Variation ◽  
Weather Conditions ◽  
Weather Data ◽  
Cereal Straw ◽  
Grain Moisture ◽  
Machine Systems ◽  
Variable Weather

All harvestable cereal straw cannot be collected every year in regions where wet periods are probable during the baling season, so some Swedish studies have used 'recovery coefficients’ to estimate potential harvestable amounts. Current Swedish recovery coefficients were first formulated by researchers in the early 1990s, after discussions with crop advisors, but there are no recent Swedish publications on available baling times and recovery proportions. Therefore, this study evaluated baling operations over a series of years for representative virtual farms and machine systems in four Swedish regions, to determine the available time for baling, baled straw ratio and annual variation in both. The hourly grain moisture content of pre-harvested cereals and swathed straw was estimated using moisture models and real weather data for 22/23 years, and the results were used as input to a model for simulating harvesting and baling operations. Expected available baling time during August and September was estimated to be 39–49%, depending on region, with large annual variation (standard deviation 22%). The average baling coefficient was estimated to be 80–86%, with 1400 t·year−1 harvestable straw and 15 t·h−1 baling capacity, and the annual variation was also considerable (s.d. 20%).

Impact of weather conditions on variability in sunflower yield over years in eastern parts of Croatia and Hungary

2012 ◽  
Vol 60 (4) ◽  
pp. 397-405 ◽  
Author(s):  
A. Mijić ◽  
I. Liović ◽  
V. Kovačević ◽  
P. Pepó
Keyword(s):  
Meteorological Data ◽  
Correlation Coefficients ◽  
Weather Conditions ◽  
Vegetation Period ◽  
Soil Conditions ◽  
Field Crops ◽  
Oil Crops ◽  
Air Temperatures ◽  
Important Field ◽  
The Impact

Oil crops constitute the second most important field crops worldwide and are important both in Hungary and Croatia. Among the oil crops, sunflower has a significant role in Hungary (∼550,000 ha) and Croatia (∼30,000 ha). The main aim of this study was to compare sunflower yields and their variation over years (2000–2007) in the eastern parts of Hungary and Croatia, with the emphasis on the impact of rainfall and temperature regime, and using a rain factor (RFm) calculated monthly as the quotient of precipitation (mm) and mean air temperatures (°C). The results showed that the year had a different effect on the yield of sunflower in the different counties of eastern Hungary and Croatia, because of their different soil conditions. The results proved that the highest yields of sunflower (2140–2710 kg ha−1) were obtained in years when the rainfall before and during the vegetation period was 110–130 mm and 350–420 mm, which was very similar to the 30-year mean data (82–108 mm and 305–346 mm, respectively). The strongest correlations (positive and negative r values) between meteorological data and sunflower yields were found in counties with unfavourable soil conditions. In counties with better soil fertility the correlation coefficients were smaller, indicating that better soil conditions can compensate for unfavourable year effects (especially temporary shortage of rainfall or unfavourable rainfall distribution).

scholarly journals Design of meteorological information database for forecasting and clustering in microgrid system

2020 ◽  
pp. 55-66
Author(s):  
V. Osypenko ◽  
◽  
N. Kiktev ◽  
T. Lendiel ◽  
◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Information System ◽  
Alternative Energy ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Time Of Day ◽  
Weather Data ◽  
Energy Sources ◽  
Distributed Information System ◽  
Distributed Information

To build Microgrid systems, it is necessary to obtain data from the meteorological service, process them and make decisions about which source of electricity is advisable to use at a given time of day, season, under current weather conditions. The aim of the study is to develop and create a distributed information system database for cluster analysis, processing and storage of incoming meteorological data, a weather forecasting algorithm based on the values of the selected indicators to further determine the type of alternative energy sources used based on the forecast. The article describes designed and implemented distributed information system for reading from the Internet, storing and further processing meteorological data for any region with the aim of forecasting for the effective use of renewable energy sources in Microgrid system. The project is implemented on the basis of a relational database Microsoft SQL Server. Each of the tables has fields that describe the weather conditions necessary to solve the task – to determine the source of electricity, the use of which is cost-effective in a given period of the year, time of day, geographical location and weather conditions. The application that operates with a database has been developed in C # according to the Windows Forms Application template. The distribution of temperature indicators is realized depending on the time of the conducted research for a certain period using cluster analysis. Forecasting weather data is performed using an autoregressive time series model. The user interface was created with Microsoft Visual Studio tools. All data processing is performed on the local server side.

Accuracy of Weather Data in Long-Term Pavement Performance Program Database

2000 ◽  
Vol 1699 (1) ◽  
pp. 151-159 ◽  
Author(s):  
Chung-Lung Wu ◽  
Gonzalo R. Rada ◽  
Aramis Lopez ◽  
Yingwu Fang
Keyword(s):  
Weather Conditions ◽  
Test Site ◽  
Climatic Conditions ◽  
Weighting Scheme ◽  
Pavement Performance ◽  
Weather Data ◽  
Climatic Data ◽  
National Climatic Data Center ◽  
Weather Stations

To provide accurate climatic data for pavements under the Long-Term Pavement Performance (LTPP) Program, a climatic database was developed in 1992 and subsequently revised and expanded in 1998. In the development of this database, up to five nearby weather stations were selected for each test site. Pertinent weather data for the selected weather stations were obtained from the U.S. National Climatic Data Center and the Canadian Climatic Center. With a 1/ R2 weighting scheme, site-specific climatic data were derived from the nearby weather station data. The derived data were referred to as “virtual”weather data. To evaluate the effect of environmental factors on pavement performance and design, automated weather stations (AWS) were installed at LTPP Specific Pavement Study Projects 1, 2, and 8 to collect on-site weather data. Since the virtual weather data were developed for all LTPP test sites and will be used for future pavement performance studies, it is essential that the derived virtual data be accurate and representative of the actual onsite climatic conditions. The availability of the AWS weather data has provided an opportunity to evaluate whether virtual weather data can be used to represent on-site weather conditions. Daily temperature data and monthly temperature and precipitation data were used in this experiment. On the basis of the comparisons made between the virtual and onsite measured (AWS) data, it appears that climatic data derived from nearby weather stations using the 1/R2 weighting scheme estimate the actual weather data reasonably well and thus can be used to represent on-site weather conditions in pavement research and design.

scholarly journals Examining the role of wind in human illness due to pesticide drift in Washington state, 2000–2015

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Edward J. Kasner ◽  
Joanne B. Prado ◽  
Michael G. Yost ◽  
Richard A. Fenske
Keyword(s):  
Wind Speed ◽  
Precision Agriculture ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Complete Information ◽  
Washington State ◽  
Weather Data ◽  
Target Movement ◽  
Pesticide Drift ◽  
Spatio Temporal

Abstract Background Pesticides play an important role in protecting the food supply and the public’s health from pests and diseases. By their nature, pesticides can be toxic to unintended target organisms. Changing winds contribute to pesticide drift— the off-target movement of pesticides—and can result in occupational and bystander illness. Methods We systematically linked historical weather data to documented pesticide drift illnesses. We used Washington State Department of Health data to identify 252 drift events that included 690 confirmed cases of illness from 2000 to 2015. To characterize wind speed and direction at the time of the events, we paired these data with meteorological data from a network of 171 state weather stations. We report descriptive statistics and the spatio-temporal extent of drift events and compare applicator-reported weather conditions to those from nearby meteorological stations. Results Most drift events occurred in tree fruit (151/252 = 60%). Ground spraying and aerial applications accounted for 68% and 23% of events, respectively; 69% of confirmed cases were workers, and 31% were bystanders. Confirmed cases were highest in 2014 (129) from 22 events. Complete applicator spray records were available for 57 drift events (23%). Average applicator-reported wind speeds were about 0.9 m •sec− 1 (2 mi •hr− 1) lower than corresponding speeds from the nearest weather station values. Conclusions Drift events result from a complex array of factors in the agricultural setting. We used known spatio-temporal aspects of drift and historical weather data to characterize these events, but additional research is needed to put our findings into practice. Particularly critical for this analysis is more accurate and complete information about location, time, wind speed, and wind direction. Our findings can be incorporated into new training materials to improve the practice of pesticide application and for better documentation of spray drift events. A precision agriculture approach offers technological solutions that simplify the task of tracking pesticide spraying and weather conditions. Public health investigators will benefit from improved meteorological data and accurate application records. Growers, applicators, and surrounding communities will also benefit from the explanatory and predictive potential of wind ramping studies.

scholarly journals Generation of a Test Reference Year for Alūksne, Latvia

2015 ◽  
Vol 33 (1) ◽  
pp. 46-54 ◽  
Author(s):  
Mārtiņš Ruduks ◽  
Arturs Lešinskis
Keyword(s):  
Selection Process ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Building Energy ◽  
Weather Data ◽  
Building Performance ◽  
Climate Data ◽  
Meteorological Observations ◽  
Energy Simulations

Abstract Precise and reliable meteorological data are necessary for building performance analysis. Since meteorological conditions vary significantly from year to year, there is a need to create a test reference year (TRY), to represent the long-term weather conditions over a year. In this paper two different TRY data models were generated and compared: TRY and TRY-2. Both models where created by analysing every 3-hour weather data for a 30-year period (1984–2013) in Alūksne, Latvia, provided by the Latvian Environment Geology and Meteorology Centre (LEGMC). TRY model was generated according to standard LVS EN ISO 15927-4, but to create second model - TRY-2, 30 year average data were applied. The generated TRY contains typical months from a number of different years. The data gathered from TRY and TRY-2 models where compared with the climate data from the Latvian Cabinet of Ministers regulation No. 379, Regulations Regarding Latvian Building Code LBN 003-01. Average monthly temperature values in LBN 003-01 were lower than the TRY and TRY-2 values. The results of this study may be used in building energy simulations and heating-cooling load calculations for selected region. TRY selection process should include the most recent meteorological observations and should be periodically renewed to reflect the long-term climate change.

scholarly journals Arduino Sensor Integrated Drone for Weather Indices: A Prototype for Pre-flight Preparation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Theodore Karachalios ◽  
Dimitris Kanellopoulos ◽  
Fotis Lazarinis
Keyword(s):  
Meteorological Data ◽  
Objective Assessment ◽  
Weather Conditions ◽  
Barometric Pressure ◽  
Local Area ◽  
Weather Data ◽  
Map Reading ◽  
Micro Scale ◽  
Arduino Microcontroller ◽  
Local Weather

Commercial weather stations can effectively collect weather data for a specified area. However, their ground sensors limit the amount of data that can be logged, thus failing to collect precise meteorological data in a local area such as a micro-scale region. This happens because weather conditions at a micro-scale region can vary greatly even with small altitude changes. For now, drone operators must check the local weather conditions to ensure a safe and successful flight. This task is often a part of pre-flight preparations. Since flight conditions (and most important flight safety) are greatly affected by weather, drone operators need a more accurate localized weather map reading for the flight area. In this paper, we present the Arduino Sensor Integrated Drone (ASID) with a built-in meteorological station that logs the weather conditions in the vertical area where the drone will be deployed. ASID is an autonomous drone-based system that monitors weather conditions for pre-flight preparation. The operation of the ASID system is based on the Arduino microcontroller running automatic flight profiles to record meteorological data such as temperature, barometric pressure, humidity, etc. The Arduino microcontroller also takes photos of the horizon for an objective assessment of the visibility, the base, and the number of clouds.

Vulnerability of sweet cherry cultivars to continuous periods of spring frosts in Plovdiv, Bulgaria

2020 ◽  
Vol 12 (4) ◽  
pp. 348-352
Author(s):  
S. Malchev ◽  
S. Savchovska
Keyword(s):  
Sweet Cherry ◽  
Weather Conditions ◽  
Frost Damage ◽  
Cold Weather ◽  
Bud Burst ◽  
Intermediate Group ◽  
Air Temperatures ◽  
Wide Range ◽  
Group Form ◽  
Sweet Cherry Cultivars

Abstract. The periods with continuous freezing air temperatures reported during the spring of 2020 (13 incidents) affected a wide range of local and introduced sweet cherry cultivars in the region of Plovdiv. They vary from -0.6°C on March 02 to -4.9°C on March 16-17. The duration of influence of the lowest temperatures is 6 and 12 hours between March 16 and 17. The inspection of fruit buds and flowers was conducted twice (on March 26 and April 08) at different phenological stages after continuous waves of cold weather conditions alternated with high temperatures. During the phenological phase ‘bud burst’ (tight cluster or BBCH 55) some of the flowers in the buds did not develop further making the damage hardly detectable. The most damaged are hybrid El.28-21 (95.00%), ‘Van’ (91.89%) and ‘Bing’ (89.41%) and from the next group ‘Lapins’ (85.98%) and ‘Rosita’ (83.33%). A larger intermediate group form ‘Kossara’ (81.67%), ‘Rozalina’ (76.00%), ‘Sunburst’ (75.00%), ‘Bigarreau Burlat’ (69.11%) and ‘Kuklenska belitza’ (66.67%). Candidate-cultivar El.17-90 ‘Asparuh’ has the lowest frost damage values of 55.00% and El.17-37 ‘Tzvetina’ with damage of 50.60%.

scholarly journals Rain Erosion Maps for Wind Turbines Based on Geographical Locations: A Case Study in Ireland and Britain

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
K. Pugh ◽  
M. M. Stack
Keyword(s):  
Wind Turbine ◽  
Western Europe ◽  
Meteorological Data ◽  
Turbine Blades ◽  
Weather Conditions ◽  
Annual Rainfall ◽  
Wind Turbine Blades ◽  
Weather Patterns ◽  
The Uk

AbstractErosion rates of wind turbine blades are not constant, and they depend on many external factors including meteorological differences relating to global weather patterns. In order to track the degradation of the turbine blades, it is important to analyse the distribution and change in weather conditions across the country. This case study addresses rainfall in Western Europe using the UK and Ireland data to create a relationship between the erosion rate of wind turbine blades and rainfall for both countries. In order to match the appropriate erosion data to the meteorological data, 2 months of the annual rainfall were chosen, and the differences were analysed. The month of highest rain, January and month of least rain, May were selected for the study. The two variables were then combined with other data including hailstorm events and locations of wind turbine farms to create a general overview of erosion with relation to wind turbine blades.

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