Updates to Hourly Climate Data for Use in AASHTOWare Pavement Mechanistic–Empirical Design

2017 ◽  
Vol 2640 (1) ◽  
pp. 11-20 ◽  
Author(s):  
Wouter Brink ◽  
Harold Von Quintus ◽  
Leon F. Osborne
Keyword(s):  
Weather Data ◽  
Data Sets ◽  
Climatic Data ◽  
Systematic Bias ◽  
Climate Data ◽  
The North ◽  
Hourly Temperature ◽  
Regional Reanalysis ◽  
Pavement Damage ◽  
Empirical Design

The AASHTOWare Pavement Mechanistic–Empirical Design software requires hourly temperature, wind speed, percentage sunshine, precipitation, and relative humidity to properly calculate pavement damage and distresses. Actual or measured values, which vary hourly throughout a day for a given site, are required to properly capture the damage caused by environmental loadings. Currently the mechanistic–empirical design hourly climatic data contain approximately 1,200 U.S. and 300 Canadian stations. The U.S. stations typically contain data from 1995 through 2005, and data from the Canadian stations vary in length from 10 to 50 years, with the exception of some weather stations. Some agencies expanded their historical weather data to include longer periods of time. This paper documents the process and data sources that were used to update the current set of climate stations with climate data dating back to 1979 using the North American Regional Reanalysis (NARR) database. The results of the comparison between new climate files and the existing older climate data files for use in pavement design are presented. Overall, the NARR-generated climate data showed a very good comparison. The paper details the background of the NARR and its limitations and compares the performance predictions made by using the old and new climate data. The results indicate there is no systematic bias between the two climate data sets.

Spurious Grid-Scale Precipitation in the North American Regional Reanalysis

2007 ◽  
Vol 135 (6) ◽  
pp. 2168-2184 ◽  
Author(s):  
Gregory L. West ◽  
W. James Steenburgh ◽  
William Y. Y. Cheng
Keyword(s):  
North American ◽  
Prediction Models ◽  
Weather Prediction ◽  
Regional Climate ◽  
Potential Temperature ◽  
Climate Data ◽  
The North ◽  
North American Regional Reanalysis ◽  
Upper Level ◽  
Regional Reanalysis

Abstract Spurious grid-scale precipitation (SGSP) occurs in many mesoscale numerical weather prediction models when the simulated atmosphere becomes convectively unstable and the convective parameterization fails to relieve the instability. Case studies presented in this paper illustrate that SGSP events are also found in the North American Regional Reanalysis (NARR) and are accompanied by excessive maxima in grid-scale precipitation, vertical velocity, moisture variables (e.g., relative humidity and precipitable water), mid- and upper-level equivalent potential temperature, and mid- and upper-level absolute vorticity. SGSP events in environments favorable for high-based convection can also feature low-level cold pools and sea level pressure maxima. Prior to 2003, retrospectively generated NARR analyses feature an average of approximately 370 SGSP events annually. Beginning in 2003, however, NARR analyses are generated in near–real time by the Regional Climate Data Assimilation System (R-CDAS), which is identical to the retrospective NARR analysis system except for the input precipitation and ice cover datasets. Analyses produced by the R-CDAS feature a substantially larger number of SGSP events with more than 4000 occurring in the original 2003 analyses. An oceanic precipitation data processing error, which resulted in a reprocessing of NARR analyses from 2003 to 2005, only partially explains this increase since the reprocessed analyses still produce approximately 2000 SGSP events annually. These results suggest that many NARR SGSP events are not produced by shortcomings in the underlying Eta Model, but by the specification of anomalous latent heating when there is a strong mismatch between modeled and assimilated precipitation. NARR users should ensure that they are using the reprocessed NARR analyses from 2003 to 2005 and consider the possible influence of SGSP on their findings, particularly after the transition to the R-CDAS.

scholarly journals Calculating Human Health Risks with General Weather Data

Eos ◽  
2021 ◽  
Vol 102 ◽  
Author(s):  
Sarah Derouin
Keyword(s):  
Human Health ◽  
Health Risks ◽  
Mortality Risk ◽  
Weather Data ◽  
Data Sets ◽  
Weather Station ◽  
Climate Data ◽  
Human Mortality ◽  
Human Health Risks ◽  
Gridded Climate Data

Gridded climate data sets are just as effective as weather station data at assessing human mortality risk related to heat and cold, researchers suggest.

An Overview of the Global Historical Climatology Network-Daily Database

2012 ◽  
Vol 29 (7) ◽  
pp. 897-910 ◽  
Author(s):  
Matthew J. Menne ◽  
Imke Durre ◽  
Russell S. Vose ◽  
Byron E. Gleason ◽  
Tamara G. Houston
Keyword(s):  
Processing System ◽  
Public Access ◽  
Climatic Data ◽  
Systematic Bias ◽  
Climate Data ◽  
Full Dataset ◽  
National Climatic Data Center ◽  
Historical Climatology ◽  
Global Historical Climatology Network ◽  
Climate Analysis

Abstract A database is described that has been designed to fulfill the need for daily climate data over global land areas. The dataset, known as Global Historical Climatology Network (GHCN)-Daily, was developed for a wide variety of potential applications, including climate analysis and monitoring studies that require data at a daily time resolution (e.g., assessments of the frequency of heavy rainfall, heat wave duration, etc.). The dataset contains records from over 80 000 stations in 180 countries and territories, and its processing system produces the official archive for U.S. daily data. Variables commonly include maximum and minimum temperature, total daily precipitation, snowfall, and snow depth; however, about two-thirds of the stations report precipitation only. Quality assurance checks are routinely applied to the full dataset, but the data are not homogenized to account for artifacts associated with the various eras in reporting practice at any particular station (i.e., for changes in systematic bias). Daily updates are provided for many of the station records in GHCN-Daily. The dataset is also regularly reconstructed, usually once per week, from its 20+ data source components, ensuring that the dataset is broadly synchronized with its growing list of constituent sources. The daily updates and weekly reprocessed versions of GHCN-Daily are assigned a unique version number, and the most recent dataset version is provided on the GHCN-Daily website for free public access. Each version of the dataset is also archived at the NOAA/National Climatic Data Center in perpetuity for future retrieval.

Our climate conditions are already changing – Should we care?

2021 ◽  
pp. 014362442110042
Author(s):  
Drury B Crawley ◽  
Linda K Lawrie
Keyword(s):  
Energy Simulation ◽  
Weather Data ◽  
Building Performance ◽  
Temperature Record ◽  
Data Sets ◽  
Climatic Data ◽  
Climate Conditions ◽  
The Past ◽  
Changing Climate ◽  
The World

The IPCC and many others predict significant changes to our climates over the rest of this century, including average temperature increases for 2–5°C. However, we can see possible indications of change already – increasing frequency of severe storms and other weather events. However, many of the major weather data sets used around the world for building energy simulation are more than 15 years old. Does it matter? This paper compares several of the major data sets used in building performance simulation against newer data derived from the past 15 years. Ten of the past 15 years are the hottest on record and this rapidly changing climate already is evident in the temperature record. We use energy simulation to demonstrate how the various data sets impact energy use. In addition, the design conditions for heating and cooling calculations are already seeing slight changes over the past 20 years. Data for 12 locations around the world is used to demonstrate the changing climate that we already see. Practical application: This paper encourages building designers to use the most up-to-date climatic data in their design and evaluation of building performance.

An empirical approach to the analysis of local and global climate and weather data and to the determination of CO2 sensitivities

2021 ◽  
Author(s):  
Wolf Timm
Keyword(s):  
Global Climate ◽  
Global Temperature ◽  
Weather Data ◽  
Data Sets ◽  
Climate Data ◽  
Local Data ◽  
Simple Method ◽  
Co2 Sensitivity ◽  
Data Points

Abstract Some freely available global temperature data sets which document the weather for a period of over 100 years, e.g. from NASA, from NOAA, additionally also local data e.g. for Germany (DWD) were analyzed in order to derive meaningful empirical long-term trends with suitable multi-annual averages. This is first demonstrated using global climate data with different approaches, whereby the results are to a high degree consistent. Analyzes of the German temperature and weather data and of climate data from other continents are carried out in a similar manner. For reliable forecasts it is important to determine the CO2 sensitivity as precisely as possible. A very simple method is to smooth out temperatures over 20 years at a time. If these values are plotted at intervals of 10 years over the associated (also averaged) CO2 content, the temperature database (since 1961) is condensed to 5 data points and a statement can be made about the quality of the linearity for the respective database. Both the NASA data and the NOAA data show an unusually good linearity with almost identical CO2 sensitivity (approx. 0.0105 K/ppm CO2). This indicates that the long-term trend in global temperature since around 1960 has been largely determined solely by greenhouse gases. If the regional weather data is used as a basis, there is also in many cases strict linearity with increasing CO2 content. The analysis of the regional data allows the conclusion that there is approximately a specific CO2 sensitivity for every region on earth with specific statistical uncertainties: For mean global land, it is 0.017 K, for Germany it is 0.022 K, and for Alaska even 0.028 K per ppm CO2 .

Effect of Applied Weather Data Sets on the Computational Assessment of Hygrothermal Performance of Historical Masonry

2017 ◽  
Author(s):  
Jan Kocí ◽  
Robert Cerný
Keyword(s):  
Meteorological Data ◽  
Warming Trend ◽  
Weather Data ◽  
Data Sets ◽  
Climatic Data ◽  
Damage Functions ◽  
Historical Building ◽  
Time Period ◽  
Hygrothermal Performance ◽  
Historical Masonry

Several historical wall assemblies together with several weather data sets are investigated in order to study the effect of environmental load on hygrothermal performance of historical buildings. The effect of weather data is assessed using several damage functions with the emphasis placed on frost induced damage. The climatic data are represented by six different weather data sets, namely by the test reference year, positive and critical weather years, together with the meteorological data measured by the autors during the time period of 2013–2015. Special attention is paid to the recent weather data as there is an apparent trend of average temperature increase in the Central Europe in last few years. The results presented in the paper confirm the warming trend which is manifested by virtually no frost induced damage observed for weather years 2014 and 2015 in the analyzed historical building envelopes.

scholarly journals Bin Weather Data for HVAC Systems Energy Calculations

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3501
Author(s):  
Konstantinos T. Papakostas ◽  
Dimitrios Kyrou ◽  
Kyrillos Kourous ◽  
Dimitra Founda ◽  
Georgios Martinopoulos
Keyword(s):  
Air Temperature ◽  
Air Conditioning ◽  
Energy Performance ◽  
Climatic Conditions ◽  
Weather Data ◽  
Climatic Data ◽  
Outdoor Air ◽  
Energy Demands ◽  
Hourly Temperature

The increase in global air temperature is well documented, as during the last several years each decade has been consecutively warmer than the preceding. As climatic conditions affect the energy performance of buildings, the changes in outdoor air temperature and humidity will inevitably lead to significant alterations in energy consumption and costs for the heating, ventilating and air conditioning (HVAC) of buildings. The availability and quality of climatic data play an important role in the accuracy of energy analysis results. In this study, the hourly temperature and relative humidity of outdoor air measurements, for a period of three decades (1983–2012), recorded at the climatic station of the National Observatory of Athens were processed, and an up-to-date set of specific data for the application of bin methods was produced and presented. The data were then used to calculate changes in the energy demands in a typical office building throughout the specified period. Results showed a progressive reduction in the low and increase in the high temperature intervals, leading to an increase in the building’s annual energy requirements for air conditioning of up to 14.5% from the first to the third decade, with decrease in the energy demands for heating and increase in the energy demands for cooling.

scholarly journals Relationship between Environmental Covariates and Ceylon Tea Cultivation in Sri Lanka

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 476 ◽  
Author(s):  
Sadeeka Layomi Jayasinghe ◽  
Lalit Kumar ◽  
Md Kamrul Hasan
Keyword(s):  
Sri Lanka ◽  
Solar Radiation ◽  
Global Climate ◽  
Climatic Data ◽  
Climate Data ◽  
North West ◽  
The North ◽  
Systems Research ◽  
Elevation Data ◽  
Terrain Elevation

How the current distribution of tea cultivation is influenced by specific environmental conditions in Sri Lanka is yet to be explored. Therefore, this study aims to assess the differences between tea and non-tea growing areas with respect to climatic and topographic covariates, and to determine the major covariates that control tea distributions. Climatic data of temperature and rainfall were extracted from WorldClim-Global Climate Data; the elevation, slopes, and aspects were obtained from Global Multi-resolution Terrain Elevation Data; and the solar radiation data was computed using a clear-sky solar radiation model. Random points were created on rasterised environmental layers for tea-growing and non-tea growing areas, stratified into low, mid, and high regions, using ArcGIS version 10.4.1 (Environmental Systems Research Institute: ESRI Redlands, CA, USA).Correlations were derived between covariates and tea and non-tea growing areas. According to the logistic regression analysis, there was no significant influence of the south-west, west, and north-west aspect compared to the north aspect when all other covariates were held constant. The odds ratio indicated that an area with a one-unit higher solar radiation was 1.453 times more likely to be a tea growing area. Similarly, a per unit increase in slope increases the likelihood of an area being suitable for tea cultivation by 1.039 times. When the annual mean temperature increased, the suitability of tea cultivation decreased, but an increased rainfall had increased the suitability of an area for tea cultivation. Areas with a north facing slope had the highest suitability for tea cultivation. This research demonstrated that tea growing could be expanded into a variety of locations as long as these variables are either found or managed in order to obtain the critical levels. In addition, it is proposed that the results of this study could be utilised in the assessment of the climate or/and land suitability for tea.

HOW TO USE CROP GROWTH MODEL WOFOST FOR FORECASTING GROWTH AND YIELD OF A CROP

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
LAL SINGH ◽  
PARMEET SINGH ◽  
RAIHANA HABIB KANTH ◽  
PURUSHOTAM SINGH ◽  
SABIA AKHTER ◽  
...  
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
Input Data ◽  
Growth And Yield ◽  
Weather Data ◽  
Data Sets ◽  
Crop Growth Model ◽  
Control Center ◽  
Field Crops ◽  
Crop Calendar

WOFOST version 7.1.3 is a computer model that simulates the growth and production of annual field crops. All the run options are operational through a graphical user interface named WOFOST Control Center version 1.8 (WCC). WCC facilitates selecting the production level, and input data sets on crop, soil, weather, crop calendar, hydrological field conditions, soil fertility parameters and the output options. The files with crop, soil and weather data are explained, as well as the run files and the output files. A general overview is given of the development and the applications of the model. Its underlying concepts are discussed briefly.

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