UPDATES TO THE HOURLY CLIMATE DATA FOR USE IN PAVEMENT-ME. Word count: 4480 words text + 9 tables/figures x 250 words (each) = 6730 words

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1 0 0 0 UPDATES TO THE HOURLY CLIMATE DATA FOR USE IN PAVEMENT-ME Wouter Brink, Corresponding Author Applied Research Associates 00 Trade Center Dr. Suite 00, Champaign, IL, 0 Tel: --00 Fax: --0; wbrink@ara.com Harold Von Quintus Applied Research Associates 00 Trade Center Dr. Suite 00, Champaign, IL, 0 Tel: --00 Fax: --0; hvonquintus@ara.com Leon F. Osborne, Jr. Department of Atmospheric Sciences University of North Dakota 0 Campus Drive Grand Forks, North Dakota 0 Tel: 0--; osborne@aero.und.edu Word count: 0 words text + tables/figures x 0 words (each) = 0 words Submission Date: August 0

2 Brink, Von Quintus, Osborne 0 ABSTRACT The AASHTOWare ME-Design software requires hourly temperature, wind speed, percent 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 ME-Design Hourly Climatic Data (HCD) contains approximately,00 US and 00 Canadian stations. The US stations typically contain data from through 00, while the Canadian stations vary in length from 0 to 0 years with the exception of some weather stations. Some agencies expanded their historical weather data to include longer periods of time. The paper documents the process and data sources which were used to update the current set of climate stations with climate data dating back to using the North American Regional Reanalysis (NARR) database. The results of the comparison between new climate files and the existing or older climate data files for use in pavement design were presented. Overall, the NARR generated climate data showed a very good comparison. The paper details the background of the NARR, its limitations, and a comparison of the performance predictions using the old and new climate data. The results indicate that there is no systematic bias between the two climate datasets. Keywords: Pavement-ME, Climate, Rigid Pavements, Flexible Pavements

3 Brink, Von Quintus, Osborne INTRODUCTION The AASHTOWare ME-Design software requires hourly temperature, wind speed, percent 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 capture the damage caused by environmental loadings. Average values for the inputs limit the potential to capture the full range of environmental loadings, and therefore are inadequate for use in ME-Design analysis.() Forensic pavement analysis, using the actual climatic data at a particular location over the time period being modelled, gives the best match between measured and predicted distresses. When modeling future pavement performance for new or rehabilitated pavements, it is not possible to know what the actual climatic data will be. Therefore, the ME-Design software recommends using the historic climatic data collected near the geographic location of the pavement being designed. While use of longer historic climatic records is preferable, a minimum of 0 years of historic observed data is recommended. If the pavement analysis period exceeds the length of the available climatic data, the ME-Design software repeats the available data to completely model the pavement analysis period. Currently the ME-Design Hourly Climatic Data (HCD) contains approximately 0 climate stations. The US stations typically contain data from through 00, while the Canadian stations vary in length from 0 to 0 years with the exception of some weather stations. Some agencies expanded their historical weather data to include longer periods of time. In addition, some of the existing weather stations contained climate data dating back as far as. The quality and accuracy of this data was checked using the data quality control checks that have been in place for years. A more rigorous check on the accuracy of this older data, however, has only recently been completed. The purpose of this paper is to document the process and data sources from which the climate data for the existing weather stations were expanded for all stations dating back to. This expansion of the climate data included identifying anomalous and missing data. In addition, the document provides a comparison of the new and existing or older climate data files for use in pavement design. Historical climate data The original HCD was compiled in late 0s from the National Centers for Environmental Information (NCEI), formerly, National Climatic Data Center (NCDC) to support the FHWA Integrated Climatic Model (ICM). Originally it contained just three years of data. The length of the data set was limited by what was available for electronic download from the NCEI. The ICM (and therefore the ME-Design analyses dependent on the ICM) requires complete input data over the design period to properly execute. The Unedited Local Climatological Data (ULCD) by design is a raw dataset with minimal quality control and with data fields with missing data left blank. The ULCD also contains a small quantity of erroneous data. Early use of the HCD required user input to fix missing or obviously incorrect climatic data. In 00, additional data were added to the climatic database extending the period covered to years. As the original MEPDG software did not have a utility to edit the climate files, it was determined that all missing data would be in-filled prior to delivery to the user. A software utility was developed to programmatically in-fill missing and correct bad data. A multi-step process was deployed that identified missing and erroneous data and created a log file of changes made to the ULCD to create the HCD. This utility has been used to infill and correct all currently published climate data files (). The previous method for fixing the files included the following steps:. Find missing data.. Interpolate missing data, if less than hours.. If more than twelve hours, repeat the day from previous good day.. If more than a week is missing, mark the month as incomplete.. Check for values out of range (i.e. relative humidity of over 00, temperatures over 0 F, etc.).. Create a log file of any interpolations or corrections. Stations with incomplete months are available to be used for an analysis but require interpolation between nearby stations to complete the missing months. The HCD has been improved at different times to increase the amount of time series data and reduce inconsistencies and anomalies in the dataset. The following is a

4 Brink, Von Quintus, Osborne short description of the dates the HCD was modified: In 00, the HCD was updated using the above method for the US and Canada to include climatic data up to December, 00. At this time, the HCD was recompiled with the raw data starting from. In 0, the Canadian stations in the HCD were updated using data from Environment Canada, which added data from the 0s to December 0. The HCD accompanying the Pavement-ME software has been found to have some missing and/or erroneous data. Some of these anomalies have been identified and flagged. Once they have been flagged, the missing and/or erroneous data can be populated and/or replaced to provide a more representative and accurate dataset(, ). Hourly climate data Each climate or weather station file in the Pavement-ME database contains hourly data of the following parameters:. Date/Hour. Air Temperature ( F). Wind speed (mph). Sunshine (percent: 0 cloudy, 00 no clouds). Precipitation (inch). Relative Humidity (percent) The hourly climate data is used in the EICM to calculate various other parameters such as long-wave radiation, convective heat transfer from pavement surface-to-air, and several others. The climatic data measurements took place over several years. As discussed previously, several stations have missing data or measurement errors. To address the erroneous measurements, quality control needs to be performed to ensure that the measurements are within reasonable ranges (minimum and maximum) and that hour-byhour changes are realistic(). Current dataset The climate files included in the Pavement-ME were populated using data starting in and ending in 00 (US sections). Some climate files have more data compared to others. The current dataset consists of,0 climate files for the United States (US) and Canada. The Canadian stations were updated more recently compared to the US stations. Additionally, only climate stations that have complete monthly data are available as a preset station in the Pavement-ME. A complete dataset for all monthly climate measurements is available for 0 climate stations in the US and Canada. The climate stations in the Pavement-ME do not cover all States and Provinces and only have a limited number of stations for a particular area. For example, Nebraska currently has no climate stations available in the Pavement-ME. In order to determine the current condition of the climate stations, the climate data were analyzed to determine the total number of years of available data. Figure a summarizes the range of climate data for all climate files currently available in the Pavement-ME. The results show that almost 00 of the,0 stations had climate data between and 0 years. About 00 stations had climate data ranging between 0 and years. Based on the results, it is observed that many of the climate stations have varying degrees of available hourly climate data. Inconsistent data range is a limitation that currently exists in Pavement-ME software. Additionally, each State has a certain amount of climate stations available. Figure b shows the frequency of climate stations for each State. Due to these limitations, the climate data needs an update to increase the number of data points.

5 Brink, Von Quintus, Osborne (a) Data Range for Climate files (b) Number of climate stations in each State FIGURE. Current climate dataset information. METHOD FOR UPDATING CLIMATE DATA North American Regional Reanalysis Database The second method consists of using the NARR climate database. The NARR is used primarily for atmospheric research requiring historical atmospheric conditions and to study the variability of climate conditions. The NARR was developed by the National Centers for Environmental Predictions to model or assimilate observational data to produce a long-term overview of weather over North America. The model is initialized by using real world temperature, winds, precipitation, and moisture conditions from surface observations. Many different sources were used to develop the NARR, some of these sources were also used in a global reanalysis. The focus of the NARR was to develop a more accurate reanalysis specific to North America. Additional sources were used to improve upon the global reanalysis. These sources include: National Centers for Environmental Prediction National Center for Atmospheric Research Global Reanalysis Climate Prediction Center National Environmental Satellite, Data, and Information Service Environmental Modeling Center Center for Ocean-Land-Atmosphere Studies Great Lakes Environmental Research Laboratory Lawrence Livermore National Laboratory Additional details for which datasets were used for each source can be found in the article titled North American Regional Reanalysis by Mesinger, et al.(). The NARR data is available for a x km (0 x 0 mi.) grid across North America. The data are available in -hour, daily, and monthly values from to present. The years of continuous data provides a consistent climate file for all stations currently available in the Pavement-ME. A longer timeframe of available climate data is a significant improvement over the ranges currently available in the Pavement-ME. The years of continuous data is significant because the climate of a location is defined based on the weather data from the previous 0 years. Therefore, the NARR data provides a more accurate representation of climate for any location across North America. The NARR dataset has already gone through several quality control checks and does not need further data smoothing or quality assurance and control. This is a large advantage given the amount of climate data needed for the Pavement-ME climate files. Additionally, a climate file can be generated for

6 Brink, Von Quintus, Osborne any latitude or longitude across North America since the NARR dataset is based on a grid system, which eliminates the use of a physical climate station that may not be close to the actual pavement location. It should be noted that the assimilation process in the NARR uses the available observed values in a particular x km grid. The number of available observed values and the topography can affect the assimilation results and impact the quality of the model for some locations. NARR Data Extraction The new HCD files were generated using the NARR database. The NARR database consists of three-hourly climate data across the entire North America. The hourly values were obtained by linearly interpolating between the three hourly values. The NARR data were available starting from and ending in 0. As more data becomes available, the new climate data can be easily added to the existing files. The NARR database consists of many different climate parameters. For the purpose of updating the current climate station data, only the parameters needed in the EICM were included. These parameters include: Temperature ( F) Wind speed (mph) Percent Sunshine (%) Precipitation (inch) Relative Humidity (%) The temperature, wind speed, precipitation and relative humidity are directly compatible with the parameters in the current climate files. Percent sunshine is slightly different. The current climate files report percent sunshine in percent increments. The NARR percent sunshine is measured in a continuous range between 0 and 00. The broader range will provide a better representation of actual conditions. The new HCD files were populated using the NARR database by identifying the latitude and longitude of the existing stations currently included in the Pavement-ME. The location details were obtained from the station.dat file which summarizes all the current stations. The closest NARR grid point to the existing weather station was selected because all stations are on a x km (0 x 0 mi.) grid. Thus, the maximum distance between an existing weather station and the closest NARR grid point is about km ( mi.). This maximum distance was considered close based on the average distance between the existing weather stations currently being used to create a virtual weather station for a specific project location. RESULTS Data Processing and Matching After all the HCD files were compiled and processed, the next step compared the climate data between the new and old HCD files. The current climate file data varies in length from station-to-station. As shown in Figure, large differences in age between available climate data were observed. However, the NARR dataset is consistent for all climate stations. Each station has years of hourly climate data. This does affect the file size of each HCD file, but is an improvement on the current set because most pavement designs are between 0 and 0 years. The new climate data avoids repeatition which is currently required when climate data are unavailable for the entire pavement design period. A complete comparison for the entire year NARR generated HCD file cannot be made since the existing data does not have any data for such a long period. Comparisons can only be made where dates and times are available in both climate files. There are a total of 0 climate stations in the current database, only stations did not have any matching dates between the new and existing HCD files. These sections had climate data starting and ending prior to. These climate stations will be replaced by the NARR generated HCD files.

7 Brink, Von Quintus, Osborne 0 0 Data Comparison An overall comparison between the current and new HCD files was performed to determine if the new HCD files were comparable with the current HCD files. The mean difference between the new and current climate data was calculated for each station. Theoretically, a mean difference of zero or close to zero indicates that there is no difference between the current and new HCD files. Differences will exist for most stations because of the differeces in data sources. If consistent differences between the two datasets are found, then recalibration of the transfer functions may be needed. If the average residual error for all stations are close to zero it would mean that the new HCD files are a sufficient replacement of the current HCD files and a recalibration may not be needed at this time. The mean residual error was calculated for all parameters. The mean temperature difference among all stations is 0. F with a standard deviation of. F. The mean wind speed difference is. mph with a standard deviation of. mph. The mean difference for percent sunshine is -. % with a standard deviation of. %. There is a larger difference between the current and new HCD files due to the way the parameter is measured. The mean difference for precipitation is 0 inches with a standard deviation of 0.0 inches, which suggest there is no difference between the two climate files. The mean difference for relative humidity was. percent with a standard deviation of. percent. There seemed to be a larger difference between the current and new HCD files for relative humidity. Another analysis was completed to determine the number of climate stations which had a residual error greater than ±, ± and ± F. The total number of stations were,, and 0 respectively out of a total number of 0 stations. Only % of the climate stations showed an error greater than ± F. The majority of stations with large errors were located in areas with significant elevation differences within short distances (Colorado, California). Therefore, the larger differences are expected since the NARR is an assimilation of various data sources in a grid pattern. The NARR gridpoint may not coincide perfectly with the original weather station. Based on these findings, it was determined that any station with a mean temperature difference greater than F should have multiple versions at different elevations. This will allow the user to select the most representative station for the location and elevation of the design project. Overall, the difference between the two climate files are not drastically different and the new files can be used to replace the current files.

8 Brink, Von Quintus, Osborne 0 FIGURE. Residual plot for climate data parameters. As a representative example, the comparison data summaries are presented for station located near Bradford, PA. The data analsys was performed to compare the old and new climate files. Figure shows the comparison between the old and new HCD files for each variable included in the climate station. The results show that the two files are comparable to each other. Wind speed and percent sunshine showed the largest difference for this particular station. The wind speed showed a similar range but with a larger magnitude for the new HCD file. The new climate file relative humidity data showed a smaller range compared to the old files. Overall, the two datasets compare well to each other.

9 Brink, Von Quintus, Osborne 0 FIGURE. Climate data boxplot comparisons between old and new HCD files for station. Time-series analysis Figures shows the time-series temperature data for climate station. Based on Figure a, the current temperature file showed very similar temperatures compared to the NARR generated temperature file. A more detailed visualization was performed to see the hourly trends for the climate station. The results shown in Figure b indicate that there is a good match between the two climate files. This particular station can directly replace the current climate file. There are slight differences between the two climate files. These differences are expected since the climate data is obtained from two different sources. The original climate files are populated using actual weather station measurement data whereas the NARR data is an assimilation of multiple weather sources for a particular area.

10 Brink, Von Quintus, Osborne 0 (a) Timeseries for matching data 0 (b) 0 day timeseries subset in FIGURE. 0 day time-series temperature data for climate station. Some climate stations had large differences between the two datasets. The climate station in Aspen, CO showed a large difference between the old and new climate station. Figure shows a timeseries graph of days in May. The old climate file shows very distinct linear interpolation beween the minimum and maximum temperature for a particular day. The old climate file is clearly not an accurate representation of the real daily temperature fluctuations and therefore caused a large error between the new and old HCD files.

11 Brink, Von Quintus, Osborne 0 0 FIGURE Time-series example of station 0; Aspen, CO PAVEMENT-ME PREDICTIONS The new NARR generated climate files were tested to compare the distress predictions for flexible and rigid pavements. The test was set up to include several locations throughout the United States. Many states were selected to ensure that all climate regions are represented. Figure shows the selected States. The climate stations for the following cities were compared:. Dallas, TX. Detroit, MI. Fargo, ND. Hartford, CT. Kansas City, MO. Los Angeles, CA. Miami, FL. Raleigh, NC. Seattle, WA Table shows the pavement structure for both flexible and rigid pavement used in the comparison. All input variables were held constant except for the climate files.

12 Brink, Von Quintus, Osborne TABLE. Basic Cross-Section Information Structural Layer AC PCC Pavement Thickness 0 Crushed gravel Base A- Subbase 0 0 A- Subgrade Semi-infinite Semi-infinite 0 FIGURE. States used for testing Pavement-ME Design predictions. Rigid Pavements The Pavement-ME predictions were extracted and compared. Figure shows the one-to-one comparison for transverse cracking, faulting and IRI. The IRI results show that there is a slight difference between the IRI predictions for the two datasets. Some differences were expected because the climate inputs have changed slightly, which affects some climate input calculations and distress predictions. Additionally, the new climate data does not repeat any data because years of continuous hourly data are available. Similar differences were observed for transverse cracking and faulting. The main purpose of the one-to-one comparisions were to determine if there is a systematic bias (over or underprediction). Based on the R and visual trends, there does not seem to be a systematic bias for the climate stations selected. Minimal transverse cracking was predicted for this particular structural design. The magnitude of percent slabs cracked is very low.

13 Brink, Von Quintus, Osborne (a) Transverse Cracking (b) Faulting (c) IRI FIGURE. One to one comparison for rigid pavement distresses. Flexible Pavements The flexible pavement distress predictions were extracted and compared. The IRI, rutting, and fatigue cracking distresses were compared and are shown in Figure. The results show that there are slight differences between the two climate files. The difference between the two datasets is less than observed for rigid pavements. These results are also expected since the thermal behavior of concrete is greater compared to asphalt pavements. Rutting showed the largest difference between the two climate files, as expected. A systematic bias was not observed for any of the flexible pavement distresses.

14 Brink, Von Quintus, Osborne (a) IRI (b) Rutting 0 (c) Fatigue cracking FIGURE. Flexible pavement comparisons between old and new climate data for all locations. SUMMARY Currently, the climate files available in the Pavement-ME vary in available hourly data. Some locations may have 0- years of data whereas others have less than years. Some of these climate station data also have missing values which requires additional interpolation to create a complete dataset. Therefore, the climate data was re-evaluated to improve upon the current limitations. The climate station data was repopulated using the NARR database. The temperature, wind speed, precipitation, percent sunshine and relative humidity data were updated for all the current climate stations available in the Pavement-ME. The NARR data are available from 0. All,0 climate stations were updated with -year hourly climate data from the NARR. Two comparisons were made to study the differences between the two climate data sets. The first comparison directly compares the hourly data between all five parameters. Overall, the matching timeseries data showed a very good comparison. The average mean difference across all,0 climate stations

15 Brink, Von Quintus, Osborne for the various parameters used is: Temperature: 0. F Wind speed:. mph Percent Sunshine: -.% Precipitation: 0 in. Relative Humidity:. % Additional analyses were performed to further investigate the stations which exhibited large differences between the existing and NARR generated climate files. The majority of the differences were seen in locations with highly variable elevations (i.e. Colorado). For these stations, the freezing index and mean temperature difference were compared as well as the differences in predicted transverse cracking and faulting. Based on the results, it was determined that any station with a mean temperature difference greater than F should have multiple versions at different elevations. This will allow the user to select the most representative station for the location of the design project. The second method compares the Pavement-ME performance predictions for various locations across the United States. The comparison was made between nine geographic locations across the United States for both flexible and rigid pavements. Based on the results, there are slight differences between the two datasets. These differences are expected due to the -year hourly data available for the new climate stations. Overall, the pavement performance prediction for both climate files was similar for both flexible and rigid pavements and a systematic bias was not observed. As a result, the updated climate data can be used to replace the current or existing climate data. REFERENCES. AASHTO. Mechanistic-Empirical Pavement Design Guide: A Manual of Practice: Interim Edition. American Association of State Highway and Transportation Officials, 00.. Schwartz, C. W., G. E. Elkens, R. Li, et al. Evaluation of LTPP Climatic Data for Use in Mechanistic-Empirical Pavement Design Guide Calibration and Other Pavement Analysis. Publication FHWA-HRT--0. Federal Highway Administration, McLean, Virginia, 0.. Mallela, J., L. Titus-Glover, S. Sadasivam, et al. Implementation of the AASHTO Mechanistic-Empirical Pavement Design Guide for Colorado. Colorado Department of Transportation, 0.. Mesinger, F., G. DiMego, E. Kalnay, et al. North American regional reanalysis. Bulletin of the American Meteorological Society, Vol., No., 00, pp. 0.