Evaluation of Long-Term Pavement Performance (LTPP) Climatic Data for Use in Mechanistic-Empirical Pavement Design Guide (MEPDG) Calibration and Other Pavement Analysis

executive summary

The Long-Term Pavement Performance (LTPP) Program has performed pioneering work to characterize and summarize site-specific climatic data for use in evaluating the performance of its General Pavement Studies (GPS) and Specific Pavement Studies (SPS) test sections. Improvements in these data are needed to support current and future research into climate effects on pavement materials, design, and performance. The calibration and enhancement of the Mechanistic-Empirical Pavement Design Guide (MEPDG) is just one example of these emerging needs.

The original objectives of this study were the following: (1) examine current and emerging needs in climate data collection for transportation infrastructure applications such as the MEPDG, Superpave binder specification, and bridge and other types of asset management models; (2)develop a methodology for incorporating temporal changes in position and measurement characteristics of operating weather stations (OWS) into the computation of climate indices; (3)apply this new methodology to update the climate statistics in the LTPP database; (4)examine the need for additional climate-soils parameters, such as the Thornthwaite Moisture Index (TMI) to the LTPP database; and (5) examine the need for continued location-specific solar radiation measurements to capture the effects of climate change on pavement and other infrastructure performance. However, during the project, the study team discovered a newly emerging source of weather data that resulted in a change of direction. This data source, the Modern-Era Retrospective Analysis for Research and Applications (MERRA), developed by the National Aeronautics and Space Administration (NASA) for its own in-house modeling needs, provides continuous hourly weather data starting in 1979 on a relatively fine-grained uniform grid. MERRA is based on a reanalysis model that combines computed model fields (e.g., atmospheric temperatures) with ground-, ocean-, atmospheric-, and satellite-based observations that are distributed irregularly in space and time. The result is a uniformly gridded dataset of meteorological data derived from a consistent modeling and analysis system over the entire data history. MERRA data are provided at an hourly temporal resolution and a 0.5 degrees latitude by 0.67 degrees longitude (approximately 31.1 mi by 37.3 mi at mid-latitudes) spatial resolution over the entire globe.

The direction of the project was therefore shifted to evaluating whether MERRA is a viable alternative to conventional ground-based climate data sources and whether it satisfied (or made moot) all of the original project objectives. MERRA data were compared against the best available ground-based observations both statistically and in terms of effects on pavement performance as predicted using the MEPDG. These analyses included a systematic quantitative evaluation of the sensitivity of MEPDG performance predictions to variations in fundamental climate parameters. Key conclusions from these investigations are summarized as follows:

Sensitivity of MEPDG performance predictions to fundamental climate parameters

• Average annual temperature and average annual temperature range were the most sensitive climate characteristics for both flexible and rigid pavements. Average daily temperature range also had a very significant influence on jointed plain concrete pavement (JPCP) slab cracking but almost no effect on flexible pavement performance. The sensitivity of JPCP slab cracking to percent sunshine was also very high.
• Percent sunshine and wind speed were moderately important climate characteristics for both flexible and rigid pavements.
• Precipitation had negligible influence on either flexible or rigid pavement performance. This was sensible given that the current version of the MEPDG does not include the effects of surface infiltration in its modeling of temperature and moisture within the pavement.
• Asphalt rutting, total rutting, and longitudinal cracking were the flexible pavement distresses that were most sensitive to climate characteristics.
• Slab cracking was the rigid pavement distress most sensitive to climate characteristics.

Comparisons of MERRA versus Automated Weather Station (AWS) and OWS weather data statistics

• There was generally good agreement in air temperature, precipitation, and relative humidity frequency distributions and statistics from all data sources.
• There was generally poorer agreement in percent sunshine and wind speed frequency distributions and statistics from the various data sources. These discrepancies may be the result of the methods used to infer some data elements (percent sunshine for AWS), unexplained anomalies in the recorded data (wind speed for OWS), discrete versus continuous recording of data (wind speed), and potentially inaccurate quantification of cloud cover conditions (wind speed for OWS).

Comparison of MEPDG distress predictions using MERRA versus AWS/OWS weather data

• About a third of the 12 sites analyzed exhibited generally good agreement between the MERRA- and AWS/OWS-based distress predictions.
• There were no systematic patterns in the discrepancies of MEPDG predicted distresses using AWS versus MERRA versus virtual weather station (VWS) versus OWS weather data sources. The results suggest that the match between MERRA and AWS data in overall terms is at least as good as the agreement between VWS/OWS and AWS data.
• Weak trends were observed between differences in MEPDG distress predictions using MERRA versus OWS weather inputs and site terrain and latitude. Good agreement of MEPDG distress predictions was more likely for flat terrain and northern sites while poorer agreement was more likely for varying/mountainous terrain and southern sites.
• Total incoming shortwave radiation, a key driver for pavement temperatures, must be determined indirectly from the OWS data using estimates of top-of-atmosphere incoming solar radiation, measured percent sunshine/cloud cover, and an empirical relation for atmospheric diffuse scattering and absorption. Total incoming shortwave radiation is provided explicitly in the MERRA data, but it cannot be used directly as an input in the current version of the MEPDG.
• Examination of the underlying formulation of the Enhanced Integrated Climate Model (EICM) in the MEPDG in the context of MERRA versus OWS weather inputs suggests that discrepancies in MEPDG predictions using the two data sources are related to differences in absorbed energy at the surface of the pavement, and more specifically, to differences in incoming shortwave solar radiation at the pavement surface from the two data sources.

These conclusions strongly support recommendation of MERRA as a source of climate data for LTPP and for weather inputs for the MEPDG and other infrastructure applications. MERRA data satisfy all of the major study objectives. They meet the climate data needs for current infrastructure applications such as the MEPDG, LTPPBind, HIPERPAV®, and bridge management. The broad range of MERRA data means that they will likely meet the climate data needs for future applications as well. The attention to quality and continuity in the MERRA data eliminates the need to deal with temporal changes in position and/or measurement details of OWS histories. The close and uniform spacing of MERRA grid points also eliminate the need for improved weather data interpolation and VWS. Lastly, MERRA makes moot the issue of continued location-specific solar radiation measurement, because MERRA provides this information directly at every grid point.

Initial evaluations of the MERRA data suggested that it is as good as, and in many ways superior to, weather data time series from conventional surface-based OWSs. The recommendations from these initial evaluations were that LTPP adopt MERRA as the data source for its next update to the climate data module and develop a tool to extract and use this data for engineering applications.

After review of the initial evaluations by the Transportation Research Board’s Expert Task Group (ETG) on LTPP Special Activities, Federal Highway Administration (FHWA) experts, and LTPP staff, two primary comments necessitate additional analysis with the following primary objectives:

1. More extensive analysis of MERRA data.
2. Development of a tool to disseminate MERRA data.

The more extensive analysis of MERRA included the following specific study activities:

• If possible, establish an appropriate ground truth for climate data.
• Perform statistical comparisons of ground truth, OWS, and MERRA.
• Evaluate the correctness of MEPDG surface shortwave radiation (SSR) calculations.
• Compare MEPDG pavement performance predictions using ground truth, OWS, and MERRA climate data.

A variety of data sources were examined in this phase of the study. Ground-based climate data provided as part of the MEPDG served as the standard input for flexible and rigid pavement simulations using the Pavement ME Design® software. Additional data sources employed for comparisons with the MEPDG climate files include the U.S. Climate Research Network (USCRN), the National Weather Service (NWS) Cooperative Observer Program (COOP), the Department of Energy Solar Infrared Radiation System (SIRS) stations, and NASA’s MERRA.

Statistical analyses were conducted comparing the different data sources relative to USCRN (i.e., USCRN treated as the reference measurement) for the approximately 17-year period of July1,1996, through September 1, 2013. This time period corresponds to the approximate temporal overlap of all of the available data sources used in this study. The emphasis of the statistical evaluation was on temperatures because prior studies had shown that pavement performance was most sensitive to these climate.^(1,2) Wind speed and cloud cover are the next most sensitive climate inputs; however, the USCRN data do not contain these data elements and consequently they could not be evaluated. Although the MEPDG in its current form assumes no infiltration of surface water into the pavement layers, precipitation data from various climate data products were nevertheless compared. Cloud cover, wind speed, and humidity were also compared to a lesser extent. Cloud cover is important primarily because of its impact on incoming SSR at the ground surface. Although SSR is not a direct input in the MEPDG, it is the principal driver for pavement heating and cooling. To evaluate the SSR issue, SIRS observations were used to supplement the USCRN SSR observations. Hence, the following meteorological analyses were conducted in-depth: (1) near-surface air temperatures, (2) precipitation at the ground surface, and (3) shortwave radiation at the ground surface.

The overall conclusions from the statistical comparisons of the various climate data sources can be summarized as follows:

• Although in concept the USCRN data are the closest thing to ground truth, it is the opinion of the project team that the concept of ground truth does not truly exist for climate data. Given the inevitable measurement errors and the spatial variability of weather data over even short distances, even two ground truth stations separated by only a few hundred meters will inevitably give slightly different climate data time series.
• The statistical comparisons of hourly data found that the Quality Controlled Local Climatological Data (QCLCD) and MERRA data have small and roughly comparable differences from the USCRN values. The MERRA data are slightly warmer on average than the QCLCD values, but only by less than 2 °F.
• The statistical comparisons of daily temperature data found that the COOP and MERRA data have small but roughly comparable differences from the USCRN values. The MERRA data are slightly warmer than the COOP values, but in most cases by less than 1° F.
• The comparisons for MEPDG surface shortwave calculations against predicted MERRA and measured SIRS values found that the bias was generally small in comparison to peak solar radiation values. However, the MEPDG values had higher positive bias and variability than MERRA during critical low-percent cloud cover conditions and hot summer months and lower positive bias during the less important late winter months. The project team recommends that the Pavement ME Design® Task Force explore the option of using SSR as a direct input rather than percent cloud cover.

Pavement performance as predicted by the MEDPG models incorporated in the Pavement ME Design® software was evaluated using the MEPDG weather data files provided with the software (derived from the QCLCD and Unedited Local Climatological Data products from the National Climate Data Center (NCDC)) and the MERRA climate data for collocated sites and congruent time series. A total of 20 sites were analyzed.

Both new flexible pavements and new JPCP were analyzed. The pavement structures, traffic loads, material properties, and other inputs for the analysis correspond to the medium traffic cases for the sensitivity analyses described in.⁽¹⁾ All analyses were performed using Version 2.0 of the Pavement ME Design® software.

Overall, the comparisons in MEPDG predicted performance for both flexible and rigid pavements using MERRA versus MEPDG weather data are close and acceptable for engineering design. Based on the earlier statistical comparisons among the various climate data sources, the agreement in predicted performance using MERRA versus USCRN ground truth and/or MEPDG versus USCRN would likely show similar agreement. However, it is impossible to demonstrate this agreement because the USCRN data lack the wind speed and cloud cover inputs required by the MEPDG software.

The results of the more extensive statistical and pavement performance comparisons reported here support the original recommendation that LTPP should adopt MERRA as a primary data source for its next update to the climate data module and develop a tool to extract and use this data for engineering applications.

MERRA offers the following benefits compared with conventional ground-based OWS data:

• Denser, more uniform, and broader spatial coverage. The network of first-order ground-based OWS provides data at approximately 1,000 locations in the contiguous United States. These locations are not distributed uniformly, and vast areas of the country have sparse or no coverage. MERRA data, by contrast, are currently available at more than 3,000 grid points in the contiguous United States. MERRA grid points are uniformly distributed at a horizontal spacing of approximately 31.1 by 37.3 mi; no point on the globe is more than 24.9 mi from the nearest MERRA grid point. This distance will become dramatically smaller when an enhanced MERRA having approximately 0.62 by 0.62 mi horizontal grid spacing is made available to the public within the next few years.
• Better temporal frequency and continuity. MERRA provides weather data at hourly time intervals as required by current state-of-the-art infrastructure modeling applications. Daily, monthly, and/or annual statistics are also available directly from MERRA or can be aggregated from the hourly data. There are no gaps in the MERRA histories as often appear in the AWS data. All MERRA data are consistently referenced to Greenwich Mean Time.
• Excellent data consistency and quality. To meet its in-house needs for modeling and satellite retrieval algorithms, NASA performs rigorous and sophisticated quality control (QC) checks to ensure that all MERRA data are consistent and correct, even as the mix of satellites and other sources of measurement data inevitably change across time and location.
• Focus on fundamental physical quantities. MERRA data include data elements that are much more relevant to the fundamental inputs required by thermodynamics-based infrastructure modeling than are available from the OWS data. For example, MERRA directly provides the shortwave radiation fluxes at the top of atmosphere and at the ground surface. In the MEPDG, these quantities are estimated using empirical and semi-empirical relationships that are functions of location, time, and percent sunshine category. Given that net shortwave radiation flux at the surface is the primary driver for pavement temperature distributions, the MERRA data are much more suitable. The ready availability of MERRA data will likely foster improvements to current infrastructure modeling applications such as the MEPDG.
• Richer and more versatile datasets. To meet NASA’s diverse modeling requirements, MERRA reports hundreds of data elements, although not all of these data elements are at the highest temporal and spatial resolutions. Many of these data elements may be useful to future infrastructure and other modeling applications.
• Potential for automated updates to LTPP database. The process of requesting MERRA data, downloading it from the server, extracting and processing the data elements relevant to LTPP needs, and importing these data into the LTPP database has the potential to be highly automated. This could enable more frequent updates to the climate (CLM) module at significantly less cost.
• Improvement over time. NASA is currently enhancing MERRA to an approximately 1km spatial resolution. This means that no location will be more than 2,297 ft from the nearest MERRA grid point. Significant improvement in conventional ground-based OWS coverage is very unlikely.
• Reliability analysis capabilities. MERRA is only, albeit the most comprehensive, retrospective reanalysis system available. Others have been developed in Europe, Japan, and elsewhere. These various modeling applications could be applied simultaneously to develop ensembles of weather histories that can be characterized statistically for rationally quantifying the uncertainty of predicted infrastructure performance caused by the weather inputs.

MERRA offers many benefits and very few if any significant limitations for use as the source of climate data for transportation infrastructure modeling applications. Therefore, it is recommended that MERRA be the future source of climate data in LTPP. Guidelines are provided for incorporating hourly MERRA data into the LTPP database. Topics addressed include unit conversions, elimination of VWS, new data elements, new table designs, nomenclature, data storage, and data release policies. Recommendations are also made for archiving of data in the current LTPP CLM module.

Data used in this effort were acquired as part of the activities of NASA’s Science Mission Directorate and are archived and distributed by the Goddard Earth Sciences Data and Information Services Center.