Evaluation of LTPP Climatic Data for 

Use in Mechanistic-Empirical Pavement 

Design Guide Calibration and Other 

Pavement Analysis

PUBLICATION NO. FHWA-HRT-15-019 MAY 2015

Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296
FOREWORD

This document presents the results of an evaluation of climate data from Modern-Era Retrospective 

Analysis for Research and Applications (MERRA) for use in the Long Term Pavement

Performance (LTPP) Program and for other infrastructure applications. 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 Mechanistic-Empirical Pavement Design

Guide (MEPDG). These analyses included a systematic quantitative evaluation of the sensitivity 

of MEPDG performance predictions to variations in fundamental climate parameters.

A more extensive analysis of MERRA data included additional statistical analysis comparing

operating weather station (OWS) and MERRA data, evaluation of the correctness of MEPDG 

surface shortwave radiation (SSR) calculations and comparison of MEPDG pavement 

performance predictions using OWS and MERRA climate data for more sections. The principal 

conclusion from these evaluations was that the MERRA climate data were as good as and in many 

cases substantially better than equivalent ground-based OWSs. MERRA is strongly recommended 

as the new future source for climate data in LTPP. Recommendations are provided for 

incorporating hourly MERRA data into the LTPP database. 

The LTPP program is an ongoing and active program. To obtain current information and access to

other technical references, LTPP data users should visit the LTPP Web site at 

http://www.tfhrc.gov/pavement/ltpp/ltpp.htm. LTPP data requests, technical questions, and data

user feedback can be submitted to LTPP customer service via e-mail at ltppinfo@fhwa.dot.gov.

Jorge E. Pagán-Ortiz

Director, Office of Infrastructure

Research and Development

Notice

This document is disseminated under the sponsorship of the U.S. Department of Transportation 

in the interest of information exchange. The U.S. Government assumes no liability for the use 

of the information contained in this document. This report does not constitute a standard, 

specification, or regulation. 

The U.S. Government does not endorse products or manufacturers. Trademarks or 

manufacturers’ names appear in this report only because they are considered essential to the 

objective of the document. 

Quality Assurance Statement

The Federal Highway Administration (FHWA) provides high-quality information to serve 

Government, industry, and the public in a manner that promotes public understanding. Standards 

and policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its 

information. FHWA periodically reviews quality issues and adjusts its programs and processes to 

ensure continuous quality improvement. 
TECHNICAL DOCUMENTATION PAGE

1. Report No.

FHWA-HRT-15-019

2. Government Accession No. 3. Recipient’s Catalog No.

4. Title and Subtitle

Evaluation of Long-Term Pavement Performance (LTTP) Climatic Data for 

Use in Mechanistic-Empirical Pavement Design Guide(MEPDG) Calibration 

and Other Pavement Analysis

5. Report Date

May 2015

6. Performing Organization Code

7. Author(s)

Charles W. Schwartz, Gary E. Elkins, Rui Li, Beth A. Visintine, Barton

Forman, Gonzalo R. Rada, Jonathan L. Groeger 

8. Performing Organization Report No.

9. Performing Organization Name and Address

AMEC Environment and Infrastructure, Inc.

12000 Indian Creek Court, Suite F

Beltsville, MD 20705-1242

University of Maryland—College Park 

Department of Civil and Environmental Engineering

1173 Glenn L. Martin Hall

College Park, MD 20742

10. Work Unit No. (TRAIS)

11. Contract or Grant No.

DTFH61-11-C-00030

12. Sponsoring Agency Name and Address

Office of Infrastructure Research and Development

Federal Highway Administration

6300 Georgetown Pike

McLean, VA 22101-2296

13. Type of Report and Period Covered

Final Report, May 2011–October 2014

14. Sponsoring Agency Code

15. Supplementary Notes

The Contracting Officer’s Representative (COR) was Larry Wiser.

16. Abstract

Improvements in the Long-Term Pavement Performance (LTPP) Program’s climate 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. A newly emerging 

climate 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. MERRA data are available at an hourly temporal resolution and 0.5 degrees

latitude by 0.67 degrees longitude (approximately 31.1 by 37.30 mi at mid-latitudes) spatial resolution over the entire globe. 

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. More extensive analysis of 

MERRA data included additional statistical analysis comparing operating weather station (OWS) and MERRA data, 

evaluation of the correctness of MEPDG surface shortwave radiation (SSR) calculations and comparison of MEPDG 

pavement performance predictions using OWS and MERRA climate data for more sections. The principal conclusion from 

these evaluations was that the MERRA climate data were as good and in many cases substantially better than equivalent 

ground-based OWS data. Given these many benefits and very few if any significant limitations, MERRA is strongly 

recommended as the new future source for climate data in LTPP. Recommendations are provided for incorporating hourly 

MERRA data into the LTPP database.

17. Key Words

Long-Term Pavement Performance, LTPP, climate data, pavement

design, pavement performance, solar radiation, virtual weather stations, 

Modern-Era Retrospective Analysis for Research and Applications, 

MERRA, Mechanistic Empirical Pavement Design Guide, MEPDG, 

hourly weather data

18. Distribution Statement

No restrictions. This document is available to 

the public through the National Technical 

Information Service, Springfield, VA 22161.

http://www.ntis.gov

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages

138

22. Price

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized
ii
TABLE OF CONTENTS

EXECUTIVE SUMMARY ...........................................................................................................1

CHAPTER 1. INTRODUCTION.................................................................................................9

STUDY BACKGROUND AND OBJECTIVES....................................................................9

TERMINOLOGY ....................................................................................................................9

CHANGE IN PROJECT WORK PLAN.............................................................................10

MORE EXTENSIVE ANALYSIS OF MERRA DATA.....................................................10

REPORT ORGANIZATION................................................................................................11

CHAPTER 2. LEGACY LTPP APPROACH TO CLIMATE DATA....................................13

VIRTUAL WEATHER STATION CONCEPT..................................................................13

DATA PROCESSING AND STORAGE.............................................................................15

ISSUES WITH LEGACY LTPP CLIMATE DATA APPROACH..................................19

CHAPTER 3. CLIMATE DATA APPLICATIONS AND SOURCE CANDIDATES..........23

INFRASTRUCTURE APPLICATIONS.............................................................................23

Empirical Pavement Design...............................................................................................23

Mechanistic-Empirical Pavement Design..........................................................................23

Superpave Binder Specification.........................................................................................25

HIPERPAV®.....................................................................................................................25

Bridge Management...........................................................................................................26

Summary of Existing Applications....................................................................................26

CONVENTIONAL SOURCES OF CLIMATIC DATA....................................................27

Automated Surface Observing System (ASOS) ................................................................29

Road Weather Information Systems (RWIS) ....................................................................29

Solar Radiation Data ..........................................................................................................29

MERRA ..................................................................................................................................31

CHAPTER 4. MODERN ERA RETROSPECTIVE-ANALYSIS FOR RESEARCH AND 

APPLICATIONS (MERRA).......................................................................................................33

CHAPTER 5. EVALUATION OF MERRA FOR USE IN PAVEMENT AND OTHER

INFRASTRUCTURE APPLICATIONS...................................................................................37

INTRODUCTION..................................................................................................................37

SENSITIVITY OF MEPDG PERFORMANCE PREDICTIONS TO CLIMATE 

PARAMETERS......................................................................................................................37

Sensitivity Analysis Methodology.....................................................................................38

Sensitivity Metrics.............................................................................................................38

Base Cases .........................................................................................................................39

Weather Data Inputs ..........................................................................................................41

Results and Interpretation ..................................................................................................43

Sensitivity Analysis Conclusions.......................................................................................47

STATISTICAL COMPARISON OF MERRA VERSUS AWS/OWS WEATHER 

DATA ......................................................................................................................................48

MEPDG OWS....................................................................................................................48

LTPP AWSs.......................................................................................................................49

MERRA .............................................................................................................................49

Data Quality Checks..........................................................................................................50

iii
Weather Sites for the Study ...............................................................................................51

Statistical Comparisons......................................................................................................52

Statistical Comparison Conclusions ..................................................................................67

COMPARISON OF MEPDG DISTRESS PREDICTIONS USING MERRA 

VERSUS OWS WEATHER DATA .....................................................................................67

Enhanced Integrated Climate Model .................................................................................67

MEPDG Prediction Results...............................................................................................68

Temperature .......................................................................................................................71

Wind Speed........................................................................................................................71

Percent Sunshine................................................................................................................72

Combined Effects of Wind Speed and Percent Sunshine ..................................................74

CHAPTER 6. ADDITIONAL MERRA DATA VALIDATION..............................................83

BACKGROUND AND OBJECTIVES ................................................................................83

CLIMATE DATA SOURCES ..............................................................................................83

MEPDG..............................................................................................................................83

COOP.................................................................................................................................85

SIRS ...................................................................................................................................85

MERRA .............................................................................................................................85

Measurement Product Collocation.....................................................................................85

Elevation and Temperature Correction..............................................................................88

Statistical Comparisons of Data Sources...........................................................................89

Comparisons of USCRN, QCLCD, and MERRA Hourly Data ........................................89

Comparisons of USCRN, COOP, and MERRA Daily Temperatures...............................94

Comparisons of Hourly SIRS, QCLCD, and MERRA SSR..............................................98

Conclusions From Statistical Comparisons.....................................................................102

COMPARISONS OF PREDICTED PAVEMENT PERFORMANCE..........................104

RECOMMENDATIONS.....................................................................................................109

CHAPTER 7. RECOMMENDATIONS ..................................................................................111

SUMMARY OF RECOMMENDATIONS........................................................................111

BENEFITS OF USING MERRA DATA ...........................................................................111

CLIMATE INDICES...........................................................................................................116

INCORPORATING MERRA DATA INTO THE LTPP DATABASE .........................116

REFERENCES...........................................................................................................................121

iv
LIST OF FIGURES

Figure 1. Illustration. Illustration of VWS concept .......................................................................16

Figure 2. Equation. Gravity model equation..................................................................................16

Figure 3. Illustration. LTPP parsing, QC checks, and data flow relationships among the 

CLM_OWS* tables obtained from external data sources..................................................17

Figure 4. Illustration. LTPP CLM VWS computational relationship structure.............................18

Figure 5. Graph. Percentage of OWSs represented in each daily VWS statistic by number of 

OWSs. ................................................................................................................................21

Figure 6. Map. U.S. Climate Research Network stations. .............................................................28

Figure 7. Map. Map of MERRA grid points over the continental United States where each 

grid point is approximately 31.1 by 37.3 mi at mid-latitudes............................................34

Figure 8. Map. Spatial distribution and density of first-order ASOS stations over the 

continental United States. ..................................................................................................34

Figure 9. Illustration. Schematic of the IAU procedure in MERRA (from Rienecker et al.) 

shown in Greenwich Mean Time.......................................................................................35

Figure 10. Equation. Design limit normalized sensitivity index. ..................................................39

Figure 11. Graph. Sensitivity analysis results for flexible pavements...........................................45

Figure 12. Graph. Sensitivity analysis results for rigid pavements ...............................................46

Figure 13. Equation. Shortwave radiation regression equation .....................................................49

Figure 14. Graph. Data quality error distribution for OWS climate files in MEPDG database. ...51

Figure 15. Map. Locations of ground-based weather stations investigated in this study ..............52

Figure 16. Graph. Site M93817 (Evansville, IN) with good agreement in MEPDG predicted 

distresses: distributions of hourly temperature. .................................................................54

Figure 17. Graph. Site M93817 (Evansville, IN) with good agreement in MEPDG predicted 

distresses: distributions of hourly wind speeds..................................................................54

Figure 18. Graph. Site M93817 (Evansville, IN) with good agreement in MEPDG predicted 

distresses: distributions of hourly percent sunshine values ...............................................55

Figure 19. Graph. Site M93817 (Evansville, IN) with good agreement in MEPDG predicted 

distresses: distributions of hourly precipitation. ................................................................55

Figure 20. Graph. Site M93817 (Evansville, IN) with good agreement in MEPDG predicted 

distresses: distributions of hourly relative humidity values...............................................56

Figure 21. Graph. Site M93817 (Evansville, IN) with good agreement in MEPDG predicted 

distresses: hourly temperature means and standard deviations..........................................56

Figure 22. Graph. Site M93817 (Evansville, IN) with good agreement in MEPDG predicted 

distresses: hourly wind speed means and standard deviations...........................................57

Figure 23. Graph. Site M93817 (Evansville, IN) with good agreement in MEPDG predicted 

distresses: hourly percent sunshine means and standard deviations..................................57

Figure 24. Graph. Site M93817 (Evansville, IN) with good agreement in MEPDG predicted 

distresses: hourly precipitation means and standard deviations ........................................58

Figure 25. Graph. Site M93817 (Evansville, IN) with good agreement in MEPDG predicted 

distresses: hourly relative humidity means and standard deviations. ................................58

Figure 26. Graph. Site M13957 (Shreveport, LA) with poor agreement in MEPDG predicted 

distresses: distributions of hourly temperatures.................................................................59

Figure 27. Graph. Site M13957 (Shreveport, LA) with poor agreement in MEPDG predicted 

distresses: distributions of hourly wind speeds..................................................................59

v
Figure 28. Graph. Site M13957 (Shreveport, LA) with poor agreement in MEPDG predicted 

distresses: distributions of hourly percent sunshine values ...............................................60

Figure 29. Graph. Site M13957 (Shreveport, LA) with poor agreement in MEPDG predicted 

distresses: distributions of hourly precipitation. ................................................................60

Figure 30. Graph. Site M13957 (Shreveport, LA) with poor agreement in MEPDG predicted 

distresses: distributions of hourly relative humidity..........................................................61

Figure 31. Graph. Site M13957 (Shreveport, LA) with poor agreement in MEPDG predicted 

distresses: hourly temperature means and standard deviations..........................................61

Figure 32. Graph. Site M13957 (Shreveport, LA) with poor agreement in MEPDG predicted 

distresses: hourly wind speed means and standard deviations...........................................62

Figure 33. Graph. Site M13957 (Shreveport, LA) with poor agreement in MEPDG predicted 

distresses: hourly percent sunshine means and standard deviations..................................62

Figure 34. Graph. Site M13957 (Shreveport, LA) with poor agreement in MEPDG predicted 

distresses: hourly precipitation means and standard deviations ........................................63

Figure 35. Graph. Site M13957 (Shreveport, LA) with poor agreement in MEPDG predicted 

distresses: hourly relative humidity means and standard deviations. ................................63

Figure 36. Graph. Comparison of key weather statistics: differences among MERRA versus 

AWS versus VWS versus OWS datasets...........................................................................66

Figure 37. Graph. Influence of site terrain and latitude on agreement of MEPDG distress 

predictions using MERRA versus OWS weather inputs...................................................66

Figure 38. Equation. Design limit normalized difference..............................................................69

Figure 39. Graph. Summary of analysis results.............................................................................70

Figure 40. Graph. DLND in predicted asphalt concrete rut depth (ACRD) versus mean 

hourly wind speed difference.............................................................................................72

Figure 41. Graph. DLND in predicted slab cracking versus mean hourly wind speed 

difference. ..........................................................................................................................72

Figure 42. Graph. DLND in predicted ACRD versus mean hourly percent sunshine 

difference. ..........................................................................................................................73

Figure 43. Graph. DLND in predicted slab cracking versus mean hourly percent sunshine 

difference. ..........................................................................................................................73

Figure 44. Equation. Net heat flux available for absorption..........................................................74

Figure 45. Equation. Shortwave radiation regression equation .....................................................74

Figure 46. Equation. Shortwave radiation heat flux from OWS data............................................75

Figure 47. Equation. Shortwave radiation heat flux from MERRA data.......................................75

Figure 48. Equation. Incoming longwave radiation heat flux. ......................................................75

Figure 49. Equation. Outgoing longwave radiation heat flux........................................................75

Figure 50. Equation. Uncorrected incoming longwave radiation heat flux...................................76

Figure 51. Equation. Uncorrected outgoing longwave radiation heat loss....................................76

Figure 52. Equation. Convective heat loss.....................................................................................76

Figure 53. Equation. Convection heat transfer coefficient. ...........................................................77

Figure 54. Graph. Absorbed energy density versus time during a typical day at site M93817 

(Evansville, IN)..................................................................................................................78

Figure 55. Graph. Mean and standard deviation of daily absorbed energy density at site 

M93817 (Evansville, IN). ..................................................................................................78

Figure 56. Graph. Distribution of daily absorbed energy density at site M93817 

(Evansville, IN)..................................................................................................................78

vi
Figure 57. Graph. Absorbed energy density versus time during a typical day at site M13957 

(Shreveport, LA)................................................................................................................79

Figure 58. Graph. Mean and standard deviation of daily absorbed energy density at 

site M13957 (Shreveport, LA)...........................................................................................79

Figure 59. Graph. Distribution of daily absorbed energy density at site M13957 

(Shreveport, LA)................................................................................................................79

Figure 60. Graph. DLND in predicted ACRD versus absorbed energy differences......................80

Figure 61. Graph. DLND in predicted slab cracking versus absorbed energy differences. ..........80

Figure 62. Histogram. Histogram of separation distances (in mi) relative to QCLCD for 

MEPDG stations................................................................................................................86

Figure 63. Histogram. Histogram of separation distances (in mi) relative to QCLCD for 

USCRN stations.................................................................................................................87

Figure 64. Histogram. Histogram of separation distances (in mi) relative to QCLCD for 

MERRA grid points. ..........................................................................................................87

Figure 65. Map. Collocated USCRN, QCLCD, and MERRA data sets........................................88

Figure 66. Equation. Adiabatic lapse rate temperature correction equation..................................88

Figure 67. Graph. Typical comparison of diurnal temperature variations for MERRA versus 

MEPDG (QCLCD) data.....................................................................................................90

Figure 68. Graph. Typical comparison of hourly temperature frequency distributions for 

MERRA versus MEPDG (QCLCD) data. .........................................................................90

Figure 69. Graph. Frequency distribution of bias for QCLCD versus USCRN and MERRA 

versus USCRN hourly temperature values for a single site (outside Chattanooga, 

TN)—typical results...........................................................................................................91

Figure 70. Graph. Frequency distribution of bias for QCLCD versus USCRN and MERRA 

versus USCRN hourly temperature values for a single site (near Jacksonville, FL)—

worst-case results...............................................................................................................91

Figure 71. Graph. Frequency distribution of average hourly temperature bias across all sites 

for QCLCD versus USCRN and MERRA versus USCRN climate data...........................92

Figure 72. Graph. Frequency distribution of RMSE of QCLCD versus USCRN and MERRA 

versus USCRN hourly temperature values across all sites................................................93

Figure 73. Graph. Frequency distribution of average hourly precipitation bias across all sites 

for QCLCD versus USCRN and MERRA versus USCRN climate data...........................94

Figure 74. Graph. Frequency distribution of RMSE of QCLCD versus USCRN and MERRA 

versus USCRN hourly precipitation rates across all sites..................................................94

Figure 75. Map. Collocated USCRN, COOP, and MERRA data sets...........................................95

Figure 76. Graph. Frequency distribution of daily mean temperature bias across all sites for 

COOP versus USCRN and MERRA versus USCRN climate data. ..................................96

Figure 77. Graph. Frequency distribution of RMSE of COOP versus USCRN and MERRA 

versus USCRN daily mean temperature values across all sites.........................................96

Figure 78. Graph. Frequency distribution of daily minimum temperature bias across all sites 

for COOP versus USCRN and MERRA versus USCRN climate data..............................97

Figure 79. Graph. Frequency distribution of RMSE of COOP versus USCRN and MERRA 

versus USCRN daily minimum temperature values across all sites..................................97

Figure 80. Graph. Frequency distribution of daily maximum temperature bias across all sites 

for COOP versus USCRN and MERRA versus USCRN climate data..............................98

vii
Figure 81. Graph. Frequency distribution of RMSE of COOP versus USCRN and MERRA 

versus USCRN daily maximum temperature values across all sites. ................................98

Figure 82. Equation. Downwelling shortwave radiation. ..............................................................99

Figure 83. Graph. Frequency distribution of SSR bias across all sites for QCLCD versus 

USCRN and MERRA versus USCRN climate data. .......................................................100

Figure 84. Graph. Frequency distribution of RMSE of QCLCD versus USCRN and MERRA 

versus USCRN SSR values across all sites......................................................................100

Figure 85. Graph. SSR bias as a function of cloud cover for QCLCD versus SIRS and 

MERRA versus SIRS climate data ..................................................................................101

Figure 86. Graph. SSR RMSE as a function of cloud cover for QCLCD versus SIRS and 

MERRA versus SIRS climate data. .................................................................................101

Figure 87. Graph. SSR bias as a function of season for QCLCD versus SIRS and MERRA 

versus SIRS climate data. ................................................................................................102

Figure 88. Graph. SSR RMSE as a function of season for QCLCD versus SIRS and 

MERRA versus SIRS climate data ..................................................................................102

Figure 89. Map. Sites used for evaluation of MEPDG performance predictions using 

MEPDG and MERRA climate data .................................................................................104

Figure 90. Graph. Comparison of MEPDG total rutting predictions (inches) using MERRA 

versus MEPDG weather data ...........................................................................................105

Figure 91. Graph. Comparison of MEPDG AC rutting predictions (inches) using MERRA 

versus MEPDG weather data ...........................................................................................105

Figure 92. Graph. Comparison of MEPDG alligator fatigue cracking predictions using 

MERRA versus MEPDG weather data. ...........................................................................106

Figure 93. Graph. Comparison of MEPDG top-down fatigue cracking predictions (ft/mi) 

using MERRA versus MEPDG weather data. .................................................................106

Figure 94. Graph. Comparison of MEPDG flexible pavement IRI predictions (inches/mi) 

using MERRA versus MEPDG weather data. .................................................................107

Figure 95. Graph. Comparison of MEPDG JPCP transverse cracking predictions using 

MERRA versus MEPDG weather data. ...........................................................................107

Figure 96. Graph. Comparison of MEPDG JPCP joint faulting predictions (inches) using 

MERRA versus MEPDG weather data. ...........................................................................108

Figure 97. Graph. Comparison of MEPDG rigid pavement IRI predictions (inches/mi) using 

MERRA versus MEPDG weather data. ...........................................................................108

Figure 98. Map. LTPP climate zone map ....................................................................................115

Figure 99. Map. Example of more realistic climate zone map ....................................................115

Figure 100. Flowchart. Conceptual computational and data storage structure for 

MERRA-based LTPP climate data ..................................................................................119

viii
LIST OF TABLES

Table 1. Derived climatic data statistics and computed indices stored in the LTPP database ......19

Table 2. MERRA data elements available to develop MEPDG weather history inputs................35

Table 3. Examples of other MERRA data elements of potential interest for transportation 

infrastructure applications..................................................................................................36

Table 4. Climate categories for base cases. ...................................................................................39

Table 5. Traffic and pavement layer thickness for base cases.......................................................40

Table 6. Pavement design properties for base cases......................................................................40

Table 7. Surface layer properties for base cases............................................................................41

Table 8. Details of weather stations in MEPDG............................................................................42

Table 9. Climate property inputs and treatments...........................................................................42

Table 10. Details of ground-based weather stations. .....................................................................53

Table 11. ASOS sky condition categories. ....................................................................................64

Table 12. Variables employed from each measurement product for use during analysis .............84

Table 13. Summary of statistical comparisons of QCLCD versus USCRN and MERRA 

versus USCRN hourly climate data .................................................................................103

Table 14. Summary of statistical comparisons of COOP versus USCRN and MERRA 

versus USCRN daily climate data....................................................................................103

ix
LIST OF ACRONYMS AND SYMBOLS

AADTT Average Annual Daily Truck Traffic

AASHTO American Association of State Highway and Transportation Officials

AC Asphalt Concrete

AR Asphalt Concrete Rutting

ASOS Automated Surface Observing System

AWS Automated Weather Station

CCC Canadian Centre for Climate

CLM Climate module (LTPP environment/climate data module)

CNCDIA Canadian National Climate Data and Information Archive

COOP Cooperative Observer Program

CRCP Continuously Reinforced Concrete Pavement 

DLND Design Limit Normalized Difference

DST Daylight Savings Time 

EICM Enhanced Integrated Climate Model 

ESS Environmental Sensor Station 

ETG Expert Task Group

F Faulting

FAA Federal Aviation Administration

FHWA Federal Highway Administration

GEOS-5 Goddard Earth Observation System Version 5

GOES Geostationary Operational Environmental Satellites

GPS General Pavement Studies

GPSR Global Position Satellite Receiver

GSI Grid-Point Statistical Interpolation

HDF Hierarchical Data Format

IAU Incremental Analysis Update

IRI International Roughness Index

JPCP Jointed Plain Concrete Pavement

LC Longitudinal Cracking

LTBP Long-Term Bridge Performance

LTPP Long-Term Pavement Performance

MDISC Modeling and Assimilation Data and Information Services Center

MEPDG Mechanistic-Empirical Pavement Design Guide

MERRA Modern-Era Retrospective Analysis for Research and Application

METAR Météorologique Aviation Régulière

NASA National Aeronautics and Space Administration

NCDC National Climate Data Center

NCHRP National Cooperative Highway Research Program

NetCDF Network Common Data Format 

NOAA National Oceanic and Atmospheric Administration 

NSI Normalized Sensitivity Index

NSRDB National Solar Radiation Data Base

NWS National Weather Service

OAT One at a Time

OWS Operating Weather Station

x
PCC Portland Cement Concrete

PG Performance Grade

PPDB Pavement Performance Database

QA Quality Assurance

QC Quality Control

QCLCD Quality Controlled Local Climatological Data

RCC Regional Climatic Center

RMSE Root Mean Squared Error

RWIS Road Weather Information System

SC Slab Cracking

SDR Standard Data Release

SIRS Solar Infrared Radiation System

SMP Seasonal Monitoring Program

SPS Specific Pavement Studies

SSR Surface Shortwave Radiation

TMI Thornthwaite Moisture Index

TR Total Rutting

ULCD Unedited Local Climatological Data

UMD University of Maryland

USCRN U.S. Climate Research Network

VWS Virtual Weather Station

xi

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.

1
• 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

2
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.

3
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 

July 1, 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 

4
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

5
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 1 km 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 

6
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.

7

CHAPTER 1. INTRODUCTION

STUDY BACKGROUND AND OBJECTIVES

The importance of climate to the performance of pavements and many other transportation 

infrastructure assets is beyond debate. The Long-Term Pavement Performance (LTPP) Program 

has performed pioneering work to characterize and summarize site-specific climatic data for its 

General Pavement Studies (GPS) and Specific Pavement Studies (SPS) test sections. However, 

improvements in climatic data collection are needed to support current and future research on 

climate effects pertaining to 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.(3)

To address these needs, the study had the following original objectives:

• Examine current and emerging needs in climate data collection and engineering 

indices for use in MEPDG calibration, changes in Superpave binder performance 

grading, and development of future mechanistic-based infrastructure management 

applications, including pavement, bridge, and other types of asset management 

models.

• Develop a methodology for characterizing location-specific historical climate indices 

that includes temporal changes in the position and measurement characteristics of the 

operating weather stations (OWS) used for the computation. This new methodology 

will include an estimate of the variability or uncertainty caused by the spatial 

averaging process used to develop the baseline indices.

• Provide recommendations to update the climate statistics in the LTPP database.

• Examine the need to add a climate-soils parameter such as the Thornthwaite Moisture 

Index (TMI) to the LTPP database. Examine the applicability of TMI to other 

transportation infrastructure applications.

• Examine the need for continued location-specific solar radiation measurements to 

capture the effect of climate change on pavement and other infrastructure 

performance. Determine whether existing data sources can be used to fulfill this need.

TERMINOLOGY

Some clarification of terminology is appropriate here. The National Oceanic and Atmospheric 

Administration (NOAA) provides the following definitions:

“Weather refers to [atmospheric] conditions at a given point in time (e.g., today’s high 

temperature), whereas Climate refers to the “average” weather conditions for an area over a long 

period of time (e.g., the average high temperature for today’s date).”(4)

9
The Intergovernmental Panel on Climate Change provides the following even more specific

definition:

“Climate in a narrow sense is usually defined as the “average weather,” or more rigorously, as 

the statistical description in terms of the mean and variability of relevant quantities over a period 

ranging from months to thousands or millions of years. The classical period is 30 years, as 

defined by the World Meteorological Organization (WMO). These quantities are most often 

surface variables such as temperature, precipitation, and wind. Climate in a wider sense is the 

state, including a statistical description, of the climate system.”(5)

The atmospheric data stored in the LTPP database are a combination of “weather,” which is 

currently the daily extremes, and “climate” statistics summarized at the monthly and annual 

level. To minimize potential confusion, this report uses the general term “climate” to represent 

the larger subject area, but may also use weather data to describe specific short-term 

observations or measurements. 

CHANGE IN PROJECT WORK PLAN

During the execution of Phase 2 of the project, the study team discovered a newly emerging 

source of weather data that resulted in a change of direction in the project work plan. This 

discovery is credited to the University of Maryland (UMD) members of the research project 

team. A UMD Civil Engineering faculty member advised the study team of the existence of a 

new source of weather and climate data based on his recent work at the Goddard Space Flight 

Center in Greenbelt, MD. The name of the data source is Modern-Era Retrospective Analysis for 

Research and Applications (MERRA). The primary attribute of this data source that motivated 

the project direction change is the availability of continuous hourly weather data starting in 1979 

on a relatively fine-grained uniform grid. MERRA also contains more fundamental scientific 

data elements than are available from any of the other data sources identified in phase 1 of this 

project. If MERRA data proved to be a viable alternative, then all of the project objectives could 

be satisfied. Chapter 4 of this report provides more details and information on MERRA data.

The significant change in the phase 2 work plan was to evaluate MERRA data against available 

ground-based observations, both in statistical terms and using the MEPDG. The MEPDG, 

developed under National Cooperative Highway Research Program (NCHRP) Project 1-37A and 

recently officially adopted by the American Association of State Highway and Transportation 

Officials (AASHTO), was selected because it contains the most advanced models on pavementclimate interaction that have ever been officially adopted by AASHTO.(6,3) The MEPDG is also a 

major focus of the objectives of the present study. Chapter 5 documents the findings of the 

comparisons between MERRA and best available ground-based weather data sources with 

significant time coverage. 

MORE EXTENSIVE ANALYSIS OF MERRA DATA

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 

10
the Climate (CLM) 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.

REPORT ORGANIZATION

To provide a complete overview of the status of climate data in the LTPP program, chapter 2 

contains a summary of the legacy LTPP approach to climate data in current use. While some of 

this information is available in other LTPP documents, some new information is presented here 

that provides a context to evaluate MERRA data against the legacy approach, not only as used by 

LTPP, but also for other infrastructure applications. 

Chapter 3 provides a summary of infrastructure climate data needs and candidate data sources 

reviewed by the study team. While most of this information was contained in the phase 1 report, 

it is being repeated in this document for reviewer convenience.

Chapter 4 contains a description of the MERRA product. This includes a conceptual description 

of the modeling approach, the data used and produced by MERRA, and comments on quality 

control (QC) procedures. 

Chapter 5 presents the findings of the evaluation of MERRA data against existing ground-based 

observations both statistically and using the MEPDG models. The analyses include a systematic 

quantitative evaluation of the sensitivity of MEPDG predicted pavement distresses to climate 

inputs, a statistical comparison of MERRA versus ground-based weather history inputs, and a 

critical comparison of MEPDG predicted pavement distresses using MERRA versus groundbased weather history data. 

Chapter 6 presents the findings of the more extensive analysis of MERRA data, including 

additional statistical analysis comparing OWS and MERRA data, evaluation of the correctness of 

11
MEPDG SSR calculations, and comparison of MEPDG pavement performance predictions using 

OWS and MERRA climate data for more sections. 

Chapter 7 presents the recommendations and findings of this research effort. These include the 

benefits of MERRA data, recommendations on inclusion of legacy climate indices, and initial 

concepts for implementation of MERRA data into the LTPP database. 

12
CHAPTER 2. LEGACY LTPP APPROACH TO CLIMATE DATA

A brief overview of the current approach for collection, processing, and storage of data to 

represent climate conditions at each test site is presented in this chapter. This discussion is 

limited to the methodology to include continuous long-term data stored in the climate module for 

all test sections because this was the objective of this portion of the study. 

VIRTUAL WEATHER STATION CONCEPT

To develop climate statistics to represent site conditions at each test site, LTPP developed the 

“virtual” weather station (VWS) concept. Because LTPP test sections are rarely located near an 

OWS, a method was needed to interpolate data from nearby sites over the required duration of 

the test section life. The methodology LTPP selected was to call the statistical climate data 

interpolated from nearby OWS a VWS. 

The selection of the OWS to form the statistical basis for a VWS is a critical consideration. The 

objective of the OWS selection process was to identify weather measurement locations that are 

expected to be representative of conditions at the VWS site and that satisfy the data needs of the 

intended application. Some of the factors taken into consideration in the LTPP OWS selection 

process included the following: 

• Distance between the OWS and VWS location. 

• Elevation difference between the OWS and VWS location.

• Terrain features between OWS and VWS locations. Mountains and large bodies of 

water can influence temperature, precipitation, humidity, and wind patterns.

• Types of data available from OWS because weather stations vary in recorded data.

• OWS data reporting frequency. Some weather data measurement sources only include 

daily extremes while others contain hourly or more frequent data. 

• Temporal coverage of data from the selected OWS. 

• Data QC measures applied to OWS instrumentation. 

The OWS selection method used by the LTPP program developed over time. It began with 

definition of a perfect weather station to describe the climate at LTPP test sections. The “perfect”

weather station had to be within 5 mi and include data equivalent to a first order weather 

station.(7) In theory, only one perfect weather station is needed to describe the weather events 

occurring at a pavement test section location that can account for events affecting the 

performance of the test pavement structure. When it became apparent that no perfect weather 

stations existed for the LTPP test sections at the start of the program, the following OWS 

selection process was used based on first-order and cooperative weather stations in the United 

States. First-order weather stations are those maintained by the National Weather Service (NWS)

that operate 24 hours a day with frequent data collection intervals collecting variables such as 

temperature, precipitation, surface pressure, humidity, wind speed and direction, cloud cover, 

13
snow depth, visibility, and solar radiation; cooperative weather stations generally collect only 

temperature and/or precipitation at less frequent intervals, and collection is performed by 

volunteers. Equivalent categories of weather stations were also applied to available data from 

Canada. The following OWS selection process used by the LTPP program emerged over time: 

A. Using automated methods, develop a list of candidate OWSs based on the following 

rules:

1. Identify at least one active first-order weather station with at least 50-percent

coverage of the desired time length.

2. Identify the closest active cooperative weather stations satisfying the following 

criteria:

i. Has at least 50-percent temporal coverage over the desired coverage period.

ii. Has temporal coverage greater than or equal to pavement age or 5 years.

iii. Contains the following mandatory data elements: minimum and maximum daily 

air temperature, daily precipitation, and daily snowfall (where applicable).

3. Identify at least three other nearest active or inactive weather stations that provide data 

over the coverage time period.

B. Submit the list of candidate OWSs to LTPP regional contractors for further evaluation 

and development of recommendations on final selection of up to five weather stations. 

The following factors were taken into account by LTPP regional contractors in 

developing recommendations on the selected OWS:

1. Correction of test section location coordinates when necessary. When LTPP started, 

the use of Global Position Satellite Receiver (GPSR) technology was limited, and 

many of the initial pavement test section coordinates were not correct. A manual 

plotting of test section coordinates on a suitable paper map and comparison to other 

information on the known location of a test section was the best available method to 

confirm test section location coordinates. 

2. Representativeness of the selected active first-order weather stations based on 

distance from the test section relative to elevation differences, mountains, large lakes, 

and other terrain features. The regional contractors were encouraged to contact State 

climatologists to obtain input on known micro-climate effects and other known OWS 

anomalies in the area. In some cases, it was not possible to associate a first-order

weather station with a LTPP test section site.

3. Representativeness of the selected active cooperative weather stations meeting the 

criteria listed under A.2 above, using the same criteria mentioned above for first-order

weather stations.

4. Representativeness of the other candidate OWSs.

14
C. Conduct a centralized evaluation of the recommended OWS list. Initial evaluation was 

based on reasonableness considerations on the basis for the recommended OWS, and 

secondarily on results of analysis of data obtained from the final list of selected OWS 

sites.

D. Repeat the OWS selection process over time as new test sections and test sites are added. 

In most instances, because new test sections in the SPS were added at existing LTPP test 

section sites, the same OWS previously selected could be used to generate the VWS 

statistics for the location. For the new pavement construction SPS experiments, i.e., SPS1, SPS-2, and SPS-8, using the original concept of perfect weather stations, automated 

weather stations (AWS) were installed and operated by the LTPP program at or near the 

SPS projects.

As it turns out, based on LTPP experience, there is no such thing as a perfect weather station. As 

is subsequently explained, all weather measurement instrumentation requires continual 

monitoring, evaluation, and maintenance to provide reasonable data. This is compounded by 

secondary issues related to time conventions, equipment calibrations, and changes in 

measurement equipment over time.

Using the selected representative OWSs, LTPP used a VWS interpolation methodology based on 

a 1/R2 “gravity” model for combining data from up to five nearby weather stations. As illustrated 

conceptually in figure 1, the interpolated value for data element V for day m at the VWS location 

is determined from the data elements Vmi recorded at each of the k OWSs on day m as seen in 

figure 2 in which Ri is the distance of weather station i from the VWS location at the project site. 

In figure 1, OWS3 is highlighted to represent the closest first-order weather station. As 

subsequently explained, 95 percent of the daily VWS weather statistics for wind and humidity 

are based on only one OWS, which is typically a first-order weather station.

The LTPP VWS interpolation methodology does not directly account for elevation differences 

between the measurement locations and project site. This is the reason that the selection criteria 

for the OWSs included a limit to the difference in elevation to the VWS site. (Note: The VWS 

algorithm in the MEPDG is very similar to that used by LTPP except that it does include an 

elevation correction using the temperature lapse rate.)

DATA PROCESSING AND STORAGE

The LTPP climate data obtained from external sources are stored in the CLM module in the 

pavement performance database (PPDB). A two-tiered data storage structure is used. The first 

tier contains raw and processed data from OWSs selected for use in computing the second tier 

VWS statistics. 

15
Figure 1. Illustration. Illustration of VWS concept.

Figure 2. Equation. Gravity model equation.

All of the table names in the CLM module start with CLM as the first three-letter prefix. In the 

table relationship figures in this part of the report, the CLM prefix has been omitted for 

presentation convenience.

Figure 3 illustrates the organization of the CLM_OWS tables contained in the PPDB. Only the 

CLM_OWS_LOCATION table is distributed as a part of the LTPP Standard Data Release 

(SDR). All of the other CLM_OWS_* tables shown in this figure are stored centrally on the 

LTPP PPDB database server and are disseminated by request only. The data obtained from the 

United States and Canadian climate data sources, National Climate Data Center (NCDC) and 

Canadian Centre for Climate (CCC), respectively, are split into four data types and stored by 

time period, i.e., daily, monthly, and annual. This format was implemented in 2004 as part of the 

2005 data release to address data discrepancies found in the raw data from NCDC. 

OWS

1

OWS2

OWS3

OWS4

OWS5

VWS

R1

R2

R3

R4

R5

∑

∑

=

= = k

i i

k

i i

mi

m

R

R

V

V

1

2

1

2

1

16
Figure 3. Illustration. LTPP parsing, QC checks, and data flow relationships among the 

CLM_OWS* tables obtained from external data sources. 

Only the OWS_LOCATION and OWS_data-type_DAILY tables are subjected to automated 

LTPP QC checks because they are used in the computation process for the CLM_VWS tables 

contained in the SDR. The four data types include precipitation, temperature, wind, and 

humidity. The OWS monthly and annual tables are not subject to LTPP QC checks. These tables 

have been used in the past by the LTPP engineering staff to perform reasonableness checks on 

the results of the VWS monthly and annual tables. In essence, the monthly computations from 

the OWS sources are compared with those computed from the VWS daily data when a new data 

upload is performed. 

Figure 4 illustrates the CLM VWS computational structure relationships. OWSs are linked to 

pavement test sections through a process that considers their locations relative to the test section 

locations. The results of this selection process are contained in the VWS_OWS_LINK table, 

which controls which OWS data are included in each VWS statistic. The SPS_GPS_LINK and 

SITE_VWS_LINK tables are used to associate CLM_VWS statistics with collocated test 

LTPP QC Applied No LTPP QC

Data Sources

NCDC

CCC

OWS_LOCATION

OWS_PRCIP_DAILY OWS_PRECIP_MONTH OWS_PRECIP_ANNUAL

OWS_TEMP_DAILY OWS_TEMP_MONTH OWS_TEMP_ANNUAL

OWS_WIND_DAILY OWS_WIND_MONTH OWS_WIND_ANNUAL

OWS_HUMIDITY_DAILY OWS_HUMIDITY_MONTH OWS_HUMIDITY_ANNUAL

17
sections at project sites with more than one test section. The dashed arrows in figure 4 show the 

hierarchical statistical summaries based on level E data from the underlying temporal statistics.

Level E means that the data have passed all of the automated LTPP QC checks. 

Figure 4. Illustration. LTPP CLM VWS computational relationship structure. 

After the VWS daily tables are created, the VWS monthly tables are computed. The monthly 

tables are computed using daily data that have passed all of the daily data QC checks. In addition 

to the checks on the daily tables, the monthly data table calculations are subjected to QC checks 

on the number of valid days in each month’s daily data. Likewise, annual statistics are based on 

the monthly statistics and subjected to QC checks related to the number of valid days in the year 

for which data for each data type is available.

The data in CLM_VWS_* tables contained in the SDR includes all data at all levels of 

RECORD_STATUS. While only level E data are used to compute the higher-level temporal 

aggregation statistics, data failing the QC checks were retained for research purposes.

OWS.LOCATION TEST.SECTION.COORDINATES

VWS-OWS 

Selection 

P

VWS.OWS.LINK SITE.VWS.LINK

SPS.GPS.LINK

OWS.{PRECIP/TEMP/WIND/HUMIDITY}.DAILY

VWS.PRECIP.DAILY VWS.PRECIP.MONTH VWS.PRECIP.ANNUAL

OWS.PRECIP.ANNUAL

VWS.TEMP.DAILY VWS.TEMP.MONTH VWS.TEMP.ANNUAL

VWS.WIND.DAILY VWS.WIND.MONTH VWS.WIND.ANNUAL

VWS.HUMIDITY.DAILY VWS.HUMIDITY.MONTH VWS.HUMIDITY.ANNUAL

 Level E Only Computations 

18
The derived monthly and annual climate statistics and indices computed from the daily data are 

shown in table 1. In addition to common statistical descriptive measures, the LTPP CLM module 

includes the following climate indices:

• Freeze index.

• Freeze–thaw cycles

• Intense precipitation days.

• Number of wet days.

• Number of days above freezing.

• Number of days below freezing.

Table 1. Derived climatic data statistics and computed indices stored in the LTPP database. 

Data Element

Monthly Annual

Average

Std.

Dev.

No. of

Days Value Average

Std.

Dev.

No. of 

Days Value

Mean temperature X X X X X

Maximum temperature X X X X X

Minimum temperature X X X X X

Absolute maximum 

temperature X X

Absolute minimum 

temperature X X

Number of days above 90 °F X X

Number of days below 32 °F X X

Freeze index X X X X

Freeze–thaw cycles X X X X

Maximum humidity X X X X X

Minimum humidity X X X X X

Total precipitation X X X X

Number of intense 

precipitation days (daily 

precipitation > 0.5 inches)

X X

Number of wet days (daily 

precipitation > 0.01 inches) X X

Total snowfall X X X

Number of snow covered days X

Mean wind speed X X X X

Maximum wind speed X X X X

Std. Dev. = Standard Deviation

ISSUES WITH LEGACY LTPP CLIMATE DATA APPROACH

The legacy LTPP program approach to general climate statistics for test sections dates back to 

technology available in 1991. Over time, LTPP has pursued an active and informed approach to 

provision of climate data statistics that has altered and adapted to changing technology. The 

following are some difficulties, limitations, and issues with this approach that the program has 

attempted to deal with over the last 20 years and that represent future challenges to its legacy 

approach:

19
• Limited spatial and temporal coverage of ground-based OWSs, close enough to test 

sections to be used as part of the VWS computation process, result in an uneven 

number of OWSs by data type. Figure 5 shows the number of OWS that provide data 

for a LTPP VWS by data type. The vertical scale is percentage of total daily OWS 

observations in each VWS climate data category. The horizontal scale is the number 

of OWSs used in each daily VWS statistic. When wind and humidity data are 

available, 95 percent of the VWS daily data come from only one OWS, whereas less 

than 10 percent of the VWS daily data for temperature and precipitation come from 

one OWS. At the other extreme, almost 50 percent of the VWS statistics for 

temperature and precipitation are based on the maximum of five OWSs. This 

situation creates an uneven balance in the basis of the VWS computed parameters for 

each study site. 

• Selected OWSs contain data gaps or do not provide temporal coverage over the 

desired timeframe. 

• Existing OWSs change location over time while keeping the same name. This 

requires changes to the current LTPP CLM data storage and computational structures 

to accommodate. 

• If all of the selected OWSs do not contain a desired data element, the resulting VWS 

climate data statistic is left null or a record is not included in the LTPP database. 

• New OWSs are introduced over time. To proceed with the legacy LTPP climate 

approach requires a new OWS selection process. While technological advances have 

been made to make this process more streamlined than in the past, the OWS selection 

process requires expenditures of significant program resources. 

20
Figure 5. Graph. Percentage of OWSs represented in each daily VWS statistic by number 

of OWSs. 

• The MEPDG software requires hourly climate inputs. The LTPP climate database 

contains only daily climate extremes. 

• The existing onsite hourly weather measurements collected by LTPP as part of the 

AWS effort contain significant data gaps due to equipment failure and thus do not 

provide continuous coverage over the operational time period. This issue is the reason 

that the U.S. Climate Reference Network (USCRN) includes redundant measurement 

instrumentation at each site.

• No adjustments are made in the interpolation process to account for elevation 

differences between the OWS and VWS.

These issues are not criticisms of the LTPP program; they merely reflect the issues and 

challenges that all infrastructure research projects requiring sit- specific climate data have faced 

over the last 20 years. Even today, using ground-based weather data observations has some of 

these same issues.

The next chapter of this report contains a summary of climate data sources that are available to 

meet emerging needs of the modern generation of infrastructure design and performance 

prediction engineering models.

21

CHAPTER 3. CLIMATE DATA APPLICATIONS AND SOURCE CANDIDATES

INFRASTRUCTURE APPLICATIONS

Effective identification of potential climate data sources requires knowledge of the types of 

applications in which these data will be used. The following subsections describe current 

pavement and other infrastructure applications that require climate data as part of their inputs.

Empirical Pavement Design

Until very recently, most major pavements in the United States have been designed using the 

empirical AASHTO method.(8) This method includes climate influences and climate data inputs, 

but only in very indirect ways. For example, the design subgrade resilient modulus for both 

flexible and rigid pavement designs is specified as a seasonally averaged value reflecting the 

variations in foundation stiffness during the year, particularly in northern climates subjected to 

freeze–thaw cycles. However, climate data per se are not explicit inputs into the seasonal 

adjustment calculations. Both the flexible and rigid design procedures include drainage 

coefficients to account for the speed with which climate-related water infiltration is removed 

from the pavement structure. However, the climate data input for determining the drainage 

coefficients is nebulously defined in terms of the percentage of the year that the pavement layers 

will be at saturation levels near 100 percent. The only explicit climate input in the 1993 

AASHTO method is the depth of frost penetration, which is used for computing serviceability 

loss due to frost heave; however, very few States use this part of the AASHTO design method. 

Explicit climate inputs in the 1998 AASHTO supplement for rigid pavements are the mean 

annual wind speed, temperature, and precipitation used for estimating the effective positive 

temperature differential through the concrete slab; however, relatively few States use the 1998 

AASHTO supplement.

Mechanistic-Empirical Pavement Design

The lack of explicit treatment of climate influences in the most widely used empirical AASHTO 

design methods was one of the motivations for the development of mechanistic-empirical 

pavement design procedures. These procedures couple mechanistic calculation of pavement 

primary responses—e.g., stresses and strains at critical locations—resulting from traffic loads 

and climate influences with empirical predictions of pavement structural distresses—e.g., rutting 

and cracking in flexible pavements, joint faulting and slab cracking in rigid pavements, and 

reflection cracking in rehabilitation overlays. Mechanistic-empirical pavement design methods 

have been developed in the past by several State highway agencies (e.g., Illinois, Kentucky, and 

Washington) and by industry groups (e.g., Shell Oil, The Asphalt Institute, and the Portland 

Cement Association). The most recent and comprehensive of these procedures is the MEPDG

developed under NCHRP Project 1-37A and recently officially adopted by AASHTO.(6.3) The 

MEPDG procedures are implemented in the AASHTOWare Pavement ME Design® software 

released by AASHTO in April 2011. 

The temperature and moisture analyses performed by the MEPDG’s Enhanced Integrated 

Climate Model (EICM) require five weather-related parameters on an hourly basis over the 

entire design life of the project: air temperature, wind speed, percent sunshine, relative humidity, 

23
and precipitation. Details on how these weather history inputs are used in the EICM are 

presented later in chapter 5.

Weather history information is obtained from weather stations located near the project site. The 

MEPDG software includes a database of approximately 800 weather stations throughout the 

United States. If needed, interpolation of climatic data from multiple nearby stations can be 

stored as a VWS. 

Although no formal, documented QC checks have been performed on the climatic data 

distributed with the current version of the MEPDG software, there is general consensus and 

concern that the information for some of the weather stations may be flawed. Studies by 

Zaghloul et al. and Johanneck and Khazanovich well-illustrate some of the reasons for these 

concerns.(9,10)

A major difficulty in evaluating the consequences of MEPDG weather data quality is that the 

sensitivity of the pavement performance predictions from the MEPDG to climatic inputs is 

largely unknown. Numerous studies have evaluated the sensitivity of MEPDG performance 

predictions to traffic, geometric, and material design input parameters, but no comprehensive 

sensitivity study of the effects of climate on performance predictions has been performed. Nearly 

all “sensitivity analyses” of MEPDG performance predictions to climatic inputs to date have 

simply compared results using one weather station to another, usually from a distinctly different 

climatic zone. (See references 11 through 26, 9, 27, and 28.) This type of anecdotal approach 

cannot provide any organized comprehensive insights. A recent study by Li et al. examined in a 

quantitative but very limited way the sensitivity MEPDG predicted performance to changes in 

average temperature and precipitation.(29) Some of the findings were unsurprising, e.g., asphalt 

rutting increases with increasing average temperature. However, other findings were more 

perplexing, e.g., alligator cracking decreased with decreasing average precipitation; although this 

might be intuitively expected, it is surprising to find this in the MEPDG predictions because, as 

described later in chapter 5, the current version of the MEDPG ignores any infiltration of 

precipitation into the pavement structure. Some insights into the influence of individual weather 

components on pavement performance can also be gleaned from the effective temperature 

relations for rutting and fatigue developed by El-Basyouny and Jeong, which are functions of 

annual average temperature, the variability of the maximum annual temperature, wind speed, 

percent sunshine, and precipitation as well as loading frequency and, for rutting, depth within the 

pavement.(30)

There have been many attempts to compare predictions of pavement temperature histories 

against measured values for the LTPP Seasonal Monitoring Program (SMP) using finite 

difference- or finite element-based transient heat balance simulation models (e.g., Hermansson,

Zubair et al., and Ho and Romero). (See references 31 through 35.) These comparisons have 

been quite close in most studies. However, the predictions exhibited low sensitivity to some 

climate inputs and high sensitivity to other climate-related inputs that are not even measured 

(e.g., atmospheric down-welling longwave radiation and surface albedo, the inverse of surface 

shortwave absorptivity). Zuo et al. demonstrated that pavement critical responses (e.g., 

maximum strains) are quite sensitive to the temperature and moisture gradients within the 

pavement, which in turn can be quite sensitive to the details of the weather history.(36) Zuo et al.

also found that the averaging period for weather data (hourly versus daily versus monthly) had a 

24
significant effect; as would be expected, longer averaging periods reduced the impact of the peak 

conditions when disproportionate pavement distress may occur.

Superpave Binder Specification

The Superpave performance grade (PG) binder specification requires as input the annual 

minimum and the annual maximum 7-day average pavement temperature. The Superpave PG 

specification recommends a corresponding high and low temperature binder grade to ensure that 

the binder has suitable viscoelastic stiffness and creep properties at the expected high and low 

pavement temperatures and target reliability level.

LTPPBind (http://www.fhwa.dot.gov/pavement/ltpp/ltppbind.cfm) is a software tool developed 

by LTPP to help highway agencies select the appropriate Superpave PG for a particular site. 

LTPPBind features a database of air temperatures (minimum, mean, maximum, standard 

deviation, and number of years) for nearly 8,000 U.S. and Canadian weather stations. 

Enhancements to the Superpave PG specification are currently under active consideration by the 

FHWA Binder ETG and other groups to address fatigue cracking resistance under immediate 

temperature conditions. These discussions may lead to additions/changes in the climate inputs 

for the Superpave PG specification.

HIPERPAV®

The HIPERPAV® analysis software (http://www.hiperpav.com/) assesses the influence of 

pavement design, concrete mix design, construction methods, and environmental conditions on 

the early-age behavior of Portland cement concrete (PCC) pavements. The service life of 

concrete pavements is highly dependent on curing behavior during the first 72 hours following 

placement. Stresses in concrete develop from the combined effects of curling and warping and 

the restraint of movements along the slab-subbase interface. These stresses may be of sufficient 

magnitude to cause cracking while the concrete strength is still relatively low. Prediction and/or 

monitoring of the stresses during this time is extremely important because problems during 

curing may lead to loss of long-term pavement performance. 

Environmental inputs required by the HIPERPAV® software include hourly temperature, wind 

speed, humidity, and cloud cover for the first 72 hours after placement; minimum and maximum 

air temperatures during the critical curing period, defined as after 72 hours to first traffic 

application; minimum and maximum air temperatures for the duration after the critical curing 

period; and typical monthly rainfall for a 12-month period.

HIPERPAV® implicitly requires solar radiation input for the heat balance equations at the 

pavement surface. Solar radiation is estimated internally in the software from latitude, elevation, 

and percent cloud cover.

Good comparisons have been found between HIPERPAV® predicted and actual measured 

temperatures.(37) Although HIPERPAV® is targeted specifically at concrete pavements, stresses 

versus strength gain as the concrete matures is an issue for all concrete structures. These include 

bridge decks, bridge columns and abutments, and many other transportation infrastructure 

components.

25
Bridge Management

Bridge deterioration should logically depend on climate factors as well as many other variables. 

For example, bridge decks and structural elements in northern tier states with freeze–thaw cycles 

and use of deicing salts can be expected to deteriorate more rapidly than those in warmer 

climates. Expansion joints in locales that have large temperature swings can be expected to 

deteriorate more quickly than those in more temperate locales.

Pontis is the AASHTO bridge management system used by most U.S. transportation agencies for 

managing bridge inventories and making decisions about preservation and functional 

improvements for bridge structures. Pontis stores bridge inventory and inspection data, including 

detailed element conditions; supports network-wide preservation and improvement policies for 

use in evaluating the needs of each bridge in a network; makes project recommendations to 

derive maximum benefit from scarce funds; reports network and project-level results; and 

forecasts individual bridge lifecycle deterioration and costs. 

Although bridge deterioration should logically depend on climate factors, the inclusion of 

climate data elements in the current version of Pontis is very limited and qualitative. Pontis has 

four climate conditions defined as Benign, Fair, Moderate, and Severe. The appropriate climate 

condition is not based on any specific environmental/climate/weather data elements but is simply 

selected by the user.

In April 2008, FHWA launched the Long-Term Bridge Performance (LTBP) Program, a major 

new strategic initiative designated as a flagship research project. The LTBP Program is intended 

to be a 20-year undertaking. Its major objective is to compile a comprehensive database of 

quantitative information from a representative sample of bridges nationwide, looking at every 

element of a bridge. By taking a holistic approach and analyzing all of the physical and 

functional variables that affect bridge performance, the study will provide a more detailed and 

timely picture of bridge health and better bridge management tools. The LTBP Program 

completed a pilot study on seven bridges to validate protocols for assessment, data collection,

and management. Data collection in two clusters in mid-Atlantic States began in March 2013 

after completion of the pilot study.

Climate data for the LTPB are being extracted from the Clarus weather station network 

(described further in the next section) and online from Weather Underground

(www.wunderground.com). Key data elements include humidity, temperature, and number of 

snowfalls greater than 1 inch. Current temporal frequency for climate data is low. However, 

future deterioration models may require more frequent data, e.g., to model thermal stresses in 

bridges with frozen bearings. Some QC checks of the climate data are performed in Clarus and 

others by LTBP. 

Summary of Existing Applications

It is now increasingly possible to perform complex thermodynamic modeling of the influence of 

climate and other environmental considerations on infrastructure performance. However, the data 

needs for these models often exceed what has been required (or available) in the past. New/

emerging pavement modeling tools such as the MEPDG and HIPERPAV® require more and 

26
finer-grained (e.g., hourly) climate data as inputs than do older techniques (e.g., 1993 

AASHTO).(8) These finer-grained climate data are available in the current LTPP database only 

for the relatively small number of AWS locations.

A common development arc for modeling is to start with very complex models that take into 

account second- and third-order interactions. Exercising these models helps identify the 

important controlling factors that dominate the intended use of the model so that the model can 

subsequently be simplified. This philosophy can be paraphrased as “You have to make things 

complicated before you can make them simple.” High-quality, finer-grained climate data are 

essential to this process.

In addition to the uses of climate data in infrastructure performance prediction, climate 

monitoring during infrastructure construction can also be highly beneficial. This monitoring can 

help to optimize closure times or times to initiation of the next construction phase based on 

climate-material interaction considerations.

CONVENTIONAL SOURCES OF CLIMATIC DATA

NOAA is the principal U.S. scientific agency focused on the conditions of the oceans and the 

atmosphere of the planet. NOAA’s NCDC is the world’s largest active archive of weather data. 

The NCDC has more than 150 years of data on hand with 224 gigabytes of new information 

added each day. The NCDC archives contain more than 320 million paper records, 2.5 million 

microfiche records, and more than 1.2 petabytes of digital data. Data are received from a wide 

variety of sources, including satellites, radar, automated airport weather stations, NWS 

cooperative observers, aircraft, ships, radiosondes, wind profilers, rocketsondes, solar radiation 

networks, and NWS forecast/warnings/analyses. 

NCDC also manages NOAA’s Regional Climate Centers (RCC). The RCCs provide access to 

essential climate variables through the Applied Climate Information System, a part of NCDC’s 

National Virtual Data System.

Additional local climatic data are available from State climatologist offices. Forty-seven States 

and Puerto Rico currently have State climatologists. They work closely with NCDC, the RCCs, 

and the NWS to provide improved climate services through greater integration of data quality 

control and improved communication and coordination.

The Canadian National Climate Data and Information Archive (CNCDIA) operated by 

Environment Canada contains the official climate and weather observations for Canada. The 

CNCDIA includes climate elements such as temperature, precipitation, relative humidity, 

atmospheric pressure, wind speed, wind direction, visibility, cloud types, cloud heights and 

amounts, soil temperature, evaporation, solar radiation and sunshine and occurrences of 

thunderstorms, hail, and other weather phenomena.(38)

The Clarus Initiative is a collaborative effort of the FHWA Road Weather Management Program 

and the Intelligent Transportation Systems Joint Program Office to reduce the impact of adverse 

weather conditions on surface transportation users.(39) To achieve this goal, a robust data 

assimilation, quality checking, and data dissemination system needed to be created so that near 

real-time atmospheric and pavement observations could be provided. By working in partnership 

27
with agencies, Clarus connects existing sensors into a nationwide network. The Clarus system 

data can be used to support transportation operations resources such as Enhanced Road Weather 

Forecasting, Seasonal Weight Restriction Decision Support Tool, Non-Winter Maintenance and 

Operations Decision Support Tool, Multi-State Control Strategy Tool, and Enhanced Road 

Weather Content for Traveler Advisories. New quality checking algorithms have recently been 

implemented to enhance the capabilities of the current Clarus system.

Clarus consolidates data from existing sensors operated by a network of different agencies. As a 

consequence, the available data vary significantly by location/agency. There are currently 

approximately 75 different observation types collected by 140 sensor systems operated by the 

various agencies.

METAR, which roughly translates from French as Aviation Routine Weather Report, is the 

international standard for reporting hourly meteorological data.(40) METAR reports wind, 

visibility, runway visual range, present weather, sky condition, temperature, dew point, and 

altimeter setting. METAR reports are provided by the NWS, the Federal Aviation Administration 

(FAA), and others via the Automated Weather Observing System, Automated Surface Observing 

System (ASOS), and Automated Weather Sensor System.(41) METAR data and the related 

Terminal Aerodrome Forecasts data are available at http://weather.noaa.gov/weather/coded.html.

For the purpose of tracking climate change, NOAA has developed the USCRN. The USCRN 

consists of 120+ research-grade stations collecting high-quality climate data, including 

temperature and precipitation, solar radiation, surface skin temperature, surface winds, relative 

humidity, and (in the future) soil moisture and soil temperature at five depths.(42) The USCRN 

program will collect data for 50 years to track climate change. Figure 6 shows the coverage of 

the USCRN stations. 

Source: NOAA

Figure 6. Map. U.S. Climate Research Network stations.(42)

28
Automated Surface Observing System (ASOS)

ASOS is a network of first-order climate stations operated cooperatively by the NWS, the FAA, 

and the Department of Defense. These data are available through the NCDC in NOAA. ASOS is

the Nation’s primary surface weather observing network. Observations from ASOS are updated 

every minute, 24 hours a day, 365 days a year. The following weather elements are reported by 

ASOS:(43)

• Sky condition: cloud height and amount (clear, scattered, broken, overcast) up to 

12,000 ft. 

• Visibility (to at least 10 statute mi). 

• Basic present weather information: type and intensity for rain, snow, and freezing 

rain. 

• Obstructions to vision: fog, haze. 

• Pressure: sea-level pressure, altimeter setting. 

• Ambient temperature, dew point temperature. 

• Wind: direction, speed, and character (gusts, squalls). 

• Precipitation accumulation. 

• Selected significant remarks, including variable cloud height, variable visibility, 

precipitation beginning/ending times, rapid pressure changes, pressure change 

tendency, wind shift, and peak wind. 

Although there are roughly 1,000 ASOS located throughout the United States, there are vast 

areas without coverage. 

Road Weather Information Systems (RWIS)

Many States maintain RWIS equipment as part of their safety management for roadways. A key 

component of the RWIS is an Environmental Sensor Station (ESS) that measures atmospheric, 

surface and/or hydrologic conditions using one or more sensors. There is no standardized ESS 

sensor configuration. An individual ESS may include a wind sensor, camera, solar radiation 

sensor, temperature/dew point sensor, precipitation sensor, visibility sensor, and snow depth 

sensor on a tower. Sensors located away from the tower may include road surface temperature, 

subsurface temperature, flooding water level, and precipitation accumulation sensors. 

Solar Radiation Data 

Solar radiation data are a principal input to thermodynamics-based climate-structure interaction 

models for predicting temperature and/or moisture distributions in pavements and other 

29
transportation infrastructure systems. Unfortunately, the LTPP OWS CLM data module does not 

contain any solar radiation data, not even indirect measures such as percent cloud cover. 

Measured hourly solar radiation data are available from the 42 LTPP AWS sites, and this

information is stored in the offline AWS climate tables. All of the LTPP AWS climate data are 

located near LTPP SPS test sites. Additional AWSs are located at sites included in the SMP 

program. However, the LTPP AWS monitoring measurements have been terminated. None of 

the AWS solar radiation data have ever been compared and/or measured against other data 

sources, nor has solar radiation data from other data sources been assessed for widespread 

inclusion in the LTPP program.

The amount of publically available solar radiation data from ground stations has increased over 

the past 20 years. The NCDC has made available historic solar radiation databases from the 1952

to 1976 period and now has online the updated National Solar Radiation Data Base (NSRDB) for 

the 1991 through 2010 time period. The updated NSRDB contains hourly solar radiation 

(including global, direct, and diffuse) and meteorological data for 1,454 stations, up from the 

239 stations in the earlier 1961 through 1990 NSRDB. The update includes the conventional 

time series for NSRDB ground stations as well as a 0.1-degree gridded dataset that contains 

hourly solar records for 8 years (1998 through 2005) for the United States (except Alaska above 

60 degrees latitude) at about 100,000 pixel locations (nominal 10- by 10-km pixel size). The 

National Renewable Energy Laboratory (formerly the Solar Energy Research Institute) in 

Golden, CO, also maintains a database of solar radiation data. 

Newer satellite-based solar radiation sensors are a potential replacement for ground-based 

sensors in determining local solar radiation inputs for infrastructure performance models. Highquality solar radiation data are readily available from Geostationary Operational Environmental 

Satellites (GOES) operated by NOAA’s National Environmental Satellite Data and Information 

Service. Two geostationary satellites, one over the eastern and another over the western United 

States, provide complete coverage for most of the contiguous United States and much of 

southern Canada. Data for more northern locations can be obtained from polar orbit satellites by 

request from NOAA. 

Access to these data (http://www.atmos.umd.edu/~srb/gcip/) is an outgrowth of the ongoing 

activity at the Department of Atmospheric and Oceanic Science, University of Maryland, to 

develop and validate an operational model for deriving surface and top of the atmosphere 

shortwave radiative fluxes from GOES in support of the Global Continental International Project 

activities and regional weather prediction models. Instantaneous, hourly, daily, and monthly 

mean information on surface downwelling shortwave, top of the atmosphere downwelling and 

upwelling radiative fluxes, photosynthetically active radiation, cloud amount, and surface skin 

temperature are provided for an area bounded by 70 to 125 degrees W longitude and 25 to 

50 degrees N latitude. Validation results against ground truth are also available. Historical data at 

a 0.5 degrees (approximately 37.3 mi) spatial resolution are available from 1996 onward. The 

historical data are currently being reprocessed at a 0.125 degrees (approximately 9.3 mi) spatial 

resolution; these reprocessed data are currently available for 1996 through 2000.

30
MERRA

A promising new source of hourly climate data, which became known to the study team in spring 

2012, is MERRA. MERRA contains reprocessed atmospheric observations from 1979 to the 

present using the National Aeronautics and Space Administration (NASA) Goddard Earth 

Observation System Version 5 (GEOS-5). This represents a merger of physics-based modeling 

with satellite, airborne, ship, radiosonde, and buoy measurements. More than 4 million 

observations are assimilated into the MERRA models every 6 h. MERRA can provide a 

continuous record of hourly values for all inputs to current advanced infrastructure models. The 

basis for MERRA as well as QC, data availability, evaluation of MERRA data for use in 

pavement and other infrastructure applications, and benefits of MERRA data are discussed in the 

following chapters.

31

CHAPTER 4. MODERN ERA RETROSPECTIVE-ANALYSIS FOR RESEARCH AND 

APPLICATIONS (MERRA)

The MERRA product from NASA is a new alternative for obtaining high-quality atmospheric 

and surface weather history data.(44,45) MERRA is a physics-based 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 model 

and analysis system over the entire data history. MERRA improves on earlier generations of 

reanalysis models such as those developed by NOAA’s National Center for Environmental 

Prediction, the European Centre for Medium-Range Weather Forecasts, and the Japan 

Meteorological Agency.(46,47,48)

Distribution of MERRA data is funded by NASA’s Science Mission Directorate. The data are 

not copyrighted and are open to all for both commercial and noncommercial uses. NASA uses 

MERRA to help verify seasonal climate forecasting systems, generate climate data records, serve 

as input to satellite retrieval algorithms, and provide atmospheric forcings for hydrologic and 

land surface process studies.(49) In addition, MERRA is regularly evaluated and validated to 

ensure continuity and consistency because the data product is produced in near real-time.

MERRA data are provided at an hourly temporal resolution and a 0.5-degree by 0.67-degree

(latitude/longitude) spatial resolution from 1979 to the present. Figure 7 illustrates graphically 

the spatial density of MERRA grid points over the continental United States; MERRA spans the 

entire globe at this spatial resolution. For contrast, figure 8 shows the spatial distribution and 

density (computed as the number of ASOS stations per MERRA grid pixel) of first-order ASOS 

ground-based weather stations over the continental United States; the ASOS coverage is limited 

to the United States. The ASOS OWSs are the primary source of climate data for the MEPDG 

weather database. The higher spatial resolution and larger geographic scope of the MERRA data 

are clear. In addition, NASA is currently upgrading MERRA to a 0.62- by 0.62-mi horizontal 

resolution.

The merger of the GEOS-5 model with observations is based on the Grid-Point Statistical 

Interpolation (GSI), which is a three-dimensional variational data assimilation analysis 

algorithm. Prior to assimilation, the available observations undergo a sophisticated QC/quality 

assurance (QA) procedure. Only observations that pass the QC/QA procedure are used during 

assimilation. For a given 6-h assimilation window, the GEOS-5 model first predicts the 

background (predictor) states over which the GSI analysis is computed (figure 9). Next, an 

incremental analysis update (IAU) procedure is conducted during which the analysis correction 

is applied to the forecast model gradually over the 6-h assimilation window. In essence, the IAU 

serves to move the model forecast toward closer agreement with the assimilated observations 

without introducing abrupt discontinuities or physical inconsistencies into the model dynamics. 

Once the IAU procedure has completed the given 6-h assimilation window, the model advances 

forward in time to the next 6-h window. This process is repeated over the course of the more 

than 30-year observation record. More than 4 million observations (mostly satellite-derived) are 

typically ingested during a 6-h assimilation cycle. For more details on the GEOS-5 model and 

the GSI procedure, the reader is referred to Rienecker et al.(44)

33
Figure 7. Map. Map of MERRA grid points over the continental United States where each 

grid point is approximately 31.1 by 37.3 mi at mid-latitudes.

Figure 8. Map. Spatial distribution and density of first-order ASOS stations over the 

continental United States.

34
Source: M.M. Rienecker et al.

Figure 9. Illustration. Schematic of the IAU procedure in MERRA (from Rienecker et al.)

shown in Greenwich Mean Time.(44)

MERRA is capable of providing all of the weather history inputs required by the MEPDG and 

other current infrastructure applications. Table 2 contains the MERRA data elements used to 

develop MEPDG weather history inputs. In addition, MERRA contains additional data elements 

useful for enhancements of current infrastructure applications and/or for support of future 

applications. Samples of available data elements are provided in table 3. A complete listing of all 

MERRA data elements can be found at 

http://gmao.gsfc.nasa.gov/products/documents/MERRA_File_Specification.pdf.

Table 2. MERRA data elements available to develop MEPDG weather history inputs.(45)

Element Description Units

CF Total cloud fraction fraction

PPT Precipitation flux incident upon the ground surface kg H2O m2 s-1

PS Surface pressure at 2 m above ground surface Pa

Q Specific humidity at 2 m above ground surface kg H2O kg-1 air

Rsw Shortwave radiation incident upon the ground surface W m-2

Rtoa Shortwave radiation incident at the top of atmosphere W m-2

T Air temperature at 2 m above ground surface K

U Eastward wind at 2 m above ground surface m s-1

V Northward wind at 2 m above ground surface m s-1

35
Table 3. Examples of other MERRA data elements of potential interest for transportation 

infrastructure applications.(45)

Element Description Units

T Air temperature at 10-meters above ground surface1 K

U Eastward wind at 10-meters above ground surface1 m s-1

V Northward wind at 10-meters above ground surface1 m s-1

PRMC Total profile soil moisture content m3 m-3

RZMC Root zone soil moisture content m3 m-3

SFMC Top soil layer soil moisture content m3 m-3

TSURF Mean land surface temperature (including snow) K

TSOIL Soil temperature in layer (available for 6 soil layers) K

PRECSNO Surface snowfall kg m-2 s-1

SNOMAS Snow mass kg m-2

SNODP Snow depth m

EVPSOIL Bare soil evaporation W m-2

EVPTRNS Transpiration W m-2

EVPSBLN Sublimation W m-2

QINFIL Soil water infiltration rate kg m-2 s-1

SHLAND Sensible heat flux from land W m-2

LHLAND Latent heat flux from land W m-2

EVLAND Evaporation from land kg m-2 s-1

LWLAND Net downward longwave flux over land W m-2

SWLAND Net downward shortwave flux over land W m-2

EMIS Surface emissivity fraction

ALBEDO Surface albedo fraction 1

Also available at approximately 100 m and other elevations.

MERRA is freely available to all research agencies and universities through the NASA Modeling 

and Assimilation Data and Information Services Center (MDISC) at 

http://disc.sci.gsfc.nasa.gov/daac-bin/DataHoldings.pl. Subsets of the MERRA data at an hourly 

resolution as a function of time and space can be requested from MDISC, including specification 

of the desired data element(s). Once the timeframe, region of the globe, and MERRA data 

elements of interest are selected, the data files are retrieved by NASA in Hierarchical Data 

Format (HDF) or Network Common Data Form (NetCDF) format. The HDF and NetCDF 

supercomputer data formats are the only data formats currently supported by NASA to keep 

storage of the large dataset sizes tractable. Once NASA retrieves the requested data files, they 

are posted to a public FTP server for access by the user. A number of computing languages (e.g., 

C++, FORTRAN, IDL, and MATLAB®) contain HDF and NetCDF libraries that can be 

employed to open the MERRA data files, extract the relevant locations in space and time, and 

prepare the data for subsequent use in MEPDG or any other infrastructure application of interest.

36
CHAPTER 5. EVALUATION OF MERRA FOR USE IN PAVEMENT AND OTHER 

INFRASTRUCTURE APPLICATIONS

INTRODUCTION

A series of analyses were performed to evaluate the suitability of using MERRA for pavement 

and other infrastructure applications. Because the MEPDG is the most demanding current 

application in terms of climate inputs as well as a major focus of many LTPP and other research 

efforts, the evaluation of MERRA focuses on its suitability for use in predicting pavement 

performance using the MEPDG. The following three sets of evaluation analyses have been 

performed:

• A systematic quantitative evaluation of the sensitivity of MEPDG distress predictions 

to fundamental climate parameters. This evaluation, which has not been done before, 

is essential for establishing which elements of climate data are most important to 

pavement infrastructure performance (according to the MEPDG). This provides a 

context for the subsequent analyses described in this chapter.

• A statistical comparison of climate parameters from MERRA versus ground-based 

OWS and AWS instruments.

• A quantitative comparison of the pavement performance predicted by MEPDG using 

MERRA versus ground-based OWS and AWS weather histories.

SENSITIVITY OF MEPDG PERFORMANCE PREDICTIONS TO CLIMATE 

PARAMETERS

The analysis methodology developed in the AASHTO MEPDG and accompanying software 

places great emphasis on the influence of climate on pavement performance.(3) Hourly measured 

values of air temperature, precipitation, wind speed, percentage sunshine, and relative humidity 

over multiple years are required as climate inputs. Data from more than 800 OWSs across the 

United States were obtained for this purpose from the NCDC and incorporated into a weather 

station database that accompanies the MEPDG software.

Current understanding of the sensitivity of MEPDG predicted pavement performance to 

environmental factors is limited. Zaghloul et al., in a study of the New Jersey Turnpike, found 

that MEPDG predictions were surprisingly sensitive to the subset of weather stations used to 

derive the project-specific VWS data.(9) Tighe et al. used the MEPDG to quantify the impacts of 

climatic change on low traffic flexible pavement performance in southern Canada and found that 

rutting (asphalt, base, and subbase layers) and both longitudinal and alligator cracking are

exacerbated by climate change, with transverse cracking becoming less of a problem.(50) Li et al. 

made a rudimentary attempt to systematically evaluate the influence of climate on MEPDG 

predictions by studying the effects of changes in average temperature and monthly precipitation 

on the performance of a flexible and rigid pavement section for Arkansas conditions.(29) They 

found that the increase in average temperature increased asphalt rutting significantly and rigid 

pavement faulting slightly. The increase in average monthly precipitation increased rigid 

pavement faulting slightly but had a nearly negligible effect on flexible pavement rutting. 

37
Systematic sensitivity studies of flexible and rigid pavement performance have found that 

material environmental properties (e.g., thermal conductivity, hydraulic conductivity) generally 

have only a small influence on predicted performance; the exception is the high sensitivity of 

nearly all predicted performance to surface shortwave absorptivity. (See references 51

through 54.)

The objective of this evaluation was to quantify systematically the sensitivity of MEPDG 

pavement performance predictions to specific characteristics of the climate inputs. The pavement 

performance measures considered included longitudinal cracking, alligator cracking, asphalt 

concrete (AC) rutting, total rutting, and international roughness index (IRI) for flexible 

pavements and faulting, slab cracking, and IRI for rigid jointed plain concrete pavements (JPCP). 

The characteristics of the climate inputs examined in the study were the average annual 

temperature, average annual temperature range, average daily temperature range, wind speed, 

percent sunshine (or cloud cover), precipitation, and relative humidity. Pavement scenarios were 

examined for three generic climate conditions (cold-wet, hot-dry, and temperate) and three traffic

levels (low, medium, and high) to assess sensitivity over a broad domain. More than 300 

MEPDG (v1.100) runs were conducted for this study. The procedures and the results of the 

sensitivity analyses described in this study provide guidance on the conditions under which the 

various climate properties significantly influence pavement performance. 

Sensitivity Analysis Methodology

Formalized rigorous approaches for sensitivity analyses of complex models have evolved 

substantially in recent years. Recent surveys of many of the sensitivity analysis techniques can 

be found in Cacuci, Saltelli et al.,, and elsewhere.(55,56) Most sensitivity studies of the MEPDG to 

date have focused largely on confirmation that the model is a realistic simulation of pavement 

system performance—i.e., to verify that the model is fundamentally sound. Most of these past 

studies have employed a “one at a time” (OAT) methodology in which one or more baseline 

cases are defined, and each input (e.g., average annual temperature) is then varied individually 

for each case to evaluate its effect on the output (e.g., quantity of fatigue cracking). Only the 

sensitivities around the reference input values for the baseline cases are evaluated—i.e., the 

evaluation is only for very small regions of the overall solution space. This provides only a 

“local” as opposed to a “global” sensitivity evaluation. Although global sensitivity analyses are 

more appropriate for complex nonlinear models with many inputs, local OAT evaluations are 

suitable for exploratory analyses and/or for simpler models having relatively few input 

parameters.(52) The local OAT sensitivity analysis approach has been adopted for the present 

exploratory study.

Sensitivity Metrics

A wide variety of metrics have been used to quantify sensitivity of model outputs to model 

inputs. These include coefficients from multivariate linear regressions, correlation coefficients 

(Pearson linear and Spearman rank), and a variety of partial derivative formulations.

(52)

Although no individual metric is “perfect,” the best for interpreting the OAT analysis results in 

this study was a “design limit” normalized sensitivity index as shown in figure 10.

38
Figure 10. Equation. Design limit normalized sensitivity index.

Where:

Xk = baseline value of design input k.

Xk = change in design input k about the baseline.

Yj = change in predicted distress j corresponding to Xk.

DLj = design limit for distress j.

This design limit normalized sensitivity index was notated more simply as NSI. The NSI always 

used the design limit as the normalizing factor for the predicted distress. NSI can be interpreted 

as the percentage change in predicted distress relative to the design limit caused by a given 

percentage change in the design input. The sensitivity of NSI follows: Hypersensitive, NSI > 5; 

Very Sensitive, 1 < NSI < 5; Sensitive, 0.1 < NSI < 1; and Non-Sensitive, NSI < 0.1. For 

example, consider total rutting as the predicted distress with a design limit of 0.75 inches. An 

NSI of -0.25 for the sensitivity of total rutting to average wind speed implies that a 10-percent

reduction in average wind speed will increase total rutting Yj by NSI*( Xk/Xk)*DLj =

-0.25*-0.1*0.75 = 0.01875 inches.

Base Cases

The sensitivity analysis was conducted for a wide range of the model inputs and outputs. 

However, not all the combinations of model input values are physically plausible. For example, a 

thin asphalt layer on a thin granular base layer subjected to high traffic volume does not 

represent a realistic scenario likely to be encountered in practice. Therefore, a set of base cases 

were developed to cover the ranges of commonly encountered climatic conditions and traffic 

levels with associated surface layer and base layer thickness. A total of nine base cases 

encompassing three climatic zones, and three traffic levels were developed for each of the 

flexible pavement scenarios and the rigid pavement scenarios.

The three climatic zones used for base cases were cold-wet, hot-dry, and temperate. Table 4

summarizes the specific locations and the weather stations used to generate the reference climate 

files for each of the three climatic zones.

Table 4. Climate categories for base cases.

Climate 

Category Location Weather Station

Cold-Wet Portland, ME PORTLAND INTL JETPORT ARPT

Hot-Dry Phoenix, AZ PHOENIX SKY HARBOR INTL AP

Temperate Los Angeles, CA LOS ANGELES INTL AIRPORT

The three traffic levels analyzed are summarized in table 5. The ranges of average annual daily 

truck traffic (AADTT) values span the low (< 5,000), medium (5,000–10,000), and high 

(> 15,000) truck volume categories in Alam et al.

(57) To put these traffic volumes into a more 

familiar context, estimates of the approximate numbers of equivalent single axle loads are also 

SSjjjj

DDDD �

∆YYjj

DD jj

� �

∆XX

XX � �

∆

∆ ∆

∆ ∆

39
included in table 5. The surface layer and base layer thicknesses for each traffic category are also 

listed. Higher traffic levels require correspondingly thicker surface and base layers.

Table 5. Traffic and pavement layer thickness for base cases.

Traffic Properties

Traffic Level

Low Traffic Medium Traffic High Traffic

Nominal AADTT1 1,000 7,500 25,000

Flexible ESALs (millions)2 1.79 9.82 29.77

AC Thickness 6.5 10 12.5

Base Thickness 6 7 9

Rigid ESALs (millions)2 4.69 25.78 78.13

JPCP Thickness 8 10 12

Base Thickness 4 6 8 1

Based on MEPDG Interstate Highway TTC4 Level 3 default vehicle distribution.

2

Based on design life of 15 years for flexible pavements/25 years for rigid pavements.

AADTT = Average Annual Daily Truck Traffic

ESAL = Equivalent Single-Axle Load

AC = Asphalt Concrete

JPCP = Jointed Plain Concrete Pavement

Table 6 and table 7 summarize the values of the major pavement design inputs required by the 

MEPDG. All analyses were performed using v1.100 of the public domain MEPDG software. 

Note that reliability was set at 50 percent, which means the mean predictions of pavement 

distresses from the MEPDG were directly used without any adjustments.

Table 6. Pavement design properties for base cases.

Input Parameter Value

Design Life 15 years for flexible pavements/25 years for rigid pavements

Construction Month August 2006

Reliability 50 percent for all distresses

AADTT Category Principal Arterials—Interstate and Defense Rout

TTC 4

Number of Lanes in Design Direction 2 for low traffic/3 for medium and high traffic

Truck Direction Factor 50

Truck Lane Factor 75 for low traffic/55 for medium traffic/50 for high traffic

Default Growth Rate No growth 

First Layer Material Type HMA for flexible pavements/

PCC for flexible pavements

Second Layer Material Type Granular Base 

Subgrade Material Type Soil 

Base Resilient Modulus 25,000

Base Poisson’s Ratio 0.35

Subgrade Resilient Modulus 15,000

Subgrade Poisson’s Ratio 0.35

AADTT = Average Annual Daily Truck Traffic

HMA = Hot Mix Asphalt

PCC = Portland Cement Concrete

TCC = Truck Traffic Classification

40
Table 7. Surface layer properties for base cases.

Material Properties Baseline Value

Hot Mix Asphalt 

(HMA)

Surface Shortwave Absorption 0.85

Endurance Limit 100 

HMA Unit Weight 149.9 lb/ft3

HMA Poisson’s Ratio 0.35

HMA Thermal Conductivity 0.67 Btu/(ft)(fr)(oF)

HMA Heat Capacity 0.23 Btu/(lb)(oF)

Delta in HMA Sigmoidal Curve 2.83

Alpha in HMA Sigmoidal Curve 3.90

Effective Binder Content in HMA 10.1 percent

Air Void in HMA 6.5 percent

Tensile Strength at 14 °F 500 psi

Aggregate Coefficient of Contraction in HMA 5E-6 (1/oF)

Design Lane Width 12 ft

Portland Cement 

Concrete (PCC)

Joint Spacing 15 ft

Dowel Diameter Various

Edge Support—LTE 5 (no support)

Edge Support—Widened Slab 12 (no support)

Erodibility Index 3

Surface Shortwave Absorption 0.85

PCC Unit Weight 150 lb/ft3

PCC Poisson Ratio 0.15

PCC Coefficient of Thermal Expansion 5.0E-6 1/oF

PCC Thermal Conductivity 1.25 Btu/(ft)(fr)(oF)

PCC Cement Content 500 lb/yd3

PCC W/C Various

PCC 7-Day MOR 572 psi

PCC 7-Day E 3,650,255 psi

LTE = Load Transfer Efficiency

W/C = Water to Cement

MOR = Modulus of Rupture

E = Modulus of Elasticity 

Weather Data Inputs

A total of three weather stations in the MEPDG were selected to represent the three different 

climatic zones. Details for the selected weather stations are summarized in table 8. All of the 

weather stations have more than 9 years of continuous climate data.

The EICM in the MEPDG requires five climate elements at hourly intervals: air temperature, 

percent sunshine, wind speed, precipitation, and relative humidity. These hourly values are 

directly adjusted in the climate input files in the sensitivity analyses. Seven climate 

characteristics were considered in the sensitivity analyses: average annual temperature, average 

annual temperature range, average daily temperature range, percent sunshine, wind speed, 

precipitation, and relative humidity. Hourly percent sunshine, wind speed, precipitation, and 

relative humidity values were varied by a fixed percentage across the entire weather history. 

Changes in average annual air temperature were implemented by shifting each hourly 

temperature value by a prescribed offset across the entire weather history. In addition to average 

air temperature, the amplitudes of the daily and annual temperature variations can also influence 

pavement performance. Consequently, the average daily and annual temperature ranges were 

μ ε

41
included in the sensitivity analyses. For each day/year, the average temperature values were first 

calculated. Then a fixed small percentage variation was applied to the difference between each 

hourly value and the average value for that day/year. In other words, the daily/annual air 

temperature variations were extended or shrunk by a defined percentage relative to the center of 

the variation. Table 9 summarizes the manipulations of climate inputs used in the sensitivity 

analyses.

Table 8. Details of weather stations in MEPDG.

Weather Station

Portland International

Jetport Airport

Phoenix Sky Harbor 

International Airport

Los Angeles 

International Airport

Climatic Zone Cold-Wet Hot-Dry Temperate

Associated .hcd file 14764.hcd 23183.hcd 23174.hcd

Data starts at 19960701 19960701 19970301

Data ends at 20060228 20060208 20060228

Location Portland, ME Phoenix, AZ Los Angeles, CA

Elevation (ft) 72 1,106 326

Latitude 43.38 33.26 33.56

Longitude 70.18 111.59 118.25

Months available data 116 116 108

Table 9. Climate property inputs and treatments.

Climate Property Input Treatments

Average Annual Temperature 5 percent of the average temperature over the entire weather history is 

added to/subtracted from each hourly value

Average Annual Temperature Range The difference between each hourly value and the annual average value is 

varied by ±5 percent

Average Daily Temperature Range The difference between each hourly value and the daily average value is 

varied by ±10 percent

Percent Sunshine Each hourly value is varied by ±5 percent

Wind Speed Each hourly value is varied by ±5 percent

Precipitation Each hourly value is varied by ±5 percent

Relative Humidity Each hourly value is varied by ±5 percent

Because there is no direct way to vary these climate property inputs in the MEPDG, the 

systematic changes in the climate characteristics were incorporated by modifying the MEPDG 

climate data input file for a given weather station using the following procedures:

• The .hcd file representing a particular weather station of interest was first found from 

the hcd folder in MEPDG installation directory (e.g., 14764.hcd for the PORTLAND 

INTL JETPORT ARPT for the Cold-Wet condition).

• All the values contained in the .hcd file were loaded into a MATLAB® calculation 

script. 

• Modifications to the numbers were processed according to table 9 (e.g., all the hourly 

precipitation values are increased by 5 percent).

42
• The modified values were exported into another .hcd file with the same format as the

original file (e.g., 14764_Prep_P.hcd with the same format as 14764.hcd).

• The filename of the newly created file was changed to the original filename so

MEDPG would read the file and provide pavement performance predictions based on

the modified condition (e.g., change 14764_Prep_P.hcd to 14764.hcd and let the

MEPDG read it as PORTLAND INTL JETPORT ARPT to get pavement

performance prediction for the climate condition in which hourly precipitation values

have been increased by 5 percent).

Results and Interpretation

The design limit normalized sensitivity index values for each distress and climate parameter 

combination as computed from the OAT analysis results are shown in figure 11 for the flexible 

pavement cases. The following abbreviations are used because of space limitations: longitudinal 

cracking (LC), alligator cracking (AC), AC rutting (AR), TR total rutting (TR), and international 

roughness index (IRI). The results are based on three climatic zones and three traffic levels. Each 

column of subplots represents one climatic factor of interest, e.g., precipitation, and each row 

represents one traffic level. Note that relative humidity is not included in these plots, because it is 

used neither in the EICM nor in the distress models for flexible pavements. The different climate 

zones are represented by the different bars in each subplot. For example, the highest grey bar in 

the subplot in row 3 and column 5 means that the NSI of AC rutting to average annual 

temperature is slightly greater than 5 (hypersensitive) for the hot-dry climatic zone and high

traffic condition.

Key observations from the flexible pavement sensitivity analysis results were as follows:

• Pavement performance is most sensitive to average annual temperature and average

annual temperature range. This was most pronounced for AR but also significant for

LC and TR. The normalized sensitivity index (NSI) values were all positive, which

means that increasing annual average temperature and annual temperature range

increase pavement distress. This observation, which matches engineering intuition,

may have relevance to the evaluation of potential effects of climate change on

pavement infrastructure.

• Percent sunshine and wind speed were consistently the next most sensitive climate

parameters. As for temperature, this was most pronounced for AR but also significant

for LC and TR. Percent sunshine affects the amount of upper atmosphere solar

radiation that reaches the pavement surface and is available for heating the asphalt

layer; AR sensibly increases as percent sunshine increases (positive NSI value). Wind

speed had negative NSI values for all distresses, which means that pavement

distresses decreased as wind speed increased; this was consistent with the inverse

relationship between wind speed and pavement temperature.

• Flexible pavement performance had only a low sensitivity to average daily

temperature range.

43 
• The sensitivity of performance to precipitation was negligibly small. This was

expected because the EICM does not include the effects of surface precipitation and

infiltration in its modeling of temperature and moisture within the pavement.

• With regard to overall climate zone, the highest sensitivity values were generally

found for the hot-dry climate condition.

The OAT sensitivity analysis results for JPCP rigid pavements are shown in figure 12. Again, 

these results are based on three climatic zones and three traffic levels. Similar to figure 11, each 

column of subplots represents one climatic factor of interest, each row of subplots represents one 

traffic level, and the bars represent the different climate zones. The rigid pavement distress 

abbreviations are F for faulting, SC for slab cracking, and IRI for international roughness index. 

44
Figure 11. Graph. Sensitivity analysis results for flexible pavements.

Low Traffic

Medium Traffic

High Traffic

Normalized Sensitivity Index Normalized Sensitivity Index Normalized Sensitivity Index

45 
Figure 12. Graph. Sensitivity analysis results for rigid pavements. 

Normalized Sensitivity Index Normalized Sensitivity Index Normalized Sensitivity Index

Low Traffic

Medium Traffic

High Traffic

46 
Key observations from the JPCP rigid pavement analyses are as follows:

• Average annual and daily temperature ranges exhibited the highest sensitivity values, 

followed closely by percent sunshine. The NSI values for these climate parameters 

were all positive, meaning that increases in annual or daily temperature ranges or 

percent sunshine cause increased F, SC, and IRI. These trends were sensible, 

especially for average daily temperature range because this directly affects the 

amount of diurnal thermal curling.

• The sensitivities of the rigid pavement distresses to annual average temperature were 

moderately large but inconsistent. As design traffic and slab thickness increased, the 

NSI values increased for F, become decreasingly negative for SC, and changed sign 

for IRI. Annual average temperature thus had mixed effects on rigid pavements 

depending on the thickness of the slabs.

• The rigid pavement distresses were only moderately sensitive to wind speed and 

relative humidity. The NSI values for both climate characteristics were negative for 

all distresses, meaning that higher wind speed or relative humidity produced less 

pavement distress.

• The sensitivity of rigid pavement performance to precipitation was negligibly small. 

Again, this was expected given that the EICM does not include the effects of surface 

precipitation and infiltration in its modeling of temperature and moisture within the 

pavement.

• Overall, slab cracking was the JPCP distress that was consistently most sensitive to 

climate effects.

• The NSI values tended to be larger for thinner slabs. This implies that heavily 

trafficked rigid pavements having thicker slabs were relatively less sensitive to 

climate effects.

• In general, the NSI values tended to be smaller in the temperate climate zone.

Sensitivity Analysis Conclusions

The sensitivity of MEPDG pavement performance predictions to key climate characteristics was

systematically and quantitatively evaluated. Three climate zones and three traffic levels were 

considered to assess sensitivity over an extended MEPDG problem domain. Sensitivity of 

predicted pavement distress to change in climate characteristic was quantified in terms of NSI 

that relates the change in distress relative to its design limit to the percentage change in the 

climate characteristic from its baseline value. Key conclusions from the results include the 

following:

• 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 JPCP slab cracking but 

47
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. Percent sunshine affects the amount of upper 

atmosphere solar radiation that reaches the pavement surface and is available for 

heating. Wind speed had negative NSI values for all distresses, which means that 

pavement distresses decreased as wind speed increased; this was consistent with the 

inverse relationship between wind speed and pavement temperature.

• Precipitation had negligible influence on either flexible or rigid pavement 

performance. This was sensible given that the EICM does not include the effects of 

surface precipitation and 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.

STATISTICAL COMPARISON OF MERRA VERSUS AWS/OWS WEATHER DATA

Hourly weather histories were obtained from three different data sources: MEDPG OWSs, LTPP 

AWSs, and MERRA. Statistical evaluations of the MEPDG-related climate inputs from these 

three sources provided an initial assessment of their similarities and differences and helped

identify any potential data issues. The MEPDG hourly climate inputs are air temperature, wind 

speed, percent sunshine, relative humidity, and precipitation.

MEPDG OWS

The hourly weather data in the MEPDG OWS files are based on first-order ASOS climate 

stations described previously in chapter 3. Most of the MEPDG climate data are obtained from 

the Unedited Local Climatological Data (ULCD) product, which covers the period of July 1996 

through December 2004. Only minimal quality checks have been performed by the NCDC for 

most of these historical data. As described later, additional quality checking of the MEDPG 

OWS data was performed as part of this study. Beginning in January 2005, data are available 

from the Quality Controlled Local Climatological Data (QCLCD) product 

(http://www.ncdc.noaa.gov/land-based-insitu-station-data/quality-controlled-localclimatological-data-qclcd). 

The MEPDG requires a minimum of 24 months of actual weather station data; for many of the 

800 OWSs in the MEPDG database, there is approximately 10 years of data. Pavement 

performance prediction periods longer than the weather time series length are accommodated by 

“recycling” the weather history—i.e., once the end of the weather time series is reached, it is 

repeated again from the start. 

48
LTPP AWSs

LTPP collected hourly weather data from AWSs at SPS sites (1994 through 2008) and SMP sites

(1993 through 2004) across the United States. Most AWSs have hourly weather series longer 

than 10 years. Significant quality checks are applied to the AWS data before they are uploaded 

into the LTPP database. However, as described later, data gaps and time misalignments are not 

rare, and these issues can create problems when trying to use AWS data as weather inputs to the 

MEDPG.

One weather input required by the MEPDG, percent sunshine, is not collected by the LTPP 

AWS; instead, the actual incoming shortwave solar radiation is measured and recorded.

Although solar radiation is more directly relevant to daytime pavement temperature modeling 

than percent sunshine, estimation of net longwave radiation fluxes requires percent sunshine 

information (or percent cloudiness at night, where percent cloudiness is the complement of 

percent sunshine). To address this data limitation, synthetic percent sunshine data were generated 

by using the shortwave radiation regression equation incorporated in the MEPDG as shown in 

figure 13.

(58)

Figure 13. Equation. Shortwave radiation regression equation.

Where:

Qi = incoming shortwave radiation received at ground level.

R* = shortwave radiation incident on a horizontal surface at the top of the atmosphere; this 

depends on the latitude of the site and the seasonally varying solar declination.

A, B = empirical constants that account for diffuse scattering and adsorption by the atmosphere; 

the values of A and B incorporated in the MEPDG, which are based on data for the upper 

Midwest and Alaska, equal 0.202 and 0.539, respectively.(58)

Sc = average percent sunshine.

The Qi values from the LTPP AWS data and the R* values provided by MERRA can be used in 

the equation in figure 13 to solve for Sc. The average percent cloudiness at night is assumed 

equal to the average percent cloudiness during the day, computed as 100 – Sc.

Note that R* could also be estimated using the algorithms and data incorporated in the EICM. 

These estimates are generally close to the measured values from MERRA; this is discussed 

further later.

MERRA

The MERRA data were obtained electronically from the servers at NASA. The hourly MERRA 

data are provided at 0.5 degrees (latitude) by 0.67 degrees (longitude) horizontal spatial grid 

point resolution (approximately 31.1 by 37.3 mi at mid-latitudes) and at 72 atmospheric 

QQii = RR ∗ �AA + BB100

SScc �

49
elevations, including the ground surface. In this study, MERRA data were obtained from the 

surface grid point closest horizontally to the corresponding AWS or OWS location. The raw 

MERRA data were used to define the hourly temperature, wind speed, percent sunshine, 

precipitation, and relative humidity values. If there is a difference between MERRA grid point 

elevation and the target ground-based meteorological station, the MERRA air temperature is 

adjusted using the adiabatic lapse rate, defined as the rate of temperature change with respect to 

elevation. If the weather station elevation is greater than the MERRA elevation, the lapse rate is 

assumed to equal -8.0 °C/km—i.e., the MERRA temperature values are adjusted downward. 

Conversely, if the weather station elevation is lower than the MERRA elevation, the ambient 

lapse rate is assumed to equal 6.5 oC/km—i.e., the MERRA temperature values are adjusted 

upward.(59) The difference between the two lapse rates accounts for the possibility of 

condensation during adiabatic cooling with increasing elevation. Adiabatic air temperature 

adjustments were typically small (less than 33.8 °F) because the majority of locations used in 

this study had elevation differences smaller than 328 ft. However, a few study locations in 

complex terrain contained elevations differences upwards of 1,640 ft, which can result in 

temperature adjustments as large as 39.2 °F.

Data Quality Checks

All data were subjected to extensive quality checks prior to performing the comparison analyses. 

OWS From MEDPG Database

Data quality checks for the MEPDG OWS weather files included the following:

1. Gaps in the time history—i.e., missing hourly data records.

2. Missing data values for any record. 

3. Incorrect data formats—e.g., non-numerical “*” to designate missing data. 

4. Physically irrational data values—e.g., negative wind speed, negative precipitation, 

percent sunshine less than 0 or greater than 100, relative humidity less than 0 or 

greater than 100.

5. Inconsistent temperatures, defined arbitrarily as adjacent hourly temperature values 

that differed by more than 50 °F.

6. Inadequate duration, defined as less than 8 years of continuous and complete weather 

data.

Any OWS file that failed any of these data quality checks was eliminated from further 

consideration. All of the 851 OWS weather files in the MEPDG database were checked, and only 

21 stations passed all quality checks. Figure 14 summarizes the distribution of data quality errors 

in the MEPDG OWS files.

50
Figure 14. Graph. Data quality error distribution for OWS climate files in MEPDG 

database.

AWS From LTPP Database

The weather data from the LTPP AWSs are time-referenced using the local time at each site. All 

of the AWS weather files had problems accommodating the change to/from daylight savings 

time (DST) properly. When DST starts each year and the local time jumps forward from 

2:00 a.m. to 3:00 a.m., no 2:00 a.m. data are stored in the database. When DST ends each year 

and the local time jumps backward from 2:00 a.m. to 1:00 a.m., there should be a duplicate 

1:00 a.m. weather record. However, because the date and time fields in the LTPP database are 

key fields that prohibit duplicate values, only one of the 1:00 a.m. data records can be stored. 

This was corrected by shifting all DST records back to standard time and inserting the missing 

data values at the end of DST by interpolating adjacent records.

In addition to the DST issues, time gaps ranging from several hours to several days were found 

in many AWS weather files. These may be the result of equipment malfunctions or routine 

maintenance. Corrections were applied where possible. The time period for the analyses was 

selected to minimize the number of record gaps. One AWS site had to be dropped from 

consideration because of excessive record gaps.

MERRA Data

No quality-related issues were found in the MERRA data when the OWS and AWS quality 

checks were applied.

Weather Sites for the Study

A total of 12 sites were selected for the study after all quality checks and possible corrections to 

weather files were applied. Figure 15 shows the site locations. Table 10 summarizes the 

coordinates and other identifying information for each station. The stations beginning with “M”

are OWSs from the MEPDG software. All other stations are AWSs from the LTPP database.

These stations spanned a range of topographical conditions. For example, sites 300800, 490800, 

and 040200 were located in topographical challenging mountainous locations while sites 

M94982, 530200, and M93817 were in more benign flat topography far from major water 

bodies.

0

10

20

30

40

50

60

70

80

90

0123456

789

1011121314151617181920212223242526272829303132333435363738394041424344454647484950

More

File Quantity

Number of Errors

 

51
Figure 15. Map. Locations of ground-based weather stations investigated in this study.

Statistical Comparisons

Hourly weather histories for congruent time periods were selected from MERRA and the AWS 

and/or OWS at each site. Two sets of MEPDG runs were then performed for each site, one using 

the weather history from MERRA and the other using either the AWS or OWS data for the site. 

Each set of analyses contained one flexible and one rigid pavement scenario. The MEPDG 

predicted results at the end of design life (20 years for flexible pavements and 25 years for rigid 

pavements) were recorded and compared. Details of the MEPDG analyses are presented later. 

The overall agreement between the predicted MEDPG distresses using the MERRA versus

AWS/OWS data was categorized as good, fair, or poor. The sites in each category were as 

follows:

• Good: AWS 530200; OWS M13987, M93817, M94982.

• Fair: AWS 040200, 300800; OWS M03026, M93738.

• Poor: AWS 490800; OWS M03966, M13883, M13957.

52
Table 10. Details of ground-based weather stations.

Site ID Latitude Longitude

Elevation 

(ft)

Weather 

Sources

Ground-Based 

Weather Station Location

M93738 38.95 -77.45 312 OWS, MERRA DULLES ITL AIRPORT, VA

040200 33.43 -112.70 1096 AWS, MERRA BUCKEYE MUNICIPAL AIRPORT, AZ

300800 46.14 -112.89 4006 AWS, MERRA near ANACONDA, MT

490800 40.56 -111.13 6486 AWS, MERRA near WOODLAND, UT

530200 47.12 -118.38 1811 AWS, MERRA near RITZVILLE, WA

M03026 39.23 -102.28 4199 OWS, MERRA KIT CARSON COUNTY AIRPORT, CO

M03966 38.67 -90.67 492 OWS, MERRA SPIRIT OF ST LOUIS AIRPORT, MO

M13883 33.93 -81.12 243 OWS, MERRA COLUMBIA METROPOLITAN ARPT, SC

M13957 32.45 -93.82 276 OWS, MERRA SHREVEPORT REGIONAL ARPT, LA

M13987 37.15 -94.50 984 OWS, MERRA JOPLIN REGIONAL AIRPORT, MO

M93817 38.03 -87.53 420 OWS, MERRA EVANSVILLE REGIONAL ARPT, IN

M94982 41.62 -90.58 751 OWS, MERRA DAVENPORT MUNICIPAL AIRPT, IA

Statistical analyses of hourly air temperature, wind speed, percent sunshine, precipitation, and 

relative humidity were performed for each of the weather history datasets. Example frequency 

distributions and comparisons of means and standard deviations are given in figure 16 through 

figure 25 and figure 26 through figure 35 for the respective sites having good (Evansville, IN, 

M93817) and poor (Shreveport, LA, M13957) agreement in MEPDG distresses using the 

different weather data time series. The differences in the weather statistics between these two 

sites were generally small except for wind speed and percent sunshine. The differences in 

MEPDG predicted distresses using MERRA versus OWS weather histories were most probably 

the consequence of the differences in the wind speed and percent sunshine in the two datasets. 

Examination of the similar figures at the other 10 sites confirms that the agreement between 

MERRA and AWS/OWS weather statistics is consistently quite close for temperature, 

precipitation, and relative humidity, and relatively larger for wind speed and percent sunshine.

53
Figure 16. Graph. Site M93817 (Evansville, IN) with good agreement in MEPDG predicted 

distresses: distributions of hourly temperature.

Figure 17. Graph. Site M93817 (Evansville, IN) with good agreement in MEPDG predicted 

distresses: distributions of hourly wind speeds.

54
Figure 18. Graph. Site M93817 (Evansville, IN) with good agreement in MEPDG predicted 

distresses: distributions of hourly percent sunshine values.

Figure 19. Graph. Site M93817 (Evansville, IN) with good agreement in MEPDG predicted 

distresses: distributions of hourly precipitation.

55
Figure 20. Graph. Site M93817 (Evansville, IN) with good agreement in MEPDG predicted 

distresses: distributions of hourly relative humidity values.

Figure 21. Graph. Site M93817 (Evansville, IN) with good agreement in MEPDG predicted 

distresses: hourly temperature means and standard deviations.

56
Figure 22. Graph. Site M93817 (Evansville, IN) with good agreement in MEPDG predicted 

distresses: hourly wind speed means and standard deviations.

Figure 23. Graph. Site M93817 (Evansville, IN) with good agreement in MEPDG predicted 

distresses: hourly percent sunshine means and standard deviations.

57
Figure 24. Graph. Site M93817 (Evansville, IN) with good agreement in MEPDG predicted 

distresses: hourly precipitation means and standard deviations.

Figure 25. Graph. Site M93817 (Evansville, IN) with good agreement in MEPDG predicted 

distresses: hourly relative humidity means and standard deviations.

58
Figure 26. Graph. Site M13957 (Shreveport, LA) with poor agreement in MEPDG 

predicted distresses: distributions of hourly temperatures.

Figure 27. Graph. Site M13957 (Shreveport, LA) with poor agreement in MEPDG 

predicted distresses: distributions of hourly wind speeds.

59
Figure 28. Graph. Site M13957 (Shreveport, LA) with poor agreement in MEPDG 

predicted distresses: distributions of hourly percent sunshine values.

Figure 29. Graph. Site M13957 (Shreveport, LA) with poor agreement in MEPDG 

predicted distresses: distributions of hourly precipitation.

60
Figure 30. Graph. Site M13957 (Shreveport, LA) with poor agreement in MEPDG 

predicted distresses: distributions of hourly relative humidity.

Figure 31. Graph. Site M13957 (Shreveport, LA) with poor agreement in MEPDG 

predicted distresses: hourly temperature means and standard deviations.

61
Figure 32. Graph. Site M13957 (Shreveport, LA) with poor agreement in MEPDG 

predicted distresses: hourly wind speed means and standard deviations.

Figure 33. Graph. Site M13957 (Shreveport, LA) with poor agreement in MEPDG 

predicted distresses: hourly percent sunshine means and standard deviations.

62
Figure 34. Graph. Site M13957 (Shreveport, LA) with poor agreement in MEPDG 

predicted distresses: hourly precipitation means and standard deviations.

Figure 35. Graph. Site M13957 (Shreveport, LA) with poor agreement in MEPDG 

predicted distresses: hourly relative humidity means and standard deviations.

The discrepancies in hourly wind speed and percent sunshine values merited further evaluation. 

In general, mean wind speeds from the MERRA data tended to be lower than from 

AWSs/OWSs. The frequency distributions of hourly wind speed data from the MERRA and 

AWS/OWS data sources provided additional insights. Hourly wind speed frequency distributions 

are shown in figure 17 for site 93817 in Evansville, IN, and figure 27 for site 13957 in 

Shreveport, LA. These respectively had very good and very poor agreement in the MEDPG 

predicted distresses using the MERRA versus AWS/OWS weather data. The frequency 

distributions in figure 17 show very similar wind speed frequency distributions and statistics for 

the site with good agreement in MEPDG predicted distresses. However, figure 27 for the poor 

distress agreement site shows a frequency distribution for the MERRA data that is smooth with a 

well-defined single peak while the distribution for the OWS data from the MEPDG database is 

bimodal and has almost zero measurements of wind speed in the range of about 1 to 3 mi/h. This 

63
“dropout” in the OWS wind speed data corresponds to the exact location of the peak frequency 

in the MERRA data. This “dropout” is also reflected in the discrepancies in the wind speed mean 

and standard deviations as shown in the bar chart in figure 32. Further examination of all OWSs

in the current MEPDG database revealed that nearly all OWS weather files have almost zero 

measurements over the wind speed range of about 1 to 3 mi/h. The reason for this is unknown 

and difficult to hypothesize.

There were also issues with the percent sunshine measurements in both the AWS and OWS data 

sources. As described previously, the AWSs do not measure percent sunshine and thus the values 

of percent sunshine (and cloud cover) must be inferred. Percent sunshine is measured on an 

hourly basis at the OWSs, but the values are recorded as five discrete categories of 0, 25, 50, 75, 

and 100 percent. This is clearly evident in the OWS percent sunshine frequency distributions in

figure 18 and figure 28. The hourly MERRA percent sunshine values, on the other hand, are 

recorded on a continuous scale from 0 to 100 percent. 

There is a second potential issue with the OWS percent sunshine values. The OWS data in the 

MEPDG database are obtained primarily from ASOS ground stations. The ASOS ground stations 

measure percent sky cover based on reflections from a laser beam ceilometer).(60) The processing 

of this reflection data to determine sky cover is complex, but the end result is a reporting of sky 

cover in one of five categories. The five sky condition categories reported in the ASOS data, the 

ranges of electronically measured sky cover corresponding to each, and the human equivalent in 

oktas (one of eight increments of visual sky cover) are summarized in table 11. The presumed 

MEPDG OWS percent sky cover values (computed as 100 minus the percent sunshine) 

corresponding to the five reported percent sunshine categories are also given in the last column 

of table 11. These MEPDG values are presumed because there is no relevant documentation 

available for the MEDPG OWS files, but it seems plausible that the five MEPDG percent 

sunshine categories correspond to the five ASOS sky condition categories. If this is true, the data 

in table 11 suggest that there is an inconsistent mapping between ASOS and MEPDG OWS 

percent sky cover values for the various categories, which may introduce biases into the MEDPG 

OWS percent sunshine data.

Table 11. ASOS sky condition categories.

Sky Condition 

Category

ASOS 

Measured 

Percent Sky 

Cover (Range)

ASOS Measured 

Percent Sky 

Cover 

(Midpoint)

Human 

Equivalent 

(Oktas)

MEDPG OWS 

Percent Sky Cover

Clear (CLR) 0 to < 5 2.5 0 0

Few Clouds (FEW) > 5 to < 25 15 > 0 to < 2/8 25

Scattered Clouds (SCT) > 25 to < 50 37.5 > 2/8 to < 4/8 50

Broken Clouds (BKN) > 50 to < 87 68.5 > 4/8 to < 8/8 75

Overcast (OVC) > 87 to < 100 93.5 8/8 100

Figure 36 summarizes comparisons of statistics on the differences of selected weather inputs for 

the four sites that had full sets of data from AWS, MERRA, and two or more nearby OWSs. 

VWS weather histories were also generated from the OWS data using the MEPDG interpolation 

algorithms. Although the MEPDG VWS algorithm is not documented, through reverse 

64
engineering, it appears to be based on an inverse square distance relationship with an elevation 

temperature correction using a 0.00349 °F/ft lapse rate in both the upward and downward 

directions. For the purposes of these comparisons, the AWS data were regarded as ground truth,

and the evaluation was in terms of how well the weather statistics for the other data sources 

agree with those from the AWS. The data in figure 36 confirmed the earlier observation of 

generally good agreement in hourly temperature statistics among the different weather data 

sources. Although not shown in figure 36, this observation applies to precipitation and relative 

humidity statistics as well. Bar heights indicates mean value of the differences in weather 

parameters, error bars indicate standard deviations of the differences. Mean and standard 

deviation values of zero imply complete agreement between datasets. The largest discrepancies 

were again in the statistics for daily wind speed and percent sunshine. However, there were no 

patterns in these discrepancies. The MERRA data agreed best with the AWS in some cases 

(temperature for site 490800; wind speed for sites 300800, 490800). In other cases, the MERRA 

data agreed best with the VWS/OWS statistics but none of these agree very well with the AWS 

(wind speed for 040200, 530200; percent sunshine for sites 300800, 530200). Examination of all 

of these results suggests that the match between MERRA and AWS data in overall terms was at 

least as good as the agreement between VWS/OWS and AWS data.

Influence of site terrain and latitude were investigated in a final attempt to find some systematic 

explanation for the effect of the weather data source on MEPDG distress predictions. Agreement 

of MEPDG distress predictions using MERRA versus OWS weather inputs was classified into 

the three categories of good, fair, and poor, as described previously. Site terrain is categorized as 

flat, varying elevation, or mountainous. Latitude represents how far north/south the site was 

located. Trends between the agreement of MEPDG distress predictions and site terrain and 

latitude are illustrated in figure 37. Although the trends were weak, the data in figure 37 suggest 

that good agreement of MEPDG distress predictions is more likely for flat terrain and northern 

sites while poorer agreement is more likely for varying/mountainous terrain and southern sites. 

The influence of latitude on MEPDG distress predictions is addressed further in a later section.

65
Figure 36. Graph. Comparison of key weather statistics: differences among MERRA 

versus AWS versus VWS versus OWS datasets. 

Figure 37. Graph. Influence of site terrain and latitude on agreement of MEPDG distress 

predictions using MERRA versus OWS weather inputs.

66 
Statistical Comparison Conclusions

The following key conclusions were reached from the statistical comparison of MERRA versus

AWS/OWS hourly weather data:

• 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 

have been 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 mapping of cloud cover conditions (wind speed for OWSs).

• There were no systematic patterns in the discrepancies of MEPDG predicted 

distresses using AWS versus MERRA versus VWS versus OWS weather data 

sources. The results suggested that the match between MERRA and AWS data in 

overall terms was at least as good as the agreement between VWS/OWS and AWS 

data.

• There were weak trends in the differences in MEPDG distress predictions using 

MERRA versus OWS weather inputs, site terrain, and latitude. Good agreement of 

MEPDG distress predictions is more likely for flat terrain and northern sites while 

poorer agreement is more likely for varying/mountainous terrain and southern sites.

Overall, these conclusions support the use of MERRA as a source of weather data inputs for the 

MEPDG and other infrastructure applications.

COMPARISON OF MEPDG DISTRESS PREDICTIONS USING MERRA VERSUS

OWS WEATHER DATA

The influences of different weather histories on MEPDG predicted distresses were examined in a 

preliminary statistical fashion previously. These influences are scrutinized from a more 

rigorously mechanistic perspective in this section. The suitability of MERRA as an alternative 

weather data source was evaluated by comparing the MEPDG prediction results using MERRA 

weather data inputs with predictions at the same location using conventional ground-based 

historical weather data. The ground-based historical weather data were obtained from the OWSs

provided with the MEPDG software and the AWSs available in the LTPP database. Differences 

in absorbed solar energy at the pavement surface using these different weather history sources 

were examined as the possible cause for the differences in predicted distresses. 

Enhanced Integrated Climate Model

Modeling of temperature and moisture conditions in the pavement structure in response to 

weather inputs is modeled in the MEDPG using the EICM. The EICM is a one-dimensional heat 

and moisture flow model that simulates pavement temperatures and moisture over depth and 

time in response to environmental inputs from the pavement surface. The analysis approach was 

67
originally developed for the FHWA in the 1980s, revised in the 1990s, and then subsequently 

enhanced for implementation in the MEPDG. (See references 62, 63, 6, and 64.) The EICM 

requires the user to input the following weather data at hourly intervals over the entire design life 

of the project: air temperature, wind speed, percentage sunshine, relative humidity, and 

precipitation. The air temperature is used in the EICM heat balance equation for calculating 

longwave radiation and convective heat transfer. It is also used to define freeze–thaw periods, to 

determine the number of freeze–thaw cycles, and to estimate the TMI for setting the equilibrium 

saturation and matric suction conditions in the unbound layers. Wind speed is required for 

computing the convective heat transfer coefficient at the pavement surface. The percentage 

sunshine, along with latitude and date for computing solar declination, is needed for estimating 

the incoming solar radiation during the day and reflected longwave radiation at night. Relative 

humidity is used indirectly in the EICM to estimate net longwave radiation heat fluxes and in the 

MEPDG empirical distress models to compute the drying shrinkage of JPCP and continuously 

reinforced concrete pavement (CRCP) and also to determine the crack spacing and initial crack 

width in CRCP. Precipitation was required in early versions of the EICM to compute infiltration 

through the surface. However, infiltration modeling is disabled in the current version of the 

EICM, and any influence of surface moisture on surface heat flux is also neglected.

Although the MEPDG requires weather data inputs on an hourly basis, these values are 

interpolated and/or aggregated in different ways during the pavement prediction calculations. 

Hourly air temperatures are interpolated to the 0.1-h actual time interval used internally in the 

EICM finite difference integrations. Hourly wind speed is aggregated to a daily average value. 

Hourly percent sunshine (or its complement, percent cloud cover) is converted to average 

daytime and nighttime values. Relative humidity is converted to a daily average for estimating 

the longwave radiation heat losses from the pavement surface and to a monthly average for use 

in the MEPDG JPCP and CRCP distress models. Precipitation is not used in the current version 

of the EICM, but the hourly input values are converted to annual averages for use in the MEPDG 

distress models to determine the IRI site factor for flexible pavements and the erosion/loss of 

base support for rigid pavements.

MEPDG Prediction Results

Pavement distresses for the 12 sites identified previously in table 10 were predicted using the 

MEPDG and the MERRA and AWS/OWS weather histories. Both flexible and rigid pavement 

scenarios were investigated. The flexible pavement section consisted of 8 inches of asphalt 

concrete over 8 inches of A-1-a granular base layer over a semi-infinite A-6 subgrade layer. 

Traffic was set at an initial AADTT of 1,500 with 95 percent of trucks in the design lane and a 

compound traffic growth rate of 4 percent. The binder PG grade was selected using the 

LTPPBind program for the climate conditions at each site. The rigid pavement section consisted 

of 10 inches of JPCP over an A-2-4 granular base over an A-7-5 semi-infinite subgrade. Traffic 

was set at an initial AADTT of 7,500 with 55 percent of truck in design lane and no traffic 

growth.

A total of eight distresses (five for flexible pavements, three for rigid JPCP) were predicted by 

the MEPDG. For the flexible pavement scenario, AR was found to be the dominant distress, 

while slab cracking was the dominant distress for the rigid pavement scenario. Consequently, the 

subsequent discussion focuses on just these two distresses. 

68
A design limit normalized difference (DLND) was used to present the differences of distress 

predictions between AWSs/OWSs and MERRA. The DLND is expressed in figure 38:

Figure 38. Equation. Design limit normalized difference.

Where:

DDDDDDDDii

ss = design limit normalized difference of distress i for site s. 

DDii,MMMMMMMMMM

ss = predicted value of distress i for site s when using weather history from MERRA.

DDii, AAAA/OO ss = predicted value of distress i for site s when using weather history from AWSs or 

OWSs.

DDDDii = design limit for distress i—0.25 inches for AR and 15 for percent slab cracking.

The DLND represents the size of the differences in predicted distress using MERRA versus

AWS/OWS weather data compared with the respective distress design limit. For example, if the 

DLND of AR is 0.5, this means that the MERRA data gave a predicted AR value that was higher 

than the predicted AR value using AWSs/OWSs by an amount equal to 0.5 times the design limit 

of AR, i.e., 0.5*0.25 inches = 0.125 inches.

Figure 39 summarizes the differences in predicted distresses as well as the statistics of the hourly 

weather input data for all sites. From left to right in figure 39 are 12 sets of bars, with each set 

corresponding to one site. The first row shows the DLND for AR and slab cracking at each 

station. The subsequent rows show the means and standard deviations for key weather 

parameters from each data source. Precipitation is not included because it is not an influential 

factor in the current EICM model. The DLND generally increases from left to right in the figure; 

in other words, the differences in predicted distress using MERRA weather data versus AWS or 

OWS data increases when going from left to right. The OWS in the legend represents the 

ground-based weather station, which in the context of the discussion in this section can be either 

AWS or OWS.

Noteworthy observations from figure 39 include the following:

• About a third of sites exhibited generally good agreement between the MERRA- and 

AWS/OWS-based distress predictions.

• The means and standard deviations of air temperature were consistently similar for 

the MERRA and AWS/OWS data.

• The differences between the MERRA and AWS/OWS mean daily wind speed 

increases in general from left to right—i.e., in parallel with the differences between 

predicted distresses. 

DDDDDDDDii

ss = DDii,MMMM

ss −DDii,AAAA /OO ss

DDDDii

69
Figure 39. Graph. Summary of analysis results. 

70 
• Although the differences in the statistics for percent sunshine can be sizable, there is 

no obvious systematic trend moving from left to right as is the case for wind speed.

• The relative humidity statistics for the MERRA and AWS/OWS were similar at all 

sites.

The results in figure 39 were insufficient to explain the differences between the MERRA- and 

AWS/OWS-based distress predictions even for the cases where these differences were 

substantial. The results are also insufficient to establish which weather data source is more 

“correct.” A more detailed examination of distress prediction agreement versus the weather data 

from the alternative sources is required. This additional analysis was completed and discussed in 

chapter 6. 

Temperature

Although the air temperature mean and standard deviation values for the MERRA and 

AWS/OWS data generally agreed quite well, there were some exceptions. As shown in figure 39, 

sites M03026 and 300800 exhibited the largest temperature differences between MERRA and 

AWS/OWS data and also had moderate to large differences in predicted AR and slab cracking. 

Both of these sites are located in topographically challenging mountainous regions. Even though 

the MERRA air temperatures are corrected for elevation differences between the MERRA grid 

point and the AWS/OWS location, this was not sufficient to match the ground-based weather 

station data. More than just elevation varies rapidly over short distances in mountainous terrain; 

for example, the MERRA grid point might be on one side of a ridge and the AWS/OWS on the 

other side in a different microclimate. This will always be an issue in these types of terrain and 

will also plague ground-based weather data from stations that are not collocated with the 

pavement site. In other words, the only source for truly accurate weather data at pavement sites 

in topographically challenging areas is an onsite OWS. 

Wind Speed

In general, mean wind speed from the MERRA data tended to be lower than from AWS/OWS

data. As described previously, the frequency distributions for the MERRA data tend to be 

smooth with well-defined single peaks while the distributions for the OWS data from the 

MEPDG database are frequently bimodal with almost zero measurements of wind speed in the

range of about 1 to 3 mi/h (figure 27 and figure 32). This “dropout” in the OWS wind speed data 

corresponds to the exact location of the peak frequency in the MERRA data.

Figure 40 and figure 41 depicts the trends of DLND for AR and slab cracking versus the 

difference between MERRA and AWS/OWS average hourly wind speeds, respectively. The 

different shapes of the data points correspond to the level of prediction agreement (good, fair,

and poor). Overall, DLND decreases with increasing difference in average wind speed (in an 

absolute value sense). This is more pronounced for AR than for slab cracking. Although the R2

values are low, the trends nevertheless suggest that biases in wind speed will produce biases in 

the MEPDG pavement predictions. Given the dropout in low wind velocities in the OWS data 

(figure 27 and figure 32), it would appear that the ground-based wind speed data are more 

suspect.

71
One additional note regarding wind speed: The wind speed used in the convection boundary 

condition for the EICM is defined at an elevation of 6.5 ft above the ground surface.(65) The 

MERRA data wind speed data are reported at this 6.5-ft elevation, but the precise elevations for 

OWS measurements are not known.

Figure 40. Graph. DLND in predicted asphalt concrete rut depth (ACRD) versus mean 

hourly wind speed difference.

Figure 41. Graph. DLND in predicted slab cracking versus mean hourly wind speed 

difference.

Percent Sunshine

As described earlier, there were issues with the percent sunshine measurements in both the AWS 

and OWS data sources. The AWSs do not measure percent sunshine and thus the values of 

percent sunshine (and cloud cover) must be inferred. Percent sunshine is measured on an hourly 

72
basis at the OWS, but the values are recorded as five discrete categories (0, 25, 50,75, and 

100 percent—see figure 18 and figure 28), and there is some question whether these categories 

are mapped correctly from the underlying ASOS categories (table 11). The hourly MERRA 

percent sunshine values are recorded on a continuous scale from 0 to 100 percent. 

Figure 42 and figure 43 depicts the trends of DLND for AR and slab cracking versus the 

difference between MERRA and OWS average percent sunshine, respectively. The meaning of 

the shapes of the points is the same as in figure 40 and figure 41. Censoring the one outlier on 

the far right, the results suggest that the differences in the MEPDG predicted distress are 

correlated quite strongly with the differences in the percent sunshine values in the weather data 

sets. Because there were issues with the AWS and OWS measurement of this parameter, these 

data would appear to be the most suspect. 

Figure 42. Graph. DLND in predicted ACRD versus mean hourly percent sunshine 

difference.

Figure 43. Graph. DLND in predicted slab cracking versus mean hourly percent sunshine 

difference.

73 
Combined Effects of Wind Speed and Percent Sunshine

Wind speed and percent sunshine both have a strong influence on pavement temperature 

distributions and thus on AR and PCC slab cracking. Higher wind speed increases convective 

losses at the surface and thus decreases the solar radiation available to heat the pavement. Higher 

percent sunshine, on the other hand, increases the solar radiation reaching the pavement surface 

and thus increases pavement temperatures. As described in the preceding section, MERRA 

tended to have lower wind speeds and lower percent sunshine values than for ground-based 

weather data. Given the physics of the heat transfer at the pavement surface, these two trends 

will cancel each other to some unknown extent.

To evaluate the combined effects of wind speed and percent sunshine, the relevant components 

of the EICM formulation can be employed to evaluate the net energy absorbed by the pavement 

surface. The relevant equations are given in the NCHRP Project 1-37A final report.(6) The net 

heat flux available for absorption through the pavement surface Qg is shown in figure 44.

Figure 44. Equation. Net heat flux available for absorption.

Where:

Qi = heat flux due to incoming shortwave radiation.

Qr = heat loss due to reflected shortwave radiation.

Qa = heat flux due to incoming longwave radiation.

Qe = heat loss due to outgoing longwave radiation.

Qh= heat loss due to transpiration, condensation, evaporation, and sublimation.

Qc = heat loss due to convection.

Transpiration, condensation, evaporation, and sublimation heat losses are ignored in the EICM, 

i.e., Qh = 0. All heat fluxes and losses are in units of energy per time and area, e.g., Btu/hr-ft2. 

(Note on units: The derivations in this section are based on the NCHRP Project 1-37A 

documentation of the EICM. This documentation employs a variety of mixed and often 

inconsistent units. No attempt has been made here to remedy this.)

The net incoming shortwave solar radiation heat flux Qi at the pavement surface is estimated 

indirectly in the EICM using the equation in figure 13, which is repeated here for convenience in 

figure 45. 

Figure 45. Equation. Shortwave radiation regression equation.

As before, R* is the extraterrestrial radiation incident on a horizontal surface at the outer 

atmosphere; A and B are empirical coefficients that account for diffuse scattering and adsorption 

by the atmosphere; and Sc is the percent sunshine, equivalent to 100 minus the percent cloud 

cover. Values of A and B incorporated in the EICM are based on 10 years of data for the upper 

Midwest and Alaska and are equal 0.202 and 0.539, respectively.(58) Only EICM OWS data are 

considered here because the AWSs do not provide a direct measurement of Sc.

QQgg = (QQii − QQrr ) + (QQaa − QQee ) − QQh − QQcc

QQii = RR ∗ �AA + BB100

SScc �

74
There are two choices for determining Qi in MERRA. The first, similar to what is done in the 

EICM, is to estimate it from the equation in figure 45 using Sc values derived from the MERRA 

cloud cover data. However, MERRA also provides a direct measure of incoming shortwave heat 

flux as the Rsw data element in units of W/m2

. This latter approach, which eliminates the need for 

the empirical A and B parameters in the equation in figure 45, was employed for the present 

analyses.

The net incoming shortwave radiation heat flux Qs after subtracting reflected radiation, is 

calculated from the OWS data as shown in figure 46:

Figure 46. Equation. Shortwave radiation heat flux from OWS data.

Qs can also be calculated from the MERRA data as (after appropriate unit conversions) using the 

equation shown in figure 47:

Figure 47. Equation. Shortwave radiation heat flux from MERRA data.

Where as is the surface shortwave absorptivity (the complement of albedo) set equal to 0.85 for 

both asphalt and PCC surfaces and the other terms are as previously defined. Note that the Qs

values for the MERRA and OWS/EICM approaches will not be identical.

The incoming and outgoing longwave radiation heat fluxes are given in figure 48 and figure 49

respectively.

Figure 48. Equation. Incoming longwave radiation heat flux.

Figure 49. Equation. Outgoing longwave radiation heat flux.

Where:

N = cloud base factor; 0.9 to 0.8 for cloud heights of 1,000 to 6,000 ft.

(66)

W = average cloud cover during day or night.

Qz = heat flux due to incoming longwave radiation without correction for cloud cover.

Qx = heat loss due to outgoing longwave radiation without correction for cloud cover.

Note that both the incoming and outgoing longwave radiation heat fluxes in the equations found 

in figure 48 and figure 49 are functions of the average cloud cover, which will in general be 

different for the MERRA and OWS weather histories. 

QQss = (QQii − QQrr ) = aassRR �AA + BB

SScc

100�

QQss = (QQii − QQrr ) = aassRRssss

QQaa = QQzz �1 − 100�

QQee = QQxx �1 − 100�

75
The uncorrected incoming longwave radiation heat flux Qz is computed in figure 50:

Figure 50. Equation. Uncorrected incoming longwave radiation heat flux.

Where:

sb = Stefan-Boltzman constant, 0.172x10-8 Btu/(hr-ft2

-°F4).

Tair = air temperature in degrees Rankine (°R).

G = 0.77.

J = 0.28.

= 0.074.

p = vapor pressure of the air (1 to 10 mm Hg), which is related to relative humidity.

The uncorrected outgoing longwave radiation heat loss Qx is computed in figure 51:

Figure 51. Equation. Uncorrected outgoing longwave radiation heat loss.

Where:

= emissivity of the pavement, which depends on pavement color, texture, and temperature; a 

typical value is 0.93.

Ts = pavement surface temperature in °R.

The final term remaining in the equation in figure 44 is the convective heat loss Qc, which is 

given by figure 52:

Figure 52. Equation. Convective heat loss.

Where Tair and Ts are as defined previously, and H is the convection heat transfer coefficient. 

The convective heat transfer coefficient depends on many variables and is thus difficult to 

define. The expression employed in the EICM is shown in figure 53:

(65)

QQzz = σσssssTTaa

4 �GG − JJ

10ρρpp �

σ

ρ

QQxx = σσssss εεTTss

4

ε

QQcc = HH(TTaa − TTss)

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Figure 53. Equation. Convection heat transfer coefficient.

Where:

Ts = pavement surface temperature, in °K.

Tair = air temperature, in °K.

Tm = average of surface and air temperatures, in °K.

U = average daily wind speed at an elevation of 2 m above the surface, in m/sec.

The EICM documentation recommends a maximum value for H of 3.0 Btu/(hr-ft2

-°F). Note that 

the average daily wind speed U will in general be different for the MERRA and OWS weather 

histories.

The equations in figure 44 through figure 53 enable comparisons of the net heat flux Qg into the 

pavement for both the MERRA and OWS weather histories. This net heat flux, when integrated 

over time, gives the energy density absorbed into the pavement. To simplify these exploratory 

analyses, only the daily average values of the hourly air temperatures were used, and the 

difference between the pavement surface temperature and the air temperature was assumed 

constant at 30 °F.

Figure 54 through figure 59 illustrate the effect of absorbed energy density on predicted 

pavement performance. The daily absorbed energy densities over 1 year are summarized in the 

graph at the top of each figure; the frequency distribution of the daily absorbed energy density is 

given in the lower right; and the mean and standard deviation of daily absorbed energy density 

are given in the bar chart in the lower left. The daily absorbed energy densities predicted using 

the MERRA data agree well with those using the OWS weather histories for site M93817 

(Evansville, IN) in figure 54. However, the agreement is not as good for site M13957 

(Shreveport, LA) in figure 57; this can be seen most clearly in the mean and standard deviation 

comparisons in the lower left, where the mean daily absorbed energy density for the MERRA 

data is about 10 to 15 percent greater than the mean for the OWS weather history. Comparing 

these results with the differences in predicted distresses given at the top of figure 39 shows very 

good correlation between distress prediction agreement and absorbed energy agreement. Site 

M93187, which had the closest agreement in the absorbed energy densities using MERRA versus

OWS, also had among the closest agreement in predicted distresses using the two weather data 

sources. Conversely, site M13957 had the worst agreement between absorbed energy densities 

and also had among the worst differences in predicted distresses using the MERRA versus OWS 

weather histories. 

HH = 122.93[0.00144TTmm

0.3UU0.7 + 0.00097(TTss − TTaa )0.3]

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Figure 54. Graph. Absorbed energy density versus time during a typical day at site M93817 

(Evansville, IN).

Figure 55. Graph. Mean and standard deviation of daily absorbed energy density at site 

M93817 (Evansville, IN).

Figure 56. Graph. Distribution of daily absorbed energy density at site M93817 (Evansville, 

IN).

78
Figure 57. Graph. Absorbed energy density versus time during a typical day at site M13957 

(Shreveport, LA).

Figure 58. Graph. Mean and standard deviation of daily absorbed energy density at site 

M13957 (Shreveport, LA).

Figure 59. Graph. Distribution of daily absorbed energy density at site M13957 

(Shreveport, LA).

Figure 60 and figure 61 depict the trend between DLND in predicted distresses (ACRD and slab 

cracking, respectively) and the mean daily absorbed energy density for all of the OWS sites 

analyzed. The prediction differences for both AR and PCC slab cracking consistently increase 

79
with increased differences in the mean daily absorbed energy density from the MERRA versus

OWS weather histories. These differences are due to the combined effects of differences in 

estimates of total incoming solar radiation and differences in measured wind speed and percent 

cloud cover (or conversely percent sunshine). The MERRA data for these parameters are judged 

to be the most reliable for the following reasons:

• Total incoming shortwave radiation is provided explicitly in the MERRA data while 

it must be determined indirectly from the OWS data using estimates of top-ofatmosphere incoming solar radiation, measured percent sunshine/cloud cover, and an 

empirical relation for atmospheric diffuse scattering and absorption.

• Many of the OWS histories have dropouts of low wind speeds in the 1 to 3 mi/h range 

while the MERRA wind speed data exhibit more physically plausible distributions.

• The OWS percent sunshine/cloud cover is reported as five discreet categories 

compared with the continuous reporting in the MERRA data. There is also a question 

of whether the mapping of cloud cover from the underlying ASOS data to the OWS 

categories is done correctly.

Figure 60. Graph. DLND in predicted ACRD versus absorbed energy differences.

Figure 61. Graph. DLND in predicted slab cracking versus absorbed energy differences.

The first bullet point above supports the more qualitative observations made in the previous 

section. Figure 37 suggested that the discrepancies in MEPDG distress predictions tended to 

increase at lower latitudes—i.e., at more southerly site locations. The A and B calibration 

80
coefficients in the empirical relation for atmospheric diffused scattering and absorption in 

equation in figure 45 are based on conditions in the northern continental United States (including 

Alaska). This calibration becomes less appropriate for southern States, which is consistent with 

the observed increased differences in absorbed energy density and consequent differences in 

MEPDG distress predictions for the MERRA versus OWS weather histories.

The next chapter provides details on a more in-depth statistical and pavement performance 

comparison than contained in this chapter.

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CHAPTER 6. ADDITIONAL MERRA DATA VALIDATION

BACKGROUND AND OBJECTIVES

Initial evaluations of the MERRA data presented in the previous chapter suggest that they are 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 should adopt 

MERRA as the data source for its next update to the climate data module and develop a tool to 

extract and use these data for engineering applications. 

These recommendations were presented as a draft phase 2 report that was subsequently reviewed 

by the Transportation Research Board’s ETG on LTPP Special Activities, FHWA experts, and 

LTPP staff.(1) The project team received and addressed 118 comments on the draft phase 2 

report. 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. 

This chapter addresses the first of these objectives. It can be broken down into the following set 

of 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 SSR calculations.

• Compare MEPDG pavement performance predictions using ground truth, OWS, and 

MERRA climate data.

CLIMATE DATA SOURCES

A variety of data sources were examined in this study. Ground-based climate data provided as 

part of the MEPDG serve 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 USCRN, the NWS Cooperative Observer Program 

(COOP), the Department of Energy Solar Infrared Radiation System (SIRS), and NASA’s 

MERRA. Further details on MEPDG, USCRN, COOP, SIRS, and MERRA are provided below.

MEPDG

The MEPDG methodology is implemented in the Pavement ME Design® software product from 

AASHTO. The climate data files needed as inputs to the MEPDG methodology and supplied 

with the Pavement ME Design® software are derived from two data products provided by the 

NCDC: the ULCD for times prior to 1 January 2005 and the QCLCD for times after 1 January 

2005. Many of the ULCD and QCLCD sites are part of the NWS ASOS. Despite the limited QC

83
incorporated in the QCLCD product and additional QC measures undertaken by the MEPDG 

model developers, these reference inputs still contain numerous measurement and/or data 

processing errors. However, a nonexhaustive review of the data found that the edited QCLCD 

data after 1 January 1, 2005, contains fewer processing errors than the unedited ULCD data prior 

to January 1, 2005. The QCLCD product includes hourly meteorological measurements for 

approximately 1,600 stations located across the United States. 

The MEPDG climate data are the inputs to the EICM. The developers of MEPDG performed 

additional QC steps on the ULCD and QCLCD products for use with the EICM. Nonetheless, the 

climate data provided with the MEPDG still contain some measurement and data coding errors

and time series gaps, albeit reduced as a result of the additional quality control steps. 

Table 12 summarizes the meteorological data evaluated in this study, both from the MEDPG 

climate files and from the other climate data sources described in the subsequent sections.

Table 12. Variables employed from each measurement product for use during analysis.

Variable of Interest QCLCD MEPDG USCRN MERRA COOP SIRS

Air Temperature X X X X X4 —

Dew Point Temperature1 — — — — X5 —

Specific Humidity1 X X X X — —

Wind Speed X X — X X5 —

Precipitation X X X X X5 —

Shortwave Radiation — — X X — X

Cloud Cover Fraction2 — X — X — —

Sky Condition3 X — — — — — 1

Dew point temperature and specific humidity provide equivalent information regarding humidity 2

Cloud cover fraction serves as a proxy for shortwave radiation 3

Sky condition serves as a proxy for cloud cover fraction, and hence, shortwave radiation 4

Only daily maximum / minimum / average values are available to the public 5

Only daily average values are available to the public

X = measurement/estimate is available at the majority of locations

— = Data not included in source

QCLCD = Quality Controlled Local Climatological Data

MEPDG = Mechanistic Empirical

USCRN = United States Climate Reference Network

MERRA = Modern-Era Retrospective Analysis for Research and Application

COOP = Cooperative Observer Program

SIRS = Solar Infrared Radiation System

USCRN

A summary describing the USCRN was provided in chapter 3. Observations collected by the 

USCRN were of particular interest in this study because they are derived from a minimum of 

three clustered sensors that are then averaged together prior to public distribution. The averaging 

of multiple measurements at each point in time serves to reduce measurement error and hence 

provides what is arguably the closest estimate of the ground truth. It is worth emphasizing here 

that all measurements inherently contain error. In addition, weather data are spatially variable, 

and measurements separated by even only a few hundred meters will be slightly different. 

However, for purposes of this study the USCRN measurements were considered as “reference”

measurements of the true value of the meteorological data at the site. 

84
COOP

Daily measurements of meteorological variables are available through the NWS COOP. COOP 

measurements collected from more than 570 individual station locations, were employed for use 

in this study. As opposed to the hourly (or shorter) measurements collected for the data products 

outlined in the preceding sections, COOP only provides daily averages and the daily range (i.e., 

maximum and minimum values) for certain meteorological variables. As a result, COOP data 

were used in this study to examine more closely the diurnal range of values from the hourly 

products in table 12 and to provide further insights regarding the fidelity of the different hourly 

products. 

SIRS

Ground-based radiometer measurements from the SIRS program operated by the Atmospheric 

Radiation Measurement Program under the auspices of the United States Department of Energy 

were used for direct comparison against model estimates of shortwave radiation.(67) Only the 

USCRN and MERRA products contain estimates of downwelling shortwave radiation at the 

surface. MEPDG estimates of downwelling shortwave radiation are inferred from cloud cover 

conditions (fraction) by employing an empirical expression described later in this report. Using 

this approach, it was possible to compare different solar radiation estimates even though not all 

of the data products contained direct estimates of downwelling shortwave radiation at the 

surface. 

MERRA

Meteorological inputs used to force the model were obtained from the MERRA product 

(http://gmao.gsfc.nasa.gov/merra/).(45) MERRA is provided at an hourly temporal resolution, 

0.5 by 0.67 degrees (latitude/longitude) spatial resolution, and is available from 1979 to the 

present. Chapter 4 provides a summary description of MERRA.

The MERRA product results from the merger of a physically based model (i.e., the NASA

GEOS-5 Version 5.2.0) with satellite, airborne, ship, and radiosonde observations of the 

atmosphere. NASA frequently uses the MERRA product to help verify seasonal climate 

forecasting systems, generate climate data records, serve as input to satellite retrieval algorithms, 

and provide atmospheric forcings for hydrologic and land surface process studies.(49) In addition, 

MERRA is regularly analyzed and validated to ensure continuity and consistency because the 

data product is produced in near real-time.

A major advantage of MERRA over ground-based climate data sources is the uniform spatial 

coverage. The ground-based ASOS stations that provide much of the MEPDG/ULCD/QCLCD 

climate data are mostly located at airports and therefore clustered along the east and west coasts 

of the United States and around major population centers. 

Measurement Product Collocation

To compare measurements from one measurement network against those from another, it is first 

necessary to collocate the measurement networks in space prior to collocating the measurement 

sequences in time. The collocation process was conducted for the QCLCD station locations (and 

85
hence the MEPDG station locations by association). That is, all computed distances treat the 

given QCLCD station as located at the center of the search area. Next, for a given QCLDC 

station, the horizontal distance was computed for every station in each of the measurement 

networks outlined above. A minimum separation distance of 0.5 degrees (approximately 31.1 mi

at mid-latitudes) was specified. This was done so that each collocated set of measurement 

stations would be representative of the same local topographic and climate conditions. In 

addition, given the spatial resolution of the MERRA product, this guaranteed that at least one 

MERRA location (grid cell) would correspond to every QCLCD (and hence every MEPDG) 

station location. If at least one station location from each of the measurement networks outlined 

above was within 0.5 degrees of the given QCLCD measurement station, then those respective 

measurement stations from each measurement network were used during the statistical analysis 

discussed later.

Figure 62 through figure 64 show the histograms of the horizontal separation distances from the 

collocation procedure. As illustrated in figure 62, the MEPDG weather stations are derived 

directly from the QCLCD data set, and hence the separation distance is often equal to zero. 

Because there are many more QCLCD stations than MEPDG stations, sometimes more than one 

QCLCD station falls within the spatial threshold available for comparing against a given 

MEPDG station—this results in the occasional nonzero separation distances between QCLCD 

and MEPDG station locations. The MERRA data, as expected, showed the greatest range in 

separation distance (see figure 64).

Figure 65 shows the locations within the contiguous United States of the various climate data 

products examined in this study. A total of 354 combinations of USCRN, QCLCD, and MERRA 

data sites (diamond symbol) were collocated to within 0.5 degrees of horizontal separation.

Figure 62. Histogram. Histogram of separation distances (in mi) relative to QCLCD for 

MEPDG stations.

86
Figure 63. Histogram. Histogram of separation distances (in mi) relative to QCLCD for 

USCRN stations.

Figure 64. Histogram. Histogram of separation distances (in mi) relative to QCLCD for 

MERRA grid points.

87
Figure 65. Map. Collocated USCRN, QCLCD, and MERRA data sets.

Elevation and Temperature Correction

Discrepancies in measurement elevation arise because not all collocated measurement locations 

are at precisely the same location in space. These discrepancies are greatest in regions of 

complex terrain (e.g., Rocky Mountains) and are less apparent in regions of low topographic 

relief. Because elevation differences can cause differences in air temperature, an air temperature 

correction was implemented to remove any systematic biases associated with elevation 

differences. The process was implemented in the MERRA product; it was not deemed necessary 

in the ground-based measurement products because most of the ground-based measurement 

stations are located in relatively flat terrain (e.g., at airports).

If differences exist between the land surface elevation at the MERRA grid point and the groundbased meteorological station, the MERRA air temperature is adjusted using an adiabatic lapse 

rate. If the elevation difference is positive (i.e., the ground station elevation is greater than the 

MERRA elevation), the ambient lapse rate is assumed to equal -8.0 °K/km. Conversely, if the 

elevation difference is negative (i.e., the station elevation is less than the MERRA elevation), the 

ambient lapse rate is assumed to equal -6.5 °K/km.(59) The difference between the two lapse rates 

accounts for the possibility of condensation during adiabatic cooling. Using the appropriate lapse 

rate, , the temperature correction is then computed as shown in figure 66:

Figure 66. Equation. Adiabatic lapse rate temperature correction equation

γγ

TTaa ,cccc cccc cc = TTaa + γγ ⋅ Δzz

88
Where zz is the elevation difference between the ground station and the nearest MERRA grid 

point and is defined as positive in the upward direction. Adiabatic air temperature adjustments 

were typically small (less than 1 °K) because the majority of locations used in this study had 

elevation differences less than 328 ft. However, a few study locations in complex terrain 

contained elevation differences upwards of 1,640 ft, which could result in temperature 

adjustments as large as 4 °K.

Statistical Comparisons of Data Sources

Statistical analyses were conducted between the different data sources relative to USCRN (i.e., 

USCRN treated as the reference measurement) for the approximately 17-year period of 

July 1, 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 inputs.(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 the 

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.

Comparisons of USCRN, QCLCD, and MERRA Hourly Data

Near-Surface Air Temperature

Hourly air temperatures were compared for QCLCD versus USCRN and MERRA versus

USCRN data, where the USCRN data are the assumed ground reference values. (Note: Because 

MEPDG data are derived from QCLCD, MEPDG and QCLCD are used interchangeably here.) 

For a given collocated set of USCRN, QCLCD, and MERRA locations, only those portions of 

the air temperature time series that were present in all three records were used for the statistical 

analysis. Some of the spatially collocated datasets did not have any temporal overlaps. As a 

consequence, only 275 of the 354 spatially collocated data sets in figure 65 could be included in 

the statistical comparisons.

Figure 67 shows a typical comparison of the diurnal temperature variations from the MERRA 

and MEPDG/QCLCD datasets for a single site. Overall, the agreement is very good. Figure 68

shows a typical comparison of the hourly temperature frequency distributions from the MERRA 

and MEDPG/QCLCD datasets over the entire 17-year analysis period for a single site. Again, the 

overall agreement between the two datasets is very good.

∆

89
Figure 67. Graph. Typical comparison of diurnal temperature variations for MERRA 

versus MEPDG (QCLCD) data.

Figure 68. Graph. Typical comparison of hourly temperature frequency distributions for 

MERRA versus MEPDG (QCLCD) data.

The differences between QCLCD versus USCRN and MERRA versus USCRN hourly 

temperatures at coincident time points at a given location were used to compute bias and root 

mean squared error (RMSE). Figure 69 shows a typical frequency distribution for bias in the 

hourly temperatures for a single site located outside Chattanooga, TN. At this site, the MERRA 

data (average bias = 0.62 °F) compared better with the USCRN reference than did the QCLCD 

data (average bias = 2.00 °F), although the spread in the MERRA bias distribution was 

somewhat larger. At other sites, the QCLCD data compared better with the USCRN reference 

than did the MERRA data, but in most cases the average bias values were small—on the order of 

approximately 2 °F. Figure 70 is an example of a “worst-case” scenario for a site near 

Jacksonville, FL. Here the MERRA data (average bias = 5.52 °F) compared less well with the 

90
USCRN reference than did the QCLCD data. This is not surprising for a location right on the 

coast where half of the MERRA grid cell may be over land and half over water.

Figure 69. Graph. Frequency distribution of bias for QCLCD versus USCRN and MERRA 

versus USCRN hourly temperature values for a single site (outside Chattanooga, TN)—

typical results.

Figure 70. Graph. Frequency distribution of bias for QCLCD versus USCRN and MERRA 

versus USCRN hourly temperature values for a single site (near Jacksonville, FL)—worstcase results.

The calculations behind figure 69 and figure 70 were repeated at each of the other 273 collocated 

data sets to determine the average bias and RMSE in the QCLCD versus USCRN and MERRA 

91
versus USCRN hourly temperature values at each site. The distributions of the average bias and 

RMSE values are illustrated in figure 71 and figure 72, respectively. The average of the average 

bias across all 275 data sets was 1.14 °F for QCLCD versus USCRN and 2.53 °F for the 

MERRA versus USCRN comparisons. The spread of the MERRA bias distribution is slightly 

broader than for the QCLCD data. The average RMSE values (figure 72) were 3.68 °F for the 

QCLCD versus USCRN and 5.91 °F for the MERRA versus USCRN comparisons. Overall, both 

the QCLCD and MERRA data were different and warmer than the USCRN reference values, 

with the MERRA data being slightly warmer and more variable. 

Figure 71. Graph. Frequency distribution of average hourly temperature bias across all 

sites for QCLCD versus USCRN and MERRA versus USCRN climate data.

92
Figure 72. Graph. Frequency distribution of RMSE of QCLCD versus USCRN and 

MERRA versus USCRN hourly temperature values across all sites.

Precipitation at the Ground Surface

Analyses similar to those behind figure 71 and figure 72 were conducted for hourly precipitation 

rates for collocated USCRN, QCLCD, and MERRA stations. Only non-zero precipitation events 

were considered to better evaluate storm effects. More specifically, if at least one climate data 

source contained a non-zero hourly precipitation value, then that hour was included in the 

evaluation. If all of the data sources registered zero precipitation for an hour, then that hour was 

excluded. As shown in figure 73 and figure 74, both the QCLCD and MERRA data closely agree 

with USCRN precipitation measurements, but MERRA has 50 percent less average bias than 

does QCLCD (-0.00059 inches/h versus -0.00106 inches/h). Further, data from numerous 

QCLCD stations contain significant negative bias relative to USCRN, which is consistent with 

rain gauge “under catch,” which is a known and pervasive problem with point-scale rain 

gauges.(68) The RMSE is also slightly lower in the MERRA estimates (0.032 inches/h versus

0.036 inches/h for QCLCD).

93
Figure 73. Graph. Frequency distribution of average hourly precipitation bias across all 

sites for QCLCD versus USCRN and MERRA versus USCRN climate data.

Figure 74. Graph. Frequency distribution of RMSE of QCLCD versus USCRN and 

MERRA versus USCRN hourly precipitation rates across all sites.

Comparisons of USCRN, COOP, and MERRA Daily Temperatures

The statistical analysis approach employed for hourly temperature data was repeated for daily 

mean, minimum, and maximum temperatures. The daily USCRN measurements were compared 

against daily values from the NWS COOP data as well as daily values derived from the original 

hourly MERRA estimates. Figure 75 shows the distribution of the collocated USCRN, COOP, 

and MERRA sites. The daily mean, minimum, and maximum temperatures from USCRN and 

MERRA were extracted from the original hourly temperature data. There was insufficient 

94
temporal overlap of the data for some of the sets of collated sites to enable computation of 

meaningful statistics. Statistical comparisons were made only where a large (> 100) number of 

daily readings were available for comparison.

Figure 75. Map. Collocated USCRN, COOP, and MERRA data sets.

The average bias and RMSE for the COOP versus USCRN and MERRA versus USCRN daily 

mean temperature values were computed for each set of collocated sites. The distributions of 

these bias and RMSE values are illustrated in figure 76 and figure 77, respectively. The average 

of the average bias across all sites (figure 76) was 1.85 °F for COOP versus USCRN and 2.73 °F

for the MERRA versus USCRN comparisons. The average RMSE values (figure 77) were 

3.03 °F for the COOP versus USCRN and 4.20 °F for the MERRA versus USCRN comparisons. 

Overall, both the COOP and MERRA daily mean temperatures were different and warmer than 

the USCRN reference values, with the MERRA data being slightly warmer and more variable. 

Figure 78 and figure 79 summarize the average bias and RMSE for the COOP versus USCRN

and MERRA versus USCRN daily minimum temperature values. The average of the average 

bias across all sites (figure 78) was 0.68 °F for COOP versus USCRN and 3.61 °F for the 

MERRA versus USCRN comparisons. The average RMSE values (figure 79) were 3.34 °F for 

the COOP versus USCRN and 6.04 °F for the MERRA versus USCRN comparisons. These 

findings suggest COOP does a better job of capturing the lower range of the diurnal air 

temperature than does MERRA. This is likely due to the spatial mismatch between the pointscale observations (i.e., USCRN and COOP) and grid cell scale estimates produced by MERRA. 

The adiabatic air temperature correction for elevation differential may also contribute to these 

differences.

95
Figure 76. Graph. Frequency distribution of daily mean temperature bias across all sites 

for COOP versus USCRN and MERRA versus USCRN climate data.

Figure 77. Graph. Frequency distribution of RMSE of COOP versus USCRN and MERRA 

versus USCRN daily mean temperature values across all sites.

96
Figure 78. Graph. Frequency distribution of daily minimum temperature bias across all 

sites for COOP versus USCRN and MERRA versus USCRN climate data.

Figure 79. Graph. Frequency distribution of RMSE of COOP versus USCRN and MERRA 

versus USCRN daily minimum temperature values across all sites.

Figure 80 and figure 81 summarize the average bias and RMSE for the COOP versus USCRN

and MERRA versus USCRN daily maximum temperature values. The average of the average 

bias across all sites (figure 80) was 2.46 °F for COOP versus USCRN and 2.88 °F for the 

MERRA versus USCRN comparisons. The average RMSE values (figure 81) were 3.46 °F for 

the COOP versus USCRN and 5.21 °F for the MERRA versus USCRN comparisons. Overall, 

both the COOP and MERRA daily maximum temperatures were different and warmer than the 

USCRN reference values, with the MERRA data being slightly warmer and more variable.

97
Figure 80. Graph. Frequency distribution of daily maximum temperature bias across all 

sites for COOP versus USCRN and MERRA versus USCRN climate data.

Figure 81. Graph. Frequency distribution of RMSE of COOP versus USCRN and MERRA 

versus USCRN daily maximum temperature values across all sites.

Comparisons of Hourly SIRS, QCLCD, and MERRA SSR

Although SSR is not a direct input in the MEPDG, it is the principal driver for pavement heating 

and cooling. Both USCRN and the SIRS were used as ground truth to evaluate the SSR issues. 

The USCRN and SIRS stations use radiometers to directly measure the SSR at the site. The 

QCLCD sites (and, by association, the MEPDG weather station data) do not have radiometers 

and thus do not measure SSR. In the EICM calculations embedded in the MEPDG software, the 

98
downwelling shortwave radiation is determined using an empirical equation that is a function of 

cloud cover as shown in figure 82:

Figure 82. Equation. Downwelling shortwave radiation. 

Where QQss downwelling (incoming) shortwave radiation, aass is the surface shortwave absorptivity 

of the pavement, and RR is the shortwave radiation at the top of the atmosphere that is a function 

of latitude and seasonally varying solar declination. The term SScc is the percentage of sunshine 

computed as Sc=1–NW, where N is a cloud base factor equal to 0.9 to 0.8 for cloud heights of 

1,000 to 6,000 ft, and W is the average cloud cover during day or night.

(66) The constants AA and BB

are empirical terms that account for diffuse scattering and adsorption by the atmosphere; the 

values of A and B incorporated in the MEDPG, which are based on data for the upper Midwest 

and Alaska, equal 0.202 and 0.539, respectively.(58) This empirical expression is required only 

with the QCLCD measurements because USCRN, SIRS, and MERRA all provide direct 

measurement or estimates of downwelling shortwave radiation.

Figure 83 and figure 84 summarize the average bias RMSE for the QCLCD versus USCRN and 

MERRA versus USCRN SSR values across all collocated data sets. Recall that each point in 

figure 83 represents the average of the hourly SSR bias values over the entire duration of 

temporally matched data series at a single site. Each point in figure 84 represents the RMSE of 

the hourly SSR values at a single site. The average of the average bias across all sites (figure 83) 

was 20 W/m2 for QCLCD versus USCRN and 51 W/m2 for the MERRA versus USCRN 

comparisons. For perspective, these values can be compared against the maximum shortwave 

radiation at the top of atmosphere equal to 1,366 W/m2; the bias values are less than 4 percent of 

this maximum. The average RMSE values (figure 84) were 176 W/m2 for the QCLCD versus

USCRN and 166 W/m2 for the MERRA versus USCRN comparisons. Overall, both the QCLCD 

and MERRA SSR values were different and slightly higher than the USCRN reference values.

QQss = aassRR (AA + BB

SScc

100)

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Figure 83. Graph. Frequency distribution of SSR bias across all sites for QCLCD versus

USCRN and MERRA versus USCRN climate data.

Figure 84. Graph. Frequency distribution of RMSE of QCLCD versus USCRN and 

MERRA versus USCRN SSR values across all sites.

Five SIRS sites were used for a more in-depth evaluation of SSR differences. The SIRS sites, 

which are all located in the southern Great Plains, were designed specifically for collecting SSR 

data using very sophisticated and accurate radiometers. Figure 85 and figure 86 summarize the 

bias and RMSE for QCLCD versus SIRS and MERRA versus SIRS climate data as a function of 

cloud cover. Several important insights can be drawn from these figures. During periods of low 

cloud cover—i.e., periods of maximum SSR and pavement heating—the QCLCD has high 

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positive biases and variability relative to MERRA. MERRA has a greater positive bias during 

periods of heavy cloud cover but these conditions generally do not correspond to the extremes 

for pavement performance. 

Figure 85. Graph. SSR bias as a function of cloud cover for QCLCD versus SIRS and 

MERRA versus SIRS climate data.

Figure 86. Graph. SSR RMSE as a function of cloud cover for QCLCD versus SIRS and 

MERRA versus SIRS climate data.

A complementary trend can be observed as a function of season. Figure 87 shows the bias in 

daily averaged SSR for QCLCD versus SIRS and MERRA versus SIRS for each month during 

the analysis period. The QCLCD data show much higher biases than MERRA during the 

summer months of May through September—the critical period of maximum pavement heating. 

Conversely, the MERRA data show higher biases during the later winter and spring months of 

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January through April; these are generally less critical periods for pavement heating and 

performance. The RMSE values for both sets of climate data are relatively similar (figure 88).

Figure 87. Graph. SSR bias as a function of season for QCLCD versus SIRS and MERRA 

versus SIRS climate data. 

Figure 88. Graph. SSR RMSE as a function of season for QCLCD versus SIRS and 

MERRA versus SIRS climate data. 

The overall conclusions drawn from these data are as follows. The SSR values from the QCLCD 

data estimated empirically using the equation in figure 82 and the values predicted by the 

MERRA approach show similar bias on an annual basis. However, when disaggregated into bias 

as a function of cloud cover and bias as a function of month of year, the QCLCD data tend to 

have higher positive biases during the critical extreme-temperature periods for the pavement—

low cloud cover and summer months—compared with the MERRA data. This may be a 

consequence of the empirical relation used in the EICM to estimate SSR based on top-ofatmosphere radiation and cloud cover. Use of the MERRA direct SSR data eliminates the need 

for this type of empirical relationship.

Conclusions From Statistical Comparisons 

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 expected measurement errors and the spatial variability of

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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 QCLCD and MERRA data 

have small and roughly comparable differences from the USCRN values. The average 

bias and RMSE computed for the QCLCD and MERRA data compared with the 

USCRN reference for average hourly temperature are summarized in table 13. (Recall 

that temperature is the most sensitive climate data input for the MEPDG models.) The 

MERRA data are slightly warmer on average than the QCLCD values but only by

slightly more than 1 °F in most cases.

• 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 average bias and RMSE computed for the COOP and MERRA data 

compared with the USCRN reference for mean, minimum, and maximum daily 

temperatures are summarized in table 14. The MERRA data are slightly warmer than 

the COOP values, but in most cases by less than 1 °F.

• The comparisons for MEPDG SSR 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.

Table 13. Summary of statistical comparisons of QCLCD versus USCRN and MERRA 

versus USCRN hourly climate data.

Bias RMSE

QCLCD MERRA QCLCD MERRA

Average Hourly Temperature (°F) 1.14 2.53 3.68 5.91

Table 14. Summary of statistical comparisons of COOP versus USCRN and MERRA 

versus USCRN daily climate data.

Bias RMSE

COOP MERRA COOP MERRA

Average Daily Mean Temp (°F) 1.85 2.73 3.03 4.20

Average Daily Min Temp (°F) 0.68 3.61 3.34 6.04

Average Daily Max Temp (°F) 2.46 2.88 3.46 5.21

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COMPARISONS OF PREDICTED PAVEMENT PERFORMANCE

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 ULCD products from NCDC) and the MERRA climate 

data for collocated sites and congruent time series. A total of 20 sites were analyzed; their 

distribution across the contiguous United States is shown in figure 89. 

It would have been ideal to evaluate MEPDG performance predictions using the USCRN ground 

truth data in addition to the MEDPG and MERRA weather time series. However, the USCRN 

data do not include the wind speed and cloud cover data required for the MEPDG models. 

Several attempts were made to synthesize these missing data from other sources but none was

satisfactory.

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 Schwartz et al.

(1) All analyses were performed 

using version 2.0 of the Pavement ME Design® software. 

©2014 Google®

Figure 89. Map. Sites used for evaluation of MEPDG performance predictions using 

MEPDG and MERRA climate data.(69)

Comparisons of flexible pavement performance as predicted by the MEPDG using MERRA 

versus MEPDG weather data are shown in figure 90 for total rutting, figure 91 for AC rutting, 

figure 92 figure 92 for alligator fatigue cracking, figure 94 for top-down fatigue cracking, and 

figure 95 for roughness. In all cases, the predictions are clustered tightly although not perfectly 

104 
along the respective lines of equality. This is consistent with the close but not perfect agreement 

found among these climate data time series in the statistical comparisons described previously. 

The worst agreement in performance predictions is for top-down fatigue cracking (figure 94). 

However, this model is also generally viewed as unreasonably sensitive and unrealistic; a 

replacement for the current top-down fatigue cracking model is currently being developed in 

NCHRP Project 1-52. 

Figure 90. Graph. Comparison of MEPDG total rutting predictions (inches) using MERRA 

versus MEPDG weather data.

Figure 91. Graph. Comparison of MEPDG AC rutting predictions (inches) using MERRA 

versus MEPDG weather data. 

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Figure 92. Graph. Comparison of MEPDG alligator fatigue cracking predictions using 

MERRA versus MEPDG weather data.

Figure 93. Graph. Comparison of MEPDG top-down fatigue cracking predictions (ft/mi) 

using MERRA versus MEPDG weather data.

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Figure 94. Graph. Comparison of MEPDG flexible pavement IRI predictions (inches/mi) 

using MERRA versus MEPDG weather data.

Comparisons of rigid JPCP pavement performance as predicted by the MEPDG using MERRA 

versus MEPDG weather data are shown in figure 95 for transverse cracking, figure 96 for joint 

faulting, and figure 97 for roughness. In all cases, the predictions are clustered tightly although 

not perfectly along the respective lines of equality. This is consistent with the close but not 

perfect agreement found among these climate data time series in the statistical comparisons 

described previously. The agreement between the MERRA versus MEPDG weather data cases 

for rigid pavement performance is somewhat less than for flexible pavements. However, this is 

consistent with the fact that rigid pavement performance is more sensitive to shorter term (e.g., 

diurnal) temperature variations and thus to the differences between MERRA versus MEPDG 

weather data over short time periods.

Figure 95. Graph. Comparison of MEPDG JPCP transverse cracking predictions using 

MERRA versus MEPDG weather data.

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Figure 96. Graph. Comparison of MEPDG JPCP joint faulting predictions (inches) using 

MERRA versus MEPDG weather data.

Figure 97. Graph. Comparison of MEPDG rigid pavement IRI predictions (inches/mi) 

using MERRA versus MEPDG weather data.

Overall, the comparisons of 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 scatter in agreement as seen in figure 90 through

figure 97. However, it is impossible to demonstrate this because the USCRN data lack the wind 

speed and cloud cover inputs required by the MEPDG software. 

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RECOMMENDATIONS

The results of the more extensive statistical and pavement performance comparisons reported 

here support the original recommendation that LTPP should adopt MERRA as a data source for 

its next update to the climate data module and develop a tool to extract and use this data for 

engineering applications.

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CHAPTER 7. RECOMMENDATIONS

This study effort started out on the premise that improvements to the existing LTPP approach 

using ground-based weather observations to describe general climate statistics at its test sites 

could be used to satisfy current MEPDG climate input requirements and emerging infrastructure 

research needs. However, in the course of this research, the study team discovered a new source 

of climate data, MERRA, which provides a continuous hourly estimate of all of the climaterelated data based on state-of-the-art global modeling.

The current MEPDG pavement performance models were used as an evaluation tool to compare 

and contrast the influence of weather histories obtained from onsite AWS, OWS, VWS, and 

MERRA data sources. In this study, limitations in the current climate data and modeling 

incorporated in the MEPDG and accompanying AASHTOWare Pavement ME Design®

software were also discovered. These findings either suggest that the current MEPDG models are 

not appropriate for this type of climate data evaluation or that these models can be improved, 

especially if MERRA is the source of climate data. Regardless, the MEPDG models were chosen 

as the evaluation tool because they represent the most advanced models in pavement 

performance forecasting today.

SUMMARY OF RECOMMENDATIONS

Based on the results of this phase of the research effort, the study team recommends the 

following:

1. The LTPP program should use the MERRA dataset as the basis for continuous hourly 

climate data histories for its test locations.

2. Using the MERRA data set, LTPP should calculate the same derived computed 

climate statistics as shown in table 1. 

3. The LTPP program should not add the TMI as a new computed parameter to the 

climate module. There is no compelling reason to include it in the LTPP database. It is 

no longer needed if the MERRA dataset is adopted by LTPP, because MERRA 

contains the data used to determine TMI. 

4. The CLM module in the LTPP database should be modified to contain MERRA data 

for the cells where LTPP test sections are currently located.

5. The existing LTPP CLM data module should be retained in the LTPP Information 

Management System as archived data so that the data are still available upon request.

The following portions of this chapter provide more details on the basis of these 

recommendations. 

BENEFITS OF USING MERRA DATA

MERRA is a new source of weather data for use in pavement and other transportation 

infrastructure modeling applications. As described in chapter 4, MERRA provides continuous 

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hourly weather data on a relatively fine-grained uniform special grid for 1979 to the present. 

Most MERRA data elements are fundamental physics-based quantities, many of which are not 

available from any ground-based or other conventional climate data source. Only a small subset 

of the MERRA data elements is needed to develop weather inputs for current infrastructure 

applications such as the MEPDG. The full set of MERRA data elements may enable 

development of much more powerful infrastructure applications in the future. 

MERRA data satisfy all of the major study objectives. They meet the climate data needs for 

current infrastructure applications such the MEPDG, LTPPBind, HIPERPAV®, and bridge 

management. The broad range of MERRA data means that it will likely meet the climate data 

needs for future applications as well. The attention to quality and continuity in 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 use of VWS. Lastly, MERRA makes moot the issue of 

continued location-specific solar radiation measurement, as MERRA provides this information 

directly at every grid point.

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

• Denser, more uniform, and broader spatial coverage. The ASOS network of firstorder ground-based weather stations provides data at approximately 1,000 locations 

in the contiguous United States. These locations are not distributed uniformly across 

the country but rather are concentrated in areas with high population density (and an 

airport). Vast areas of the country have sparse or no ASOS coverage. MERRA data, 

by contrast, are currently available at more than 3,000 grid points in the contiguous 

United States and worldwide coverage at similar resolution. MERRA grid points are 

uniformly distributed at a horizontal spacing of approximately 31.1 mi by 37.3 mi. In 

other words, no point in the continental United States is more than 24.9 mi from the 

nearest MERRA grid centroid. This nearest grid point distance will become 

dramatically smaller when MERRA moves to an approximately 0.62 mi by 0.62 mi 

horizontal grid spacing; NASA is currently using this higher resolution data in-house,

and it is expected to be 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 such as the MEPDG and HIPERPAV®. 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 and other OWSs. All MERRA data are referenced to Greenwich Mean Time, 

eliminating issues of local time conversions and discontinuities in the data at changes 

to/from Daylight Savings Time.

• Excellent data consistency and quality. NASA developed MERRA for use in its 

own modeling applications and satellite retrieval algorithms. To meet these in-house 

needs, NASA performs rigorous and sophisticated 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. LTPP’s need for 

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extensive QC checks such as those in place for the current CLM module will be 

greatly reduced.

• 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 first-order ASOS data. For 

example, MERRA directly provides the shortwave radiation fluxes at the top of 

atmosphere and ground. 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 

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 CLM module at significantly less cost.

• Improvement over time. NASA is currently enhancing MERRA to an 

approximately 1 km spatial resolution. This means that no location will be more than 

2,297 ft from the nearest MERRA grid centroid. Significant improvement in 

conventional ground-based OWS coverage is very unlikely.

• Reliability analysis capabilities. MERRA is only one, 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. Statistical 

characterization of these ensembles could provide a rational basis for quantifying the 

uncertainty of predicted infrastructure performance due to the weather inputs.

MERRA does, of course, have some limitations, including the following:

• Grid points are not at project location. The current version of MERRA will have a 

grid point centroid no further than about 40 km from any project location. This is 

generally sufficient for the roughly 70 percent of the United States with relatively flat

terrain. This distance may not be sufficient in mountainous regions with isolated 

microclimates but in general, there will be no OWS that can realistically represent 

these microclimate weather histories either. The current version of MERRA is thus no 

worse than the OWSs in this regard, and MERRA will be significantly better after the 

1 km horizontal resolution upgrade is released to the public. AWSs can obviously 

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capture future (but not historical) climate conditions at a specific site, but it is 

unrealistic to expect deployment of these instruments at project sites other than for 

research purposes.

• Spatial averaging over grid cell volume. Some MERRA data represent averages 

over the grid cell volume as opposed to point measures provided by OWSs. However, 

given the MERRA grid points are often located closer to project sites than any OWS, 

the question is “Which is worse—spatially averaged values around the vicinity of the 

site or point values at the wrong point?” This arguable limitation of the MERRA data 

will become less important as the MERRA grid point resolution increases and 

associated grid cell volume decreases in the future.

• MERRA data begin in 1979. For LTPP test sections built before 1979, MERRA will 

not be able to provide a complete climate history over the entire service life of the 

project. However, OWS data prior to 1980 are also very sparse, especially at hourly 

time intervals. MERRA is thus no worse than OWS data in this regard. In addition, it 

is unclear how important these earlier climate data are to current infrastructure 

modeling applications.

• MERRA hourly data storage will require changes to LTPP database. Hourly 

weather data are required for current state-of-the-art infrastructure modeling 

applications such as the MEPDG and HIPERPAV®. The LTPP database will 

therefore need to be restructured to accommodate these data regardless of whether 

they come from MERRA or OWSs.

In summary, MERRA offers many benefits and very few if any significant limitations for use as 

the source of climate data for transportation infrastructure modeling applications. It is thus clear 

that MERRA should be the source for climate data in LTPP. 

In addition to providing the weather inputs required for applications such as the MEPDG, the 

uniformly gridded MERRA data will also make the development of more precise climate zone 

maps easier. For example, figure 98 shows a LTPP climate zone classification map that was 

published during the test section recruitment phase of the program. In some places, this map was 

altered to adhere to State boundaries to make it easier for participating highway agencies to 

complete the test section nomination form. Climate zones do not follow State boundaries. 

Unfortunately, this stylized climate zone map has been inappropriately used for other 

applications; for example this figure appears in the early MEPDG literature. The uniformly 

gridded MERRA data will make it much easier to develop an updated climate zone map in the 

style of figure 99 that much more precisely demarcates the zone boundaries.

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Figure 98. Map. LTPP climate zone map.

Source: M.I. Darter

Figure 99. Map. Example of more realistic climate zone map.(70)

Another potential LTPP product made possible by MERRA is a “weather anywhere” interactive 

application. Using a graphical map, a user can click a location and the program returns summary 

climate statistics. The output could be further refined to include inputs tailored to the 

AASHTOWare Pavement ME Design® software, HIPERPAV®, and other infrastructure 

modeling software. This tool is currently under production by the project team.

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CLIMATE INDICES

Based on the project team’s review of the existing LTPP CLM module, there is no compelling 

need to eliminate any of the climate indices currently in the database. These indices all have 

valid use over a range of applications, and their implementation in the LTPP database involves 

minimal storage and processing. 

One specific objective of this project was to examine the calculation and storage of the TMI in 

the LTPP database. The TMI is a semi-empirical method for classifying the climate of a given 

location that quantifies the aridity or humidity of a soil-climate system by summing the effects of 

annual precipitation, potential evapotranspiration, storage deficit, and runoff.(71,64) Inclusion of 

TMI in the LTPP database was considered early in the program because some existing work on 

climate zones (e.g., climate zone maps) used it in its formulations. However, TMI has the 

disadvantage of being a largely empirical index formulated for continental United States 

conditions and therefore cannot be easily extended to other locations. Zapata et al. suggest a 

method for extending TMI beyond the continental United States, but the robustness of this 

method is unclear and its general validity needs further evaluation.(64) The TMI has seen a 

resurgence of interest in recent years because the MEPDG uses it to determine the equilibrium 

moisture contents of the unbound pavement materials far above the groundwater table.

The project team has found no compelling reason to incorporate TMI into the LTPP database. 

The following are the three principal reasons for this conclusion: 

1. Although used currently in the MEPDG, TMI is a calculated internal quantity, not a 

direct model input. TMI values stored in the LTPP database could not be entered as 

input to the AASHTOWare Pavement ME Design® software and thus would have 

little direct usefulness to the MEPDG.

2. Because TMI is a semi-empirical index with limitations (e.g., its inability to be 

calculated outside the continental United States), it is unlikely to be used as a direct 

model input in any future infrastructure modeling application.

3. In the rare event that TMI values may be desired and assuming that MERRA data are 

incorporated into the LTPP database, TMI can be calculated on the fly from the 

appropriate MERRA data elements. 

INCORPORATING MERRA DATA INTO THE LTPP DATABASE

If the recommendation to use MERRA data as an additional source of climate data for LTPP test 

sections is approved, the next consideration is how to add these data to the LTPP database. The 

MERRA dataset requires adaptation to specific user needs. The following activities and data 

storage constructs are recommended for adaptation of MERRA data to LTPP needs:

• Some MERRA data units need to be converted to conventional weather units 

common to infrastructure uses. For example, precipitation in MERRA is expressed as 

a flux rate that needs to be translated into more traditional units of “depth” over a 

prescribed period of time. For example, rain is traditionally measured by inches per 

hour or day whereas MERRA units of precipitation are in units of mass/(area * time). 

116
Wind data are presented in eastward and northward vector components to avoid timebased averaging issues related to wind direction.

• The MERRA grid data represent the weather data for all test sections included in the 

grid boundaries. Interpolation of weather data is no longer needed. MERRA in 

essence performs a physics-based interpolation at each grid cell location.

• A new table needs to be developed to associate LTPP test site locations to the 

appropriate MERRA grid cell.

• Based on previous LTPP experience with weather data, it is recommended that the 

hourly MERRA data should be parsed into base line datasets containing distinct types 

of data. While this results in more datasets/tables than originally contained in the 

MERRA data files, it also provides a rational basis for the higher order climate 

statistics using existing LTPP database software code. 

• A new LTPP database nomenclature needs to be developed to distinguish between 

MERRA data and the older OWS/VWS ground-based observations. The 

nomenclature used in this report is CLM_MERRA_datataype_time, where datatype 

follows the current climate categories of PRECIP, TEMP, WIND, and HUMIDITY, 

plus the new SOLAR category for solar radiation related data. Time is HOUR, DAY, 

MONTH, and ANNUAL. 

• New data can be added to tables. Examples of potential new data additions include

the following:

o Evaporation to the PRECIP tables.

o Snow to the PRECIP tables.

o Average soil moisture at various depths to the PRECIP tables.

o Albedo, surface emissivity, shortwave radiation, longwave radiation, and cloud 

cover to the new solar radiation tables.

• MERRA data are delivered in a flat file format. One of the first steps will be to 

translate the file format into a relational database format. The raw MERRA data will 

be stored in tables in native units for QC purposes and to make automating updates, 

extraction, and population of the new hourly climate data tables easier. Three 

MERRA analysis products are recommended for LTPP, and some of the data of 

interest in the files include the following: 

o tavg1_2d_slv-Nx—This includes hourly specific humidity and air temperature 

data. 

o tavg1_2d_flx_Nx—This includes surface evaporation, snowfall, precipitation, 

total precipitation, and total reevaporation of precipitation.

117
o tavg1_2d_rad_Nx—This includes data associated with solar radiation, including 

albedo, shortwave radiation, longwave radiation, and cloud fraction. 

• The raw MERRA database tables are not envisaged as being included in the LTPP 

SDR because they are intended to mirror the raw data structure, including field 

names, obtained from the data source. This will decrease data user support 

requirements by LTPP because the raw data formats are not in customary units 

employed for civil engineering infrastructure modeling purposes and use nonintuitive 

variable names.

Figure 100 shows the conceptual computational and structure for climate data for LTPP based on 

MERRA data. The process starts with obtaining the MERRA data analysis products, which are 

delivered in a data file format. In the step that transforms the data file formats to database tables, 

the MERRA raw hourly data are parsed down to only the grid cells where LTPP test sections are 

located to save data storage space on the LTPP server because the total size of the three primary 

files are more than 17 terabytes. The MERRA raw hourly data are then both transformed into 

customary civil engineering units and split into five climate data categories to populate the 

CLM_MERRA_datataype_HOUR tables. These tables are recommended for inclusion in the 

LTPP SDR as are the remaining DAY, MONTH, and ANNUAL tables. After computation of the 

day tables from the hourly tables, the remaining month and annual tables will contain the climate 

indices included in the current LTPP CLM tables. Not shown in this figure is the table that will 

link test sections to MERRA grid cells, because the key fields in the 

CLM_MERRA_datataype_time tables will be based on MERRA grid cells to save data storage 

space.

118
Figure 100. Flowchart. Conceptual computational and data storage structure for MERRA-based LTP

119 

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