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

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, 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 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 (https://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.

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 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 timesto 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 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⁾

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 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 239stations 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 8years (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. High-quality 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.

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.