The National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2 ) utilized data from the Earth Observing System satellites to improve precipitation and water vapor climatology beginning in 1979. The Federal Highway Administration (FHWA) and the American Association of State Highway Transportation Officials (AASHTO) have adopted its use and translated the variables into civil engineering applications, specifically pavement design. FHWA’s Long Term Pavement Performance (LTPP) Climate Tool provides access to the MERRA-2 database and generates site-specific climate data in compatible formats for AASHTO Pavement ME Design (Pavement ME). AASHTO will soon use MERRA-2 climatic data for AASHTOWare applications. MERRA-2 data sets include accurate hourly solar radiation values based on measured cloud-covered fractions which can significantly improve the accuracy of predicted pavement temperatures.
Studies reveal challenges in matching the predictions from the Enhanced Integrated Climatic Model (EICM) with field observations and pavement performance. Variations in measurement of climatic attributes have also been reported to Operating Weather Stations (OWS). MERRA-2 data provides opportunities for enhancements to the climatic parameters and climatic module calculations for pavement design using Pavement ME. It provides improved climatology, higher frequency outputs including hourly data updates, and additional locations beyond the United States. Available data categories include: temperature, precipitation, surface pressure, cloud cover, humidity, wind speed, and solar radiation.
The objectives of this project are to (1) evaluate the impact of using NASA’s Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) through the FHWA LTPP Climate Tool to improve the climatic inputs and related models for Pavement ME; (2) enhance and simplify climate input parameters for Pavement ME that can be implemented by transportation agencies; and (3) develop climate-related models based on identified parameter enhancements.