Generating Hourly Climatic Data for Pavement Design from Available Weather Information

Abstract

This study addressed the need for more detailed weather information in the context of pavement design with the Mechanistic–Empirical Pavement Design Guide from weather information available for Chile. An initial database was developed with the available hourly data in the country, and the sensitivity of distress predictions for Chilean weather was studied. Because of the limited availability of hourly climatic data, different methods were proposed for completing the missing data. As their inputs, the developed models used weather information that was more generally available. A sinusoidal–exponential model was used for the minimum and maximum daily temperature. An exponential model for hourly humidity based on maximum and minimum daily humidity was also used. Linear interpolation between the available hours with data for wind speed and cloud cover was sufficient to complete the hourly information for these variables. The precision of the distress predictions with these models proved to be accurate compared with the predictions with actual hourly weather data. The models are being successfully used to expand the number of available weather stations for pavement design in Chile. The models are also useful for validating the existing weather stations and detecting inaccuracies and errors in the recorded hourly data.

Development and Impact of the Egyptian Climatic Conditions on Flexible Pavement Performance

“…For the unbound layers and subgrade soils, the program uses a monthly or semi-monthly temperature (if the pavement is exposed to freeze and thaw cycles) to compute the modulus. In addition, a recently published study concluded that the MEPDG predicted performance was not significantly affected when average daily climatic data was used instead of actual hourly climatic data [15].…”

“…Delgadillo et al developed an initial climatic database comprising of 17 weather stations across Chili [15]. They also studied the sensitivity of the distress predictions for the Chilean weathers.…”

“…Some of these reasons are maintenance of the weather station, malfunction, or extreme weather [3,4,14,15]. In these cases, researchers used different techniques to account for the missing weather data.…”