Assessment of satellite-based MERRA climate data in AASHTOWare pavement mechanistic-empirical design

Author(s):  
Praveen Gopisetti ◽  
Halil Ceylan ◽  
Bora Cetin ◽  
Sunghwan Kim
Author(s):  
Wouter Brink ◽  
Harold Von Quintus ◽  
Leon F. Osborne

The AASHTOWare Pavement Mechanistic–Empirical Design software requires hourly temperature, wind speed, percentage sunshine, precipitation, and relative humidity to properly calculate pavement damage and distresses. Actual or measured values, which vary hourly throughout a day for a given site, are required to properly capture the damage caused by environmental loadings. Currently the mechanistic–empirical design hourly climatic data contain approximately 1,200 U.S. and 300 Canadian stations. The U.S. stations typically contain data from 1995 through 2005, and data from the Canadian stations vary in length from 10 to 50 years, with the exception of some weather stations. Some agencies expanded their historical weather data to include longer periods of time. This paper documents the process and data sources that were used to update the current set of climate stations with climate data dating back to 1979 using the North American Regional Reanalysis (NARR) database. The results of the comparison between new climate files and the existing older climate data files for use in pavement design are presented. Overall, the NARR-generated climate data showed a very good comparison. The paper details the background of the NARR and its limitations and compares the performance predictions made by using the old and new climate data. The results indicate there is no systematic bias between the two climate data sets.


Nature ◽  
2019 ◽  
Vol 574 (7780) ◽  
pp. 605-606 ◽  
Author(s):  
Linda Nordling

Agronomie ◽  
2001 ◽  
Vol 21 (1) ◽  
pp. 45-56 ◽  
Author(s):  
Pandi Zdruli ◽  
Robert J.A. Jones ◽  
Luca Montanarella

2019 ◽  
Vol 25 (1) ◽  
Author(s):  
MASROOR ALI KHAN ◽  
KHALID AL GHAMDI ◽  
JAZEM A. MEHYOUB ◽  
RAKHSHAN KHAN

The focus of this study is to find the relationship between El Nino and dengue fever cases in the study area.Mosquito density was recorded with the help of light traps and through aspirators collection. Climate data were obtained from National Meteorology and Environment centre. (Year wise El Nino and La Nina data are according to NOAA & Golden Gate Weather Services). Statistical methods were used to establish the correlation coefficient between different factors. A high significant relationship was observed between Relative Humidity and Dengue fever cases, but Aedes abundance had no significant relationship with either Relative humidity and Temperature. Our conclusion is that the El Nino does not affect the dengue transmission and Aedes mosquito abundance in this region, which is supported by earlier works.


Author(s):  
Mohammad Shohidul Islam ◽  
Sultana Easmin Siddika ◽  
S M Injamamul Haque Masum

Rainfall forecasting is very challenging task for the meteorologists. Over the last few decades, several models have been utilized, attempting the successful analysing and forecasting of rainfall. Recorded climate data can play an important role in this regard. Long-time duration of recorded data can be able to provide better advancement of rainfall forecasting. This paper presents the utilization of statistical techniques, particularly linear regression method for modelling the rainfall prediction over Bangladesh. The rainfall data for a period of 11 years was obtained from Bangladesh Meteorological department (BMD), Dhaka i.e. that was surface-based rain gauge rainfall which was acquired from 08 weather stations over Bangladesh for the years of 2001-2011. The monthly and yearly rainfall was determined. In order to assess the accuracy of it some statistical parameters such as average, meridian, correlation coefficients and standard deviation were determined for all stations. The model prediction of rainfall was compared with true rainfall which was collected from rain gauge of different stations and it was found that the model rainfall prediction has given good results.


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