Evaluation of Meteorological Base Models for Estimating Hourly Global Solar Radiation in Texas Conference Paper uri icon


  • 2014 Published by Elsevier Ltd. Building thermal performance and potential solar applications depend on the quality of the solar resource data available. Unfortunately, most of the locations do not account for measured solar radiation data and, as a result, rely on the values from typical meteorological years. Texas, in a similar fashion as other states in the US, does not have an active network for solar radiation data and has a variety of weather conditions that could be integrated into more than three climate zones. Therefore, in order to estimate reliable solar radiation for different locations in Texas, this paper presents the comparison and the adjustment between two models that use the meteorological data available from the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA). The comparison study was based on sixteen solar stations that cover three climate zones in Texas and use hourly solar radiation data that was recorded from 2000 through 2002 and 2010 through 2012. In this study, the estimated and measured hourly global solar radiations were compared to evaluate which model would be most suitable in each location in Texas. The two models that were studied were a modified Cloud-cover Radiation Model (CRM) by Muneer and the model developed by Zhang and Huang. These models are regression type models that use location or site specific coefficients, which have shown a good correlation during the past years between measured global solar radiation and local meteorological parameters. Most of the locations in climate zone 2, in general, fit the Zhang-Huang Model better, whereas the CRM model presents a better correlation for climate zones 3 and 4.

published proceedings


author list (cited authors)

  • Kim, K. H., Baltazar, J., & Haberl, J. S.

citation count

  • 15

complete list of authors

  • Kim, Kee Han||Baltazar, Juan-Carlos||Haberl, Jeff S

publication date

  • January 2014