Development of a Wind Resource Assessment Framework Using Weather Research and Forecasting (WRF) Model, Python Scripting and Geographic Information Systems
Wind energy is rapidly emerging as the primary
source of electricity in the Philippines, although developing an
accurate wind resource model is difficult. In this study, Weather
Research and Forecasting (WRF) Model, an open source mesoscale
Numerical Weather Prediction (NWP) model, was used to produce a
1-year atmospheric simulation with 4 km resolution on the Ilocos
Region of the Philippines. The WRF output (netCDF) extracts the
annual mean wind speed data using a Python-based Graphical User
Interface. Lastly, wind resource assessment was produced using a
GIS software. Results of the study showed that it is more flexible to
use Python scripts than using other post-processing tools in dealing
with netCDF files. Using WRF Model, Python, and Geographic
Information Systems, a reliable wind resource map is produced.
[1] D. B. Mde, T. D. M. Dep, M. K. N. Dec, W. H. N. Dec, G. S. N. Dec, M. K. V. Deq And D. B. I. Dnr, "Sensitivity testing of WRF physics parameterizations for meteorological modeling and protocol in support of regional SIP air quality modeling in the OTR," 2009.
[2] W. Wang, C. Bruyere, M. Duda, J. Dudhia, D. Gill, H. C. Lin and J. Mandel, "ARW version 3 modeling system user’s guide. Mesoscale & Miscroscale Meteorology Division. National Center for Atmospheric Research," January 2015. (Online). Available: http://www.mmm.ucar.edu/wrf/users/docs/user_guide_V3/ARWUsersGuideV3. (Accessed 17 October 2015).
[3] J. W. B. Lin, "Why python is the next wave in earth sciences computing," Bulletin of the American Meteorological Society, vol. 93, no. 12, pp. 1823-1824, 2012.
[4] M. Rainer, "Multidimensional Marine Environmental Data Conversion and Visualization Using Python and GIS".
[5] N. C. f. E. P. W. S. D. o. C. C. f. E. P. W. S. D. o. Commerce, "NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999," Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, 2000, updated daily. (Online). Available: http://dx.doi.org/10.5065/D6M043C6. (Accessed 17 10 2015).
[6] J. W. Shipman, "Tkinter reference: a GUI for Python," Technical report, New Mexico Tech Computer Center, 2005.
[7] "netCDF4 module," (Online). Available: http://unidata.github.io/ netcdf4-python/ (Accessed 17 October 2015).
[8] J. McNamara, "Creating Excel files with Python and XlsxWriter," (Online). Available: http://xlsxwriter.readthedocs.org/ (Accessed 1 July 2015).
[9] G. Zhao, B. A. Bryan, D. King, X. Song and Q. Yu, "Parallelization and optimization of spatial analysis for large scale environmental model data assembly," Computers and electronics in agriculture, vol. 89, pp. 94-99, 2012.
[1] D. B. Mde, T. D. M. Dep, M. K. N. Dec, W. H. N. Dec, G. S. N. Dec, M. K. V. Deq And D. B. I. Dnr, "Sensitivity testing of WRF physics parameterizations for meteorological modeling and protocol in support of regional SIP air quality modeling in the OTR," 2009.
[2] W. Wang, C. Bruyere, M. Duda, J. Dudhia, D. Gill, H. C. Lin and J. Mandel, "ARW version 3 modeling system user’s guide. Mesoscale & Miscroscale Meteorology Division. National Center for Atmospheric Research," January 2015. (Online). Available: http://www.mmm.ucar.edu/wrf/users/docs/user_guide_V3/ARWUsersGuideV3. (Accessed 17 October 2015).
[3] J. W. B. Lin, "Why python is the next wave in earth sciences computing," Bulletin of the American Meteorological Society, vol. 93, no. 12, pp. 1823-1824, 2012.
[4] M. Rainer, "Multidimensional Marine Environmental Data Conversion and Visualization Using Python and GIS".
[5] N. C. f. E. P. W. S. D. o. C. C. f. E. P. W. S. D. o. Commerce, "NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999," Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, 2000, updated daily. (Online). Available: http://dx.doi.org/10.5065/D6M043C6. (Accessed 17 10 2015).
[6] J. W. Shipman, "Tkinter reference: a GUI for Python," Technical report, New Mexico Tech Computer Center, 2005.
[7] "netCDF4 module," (Online). Available: http://unidata.github.io/ netcdf4-python/ (Accessed 17 October 2015).
[8] J. McNamara, "Creating Excel files with Python and XlsxWriter," (Online). Available: http://xlsxwriter.readthedocs.org/ (Accessed 1 July 2015).
[9] G. Zhao, B. A. Bryan, D. King, X. Song and Q. Yu, "Parallelization and optimization of spatial analysis for large scale environmental model data assembly," Computers and electronics in agriculture, vol. 89, pp. 94-99, 2012.
@article{"International Journal of Earth, Energy and Environmental Sciences:71539", author = "Jerome T. Tolentino and Ma. Victoria Rejuso and Jara Kaye Villanueva and Loureal Camille Inocencio and Ma. Rosario Concepcion O. Ang", title = "Development of a Wind Resource Assessment Framework Using Weather Research and Forecasting (WRF) Model, Python Scripting and Geographic Information Systems", abstract = "Wind energy is rapidly emerging as the primary
source of electricity in the Philippines, although developing an
accurate wind resource model is difficult. In this study, Weather
Research and Forecasting (WRF) Model, an open source mesoscale
Numerical Weather Prediction (NWP) model, was used to produce a
1-year atmospheric simulation with 4 km resolution on the Ilocos
Region of the Philippines. The WRF output (netCDF) extracts the
annual mean wind speed data using a Python-based Graphical User
Interface. Lastly, wind resource assessment was produced using a
GIS software. Results of the study showed that it is more flexible to
use Python scripts than using other post-processing tools in dealing
with netCDF files. Using WRF Model, Python, and Geographic
Information Systems, a reliable wind resource map is produced.", keywords = "Wind resource assessment, Weather Research and
Forecasting (WRF) Model, python, GIS software.", volume = "9", number = "12", pages = "1340-4", }