Open Access Open Access  Restricted Access Subscription or Fee Access

Towards Renewable Energy Transition: Evaluating Wind Resources in Odisha

Dibya Jyoti Mohanty, Dr. Jajnaseni Rout


The urgency to transition to renewable energy sources is underscored by the environmental crises stemming from our reliance on non-renewable fuels. This study focuses on assessing wind energy potential in Odisha, India, utilizing satellite data and Geographic Information System (GIS) technologies. The research addresses the critical need for strategic planning and site selection before investing in renewable energy infrastructure. By employing a model that integrates various free satellite datasets and leverages fundamental physical principles, the study calculates wind power density (WPD) at a height of 90 meters above the surface for both onshore and offshore locations. The methodology involves acquiring and processing datasets such as temperature, wind speed, digital elevation model (DEM), pressure, air density, and land use/land cover (LULC) classifications. The model applies equations derived from physical laws to determine key parameters necessary for calculating WPD. Specifically, temperature and pressure data are used to estimate air density, while surface roughness is assigned based on LULC classes with windspeed at 10m to extrapolate wind speed at 90 meters above ground level. The method can be used at any hub height. Results reveal significant wind energy potential in Odisha, particularly along the coastal regions. Jagatsinghpur and Puri emerge as areas with high WPD onshore, while the offshore exclusive economic zone (EEZ) of Odisha exhibits substantial wind energy resources. The model outputs provide valuable insights for various studies related to renewable energy and facilitate informed decision-making in site selection analyses. Furthermore, the study emphasizes the simplicity and effectiveness of the developed model,
making it a practical tool for assessing wind energy potential in other regions as well. Overall, this research contributes to the global effort towards transitioning to sustainable energy sources and combating climate change. By highlighting the renewable energy potential of Odisha, it underscores the importance of harnessing wind energy as a viable pathway towards a cleaner, greener future.

Full Text:



 Abdelaziz, Almoataz, Mekhamer, S., & Mohamed, Amany. (2012). Geographic Information Systems (GIS) Application in Wind Farm Planning. The Online Journal on Power and Energy Engineering (OJPEE), 3, 279-283.

 Alpert, J. (2006). Sub-Grid Scale Mountain Blocking at NCEP. 20th Conference on Weather Analysis and Forecasting/16th Conference on Numerical Weather Prediction (P2.4).

 Alpert, J., Kanamitsu, M., Caplan, P. M., Sela, J. G., White, G. H., & Kalnay, E. (1988). Mountain induced gravity wave drag parameterization in the NMC medium-range forecast model. Pre-prints, Eighth Conference on Numerical Weather Prediction, Baltimore, MD,

Amer. Meteor. Soc., 726-733.

 Baker-Yeboah, S., Saha, K., Zhang, D., Casey, K. S., Evans, R., & Kilpatrick, K. A. (2016). Pathfinder Version 5.3 AVHRR Sea Surface Temperature Climate Data Record. Fall AGU 2016 Poster (manuscript in progress).

 Buehner, M., Morneau, J., & Charette, C. (2013). Four-dimensional ensemble-variational data assimilation for global deterministic weather prediction. Nonlinear Processes in Geophysics, 20, 669–682.

 Cristea, Catalina & Jocea, Andreea. (2016). GIS Application for Wind Energy. Energy Procedia, 85.

 Copernicus Climate Change Service (C3S). (2017). ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store (CDS). Retrieved from!/home.

 Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., & Alsdorf, D. (2007). The Shuttle Radar Topography Mission. Reviews of Geophysics, 45, RG2004.

 Han, J., & Pan, H.-L. (2011). Revision of convection and vertical diffusion schemes in the NCEP global forecast system. Weather and Forecasting, 26, 520-533.

 Han, J., Witek, M., Teixeira, J., Sun, R., Pan, H.-L., Fletcher, J. K., & Bretherton, C. S. (2016). Implementation in the NCEP GFS of a hybrid eddy-diffusivity mass-flux (EDMF) boundary layer parameterization with dissipative heating and modified stable boundary layer mixing. Weather and Forecasting, 31, 341-352.

 Johansson, Ake. (2008). Convectively Forced Gravity Wave Drag in the NCEP Global Weather and Climate Forecast Systems. SAIC/Environmental Modelling Center internal report.

 Juang, H-M, et al. (2014). Regional Spectral Model workshop in memory of John Roads and Masao Kanamitsu. BAMS.

 Kalnay et al. (1996). The NCEP/NCAR 40-Year Reanalysis Project. Bulletin of the American Meteorological Society, 77, 437-471.<0437:TNYRP>2.0.CO;2.

 Kleist, D. T. (2012). An evaluation of hybrid variational-ensemble data assimilation for the NCEP GFS. Ph.D. Thesis, Dept. of Atmospheric and Oceanic Science, University of Maryland-College Park.

 Lott, F., & Miller, M. J. (1997). A new subgrid-scale orographic drag parameterization: Its formulation and testing. Quarterly Journal of the Royal Meteorological Society, 123, pp101-127.

 Meyer, D., & Riechert, M. (2018). The GIS4WRF Plugin. Zenodo.

 Meyer, D., & Riechert, M. (2019). Open source QGIS toolkit for the Advanced Research WRF modelling system. Environmental Modelling & Software, 112, 166–178.

 Muñoz Sabater, J. (2019). ERA5-Land monthly averaged data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). doi:10.24381/cds.68d2bb30.

 Ramachandra, T. V. (2007). Solar energy potential assessment using GIS. Energy Education Science and Technology, 18, 101-114.

 Roy, P. S., Meiyappan, P., Joshi, P. K., Kale, M. P., Srivastav, V. K., Srivasatava, S. K., Krishnamurthy, Y. V. N. (2016). Decadal Land Use and Land Cover Classifications across India, 1985, 1995, 2005. ORNL DAAC.

 Sela, J. (2009). The implementation of the sigma-pressure hybrid coordinate into the GFS. NCEP Office Note #461.

 Sela, J. (2010). The derivation of sigma-pressure hybrid coordinate semi-Lagrangian model equations for the GFS. NCEP Office Note #462.

 Tozer, B., Sandwell, D. T., Smith, W. H. F., Olson, C., Beale, J. R., & Wessel, P. (2019). Global bathymetry and topography at 15 arc sec: SRTM15+. Distributed by OpenTopography.

 Yang, F. (2009). On the Negative Water Vapor in the NCEP GFS: Sources and Solution. 23rd Conference on Weather Analysis and Forecasting/19th Conference on Numerical Weather Prediction, 1-5 June 2009, Omaha, NE.



  • There are currently no refbacks.

eISSN: 2230-7990