Volume IV-5
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-5, 303-310, 2018
https://doi.org/10.5194/isprs-annals-IV-5-303-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-5, 303-310, 2018
https://doi.org/10.5194/isprs-annals-IV-5-303-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  15 Nov 2018

15 Nov 2018

EVALUATION OF HIGH RESOLUTION URBAN LULC FOR SEASONAL FORECASTS OF URBAN CLIMATE USING WRF MODEL

M. Bhavana1, K. Gupta2, P. K. Pal1, A. S. Kumar1, and J. Gummapu3 M. Bhavana et al.
  • 1Centre for Space Science and Technology Education in Asia and the Pacific, Dehradun, India
  • 2Indian Institute of Remote Sensing, Dehradun, India
  • 3Department of Geo-Engineering, Andhra University, Visakhapatnam, Andhra Pradesh, India

Keywords: UCM, WRF Model, urban physics, Updated LULC

Abstract. In all mesoscale models with urban parameterizations, urban area represented as a single entity to represent the influence of urban morphology. In the last few years, many Urban Canopy Models (UCM) have been developed by many researchers to model the urban energy fluxes, but their spatial resolution is too coarse. These models proves to be a hindrance in obtaining improved results for urban climatic studies due to their coarser resolution. So downscaling of climatic variables in an urban area is primary significance for urban climatic studies. Weather Research Forecasting Model (WRF) is the one of the models that has been used widely for downscaling the climatic variables at urban scale and it has been also integrated with UCM along with a number of urban sub physics options. In this study, modified high resolution Land Use Land Cover (LULC) representing three urban classes for the city of Chandigarh has been ingested into the model to examine and validate the model output with respect to ground observations. The model has been configured with two domains with a resolution of 3KM and 1KM and simulations were carried out for three days of the of four seasons of India, winter, summer, monsoon and post-monsoon for the analysis of seasonal variation. Improved values of Root Mean Square Error (RMSE) for surface temperature, relative humidity and wind speed was observed with modified high resolution LULC with BEM option as compared to single urban built up class. In terms of temperature, summer season showed very less RMSE than other seasons, i.e, 0.76°C and . In terms of relative humidity, monsoon season showed very less RMSE than other seasons, i.e., 2.63% and in terms of wind speed, post monsoon season is giving less RMSE i.e., 1.01m/s.