Urban heat island effect has been assessed using weather research and forecasting model (WRF v3.5) focusing on air temperature and surface skin temperature in the sub-tropical urban Indian megacity of Delhi. Impact of urbanization-related changes in land use/land cover (LULC) on model outputs has been analyzed. Four simulations have been carried out with different types of LULC data viz. (1) USGS, (2) MODIS, (3) user-modified USGS and (4) user-modified land use data coupled with urban canopy model (UCM) for incorporation of canopy features. Heat island intensities have been estimated based on these simulations and subsequently compared with those derived from in situ and satellite observations. There is a significant improvement in model performance with modification of LULC and inclusion of UCM. Overall, RMSEs for near surface temperature improved from 6.3 to 3.9 °C and index of agreement for mean urban heat island intensities (UHI) improved from 0.4 to 0.7 with modified land use coupled with UCM. In general, model is able to capture the magnitude of UHI as well as high UHI zones well. A simple method of bias correction in model has been applied to improve model results for further application. The study highlights the importance of appropriate and updated the representation of land use–land cover and urban canopies for improving predictive capabilities of the mesoscale models. Urban heat island has been known to have effect on human thermal comfort. In the present study, Heat Index, a commonly used indicator of thermal comfort, is assessed spatially using WRF-UCM derived results. Urban areas were found to have higher Heat Index than non-urban areas by a difference of about 1.5–2 °C. Further, it was found that urban canopy effect leads to rise in thermal discomfort by increasing Heat Index. There is an increase in Heat Index of about 2.0–2.5 °C at dense built-up stations. Decrease in thermal comfort causes a significant impact on energy demand. Hence, analysis of urban heat island effect vis-a-vis thermal comfort provides useful information with regard to impact on human comfort and welfare.

The prediction of fog onset remains difficult despite the progress in numerical weather prediction. It is a complex process and requires adequate representation of the local perturbations in weather prediction models. It mainly depends upon microphysical and mesoscale processes that act within the boundary layer. This study utilizes a multirule based diagnostic (MRD) approach using postprocessing of the model simulations for fog predictions. The empiricism involved in this approach is mainly to bridge the gap between mesoscale and microscale variables, which are related to mechanism of the fog formation. Fog occurrence is a common phenomenon during winter season over Delhi, India, with the passage of the western disturbances across northwestern part of the country accompanied with significant amount of moisture. This study implements the above cited approach for the prediction of occurrences of fog and its onset time over Delhi. For this purpose, a high resolution weather research and forecasting (WRF) model is used for fog simulations. The study involves depiction of model validation and postprocessing of the model simulations for MRD approach and its subsequent application to fog predictions. Through this approach model identified foggy and nonfoggy days successfully 94% of the time. Further, the onset of fog events is well captured within an accuracy of 30–90 minutes. This study demonstrates that the multirule based postprocessing approach is a useful and highly promising tool in improving the fog predictions.