Implementation of the urban parameterization scheme in the Delhi model with an improved urban morphology

The current study highlights the importance of a detailed representation of urban processes in numerical weather prediction models and emphasizes the need for accurate urban morphology data for improving the near‐surface weather prediction over Delhi, a tropical Indian city. The Met Office Reading Urban Surface Exchange Scheme (MORUSES), a two‐tile urban energy‐budget parameterization scheme, is introduced in a high‐resolution (330‐m) model of Delhi. A new empirical relationship is established for the MORUSES scheme from the local urban morphology of Delhi. The performance is evaluated using both the newly developed empirical relationships (MORUSES‐IND) and the existing empirical relationships for the MORUSES scheme (MORUSES‐LON) against the default one‐tile configuration (BEST‐1t) for clear and foggy events and validations are performed against ground observations. MORUSES‐IND exhibits a significant improvement in the diurnal evolution of the wind speed in terms of amplitude and phase, compared with the other two configurations. Screen temperature (Tscreen$$ {T}_{\mathrm{screen}} $$) simulations using MORUSES‐IND reduce the warm bias, especially during the evening and night hours. The root‐mean‐square error of Tscreen$$ {T}_{\mathrm{screen}} $$ is reduced up to 29% using MORUSES‐IND for both synoptic conditions. The diurnal cycle of surface‐energy fluxes is reproduced well using MORUSES‐IND. The net longwave fluxes are underestimated in the model and biases are more significant during foggy events, partly due to the misrepresentation of fog. An urban cool island (UCI) effect is observed in the early morning hours during clear‐sky conditions, but it is not evident on foggy days. Compared with BEST‐1t, MORUSES‐IND represents the impact of urbanization more realistically, which is reflected in the reduction of the urban heat island and UCI in both synoptic conditions. Future works would improve the coupling between the urban surface energy budget and anthropogenic aerosols by introducing MORUSES‐IND in a chemistry aerosol framework model. Urban morphology parameters: (a) planar‐area index, (b) building height, and (c) frontal‐area index as a function of urban fraction derived from ISRO high‐resolution LULC data.