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

Abstract

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.

Unraveling the Mechanism of the Holes in the Blanket of Fog Over the Indo‐Gangetic Plains: Are They Driven by Urban Heat Islands or Aerosol?

“…(2021) and Anurose et al. (2023). DM‐Chem includes the state of the art UK Chemistry and Aerosol scheme (UKCA).…”

“…Two widespread dense fog events, 22 January 2016 and 23 December 2019 over the IGP region are selected for the case study. These fog cases are chosen by considering the fog hole formation and the satellite coverage over the domain and the details of these events are reported in the earlier literature (Anurose et al., 2023; Dhangar et al., 2021; Jayakumar et al., 2021).…”

Implementation of WRF-Urban Asymmetric Convective Model (UACM) for Simulating Urban Fog over Delhi, India

“…The study conducted by Theethai Jacob et al (2023) involved the integration of a comprehensive urban surface-flux scheme into a high-resolution Delhi Model with Chemistry and aerosol framework (DM-Chem), utilizing urban morphology data specific to the Delhi region obtained from empirical relationships. Their aim was to simulate the UHI and urban cool island (UCI) effects under clear sky and foggy conditions.…”

Deep Learning for Built-up Fractional Mapping using Sentinel-2 images: A Case Study in Delhi, India

“…Urbanization significantly impacts climate at different scales and with the need and want for more and more infrastructure to support the growing populations especially in developing countries where still construction is done using concrete and other heat-absorbing elements rather than sustainable elements, the Urban Heat Island (UHI) effect is very profound [2]. Urban populations in Asia and Africa are projected to double by 2050, with limited data available on settlement patterns, particularly in low and middle-income countries [3][4][5]. The dynamic transition between rural and urban areas morphology further complicates the evolving structure of cities and regions, challenging mapping, and monitoring efforts of built-up in those regions [4,6].…”