Forecasts of fog events in northern India dramatically improve when weather prediction models include irrigation effects

Dense wintertime fog regularly impacts Delhi, severely affecting road and rail transport, aviation and human health. Recent decades have seen an unexplained increase in fog events over northern India, coincident with a steep rise in wintertime irrigation associated with the introduction of double-cropping. Accurate fog forecasting is challenging due to a high sensitivity to numerous processes across many scales, and uncertainties in representing some of these in state-of-the-art numerical weather prediction models. Here we show fog event simulations over northern India with and without irrigation, revealing that irrigation counteracts a common model dry bias, dramatically improving the simulation of fog. Evaluation against satellite products and surface measurements reveals a better spatial extent and temporal evolution of the simulated fog events. Increased use of irrigation over northern India in winter provides a plausible explanation for the observed upward trend in fog events, highlighting the critical need for optimisation of irrigation practices

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

The current study highlights the importance of a detailed representation of urban processes in a numerical weather prediction model 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 to the other two configurations. The screen temperature (Tscreen) simulations using MORUSES-IND reduce the warm bias, especially during the evening and night hours. The root-mean-square error of Tscreen 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 the foggy events partly due to the misrepresentation of fog. An urban cool island (UCI) effect is observed in the early morning hours during the clear sky conditions but it is not evident on foggy days. Compared to BEST-1t, MORUSES-IND represents the impact of urbanization more realistically which is reflected in the reduction of 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 the MORUSES-IND in a chemistry aerosol framework model