Operational Probabilistic Fog Prediction Based on Ensemble Forecast System: A Decision Support System for Fog

One of the well-known challenges of fog forecasting is the high spatio-temporal variability of fog. An ensemble forecast aims to capture this variability by representing the uncertainty in the initial/lateral boundary conditions (ICs/BCs) and model physics. The present study highlights a new operational Ensemble Forecast System (EFS) developed by the Indian Institute of Tropical Meteorology (IITM), Pune, to predict the fog over the Indo-Gangetic Plain (IGP) region using the visibility (Vis) diagnostic algorithm. The EFS framework comprises the WRF model with a 4 km horizontal resolution, initialized by 21 ICs/BCs. The advantages of probabilistic fog forecasting have been demonstrated by comparing control (CNTL) and ensemble-based fog forecasts. The forecast is verified using fog observations from the Indira Gandhi International (IGI) airport during the winter months of 2020–2021 and 2021–2022. The results show that with a probability threshold of 50%, the ensemble forecasts perform better than the CNTL forecasts. The skill scores of EFS are relatively promising, with a Hit Rate of 0.95 and a Critical Success Index of 0.55; additionally, the False Alarm Rate and Missing Rate are low, with values of 0.43 and 0.04, respectively. The EFS could correctly predict more fog events (37 out of 39) compared with the CNTL forecast (31 out of 39) and shows the potential skill. Furthermore, EFS has a substantially reduced error in predicting fog onset and dissipation (mean onset and dissipation error of 1 h each) compared to the CNTL forecasts

Operational Probabilistic Fog Prediction Based on Ensemble Forecast System: A Decision Support System for Fog

One of the well-known challenges of fog forecasting is the high spatio-temporal variability of fog. An ensemble forecast aims to capture this variability by representing the uncertainty in the initial/lateral boundary conditions (ICs/BCs) and model physics. The present study highlights a new operational Ensemble Forecast System (EFS) developed by the Indian Institute of Tropical Meteorology (IITM), Pune, to predict the fog over the Indo-Gangetic Plain (IGP) region using the visibility (Vis) diagnostic algorithm. The EFS framework comprises the WRF model with a 4 km horizontal resolution, initialized by 21 ICs/BCs. The advantages of probabilistic fog forecasting have been demonstrated by comparing control (CNTL) and ensemble-based fog forecasts. The forecast is verified using fog observations from the Indira Gandhi International (IGI) airport during the winter months of 2020–2021 and 2021–2022. The results show that with a probability threshold of 50%, the ensemble forecasts perform better than the CNTL forecasts. The skill scores of EFS are relatively promising, with a Hit Rate of 0.95 and a Critical Success Index of 0.55; additionally, the False Alarm Rate and Missing Rate are low, with values of 0.43 and 0.04, respectively. The EFS could correctly predict more fog events (37 out of 39) compared with the CNTL forecast (31 out of 39) and shows the potential skill. Furthermore, EFS has a substantially reduced error in predicting fog onset and dissipation (mean onset and dissipation error of 1 h each) compared to the CNTL forecasts

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