Analysis of WRF Model Performance over Subtropical Region of Delhi, India

Model performance and sensitivity to model physics options are studied with the Weather Research and Forecasting model (version 3.1.1) over Delhi region in India for surface and upper air meteorological parameters in summer and winter seasons. A case study with the model has been performed with different configurations, and the best physics options suited for this region have been, determined. Comparison between estimated and observed data was carried out through standard statistical measures. Generally, the combination of Pleim-Xiu land surface model, Pleim surface layer scheme, and Asymmetric Convective Model has been found to produce better estimates of temperature and relative humidity for Delhi region. Wind speed and direction estimations were observed best for MM5 similarity surface layer along with Yonsei University boundary layer scheme. Nested domains with higher resolutions were not helpful in improving the simulation results as per the current availability of the data. Overall, the present case study shows that the model has performed reasonably well over the subtropical region of Delhi

Industrial heat island: a case study of Angul-Talcher region in India

Most of the urban heat island (UHI) studies are carried out in densely populated cities but core industrial areas are also potential sites of heat island effect despite having a comparatively lower population. In the present study, heat island assessment has been carried out for Angul-Talcher industrial area (ATIA) which is one of the oldest industrial areas of India and is still undergoing a transformation to accommodate more industries and mining operations. As the major contributors towards influencing local meteorology were expected to be industrial (and mining) activities, the heat island was studied as "industrial heat island" (IHI) rather than urban heat island. Industrial and mining sites were the most frequent nighttime canopy-layer heat island intensity (HIN) hotspots due to anthropogenic heat of associated industrial processes as well as built structures. During the daytime, croplands experienced the most frequent canopy-layer HIN hotspots which could be attributed to low moisture of the soils during the non-farming period of the field campaign. Hourly maximum atmospheric heat island intensities were observed in the range of 7-9 degrees C. Monthly maximum HINs ranged from 2.97 to 4.04 degrees C while 3-month mean HINs varied from 1.45 to 2.74 degrees C. Amongst different land use/land cover classes, the highest mean canopy-layer heat island intensity for the entire 3-month-long duration of field campaign during nighttime was assessed at the mining sites (3-month mean 2.74 degrees C) followed in decreasing order by the industrial sites (2.52 degrees C), rural and urban settlements (2.13 degrees C), and croplands (2.06 degrees C). Corresponding daytime canopy-layer heat island intensity was highest for the croplands (2.07 degrees C) followed in decreasing order by the mining sites (1.70 degrees C), rural and urban settlements (1.68 degrees C), and industry (1.45 degrees C)

WRF-urban canopy model evaluation for the assessment of heat island and thermal comfort over an urban airshed in India under varying land use/land cover conditions

Abstract 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

Air pollution control Technologies in the transport sector Special Feature : Air Pollution Control Technologies

Automobiles are a major source of air pollution in urban areas. This article explores technical and non-technical ways to control this pollution. It examines regulation of vehicular emission in relation to fuel characteristics, impact on air quality and standards enforcement in various Asian nations. In addition to the various technical and non-technical ways, vehicular pollution can also be mitigated by the development of an efficient public transport system. The article discusses the cases of two modes of public transport systems -Rapid Bus Transit and Metro rail -in Delhi, India. While growth in the number of vehicles cannot be contained, the right means can ensure that the air we breathe does not damage our health

Assessment of Air Pollution Mitigation Measures on Secondary Pollutants PM10 and Ozone Using Chemical Transport Modelling over Megacity Delhi, India

Sporadic efforts have been introduced to control emissions in Delhi, but the air quality has declined further due to the rapid development of different sectors. In this study, the impact of various mitigation scenarios on air quality for PM10, ozone, and its precursors are studied using a chemical transport model, namely WRF-Chem. The Emission Database for Global Atmospheric Research emission inventory was modified and introduced into the WRF-Chem model to assess the impact of selected emission control scenarios on different sectors. The simulations were conducted with reduced emissions for these sectors over the study domain: (a) implementation of Bharat Stage—VI norms in the transport sector, (b) conversion of fuel from coal to natural gas in the energy sector, and (c) fuel shift to LPG in the residential sector. The transport sector noted a decrease of 4.9% in PM10, 44.1% in ozone, and 18.9% in NOx concentrations with emission reduction measures. In the energy sector, a marginal reduction of 3.9% in NOx concentrations was noted, and no change was observed in PM10 and ozone concentrations. In the residential sector, a decrease of 8% in PM-10, 47.7% in ozone, and 49.8% in NOx concentrations were noted. The VOC-to-NOx ratios were also studied, revealing the ozone production over the study domain was mostly VOC-limited. As the inclusion of control measures resulted in varying levels of reduction in pollutant concentrations, it was also studied in the context of improving the air quality index. The WRF-Chem model can be successfully implemented to study the effectiveness of any regulated control measures

Assessment of Air Pollution Mitigation Measures on Secondary Pollutants PM₁₀ and Ozone Using Chemical Transport Modelling over Megacity Delhi, India

Sporadic efforts have been introduced to control emissions in Delhi, but the air quality has declined further due to the rapid development of different sectors. In this study, the impact of various mitigation scenarios on air quality for PM10, ozone, and its precursors are studied using a chemical transport model, namely WRF-Chem. The Emission Database for Global Atmospheric Research emission inventory was modified and introduced into the WRF-Chem model to assess the impact of selected emission control scenarios on different sectors. The simulations were conducted with reduced emissions for these sectors over the study domain: (a) implementation of Bharat Stage—VI norms in the transport sector, (b) conversion of fuel from coal to natural gas in the energy sector, and (c) fuel shift to LPG in the residential sector. The transport sector noted a decrease of 4.9% in PM10, 44.1% in ozone, and 18.9% in NOx concentrations with emission reduction measures. In the energy sector, a marginal reduction of 3.9% in NOx concentrations was noted, and no change was observed in PM10 and ozone concentrations. In the residential sector, a decrease of 8% in PM-10, 47.7% in ozone, and 49.8% in NOx concentrations were noted. The VOC-to-NOx ratios were also studied, revealing the ozone production over the study domain was mostly VOC-limited. As the inclusion of control measures resulted in varying levels of reduction in pollutant concentrations, it was also studied in the context of improving the air quality index. The WRF-Chem model can be successfully implemented to study the effectiveness of any regulated control measures

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