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.

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There has been paucity of field campaigns in India in past few decades on the urban heat island intensities (UHI). Remote sensing observations provide useful information on urban heat island intensities and hotspots as supplement or proxy to in-situ surface based measurements. A case study has been undertaken to assess and compare the UHI and hotspots based on in-situ measurements and remote sensing observations as the later method can be used as a proxy in absence of in-situ measurements both spatially and temporally. Capital of India, megacity Delhi has grown by leaps and bounds during past 2 - 3 decades and strongly represents tropical climatic conditions where such studies and field campaigns are practically non-existent. Thus, a field campaign was undertaken during summer, 2008 named DELHI-I (Delhi Experiments to Learn Heat Island Intensity-I) in this megacity. Urban heat island effects were found to be most dominant in areas of dense built up infrastructure and at commercial centers. The heat island intensity (UHI) was observed to be higher in magnitude both during afternoon hours and night hours (maximum up to 8.3?C) similar to some recent studies. The three high ranking urban heat island locations in the city are within commercial and/or densely populated areas. The results of this field campaign when compared with MODIS-Terra data of land surface temperature revealed that UHI hotspots are comparable only during nighttime. During daytime, similar comparison was less satisfactory. Further, available relationship of maximum UHI with population data is applied for the current measurements and discussed in the context of maximum UHI of various other countries

Regulatory models are useful tools for air quality management. However, application of models without proper evaluation may lead to erroneous conclusions and thus systematic model evaluation studies are essential prior to model application. Often, models are evaluated for a specific source and climatic condition and then find application to another source and climatic condition without this realization. In this context, two well known regulatory models namely; AERMOD (07026) and ADMS-Urban (2.2) are applied throughout the world in various countries without rigorous evaluation procedures. An attempt is made here to undertake performance evaluation of these models for a tropical city such as Delhi in India which is a well known megacity of the world. The models have been applied to estimate ambient particulate matter concentrations for the years 2000 and 2004 over seven sites in Delhi and model evaluation and inter-comparison is performed. Concentrations have been estimated for winter season in both years as the low temperature and low speed wind conditions in this season make it most significant from air pollution point of view. It has been found that though both the models have a tendency towards under-prediction, estimated values by both models agree with the observed concentrations within factor of two. However ADMS-Urban results show better trend correlation with observed values while bias between observed and estimated values is lower for AERMOD Results. The models include all the urban sources (ie. elevated point sources, vehicular traffic, domestic and other sources) in the city. The model validation is discussed in the light of emission inventory, requisite meteorological inputs and statistical performance measures. Performance evaluation of the above models is examined based on boundary layer parameterisations used in these models. Intercomparison of the model performances is envisaged to be useful for application to air quality management and further development of these models.

A thermal stress index of a geographic location over a period of time can provide knowledge of overall climate perceptible to the general public. Out of the three approaches to assessing thermal comfort namely, rational, empirical and direct, the direct approach is being used in the present study because of easy availability of all inputs and reasonable comprehension of the assessments. Assessment and ranking of cities using this approach based on the percentage of comfortable hours alone may however be erroneous and misleading as this approach does not consider the percentages of uncomfortable classes which could often be substantially high. The modified approach for thermal comfort classification demonstrates cumulative representation of all classes of thermal comfort including uncomfortablity and provides relative ranking of cities. Analysis of the results is presented here for five megacities (Delhi, Mumbai, Chennai, Kolkata and Hyderabad) representing varying geographical and climatic locations of India. These cities are ranked based on the routine and modified approaches and results are discussed in detail on monthly, seasonal and annual average basis. When the cities are compared only on the basis of comfortable hours, the decreasing order of comfortability is Hyderabad, Kolkata, Delhi, Chennai and Mumbai. However, considering the second methodology, it is revealed that the contribution of uncomfortable hours is greater in Kolkata and Chennai in comparison to Mumbai. The proposed methodology could be an improvement over the current practices and provides a more rational method for relative ranking of cities that could be used for tourism and energy demands.