Assessment of universal thermal climate index (UTCI) using the WRF-UCM model over a metropolitan city in India.

Rapid urbanization increases urban air temperature, considerably affecting health, comfort, and the quality of life in urban spaces. The accurate assessment of outdoor thermal comfort is crucial for urban health. In the present study, a high-resolution mesoscale model coupled with a layer Urban Canopy Model (WRF-UCM) is implemented over the city of Hyderabad (17.3850° N, 78.4867° E) to simulate urban meteorological conditions during the summer and winter period of 2009 and 2019. The universal thermal climate index (UTCI) has been estimated using the model-derived atmospheric variables and a human biometeorology parameter to assess the linkages between the outdoor environment and thermal comfort. Results revealed that during summer, the city experiences nearly 50 h of very strong thermal stress, whereas about 120 h of slight cold stress are experienced during winter. The urban area in Hyderabad expanded from 5 to 15% during the study period, leading to a 2.5℃ (2.8 ℃) increase in land surface temperature, and a 1.2 (1.9 ℃) rise in air temperature at 2 m height and 1.5 (2.5 ℃) UTCI during summer (winter) time. The analysis reveals that the maximum UTCI values were noticed over built-up areas compared to other land classes during daytime and nighttime. The results derived from the present study have shown that the performance of WRF-UCM-derived UTCI reasonably portrayed the significant impact of urbanization on thermal comfort over the city and provided useful insights with regard to urban comfort and welfare.

Retrieval of high-resolution aerosol optical depth (AOD) using Landsat 8 imageries over different LULC classes over a city along Indo-Gangetic Plain, India.

Aerosol optical depth (AOD) serves as a crucial indicator for assessing regional air quality. To address regional and urban pollution issues, there is a requirement for high-resolution AOD products, as the existing data is of very coarse resolution. To address this issue, we retrieved high-resolution AOD over Kanpur (26.4499°N, 80.3319°E), located in the Indo-Gangetic Plain (IGP) region using Landsat 8 imageries and implemented the algorithm SEMARA, which combines SARA (Simplified Aerosol Retrieval Algorithm) and SREM (Simplified and Robust Surface Reflectance Estimation). Our approach leveraged the green band of the Landsat 8, resulting in an impressive spatial resolution of 30 m of AOD and rigorously validated with available AERONET observations. The retrieved AOD is in good agreement with high correlation coefficients (r) of 0.997, a low root mean squared error of 0.035, and root mean bias of - 4.91%. We evaluated the retrieved AOD with downscaled MODIS (MCD19A2) AOD products across various land classes for cropped and harvested period of agriculture cycle over the study region. It is noticed that over the built-up region of Kanpur, the SEMARA algorithm exhibits a stronger correlation with the MODIS AOD product compared to vegetation, barren areas and water bodies. The SEMARA approach proved to be more effective for AOD retrieval over the barren and built-up land categories for harvested period compared with the cropping period. This study offers a first comparative examination of SEMARA-retrieved high-resolution AOD and MODIS AOD product over a station of IGP.