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.

Assessment of groundwater vulnerability using integrated remote sensing and GIS techniques for the West Bengal coast, India.

Groundwater in the coastal districts of West Bengal, India is highly susceptible to various factors such as over-pumping, variations in rainfall, lower elevation and risk due to sea level rise. In addition to these factors, tropical cyclone induced storm surge and saltwater intrusion also induce potential risks to the quality of the coastal aquifers. There are several knowledge gaps, as many of these factors have not previously been systematically and rigorously analysed; furthermore, up-to-date information is either unavailable or insufficient. Accordingly, the present study analysed the groundwater vulnerability during the pre- and post-monsoon months for the period from 2001 to 2010 at three main coastal districts of West Bengal: East Midnapore, South 24 Parganas and North 24 Parganas (administrative regions). The GALDIT index-based model was employed to assess salt-water intrusion into the groundwater using Geographic Information System (GIS). Spatial distribution maps were also generated to identify highly vulnerable groundwater locations. Map removal and single parameter sensitivity analyses were performed to understand the sensitivity of the parameters. The study reveals that the depth of ground-water levels for the three districts increased and also the rainfall exerts a significant effect on the groundwater depth. The chemical constituents TDS and chloride contents in groundwater during the period 2004 to 2010 were analysed. The average TDS range values for pre- and post-monsoon seasons were observed to vary in the range between 100 and 3874 mg/l and 83-1929 mg/l respectively. Reports indicate that, groundwater in the area is highly saturated with iron containing minerals like Fe(OH)3, goethite, and hematite and is also moderately saturated with the calcite, chalcedony, dolomite and quartz, whereas under-saturated with anhydrite and gypsum. The implications of the research points to the urgent need for remedial action and appropriate responses at policy-level to protect groundwater.

Impact of urbanisation on urban heat island intensity during summer and winter over Indian metropolitan cities.

Industrial and economic development along with the rapid growth in urban population over the global cities is generating a warmer inner core in comparison to the surrounding natural landscape, the common feature of the phenomena of urban heat island (UHI). In the earlier part of the study, the spatial relationship of the changes in the land cover types and land surface temperature (LST) is analysed for metropolitan cities of India during winter season using remote sensing techniques. In the present study, an attempt has been made to estimate the UHI intensities over the metropolitan cities of India during summer season. The LST estimated using the Landsat 7 ETM+ images reveals the presence of a unique form of surface UHIs in the dense built-up areas of different cities. The intensities of UHIs over these cities during summer season are noticed to be in the range of 10.5-14 °C. Delhi possesses the highest UHI intensity in the range of 13.4-14.0 °C, and Kolkata possessed the lowest UHI intensity in the range of 10.5-11.7 °C. Higher-temperature zones (hotspots) are noticed to be increasing within the built-up areas and barren lands. The temperature of hotspots for the northern cities Delhi and Jaipur is observed to be in the range of 45-50 °C and is highest among the cities under consideration. On comparison with the winter results, the LST ranges, UHI intensities and the hotspot ranges for summer season are found be noticeably higher. The LST distribution and the UHI pattern of these cities are found to different during summer season in comparison to the winter season.

Higher-order statistics based multifractal predictability measures for anisotropic turbulence and the theoretical limits of aviation weather forecasting.

Theoretical predictability measures of turbulent atmospheric flows are essential in estimating how realistic the current storm-scale strategic forecast skill expectations are. Atmospheric predictability studies in the past have usually neglected intermittency and anisotropy, which are typical features of atmospheric flows, rendering their application to the storm-scale weather regime ineffective. Furthermore, these studies are frequently limited to second-order statistical measures, which do not contain information about the rarer, more severe, and, therefore, more important (from a forecasting and mitigation perspective) weather events. Here we overcome these rather severe limitations by proposing an analytical expression for the theoretical predictability limits of anisotropic multifractal fields based on higher-order autocorrelation functions. The predictability limits are dependent on the order of statistical moment (q) and are smaller for larger q. Since higher-order statistical measures take into account rarer events, such more extreme phenomena are less predictable. While spatial anisotropy of the fields seems to increase their predictability limits (making them larger than the commonly expected eddy turnover times), the ratio of anisotropic to isotropic predictability limits is independent of q. Our results indicate that reliable storm-scale weather forecasting with around 3 to 5 hours lead time is theoretically possible.