The present study concentrates on the assimilation of clear‐sky radiances from the recently launched INSAT‐3D satellite. The imager and sounder are two primary meteorological instruments aboard the INSAT‐3D satellite. Pre‐assimilation monitoring of the radiances has been carried out from April to July 2014. A double‐difference technique using the High Resolution Infrared Sounder (HIRS) on MetOp‐A is employed to remove the effect of deficiencies in the model‐analysed profiles which can contribute to the biases. Observed radiances of both instruments appear to be colder (∼1.5–2 K) with respect to radiative transfer model (RTM) simulated radiances, except water vapour channel radiances, which are warmer (0.5–1 K). The standard deviations of observed minus RTM‐simulated radiances are between 0.5 and 1.5 K. Prior to data assimilation, these biases are corrected using a variational bias correction scheme. The largest impacts on the analyses, when assimilating imager radiances, are found in the mid‐ and upper‐tropospheric moisture, while assimilation of sounder radiances impacted both moisture and temperature throughout the troposphere. The more accurate analyses with the INSAT‐3D radiance assimilation lead to improved moisture, wind, temperature and precipitation forecasts compared to the control case in which only conventional observations were assimilated. The results demonstrate the ability of temperature‐ and water vapour‐sensitive radiances to improve not only the temperature and moisture fields, but also the wind fields, enhancing their importance, particularly over tropical regions where wind observations are more important.

Amid global concerns regarding climate change and urbanization, understanding the interplay between land use/land cover (LULC) changes, the urban heat island (UHI) effect, and land surface temperatures (LST) is paramount. This study provides an in-depth exploration of these relationships in the context of the Kamrup Metropolitan District, Northeast India, over a period of 22 years (2000–2022) and forecasts the potential implications up to 2032. Employing a high-accuracy supervised machine learning algorithm for LULC analysis, significant transformations are revealed, including the considerable growth in urban built-up areas and the corresponding decline in cultivated land. Concurrently, a progressive rise in LST is observed, underlining the escalating UHI effect. This association is further substantiated through correlation studies involving the normalized difference built-up index (NDBI) and the normalized difference vegetation index (NDVI). The study further leverages the cellular automata–artificial neural network (CA-ANN) model to project the potential scenario in 2032, indicating a predicted intensification in LST, especially in regions undergoing rapid urban expansion. The findings underscore the environmental implications of unchecked urban growth, such as rising temperatures and the intensification of UHI effects. Consequently, this research stresses the critical need for sustainable land management and urban planning strategies, as well as proactive measures to mitigate adverse environmental changes. The results serve as a vital resource for policymakers, urban planners, and environmental scientists working towards harmonizing urban growth with environmental sustainability in the face of escalating global climate change.

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