Urban areas, though small in terms of Earth’s total land coverage, are home to a majority of the global population. Understanding urban climate is crucial given its significant impact on the environment and human life. However, acquiring detailed and accurate urban climate data presents challenges.
Reanalysis data, for instance, often fails to accurately represent the intricate structures of urban environments due to the low resolution of climate models and the inherent limitations of model-based approaches. On the other hand, while station-based data offers more detailed insights, its usefulness is constrained by limited spatial coverage, particularly in urban areas, due to a bias towards rural station placement.
Satellite measured data play a pivotal role in urban climate analysis, yet they come with notable limitations. High spatial resolution satellites, such as MODIS and ECOSTRESS, are polar-orbiting, limiting their revisit frequency to specific locations on Earth. Conversely, geostationary satellites like GOES offer continuous observation of fixed locations but at the cost of lower spatial resolution. My research focuses on overcoming these constraints by developing methods to enhance satellite data’s spatiotemporal resolution. This involves ‘Downscaling’ – a process of merging data from multiple satellites using statistical and machine-learning techniques to achieve highly detailed and frequent observations. This area of research is a key interest of mine, aiming to bridge the gap between spatial detail and temporal frequency in satellite-based urban climate analysis.
Another limitation of satellite data in urban climate studies is its inability to directly measure certain climate indices. While satellites effectively estimate Land Surface Temperature (LST), they fall short in gauging Near Surface Air Temperature (T2M), a crucial metric for socio-economic impact assessments. LST provides valuable insights, but T2M, which measures air temperature approximately two meters above the ground, often has more direct socio-economic implications. Addressing this gap, my research aims to supplement satellite data with climate models and in-situ measurements. This approach is designed to derive comprehensive datasets, capturing both LST and T2M, thereby providing a more holistic understanding of urban climates and their socio-economic impacts.
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