Modeling Spatial Variability of Urban Microclimate

Cities modify their local microclimate through changes in radiative, morphological, and thermal properties. Cities are also highly heterogeneous, leading to spatial variability in environmental exposure and climate risks. For instance, poor and disadvantaged communities in U.S cities often live in the warmest neighborhoods. While a lot of work has been done to improve urban representation in models to isolate the overall urban climate signals, these models are not ideal for examining spatial variability within cities due to poor structural and parameter constraints at these scales. In this talk, I will give an overview of this spatial variability and its importance, our current limitations in capturing this variability, and potential ways forward by leveraging current-generation satellite observations. Of note, the talk will cover both process-based numerical models as well as data-driven models with a focus on distributional inequality in urban heat exposure based on some recently published and upcoming papers. The lessons learned from these multiple past studies can hopefully guide future urban model development efforts to enable them to more accurately inform neighborhood-scale climate mitigation and adaptation strategies.
Dr. Tirthankar Chakraborty (goes by TC) is an Earth Scientist at the Pacific Northwest National Laboratory (PNNL) with interest in atmosphere-biosphere interactions. Before joining PNNL, TC finished his PhD from Yale University, where he developed a surface-energy budget perspective on aerosol-climate interactions. He has also worked extensively on urbanization-induced warming across scales by leveraging satellite measurements, crowdsourced weather station data, and modeling frameworks. TC is interested in the role of big data, machine learning, and urban informatics to better understand cities and their complexities. He is currently working on improving urban representation in land models and examining extreme events over coastal cities. He often uses the Google Earth Engine (GEE) cloud computing platform for geospatial analyses and was one of 26 inaugural GEE Developer Experts in the world. TC’s work has been featured in high impact scientific publications like Nature Communications, Science Advances, One Earth, etc. as well as in popular media outlets like the New York Times and the Washington Post. He recently received the U.S. Department of Energy Early Career grant to improve urban representation in Earth system models through planetary-scale data-model integration and a NASA Interdisciplinary Research in Earth Sciences grant to combine machine learning and remote sensing to examine disparities in heat exposure within U.S. cities.