Abstract by Lynsie Warr

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Presenter's Name

Lynsie Warr

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Faculty Advisor

Matthew Heaton


Validating Climate Data Products in High Mountain Asia using Spatially-varying Mixture Models


The high mountain regions of Asia contain more glacial ice than anywhere on the planet outside the poles. Because of the large population reliant on melt from these glaciers, understanding factors that affect glacial melt along with the impacts of climate change on the region is important for managing these resources. While there are multiple climate data products (e.g. reanalysis and global climate models) available for the region, each product has a different amount of skill in projecting a given climate variable, such as precipitation. In this research, we develop a spatially varying mixture model to compare the distribution of precipitation in the high mountain Asia region as produced by climate models with the corresponding distribution from in situ observations from the Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) data product. Parameter estimation is carried out via a computationally efficient Markov chain Monte Carlo algorithm. Each estimated distribution from each climate data product is then validated against APHRODITE using a spatially varying Kullback-Leibler divergence measure.