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B0285
Title: Distributional validation of precipitation data products with spatially varying mixture models Authors:  Lynsie Warr - University of California Irvine (United States) [presenting]
Matthew Heaton - Brigham Young University (United States)
William Christensen - Brigham Young University (United States)
Philip White - Brigham Young University (United States)
Summer Rupper - University of Utah (United States)
Abstract: The high mountain regions of Asia contain more glacial ice than anywhere on the planet outside of the polar regions. Because of the large population living in the Indus watershed region who are reliant on melt from these glaciers for freshwater, understanding the factors that affect glacial melt along with the impacts of climate change on the region is important for managing these natural resources. While there are multiple climate data products (e.g. reanalysis and global climate models) available to study the impact of climate change on this region, each product will have a different amount of skill in projecting a given climate variable, such as precipitation. 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 climate distribution from each climate data product is then validated against APHRODITE using a spatially varying Kullback-Leibler divergence measure.