EcoSta 2022: Start Registration
View Submission - EcoSta2022
A0478
Title: Distributional validation of precipitation data products with spatially varying mixture models Authors:  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)
Lynsie Warr - University of California Irvine (United States) [presenting]
Abstract: The high mountain regions of Asia contain more glacial ice than anywhere on the planet outside the polar regions. Because the large populations living in the Indus watershed region are reliant on glacial melt for freshwater, understanding the factors that affect glacial melt and 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 these factors and impacts, each product has 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 an efficient Markov chain Monte Carlo algorithm. Each estimated distribution from each climate data product is validated against APHRODITE using a spatially varying Kullback-Leibler divergence measure.