|
Activity Number:
|
414
- ENVR Student Paper Award Winners
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Wednesday, August 10, 2022 : 10:30 AM to 12:20 PM
|
|
Sponsor:
|
Section on Statistics and the Environment
|
|
Abstract #322325
|
|
|
Title:
|
Distributional Validation of Precipitation Data Products with Spatially Varying Mixture Models
|
|
Author(s):
|
Lynsie R Warr* and Matthew Heaton and William F Christensen and Philip White and Summer B Rupper
|
|
Companies:
|
Brigham Young University and BYU and Brigham Young University and BYU and University of Utah
|
|
Keywords:
|
High Mountain Asia;
Latent Variables;
Ordered Categorical Data
|
|
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.
|
Authors who are presenting talks have a * after their name.
