Distributional Validation of Precipitation Data Products with Spatially Varying Mixture Models

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 fresh water, understanding the factors that affect glacial melt al...

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Bibliographic Details
Published in:Journal of agricultural, biological, and environmental statistics biological, and environmental statistics, 2023-03, Vol.28 (1), p.99-116
Main Authors: Warr, Lynsie R., Heaton, Matthew J., Christensen, William F., White, Philip A., Rupper, Summer B.
Format: Article
Language:English
Subjects:
Agriculture
Algorithms
Biostatistics
Climate change
Climate models
Climatic data
Data integration
Environmental impact
Fresh water
Glaciers
Global climate
Global climate models
Global temperature changes
Health Sciences
Hydrologic data
Markov chains
Markov processes
Mathematics and Statistics
Medicine
Monitoring/Environmental Analysis
Monte Carlo method
Mountain regions
Mountains
Natural resources
Parameter estimation
Polar environments
Precipitation
Precipitation (Meteorology)
Probabilistic models
Statistics
Statistics for Life Sciences
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Summary: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 fresh water, 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. 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 of the estimated climate distributions from each climate data product is then validated against APHRODITE using a spatially varying Kullback–Leibler divergence measure. Supplementary materials accompanying this paper appear online.
ISSN:1085-7117
1537-2693
DOI:10.1007/s13253-022-00515-0