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Distributional Validation of Precipitation Data Products with Spatially Varying Mixture Models

Author

Listed:
  • Lynsie R. Warr

    (University of California Irvine)

  • Matthew J. Heaton

    (Brigham Young University)

  • William F. Christensen

    (Brigham Young University)

  • Philip A. White

    (Brigham Young University)

  • Summer B. Rupper

    (The University of Utah)

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 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.

Suggested Citation

  • Lynsie R. Warr & Matthew J. Heaton & William F. Christensen & Philip A. White & Summer B. Rupper, 2023. "Distributional Validation of Precipitation Data Products with Spatially Varying Mixture Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(1), pages 99-116, March.
    • Handle: RePEc:spr:jagbes:v:28:y:2023:i:1:d:10.1007_s13253-022-00515-0
    DOI: 10.1007/s13253-022-00515-0
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    1. Sudipto Banerjee & Alan E. Gelfand & Andrew O. Finley & Huiyan Sang, 2008. "Gaussian predictive process models for large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 825-848, September.
    2. Schliep, Erin M. & Hoeting, Jennifer A., 2015. "Data augmentation and parameter expansion for independent or spatially correlated ordinal data," Computational Statistics & Data Analysis, Elsevier, vol. 90(C), pages 1-14.
    3. Higgs, Megan Dailey & Hoeting, Jennifer A., 2010. "A clipped latent variable model for spatially correlated ordered categorical data," Computational Statistics & Data Analysis, Elsevier, vol. 54(8), pages 1999-2011, August.
    4. Jie Chen & François P. Brissette & Xunchang J. Zhang & Hua Chen & Shenglian Guo & Yan Zhao, 2019. "Bias correcting climate model multi-member ensembles to assess climate change impacts on hydrology," Climatic Change, Springer, vol. 153(3), pages 361-377, April.
    5. John Hughes & Murali Haran, 2013. "Dimension reduction and alleviation of confounding for spatial generalized linear mixed models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(1), pages 139-159, January.
    6. Matthew Stephens, 2000. "Dealing with label switching in mixture models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 795-809.
    7. Noel Cressie & Gardar Johannesson, 2008. "Fixed rank kriging for very large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 209-226, February.
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