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Guest Editors’ Introduction to the Special Issue on “Climate and the Earth System”

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  • Dorit Hammerling

    (Colorado School of Mines)

  • Brian J. Reich

    (North Carolina State University)

Abstract

The Journal of Agricultural, Biological and Environment Statistics (JABES) special issue on the Climate and Earth System highlights recent statistical develops that aim to refine our understanding of this complex system. New methods are required to process the massive environmental data that often fuels climate analysis and to properly account for uncertainty in the results. This special issue proudly features eight papers that span a wide range of computational and methodological problems related to the climate and earth system. In this brief introduction, we identify common themes among the papers and point to areas of future research.

Suggested Citation

  • Dorit Hammerling & Brian J. Reich, 2019. "Guest Editors’ Introduction to the Special Issue on “Climate and the Earth System”," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 395-397, September.
    • Handle: RePEc:spr:jagbes:v:24:y:2019:i:3:d:10.1007_s13253-019-00373-3
    DOI: 10.1007/s13253-019-00373-3
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    References listed on IDEAS

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    1. Matthew J. Heaton & Abhirup Datta & Andrew O. Finley & Reinhard Furrer & Joseph Guinness & Rajarshi Guhaniyogi & Florian Gerber & Robert B. Gramacy & Dorit Hammerling & Matthias Katzfuss & Finn Lindgr, 2019. "A Case Study Competition Among Methods for Analyzing Large Spatial Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 398-425, September.
    2. Whitney K. Huang & Daniel S. Cooley & Imme Ebert-Uphoff & Chen Chen & Snigdhansu Chatterjee, 2019. "New Exploratory Tools for Extremal Dependence: $$\chi $$ χ Networks and Annual Extremal Networks," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 484-501, September.
    3. Daniela Castro-Camilo & Raphaël Huser & Håvard Rue, 2019. "A Spliced Gamma-Generalized Pareto Model for Short-Term Extreme Wind Speed Probabilistic Forecasting," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 517-534, September.
    4. Colin Lewis-Beck & Zhengyuan Zhu & Anirban Mondal & Joon Jin Song & Jonathan Hobbs & Brian Hornbuckle & Jason Patton, 2019. "A Parametric Approach to Unmixing Remote Sensing Crop Growth Signatures," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 502-516, September.
    5. Luis A. Barboza & Julien Emile-Geay & Bo Li & Wan He, 2019. "Efficient Reconstructions of Common Era Climate via Integrated Nested Laplace Approximations," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 535-554, September.
    6. Yawen Guan & Christian Sampson & J. Derek Tucker & Won Chang & Anirban Mondal & Murali Haran & Deborah Sulsky, 2019. "Computer Model Calibration Based on Image Warping Metrics: An Application for Sea Ice Deformation," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 444-463, September.
    7. Matthew Edwards & Stefano Castruccio & Dorit Hammerling, 2019. "A Multivariate Global Spatiotemporal Stochastic Generator for Climate Ensembles," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 464-483, September.
    8. Joshua Hewitt & Miranda J. Fix & Jennifer A. Hoeting & Daniel S. Cooley, 2019. "Improved Return Level Estimation via a Weighted Likelihood, Latent Spatial Extremes Model," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 426-443, September.
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