s p atial modelling of annual max temperatures using max stable processes
DESCRIPTION
S p atial Modelling of Annual Max Temperatures using Max Stable Processes. NCAR Advanced Study Program 24 June, 2011 Anne Schindler, Brook Russell, Scott Sellars, Pat Sessford , and Daniel Wright. Outline. Introduction to spatial extreme value analysis R package— SpatialExtremes - PowerPoint PPT PresentationTRANSCRIPT
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Spatial Modelling of Annual Max Temperatures using Max Stable
Processes
NCAR Advanced Study Program24 June, 2011
Anne Schindler, Brook Russell, Scott Sellars, Pat Sessford, and
Daniel Wright
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Outline• Introduction to spatial extreme value
analysis• R package—SpatialExtremes• Study area and data• Covariates• Modeling fitting and results• Summary• Challenges and future work
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Introduction to Spatial Extremes• Societal impacts of extreme events• Extreme value analysis of physical
processes– Temperature – Precipitation – Streamflow –Waves
• Characterization of the spatial dependency of extreme events
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R package—SpatialExtremes
• Developed by Dr. Mathieu Ribatet– http://spatialextremes.r-forge.r-project.org/i
ndex.php
• Several techniques for analyzing spatial extremes:– Gaussian copulas– Bayesian hierarchical model (BHM)–Max stable processes– Simulation
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Study Area and Data
• DWD Met Stations (36)– State of Hessen, Germany– Annual Max Temperature (Apr-Sept) – Elevation from 110 to 921 meters – Maximum separation distance of 200 km
• Modeling data set– 16 Stations (1964-2006) with 24 years of
overlapping data• Cross-validation data set
– 8 stations with 10 years overlapping– 5 stations with 40 years overlapping
Germany
Wikipedia.com
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Station Locations
Germany
Wikipedia.com
State of Hessen, Germany
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Covariates• Spatial
Covariates:– Latitude and
Longitude • Magnitude of
extreme events might be different depending on location
– Elevation– Avg. Summer
Temp
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Covariates• Temporal Covariate:– North Atlantic Oscillation (NAO)
Positive Phase
Negative Phase
http://www.ldeo.columbia.edu/res/pi/NAO/
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Modeling Framework• No Blue Print to follow!• Fit Marginal GEVs (station by station)• Estimate spatial dependence– Pick model for max stable process– Pick correlation structure
• Estimate marginals– Select covariates for trend surfaces
• Fit max stable model using pairwise likelihood
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Models For Max Stable Process
• Candidate models
• Correlation Structure– (an)isotropic covariance (Smith)– Whittle-Matérn, Stable, Powered Exponential,
Cauchy
*Ribatet ASP .ppt (2011)
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Model Fitting Criteria• TIC • Madogram• Parameter estimates (station by
station vs. spatial marginals)
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Station By Station (GEV)
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Spatial Dependence (Madogram)
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Spatial Dependence (Madogram)
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Geometric-Gaussian Model: Different Covariates
Location: lat, lon,elevScale: lon, avg temp
Shape: lat, lon, lat*lon
Location: lat, lon,elev,NAOScale: lon
Shape: lat, lon, lat*lon
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Parameter Estimates
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Estimated Return Levels
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Summary• High spatial dependence in annual maximum
temperature in research area (Hessen)• Spatial covariates for shape parameter fairly
complexno literature to support this (only precip examples )
• Most models and covariate combinations underestimated the spatial dependence of the data
• Different optimization methods gave different results
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Challenges and Future Work• New field of EVA, lack of examples• Spatial dependence greatly varies with earth
science variables (temperature vs. precipitation)• Small regions vs. large regions (dependence
structure?)– Computational issues?
• Optimization/composite likelihood issues • Uncertainty estimation• Simulations• Applications?
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Extra Bonus Quiz: Who Said It?a) “If you can’t solve the problem,
change the problem.”b) “If you want to stay awake, do not
go into that talk!”c) “Loading…”
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Thank you!Questions and Comments?
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References• de Haan, L. (1984). A spectral representation for max-stable
processes. The Annals of Probability, 12(4):1194-1204. • de Haan, L. and Ferreira, A. (2006). Extreme Value Theory: An
Introduction. Springer, New York. • Cooley, D., Naveau, P., and Poncet, P. (2006). Variograms for
spatial max-stable random fields. In Springer, editor, Dependence in Probability and Statistics, volume 187, pages 373-390. Springer, New York, lecture notes in statistics edition.
• Kabluchko, Z., Schlather, M., and de Haan, L. (2009). Stationary max-stable fields associated to negative definite functions. Ann. Prob., 37(5):2042-2065.
• Lindsay, B. (1988). Composite likelihood methods. Statistical Inference from Stochastic Processes. American Mathematical Society, Providence.
• Padoan, S., Ribatet, M., and Sisson, S. (2010). Likelihood-based inference for max-stable processes. Journal of the American Statistical Association (Theory & Methods), 105(489):263-277.
• Schlather, M. (2002). Models for stationary max-stable random fields. Extremes, 5(1):33-44.
• Smith, R. L. (1990). Max-stable processes and spatial extreme. Unpublished manuscript.
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ENSEMBLES Project• RCMs covering Europe, driven by
GCMs or reanalysis data (1958-2002).
• Here we focus on the Hessen (a state in Deutschland) area, with the data driven by reanalysis.....
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Observations vs. Climate Model
• Location parameters differ in places but agree on a lot, but the scale and shape parameters disagree completely; presumably the observational data are more realistic.
• BUT.... possible inconsistencies when extrapolating out of the spatial range of observation stations?? (Whereas this is not an issue with data from climate models)........
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