TY - JOUR
T1 - Stilt
T2 - Easy emulation of time series AR(1) computer model output in multidimensional parameter space
AU - Olson, Roman
AU - Ruckert, Kelsey L.
AU - Chang, Won
AU - Keller, Klaus
AU - Haran, Murali
AU - An, Soon Il
N1 - Funding Information:
For their roles in producing, coordinating, and making available the CMIP5 model output, we acknowledge the climate modeling groups, the World Climate Research Programme's (WCRP) Working Group on Coupled modeling (WGCM), and the Global Organization for Earth System Science Portals (GO-ESSP). We thank Jong-Soo Shin for help with extracting Korean temperature output, and Patrick Applegate for sharing the SICOPOLIS ice sheet model output. We acknowledge financial support from National Research Foundation of Korea (NRF-2009-0093069, NRF-2018R1A5A1024958), and from the Institute for Basic Science (project code IBS-R028-D1). This work was also co-supported by the National Science Foundation through the Network for Sustainable Climate Risk Management (SCRiM) under NSF cooperative agreement GEO-1240507 and the Penn State Center for Climate Risk Management. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation, or any other foundation or entity
Publisher Copyright:
© 2018 The R Journal.
PY - 2019
Y1 - 2019
N2 - Statistically approximating or "emulating" time series model output in parameter space is a common problem in climate science and other fields. There are many packages for spatio-temporal modeling. However, they often lack focus on time series, and exhibit statistical complexity. Here, we present the R package stilt designed for simplified AR(1) time series Gaussian process emulation, and provide examples relevant to climate modelling. Notably absent is Markov chain Monte Carlo estimation - a challenging concept to many scientists. We keep the number of user choices to a minimum. Hence, the package can be useful pedagogically, while still applicable to real life emulation problems. We provide functions for emulator cross-validation, empirical coverage, prediction, as well as response surface plotting. While the examples focus on climate model emulation, the emulator is general and can be also used for kriging spatio-temporal data.
AB - Statistically approximating or "emulating" time series model output in parameter space is a common problem in climate science and other fields. There are many packages for spatio-temporal modeling. However, they often lack focus on time series, and exhibit statistical complexity. Here, we present the R package stilt designed for simplified AR(1) time series Gaussian process emulation, and provide examples relevant to climate modelling. Notably absent is Markov chain Monte Carlo estimation - a challenging concept to many scientists. We keep the number of user choices to a minimum. Hence, the package can be useful pedagogically, while still applicable to real life emulation problems. We provide functions for emulator cross-validation, empirical coverage, prediction, as well as response surface plotting. While the examples focus on climate model emulation, the emulator is general and can be also used for kriging spatio-temporal data.
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U2 - 10.32614/RJ-2018-049
DO - 10.32614/RJ-2018-049
M3 - Article
AN - SCOPUS:85070269481
SN - 2073-4859
VL - 10
SP - 209
EP - 225
JO - R Journal
JF - R Journal
IS - 2
ER -