TY - JOUR
T1 - Quantifying uncertainty for temperature maps derived from computer models
AU - Paci, Lucia
AU - Gelfand, Alan E.
AU - Cocchi, Daniela
PY - 2015
Y1 - 2015
N2 - Computer models are often deterministic simulators used to predict several environmental phenomena. Such models do not associate any measure of uncertainty with their output since they are derived from deterministic specifications. However, many sources of uncertainty exist in constructing and employing numerical models.We are motivated by temperature maps arising from the Rapid Update Cycle (RUC) model, a regional short-term weather forecast model for the continental United States (US) which provides forecast maps without associated uncertainty.Despite a rapidly growing literature on uncertainty quantification, there is little regarding statistical methods for attaching uncertainty to model output without information about how deterministic predictions are created. Although numerical models produce deterministic surfaces, the output is not the 'true' value of the process and, given the true value and the model output, the associated error is not stochastic. However, under suitable stochastic modeling, this error can be interpreted as a random unknown. Then, we infer about this error using a Bayesian specification within a data fusion setting, fusing the computer model data with some external validation data collected independently over the same spatial domain. Illustratively, we apply our modeling approach to obtain an uncertainty map associated with RUC forecasts over the northeastern US.
AB - Computer models are often deterministic simulators used to predict several environmental phenomena. Such models do not associate any measure of uncertainty with their output since they are derived from deterministic specifications. However, many sources of uncertainty exist in constructing and employing numerical models.We are motivated by temperature maps arising from the Rapid Update Cycle (RUC) model, a regional short-term weather forecast model for the continental United States (US) which provides forecast maps without associated uncertainty.Despite a rapidly growing literature on uncertainty quantification, there is little regarding statistical methods for attaching uncertainty to model output without information about how deterministic predictions are created. Although numerical models produce deterministic surfaces, the output is not the 'true' value of the process and, given the true value and the model output, the associated error is not stochastic. However, under suitable stochastic modeling, this error can be interpreted as a random unknown. Then, we infer about this error using a Bayesian specification within a data fusion setting, fusing the computer model data with some external validation data collected independently over the same spatial domain. Illustratively, we apply our modeling approach to obtain an uncertainty map associated with RUC forecasts over the northeastern US.
KW - Computers in Earth Sciences
KW - Hierarchical modeling
KW - LogCAR process
KW - MCMC
KW - Management, Monitoring, Policy and Law
KW - Measurement error
KW - Numerical models
KW - Statistics and Probability
KW - Computers in Earth Sciences
KW - Hierarchical modeling
KW - LogCAR process
KW - MCMC
KW - Management, Monitoring, Policy and Law
KW - Measurement error
KW - Numerical models
KW - Statistics and Probability
UR - http://hdl.handle.net/10807/97758
UR - http://www.journals.elsevier.com/spatial-statistics/
U2 - 10.1016/j.spasta.2015.03.005
DO - 10.1016/j.spasta.2015.03.005
M3 - Article
SN - 2211-6753
VL - 12
SP - 96
EP - 108
JO - Spatial Statistics
JF - Spatial Statistics
ER -