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MULTIMODEL ENSEMBLE ANALYSIS WITH NEURAL NETWORK GAUSSIAN PROCESSES
Trevor Harris,
Bo Li
,
Ryan Sriver
Statistics
Climate, Meteorology and Atmospheric Sciences
National Center for Supercomputing Applications (NCSA)
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peer-review
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Keyphrases
Gaussian Process Regression
100%
Multi-model Ensemble
100%
Ensemble Analysis
100%
Neural Network Gaussian Processes
100%
Climate Models
66%
Neural Network
16%
Spatial Resolution
16%
Spatial Data
16%
Surface Temperature
16%
Downscaling
16%
Accuracy Improvement
16%
Uncertainty Quantification
16%
Information Integration
16%
Global Model
16%
High Variability
16%
Modeling Data
16%
Covariance Function
16%
Prediction Algorithms
16%
Tail Behavior
16%
Regional Climate Model
16%
Model Averaging
16%
Precipitation Forecasting
16%
Re-analysis Data
16%
Reanalysis Datasets
16%
Ensemble Model
16%
Surface Precipitation
16%
Geospatial
16%
Deep Neural Network
16%
Regional Prediction
16%
Vari
16%
Temperature Forecasting
16%
Computer Science
Spatial Information
100%
Spatial Resolution
100%
Neural Network
100%
Statistical Approach
100%
Deep Neural Network
100%
Spatial Pattern
100%
Covariance Function
100%
Earth and Planetary Sciences
Kriging
100%
Climate Modeling
83%
Interpolation
16%
Spatial Resolution
16%
Covariance
16%
Regional Climate
16%
Ground Penetrating Radar
16%
Uncertainty Modeling
16%