• Publications
  • Influence
A Multiresolution Gaussian Process Model for the Analysis of Large Spatial Datasets
We develop a multiresolution model to predict two-dimensional spatial fields based on irregularly spaced observations. The radial basis functions at each level of resolution are constructed using aExpand
A Case Study Competition Among Methods for Analyzing Large Spatial Data
This study provides an introductory overview of several methods for analyzing large spatial data and describes the results of a predictive competition among the described methods as implemented by different groups with strong expertise in the methodology. Expand
Mapping of CO2 at high spatiotemporal resolution using satellite observations: Global distributions from OCO‐2
[1] Satellite observations of CO2 offer new opportunities to improve our understanding of the global carbon cycle. Using such observations to infer global maps of atmospheric CO2 and their associatedExpand
Global CO2 distributions over land from the Greenhouse Gases Observing Satellite (GOSAT)
[1] January 2009 saw the successful launch of the first space-based mission specifically designed for measuring greenhouse gases, the Japanese Greenhouse gases Observing SATellite (GOSAT). We presentExpand
A new ensemble-based consistency test for the Community Earth System Model (pyCECT v1.0)
A new tool for evaluating climate consistency in the CESM ensemble consistency test, referred to as CESM-ECT, is developed, which is objective in nature and accessible to CESM developers and users. Expand
A multi-resolution Gaussian process model for the analysis of large spatial data sets
Abstract A multi-resolution basis is developed to predict two-dimensional spatial fields based on irregularly spaced observations. The basis functions at each level of resolution are constructed asExpand
Modeling and emulation of nonstationary Gaussian fields
Evidence is given to show that non-stationary covariance functions based on the Mat`ern family can be reproduced by the Lat- ticeKrig model, a flexible, multi-resolution representation of Gaussian processes that emulates spatial fields derived from numerical model simulations such as Earth system models. Expand
Evaluating statistical consistency in the ocean model component of the Community Earth System Model (pyCECT v2.0)
The experiments indicate that the new POP ensemble consistency test (POP-ECT) tool is capable of distinguishing cases that should be statistically consistent with the ensemble and those that should not, as well as providing a simple, subjective and systematic way to detect errors in CESM-POP due to the hardware or software stack. Expand
Evaluating lossy data compression on climate simulation data within a large ensemble
This paper reports on the results of a lossy data compression experiment with output from the CESM Large Ensemble (CESM-LE) Community Project, in which climate scientists are challenged to examine features of the data relevant to their interests, and to identify which of the ensemble members have been compressed and reconstructed. Expand
Methods for Analyzing Large Spatial Data: A Review and Comparison
The Gaussian process is an indispensable tool for spatial data analysts. The onset of the "big data" era, however, has lead to the traditional Gaussian process being computationally infeasible forExpand