Dorit Hammerling

TL;DR: A multiresolution model to predict two-dimensional spatial fields based on irregularly spaced observations that gives a good approximation to standard covariance functions such as the Matérn and also has flexibility to fit more complicated shapes.

TL;DR: 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.

TL;DR: In this article, the results of a predictive competition among the described methods as implemented by different groups with strong expertise in the methodology have been presented, and each group then wrote their own implementation of their method to produce predictions at the given location and each which was subsequently run on a common computing environment.

TL;DR: In this article, a geostatistical method was proposed to extract information about the spatial covariance structure of the CO2 field from the available CO2 retrievals, yields full coverage (Level 3) maps at high spatial resolutions, and provides estimates of the uncertainties associated with these maps.

TL;DR: It is argued that quantitatively accounting for dependence in addition to model performance, and thoroughly testing the effectiveness of the approach used will be key to a sound interpretation of the CMIP ensembles in future scientific studies.