Locations in regions with
widely spaced observations (mostly remote areas) have a larger footprint in the analysis than locations in more densely observed areas.
Not exact matches
One of the best and most
widely implemented examples is the GLOBE (Global Learning and
Observations to Support the Environment) project, a global network — supported by, among others, the National Science Foundation and the National Aeronautics and
Space Administration — of teachers, students, and scientists studying the atmosphere, water quality, soils, and local flora and fauna.
Second, orbital instrumental
observations provide only a recent record of land surface area temperature assessment, and the methods involved had to be calibrated against the prevailing standards of proximal thermometric determination, the
widely - ranged system of meteorological thermometers in these United States providing (as others here have observed) a sort of «gold standard» in terms of technology, maintenance, and reliability as compared with similar broadly
spaced systems of monitoring stations.
In each case, a «prior» ensemble (with parameters selected
widely from prior distributions) has been narrowed down to «posterior» ensembles through comparison with
observations, although the details of this process differ for each ensemble and at least in the case of the Hadley Centre ensembles, there was also an explicit goal of sampling
widely in parameter
space subject to observational constraints.