Sentence examples for surface datasets from inspiring English sources

In this case, I would trust the surface data a little more because the difference between the long term trends in the various surface datasets (NOAA, NASA GISS, HADCRUT, Berkeley, etc) are closer to each other than the long term trends from the different satellite datasets.

Surface datasets were not up-scaled by the above process as they were only available as a single z-bin.

On the basis of a long-term (1960 2008) land surface dataset, this study applies the theoretical framework to assess the effects of climate and terrestrial storage factors on the interannual and intra-annual variance in E across China.

This suggests that the satellite datasets contain more "structural uncertainty" than the surface dataset.

Thus, the actual surface topography outline was detected in individual B-scans and referenced to a smoothed surface dataset as obtained by Savitzky-Golay filtering.

The HF10 metagenome taken from HOT [ 5] and seven metagenomes from the oceans (GS001c and GS018 from the Atlantic Ocean; GS026, GS037, and GS047 from the Pacific Ocean; and GS113 and GS123 from the Indian Ocean) collected by the Global Ocean Sampling (GOS) expedition [ 4] were selected to represent global sea-surface datasets.

But surface metrology datasets are distinctly different from those images, as all the data points are in focus, compared to typical images with a subject in focus and background with various levels of out-of-focus.

Global sea surface temperature datasets are produced combining in situ and space-borne measurements [3] as well as various satellite observations, which are then validated with buoys [4, 5].

To investigate the effects of temporal compositing, BRDF-adjustment, and base pair timing on the accuracy of the resulting synthetic imagery, we employed a range of MODIS 500 m surface reflectance datasets (daily, 8-day composite, and 16-day Nadir BRDF-Adjusted Reflectance (NBAR)) as well as initial Landsat/MODIS imagery pairs from opposite ends of the growing season.

In [ 31] we presented extensions of 2D singular spectrum analysis (2D-SSA) to analyze 2D and surface 3D datasets from Drosophila confocal scans.

We applied the concept of the velocity of climate change to sea surface temperature datasets, to produce predictions for the pace and direction that species should move to track regional climate change [ 6].