scholarly journals How Long Is Too Long? Variogram Analysis of AERONET Data to Aid Aerosol Validation and Intercomparison Studies

2020 ◽  
Vol 7 (9) ◽  
Author(s):  
Andrew M. Sayer
Keyword(s):  
Variogram Analysis

Variogram-Analysis in Geomagnetic Prospection of Archaeological Sites

2004 ◽  
Author(s):  
F. Bilgili ◽  
C. Müller ◽  
W. Rabbel ◽  
H. Stümpel
Keyword(s):  
Archaeological Sites ◽  
Variogram Analysis

scholarly journals Use of variogram analysis to classify field peas with and without internal defects caused by weevil infestation

2014 ◽  
Vol 123 ◽  
pp. 17-22 ◽  
Author(s):  
Christian Nansen ◽  
Xuechen Zhang ◽  
Nader Aryamanesh ◽  
Guijun Yan
Keyword(s):  
Internal Defects ◽  
Variogram Analysis ◽  
Field Peas ◽  
Weevil Infestation

scholarly journals A systematic approach for comparing modeled biospheric carbon fluxes across regional scales

2011 ◽  
Vol 8 (6) ◽  
pp. 1579-1593 ◽  
Author(s):  
D. N. Huntzinger ◽  
S. M. Gourdji ◽  
K. L. Mueller ◽  
A. M. Michalak
Keyword(s):  
Variable Selection ◽  
Spatial Variability ◽  
Carbon Flux ◽  
Systematic Approach ◽  
Ecosystem Respiration ◽  
Net Ecosystem Exchange ◽  
Environmental Drivers ◽  
Model Formulation ◽  
Variogram Analysis ◽  
Fire Emissions

Abstract. Given the large differences between biospheric model estimates of regional carbon exchange, there is a need to understand and reconcile the predicted spatial variability of fluxes across models. This paper presents a set of quantitative tools that can be applied to systematically compare flux estimates despite the inherent differences in model formulation. The presented methods include variogram analysis, variable selection, and geostatistical regression. These methods are evaluated in terms of their ability to assess and identify differences in spatial variability in flux estimates across North America among a small subset of models, as well as differences in the environmental drivers that best explain the spatial variability of predicted fluxes. The examined models are the Simple Biosphere (SiB 3.0), Carnegie Ames Stanford Approach (CASA), and CASA coupled with the Global Fire Emissions Database (CASA GFEDv2), and the analyses are performed on model-predicted net ecosystem exchange, gross primary production, and ecosystem respiration. Variogram analysis reveals consistent seasonal differences in spatial variability among modeled fluxes at a 1° × 1° spatial resolution. However, significant differences are observed in the overall magnitude of the carbon flux spatial variability across models, in both net ecosystem exchange and component fluxes. Results of the variable selection and geostatistical regression analyses suggest fundamental differences between the models in terms of the factors that explain the spatial variability of predicted flux. For example, carbon flux is more strongly correlated with percent land cover in CASA GFEDv2 than in SiB or CASA. Some of the differences in spatial patterns of estimated flux can be linked back to differences in model formulation, and would have been difficult to identify simply by comparing net fluxes between models. Overall, the systematic approach presented here provides a set of tools for comparing predicted grid-scale fluxes across models, a task that has historically been difficult unless standardized forcing data were prescribed, or a detailed sensitivity analysis performed.

scholarly journals Detecting Radiation-Induced Injury Using Rapid 3D Variogram Analysis of CT Images of Rat Lungs

2013 ◽  
Vol 20 (10) ◽  
pp. 1264-1271 ◽  
Author(s):  
Richard E. Jacob ◽  
Mark K. Murphy ◽  
Jeffrey A. Creim ◽  
James P. Carson
Keyword(s):  
Ct Images ◽  
Variogram Analysis ◽  
Rat Lungs ◽  
Radiation Induced

Variogram analysis of helicopter magnetic data to identify paleochannels of the Omaruru River, Namibia

Geophysics ◽  
1999 ◽  
Vol 64 (3) ◽  
pp. 785-794 ◽  
Author(s):  
Stefan Maus ◽  
K. P. Sengpiel ◽  
B. Röttger ◽  
B. Siemon ◽  
E. A. W. Tordiffe
Keyword(s):  
Geomagnetic Field ◽  
Sedimentary Basins ◽  
Source Model ◽  
High Accuracy ◽  
Groundwater Exploration ◽  
Magnetic Data ◽  
Magnetic Signal ◽  
Variogram Analysis ◽  
Power Spectral ◽  
Basement Depth

The geomagnetic field over sedimentary basins is very sensitive to variations in basement depth. Therefore, magnetic surveys are widely used to map basement topography in petroleum and groundwater exploration. We propose variogram analysis as a more accurate alternative to power spectral methods. Data variograms are computed from aeromagnetic flight‐line data. To estimate depth, the data variograms are compared with model variograms for a range of source depths. We use the exact space domain counterparts of a fractal power spectral model as model variograms. To demonstrate the utility of this method for groundwater exploration, we map the basement topography of the Omaruru Alluvial Plains in Namibia. A comparison with electromagnetic (EM) resistivities and drilling information confirms the high accuracy—but also the limitations—of variogram analysis depth. Variogram analysis makes maximum use of short‐wavelength contributions to the magnetic signal, which is the key to the resolution of shallow basement topography. Moreover, by using a realistic source model and avoiding extensive data preconditioning and the transform to wavenumber domain, variogram analysis is likely to provide improved magnetic depth estimates even for deep basins.

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