Merging remotely sensed data with geophysical models

• Main Author: Searcy, Stanley Craig
• Other Authors: Layer, Paul, Stringer, William, Dean, Ken, Niebauer, Joe, Goering, Doug, Weller, Gunter
• Format: Doctoral or Postdoctoral Thesis
• Language: unknown
• Published: 1996
• Subjects: Geophysics; Remote sensing; Arctic; Fairbanks; Mackenzie River; Mackenzie river; Alaska
• Online Access: http://hdl.handle.net/11122/9449

Dissertation (Ph.D.) University of Alaska Fairbanks, 1996 Geophysical models are usually derived from the idealistic viewpoint that all required external parameters are, in principle, measurable. The models are then driven with the best available data for those parameters. In some cases, there are few measurements available, because of factors such as the location of the phenomena modeled. Satellite imagery provides a synoptic overview of a particular environment, supplying spatial and temporal variability as well as spectral data, making this an ideal source of data for some models. In other cases, although frequent satellite image observations are available, they are of little use to the modeler, because they do not provide values for the parameters demanded by the model. This thesis contains two examples of geophysical models that were derived expressly to utilize measurements and qualitative observations taken from satellite images as the major driving elements of the model. The methodology consists of designing a model such that it can be 'run' by numerical data extracted from image data sets, and using the image data for verification of the model or adjustment of parameters. The first example is a thermodynamic model of springtime removal of nearshore ice from an Arctic river delta area, using the Mackenzie River as a study site. In this example, a multi-date sequence of AVHRR images is used to provide the spatial and temporal patterns of melt, allowing the required physical observations in the model to be parameterized and tested. The second example is a dynamic model simulating the evolution of a volcanic ash cloud under the influence of atmospheric winds. In this case, AVHRR images are used to determine the position and size of the ash cloud as a function of time, allowing tuning of parameters and verification of the model.