%0 Journal Article %1 Hodges1994General %A Hodges, K. I. %B Monthly Weather Review %D 1994 %I American Meteorological Society %J Mon. Wea. Rev. %K theory storms stormtracks stormidentification %N 11 %P 2573--2586 %R 10.1175/1520-0493(1994)122%3C2573:agmfta%3E2.0.co;2 %T A General Method for Tracking Analysis and Its Application to Meteorological Data %U http://dx.doi.org/10.1175/1520-0493(1994)122%3C2573:agmfta%3E2.0.co;2 %V 122 %X Abstract Recent interest in the validation of general circulation models (GCMS) has been devoted to objective methods. A small number of authors have used the direct synoptic identification of phenomena together with a statistical analysis to perform the objective comparison between various datasets. This paper describes a general method for performing the synoptic identification of phenomena that can be used for an objective analysis of atmospheric, or oceanographic, datasets obtained from numerical models and remote sensing. Methods usually associated with image processing have been used to segment the scene and to identify suitable feature points to represent the phenomena of interest. This is performed for each time level. A technique from dynamic scene analysis is then used to link the feature points to form trajectories. The method is fully automatic and should be applicable to a wide range of geophysical fields. An example will be shown of results obtained from this method using data obtained from a run of the Universities Global Atmospheric Modelling Project GCM.

@article{Hodges1994General, abstract = {Abstract Recent interest in the validation of general circulation models (GCMS) has been devoted to objective methods. A small number of authors have used the direct synoptic identification of phenomena together with a statistical analysis to perform the objective comparison between various datasets. This paper describes a general method for performing the synoptic identification of phenomena that can be used for an objective analysis of atmospheric, or oceanographic, datasets obtained from numerical models and remote sensing. Methods usually associated with image processing have been used to segment the scene and to identify suitable feature points to represent the phenomena of interest. This is performed for each time level. A technique from dynamic scene analysis is then used to link the feature points to form trajectories. The method is fully automatic and should be applicable to a wide range of geophysical fields. An example will be shown of results obtained from this method using data obtained from a run of the Universities Global Atmospheric Modelling Project GCM.}, added-at = {2018-06-18T21:23:34.000+0200}, author = {Hodges, K. I.}, biburl = {https://www.bibsonomy.org/bibtex/244befdda6e6d14a5634ce8c1dc26f102/pbett}, booktitle = {Monthly Weather Review}, citeulike-article-id = {3912348}, citeulike-linkout-0 = {http://journals.ametsoc.org/doi/abs/10.1175/1520-0493(1994)122<2573:AGMFTA>2.0.CO;2}, citeulike-linkout-1 = {http://dx.doi.org/10.1175/1520-0493(1994)122%3C2573:agmfta%3E2.0.co;2}, day = 1, doi = {10.1175/1520-0493(1994)122%3C2573:agmfta%3E2.0.co;2}, interhash = {9178d22916617c83c39ebff3d1c04cd3}, intrahash = {44befdda6e6d14a5634ce8c1dc26f102}, journal = {Mon. Wea. Rev.}, keywords = {theory storms stormtracks stormidentification}, month = nov, number = 11, pages = {2573--2586}, posted-at = {2015-10-12 17:38:26}, priority = {2}, publisher = {American Meteorological Society}, timestamp = {2018-06-22T18:36:02.000+0200}, title = {A General Method for Tracking Analysis and Its Application to Meteorological Data}, url = {http://dx.doi.org/10.1175/1520-0493(1994)122%3C2573:agmfta%3E2.0.co;2}, volume = 122, year = 1994 }