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Difference between
version 4
and
version 3:
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| - Use Case #1 - SST Target Data Set Evaluation \\ |
| + !Use Case #1 - SST Target Data Set Evaluation \\ |
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| - |
| + \\ |
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| - The next step is to refine the list (if necessary) based on the metadata descriptions of the data. This step, at least at present, requires human intervention because the metadata associated with SST data sets is often not sufficiently complete to totally automate the procedure and because the number and character of discovered data sets is not known ahead of time. For example, the list may consist of a large number of qualitatively similar satellite-derived SST data sets which, if all were used, would overwhelm the computational capability of the modeler. The result of this step is the list of comparison data sets to be used in the analysis. |
| + The next step is to __refine the list__ (if necessary) based on the metadata descriptions of the data. This step, at least at present, requires human intervention because the metadata associated with SST data sets is often not sufficiently complete to totally automate the procedure and because the number and character of discovered data sets is not known ahead of time. For example, the list may consist of a large number of qualitatively similar satellite-derived SST data sets which, if all were used, would overwhelm the computational capability of the modeler. The result of this step is the list of comparison data sets to be used in the analysis. |
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| - Comparison data sets fall into two classes: those comprised of point (a single lat, lon, time location - XBTs) and line (either a time series at a number of locations - mooring data - or lines in space-time - drifters or ship tracks) values in space and time and those consisting of 3D (lat, lon, time - satellite-derived data sets or model outputs) SST arrays. The modeler's data set is a 3D data set. For subsequent analysis, the modeler wants to match the observed values with values from the target data set based on a space-time window. The result of this step is a set of match-up elements. Match-up elements consist of SST values from the target data set, SST value(s) from a comparison data set and metadata characterizing the element. |
| + Comparison data sets fall into two classes: those comprised of point (a single lat, lon, time location - XBTs) and line (either a time series at a number of locations - mooring data - or lines in space-time - drifters or ship tracks) values in space and time and those consisting of 3D (lat, lon, time - satellite-derived data sets or model outputs) SST arrays. The modeler's data set is a 3D data set. For subsequent analysis, the modeler wants to __match the observed values with values from the target data set__ based on a space-time window. The result of this step is a set of match-up elements. Match-up elements consist of SST values from the target data set, SST value(s) from a comparison data set and metadata characterizing the element. |
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| - In the next step, elements of the match-up data set are used to statistically compare the target data set with the comparison data sets. The modeler would like to be able to experiment with this comparison, to perform it as a function of time of year, location, comparison data set, etc. S/he would also like to use a variety of commonly used statistical tools and a variety of commonly used visualization tools. |
| + In the next step, elements of the match-up data set are used to __statistically compare the target dataset with the comparison datasets__. The modeler would like to be able to experiment with this comparison, to perform it as a function of time of year, location, comparison data set, etc. S/he would also like to use a variety of commonly used statistical tools and a variety of commonly used visualization tools. |
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