Ocean_SST_Conceptual

This is version 4. It is not the current version, and thus it cannot be edited.
[Back to current version]   [Restore this version]

Conceptual Description of REAP SST Usecase

The goal of the REAP SST usecase workflow (REAP-SST-UCW) is to compare and match-up existing sensor datasets in OpenDAP archives.

REAP-SST-UCW has three main steps:

1. User Input:
   

1. The datasets to use: For now, we assume only satellite (MODUS, HYCON, etc.) and Level 3 mapped data. "The Level 3 mapped products are global gridded data sets with all points filled even over land." Ref: ftp://podaac.jpl.nasa.gov/pub/documents/dataset_docs/modis_sst.html No InterWiki reference defined in properties for Wiki called 'Ref'!)
2. Timespan: Beginning and end timestamps.
3. Time sampling: Percentage of the dataset times within the timespan.
4. Time span delta: Maximum amount of time between a reference dataset time sample and the corresponding sample of another dataset (see Step 2.4)
   We need the ppt figure from Peter C.
5. Maximum and minimum latitudes: Latitude ranges for the spatial region
6. Maximum and minimum longitudes: Longitude ranges for the spatial region
7. Spatial sampling: Percentage of the area defined by the min/max latitudes and longitudes to sample.
   We need the ppt figure from Peter C.
8. Spatial window delta: Maximum amount of overlap between a reference dataset and the corresponding region of another dataset
   We need the ppt figure from Peter C.
9. MinNumberOfPixels:
10. Sampling ?
11. TileAveraging: A tile is a rectangular region that includes a set of pixels. A pixel is atomic an area and characterized by an SST value at a lat,long that represents the center of a pixel.
1. Build Match-Up Datasets:
   

1. Analyze Match-Up Datasets:
   

1.

2. Once the user has input these parameters, the workflow builds a set of “tiles” or “match-ups”. 2.1. The metadata of the datasets is retrieved. The metadata describes both when and where the datasets occur. 2.2. In the time span specified from Step 1.2, the workflow determines the dataset with the coarsest granularity of timestamps. 2.3. The workflow randomly chooses time samples of the reference dataset selected in the previous step. The samples are bounded by the time span from Step 1.2 and the number chosen is the percentage in Step 1.3. 2.4. For each time sample selected in the previous step, the workflow finds the closest time sample for each of the other datasets. The maximum allowable difference in time between a time sample from the coarsest dataset and any other dataset is the time span delta specified in Step 1.4. 2.5. In the spatial area determined from Step 1.5 and 1.6, the workflow determines the dataset with the coarsest spatial granularity. 2.6. The workflow randomly chooses spatial samples or “tiles” of the reference dataset of selected in the previous step, using the time samples determined in Step 2.4: these are bounded by the min. and max. latitudes and longitudes from Step 1.5 and 1.6. The spatial samples are randomly selected such that they cover the spatial percentage (specified in Step 1.7) of the reference dataset. 2.7. For each spatial sample selected in the previous step, the workflow determines the corresponding sample area in each of the other datasets. 2.8. The SST values for the spatial samples are retrieved for each dataset. 2.9. A description of the samples retrieved in the previous step is written to a database. For each sample, the description includes: 2.9.1. Latitude center 2.9.2. Longitude center 2.9.3. Descriptions of the sample for each dataset: 2.9.3.1. Time sample 2.9.3.2. Array of latitudes 2.9.3.3. Array of longitudes 2.9.3.4. SST values 2.9.3.5. Number of good SST values 2.9.3.6. Sum of SST values

ImageStackRandomlySelected.png		114248 bytes
ImageStackRandomlySelectedOne.png		115932 bytes
RandomSpatialSamples.png		9889 bytes
ImageStackRandomlySelected.pdf		146007 bytes