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| - # __Build Match-Up Datasets:__\\ |
| - # __Analyze Match-Up Datasets:__\\ |
| + # __Build Match-Up Datasets:__\\Once the user has input parameters in step 1, the workflow builds a set of “tiles” or “match-ups”. The match-up building starts with finding the coarsest granularity of timesteps depending on the metadata for the datasets. The dataset is sampled and closest timeframe of the other datasets is determined based on this coarsest dataset. |
| + For each timestep, the workflow finds the dataset with coarsest spatial granularity. Then the workflow randomly chooses spatial samples or “tiles” of this reference dataset. These are bounded by the min. and max. latitudes and longitudes. The spatial samples are randomly selected such that they cover the spatial percentage of the reference dataset. \\ |
| Lines 22-46 were replaced by lines 22-24 |
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| - 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 |
| + For each spatial sample selected in the previous step, the workflow determines the corresponding sample area in each of the other datasets. The SST values for the spatial samples are retrieved for each dataset. A description of the samples retrieved is written to a database. For each sample, the description including latitude,longitude center and descriptions of the sample for each dataset (Timeframe, Array of latitudes, Array of longitudes, SST values, Number of good SST values, Sum of SST values). |
| + # __Analyze Match-Up Datasets:__\\ The selected and saved (in an RDBMS) SST math-up dataset values can then be used in statistical comparisons. %%(color: red) To be defined in detail after the implementation of the first two steps. %% |
| + ## __MinNumberOfPixels:__ |
