Difference between revisions of "SemanticServicesUseCases"

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Bruce: Level 2g - Create a plot of direct comparison of MODIS NDVI as a timeseries for a year with the vegetative index from a flux tower.
 
Bruce: Level 2g - Create a plot of direct comparison of MODIS NDVI as a timeseries for a year with the vegetative index from a flux tower.
  
[Media:http://wiki.esipfed.org/images/a/aa/BruceUseCase_v0.pdf]
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Bruce to add narrative.
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[http://wiki.esipfed.org/images/a/aa/BruceUseCase_v0.pdf]
  
 
====Ice use case ====
 
====Ice use case ====

Revision as of 09:34, July 18, 2008

Use Cases for Ontology Development

The ESIP Solutions Use Case template is [here]

[Other Instructions]

Example

Use case: find and display in the same projection and coordinate system, sea surface temperature and land surface temperature from a global climate model.

Airborne Dust Detection in MODIS Imagery using Data Mining Services

Background: Growth of plankton in remote ocean locations is limited by the availability of iron. Dust storm from arid and semi arid regions transport dust containing iron to remote ocean locations, which spur the growth of plankton. Phytoplankton accounts for more than half of the global photosynthetic activity; they act as an important sink of atmospheric carbon dioxide and thus have the potential to impact climate. Emission of Dimethly Sulfide (DMS) is another pathway through which plankton affects climate. DMS emissions increases cloud condensation nuclei (CCN) present in the relatively clean marine atmosphere, altering the microphysical nature of marine stratus. Increase in CCN results in more but smaller size cloud droplets in marine stratus thereby increasing cloud albedo and creating a cooling effect. In order to explore connections between iron fertilization via dust storms and plankton growth, algorithms that accurately detect dust storms in satellite imagery are required. NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) provides global data with the correct spectral properties for dust storm detection. Unfortunately, there are no well accepted dust storm detection algorithms or methodologies for this data set. Different researchers have devised different dust indices along the lines of Normalized Difference Vegetation Index (NDVI)and suggest different threshold values. Data mining techniques such as unsupervised classifiers can be used for this detection. Unsupervised classification techniques such as clustering not only provide the ability to automatically group pixels in different classes but also determine the appropriate thresholds.

Use case: Find appropriate MODIS data with airborne dust, preprocess the data, select the 'optimal' clustering algorithm and apply the algorithm.

Atmospheric XXX use case

Brian: Atmospheric retrieval and validation. Compare temperature profile retrieval from AIRS and find space time match-ups between GPS occultation and AIRS temperature profile.

Chris: Plot


Bruce: Level 2g - Create a plot of direct comparison of MODIS NDVI as a timeseries for a year with the vegetative index from a flux tower.

Bruce to add narrative.

[1]

Ice use case

Ruth: Compare what atmospheric forcing component should be doing with what the sea ice motion is doing ; involves buoy, point data, and rem-sens. like Bruce's so we will proceed with the MODIS and generalize

Patterns of spatial transformation

Space-time matchup between points and satellite swath data or any gridded data (model or level 3 from instrument retrieval)

Grid to grid

Point to grid - Tile (part of a grid) to point

Point to swath

Swath to swath

Swath to grid

Moving point to ANY

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