Difference between revisions of "SemanticServicesUseCases"
|Line 103:||Line 103:|
a service operation, we will name it along with IOPs.
a service operation, we will name it along with IOPs.
Revision as of 11:48, July 6, 2009
Use Cases for Ontology Development[edit | edit source]
The ESIP Solutions Use Case template is [here]
Example[edit | edit source]
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[edit | edit source]
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 Science Mission use case[edit | edit source]
Brian: Atmospheric retrieval and validation. Compare temperature profile retrieval from AIRS and find space time match-ups between GPS occultation and AIRS temperature profile.
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.
pdf of first version of concept map
pdf of latest version of concept map
Ice use case[edit | edit source]
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
Aerosol Search Use Case[edit | edit source]
Locate the access points for aerosol data or services that would be useful and usable for characterizing the extent of the ash cloud plume from the 2 May 2008 Chaiten volcanic eruption. Useful data are those that have information that can be brought to bear on the problem; usable data are those that are available in a form that can be used in my analysis framework.
Target products would include such items as:
- MODIS L2 Aerosol product, Terra and Aqua
- MODIS L3 Aerosol product, Terra and Aqua
- MERIS Aerosol product
- GOCART Aerosol model
- CALIPSO Aerosol classification
- OMI L2 Aerosol index
- OMI L3 Aerosol index
- AIRS Brightness Temperature difference (ch ? - ch ??)
- MISR L2 Aerosol(?)
- Experimental CALIPSO aerosol sub-classification
- GOES Image with analyst's assessment of ash extent
- Hysplit model run
Target services could include:
- OGC WCS or WMS access
- OPeNDAP access
- On-the-fly virtual products
- On-demand model runs
Factors that go into the usefulness include:
- observation vs. model
- type of measurement (e.g., radiance, aerosol index, AOT/AOD, ...)
- method of measurement (e.g., infrared, visible, UV)
- source of measurement (instrument or model)
- processing algorithm
- horizontal resolution
- temporal resolution
- temporal "alignment" (e.g., averaged vs. synoptic vs. temporal progression, different types of data days)
- data product or service maturity (e.g., operational, validated research, provisional research, experimental)
Factors that go into usability include:
- data format
- spatial reference system (swath, grid; type of grid)
- access means (protocol; synchronous vs. asynchronous; machine-accessible or not...)
- access restrictions
--Clynnes 21:23, 8 January 2009 (EST)
Patterns of spatial transformation[edit | edit source]
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
Extract sub-sets of files[edit | edit source]
Extract data from a file (or set of files) using a geographic region selection. This can involve L3 (grid) or L2 (swath) types. Need to know underlying representations so that an operation can extract the relevant information. Anytime we come to a service operation, we will name it along with IOPs.
E.g. Orbiting satellite (need period and inclination angle for satellite), L2 swath width (in km from center of orbit from NADIR), how does single orbit ma[p to a granule file? sometimes a granule file = one orbit, sometime it is 1/2 orbit, sometimes someother fraction, e.g. can be greater than 1