[PROPOSAL] NASA JPL Use Case

Hi Folks,
I would like to propose another use case (as if the working group needs another one) which I used over in the Data and the Web Best Practices WG. The Use Case is allied ASO: Airborne Snow Observatory [0]. I’ve pasted it below for complexness. I want to state that although this is a copy and paste job, this use case does go straight to the heart of bullet point 2 of the WG Missions “…to determine how machines and people can discover that different facts in different datasets relate to the same place, especially when 'place' is expressed in different ways and at different levels of granularity;”.
I would really like to get feedback on the use case if anyone has any.
Thanks
Lewis
2.1 ASO: Airborne Snow Observatory

(Contributed by Lewis John McGibbney, NASA Jet Propulsion Laboratory/California Institute of Technology)
URL: http://aso.jpl.nasa.gov/

The two most critical properties for understanding snowmelt runoff and timing are the spatial and temporal distributions of snow water equivalent (SWE) and snow albedo. Despite their importance in controlling volume and timing of runoff, snowpack albedo and SWE are still largely unquantified in the US and not at all in most of the globe, leaving runoff models poorly constrained. NASA/JPL, in partnership with the California Department of Water Resources, has developed the Airborne Snow Observatory (ASO), an imaging spectrometer and scanning Lidar system, to quantify SWE and snow albedo, generate unprecedented knowledge of snow properties for cutting edge cryospheric science, and provide complete, robust inputs to water management models and systems of the future.

Elements:

  *   Domains: Digital Earth Modeling, Digital Surface Modeling, Spatial Distribution Measurement, Snow Depth, Snow Water Equivalent, Snow Albedo.
  *   Obligation/motivation: Funding provided by NASA Terrestrial Hydrology, NASA Applied Sciences, and California Department of Water Resources.
  *   Usage: Example data usage include < 24hrs turnaround of flight data which is passed on to numerous Water Resource Managers aiding in water conservation usage, policy and decision making processes. Accurate and weekly spatially distributed SWE has never been produced before, and is highly informative to reservoir managers who must make tradeoffs between storing water for summer water supply versus using water before snowmelt recedes for generation of clean hydropower. Accurate SWE information, when coupled with runoff forecasting models, can also have ecological benefits through avoidance of late-spring high flows released from reservoirs that are not part of the natural seasonal variability.
  *   Quality: Available in a number of scientific formats to customers and stakeholders based on customer requirements.
  *   Lineage: All ASO data stems directly from on-board imaging spectrometer and scanning Lidar system instruments.
  *   Size: Many many TB in size. Raw data acquisition is dependent on the basin/survey size. Recent individual flights generate in the order of ~500GB which include imaging spectrometer and Lidar data. This does however shrink considerably if we just consider the data that we would distribute.
  *   Type/format: Digital Elevation Model / binary image (not public atm), Lidar (Raw Point Clouds)/ las (not public atm), Raster Zonal Stats / text (not public atm), Snow Water Equivalent / tiff, Snow Albedo / tiff
  *   Rate of change: Recent weekly flights have provided information on a scale and timing that has never occurred before. Distributed SWE increases after storms, and decreases during melt events in patterns that have never before been measured and will be studied by snow hydrologists for years to come. Once data is captured it is not updated, however subsequent data is generated from the original data within processing pipelines which as screening for data quality control and assurance.
  *   Data lifespan: For immediate operational purposes, the last flight's data become obsolete when a new flight is made. However, the annual sequence of data sets will be leveraged by snow hydrologists and runoff forecasters during the next decade as they are used to improve models and understanding of the spatial nature of the mountain snowpack.
  *   Potential audience: (snow) hydrologists, hydrologic modelers, runoff forecasters, and reservoir operators and reservoir managers.

Positive aspects:

This use case provides insight into what a NASA funded demonstration mission looks like (from a data provenance, archival point of view).

It is an excellent opportunity to delve into an earth science mission which is actively addressing the global problem of water resource management. Recently senior officials have declared a statewide (CA) drought emergency and are asking all Californians to reduce their water use by 20 percent. California, and other U.S. states are experiencing a serious drought and the state will be challenged to meet its water needs in the upcoming year. Calendar year 2013 was the driest year in recorded history for many areas of California, and current conditions suggest no change is in sight for 2014. ASO is at the front line of cutting edge scientific research meaning that the data that backs the mission, as well as the practices adopted within the project execution, are extremely important to addressing this issue.

Project collaborators and stakeholders are sent data and information when it is produced and curated. For some stakeholders, the data (in an operational sense) they require is very small in size and in such cases ASO emphasizes speed. It's more like a sharing of information than delivering a product for the short-term turnaround of information.

Negative aspects:

Demonstration missions of this caliber also have downsides. With regards to data best practices, more work is required in the following areas:

  *   Documentation of processes including data acquisition, provenance tracking, curation of data products such as bare earth digital earth models (DEM), full surface digital surface models (DSM), snow products, snow water equivalents (SWE), etc.
  *   Currently data is not searchable, this makes retrieval of specific data difficult when data volumes grow to this size and nature
  *   There is no publicly available guidance regarding suggested tools which can be used to interact with the data sources.
  *   Quick turnarounds of operational data may be compromised when ASO moves beyond a demonstration mission and picks up new customers etc. This will most likely be attributed to the time associations for the generation and distribution of science grade products.

Challenges:

  *   Data volumes are large, and will grow by year on year. The volume of generated data grew by 50% between 2013 and 2014.
  *   On many occasions we require a very quick turn around on inferences which can be made from the data. This sometimes (but not always) comes at the cost of reducing the emphasis of best practices for the generation, storage and archival of projects data.
  *   The data takes the form of science oriented representational formats. Such formats are non-typical of the typical data many people publish on the Web. A lot of thought needs to be put in to how this data can be better accessed.

Requires: R-AccessUpToDate<http://www.w3.org/TR/dwbp-ucr/#R-AccessUpToDate>, R-Citable<http://www.w3.org/TR/dwbp-ucr/#R-Citable>, R-DataIrreproducibility<http://www.w3.org/TR/dwbp-ucr/#R-DataIrreproducibility>, R-DataMissingIncomplete<http://www.w3.org/TR/dwbp-ucr/#R-DataMissingIncomplete>, R-FormatMachineRead<http://www.w3.org/TR/dwbp-ucr/#R-FormatMachineRead>, R-GeographicalContext<http://www.w3.org/TR/dwbp-ucr/#R-GeographicalContext>, R-GranularityLevels<http://www.w3.org/TR/dwbp-ucr/#R-GranularityLevels>, R-LicenseLiability<http://www.w3.org/TR/dwbp-ucr/#R-LicenseLiability>, R-MetadataAvailable<http://www.w3.org/TR/dwbp-ucr/#R-MetadataAvailable>, R-ProvAvailable<http://www.w3.org/TR/dwbp-ucr/#R-ProvAvailable>, R-QualityCompleteness<http://www.w3.org/TR/dwbp-ucr/#R-QualityCompleteness>, R-QualityMetrics<http://www.w3.org/TR/dwbp-ucr/#R-QualityMetrics>, R-TrackDataUsage<http://www.w3.org/TR/dwbp-ucr/#R-TrackDataUsage>, R-UsageFeedback<http://www.w3.org/TR/dwbp-ucr/#R-UsageFeedback> and R-VocabDocum<http://www.w3.org/TR/dwbp-ucr/#R-VocabDocum>.

[0] http://www.w3.org/TR/dwbp-ucr/#UC-ASO

Dr. Lewis John McGibbney Ph.D., B.Sc., MAGU
Engineering Applications Software Engineer Level 2
Computer Science for Data Intensive Systems Group 398M
Jet Propulsion Laboratory
California Institute of Technology
4800 Oak Grove Drive
Pasadena, California 91109-8099
Mail Stop : 158-256C
Tel:  (+1) (818)-393-7402
Cell: (+1) (626)-487-3476
Fax:  (+1) (818)-393-1190
Email: lewis.j.mcgibbney@jpl.nasa.gov

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