Unified Metadata Model- Variables July 21 st, 2016 The material is based upon work supported by the National Aeronautics and Space Administration under Contract Number NNG15HZ39C

2 Agenda Overview of Initial Unified Metadata Model-Variables UML Data Model Analysis of Use Cases Backup slides: Examples (discuss as time allows)

3 Measurement Definition Variable Name: “Length” Variable Value: “1.5” Variable Units: “Inches” What is a Measurement? Webster’s defines it as: “the size, length, or amount of something, as established by measuring” We can take a measurement of length using a common ruler How do we measure something using an EOS instrument? We can take a measurement of TOA Fluxes in the following way: Variable Name: “SW_TOA_Clear-Sky” Variable Value: “301.8” Variable Units: “Watts per square meter” Measurements in general can be characterized by their: Variable Name, Variable Value, Variable Units, etc.

4 Unified Metadata Model-Variables Variable Class Elements Variable [1..N] Variable/Name [R] Variable/LongName [R] Variable/Units [O] Variable/DataType [R] Variable/Dimensions [R] Variable/ValidRange [O] Variable/Scale [O] Variable/Offset [O] Variable/FillValue[O] Variable/VariableType[R]

5 Required fields and showing how our examples populate the Variable class fields* NameLongNameUnitsData Type DimensionsValid RangeScaleOffsetFillValueVariable Type sea_surface_temperat ure sea_surface_subskin_ temperature kelvinshorttime=1, nj=3072, ni=4096 valid_min= -300, valid_max= 4500 scale_ factor =0.01 add_offset = Science quality_levelquality level of SST pixel bytetime=1, nj=3072, ni=4096 valid_min=0, valid_max=5 -128Quality LST_Day_1kmDaily daytime 1km grid Land-surface Temperature KshortYDim=1200, XDim=1200 valid_range= 7000, -1 scale_ factor =0.02 0Science QC_DayQuality control for daytime LST and emmissivity byteYDim=1200, XDim=1200 valid_range= 0, -1 undefinedQuality CERES_SW_Filtered_ Radiances_Upwards CERES SW Filtered Radiance, Upwards Watts per square meter per steradian floatRecords=13091, Samples=660 valid_range= -10.0, E38 Science CERES_Solar_Zenith_ at_Surface CERES Solar Zenith at Surface degfloatRecords=13091, Samples=660 valid_range= 0.0, E38 Auxiliary SW_TOA_Clear-Sky1.0 degree Regional MonthObserved TOA Fluxes Watts per square meter float E38 Science mole_fraction_of_carb on_dioxide_in_free_tr oposphere floatLatDim=91, LonDim= Science CLDCloud Coveroktasshorttime=1176, lat=360, lon=720 valid_range=0, 80 scale_ factor = Science * See EOS and CF Data Set/Variables examples detailed towards the end of the slide deck (#15 on) Indicates recommended mandatory fields for the UMM-Var class.

6 Analysis of UMM-Var Use Cases Browse Variables of a Collection –Scenario: the user starts with a collection, and wants to know what variables it includes Outcomes: Enables a user without any knowledge of the variable names to search for collections, select one, and be presented with a list of variables for that collection, grouped by measurement. Fields used:,,,

7 Analysis of UMM-Var Use Cases Faceted Browse –Scenario [a]: As a user of the Earthdata Search Client (EDSC), I can get a list of Measurement facets from the CMR. –Scenario [b]: As a user of the EDSC, I can click on a “Measurement” facet value and constrain the lists to the collections that match the selected Measurement and any other constraints I have selected. Outcomes: The EDSC user, with no knowledge of the Measurements available within the CMR, can get a list of Measurements, and can further constrain the lists to Collections which match by clicking on that Measurement, and any other constraints. Note: The user can go on and click on a Measurement to see the list of Variables available for that Measurement. Fields used:,,,,,,

8 Analysis of UMM-Var Use Cases Search Relevancy Ranking –Scenario: As a search engine (CMR), I can rank collections with a high relevance ranking when one or more of the search words appear in the measurement names for the variables in the collection, as opposed to more generic fields such as the summary or references. Outcomes: Enables users to search the CMR to get a list of Collections, ranked by relevance to a search word, in matching to a measurement. Fields used:,,,, * Source of tags could be either from the names defined in the CSDMS Standard: or alternatively the CF convention, (TBD).

9 Analysis of UMM-Var Use Cases Update Variable Associations –Scenario [a]: As a CMR client, I can associate multiple variables with a collection. –Scenario [b]: As a CMR client, I can submit a file with multiple collections and all of the variables listed for each collection. –Scenario [c]: As a metadata curator, I can populate the list of valid measurements with selections from the GCMD keyword hierarchy. Outcomes: The curator seeded the CMR with new valid variable measurements from the GCMD keyword hierarchy and allowed editors to maintain variable and collection associations. A curation tool may be used to maintain/update the variable and collection associations. Note: The metadata curator would benefit from being able to select from an alphabetically-sorted list of valid variable measurements, when associating variables with a collection. Ideally, a pull-down list could assist the curator when making each variable association. Fields used:,,,, * Source of keywords could be from the names defined in the GCMD Valids List, Version 8.1, :

10 Analysis of UMM-Var Use Cases Cross-site Data Subsetting –Scenario: As a subsetting GUI, I can present the variables for a given collection in a logically categorized way, such as by measurement, and further subset the data into more specific groups based on additional criteria. Outcomes: Enables users of a subsetting GUI to perform cross-site subsetting, based on the selection of a collection, categorized by measurement, variables. Note: in the example shown below, the measurement term used was: Ozone. This resulted in two collections being returned from the search: AIRX2TER and OMDOAO3. In the subsetting GUI, variables are grouped under the measurement term, for each collection. The user will be able to go on and subset the variable fields, for specific granules of interest. Fields used:,,,,,,,,,

11 Analysis of UMM-Var Use Cases Access Variables Data including Ancillary Variable Data (extension of Cross-Site Data Subsetting Use Case) –Scenario: The user starts with a list of variables (e.g., {,,,... }, and wants to know which collections contain variables that satisfy (and may also want to know what data quality, instrument calibration, spacecraft location, etc. variables are needed to properly understand the data) Outcomes: Enables a user without any knowledge of the collections, to locate those collections which contain variable selected from a list of variables. Allows subsequent discovery of associated variables, e.g. ancillary, calibration, location, data quality variables, which are directly related to the science variable selected. Fields used:,,,

12 Analysis of UMM-Var Use Cases Integrating GIBS with Web-Based Clients –Scenario: As a user of a GIBS client (like EDSC, WorldView, or GloVIS), I can view granules’ browse images for a particular layer to form a request to obtain the corresponding data variables, and only those data variables pertaining to this layer. Through CMR, I can locate the granules, the corresponding data variable from which the layer was generated, and any ancillary variables that need to go along with that variable (coordinates, quality, etc.). Ideally, I can transform that information into a set of subsetting request URLs that will fetch just those data variables from the appropriate granules. Outcomes: Allow users of a GIBS client to fetch data subsets based on their layer selections, and any associated variables. Note: It may be a perquisite for this use case to have a way to invoke “show layers” for a collection, and selected measurement(s), per a GIBS client. Fields used:,,,,,,,

EXAMPLES Backup Slides: EOS and CF Data Set/Variables

14 VIIRS SST NPP Data Set structure

15 sea_surface_temperature variable

16 quality_level variable

17 MOD11A1 Data Set Structure

18 LST_Day_1km variable

19 QC_Day variable

20 CER_BDS_Aqua-FM3_Edition1 Data Set structure

21 CERES_SW_Filtered_Radiances_Upwards Variable

22 CERES_Solar_Zenith_at_Surface

23 CERES_SYN_1km Data Set structure

24 SW_TOA_Clear-Sky variable structure

25 AIRS L3.CO2Std008 Data Set structure

26 mole_fraction_of_carbon_dioxide_i n_free_troposphere variable

27 ORNL DAAC THREDDS Cloud Cover Data Set structure

28 CLD variable

29 This material is based upon work supported by the National Aeronautics and Space Administration under Contract Number NNG15HZ39C.