1. Quality, Uncertainty and Bias Representations of Atmospheric Remote Sensing Information Products Peter Fox, and … others Xinformatics 4400/6400 Week 11, April 21, 2015

2. reading Audit/ Workflow Information Discovery –Information discovery graph(IDG) –Projects using information discovery –Information discovery and Library Sciences –Information Discovery and retrieval tools –Social Search Metadata –http://en.wikipedia.org/wiki/Metadatahttp://en.wikipedia.org/wiki/Metadata –http://www.niso.org/publications/press/UnderstandingMetada ta.pdfhttp://www.niso.org/publications/press/UnderstandingMetada ta.pdf –http://dublincore.org/http://dublincore.org/ 2

3. Acronyms AODAerosol Optical Depth MDSAMulti-sensor Data Synergy Advisor MISRMulti-angle Imaging Spectro-Radiometer MODIS Moderate Resolution Imaging Spectro-radiometer OWLWeb Ontology Language RESTRepresentational State Transfer UTCCoordinated Universal Time XMLeXtensible Markup Language XSLeXtensible Stylesheet Language XSLTXSL Transformation

4. Where are we in respect to the data challenge? “The user cannot find the data; If he can find it, cannot access it; If he can access it, ; he doesn't know how good they are; if he finds them good, he can not merge them with other data” The Users View of IT, NAS 1989 4

5. Data quality is an ill-posed problems because  It is not uniquely defined  It is user dependent  It is difficult to be quantified  It is handled differently by different teams  It is perceived differently by data providers and data users User question: Which data or product is better for me? PROBLEM STATEMENT

6. QUALITY CONCERNS ARE POORLY ADDRESSED  Data quality issues have lower priority than building an instrument, launching rockets, collecting/processing data, and publishing papers using the data.  Little attention on how validation measurements are passed from Level 1 to Level 2 and higher as it propagates in time and space.

7. USERS PERSPECTIVE There might be a better product somewhere but if I cannot easily find it and understand it, I am going to use whatever I have and know already.

8. (Some) Facets of Quality Accuracy: closeness to Truth –Bias: systematic deviation –Uncertainty: non-systematic deviation Completeness: how well data cover a domain –Spatial –Temporal Consistency –Spatial: absence of spurious spatial artifacts –Temporal: absence of trend, spike and offset artifacts Resolution –Temporal: time between successive measurements of the same volume –Spatial: distance between adjacent measurements Ease of Use Latency: Time between data collection and receipt

9. Pretend you’re a museum curator... Which data quality facet is most important to you?...and you’re putting together an exhibit on wildfires with some cool satellite data A – Accuracy B – Resolution (spatial and/or temporal) C – Completeness (spatial and/or temporal) D – Latency E – Ease of Use Museum Curator

10. Museum Curator Poll Which data quality facet is most important to you? A – Accuracy B – Resolution (spatial and/or temporal) C – Completeness (spatial and/or temporal) D – Latency E – Ease of Use

11. You’re an operational user and... Which data quality facet is most important to you?...you want to use satellite wildfire data to direct HotShot team deployments A – Accuracy B – Resolution (spatial and/or temporal) C – Completeness (spatial and/or temporal) D – Latency E – Ease of Use

12. Operational User / HotShot Which data quality facet is most important to you? A – Accuracy B – Resolution (spatial and/or temporal) C – Completeness (spatial and/or temporal) D – Latency E – Ease of Use

13. You’re an operational user and... Which data quality facet is most important to you?...you want to use satellite wildfire data to estimate burn scar areas for landslide prediction A – Accuracy B – Resolution (spatial and/or temporal) C – Completeness (spatial and/or temporal) D – Latency E – Ease of Use

14. Operational User / Landslide Which data quality facet is most important to you? A – Accuracy B – Resolution (spatial and/or temporal) C – Completeness (spatial and/or temporal) D – Latency E – Ease of Use

15. You’re an ecology researcher and... Which data quality facet is least important to you?...you want to use satellite wildfire data to predict extinction risk of threatened species A – Accuracy B – Resolution (spatial and/or temporal) C – Completeness (spatial and/or temporal) D – Latency E – Ease of Use

16. Ecology Researcher Which data quality facet is least important to you? A – Accuracy B – Resolution (spatial and/or temporal) C – Completeness (spatial and/or temporal) D – Latency E – Ease of Use

17. You’re a remote sensing researcher... Which data quality facet is least important to you?...you want to perfect an algorithm to detect and estimate active burning areas at night with visible and infrared radiances A – Accuracy B – Resolution (spatial and/or temporal) C – Completeness (spatial and/or temporal) D – Latency E – Ease of Use

18. Remote Sensing Researcher... Which data quality facet is least important to you? A – Accuracy B – Resolution (spatial and/or temporal) C – Completeness (spatial and/or temporal) D – Latency E – Ease of Use

19. Giovanni Earth Science Data Visualization & Analysis Tool Developed and hosted by NASA/ Goddard Space Flight Center (GSFC) Multi-sensor and model data analysis and visualization online tool Supports dozens of visualization types Generate dataset comparisons ~1500 Parameters Used by modelers, researchers, policy makers, students, teachers, etc. 19

20. Web-based tools like Giovanni allow scientists to compress the time needed for pre- science preliminary tasks: data discovery, access, manipulation, visualization, and basic statistical analysis. DO SCIENCE Submit the paper Minutes Web-based Services: Perform filtering/masking Find data Retrieve high volume data Extract parameters Perform spatial and other subsetting Identify quality and other flags and constraints Develop analysis and visualization Accept/discard/get more data (sat, model, ground-based) Learn formats and develop readers Jan Feb Mar May Jun Apr Pre- Science Days for exploration Use the best data for the final analysis Write the paper Derive conclusions Exploration Use the best data for the final analysis Write the paper Initial Analysis Derive conclusions Submit the paper Jul Aug Sep Oct The Old Way: The Giovanni Way: Read Data Reformat Analyze Explore Reproject Visualize Extract Parameter Giovanni Mirador Scientists have more time to do science! DO SCIENCE Giovanni Allows Scientists to Concentrate on the Science Filter Quality Subset Spatially

21. EXPECTATIONS FOR DATA QUALITY What do most users want?  Gridded data (without gaps) with error bars in each grid cell What do they get instead?  Level 2 swath in satellite projections with poorly defined quality flags  Level 3 monthly data with a lot of suspicious aggregations and standard deviation as an uncertainty measure (fallacy) – Standard deviation mostly reflects the variability within the grid box.  Little or no information on sampling (Level 3).

22. The effect of bad quality data is often not negligible Total Column Precipitable Water Quality BestGood Do Not Use kg/m 2 Hurricane Ike, 9/10/2008

23. Data Discovery AssessmentAccessManipulationVisualizationAnalyze Data Usage Workflow 23

24. Data Discovery AssessmentAccessManipulationVisualizationAnalyze Data Usage Workflow 24 Integration Reformat Re-project Filtering Subset / Constrain

25. Data Discovery AssessmentAccessManipulationVisualizationAnalyze Data Usage Workflow 25 Integration Planning Precision Requirements Quality Assessment Requirements Intended Use Integration Reformat Re-project Filtering Subset / Constrain

26. Challenge Giovanni streamlines data processing, performing required actions on behalf of the user –but automation amplifies the potential for users to generate and use results they do not fully understand The assessment stage is integral for the user to understand fitness-for-use of the result –but Giovanni did not assist in assessment We were challenged to instrument the system to help users understand results 26

27. 27 ProducersConsumers Quality Control Fitness for Purpose Fitness for Use Quality Assessment Trustee Trustor

28. Definitions – for an atmospheric scientist Quality –Is in the eyes of the beholder – worst case scenario… or a good challenge Uncertainty –has aspects of accuracy (how accurately the real world situation is assessed, it also includes bias) and precision (down to how many digits) 28

29. Quality Control vs. Quality Assessment Quality Control (QC) flags in the data (assigned by the algorithm) reflect “happiness” of the retrieval algorithm, e.g., all the necessary channels indeed had data, not too many clouds, the algorithm has converged to a solution, etc. Quality assessment is done by analyzing the data “after the fact” through validation, intercomparison with other measurements, self-consistency, etc. It is presented as bias and uncertainty. It is rather inconsistent and can be found in papers, validation reports all over the place.

30. Definitions – for an atmospheric scientist Bias has two aspects: –Systematic error resulting in the distortion of measurement data caused by prejudice or faulty measurement technique –A vested interest, or strongly held paradigm or condition that may skew the results of sampling, measuring, or reporting the findings of a quality assessment: Psychological: for example, when data providers audit their own data, they usually have a bias to overstate its quality. Sampling: Sampling procedures that result in a sample that is not truly representative of the population sampled. (Larry English) 30

31. Data quality needs: fitness for use Measuring Climate Change: –Model validation: gridded contiguous data with uncertainties –Long-term time series: bias assessment is the must, especially sensor degradation, orbit and spatial sampling change Studying phenomena using multi-sensor data: –Cross-sensor bias is needed Realizing Societal Benefits through Applications: –Near-Real Time for transport/event monitoring - in some cases, coverage and timeliness might be more important that accuracy –Pollution monitoring (e.g., air quality exceedance levels) – accuracy Educational (users generally not well-versed in the intricacies of quality; just taking all the data as usable can impair educational lessons) – only the best products

32. Level 2 data 32

33. Level 2 data Swath for MISR, orbit 192 (2001) 33

34. Level 3 data 34

35. MODIS vs. MERIS Same parameterSame space & time Different results – why? MODIS MERIS A threshold used in MERIS processing effectively excludes high aerosol values. Note: MERIS was designed primarily as an ocean-color instrument, so aerosols are “obstacles” not signal.

36. Spatial and temporal sampling – how to quantify to make it useful for modelers? Completeness: MODIS dark target algorithm does not work for deserts Representativeness: monthly aggregation is not enough for MISR and even MODIS Spatial sampling patterns are different for MODIS Aqua and MISR Terra: “pulsating” areas over ocean are oriented differently due to different orbital direction during day-time measurement  Cognitive bias MODIS Aqua AOD July 2009MISR Terra AOD July 2009

37. Three projects with data quality flavor Multi-sensor Data Synergy Advisor –Product-level Quality: how closely the data represent the actual geophysical state Data Quality Screening Service –Pixel-level Quality: algorithmic guess at usability of data point –Granule-level Quality: statistical roll-up of Pixel-level Quality Aerosol Statistics –Record-level Quality: how consistent and reliable the data record is across generations of measurements 37

38. Multi-Sensor Data Synergy Advisor (MDSA) Goal: Provide science users with clear, cogent information on salient differences between data candidates for fusion, merging and intercomparison –Enable scientifically and statistically valid conclusions Develop MDSA on current missions: – NASA - Terra, Aqua, (maybe Aura) Define implications for future missions 38

39. How MDSA works? MDSA is a service designed to characterize the differences between two datasets and advise a user (human or machine) on the advisability of combining them. Provides the Giovanni online analysis tool Describes parameter and products Documents steps leading to the final data product Enables better interpretation and utilization of parameter difference and correlation visualizations. Provides clear and cogent information on salient differences between data candidates for intercomparison and fusion. Provides information on data quality Provides advice on available options for further data processing and analysis. 39

40. Correlation – same instrument, different satellites Anomaly 40 MODIS Level 3 dataday definition leads to artifact in correlation

41. …is caused by an Overpass Time Difference 41

42. Correlation between MODIS Aqua AOD (Ocean group product) and MODIS-Aqua AOD (Atmosphere group product) Pixel Count distribution Only half of the Data Day artifact is present because the Ocean Group uses the better Data Day definition! Effect of the Data Day definition on Ocean Color data correlation with Aerosol data

43. Research approach Systematizing quality aspects –Working through literature –Identifying aspects of quality and their dependence of measurement and environmental conditions –Developing Data Quality ontologies –Understanding and collecting internal and external provenance Developing rulesets allows to infer pieces of knowledge to extract and assemble Presenting the data quality knowledge with good visual, statement and references

44. 44 Semantic Web Basics The triple: {subject-predicate-object} Interferometer is-a optical instrument Optical instrument has focal length W3C is the primary (but not sole) governing org. languages –RDF programming environment for 14+ languages, including C, C++, Python, Java, Javascript, Ruby, PHP,...(no Cobol or Ada yet ;- ( ) –OWL 1.0 and 2.0 - Ontology Web Language - programming for Java Query, rules, inference… Closed World - where complete knowledge is known (encoded), AI relied on this Open World - where knowledge is incomplete/ evolving, SW promotes this

45. 45 Ontology Spectrum Catalog/ ID Selected Logical Constraints (disjointness, inverse, …) Terms/ glossary Thesauri “narrower term” relation Formal is-a Frames (properties) Informal is-a Formal instance Value Restrs. General Logical constraints Originally from AAAI 1999- Ontologies Panel by Gruninger, Lehmann, McGuinness, Uschold, Welty; – updated by McGuinness. Description in: www.ksl.stanford.edu/people/dlm/papers/ontologies-come-of-age-abstract.html

46. Model for Quality Evidence 46

47. Data Quality Ontology Development (Quality flag) Working together with Chris Lynnes’s DQSS project, started from the pixel-level quality view.

48. Data Quality Ontology Development (Bias) http://cmapspublic3.ihmc.us:80/servlet/SBReadResourceServlet ?rid=1286316097170_183793435_22228&partName=htmltext

49. Modeling quality (Uncertainty) Link to other cmap presentations of quality ontology: http://cmapspublic3.ihmc.us:80/servlet/SBRead ResourceServlet?rid=1299017667444_189782 5847_19570&partName=htmltext

50. MDSA Aerosol Data Ontology Example Ontology of Aerosol Data made with cmap ontology editor http://tw.rpi.edu/web/project/MDSA/DQ-ISO_mapping

51. Multi-Domain Knowledgebase 51 Provenance Domain Earth Science Domain Data Processing Domain

52. RuleSet Development [DiffNEQCT: (?s rdf:type gio:RequestedService), (?s gio:input ?a), (?a rdf:type gio:DataSelection), (?s gio:input ?b), (?b rdf:type gio:DataSelection), (?a gio:sourceDataset ?a.ds), (?b gio:sourceDataset ?b.ds), (?a.ds gio:fromDeployment ?a.dply), (?b.ds gio:fromDeployment ?b.dply), (?a.dply rdf:type gio:SunSynchronousOrbitalDeployment), (?b.dply rdf:type gio:SunSynchronousOrbitalDeployment), (?a.dply gio:hasNominalEquatorialCrossingTime ?a.neqct), (?b.dply gio:hasNominalEquatorialCrossingTime ?b.neqct), notEqual(?a.neqct, ?b.neqct) -> (?s gio:issueAdvisory giodata:DifferentNEQCTAdvisory)]

53. Advisor Knowledge Base 53 Advisor Rules test for potential anomalies, create association between service metadata and anomaly metadata in Advisor KB

54. Data Discovery AssessmentAccessManipulationVisualizationAnalyze Re- Assessment Assisting in Assessment 54 Integration Planning Precision Requirements Quality Assessment Requirements Intended Use Integration Reformat Re-project Filtering Subset / Constrain MDSA Advisory Report Provenance & Lineage Visualization

55. Thus - Multi-Sensor Data Synergy Advisor Assemble semantic knowledge base –Giovanni Service Selections –Data Source Provenance (external provenance - low detail) –Giovanni Planned Operations (what service intends to do) Analyze service plan –Are we integrating/comparing/synthesizing? Are similar dimensions in data sources semantically comparable? (semantic diff) How comparable? (semantic distance) –What data usage caveats exist for data sources? Advise regarding general fitness-for-use and data-usage caveats 55

56. Semantic Advisor Architecture RPI

57. …. complexity 57

58. Presenting data quality to users Global or product level quality information, e.g. consistency, completeness, etc., that can be presented in a tabular form. Regional/seasonal. This is where we've tried various approaches: –maps with outlines regions, one map per sensor/parameter/season –scatter plots with error estimates, one per a combination of Aeronet station, parameter, and season; with different colors representing different wavelengths, etc.

59. Advisor Presentation Requirements Present metadata that can affect fitness for use of result In comparison or integration data sources –Make obvious which properties are comparable –Highlight differences (that affect comparability) where present Present descriptive text (and if possible visuals) for any data usage caveats highlighted by expert ruleset Presentation must be understandable by Earth Scientists!! Oh you laugh… 59

60. Advisory Report Tabular representation of the semantic equivalence of comparable data source and processing properties Advise of and describe potential data anomalies/bias 60

61. Advisory Report (Dimension Comparison Detail) 61

62. Advisory Report (Expert Advisories Detail) 62

63. Quality Comparison Table for Level- 3 AOD (Global example) Quality AspectMODISMISR Completeness Total Time Range Platform Time Range 2/2/200-present Terra2/2/2000-present Aqua7/2/2002-present Local Revisit Time Platform Time Range Terra10:30 AMTerra 10:30 AM Aqua1:30 PM Revisit Timeglobal coverage of entire earth in 1 day; coverage overlap near pole global coverage of entire earth in 9 days & coverage in 2 days in polar region Swath Width 2330 km380 km Spectral AOD AOD over ocean for 7 wavelengths (466, 553, 660, 860, 1240, 1640, 2120 nm ); AOD over land for 4 wavelengths (466, 553, 660, 2120 nm (land) AOD over land and ocean for 4 wavelengths (446, 558, 672, and 866 nm) AOD Uncertainty or Expected Error (EE) +-0.03+- 5% (over ocean; QAC > = 1) +-0.05+-20% (over land, QAC=3); 63% fall within 0.05 or 20% of Aeronet AOD; 40% are within 0.03 or 10% Successful Retrievals 15% of Time15% of Time (slightly more because of retrieval over Glint region also)

64. What they really like! 64

65. Summary Quality is very hard to characterize, different groups will focus on different and inconsistent measures of quality –Modern ontology representations and reasoning to the rescue! Products with known Quality (whether good or bad quality) are more valuable than products with unknown Quality. –Known quality helps you correctly assess fitness-for-use Harmonization of data quality is even more difficult that characterizing quality of a single data product 65

66. Summary Advisory Report is not a replacement for proper analysis planning –But provides benefit for all user types summarizing general fitness-for-usage, integrability, and data usage caveat information –Science user feedback has been very positive Provenance trace dumps are difficult to read, especially to non-software engineers –Science user feedback; “Too much information in provenance lineage, I need a simplified abstraction/view” Transparency  Translucency –make the important stuff stand out 66

67. Current Work Advisor suggestions to correct for potential anomalies Views/abstractions of provenance based on specific user group requirements Continued iteration on visualization tools based on user requirements Present a comparability index / research techniques to quantify comparability 67