Peter Fox, and … others

1. Quality, Uncertainty and Bias Representations of Atmospheric Remote Sensing Information Products Peter Fox, and … others Xinformatics 4400/6400 Week 12, April 22, 2014

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/Metadata • http://www.niso.org/publications/press/UnderstandingMetadata.pdf • http://dublincore.org/

3. Acronyms AOD Aerosol Optical Depth MDSA Multi-sensor Data Synergy Advisor MISR Multi-angle Imaging Spectro-Radiometer MODIS Moderate Resolution Imaging Spectro-radiometer OWL Web Ontology Language REST Representational State Transfer UTC Coordinated Universal Time XML eXtensible Markup Language XSL eXtensibleStylesheet Language XSLT XSL 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

5. Problem statement 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?

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... ...and you’re putting together an exhibit on wildfires with some cool satellite data 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 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... ...you want to use satellite wildfire data to direct HotShot team deployments 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

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... ...you want to use satellite wildfire data to estimate burn scar areas for landslide prediction 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

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... ...you want to use satellite wildfire data to predict extinction risk of threatened species 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

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... ...you want to perfect an algorithm to detect and estimate active burning areas at night with visible and infrared radiances 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

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.

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

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 qualitydata is often not negligible Hurricane Ike, 9/10/2008 Total Column Precipitable Water Quality Best Good Do Not Use kg/m2

23. Data Usage Workflow

24. Data Usage Workflow *Giovanni helps streamline / automate Subset / Constrain Reformat Filtering Re-project Integration

25. Data Usage Workflow Precision Requirements Integration Planning Quality Assessment Requirements *Giovanni helps streamline / automate Intended Use Subset / Constrain Reformat Filtering Re-project Integration

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

27. Producers Consumers Quality Control Quality Assessment Fitness for Purpose Fitness for Use Trustor Trustee 27

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)

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)

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

33. Level 2 data • Swathfor MISR, orbit 192 (2001)

34. Level 3 data

35. Same parameter Same space & time MODIS vs. MERIS MODIS MERIS Different results – why? 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? • MODIS Aqua AOD July 2009 • MISR Terra AOD July 2009 • 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

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

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

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.

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

41. …is caused by an Overpass Time Difference

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

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. Semantic Web Basics • The triple: {subject-predicate-object} Interferometeris-aoptical instrument Optical instrumenthasfocal 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. Ontology Spectrum Thesauri “narrower term” relation Selected Logical Constraints (disjointness, inverse, …) Frames (properties) Formal is-a Catalog/ ID Informal is-a Formal instance General Logical constraints Terms/ glossary Value Restrs. 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

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/SBReadResourceServlet?rid=1299017667444_1897825847_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