1. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Challenges of Analyzing Large Environmental Data Sets Dan Crichton, Program Manager, Earth and Planetary Science Data Systems Amy Braverman, Senior Statistician NASA/JPL

2. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Massive Data Sets in the Environmental Sciences Environmental science areas (not exhaustive): –Climate change science/climate modeling Global Regional –Environmental quality Pollution Epidemiology Land use and natural resource management – Decision support and disaster management Climate change impacts Policy decisions and treaty enforcement Disaster response (flooding, drought, volcanoes, etc.)

3. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Massive Data Sets in Climate Science Climate model output: –originally intended as laboratory experiments to play what if (explore the physics by twiddling knobs and seeing what happens) –now have greater policy implications wrt predictions into the future, attribution of causes, and characterizing uncertainties Observations: –Improve process understanding and formulate hypotheses through exploratory data analysis –Improve parameterizations (statistical description of sub-grid-scale processes) –Establishment of long term data records – Model evaluation comparison of model output against observations weighting multi-model ensemble members

4. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Architecture Drivers: Data Intensive Science Increasing data volumes requiring new approaches for data production, validation, processing, discovery and data transfer/distribution (E.g., scalability relative to available resources) –Roughly doubling in size every two years –Shift from compute to data intensive Increased emphasis on usability of the data (E.g., discovery, access and analysis) Increasing diversity of data sets and complexity for integrating across missions/experiments (E.g., common information model for describing the data) –the benefits to science in bringing together and creating fused data products from multiple sources is critical in areas such as climatology where baseline data records are needed across measurements ** Increasing distribution of coordinated processing, operations and analysis (E.g., federation) On the fly analysis Increased pressure to reduce cost of supporting new missions Increasing desire for PIs to have integrated tool sets to work with data products with their own environments (E.g. perform their own generation and distribution)

5. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California NASA Earth Science Data Pipeline Data Acquisition and Command Instrume nt Operation s EDOS/GD S L0A Processin g Science Data Processing L0B L1 L2 L3 L4 SDS EOSDIS DAAC Science Data Management Archive & Distribution Instrume nt Operation s EDOS/GD S L0A Processin g Science Data Processing L0B L1 L2 L3 L4 SDS EOSDIS DAAC Science Data Management Archive & Distribution EOSDIS Data Centers Science Data Management Archive & Distribution Science Data Processing L0B L1 L2 L3 L4 Science Data Systems Instrument Operations EDOS/Groun d Data Systems L0A Processing Science Teams Outreach Research Mission Operation s TDRS Network On Board Processing

6. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California EOSDIS DAACs Earth Observing System Data and Information System Distributed Active Archive Centers

7. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Using Satellite Observations to Enable Climate Model Evaluation How to bring as much observational scrutiny as possible to the IPCC process? How to best utilize the wealth of NASA Earth observations for the IPCC process? Next Target : IPCC AR5 Model Output Available for Analysis Spring 2011 Papers Due ~ Late 2011/Early 2012 Report Completion 2013

8. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Earth System Grid Federation DOE-funded federation to distribute climate model output to the climate modeling community Common services for access to repositories and portals/gateways Highly decoupled Open source framework (software packaged and distributed) mandated by DOE SciDAC Program A Recent question….how do you link observations and climate model output?

9. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California ESG – NASA Integration

10. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Moving to Data Intensive Science Traditional Pipelines vs. Online Dynamic Services –Convergence between static pipelines and on-the-fly data processing and services Analysis of Distributed Data through Distributed Computational Services –Push computational services to data Fused Data Products –Generate new, fused data products Virtual Research Networks –Provide a computing infrastructure for collaborative research

11. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Traditional Analysis Approach User program must encode all functionality beyond gross-level access. Requires knowledge of specific instrument characteristics such as retrieval methods, format, measurement error characteristics and biases, etc. Difficulties multiply with more than one data source. Credit: Braverman, Mattmann, Crichton

12. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Emerging Paradigm for Analysis Push as much computation as possible to locations where the data reside; minimize data movement Deploy simple services to data centers that provide access and the computational functions to enable model-to-data analysis –Embrace service-oriented style of architecture Credit: Braverman, Mattmann, Crichton

13. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Data Integration Combining AIRS and MLS requires: –Rectifying horizontal, vertical and temporal mismatch –Assessing and correcting for the instruments scene- specific error characteristics (see left diagram)

14. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Model Intercomparison: Regional Example Collect User Choices (GUI / command line) Collect User Choices (GUI / command line) Load model data Retrieve obs from database Spatial re- gridding onto common grid Time averaging Area -averaging Annual cycle compositing Metric Calculation Plot production Model file RCMET optional e.g. calculate monthly means from daily data e.g. calculate area-weighted mean over user defined masked region e.g. calculate means of all Januarys, all Februarys etc e.g. calculate bias, RMS error etc e.g. map, time series plot, Taylor diagram Observations

15. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Computational Vision Data Acquisition and Command Instrume nt Operation s EDOS/GD S L0A Processin g Instrume nt Operation s EDOS/GD S L0A Processin g Instrument Operations EDOS/Groun d Data Systems L0A Processing Mission Operation s TDRS Network On Board Processing Network w/ Cloud Storage & Computation Applications Analysis, Modeling and Application Environments/Ga teways Other Data Systems (e.g. NOAA) Other Data Systems (e.g. NOAA) Other Data Systems (e.g. NOAA) Decision Support Science Data Processing Science Data Manage NASA Mission/Multi- Mission Data & Science Centers Science Data Manage NASA Mission/Multi- Mission Data & Science Centers Science Data Manage NASA Mission/Multi- Mission Data & Science Centers Research Science Teams

16. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Research Challenges for Statistics What architectural design produces the most efcient system topology for the types of data movement that will be required given scientic objectives? Can we study this as an optimization problem? How do we design computational methods that exploit the system topology and its distributed nature? Need algorithms that operate on distributed data to produce statistics of interest, or approximations. Study this trade-off. Data analysis choreography: how to assemble algorithms most efciently given a set of analysis goals? How to optimize the movement of data? How can statistics and other disciplines (e.g., computer science) education be better aligned?

17. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Summary Signicant efciencies may be achieved by thinking of data analysis and data access together rather than thinking of them as serial operations. In this paradigm, data sets are not static entities. They are virtual, possibly streaming data structures owing across the internet, manipulated and combined on-the-y as necessary for specic analyses. We need new statistical methods and algorithms optimized for this type of environment.