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

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.)

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

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

4,000 3,500 3,000

Increased emphasis on usability of the data (E.g., discovery, access and analysis)

2,500 2,000

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 **

1,500 1,000 500 Cumulative Volume of L2+ Products at All DAACs 0 FY00 FY01 FY02 FY03 FY04 FY05 FY06 FY07 FY08 FY09 FY10 FY11 FY12 FY13 FY14 Fiscal Year

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

)

National Aeronautics and Space Administration Jet Propulsion Laboratory

California Institute of Technology Pasadena, California

NASA Earth Science Data Pipeline

TDRS Network

EOSDIS DAAC EOSDIS DAAC Centers

National Aeronautics and Space Administration Jet Propulsion Laboratory

California Institute of Technology Pasadena, California

EOSDIS DAAC’s Earth Observing System Data and Information System Distributed Active Archive Centers

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

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?

National Aeronautics and Space Administration Jet Propulsion Laboratory

California Institute of Technology Pasadena, California

ESG – NASA Integration

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

National Aeronautics and Space Administration Jet Propulsion Laboratory

California Institute of Technology Pasadena, California

Traditional Analysis Approach

Credit: Braverman, Mattmann, Crichton • 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.

National Aeronautics and Space Administration Jet Propulsion Laboratory

California Institute of Technology Pasadena, California

Emerging Paradigm for Analysis

Credit: Braverman, Mattmann, Crichton • 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

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 diagram) (see left

National Aeronautics and Space Administration Jet Propulsion Laboratory

California Institute of Technology Pasadena, California

Model Intercomparison: Regional Example

Observations Collect User Choices (GUI / command line) Retrieve obs from database Spatial re gridding onto common grid Time averaging Load model data Area -averaging Annual cycle compositing 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 Metric Calculation Plot production e.g. map, time series plot, Taylor diagram Model file

TDRS Network

National Aeronautics and Space Administration Jet Propulsion Laboratory

California Institute of Technology Pasadena, California

Computational Vision

NASA Mission/Multi Mission Data & Science Centers NASA Mission/Multi Mission Data & Science Centers NASA Mission/Multi Mission Data & Science Centers Network w/ Cloud Storage & Computation Other Data Systems

NOAA)

National Aeronautics and Space Administration Jet Propulsion Laboratory

California Institute of Technology Pasadena, California

Research Challenges for Statistics

•

What architectural design produces the most efficient system topology for the types of data movement that will be required given scientific 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 efficiently 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?

National Aeronautics and Space Administration Jet Propulsion Laboratory

California Institute of Technology Pasadena, California

Summary

•

Significant efficiencies 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 flowing across the internet, manipulated and combined on-the fly as necessary for specific analyses.

•

We need new statistical methods and algorithms optimized for this type of environment.