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Overcoming Challenges in Earth Science Data Analysis: A Comprehensive Guide

Alice's quest to accurately determine the extent of a volcanic eruption highlights the complexities of Earth science data analysis. From choosing suitable aerosol datasets across various platforms to managing multiple data formats and reprojecting them for integration, the technical hurdles can be daunting. This guide explores the challenges Alice faces and suggests enabling technologies like CF-1 and OPeNDAP that streamline data processing without extensive pre-processing. By utilizing user-friendly tools and innovative approaches, we can simplify access to critical datasets in Earth science.

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Overcoming Challenges in Earth Science Data Analysis: A Comprehensive Guide

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Presentation Transcript

  1. Earth Science Data:Why So Difficult??!@#!! Chris Lynnes

  2. The Way Things Should Be • Alice wants to determine a volcanic eruption's vertical and horizontal extent, at the highest possible spatial resolution • In her analysis tool, she types "aerosols" in a blank • The tool presents a table of aerosol choices, showing key charcteristics in a comparison table: • Alice checks desired boxes, draws a box around the volcano, sets date/time and clicks "Go" • 20 seconds later, all data are in her analysis tool, pre-subsetted, with quality screening applied • MISR aerosols from LaRC • MODIS aerosols from MRDC • CALIPSO vertical classifications from LaRC • OMI UV aerosols from GES DISC • Multi-sensor climatology computed by Giovanni • GOCART model from DataFed • CALIPSO SO2/aerosol experimental discrimination from Dr. Bob • HIRDLS profile from GES DISC • POLDER aerosols from CNES • GOES-R aerosols from NOAA • and more (ACE, GEO-CAPE, NPOESS...)

  3. The Way Things Are • Look through a bunch of directories to find the right datasets • Probably miss some good ones • Use several different search tools to get data files • Use a special interface to get the climatology • Wait for email for some data, then go pick up • Get data in at least 5 different formats, myriad data structures • Got to reformat to get them into analysis tool • Got to reproject somehow • Got to integrate vertical profile with horizontal data • Spend days reading documentation on quality • Spend more days writing code to screen data • Hear about Dr. Bob's experimental data through good luck • JPEG picture is nice but can't be integrated with data

  4. Why So Difficult???!@#$%!! All of the challenges have technical solutions or are a Simple Matter of Coding (SMOC) -- it shouldn't be so hard

  5. Enabling Technology: CF-1 AIRS Swath data in NetCDF-CF1, displayed in IDV  WITHOUT any pre-processing

  6. Enabling Technology: OPeNDAP OMI Level 3 data remotely acquired and displayed in Panoply  by providing just a URL (no preprocessing)

  7. Other Demonstrated Capabilities • Server-side quality screening • Giovanni  • CEOP Satellite Data Server • Multi-center search • Federated:  EOSDIS Version 0 • Centralized: ECHO • Self-publication (Dr. Bob's case) • Datacasting • Plug-in search modules • Firefox / OpenSearch

  8. What Would We Need? • More use of key enabling technologies • Easy-to-use, turnkey tools for deploying technologies • Gateways and proxies for those who cannot deploy technologies This sounds like a job for Technology Infusion! (If not Tech Infusion, then who???)

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