1. Brian Johnson and Doug Young
NSIDC Jupyter Hub prototyping Part of the Cross-DAAC Cloud Analysis Toolkit to Enable Earth Science (CATEES)
Brian Johnson and Doug Young

2. CATEES Overview – cloud prototyping activity
Objectives
Provide easy-to-use tools for working with EOSDIS data
Provide tools in a convenient package to users
Show users how to access EOSDIS API via scripts
Show users how use analytics optimized cloud storage for analysis
Demonstrate cross-EOSDIS development
Approach
Python Software + Other Materials
Library routines (open-source and DAAC-contrib)
Interdisciplinary Use Case examples
Data (links + local)
Sample IPython Notebooks
Deploy to cloud for usage
Develop Process for joint development

3. CATEES Roadmap

4. Status – NSIDC example in GHRC Jupyter Hub
Sample Code:
Visualize the digital SAR mosaic and elevation map of the Greenland icesheet
(Bruce Raup, Paul Madden, Doug Young; NSIDC)

5. Cross-DAAC example Goal:
Provides a “science” analytics” example that integrated data sets from more than on DAAC and extensible to other data sets, analyses and larger domains (i.e. analytics at scale)
Approach:
Build a Jupyter notebook that contains 5 fundamental “building blocks” (or functional elements) that address a common science workflow, e.g.
Get data from 3 differnet sources (“connectors”)
Access the data (“extract”)
Prepare the data (subset, regrid, bin, …)
Run a simple correlation analysis
Display the results

6. Science use case
How do different vegetation communities respond to long-term changes in water availability in Colorado?
Expect vegetation productivity (growth) to respond to long-term trends in soil moisture
Want to evaluate spatial and temporal relationships between:
Soil moisture
Vegetation greenness
Ecoregions

7. Data products SMAP soil moisture (SPL2SMP)
NSDIC DAAC: HDF 5, OPeNDAP access
MODIS Veg. Indices (MOD13A2)
LP DAAC: HDF-EOS, OPeNDAP
Precipitation (Daymet)
ORNL DAAC: netCDF-4, HTTP or THREDDS
Ecoregion map for Colorado
SMAP 36-km soil moisture
MODIS NDVI composite
DAYMET daily precip map

8. Data prep • Re-gridded the four data sets; user specified projection
• Extract Same extent (state of Colorado)
• Integrated data with different resolution

9. Analysis

10. Benefits for users