IEEE Geoscience and Remote Sensing Magazine - September 2016 - 19

CDS API

Applications

Scripts

CDS CLI

CDS Library
CDS WS Client

CDS WS Servers
Data

Exposure

Relevance
Co-Location

Convenience
Extensible

High-Performance
Compute/Storage Fabric
Storage-Proximal Analytics
Canonical Operations

Problem
Data

Worker
Node 1
Map
Master Node

Worker
Node 2

Reduce
Worker
Node n

Solution
Data

MERRA Reanalysis

Map Reduce

FIGURE 4. The MERRA/AS provides an end-to-end demonstration of the principles underlying CAaaS: important data embedded in a high-

performance storage-compute environment where analytic services are exposed via web services to client-side applications through an
easy-to-use client-side API tailored to the climate research community. CLI: command-line interface, WS: web service.

for Ecosystem Recovery wildfire decision support system
that is being used for postfire rehabilitation planning by
Burned Area Emergency Response teams within the U.S.
Department of Interior and the U.S. Forest Service [28].
This capability has led to the development of new data
products based on climate reanalysis data that until now
were not available to the wildfire community.
In our largest deployment exercise to date, the CDS client distribution package and the CDS API have been used
by the iPlant Collaborative to integrate MERRA data and
MERRA/AS functionality into the iPlant Discovery Environment. iPlant is a virtual organization created by a cooperative agreement funded by the U.S. National Science
Foundation to create cyberinfrastructure for the plant
sciences. The project develops computing systems and
software that combine computing resources, like those of
TeraGrid, and bioinformatics and computational biology
software. Its goal is easier collaboration among researchers
with improved data access and processing efficiency. Primarily centered in the United States, it collaborates interseptember 2016

ieee Geoscience and remote sensinG maGazine

nationally and includes a wide range of governmental and
private-sector partners [29].
Initial results have shown that analytic engine optimizations can yield near real-time performance of MERRA/
AS's canonical operations and that the total time required
to assemble relevant data for many applications can be significantly reduced, often by as much as two to three orders
of magnitude [24].
NExT-GENERATION CyBERINFRASTRUCTURE
FOR ENHANCED INTEROPERABILITy
Big data challenges are sometimes viewed as problems of
large-scale data management where solutions are offered
through an array of traditional storage and archive theories
and technologies. These approaches tend to view big data
as an issue of storing and managing large amounts of structured data for the purpose of finding subsets of interest. Alternatively, big data challenges can be viewed as knowledge
management problems where solutions are offered through
an array of analytic techniques and technologies. These
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Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - September 2016
