Award Abstract # 2126413
EarthCube Capabilities: CloudDrift: a platform for accelerating research with Lagrangian climate data

NSF Org: RISE
Integrative and Collaborative Education and Research (ICER)
Recipient: UNIVERSITY OF MIAMI
Initial Amendment Date: August 6, 2021
Latest Amendment Date: December 17, 2021
Award Number: 2126413
Award Instrument: Standard Grant
Program Manager: Andrew Zaffos
azaffos@nsf.gov
 (703)292-4938
RISE
 Integrative and Collaborative Education and Research (ICER)
GEO
 Directorate for Geosciences
Start Date: September 1, 2021
End Date: August 31, 2025 (Estimated)
Total Intended Award Amount: $476,876.00
Total Awarded Amount to Date: $476,876.00
Funds Obligated to Date: FY 2021 = $476,876.00
History of Investigator:
  • Shane Elipot (Principal Investigator)
    selipot@miami.edu
Recipient Sponsored Research Office: University of Miami
1251 MEMORIAL DR
CORAL GABLES
FL  US  33146-2509
(305)421-4089
Sponsor Congressional District: 27
Primary Place of Performance: University of Miami
4600 RICKENBACKER CSWY
Key Biscayne
FL  US  33149-1031
Primary Place of Performance
Congressional District:
27
Unique Entity Identifier (UEI): KXN7HGCF6K91
Parent UEI: VNZZYCJ55TC4
NSF Program(s): EarthCube
Primary Program Source: 01002122DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):
Program Element Code(s): 807400
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.050

ABSTRACT

The DriftCloud project aims at propelling forward the discovery and use of unique oceanic Lagrangian observational data principally gathered by freely drifting buoys. The tools developed will utilize existing cyberinfrastructures and established open source and open access protocols in order to bring analyses of ocean observational and numerical heterogeneously distributed geospatial data at the fingertips of users with level of proficiency ranging from high school students to statistical experts. This will potentially contribute to more equitable access to data and computing resources for a broad spectrum of specific and interdisciplinary applications ranging from marine plastics transport to the impact of surface gravity waves on satellite sea surface temperature calibrations. A wide and diverse set of users will be able to access these notebooks which will be bound to openly-accessible cloud-based executable environments deployed on existing infrastructures thanks to collaborations with the EarthCube and other cognizant communities. This project will support an early-career scientist.

The DriftCloud project will facilitate and accelerate the production and analysis of Lagrangian datasets by using the climate relevant Lagrangian data of sea surface current, sea surface temperature, and sea level pressure from the drifting buoys of the National Oceanic and Atmospheric Administration?s Global Drifter Program as a working framework. The project will generate new add-on datasets and a suite of modular and open source conversion tools to render Lagrangian datasets ready for analyses and optimized for cloud computing environments. An additional suite of open source tools will be developed for rapid and efficient visualizations and analyses of any Lagrangian data. The utilization of both suites of software tools will be fostered by creating pedagogical demonstration Jupyter Notebooks using the open source python language.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Elipot, Shane and Miron, Philippe and Curcic, Milan and Santana, Kevin and Lumpkin, Rick "Clouddrift: a Python package to accelerate the use ofLagrangian data for atmospheric, oceanic, and climate sciences" Journal of Open Source Software , v.9 , 2024 https://doi.org/10.21105/joss.06742 Citation Details
Elipot, Shane and Sykulski, Adam and Lumpkin, Rick and Centurioni, Luca and Pazos, Mayra "A dataset of hourly sea surface temperature from drifting buoys" Scientific Data , v.9 , 2022 https://doi.org/10.1038/s41597-022-01670-2 Citation Details

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