David Moroni

David Moroni
California Institute of Technology | CIT · Jet Propulsion Laboratory

MS Meteorology

About

30
Publications
5,871
Reads
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164
Citations
Citations since 2017
21 Research Items
142 Citations
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Introduction
David Moroni currently works at JPL. David has been involved in research with Applied Science System Engineering, Data/Metadata Structures, Data Assimilation, Search Relevancy, Data Quality, Uncertainty, Oceanography, and Air-Sea Interaction. His primary projects include PO.DAAC, MAIA Early Adopters, MEaSUREs-2017, and MAAP-AWS-HEC. David is serving as co-chair of the ESIP Information Quality Cluster and contributing member of the US-CLIVAR Uncertainty Quantification Working Group (UQWG).

Publications

Publications (30)
Article
Full-text available
Open-source science builds on open and free resources that include data, metadata, software, and workflows. Informed decisions on whether and how to (re)use digital datasets are dependent on an understanding about the quality of the underpinning data and relevant information. However, quality information, being difficult to curate and often context...
Article
Full-text available
Knowledge about the quality of data and metadata is important to support informed decisions on the (re)use of individual datasets and is an essential part of the ecosystem that supports open science. Quality assessments reflect the reliability and usability of data. They need to be consistently curated, fully traceable, and adequately documented, a...
Preprint
Under the auspices of the Earth Science Information Partners (ESIP) and with collaboration among the ESIP Information Quality Cluster (IQC), the Barcelona Supercomputing Center (BSC) Evaluation and Quality Control (EQC) team, and the Australia/New Zealand Data Quality Interest Group (AU/NZ DQIG), a community effort has been undertaken by internatio...
Preprint
Full-text available
Knowledge about the quality of data and metadata is important to support informed decisions on the (re)use of individual datasets and is an essential part of the ecosystem that supports open science. Quality assessments reflect the reliability and usability of data and need to be consistently curated, fully traceable, and adequately documented, as...
Preprint
This document provides background for and summarizes main takeaways of a workshop held virtually to kick off the development of community guidelines for consistently curating and representing dataset quality information in a way that is in line with the FAIR principles.
Article
Finding geospatial data has been a big challenge regarding the data size and heterogeneity across various domains. Previous work has explored using machine learning to improve geospatial data search ranking, but it usually relies on training data labelled by subject matter experts, which makes it laborious and costly to apply to scenarios in which...
Article
The Physical Oceanography Distributed Active Archive Center (PO.DAAC) has been distributing satellite oceanographic data, including altimetry data, since the early 1980s. While it started by mailing magnetic storage tapes, PO.DAAC now distributes terabytes of data accessible within seconds, which will exponentially increase with future missions, su...
Article
Full-text available
Information about the uncertainty associated with Earth science observational data is fundamental to use, re-use, and overall evaluation of the data being used to produce science and support decision making. The associated uncertainty information leads to a quantifiable level of confidence in both the data and the science informing decisions produc...
Technical Report
Please see details in https://cdn.earthdata.nasa.gov/conduit/upload/12101/ESDS-RFC-033.pdf
Technical Report
!! This article is available at "https://cdn.earthdata.nasa.gov/conduit/upload/11247/ESDS-RFC-034.pdf"
Article
Full-text available
The volume, variety, and velocity of different data, e.g., simulation data, observation data, and social media data, are growing ever faster, posing grand challenges for data discovery. An increasing trend in data discovery is to mine hidden relationships among users and metadata from the web usage logs to support the data discovery process. Web us...
Technical Report
!! This article is available at "https://cdn.earthdata.nasa.gov/conduit/upload/10743/ESDS-RFC-031v1.pdf"
Technical Report
!!This article is available at "https://cdn.earthdata.nasa.gov/conduit/upload/10744/ESDS-RFC-032v1.pdf"
Poster
Full-text available
The Earth Science Information Partners (ESIP) organization was established 20 years ago as a non-profit, volunteer and community-driven organization. ESIP aims to advance the use of Earth science data in application areas like disaster response, climate, energy and agriculture. It also promotes open data and data sharing utilizing community best pr...
Article
Full-text available
Discovering and accessing geospatial data presents a significant challenge for the Earth sciences community as massive amounts of data are being produced on a daily basis. In this article, we report a smart web-based geospatial data discovery system that mines and utilizes data relevancy from metadata user behavior. Specifically, (1) the system ena...
Article
Full-text available
Current search engines in most geospatial data portals tend to induce users to focus on one single-data characteristic dimension (e.g. popularity and release date). This approach largely fails to take account of users’ multidimensional preferences for geospatial data, and hence may likely result in a less than optimal user experience in discovering...
Article
Full-text available
It is challenging to find relevant data for research and development purposes in the geospatial big data era. One long-standing problem in data discovery is locating, assimilating and utilizing the semantic context for a given query. Most research in the geospatial domain has approached this problem in one of two ways: building a domain-specific on...
Presentation
Youtube link for plenary session: https://www.youtube.com/watch?v=senSUe-j8j8 Speakers include Dr. Carol Anne Clayson (Woods Hole Oceanographic Institution), Dr. Amy Braverman (Jet Propulsion Laboratory, California Institue of Technology, Pasadena, CA), Isla Simpson (National Center for Atmospheric Research). This session was sponsored by the Eart...
Article
Full-text available
Information about quality is always of concern to users, whether they are buying a car or some other consumer goods, or using scientific data for research or an application. To facilitate consistent quality evaluation and description of quality information on data products for the Earth Science community, we formally introduce and define four const...
Poster
Improving collection, description, discovery, and usability of information about data quality in Earth science data products is critical in ensuring data use but requires coordinated efforts of people from multiple disciplines. Under the auspices of the Federation of Earth Science Information Partners (ESIP), the Information Quality Cluster (IQC) b...
Conference Paper
The NASA Earth Science Data Information Systems Project funds and operates 12 Distributed Active Archive Center(s) (DAAC) throughout the United States. Of these 12 centers, the Physical Oceanography DAAC (PO.DAAC) is committed to providing long term archival, distribution and stewardship for NASA physical oceanographic data, primarily derived from...
Conference Paper
It is very challenging for scientists to find the right oceanographic data in a fast manner. A novel approach was proposed to analyze user access logs to explore the implicit relationship between oceanographic datasets. This paper reports a cloud-based data analytics framework to speed up the process for dealing with problems, such as (1) user acce...
Article
Full-text available
Big geospatial data are archived and made available through online web discovery and access. However, finding the right data for scientific research and application development is still a challenge. This paper aims to improve the data discovery by mining the user knowledge from log files. Specifically, user web session reconstruction is focused upo...
Article
The NASA Physical Oceanography DAAC (PO.DAAC) is responsible for distributing and maintaining satellite derived oceanographic data from a number of NASA and non-NASA missions for the physical disciplines of ocean winds, sea surface temperature, ocean topography and gravity. Currently its holdings consist of over 600 datasets with a data archive in...
Article
A new cross-calibrated, multiplatform (CCMP) ocean surface wind product with wide-ranging research applications in meteorology and oceanography became available at the Physical Oceanography Distributed Active Archive Center (PO.DACC) in May 2009. Data sets at three different levels of processing may be downloaded from http://podaac.jpl.nasa.gov/DAT...
Article
Remotely-sensed stress measurements have been long desired by many in the ocean science community. It was originally perceived that scatterometers respond well to changes in wind speed and direction in response to the changes in surface roughness over the sea surface, which led to over 30 years of extensive tuning, calibration, and development of s...
Article
Twenty turbulent flux parameterizations are compared globally and regionally with a focus on the differences associated with episodic events. The regional focus is primarily upon the Gulf Stream and Drake Passage, as these two regions contain vastly different physical characteristics related to storm and frontal passages, varieties of sea-states, a...
Thesis
Full-text available
Twenty turbulent flux parameterizations are compared globally and regionally with a focus on the differences associated with episodic events. The regional focus is primarily upon the Gulf Stream and Drake Passage, as these two regions contain vastly different physical characteristics related to storm and frontal passages, varieties of sea-states, a...

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Cited By

Projects

Projects (6)
Project
This open-source project (NASA NPO-52250-1) will deliver a set of capabilities to NASA's Multi-Mission Algorithm and Analysis Platform (MAAP; https://github.com/MAAP-Project), which will allow seamless employment of the compute/storage capacity of both AWS and NASA High-End Computing Capability (HECC) for algorithm development and data processing and analysis. MAAP's Data Processing Services (DPS) approach of converting user-friendly Jupyter science notebooks to algorithm processing steps that run at scale will be updated to deploy seamlessly across AWS and NASA HECC. Forthcoming capabilities from this project will also enable deployment containerized algorithm workloads that are interoperable across AWS and Pleiades. The data management services will also be updated to enable automatic data migration between AWS storage and local storage on Pleiades. On-demand data products generated on both AWS and Pleiades will be ingested and managed by the MAAP Data Store for further exploitation from the MAAPs existing Algorithm Development Environment (ADE). A separate MAAP development system will be established to accommodate the changes required for AWS-NASA HEC infrastructure needs and to develop new capabilities. This system will then be tested and integrated into the future MAAP Ops/Production system. To continue the open science approach of the MAAP, this project will continue to make use of open source software developed under this task from the outset. Additionally, the utilization of science algorithm software used within the MAAP will continue to be open. The Jupyter notebooks developed under MAAP and run across AWS and NASA HECC will also be open. More details on this project (NASA NPO-52250-1) are captured here: https://software.nasa.gov/software/NPO-52250-1
Project
The Working Group(s) organized by NASA ESDIS or ESIP to discuss and address matters or issues relevant to Data Quality
Project
Read for details: http://wiki.esipfed.org/index.php/Information_Quality