32 Works

Grad-Shafranov equation: MHD simulation of the new solution obtained from the Fadeev and Naval models

Arian Ojeda Gonzalez, Leandro Nunes Dos Santos, José Juan González-Avilés, Victor De La Luz & Pablo Rubén Muñoz-Gutberlet
This article aims to obtain a new analytical solution of a specific form of the Grad-Shafranov (GS) equation using Walker's formula. The new solution has magnetic field lines with X-type neutral points, magnetic islands and singular points. The singular points are located on the x-axis. The X-points and the center of the magnetic islands do not appear on the x-axis an island appears at $z>0$ and the other two at $z<0$. The aforementioned property allows...

Data from: Mechanistic insights into landscape genetic structure of two tropical amphibians using field-derived resistance surfaces

A. Justin Nowakowski, J. Andrew DeWoody, Matthew E. Fagan, Janna R. Willoughby & Maureen A. Donnelly
Conversion of forests to agriculture often fragments distributions of forest species and can disrupt gene flow. We examined effects of prevalent land uses on genetic connectivity of two amphibian species in northeastern Costa Rica. We incorporated data from field surveys and experiments to develop resistance surfaces that represent local mechanisms hypothesized to modify dispersal success of amphibians, such as habitat-specific predation and desiccation risk. Because time lags can exist between forest conversion and genetic responses,...

Data from: Bayesian hierarchical models for spatially misaligned data in R

Andrew O. Finley, Sudipto Banerjee & Bruce D. Cook
Spatial misalignment occurs when at least one of multiple outcome variables is missing at an observed location. For spatial data, prediction of these missing observations should be informed by within location association among outcomes and by proximate locations where measurements were recorded. This study details and illustrates a Bayesian regression framework for modelling spatially misaligned multivariate data. Particular attention is paid to developing valid probability models capable of estimating parameter posterior distributions and propagating uncertainty...

2013 Machine Learning Data Set for NASA's Solar Dynamics Observatory - Atmospheric Imaging Assembly

David Fouhey, Meng Jin, Mark Cheung, Abndres Munoz-Jaramillo, Richard Galvez, Rajat Thomas, Paul Wright, Alexander Szenicer, Monica G. Bobra, Yang Liu & James Mason
We present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a...

Data from: Latitudinal and altitudinal patterns of plant community diversity on mountain summits across the tropical Andes

Francisco Cuesta, Priscilla Muriel, Luis D. Llambí, Stephan Halloy, Nikolay Aguirre, Stephan Beck, Julieta Carilla, Rosa I. Meneses, Soledad Cuello, Alfredo Grau, Luis E. Gámez, Javier Irazábal, Jorge Jacome, Ricardo Jaramillo, Lirey Ramírez, Natalia Samaniego, David Suárez-Duque, Natali Thompson, Alfredo Tupayachi, Paul Viñas, Karina Yager, María T. Becerra, Harald Pauli & William D. Gosling
The high tropical Andes host one of the richest alpine floras of the world, with exceptionally high levels of endemism and turnover rates. Yet, little is known about the patterns and processes that structure altitudinal and latitudinal variation in plant community diversity. Herein we present the first continental-scale comparative study of plant community diversity on summits of the tropical Andes. Data were obtained from 792 permanent vegetation plots (1m2) within 50 summits, distributed along a...

2011 Machine Learning Data Set for NASA's Solar Dynamics Observatory - Atmospheric Imaging Assembly

David Fouhey, Meng Jin, Mark Cheung, Abndres Munoz-Jaramillo, Richard Galvez, Rajat Thomas, Paul Wright, Alexander Szenicer, Monica G. Bobra, Yang Liu & James Mason
We present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a...

PSP FIELDS Fluxgate Magnetometer (MAG) Magnetic Field Vectors, Radial-Tangential-Normal, RTN, Coordinates, 4 samples/cycle, Level 2 (L2), 3.413 ms Data

Stuart D. Bale, Robert J. MacDowall, Andriy Koval, Marc Pulupa, Timothy Quinn & Peter Schroeder
Parker Solar Probe FIELDS Instrument Suite Fluxgate Magnetometer, MAG, Data: The time resolution of the MAG time series data varies with instrument mode ranging from 2.289 samples/s to 292.9 samples/s. These two data sampling rates corresponding to 2 samples or 256 samples per 0.874 s where 0.874 s is equal to 2^25 divided 38.4 MHz. The Magnetometer has four ranges: ±1024 nT, ±4096 nT, ±16,384 nT, and ±65,536 nT. The Magnetometer Range is selected by...

PSP FIELDS Fluxgate Magnetometer (MAG) Magnetic Field Vectors, Spacecraft, SC, Coordinates, 4 samples/cycle, Level 2 (L2), 3.413 ms Data

Stuart D. Bale, Robert J. MacDowall, Andriy Koval, Marc Pulupa, Timothy Quinn & Peter Schroeder
Parker Solar Probe FIELDS Instrument Suite Fluxgate Magnetometer, MAG, Data: The time resolution of the MAG time series data varies with instrument mode ranging from 2.289 samples/s to 292.9 samples/s. These two data sampling rates corresponding to 2 samples or 256 samples per 0.874 s where 0.874 s is equal to 2^25 divided 38.4 MHz. The Magnetometer has four ranges: ±1024 nT, ±4096 nT, ±16,384 nT, and ±65,536 nT. The Magnetometer Range is selected by...

Parker Solar Probe Ephemeris, Heliocentric Trajectories, Heliographic, Heliographic Inertial, and Solar Ecliptic Coordinates, HelioWeb, Daily Data

Adam Szabo
Heliocentric trajectories for Parker Solar Probe in Heliographic, HG, Heliographic Inertial, HGI, and Solar Ecliptic, SE, Coordinates.

2012 Machine Learning Data Set for NASA's Solar Dynamics Observatory - Atmospheric Imaging Assembly

David Fouhey, Meng Jin, Mark Cheung, Abndres Munoz-Jaramillo, Richard Galvez, Rajat Thomas, Paul Wright, Alexander Szenicer, Monica G. Bobra, Yang Liu & James Mason
We present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a...

2017 Machine Learning Data Set for NASA's Solar Dynamics Observatory - Atmospheric Imaging Assembly

David Fouhey, Meng Jin, Mark Cheung, Abndres Munoz-Jaramillo, Richard Galvez, Rajat Thomas, Paul Wright, Alexander Szenicer, Monica G. Bobra, Yang Liu & James Mason
We present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a...

2018 Machine Learning Data Set for NASA's Solar Dynamics Observatory - Atmospheric Imaging Assembly

David Fouhey, Meng Jin, Mark Cheung, Abndres Munoz-Jaramillo, Richard Galvez, Rajat Thomas, Paul Wright, Alexander Szenicer, Monica G. Bobra, Yang Liu & James Mason
We present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a...

Ensemble model output of North American atmospheric CO2 simulation (full WRF-chem output)

S. Feng, T. Lauvaux, K.J. Davis, K. Keller, R. Rayner, T. Oda, K. Gurney, Y. Zhou, C. Williams, A.E. Schuh, J. Liu & I. Baker
The uncertainty in biospheric carbon dioxide (CO2) flux estimates drives divergent projections of future climate and uncertainty in prescriptions for climate mitigation. The terrestrial carbon sink can be inferred from atmospheric CO2 observations with transport models via inversion methods. Regional CO2 flux estimates remain uncertain due to the mixture of uncertainties caused by transport models, prior estimates of biospheric fluxes, large-scale CO2 boundary inflow, the assumptions in the inversion process, and the limited density of...

Dataset of Constraining fossil fuel CO2 emissions from urban area using OCO-2 observations of total column CO2

X. Ye, T. Lauvaux, E.A. Kort, T. Oda, S. Feng, J.C. Lin, E.G. Yang & D. Wu
Satellite observations of the total column dry-air CO2 (XCO2) are expected to support the quantification and monitoring of fossil fuel CO2 (ffCO2) emissions from urban areas. We evaluate the utility of the Orbiting Carbon Observatory 2 (OCO-2) XCO2 retrievals to optimize whole-city emissions, using a Bayesian inversion system and high-resolution transport modeling. The uncertainties of constrained emissions related to transport model, satellite measurements, and local biospheric fluxes are quantified. For the first two uncertainty sources,...

Data from: Targeted reforestation could reverse declines in connectivity for understory birds in a tropical habitat corridor

Matthew E. Fagan, Ruth S. DeFries, Steven E. Sesnie, J. Pablo Arroyo-Mora & Robin L. Chazdon
Re-establishing connectivity between protected areas isolated by habitat clearing is a key conservation goal in the humid tropics. In northeastern Costa Rica, payments for environmental services (PES) and a government ban on deforestation have subsidized forest protection and reforestation in the San Juan–La Selva Biological Corridor (SJLSBC), resulting in a decline in mature forest loss and the expansion of tree plantations. We use field studies and graph models to assess how conservation efforts have altered...

Data from: Disentangling competitive versus climatic drivers of tropical forest mortality

Michiel Pillet, Emilie Joetzjer, Camille Belmin, Jérôme Chave, Philippe Ciais, Aurélie Dourdain, Margaret Evans, Bruno Hérault, Sebastiaan Luyssaert & Benjamin Poulter
1. Tropical forest mortality is controlled by biotic and abiotic processes, but how these processes interact to determine forest structure is not well understood. Using long-term demography data from permanent forest plots at the Paracou Tropical Forest Research Station in French Guiana, we analyzed the relative influence of competition and climate on tree mortality. We found that self-thinning is evident at the stand level, and is associated with clumped mortality at smaller scales (< 2...

2010 Machine Learning Data Set for NASA's Solar Dynamics Observatory - Atmospheric Imaging Assembly

David Fouhey, Meng Jin, Mark Cheung, Abndres Munoz-Jaramillo, Richard Galvez, Rajat Thomas, Paul Wright, Alexander Szenicer, Monica G. Bobra, Yang Liu & James Mason
We present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a...

2015 Machine Learning Data Set for NASA's Solar Dynamics Observatory - Atmospheric Imaging Assembly

David Fouhey, Meng Jin, Mark Cheung, Abndres Munoz-Jaramillo, Richard Galvez, Rajat Thomas, Paul Wright, Alexander Szenicer, Monica G. Bobra, Yang Liu & James Mason
We present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a...

2016 Machine Learning Data Set for NASA's Solar Dynamics Observatory - Atmospheric Imaging Assembly

David Fouhey, Meng Jin, Mark Cheung, Abndres Munoz-Jaramillo, Richard Galvez, Rajat Thomas, Paul Wright, Alexander Szenicer, Monica G. Bobra, Yang Liu & James Mason
We present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a...

Radiolysis of macromolecular organic material in Mars-relevant mineral matrices

A.C. Fox, J.L. Eigenbrode & C. Herrera
The fate of organic material on Mars after deposition is crucial to interpreting the source of these molecules. Previous work has addressed how various organic compounds at millimeter depths in sediments respond to ultraviolet (UV) radiation. In contrast, this study addressed how high-energy particle radiation (200-MeV protons, simulating the effect of galactic cosmic rays at depths of < 4-5 cm) influences organic macromolecules in sediments. Specifically, we report the generation of organic-acid radiolysis products after...

High-resolution (1.3kmx1.3-km) WRF-Chem output driven with the Hestia, and two nightlight-based fossil fuel emission products over the LA basin from 5/15/2020-6/15/2020

S. Feng, T. Lauvaux, T. Oda, M.O. Román, Z. Wang, S. Maksyutov & V.L. Kalb
This high-resolution WRF-Chem model output is driven with three different fossil fuel emission products: Hestia (Gurney et al., 2018), and two nightlight-based CO2 emission data using the same setup as Feng et al (2016). The nightlight data products are based on DMSP (Oda and Maksyutov, 2011) and NASA’s Black Marble Nighttime Lights Product Suite (VNP46) (Román et al., 2018; Oda et al. 2020). This sensitivity atmospheric modeling experiment demonstrates the utility of CO2 mapping, helping...

Data from: Evidence of small-scale spatial structuring of phytoplankton alpha- and beta-diversity in the open ocean

Erik Askov Mousing, Katherine Richardson, Jørgen Bendtsen, Ivona Cetinić & Mary Jane Perry
Phytoplankton assemblages in the open ocean are usually assumed to be mixed on local scales unless large semi-permanent density discontinuities separating water masses are present. Recent modelling studies have, however, suggested that ephemeral submesoscale oceanographic features leading to only subtle density discontinuities may be important for controlling phytoplankton alpha- and beta-diversity patterns. Until now, no empirical evidence has been presented to support this hypothesis. Using hydrographic and taxonomic composition data collected near Iceland during the...

A survey of small-scale waves and wave-like phenomena in Jupiter's atmosphere detected by JunoCam

Glenn Orton, Fachreddin Tabataba-Vakili, Gerald Eichstaedt, John Rogers, Candice Hansen, Thomas Momary, Andrew Ingersoll, Shawn Brueshaber, Michael H. Wong, Amy Simon, Leigh Fletcher, Michael Ravine, Michael Caplinger, Dakota Smith, Scott Bolton, Stephen Levin, James Sinclair, Chloe Thepenier, Hamish Nicholson & Abigail Anthony
In the first 20 orbits of the Juno spacecraft around Jupiter, we have identified a variety of wave-like features in images made by its public-outreach camera, JunoCam. Because of Juno’s unprecedented and repeated proximity to Jupiter’s cloud tops during its close approaches, JunoCam has detected more wave structures than any previous surveys. Most of the waves appear in long wave packets, oriented east-west and populated by narrow wave crests. Spacing between crests were measured as...

PSP FIELDS Fluxgate Magnetometer (MAG) Magnetic Field Vectors, Radial-Tangential-Normal, RTN, Coordinates, Full Resolution, Level 2 (L2), 3.413 ms Data

Stuart D. Bale, Robert J. MacDowall, Andriy Koval, Marc Pulupa, Timothy Quinn & Peter Schroeder
Parker Solar Probe FIELDS Instrument Suite Fluxgate Magnetometer, MAG, Data: The time resolution of the MAG time series data varies with instrument mode ranging from 2.289 samples/s to 292.9 samples/s. These two data sampling rates corresponding to 2 samples or 256 samples per 0.874 s where 0.874 s is equal to 2^25 divided 38.4 MHz. The Magnetometer has four ranges: ±1024 nT, ±4096 nT, ±16,384 nT, and ±65,536 nT. The Magnetometer Range is selected by...

PSP FIELDS Fluxgate Magnetometer (MAG) Magnetic Field Vectors, Spacecraft, SC, Coordinates, Full Resolution, Level 2 (L2), 3.413 ms Data

Stuart D. Bale, Robert J. MacDowall, Andriy Koval, Marc Pulupa, Timothy Quinn & Peter Schroeder
Parker Solar Probe FIELDS Instrument Suite Fluxgate Magnetometer, MAG, Data: The time resolution of the MAG time series data varies with instrument mode ranging from 2.289 samples/s to 292.9 samples/s. These two data sampling rates corresponding to 2 samples or 256 samples per 0.874 s where 0.874 s is equal to 2^25 divided 38.4 MHz. The Magnetometer has four ranges: ±1024 nT, ±4096 nT, ±16,384 nT, and ±65,536 nT. The Magnetometer Range is selected by...

Registration Year

  • 2021
    7
  • 2020
    7
  • 2019
    12
  • 2017
    1
  • 2016
    3
  • 2014
    2

Resource Types

  • Dataset
    32

Affiliations

  • Goddard Space Flight Center
    23
  • Southwest Research Institute
    10
  • Hansen Experimental Physics Laboratory, Stanford University
    9
  • Electrical Engineering and Computer Science Department, University of Michigan
    9
  • SUPA School of Physics and Astronomy, University of Glasgow
    9
  • NASA Goddard Space Flight Center
    9
  • Center for Data Science, New York University
    9
  • Lockheed Martin Solar & Astrophysics Laboratory
    9
  • SETI Institute
    9
  • Department of Psychiatry, University of Amsterdam
    9