32 Works

CMS-Flux NBE 2020

Junjie Liu, Lartha Baskarran, Kevin Bowman, David Schimel, A. Anthony Bloom, Nick Parazoo, Tomohiro Oda, Dustin Carrol, Dimitris Menemenlis, Joanna Joiner, Roisin Commane, Bruce Daube, Lucianna V. Gatti, Kathryn McKain, John Miller, Britton B. Stephens, Colm Sweeney & Steven Wofsy
Top-down Net biosphere exchange estimates between Jan 2010 and Dec 2018 constrained by column CO2 observations from Greenhouse gases Observing Satellite and Orbiting Carbon Observatory 2. This dataset is openly shared in accordance with NASA Data and Information Policy (https://earthdata.nasa.gov/collaborate/open-data-services-and-software/data-information-policy).

Pyrocumulonimbus Events over British Columbia in August 2017: Results from the NASA GEOS Earth System Model

Sampa Das, Peter Colarco & Luke Oman

IceCube Level 1 Radiance Data and Codes

Jie Gong & Dong Wu
This zipped meta data file can be expanded into two folders. One folder contains the daily calibrated Level 1 radiance and geolocation data in HDF5 format, and the other folder contains the main IDL codes that process the data and make plots (mainly for generating plots for the paper Gong et al. 2021 that is under review for Earth Science System Data journal). Both folders contain a README file in each to guide readers through...

2014 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...

Thinner bark increases sensitivity of wetter Amazonian tropical forests to fire

Ann Carla Staver, Paulo M. Brando, Jos Barlow, Douglas C. Morton, C.E. Timothy Paine, Yadvinder Malhi, Alejandro Araujo Murakami & Jhon Pasquel
Understory fires represent an accelerating threat to Amazonian tropical forests and can, during drought, affect larger areas than deforestation itself. These fires kill trees at rates varying from < 10 to c. 90% depending on fire intensity, forest disturbance history and tree functional traits. Here, we examine variation in bark thickness across the Amazon. Bark can protect trees from fires, but it is often assumed to be consistently thin across tropical forests. Here, we show...

Data from: A segmentation algorithm for characterizing Rise and Fall segments in seasonal cycles: an application to XCO2 to estimate benchmarks and assess model bias

Leonardo Calle, Benjamin Poulter & Prabir K. Patra
There is more useful information in the time series of satellite-derived column-averaged carbon dioxide (XCO2) than is typically characterized. Often, the entire time series is treated at once without considering detailed features at shorter timescales, such as nonstationary changes in signal characteristics – amplitude, period and phase. In many instances, signals are visually and analytically differentiable from other portions in a time series. Each rise (increasing) and fall (decreasing) segment in the seasonal cycle is...

PSP FIELDS Fluxgate Magnetometer (MAG) Magnetic Field Vectors, Spacecraft, SC, Coordinates, Level 2 (L2), 1 min 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...

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Affiliations

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  • Hansen Experimental Physics Laboratory, Stanford University
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  • Electrical Engineering and Computer Science Department, University of Michigan
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  • SUPA School of Physics and Astronomy, University of Glasgow
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