Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
52
datasets available to search
ShareScore release 0.9.0
Dataset results
52 results for “ice particle”
Electrical Alignment Signatures of Ice Particles before Intracloud Lightning Activity Detected by Dual-polarized Phased Array Weather Radar
<p>lightning data from LIDEN system and the data of the Dual-polarized Phased Array Weather Radar (DP-PAWR) on August 20, 2019. </p>
Decreased dust particles amplify the cloud cooling effect by regulating cloud ice formation over the Tibetan Plateau
Open the record for dataset details and reuse information.
Data from: Export of ice nucleating particles from a watershed
Open the record for dataset details and reuse information.
Model simulation data used in "Impacts of ice-nucleating particles on cirrus clouds and radiation derived from global model simulations with MADE3 in EMAC" (Beer et al., Atmos. Chem. Phys., 2024)
<p>This dataset contains the namelist setup and the output of the EMAC global model simulations analysed and discussed in Beer et al. (<i>Atmos. Chem. Phys.</i>, 2024). For details see the README.md file and Table 2 in the paper.</p>
Dataset for "Roles of marine biota in the formation of atmospheric bioaerosols, cloud condensation nuclei, and ice-nucleating particles over the North Pacific Ocean, Bering Sea, and Arctic Ocean"
<p>Atmospheric and oceanic observations were conducted over the North Pacific Ocean, Bering Sea, and Arctic Ocean during a cruise (MR19-03C) in early autumn of 2019. The dataset includes trace gases, chemical composition, fluorescent particles in the ambient and bioindicators in the surface seawater, observed along the ship track (time stamp and coordinates are included). This dataset is for Kawana et al., "Roles of marine biota in the formation of atmospheric bioaerosols, cloud condensation nuclei, and ice-nucleating particles over the North Pacific Ocean, Bering Sea, and Arctic Ocean", Atmospheric Chemistry and Physics, 2024 (in press).</p>
Physicochemical characterization of free troposphere and marine boundary layer ice-nucleating particles collected by aircraft in the eastern North Atlantic
<p>This repository contains the MATLAB data files to generate the figures pertaining to the STXM/NEXAFS analysis given in the publication:</p> <p>Title: Physicochemical characterization of free troposphere and marine boundary layer ice-nucleating particles collected by aircraft in the eastern North Atlantic<br> Authors: Daniel A. Knopf, Peiwen Wang, Benny Wong, Jay M. Tomlin, Daniel P. Veghte, Nurun N. Lata, Swarup China, Alexander Laskin, Ryan C. Moffet, Josephine Y. Aller, Matthew A. Marcus, and Jian Wang</p> <p>Journal: Atmospheric Chemistry and Physics, accepted 2023</p>
ICE ENERGETIC PARTICLE ANISOTROPY SPECTROMETER DATA V1.0
In this G-Z dataset, observations from channels E1 to E5 (table below) are included since cometary ions were not detected in the higher energy channels. For each of these channels, particle counts are accumulated in eight azimuthal sectors of 45 deg. width in each telescope. The sectors are numbered from 1 to 8 anticlockwise when viewed from above the ecliptic, with Sector 1 centered on the sun- Earth line, i.e. it views in the sunward direction and detects ions streaming away from the Sun. At Giacobini-Zinner, a complete 3- dimensional distribution function was obtained at 32 s intervals during the main encounter phase, changing to 64 s at greater distances e.g. after 1830 UT on 12 September outbound.
ICE ENERGETIC PARTICLE ANISOTROPY SPECTROMETER DATA V1.0
In this G-Z dataset, observations from channels E1 to E5 (table below) are included since cometary ions were not detected in the higher energy channels. For each of these channels, particle counts are accumulated in eight azimuthal sectors of 45 deg. width in each telescope. The sectors are numbered from 1 to 8 anticlockwise when viewed from above the ecliptic, with Sector 1 centered on the sun- Earth line, i.e. it views in the sunward direction and detects ions streaming away from the Sun. At Giacobini-Zinner, a complete 3- dimensional distribution function was obtained at 32 s intervals during the main encounter phase, changing to 64 s at greater distances e.g. after 1830 UT on 12 September outbound.
Developing and bounding ice particle mass-and area-dimension expressions for use in atmospheric models and remote sensing
<p>Ice particle mass- and projected area-dimension (<em>m</em>-<em>D</em> and <em>A</em>-<em>D</em>) power laws are commonly used in the treatment of ice cloud microphysical and optical properties and the remote sensing of ice cloud properties. Although there has long been evidence that a single <em>m</em>-<em>D</em> or <em>A</em>-<em>D</em> power law is often not valid over all ice particle sizes, few studies have addressed this fact. This study develops self-consistent <em>m</em>-<em>D</em> and <em>A</em>-<em>D</em> expressions that are not power laws but can easily be reduced to power laws for the ice particle size (maximum dimension or <em>D</em>) range of interest, and they are valid over a much larger <em>D</em> range than power laws. This was done by combining ground measurements of individual ice particle <em>m</em> and <em>D</em> formed at temperature <em>T</em>  <  −20 °C during a cloud seeding field campaign with 2-D stereo (2D-S) and cloud particle imager (CPI) probe measurements of <em>D</em> and <em>A</em>, and estimates of <em>m</em>, in synoptic and anvil ice clouds at similar temperatures. The resulting <em>m</em>-<em>D</em> and <em>A</em>-<em>D</em> expressions are functions of temperature and cloud type (synoptic vs. anvil), and are in good agreement with <em>m</em>-<em>D</em> power laws developed from recent field studies considering the same temperature range (−60 °C  <  <em>T</em>  <  −20 °C).</p>
Growth of ice particle mass and projected area during riming
<p>There is a long-standing challenge in cloud and climate models to simulate the process of ice particle riming realistically, partly due to the unrealistic parameterization of the growth of ice particle mass (<em>m</em>) and projected area (<em>A</em>) during riming. This study addresses this problem, utilizing ground-based measurements of <em>m</em> and ice particle maximum dimension (<em>D</em>) as well as theory to formulate simple expressions describing the dependence of <em>m</em> and <em>A</em> on riming. It was observed that <em>β</em> in the <em>m</em> − <em>D</em> power law <em>m</em>  =&thinsp;<em>α</em> <em>D</em><sup><em>β</em></sup> appears independent of riming during the phase 1 (before the formation of graupel), with <em>α</em> accounting for the ice particle mass increase due to riming. This semi-empirical approach accounts for the degree of riming and renders a gradual and smooth ice particle growth process from unrimed ice particles to graupel, and thus avoids discontinuities in <em>m</em> and <em>A</em> during accretional growth. Once the graupel with quasi-spherical shape forms, <em>D</em> increases with an increase in <em>m</em> and <em>A</em> (phase 2 of riming). The treatment for riming is explicit, and includes the parameterization of the ice crystal–cloud droplet collision efficiency (<em>E</em><sub>c</sub>) for hexagonal columns and plates using hydrodynamic theory. In particular, <em>E</em><sub>c</sub> for cloud droplet diameters less than 10 µm are estimated, and under some conditions observed in mixed-phase clouds, these droplets can account for roughly half of the mass growth rate from riming. These physically meaningful yet simple methods can be used in models to improve the riming process.</p>
A Mechanistic Understanding of North American Monsoon and Microphysical Properties of Ice Particles
<p>A mechanistic understanding of the North American Monsoon (NAM) is suggested by incorporating local-and synoptic-scale processes. The local-scale mechanism describes the effect sea surface temperature (SST) in Gulf of California (GC) and how it contributes to the low-level moisture during the 2004 NAM. Before NAM onset, the strong low-level temperature inversion exists over the GC, but this inversion weakens with increasing GC SST and generally disappears once SSTs exceed 29.5 C, allowing the moist air, trapped in the MBL, to mix with free tropospheric air. This leads to a deep, moist layer that can be transported toward the NAM regions to produce thunderstorms. The synoptic scale mechanism is based on climatologies from 1983 to 2010 and explains that the warmest SSTs moving up the coast contributes to NAM convection and atmospheric heating, and consequently advancing the position of the …</p>
EAMv1 outputs Macquarie Island- Long-term variability in immersion-mode marine ice-nucleating particles from climate model simulations and observations
<p>EAMv1 outputs for the ACP publication </p> <p>https://acp.copernicus.org/articles/23/5735/2023/acp-23-5735-2023.pdf</p> <p>We have archived the outputs from the EAMv1 control simulations. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.