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7
datasets available to search
ShareScore release 0.9.0
Dataset results
7 results for “air parcels”
Deep convective cloud air parcels
<p>The dataset consists of 500 files describing the idealized deep convective cloud air parcels. Values for coordinate (X, Y, Z), velocity components (U, V, W), potential temperature (PTIL), total water content (QT), water vapor (QV), condensate density (QC), precipitation density (QR), net precipitation change (QR_FLUX), relative humidity (RH), buoyancy (BUOY), temperature (T), pressure (P), resolved TKE (KRES), SGS TKE (SGS_TKE), turbulent diffusion (TDIFF) are provided for each parcel at every time step of the simulation.</p>
Air parcel trajectories data generated by MIMICA code and simulation results generated by a trajectory box model
<p>The dataset includes trajectories of air parcels extracted from the large-eddy (cloud-resolving model) simulations of the deep convective clouds from the Amazon based on the soundings retrieved on April 8, 2020, April 23, 2020, and April 27, 2020, over Manaus, Brazil, as well as the results of the chemical box model simulations quantifying the transport of some atmospheric trace gases abundant in the Amazon.</p>
Air parcel trajectories dataset based on modeling of 16 deep convective clouds in the Amazon.
<p>The dataset contains trajectories of 16 deep convective cloud air parcels simulated with MIMICA code based on soundings retrieved over Manaus (Latitude: -3.1, Longitude: -60.0) during the wet season from April 1 until April 14, 2020 at 00 and 12 UTC.</p> <p> </p> <p>Parcel data contains values for coordinate (X, Y, Z), velocity components (U, V, W), potential temperature (PTIL), total water content (QT), water vapor (QV), condensate density (QC), precipitation density (QR), net precipitation change (QR_FLUX), relative humidity (RH), buoyancy (BUOY), temperature (T), pressure (P), resolved TKE (KRES), SGS TKE (SGS_TKE), turbulent diffusion (TDIFF) are provided for each parcel at every time step of the simulation.</p>
ATom: Data Stream for Modeling the Reactivity of ATom Air Parcels, 2016-2018
This dataset provides Modeling Data Stream (MDS) and Reactivity Data Stream (RDS) products for each of the four ATom campaigns conducted from 2016 to 2018. MDS files contain the atmospheric constituents needed to model the RDS of the air parcels along ATom flight paths. The MDS is a continuous data stream (every 10 seconds) of the atmospheric content of these key chemical species derived from the in-situ measurements collected along ATom flight paths (as reported in the comprehensive related dataset ATom: Merged Atmospheric Chemistry, Trace Gases, and Aerosols). Values for chemical species measured by multiple instruments were selected from the instrument with better coverage and/or greater precision. Missing values were filled using interpolation for short gaps. For long gaps owing to instrument failure, values were estimated using multiple linear regressions from comparable parallel flights from other ATom campaigns. All species were flagged for instrument source and values were flagged for gap-filling status. In combination, MDS and RDS provide, in essence, a photochemical climatology for each air parcel along ATom flight paths containing the reactive species that control the loss of methane and the production and loss of ozone.
ATom: Back Trajectories and Influences of Air Parcels Along Flight Track, 2016-2018
This dataset contains back trajectories, boundary layer influences, and convective influences of air parcels along NASA DC-8 aircraft's flight tracks during the four ATom campaigns that occurred from 2016 to 2018. Back trajectories were interpolated using National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) and Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2) meteorology. Back trajectory analysis determines the origin of air masses by modeling the path of an air parcel backward in time. It can be used to better understand the sources of atmospheric compounds. Boundary layer Influences were determined based on 30 Day Back Trajectories. The atmospheric boundary layer is the lowest part of the troposphere that is directly influenced by earth's surface. The boundary layer influences wind patterns and thus the dispersal of pollutants and other atmospheric compounds of interest. Convective influences were based on 10 Day Back Trajectories and NASA Langley cloud products. Convective influences model the effects of convection on the movement of water vapor through the atmosphere, which influences cloud behavior.
Air parcels extracted from LES simulations of a deep convective cloud observed during DC3 field campaign
<p>The dataset consists of 500 files describing air parcels retrieved from the simulation of the deep convective cloud event observed on May 29, 2012, during the DC3 field campaign. Values for coordinate (X, Y, Z), velocity components (U, V, W), potential temperature (PTIL), total water content (QT), water vapor (QV), condensate density (QC), precipitation density (QR), net precipitation change (QR_FLUX), relative humidity (RH), buoyancy (BUOY), temperature (T), pressure (P), resolved TKE (KRES), SGS TKE (SGS_TKE), turbulent diffusion (TDIFF) are provided for each parcel at every time step of the simulation.</p>
The trajectories of deep convective cloud air parcels over Manaus, Brazil simulated by MIMICA LES / CRM
<p>The dataset contains trajectories of the deep convective clouds simulated with MIMICA code based on soundings retrieved on April 8, 2020, April 23, 2020, and April 27, 2020, over Manaus, Brazil.</p> <p><br> OUTPUT folder contains all 500 air parcel trajectories retrieved from MIMICA.<br> OUTPUT_MEAN folder contains all the bin averaged parcels</p> <p>Parcel data contains values for coordinate (X, Y, Z), velocity components (U, V, W), potential temperature (PTIL), total water content (QT), water vapor (QV), condensate density (QC), precipitation density (QR), net precipitation change (QR_FLUX), relative humidity (RH), buoyancy (BUOY), temperature (T), pressure (P), resolved TKE (KRES), SGS TKE (SGS_TKE), turbulent diffusion (TDIFF) are provided for each parcel at every time step of the simulation.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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