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364 results for “Convection”
Dataset accompanying "Integrated nowcasting of convective precipitation with Transformer-based models using multi-source data"
<p>Dataset accompanying the article <a href="https://arxiv.org/abs/2409.10367" target="_blank" rel="noopener">Integrated nowcasting of convective precipitation with Transformer-based models using multi-source data</a>. </p> <p>Contains almost 8000 events that are sampled from the summer months (where convective precipitation events are most likely to occur) of 2019-2023, centred over Austria.</p> <p>Each sample has a temporal span of 4 hours with a spatial extent of 400 x 700 km, with following data streams:</p> <ul> <li>4 MSG infrared channels with central wavelengths of 6.2, 7.3, 8.7, and 10.8 μm</li> <li>Rain rates mosaicked from ground-based radar observations</li> <li>Lightning data from ground-based observations</li> <li>INCA precipitation analysis</li> <li>INCA convective available potential energy (CAPE) estimates</li> </ul> <p>The dataset is accompanied by elevation and coordinate information. </p> <p>Please refer to the manuscript and the <a href="https://github.com/caglarkucuk/earthformer-multisource-to-inca">GitHub repository</a> for further information and helper code for reading the data files.</p>
Is there a scalar atmospheric surface layer within a convective boundary layer? Implications for flux measurements
<p>This dataset contains the data used in the submitted manuscript of Liu, Liu, Zhou, Zhang, Desai, Ghannam, Huang, and Katul 2024. Please refer to the manuscript for the detailed description of the dataset.</p>
Analysis and emissions data for "What can lightning and shipping regulations tell us about aerosols in deeply convecting clouds?"
<p>Analysis scripts and SOx emissions data produced from STEAM emissions model (courtesy of Jukka-Pekka Jalkanen, FMI). Used in "What can lightning and shipping regulations tell us about aerosols in deeply convecting clouds?" submitted to GRL.</p>
The supplemental material to "Mean cold pool size of quasi-equilibrium convection" Part1 and Part 2
<p>The supplemental material to this two-paper series includes a hand-written mathematical derivation note (math_note.pdf), supplemental figures shared by the two parts (supplemental_figures_Parts_I_and_II.pdf), the postprocessing codes (postprocessing codes.zip), the LES namelist file for Bryan Cloud Model 1 (namelist.input), and some LES movies. The movie list is shown below:</p> <p>The movie "<a href="../api/records/10822712/draft/files/potential_temperature_near_surface.avi/content" target="_blank" rel="noopener noreferrer">potential_temperature_near_surface.avi</a>" shows the z=12.5 m potential temperature (unit: K) between t=3 days and t=5 days. Experiments Ev=0.2, 0.5, 1.0, and 2.0 are shown. </p> <p>The movie "<a href="../api/records/10822712/draft/files/w_4km.avi/content" target="_blank" rel="noopener noreferrer">w_4km.avi</a>" shows the z~4 km vertical velocity (unit: m/s) between t=3 days and t=5 days. Experiments Ev=0.2, 0.5, 1.0, and 2.0 are shown. </p> <p>The movie "<a href="../api/records/10822712/draft/files/vapor_0-551m_average.avi/content" target="_blank" rel="noopener noreferrer">vapor_0-551m_average.avi</a>" shows the z=0-551 m vertically averaged water vapor mixing ratio (unit: g/kg) between t=3 days and t=5 days. Experiments Ev=0.2, 0.5, 1.0, and 2.0 are shown. </p> <p>The movie "<a href="https://zenodo.org/api/records/13785498/draft/files/early_vapor_0-551m_average.avi/content" target="_blank" rel="noopener noreferrer">early_vapor_0-551m_average.avi</a>" is the same as "<a href="../api/records/10822712/draft/files/vapor_0-551m_average.avi/content" target="_blank" rel="noopener noreferrer">vapor_0-551m_average.avi</a>", but for the water vapor mixing ratio between t=0 day and t=3 days. It aims to show the initial spin-up stage of the simulation, which some readers might be interested in. </p> <p>The movie "<a href="https://zenodo.org/api/records/13785498/draft/files/large_domain_vapr_551m_average.mp4/content" target="_blank" rel="noopener noreferrer">large_domain_vapr_551m_average.mp4</a>" shows the z=0-551 m vertically averaged vapor mixing ratio (unit: g/kg) between t=3 days and t=5 days, using a large domain size of 144 km x 144 km (not 96 km x 96 km as for other cases) for the Ev=1.0 and 2.0 cases. Only the domain's southwest 96 km x 96 km corner is shown. </p> <p>The LES data, as well as intermediate output files for plotting (some .mat files), are available by contacting the first author Hao Fu via: haofu@uchicago.edu / haofu736@gmail.com </p> <p>Please let us know if you have any questions!</p>
Incorporating Hourly Convective Cloud data into Tropical Cyclone Rapid Intensification Forecasting with Machine Learning
<p>These models were developed to predict both the probability of RI and the binary RI classification for tropical cyclones. They are a weighted average of probabilities derived from logistic regression, random forest, decision tree, and extremely randomized tree algorithms within the standard Python scikit-learn package. The uploaded files include the hyperparameters and weights for each individual machine learning model.</p>
Radar Observations of Convective Processes Associated with Eyewall Formation during the Rapid Intensification of Typhoon Cempaka (2021)
<p>Dataset for Radar Observations of Convective Processes Associated with Eyewall Formation during the Rapid Intensification of Typhoon Cempaka (2021)</p>
Active Regions of Secondary Ice Production in a Modeled Tropical Mesoscale Convective System
<p>The animations of the modeled tropical mesoscale convective system (MCS). The red regions indicate the cloud regions with the secondary ice production rate dNice/dt >10<sup>5</sup> m<sup>-3</sup> s<sup>-1</sup>. The numerical simulation of the MCS was performed with the help of the Environment and Climate Change Canada's (ECCC) Global Environmental Multiscale (GEM) model.</p> <p>For details see Korolev, A., Z. Qu, J. Milbrandt, I. Heckman, M. Cholette, M. Wolde, C. Nguyen, G. M. McFarquhar, P. Lawson, and A. M. Fridlind: High ice water content in tropical mesoscale convective systems (a conceptual model). Atmos. Chem. Phys., 2024.</p>
Data and Code for "Updraft Width Modulates Ambient Atmospheric Controls on Convective Cloud Depth" by Varble et al. (2024)
<p>The zip file contains additional data and revised code used in the following paper: Varble, A. C., Feng, Z., Marquis, J. N., Zhang, Z., Geiss, A., Hardin, J. C., & Jo, E. (2024), Updraft Width Modulates Ambient Atmospheric Controls on Deep Convection Depth. Under review in J. Geophys. Res. Atmos.</p> <p>Datasets used in analyses that are derived from observations and model output can be found in the data folder. Python code and notebooks used to derive these datasets and to make all plots in the paper are in the code folder. The cell tracking was performed using PyFLEXTRKR software available here: https://github.com/FlexTRKR/PyFLEXTRKR.</p> <p>Parallax corrected GOES-16 satellite retrievals used in the study are downloadable at doi.org/10.5439/2008448. Taranis C-band radar retrievals are a very large dataset and in the process of being uploaded to the DOE ARM data archive. Interpolated sonde data is downloadable at doi.org/10.5439/1095316. Raw WRF model output is a very large dataset and currently stored at NERSC. To access raw model output or Taranis radar retrievals (before being available via ARM), please contact Adam Varble (adam.varble@pnnl.gov).</p> <p>If you have any further questions, please contact Adam Varble at adam.varble@pnnl.gov.</p>
DATA_Diffusion-free scaling in rotating spherical RB convection
<p>Simulation details. All simulations are run at $Pr=1$ and $\eta=0.6$. The columns from left to right indicate: case number, Rayleigh number $Ra$, Rossby number $Ro$, the number of grid points in the longitudinal, radial, and co-latitudinal direction $N_\theta \times N_r \times N_\varphi$. A rotational symmetry order $n_s$ is applied to reduce computational costs, indicating the longitude of the computational domain as $2\pi/n_s$. The average heat transfer $Nu$ across the inner and the outer sphere, and the regional heat transfer across the outer sphere $Nu_{I,II,III}$ (see figure 3(b) of the manuscript). Note that region $\mathrm{II}$ and $\mathrm{III}$ are only defined when a maximum prograde velocity is identified.</p>
Assessment of the data assimilation framework for the prototype Rapid Refresh Forecast System and impacts on forecasts of convective storms
<p>The compressed file contains the data and scripts necessary for the numerical experiments conducted in this research and the processing and visualization of the results. It includes the namelist files used for the model when using cold and warm start, the analyses in each experiment, and the grid, topography and surface climatology along with the model configuration file, the file used in the analyses to read the horizontal and vertical scales from an external file, all scripts used to execute every task of the workflow, all scripts used to process model outputs with MET, as well as all scripts and data used to create all figures of the paper.</p>
Data used for publication on 'Analysis of blue corona discharges at the top of tropical thunderstorm clouds in different phases of convection'
<p>This data was used from implementing Figures 1-4 in the main manuscript and all the Figures in the Supportive Information of the submitted publication 'Analysis of blue corona discharges at the top of tropical thunderstorm clouds in different phases of convection'.</p>
Supplmental Material to Nature of Intense Magnetism and Differential Rotation In Convective Dynamos of M-Dwarf Stars With Tachoclines
<p>Inlists and source files used for the MESA model in the paper "Nature of Intense Magnetism and Differential Rotation In Convective Dynamos of M-Dwarf Stars With Tachoclines"</p> <p>This work employed MESA version 10398.</p>
model output of Cumulus convection over land in cloud-resolving simulations with a coupled ray tracer
<p><strong>768_*</strong>: Output of cloud resolving simulations of a diurnal cycle (Augst 15th, 2016) with shallow cumulus clouds over Cabauw, the Netherlands. All simulations are performed with MicroHH, with radiative transfer computed either with a two-stream approximation or with ray tracing. </p> <ul> <li>2s_het: main simulation with two-stream solver</li> <li>2s_hom_sw: two-stream solver, horizontally averaged surface solar radiative fluxes</li> <li>2s_hom_hrsw: two-stream solver, horizontally averaged heating rates</li> <li>rt_het: main simulation with ray tracing, 256 samples per pixel </li> <li>rt_hom_sw: ray tracing, horizontally averaged surface solar radiative fluxes, 256 samples per pixel </li> <li>rt_hom_hrsw: ray tracing, horizontally averaged heating rates, 256 samples per pixel </li> <li>rt_het_128: ray tracing, 128 samples per pixel</li> <li>rt_het:_64 ray tracing, 64 samples per pixel</li> <li>rt_het:_32 ray tracing, 32 samples per pixel</li> </ul>
Intercomparison of Convective-Aggregation States with two Cloud Resolving Models: DATASET
<p>Radiative-Convective Equilibrium (RCE) is an important modeling paradigm for the tropical atmosphere. In this paradigm, cloud clustering can occur spontaneously, affecting the energy budget of the atmosphere. Here, two models, run in RCE, exhibiting this convective aggregation have been compared with each other and with the results of the Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP). The two models studied, the SAM (System for Atmospheric Modeling) and the ARPS (Advanced Regional Prediction System), are different in the physical and numerical formulation, allowing us to compare the sensitivity to processes related to the phenomenon of convective organization. In General, the two models present similarities in what concerns precipitation, warming, and drying of the atmosphere and anvil cloud area reduction. All these factors are also within the spread of the RCEMIP values. However, the two models differ both in the convective organization feedback and in the degree of organization. SAM is strongly organized and ARPS is weakly organized. SAM achieves convective organization through clouds-radiative feedback and ARPS achieves it through moisture-convection feedback. These differences can be traced back to the interaction between the microphysics and the sub-cloud layer properties. We suggest that when studying climate sensitivity, climate models should include both types of convective organization mechanisms.</p>
Dataset for Liu et al. (2022), "Convection and Clouds under Different Planetary Gravities Simulated by a Small-domain Cloud-resolving Model"
<p>Dataset for Liu et al. (2022), "Convection and Clouds under Different Planetary Gravities Simulated by a Small-domain Cloud-resolving Model".</p> <p> </p>
Data and code for Seeley and Wordsworth, "Moist convection is most vigorous at intermediate atmospheric humidity"
<p>Data and code for Seeley and Wordsworth, "Moist convection is most vigorous at intermediate atmospheric humidity"</p>
Statistics of the Subgrid Cloud of an Idealized Tropical Cyclone at Convection-Permitting Resolution
<p>Data and analysis scripts for figures of Journal article (Statistics of the Subgrid Cloud of an Idealized Tropical Cyclone at Convection-Permitting Resolution)</p>
AnkitBarik/convection_onset_radratio: convection_onset_data_v1.0.1
<ul> <li>Added requirements.txt and binder launch link for ipynb</li> </ul>
Dataset for "Global Simulation of the Madden–Julian Oscillation With Stochastic Unified Convection Scheme"
<p>Datasets for "Global Simulation of the Madden–Julian Oscillation With Stochastic Unified Convection Scheme". The global simulation outputs (climatologies and daily anomalies), calculated RMM indexes, and the results of the budget analysis are included.</p>
Solution of convection equation with compact scheme different sub-domain closures.
<p>Solution of convection equation with CFL number, Nc = 0.001. Computational domain extends from -1 to 19 and from -1 to +1 in x- and y- directions; 2001\times201 equidistant points are sub divided into 10x2 processors. The solid black lines indicate the processor boundary. RK4-OUCS3 scheme is used with CD8 sub-domain closure in the top frame and NOHAP closure is used in the bottom frame</p>
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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)
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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.