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2,170 results for “clouds”
FIGURE 8. Zadbimyia minima, male. A in Zadbimyia, a new genus of asynaptine Porricondylinae (Diptera: Cecidomyiidae) with twenty-two new species from the cloud forest of Costa Rica
FIGURE 8. Zadbimyia minima, male. A: terminalia, ventral. B: fourth flagellomere, lateral. C: ejaculatory apodeme and parameres, ventral. Scale 0.05 mm.
FIGURE 21. Zadbimyia zurqui, male. A in Zadbimyia, a new genus of asynaptine Porricondylinae (Diptera: Cecidomyiidae) with twenty-two new species from the cloud forest of Costa Rica
FIGURE 21. Zadbimyia zurqui, male. A: terminalia, ventral. B: fourth flagellomere, lateral. C: ejaculatory apodeme and parameres, ventral. Scale 0.05 mm.
FIGURE 24. Zadbimyia aberrans, male. A in Zadbimyia, a new genus of asynaptine Porricondylinae (Diptera: Cecidomyiidae) with twenty-two new species from the cloud forest of Costa Rica
FIGURE 24. Zadbimyia aberrans, male. A: terminalia, ventral. B: fourth flagellomere, lateral. C: ejaculatory apodeme and parameres, ventral. Scale 0.05 mm. postGA = posterior portion of gonocoxal apodeme.
FIGURE 18. Zadbimyia elenae, male. A in Zadbimyia, a new genus of asynaptine Porricondylinae (Diptera: Cecidomyiidae) with twenty-two new species from the cloud forest of Costa Rica
FIGURE 18. Zadbimyia elenae, male. A: terminalia, ventral. B: fourth flagellomere, lateral. C: ejaculatory apodeme and parameres, ventral. Scale 0.05 mm.
FIGURE 15. Zadbimyia marcoi, male. A in Zadbimyia, a new genus of asynaptine Porricondylinae (Diptera: Cecidomyiidae) with twenty-two new species from the cloud forest of Costa Rica
FIGURE 15. Zadbimyia marcoi, male. A: terminalia, ventral. B: ejaculatory apodeme and parameres, ventral. C: fourth flagellomere, lateral. Scale 0.05 mm.
FIGURE 5. Zadbimyia browni, male. A in Zadbimyia, a new genus of asynaptine Porricondylinae (Diptera: Cecidomyiidae) with twenty-two new species from the cloud forest of Costa Rica
FIGURE 5. Zadbimyia browni, male. A: gonostylus, ventral. B: ejaculatory apodeme and parameres, ventral. C: terminalia, ventral. D: fourth flagellomere, lateral. Scale 0.05 mm. acc gl dc = accessory gland duct, antGA = anterior portion of gonocoxal apodeme, lat shld = lateral shoulder, vtr em = ventral emargination.
FIGURE 6. Zadbimyia artborkenti, male. A in Zadbimyia, a new genus of asynaptine Porricondylinae (Diptera: Cecidomyiidae) with twenty-two new species from the cloud forest of Costa Rica
FIGURE 6. Zadbimyia artborkenti, male. A: terminalia, ventral. B: gonostylus, ventral. C: fourth flagellomere, lateral. D: ejaculatory apodeme and parameres, ventral. Scale 0.05 mm.
FIGURE 14. Zadbimyia carolinae, male. A in Zadbimyia, a new genus of asynaptine Porricondylinae (Diptera: Cecidomyiidae) with twenty-two new species from the cloud forest of Costa Rica
FIGURE 14. Zadbimyia carolinae, male. A: terminalia, ventral. B: fourth flagellomere, lateral. C: ejaculatory apodeme and parameres, ventral. Scale 0.05 mm.
FIGURE 17. Zadbimyia tapanti, male. A in Zadbimyia, a new genus of asynaptine Porricondylinae (Diptera: Cecidomyiidae) with twenty-two new species from the cloud forest of Costa Rica
FIGURE 17. Zadbimyia tapanti, male. A: terminalia, ventral. B: fourth flagellomere, lateral. C: ejaculatory apodeme and parameres, ventral. Scale 0.05 mm.
Formation of tropical anvil clouds by slow evaporation (manuscript data)
<p>Cloud-resolving model output used in the manuscript "Formation of tropical anvil clouds by slow evaporation" by Seeley et al. </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 of the Paper: Stateful Depletion and Scheduling of Containers on Cloud Nodes for Efficient Resource Usage
<p>This is the online artifact containing the code, data and evaluation log of the experiment performed for the research paper accepted at IEEE QRS 2022 with the title: </p> <p><strong>Stateful Depletion and Scheduling of Containers on Cloud Nodes for Efficient Resource Usage</strong></p> <p><strong>Abstract:</strong> Container scheduling is a fundamental part of today’s service and cloud-based applications. Schedulers operate at different levels depending on how much control the system developers have. On the one hand, container orchestration managers such as Google Kubernetes manage the scheduling of containers to different nodes. On the other hand, serverless managers, such as Google Autopilot, take care of the underlying infrastructure automatically, and developers do not need to manage the nodes. However, when it comes to container depletion, i.e., removing the assigned cloud resources to an idle container, current scheduling technologies have limitations. In this paper, we propose our approach to managing cloud resource usage when containers are idle efficiently. For this purpose, we deplete idle containers statefully, i.e., propose a novel manager that monitors idle containers, saves their state, and efficiently depletes them. This manager reconstructs a depleted container using the saved state when reconstruction is needed. In our approach, we suggest an Infrastructure as Code component to automate the creation of new nodes if a depleted container cannot be scheduled on the same node, e.g., because of being overloaded. We provide an analytical model for the stateful depletion of containers and their rescheduling and empirically evaluate the accuracy of our model. For this purpose, we ran an experiment on a private cloud infrastructure and Google Cloud Platform. Our model has a low error rate of 4.28% averaged over public and private clouds.</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>
Dataset: "Resilience of small PAHs in interstellar clouds: Efficient stabilization of cyanonaphthalene by fast radiative cooling"
<p>Data supporting the article "Resilience of small PAHs in interstellar clouds: Efficient stabilization of cyanonaphthalene by fast radiative cooling"</p>
The Effects of Aerosol on Small-Scale Cloud-Environment Mixing: Implications for Observing Inhomogeneous Mixing
<p>This is the simulation output of the submitted manuscript "The Effects of Aerosol on Small-Scale Cloud-Environment Mixing: Implications for Observing Inhomogeneous Mixing" to the Journal of Geophysical Research: Atmospheres. </p> <p>Abstract: The mixing of clouds with their environment can change the size and number of cloud droplets, two important parameters to understand the role of clouds in the climate system. Because these changes are determined by the way a cloud mixes with its environment, they have been used to characterize the mixing process. Using a highly detailed numerical cloud model coupled with a statistical turbulence model, we show that the adequate consideration of aerosol particles from which cloud droplets grow, can alter the changes of cloud droplets during mixing. This has not been considered in the past. We identify biases that need to be considered when mixing processes are analyzed.</p> <p>The raw data presented here was produced by the aforementioned model only and is completely available.</p>
Studying the wide range of relative humidity in cirrus clouds with large-ensemble parcel model simulations
<p>The model codes, data, and plot scripts used in the paper, "Studying the wide range of relative humidity in cirrus clouds with large-ensemble parcel model simulations".</p> <ul> <li>parcel model code.zip contains model code.</li> <li>Results.zip contains output data of each experiment in this study.</li> <li>Figs and scripts.zip are the NCL scripts used for figures.</li> </ul>
S5: Probing the Milky Way and Magellanic Clouds potentials with the 6-D map of the Orphan-Chenab stream
<p>The data associated with the "S5: Probing the Milky Way and Magellanic Clouds potentials with the 6-D map of the Orphan-Chenab stream" paper. </p> <p> </p> <p>We provide</p> <ul> <li>* the Stan models used to extract stellar stream properties</li> <li>The measurement of radial velocity, stream track on the the sky, proper motions and stream densities for Orphan-Chenab.</li> <li>The list of candidate OC stream members from S5 and other surveys. </li> <li>The best fit stream N-body model</li> <li>the posterior chains from the stream/potential model.</li> <li>The enclosed mass profiles for LMC and MW (figure 15 and 17 of the paper)</li> </ul>
Visualisation of Simulated Clouds over the Atlantic
<p>Video results of the thesis "Visualisation of Simulated Clouds over the Atlantic".</p> <p>GitLab with the thesis and source code for the visualisations available under:</p> <p>https://gitlab.ethz.ch/vogelpa/visualisation-of-simulated-clouds-over-the-atlantic</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.