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2,170 results for “clouds”
Distribution. The species lives in Andean cloud forest above 1500 m in Colombia, Ecuador, and possibly N Peru. in Procyonidae
Distribution. The species lives in Andean cloud forest above 1500 m in Colombia, Ecuador, and possibly N Peru.
Dataset for "Diurnal variations of cloud optical properties during day-time over China based on Himawari-8 satellite retrievals"
<p>Dataset for "Diurnal variations of cloud optical properties during day-time over China based on Himawari-8 satellite retrievals".</p>
AHI-CALIOP Collocated Data for Training and Validation of Cloud Masking Neural Networks
<p>Collocated data between AHI at 2km resolution (nadir) and CALIOP 1km cloud product v4.20 used for training and validating cloud identification neural networks. The main training and validation data from 2019 is stored in monthly directories, whilst the collocated dataset used to compare the NN, JMA and BoM cloud mask performances is the file "superdf.h5". All collocated data is stored as .h5 files and was built using the Python Pandas package. In this archive, the data has been stored as compressed directories for each month or as a single compressed file in the case of "superdf.h5" using tar with bzip2 compression or just bzip2 compression respectively.</p>
Elastic Data Analytics for the Cloud-to-Things Continuum - Demo Video
<p>The massive deployment of Internet-connected devices has led to an increase in the collection of data that are then used by companies to improve their decision-making processes. This growing trend demands more and more cloud and communications infrastructure. The limited resources, the need of sharing them, and the fact that many consumers are interested in the same data call for an efficient management of the available resources. The cloud-to-thing continuum can be used to execute different analytics closer to the data source so infrastructure consumption and data circulation can be optimized. In this paper, different dimensions for achieving elastic analytics and a framework for dynamically modifying their behavior, is proposed.</p> <p>This artifact corresponds with a descriptive video of the framework presented in the paper. </p>
Data archive for paper "Machine Learning Emulation of 3D Cloud Radiative Effects"
<p><strong>Overview</strong></p> <p>This archive contains models, data, and the Singularity image to optionally rerun experiments described in "<a href="https://doi.org/10.1029/2021MS002550">Machine Learning Emulation of 3D Cloud Radiative Effects</a>".</p> <p>For the Python tool to generate synthetic data, please refer to the <a href="https://github.com/dmey/synthia">Synthia repository</a>.</p> <p><strong>Prerequisites</strong></p> <ul> <li>Linux or macOS with Bash shell.</li> <li><a href="https://sylabs.io/singularity/">Singularity</a> (tested with version 3.6.3-1.el8)*.</li> <li><a href="https://en.wikipedia.org/wiki/Portable_Batch_System">Portable Batch System</a> (PBS) job scheduler**.</li> </ul> <p>*Please note that all steps require <a href="https://sylabs.io/">Singularity</a> to be installed on your system. If you are looking for information on how to install or use Singularity, please refer to the <a href="https://sylabs.io/docs">Singularity documentation</a>.</p> <p>**Although PBS in not a strict requirement, it is required to run all helper scripts as included in this repository. Please note that depending on your specific system settings and resource availability, you may need to modify PBS parameters at the top of submit scripts stored in the <code>hpc</code> directory (e.g. <code>#PBS -lwalltime=24:00:00</code>).</p> <p><strong>Initialization</strong></p> <p>Deflate the data archive with:</p> <pre><code>./init.sh </code></pre> <p>Compile ecRad with Singularity:</p> <pre><code>./tools/singularity/compile_ecrad.sh </code></pre> <p><strong>Usage</strong></p> <p>To reproduce the results as described in the paper, run the following commands from the <code>hpc</code> folder:</p> <pre><code>qsub -v JOB_NAME=mlp_default ./submit_grid_search_default.sh qsub -v JOB_NAME=mlp_synthia ./submit_grid_search_synthia.sh qsub submit_benchmark.sh </code></pre> <p>then, to plot stats and identify notebooks run:</p> <pre><code>qsub submit_stats.sh </code></pre> <p><strong>License</strong></p> <p>Paper code released under the <a href="./LICENSE.txt">MIT license</a>. Data released under <a href="./data/LICENSE.txt">CC BY 4.0</a>. <a href="https://confluence.ecmwf.int/display/ECRAD">ecRad</a> released under the <a href="./ecrad/LICENSE">Apache 2.0 license</a>.</p>
FIGURE 6 in A new species of Pristimantis (Anura: Strabomantidae) with a colourful venter from the cloud forests of Colombian western Andes
FIGURE 6. General (above) and detailed (below) representative pictures of the habitat of Pristimantis postducheminorum sp. nov. in the cloud forests of the Colombian northwestern Andes. Individuals were found perched on the moss covering tree branches or (one) directly sitting on bromeliad leaves. Photos by Luis A. Mazariegos-H.
FIGURE 4 in A new species of Pristimantis (Anura: Strabomantidae) with a colourful venter from the cloud forests of Colombian western Andes
FIGURE 4. Schematic lateral (A) and dorsal (B) views of the snout of Pristimantis postducheminorum sp. nov.; ventral view of the hand (C) and foot (D). All illustrations by Luis A. Mazariegos-H. based on his own photographs on the holotype (MHUA- A11550).
FIGURE 5 in A new species of Pristimantis (Anura: Strabomantidae) with a colourful venter from the cloud forests of Colombian western Andes
FIGURE 5. Representative examples of among-individuals variation in ventral colouration of Pristimantis postducheminorum sp. nov. after preservation in 70% ethanol during at least one-year. Regarding the gular area, individuals may lack patterning (A), or present a V-shaped band (C) or a blotched (B, D) pattern. Some keep the belly orange pigment (B), and some exhibit a mid-ventral white line (D). From left to right and top to down: MHUA-A 11549, ANDES-A4425, MHUA-A 11550, ANDES- A4428. Photographs not to scale.
FIGURE 3 in A new species of Pristimantis (Anura: Strabomantidae) with a colourful venter from the cloud forests of Colombian western Andes
FIGURE 3. Variation in dorsolateral and ventral coloration among some individuals of the type series. From top to down: A. MHUA-A11550 (holotype), B: ANDES-A4428, C: MHUA-A11551, D: ANDES-A4427, E: MHUA-A 1549. Photographs not to scale.
FIGURE 7 in A new species of Pristimantis (Anura: Strabomantidae) with a colourful venter from the cloud forests of Colombian western Andes
FIGURE 7. Currently known distribution of Pristimantis postducheminorum sp. nov. and the phylogenetically closest species throughout the Colombian western Andes. The light blue dot with the new species name encloses the type locality (MPNR) and only other locality (El Centello Reserve) where the species is currently known. They are separated by less than 5 km of straight line.
FIGURE 2 in A new species of Pristimantis (Anura: Strabomantidae) with a colourful venter from the cloud forests of Colombian western Andes
FIGURE 2. Two-dimensional representation of genetic uncorrected distances between individuals of Pristimantis postducheminorum sp. nov. and the phylogenetically closest species of Pristimantis (Fig. 1). The axes are unitless and derived from Classic Multidimensional Scaling of pairwise genetic distances. The Pearson's correlation coefficient between graphical distances and original genetic distances is 0.96.
FIGURE 1 in A new species of Pristimantis (Anura: Strabomantidae) with a colourful venter from the cloud forests of Colombian western Andes
FIGURE 1. Phylogenetic analysis on the relationship between Pristimantis postducheminorum sp. nov. and related species of Pristimantis based on the mitochondrial gene 16S (see Supplementary Table 1). Numbers at nodes represent ultrafast bootstrap support after 5000 pseudoreplicates, replaced by green-filled circles when the values are above 95. The taxon composed by P. euphronides and P. shrevei was used as external group. Photos: P. ferwerdai and P. postducheminorum sp. nov. by Luis A. Mazariegos-H., and P. satagius by Juan M. Daza.
Synergistic use of Far- and Mid-Infrared Spectral radiances for Polar Ice Cloud Detection from Space
<p>Modtran5 was used to generate all .plt radiance files from the tp5 files provided</p> <p>All ERA5 data used in this study is provided in .mat format </p> <p>Codes for plotting the data for figures is provided in "codes" folder </p> <p> </p>
Code for JAMES publication - Implementation of prognostic cloud ice number concentrations for the Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) microphysics scheme.
<p>In this repository, we include the source codes for WRF microphysics parameterization used in the JAMES publication "Implementation of prognostic cloud ice number concentrations for the Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) microphysics scheme"</p> <p> </p> <p>The revised WDM6 code, which predicts the number concentration of cloud ice, and the original WDM6 code are uploaded. Each code is modified so that detailed microphysical processes can be found in wrfout.</p> <p> </p> <p>Namelist files shows the namelist.input for each case.</p> <p> </p> <p>Scripts file for figures in this manuscript are uploaded</p>
Phytogeographic origin determines Tropical Montane Cloud Forest hydraulic trait composition
<p>Tropical montane cloud forests (TMCF) have unique climatic conditions, which allow the coexistence of plant lineages with different phytogeographic origins from tropical versus temperate climates. Future climate projections suggest TMCFs will be subjected to increasing drought stress due to fog uplift and higher temperatures, possibly leading to tree mortality and local extinctions, and consequently changes in forest composition and functioning. Characterising community functional composition, trade-offs among traits and the drivers of community assembly is of utmost importance to improve our capacity to predict the response of montane plant communities to forecast climate change.</p> <p>Here, we aimed to test if species from different phytogeographic origins (i.e. tropical - evergreen x deciduous - and temperate) differ in drought vulnerability and how the co-existence of these groups change the hydraulic composition of TMCF`s. We used a framework based on measurements of key hydraulic traits (i.e. xylem embolism resistance, hydraulic safety margin, stomata control, turgor loss point, minimum water potential) of 16 dominant species (> 70% of the forest basal area) within a TMCF in the Atlantic Rain Forest Domain in southeast Brazil. We used community-weighted means to model whether removing each species group would change the community hydraulic functional composition.</p> <p>Temperate, tropical deciduous and tropical evergreen groups differ in their hydraulic functioning and these differences explain forest functional composition and taxa dominance. Temperate and tropical deciduous taxa were consistently more vulnerable hydraulically (i.e. lower safety margins and embolism resistance). The coexistence of different phytogeographic lineages is a key determinant of TMCF hydraulic composition. We also used models including phylogeny to evaluate the variation of hydraulic traits across Phytogeographic groups, and the results suggest some niche conservatism associated with plant hydraulic functioning.</p> <p>Our results provide evidence of the importance of species phytogeographic origin on TMCF functioning, and niche conservatism in the evolution of hydraulic traits. The higher drought vulnerability observed in temperate group might be a mechanistic explanation for the expansion of temperate taxa distribution to wetter places during past colder and drier climate. Thus, we suggest hydraulic functional traits may be useful to predict future dynamics of TMCFs under changing climatic conditions.</p>
Data for "On the resolution-dependence of cloud fraction in radiative-convective equilibrium"
<p>Cloud-resolving model output and analysis scripts for the paper "On the resolution-dependence of anvil cloud fraction and precipitation efficiency in radiative-convective equilibrium"</p>
FIGURE 1 in The advertisement and courtship calls of Phyllobates bicolor (Anura: Dendrobatidae) from a natural population in the Colombian pacific cloud forests
FIGURE 1. Advertisement call: sonogram of a series of notes (A), spectrogram and sonogram of four consecutive notes (B), spectrogram and sonogram of a single note (C), and the spectrum of power (D). Courtship call: spectrogram and sonogram of the whole signal (E), and spectrum of power (F). Individual of P. bicolor, photo taken by JHR (G).
Six new species of Pristimantis (Anura: Strabomantidae) from Llanganates National Park and Sangay National Park in Amazonian cloud forests of Ecuador
<p>Raw data for geometric morphometrics landmarks</p>
Data for the publication "Estimation of the importance of natural cloud seeding" (Master thesis)
<p>This repository contains the data for the thesis: "Estimation of the importance of natural cloud seeding" by Ulrike Proske, submitted in April 2020 to the Department of Earth Sciences, ETH Zurich.</p>
COSP output for the TWPICE and KWAJEX case with varying cloud subgrid-scale parameters
<p>The control experiment is named SCG_LIN, in which the domain-averaged cloud fraction and condensate from CRM_LIN are used, along with median values of <em>L</em><sub>cf</sub>, <em>L</em><sub>cw</sub> and <em>v</em>. Two sensitivity experiments were carried out for each parameter using either lower or upper quartiles, while keeping the other two parameters unchanged. </p> <table> <tbody> <tr> <td> <p>CRM_LIN</p> </td> <td> <p>CRM output with LIN microphysics</p> </td> </tr> <tr> <td> <p>CRM_SAM</p> </td> <td> <p>CRM output with SAM1MOM microphysics</p> </td> </tr> <tr> <td> <p>CRM_M2005</p> </td> <td> <p>CRM output with M2005 microphysics</p> </td> </tr> <tr> <td> <p>SCG_LIN</p> </td> <td> <p>SCG output based on domain-averaged clouds from CRM_LIN and median subgrid-scale parameters (<em>L</em><sub>cf </sub>= 1.6, <em>L</em><sub>cw </sub>= 0.9, <em>v </em>= 2)</p> </td> </tr> <tr> <td> <p>SCG_SAM</p> </td> <td> <p>Same as SCG_LIN but based on clouds from CRM_SAM</p> </td> </tr> <tr> <td> <p>SCG_M2005</p> </td> <td> <p>Same as SCG_LIN but based on clouds from CRM_M2005</p> </td> </tr> <tr> <td> <p>SCG_Exact</p> </td> <td> <p>SCG output using exact parameters derived from CRM_LIN</p> </td> </tr> <tr> <td> <p>SCG_<em>L</em><sub>cf</sub>(3.2)</p> </td> <td> <p>Same as SCG_LIN but for <em>L</em><sub>cf </sub>= 3.2 in cloud overlap</p> </td> </tr> <tr> <td> <p>SCG_<em>L</em><sub>cf</sub>(0.9)</p> </td> <td> <p>Same as SCG_<em> L</em><sub>cf</sub>(3.2) but for <em>L</em><sub>cf </sub>= 0.9</p> </td> </tr> <tr> <td> <p>SCG_<em>L</em><sub>cw</sub>(1.5)</p> </td> <td> <p>Same as SCG_LIN but for <em>L</em><sub>cw </sub>= 1.5 in condensate overlap</p> </td> </tr> <tr> <td> <p>SCG_<em>L</em><sub>cw</sub>(0.5)</p> </td> <td> <table> <tbody> <tr> <td> <p>Same as SCG_<em> L</em><sub>cw</sub>(1.5) but for <em>L</em><sub>cw </sub>= 0.5</p> </td> </tr> </tbody> </table> <p> </p> </td> </tr> <tr> <td> <p>SCG_<em>v</em>(0.8)</p> </td> <td> <p>Same as SCG_LIN but for <em>v </em>= 0.8</p> </td> </tr> <tr> <td> <p>SCG_<em>v</em>(5.9)</p> </td> <td> <p>Same as SCG_<em>v</em>(0.8) but for <em>v </em>= 5.9</p> </td> </tr> </tbody> </table>
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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)
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.