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2,170 results for “clouds”
Linked collectors and determiners for: The Trichoptera of Panama. VI. Seven new species of microcaddisflies (Insecta: Trichoptera: Hydroptilidae) from Mount Totumas Cloud Forest and Biological Reserve.
Natural history specimen data linked to collectors and determiners held within, "The Trichoptera of Panama. VI. Seven new species of microcaddisflies (Insecta: Trichoptera: Hydroptilidae) from Mount Totumas Cloud Forest and Biological Reserve". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/bda85876-5e1a-46a4-83ac-bb42fd72b390">https://bionomia.net/dataset/bda85876-5e1a-46a4-83ac-bb42fd72b390</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/bda85876-5e1a-46a4-83ac-bb42fd72b390">https://gbif.org/dataset/bda85876-5e1a-46a4-83ac-bb42fd72b390</a>. Formatted as a Frictionless Data package.
Dataset to reproduce the figures of "Clouds are crucial to capture Antarctic sea ice variability"
<p>These files, further described below, contain the necessary variables to reproduce the figures of Cesana et al. (submitted to GRL), which are not all publically available.</p> <p> </p> <p><strong>Observations</strong></p> <p><a href="https://zenodo.org/api/records/13974536/draft/files/CERES_EBAF-TOA_Ed4.2_Subset_200101-202112_2x2.nc/content" target="_blank" rel="noopener noreferrer">CERES_EBAF-TOA_Ed4.2_Subset_200101-202112_2x2.nc</a> This file contains the TOA SW CRE (= toa_sw_clr_c_mon - toa_sw_all_mon) and total cloud fraction (cldarea_total_daynight_mon) variables from CERES-EBAF observations (Ed4.2, Loeb et al., 2018) from 2001 to 2021 and can be downloaded directly from the CERES ordering tool website here https://ceres-tool.larc.nasa.gov/ord-tool/jsp/EBAFTOA42Selection.jsp.</p> <p><a href="https://zenodo.org/api/records/13974536/draft/files/seaice_conc_monthly_sh_200101-202112_2x2.nc/content" target="_blank" rel="noopener noreferrer">seaice_conc_monthly_sh_200101-202112_2x2.nc</a> This file contains the sea ice area variable (cdr_seaice_conc) for the Southern Hemisphere (SH) from NSDIC CDR observations (Meier et al., 2021) and can be downloaded directly here https://noaadata.apps.nsidc.org/NOAA/G02202_V4/.</p> <p><a href="https://zenodo.org/api/records/13974536/draft/files/sia_sh_obs_2001-2021.nc/content" target="_blank" rel="noopener noreferrer">sia_sh_obs_2001-2021.nc</a> This file contains the yearly mean of SH sea ice area (sia_sh) from the NSDIC CDR observations and from 2001 to 2021. </p> <p> </p> <p><strong>Simulations</strong></p> <p><a href="https://zenodo.org/api/records/13974536/draft/files/cloud_analysis_cesm1_maps_annmean_50-90S_2001-2018.nc/content" target="_blank" rel="noopener noreferrer">cloud_analysis_cesm1_maps_annmean_50-90S_2001-2018.nc</a> This file contains the total cloud fraction (CLDTOT) and TOA SW CRE (SWCF) variables over 50-90˚S from 2001 to 2018 for CESM1 free-running and wind-nudged simulations (Blanchard-Wrigglesworth et al., 2021).</p> <p><a href="https://zenodo.org/api/records/13974536/draft/files/cloud_analysis_cesm1_conc_annmean_50-90S_2001-2018.nc/content" target="_blank" rel="noopener noreferrer">cloud_analysis_cesm1_conc_annmean_50-90S_2001-2018.nc</a> This file contains the sea ice area (aice) variable over 50-90˚S from 2001 to 2018 for CESM1 free-running and wind-nudged simulations (Blanchard-Wrigglesworth et al., 2021).</p> <p><a href="https://zenodo.org/api/records/13974536/draft/files/cloud_analysis_GISS-E2-1_maps_conc_annmean_50-90S_2001-2021.nc/content" target="_blank" rel="noopener noreferrer">cloud_analysis_GISS-E2-1_maps_conc_annmean_50-90S_2001-2021.nc</a> This file contains total cloud fraction (clt), TOA SW CRE (= rsutcs - rsut) and sea ice area (siconca) variables over 50-90˚S from 2001 to 2021 for NASA-GISS-E2-1 free-running and wind-nudged simulations (Roach et al., 2023).</p> <p><a href="https://zenodo.org/api/records/13974536/draft/files/sia_sh_GISS-E2-1_2001-2021.nc/content" target="_blank" rel="noopener noreferrer">sia_sh_GISS-E2-1_2001-2021.nc</a> and <a href="https://zenodo.org/api/records/13974536/draft/files/sia_sh_cesm1_2001-2018.nc/content" target="_blank" rel="noopener noreferrer">sia_sh_cesm1_2001-2018.nc</a> contain the yearly mean of SH sea ice area (sia_sh) from NASA-GISS-E2-1 and CESM1 simulations, respectively. </p> <p> </p> <p><strong>References</strong></p> <p>Blanchard-Wrigglesworth, E., Roach, L. A., Donohoe, A., & Ding, Q. (2021, February). Impact of Winds and Southern Ocean SSTs on Antarctic Sea Ice Trends<span> </span>and Variability. Journal of Climate, 34 (3), 949–965. doi: 10.1175/JCLI-D-20-0386.1</p> <p><span>Cesana, G., Roach, L. A. and Blanchard-Wrigglesworth, E., 2024 Clouds are crucial to capture Antarctic sea ice variability, submitted to <em>GRL.</em></span></p> <p>Loeb, N. G., Doelling, D. R., Wang, H., Su, W., Nguyen, C., Corbett, J. G., <span> </span>Kato, S. (2018). Clouds and the earth’s radiant energy system (ceres) energy<span> </span>balanced and filled (ebaf) top-of-atmosphere (toa) edition-4.0 data product.<span> </span>Journal of Climate, 31 , 895-918. doi: 10.1175/JCLI-D-17-0208.1</p> <p>Meier, W., Fetterer, F., Windnagel, A., & Stewart, S. (2021). NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 4.<span> </span>NSIDC. doi: 10.7265/EFMZ-2T65</p> <p>Roach, L. A., Mankoff, K. D., Romanou, A., Blanchard-Wrigglesworth, E., Haine,<span> </span>T. W., & Schmidt, G. A. (2023, 12). Winds and meltwater together lead to<span> </span>southern ocean surface cooling and sea ice expansion. Geophysical Research<span> </span>Letters, 50 . doi: 10.1029/2023GL105948</p> <p> </p>
Linked collectors and determiners for: Anticyphon gen. nov., a new genus of Scirtidae (Coleoptera: Scirtoidea) inhabiting high altitude Andean cloud forests and páramo formation.
Natural history specimen data linked to collectors and determiners held within, "Anticyphon gen. nov., a new genus of Scirtidae (Coleoptera: Scirtoidea) inhabiting high altitude Andean cloud forests and páramo formation". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/55c6a2a3-cf2b-41df-bb10-ee4ba1d423c3">https://bionomia.net/dataset/55c6a2a3-cf2b-41df-bb10-ee4ba1d423c3</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/55c6a2a3-cf2b-41df-bb10-ee4ba1d423c3">https://gbif.org/dataset/55c6a2a3-cf2b-41df-bb10-ee4ba1d423c3</a>. Formatted as a Frictionless Data package.
Global atmospheric particle formation from CERN CLOUD measurements: nucleation rate dataset
<p>Data S1 for paper "Dunne et al, (the CLOUD collaboration), Global atmospheric particle formation from CERN CLOUD measurements, Science 354 6316 (2016). Data contains nucleation rates and chamber conditions (including precursor gas concentrations) for inorganic binary and ternary (H2SO4-H2O) and (H2SO4-NH3-H2O) neutral and ion-induced nucleation measurements presented in the paper. These data were originally attached as supplemental to the paper, but are not currently available on the Science website (as of October 2024). </p> <p>Relative humidity units are percent. Care is needed to interpret all the data, for example not all ammonia values quoted were measured directly, some are inferred. See the supplementary materials of the paper for more discussion, and users of data at relative humidity other than (38+/-5)% are advised to discuss with the contact author Hamish Gordon. Ion production rate and nucleation rate units are per cm3 per second. The sulfuric acid units can be interpreted by noting that the value of 632 in the first data entry is 6.32x10^8 cm-3. </p> <p>Please cite the original Science article if you use these data: https://www.science.org/doi/10.1126/science.aaf2649</p>
Cloud feedback maps from CMIP5 and CMIP6 models
<p>This repository contains maps of cloud feedbacks computed using up to three methodologies from two types of experiments conducted across CMIP5 and CMIP6 models.</p> <p>Methods:</p> <ul> <li>The approximate partial radiative perturbation technique (shortwave cloud feedbacks only), denoted with 'APRP' in filename. </li> <li>The adjusted change in cloud radiative effect, denoted with 'adjCRE' in filename. </li> <li>The cloud radiative kernel method, in files without 'APRP' or 'adjCRE' in the name. </li> </ul> <p>Experiments:</p> <ul> <li>amip+4K experiments, known as amip4K and amip-p4K in CMIP5 and CMIP6, respectively</li> <li>abrupt CO2 quadrupling experiments, known as abrupt4xCO2 and abrupt-4xCO2 in CMIP5 and CMIP6, respectively</li> </ul> <p>Data formats:</p> <ul> <li>For cloud feedbacks derived using APRP and adjCRE methods, all models' maps are contained within single netcdf files.</li> <li>For cloud feedbacks derived using cloud radiative kernels, Individual netcdf files are provided for each model, which are compressed into a zip file.</li> </ul>
A Lagrangian study of interfaces at the edges of cumulus clouds
<p>The upload contains DNS data that has been used to produce the results published in Nair et al, 'A Lagrangian study of interfaces in cumulus clouds,' Journal of the Atmospheric Sciences, 2021. Please read the README file for all necessary information on how to read and analyze the data.</p>
Point clouds from terrestrial laser scanning of 97 trees in St Pancras Old Church, London
<p>Point clouds of 97 trees scanned in the grounds of St Pancras Old Church, Camden, London</p> <p>Tree species is predominantly London Plane (<em>Platanus × hispanica</em>) but also includes the <a href="https://www.atlasobscura.com/places/the-hardy-tree-london-england">Hardy ash tree</a>.</p> <p>Data was captured on 18/7/2017 (leaf-on) with a RIEGL VZ-400 terrestrial laser scanner. 38 scans were conducted from 19 positions. The weather was good, with little to no noticeable wind.</p> <p>Data is a binary PLY format with <em>xyz</em> fields in an arbitrary coordinate system. Trees have been extracted from the global point cloud and have been "cleaned" to remove the ground and neighbouring trees (however there may be some errors). Data has been downsampled to a voxel size of 0.04 m.</p> <p>Raw data can be accessed from here.</p> <p>Please acknowledge the data set authors if using this data.</p>
Point clouds from terrestrial laser scanning of 30 trees along Malet Street, London
<p>Point clouds of 30 street trees scanned along <a href="https://goo.gl/maps/7x3dutn6vHcxVPdC6">Malet Street, London, UK</a>. </p> <p>Tree species is predominantly London Plane (<em>Platanus × hispanica</em>).</p> <p>Data was captured on 8/2/2017 (leaf-off) with a RIEGL VZ-400 terrestrial laser scanner. 24 scans were conducted from 12 positions along the street. The weather was good, with little to no noticeable wind.</p> <p>Data is a binary PLY format with <em>xyz</em> fields in an arbitrary coordinate system. Trees have been extracted from the global point cloud and have been "cleaned" to remove the ground and neighbouring trees (however there may be some errors). Data has been downsampled to a voxel size of 0.04 m.</p> <p>Raw data can be accessed from here.</p> <p>Please acknowledge the data set authors if using this data.</p>
Point clouds from terrestrial laser scanning of 74 trees in Russell Square, London
<p>Point clouds of 74 trees scanned in <a href="https://www.google.co.uk/maps/place/Russell+Square/@51.5217533,-0.1280787,17z/data=!3m1!4b1!4m5!3m4!1s0x48761b310bb300bf:0xd1190a08b331324a!8m2!3d51.52175!4d-0.12589">Russell Square, London</a></p> <p>Tree species is predominantly London Plane (<em>Platanus × hispanica</em>).</p> <p>Data was captured on 8/2/2017 (leaf-off) with a RIEGL VZ-400 terrestrial laser scanner. 22 scans were conducted from 11 positions. The weather was good, with little to no noticeable wind.</p> <p>Data is a binary PLY format with <em>xyz</em> fields in an arbitrary coordinate system. Trees have been extracted from the global point cloud and have been "cleaned" to remove the ground and neighbouring trees (however there may be some errors). Data has been downsampled to a voxel size of 0.04 m.</p> <p>Raw data can be downloaded from <a href="https://doi.org/10.5281/zenodo.5070681">10.5281/zenodo.5070681</a></p> <p>Please acknowledge the data set authors if using this data.</p>
Sentinel-2 KappaZeta Cloud and Cloud Shadow Masks
<p><strong>General information</strong></p> <p>The dataset consists of 4403 labelled subscenes from 155 Sentinel-2 (S2) Level-1C (L1C) products distributed over the Northern European terrestrial area. Each S2 product was oversampled at 10 m resolution for 512 x 512 pixels subscenes. 6 L1C S2 products were labelled fully. Among other 149 S2 products the most challenging ~10 subscenes per product were selected for labelling. In total the dataset represents 4403 labelled Sentinel-2 subscenes, where each sub-tile is 512 x 512 pixels at 10 m resolution. The dataset consists of around 30 S2 products per month from April to August and 3 S2 products per month for September and October. Each selected L1C S2 product represents different clouds, such as cumulus, stratus, or cirrus, which are spread over various geographical locations in Northern Europe.</p> <p>The classification pixel-wise map consists of the following categories:</p> <ul> <li>0 – MISSING: missing or invalid pixels;</li> <li>1 – CLEAR: pixels without clouds or cloud shadows;</li> <li>2 – CLOUD SHADOW: pixels with cloud shadows;</li> <li>3 – SEMI TRANSPARENT CLOUD: pixels with thin clouds through which the land is visible; include cirrus clouds that are on the high cloud level (5-15km).</li> <li>4 – CLOUD: pixels with cloud; include stratus and cumulus clouds that are on the low cloud level (from 0-0.2km to 2km).</li> <li>5 – UNDEFINED: pixels that the labeler is not sure which class they belong to.</li> </ul> <p>The dataset was labelled using Computer Vision Annotation Tool (<a href="https://github.com/openvinotoolkit/cvat">CVAT</a>) and <a href="https://segments.ai/">Segments.ai</a>. With the possibility of integrating active learning process in Segments.ai, the labelling was performed semi-automatically.</p> <p>The dataset limitations must be considered: the data is covering only terrestrial region and does not include water areas; the dataset is not presented in winter conditions; the dataset represent summer conditions, therefore September and October contain only test products used for validation. Current subscenes do not have georeferencing, however, we are working towards including them in next version.</p> <p>More details about the dataset structure can be found in README. </p> <p><strong>Contributions and Acknowledgements</strong></p> <p>The data were annotated by Fariha Harun and Olga Wold. The data verification and Software Development was performed by Indrek Sünter, Heido Trofimov, Anton Kostiukhin, Marharyta Domnich, Mihkel Järveoja, Olga Wold. Methodology was developed by Kaupo Voormansik, Indrek Sünter, Marharyta Domnich.<br> We would like to thank Segments.ai annotation tool for instant and an individual customer support. We are grateful to European Space Agency for reviews and suggestions. We would like to extend our thanks to Prof. Gholamreza Anbarjafari for the feedback and directions.<br> The project was funded by<strong><em> European Space Agency</em></strong>, Contract No. 4000132124/20/I-DT.</p>
Sonora Bobcat: cloud-free, substellar atmosphere models, spectra, photometry, evolution, and chemistry
<p><strong>OVERVIEW</strong></p> <p>Presented here are models for non-irradiated, substellar mass objects belonging to the Sonora model series, described in Marley et al. (2021). The files presented here are model temperature-pressure structures ("structure"), emergent spectra from the top of the atmosphere ("spectra"), thermal evolution and photometry ("evolution_and_photometry"), and rainout chemical equilibrium tables used to compute the models ("chemistry"). </p> <p>Atmospheric structure and spectra .tar file names specify metallicity [M/H] and carbon-to-oxygen ratio (C/O) relative to solar. For example, "structures+0.0_co1.5<a href="../api/files/2e9ce76a-67fc-4fd6-ae5c-f88f16c610ea/structures%2B0.0_co1.5.tar.gz">.</a>tar.gz" contains the set of radiative-convective equilibrium atmospheric structures for solar metallicity ("+0.0") with C/O=1.5 times the solar abundance. The _co*.* is omitted for solar C/O, or co_1.0. The individual file naming convention is described below. All stated abundances and ratios are relative to Lodders (2010) abundances, see Marley et al. (2021) for details and use caution when referring to other abundance tabulations.</p> <p>This particular set of model atmosphere structures and associated spectra, photometry, and evolution, which we name <strong>Sonora Bobcat</strong>, are for cloudless objects with 3.25 ≤ log g (cgs) ≤ 5.5 and 200 ≤ Teff ≤ 2400K. Steps in T<sub>eff</sub> vary from 25K to 1000K and steps in log g are 0.25 or 0.5. Some combinations of model grid parameters include additional values of the gravity. Models are provided for [M/H] = -0.5, 0.0, and +0.5 and "rainout" chemical equilibrium. A limited set of models with carbon-to-oxygen ratio of 0.5 and 1.5 times solar abundance are also included. For the convenience of having a rectangular table in (T<sub>eff</sub>, gravity) space, models are calculated in regimes that are not reached by the evolution, such as very high gravity and very low T<sub>eff</sub>. Refer to the companion evolution tables to identify combinations of T<sub>eff</sub> and log g outside the bounds covered by the evolution.</p> <p><strong>ATMOSPHERIC STRUCTURE</strong></p> <p>Atmospheric structure and spectra filenames specify Teff and gravity (in mks units) along with [M/H] and (C/O) relative to solar. "co1.5" in version and spectra header nomenclature refers to 1.5 times the solar C/O ratio. _co*.* is generally omitted for 1.0, the solar value. For example, the file t1000g316nc_m-0.5.dat contains the structure of a model with T<sub>eff</sub>=1000K, g=316m/s<sup>2</sup> (the exact value of the gravity is given<sup> </sup>in the first line of the file, see below) , [Fe/H]=-0.5, and C/O=1.0 times the solar value. </p> <p>Temperature structure and spectra files have a one line header giving "Teff, grav(MKS), Y, f_sed, kz_min, [Fe/H], C/O, f_hole". Teff and grav are the effective temperature (K) and gravity (MKS), Y is the He mass fraction. f_sed is a cloud parameterization which is not relevant for these cloudless models and is arbitrarily given as 0.0. Likewise kz_min relates to the atmospheric eddy diffusion coefficient, which is also not relevant for these chemical equilibrium models and is arbitrarily set equal to a placeholder value that is not used in these models. [Fe/H] and C/O are the metallicity and C/O ratios as described above. [Fe/H] is identical to [M/H]. f_hole is another cloud parameter for cloudy models, not relevant to these cloudless models.</p> <p>Columns in the atmosphere structure files describe the atmosphere at discrete levels. Columns give: level index, P(bar), T(K), internally used check parameter, adiabatic temperature gradient (d ln T / d ln P), local temperature gradient (d ln T / d ln P), atmospheric density (g / cm<sup>3</sup>).</p> <p><strong>EVOLUTION AND PHOTOMETRY</strong></p> <p>Evolution and Photometry tables are described in detail in a README file included in that tar file. Evolution files connect mass, effective temperature, radius, age, gravity, and moment of inertia for these model sets. Each set of model spectra is complemented with tables of fluxes and of absolute magnitudes in a number of photometric systems commonly used in brown dwarf and exoplanet research (MKO, Keck, 2MASS, SDSS, WISE, Spitzer IRAC, etc). Fluxes and magnitudes for the full set of JWST filters is also included in separate tables. Magnitudes are computed on the Vega system (using the Vega spectrum of Bohlin & Gilliland 2004) or on the AB system (e.g. for SDSS).</p> <p><strong>SPECTRA</strong></p> <p>The model spectra each contain close to 362000 wavelength points. The resolving power varies with wavelength and ranges from R=6000 to 200000 but is otherwise the same for all spectra. The first line gives the model parameters in the same format as the structure files described above. This is followed by the spectrum</p> <p>Column 1: wavelength in µm</p> <p>Column 2: <strong>Radiation flux <em>F<sub>ν</sub></em></strong><sub> </sub>= \(4\pi\) x Eddington flux <em>H</em><sub>ν</sub>, in erg/cm<sup>2</sup>/s/Hz (always exercise caution with factors of \(4\pi\) when comparing to the radiation and Eddington flux, e.g., see Section 3.3 of Hubeny & Mihalas, "Theory of Stellar Atmospheres")</p> <p>The spectral fluxes are given at the top of the atmosphere and are strictly monochromatic. The model spectrum provides no information in the wavelength range between two tabulated points. Unless a spectral line or feature is well resolved,<em> interpolation in wavelength is not advised</em>. For comparison with data, the model spectra need to be convolved and binned to the instrumental resolution and sampling. In our experience, a minimum of 10 wavelength points is necessary to obtain a reasonable average flux over a wavelength interval. This is a rule of thumb and caution is advised, especially when comparing with high resolution data. The flux received at Earth is that given in the table scaled by (R/D)<sup>2</sup> where R is the radius of the object (given in the companion evolution tables) and D its distance. </p> <p>The solar spectra and photometry are the same as those archived at https://zenodo.org/record/1309035#.YOyz4S1h2X0, which did not provide the T(P) profiles available here.</p> <p><strong>CHEMISTRY</strong></p> <p>We also separately include rainout chemical equilibrium tables for these same atmospheric bulk abundances. These chemistry files are described in detail by their own README file. Additional chemistry tables, beyond those used for the models presented here, are also included for completeness. Users interested in the chemical abundances of the structure models must interpolate within the matching chemistry file for the atmospheric species of interest.</p> <p><strong>CREDITS</strong></p> <p>If you use these tables in your research, please cite Marley et al. (2021, Astrophysical Journal, Volume 920, Issue 2, id.85.)</p> <p>16 Feb 2024: Error corrected in Column 2 heading. Column 2 is the Radiation flux, not the Eddington flux as previously stated. Citation updated.</p> <p> </p>
Dataset for mammatus-like radar echoes along the bases of upper-tropospheric outflow-layer clouds of typhoons.
<p>PPI and RHI data of radar observations of mammatus-like echoes at the cloud bases of upper-level clouds of typhoons observed by the Nagoya-University cloud radar. The observations were carried out at Okinawa, Japan in 2016 and 2019, and Kobe, Japan in 2018 This dataset includes raw data at polar coordinates and grid data interpolated to Cartesian coordinates. This dataset also includes upper-level sounding observation data during the appearance of mammatus-like echoes at Okinawa, Japan in 2016.</p> <p>In this version 2, all RHI data obtained in 3 October 2016 were added.</p>
TOA and surface cloud radiative kernels calculated with RRTM
<p> These cloud radiative kernels (CRK) are calculated with RRTM, with meteorological variables from ERA-interim as inputs. The file format is netcdf4, and was created by python. To read these files, any software supporting netcdf4 can be used. </p> <p> Longwave cloud radiative effect at surface is primarily decided by cloud base properties, while top-of-atmosphere (TOA) cloud radiative effect is primarily decided by cloud top properties, so the standard version of surface CRK is a function of latitude, longitude, month, cloud optical thickness (τ) and cloud base pressure (CBP), and the TOA CRK is a function of latitude, longitude, month,τ and cloud top pressure (CTP). Considering that the cloud property histograms provided by climate models are functions of CTP instead of CBP at present, we created a set of surface CRK on CTP-τ cloud fraction histograms using the statistical relationship between CTP, CBP and τ from collocated CloudSat-MODIS observations.</p> <p> There are five individual files. "SFC_CRK_CBP2.nc" is for surface CRK on CBP-τ histograms, "SFC_CRK_CTP2.nc" is for surface CRK on CTP-τ histograms, and "TOA_CRK_CTP2.nc" is for TOA CRK on CTP-τ histograms. The atmospheric CRK can be calculated as the difference between "TOA_CRK_CTP2.nc" and "SFC_CRK_CTP2.nc". The other two files denote separate CRK for ice and liquid clouds. </p> <p> </p> <p> Notes: (1) this version is used in the submitted draft for publication, and it might be renewed in the future.</p> <p> (2) To avoid NAN values, the value of CBP/CTP is set to be the lowest level near surface for cloud bins with CBP/CTP greater than surface air temperature (cloud base or top is below surface). </p> <p> </p>
Seasonal rainfall in subtropical montane cloud forests drives demographic fluctuations in a Green-backed Tit population
<p>Montane birds are vulnerable to climate change. However, the mechanisms by which weather drives demographic processes in montane birds have seldom been investigated. We conducted a long-term study (2009–2019) on the Green-backed Tit (<em>Parus monticolus)</em>, an insectivorous passerine, in the montane cloud forest of subtropical Taiwan. We explored the effects of weather variability on the productivity and survival of adult Green-backed Tits. Nest survival was negatively associated with seasonal rainfall during the breeding season (April–July) and was lower in early clutches than in late clutches. Higher typhoon-induced precipitation during the postbreeding period (July–September) was related to reduced adult survival, but neither summer temperature nor winter weather conditions were found to be related to adult bird survival. We developed a stochastic simulation model for Green-backed Tit population dynamics based on empirical data. We compared the simulated time-series and observed population growth rates (λ) and found that 80% (8/10 yr) of the observed λ fell within the 5th and 95th percentiles of the simulated data over the 10-yr period. Moreover, the simulated average (± standard deviation) of the geometric mean of λ over 10 yr (1.05 ± 0.07) was close to that observed from 2009–2019 (0.99), which provided confidence that the model effectively simulated the population growth rate of the Green-backed Tit. We conducted a sensitivity analysis for λ, and found that juvenile and adult survival influenced by typhoon-induced rainfall were the greatest contributors to the variance in the growth rate of the Green-backed Tit population. With the onset of intensified seasonal precipitation associated with global warming, the population growth and density of Green-backed Tits will decline substantially. Our results suggest that under scenarios of high emissions of greenhouse gas, this local population of Green-backed Tits will not persist in the near future.</p>
A description of the self-deployed-cloud-based ICT infrastructure we use in the SteeleLab
<p>A description of how we manage the data in our group, including</p> <ul> <li>How we get the data off our measurement computers to our Synology Rackstation NAS</li> <li>How we make the data available our NAS to the the containers in our JupyterHub server</li> <li>How we share, maintain, and collaboratively edit our data analysis code / notebooks that run in our JupyterHub </li> </ul>
Figure 7 in Two new species of Phyllophaga of the Schizorhina species group from cloud forests of Chiapas, México and Guatemala (Coleoptera: Scarabaeidae: Melolonthinae)
Figure 7. Genital capsule of species of Phyllophaga of Schizorhina subgroup 2, from Chiapas, México and Guatemala. A) P. acacoyahuana, dorsolateral view. B) P. alvareztoroi, dorsal view, redrawn from Morón and Blas (2006). C) P. ginigra, lateral view. D) P. javepacuana, lateral view. Arrows indicate the finger-like dorsal projections of phallobase (sensu Morón 2003: 231).
Figure 6 in Two new species of Phyllophaga of the Schizorhina species group from cloud forests of Chiapas, México and Guatemala (Coleoptera: Scarabaeidae: Melolonthinae)
Figure 6. Genital capsule of holotype of Phyllophaga sechi, n. sp. A) Lateral view. B) Distal view. C. Ventral view. Arrows indicate the finger-like dorsal projections of phallobase (sensu Morón 2003: 231).
Figure 4 in Two new species of Phyllophaga of the Schizorhina species group from cloud forests of Chiapas, México and Guatemala (Coleoptera: Scarabaeidae: Melolonthinae)
Figure 4. Collecting localities of the new species. Circles: Phyllophaga badbunnyi n. sp. Cano. Square: Phyllophaga sechi n. sp. Cano.
Figure 3 in Two new species of Phyllophaga of the Schizorhina species group from cloud forests of Chiapas, México and Guatemala (Coleoptera: Scarabaeidae: Melolonthinae)
Figure 3. Genital plates of Phyllophaga badbunnyi, n. sp. A) Superior and lateral plates. B) Inferior plates (the right broken).
Figure 2 in Two new species of Phyllophaga of the Schizorhina species group from cloud forests of Chiapas, México and Guatemala (Coleoptera: Scarabaeidae: Melolonthinae)
Figure 2. Genital capsule of Phyllophaga badbunnyi, n. sp. A) Lateral view. B) Dorsal view. C) Lateral view of aedeagus. Arrows indicate the finger-like dorsal projections of phallobase (sensu Morón 2003: 231).
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
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.