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31 results for “supersaturation”
Data of "Towards a More Reliable Forecast of Ice Supersaturation: Concept of a One-Moment Ice Cloud Scheme that Avoids Saturation Adjustment"
<p>These are the data used for generating the figures in the ACP article "Towards a More Reliable Forecast of Ice Supersaturation: Concept of a One-Moment Ice Cloud Scheme that Avoids Saturation Adjustment" by Sperber and Gierens.</p> <p>The data sets labeled "Box" have been generated by the stochastic box model, "adj" refers to the parameterisation using saturation adjustment and data labeled "par" originate from the newly developed parameterisation.</p> <p>The label "const" followed by a number refers to simulations with a constant updraught of the speed specified by the number in cm/s. The label "cos" refers to the simulations in which the updraught velocity follows a cosine function in time.</p> <p>"a10" labels simulations with less initial clear sky humidity fluctuations of plus/minus 10% instead of plus/minus 25%. "al0028" labels simulations with a higher deposition rate of 0.0028 1/s instead of 0.0003 1/s. "step10" labels simulations with a longer time step of 10 minutes instead of 1 minute.</p> <p>"Box_const2_rh1.txt" contains data from a simulation similar to "Box_const2.txt" but with an initial mean relative humidity of 100% instead of 110%. "Box_het.txt" contains data from a simulation including heterogeneous nucleation. "Box_slow_nuc.txt" contains data from a simulation where the deposition rate increases over time from zero after nucleation in every air parcel. "Box_upvar.txt" contains data from a simulation, where the updraught velocity in every air parcel varies randomly between 1 cm/s and 3 cm/s and the deposition rate inside the air parcel depends on the updraught velocity at the time of nucleation.</p> <p> </p> <p>The columns in the "Box" files represent from left to right:</p> <p>1. Time since the simulation start in s</p> <p>2. Cloud fraction</p> <p>3. Mean relative humidity across all air parcels</p> <p>4. Mean specific humidity across all air parcels</p> <p>5. Mean specific ice content across all air parcels</p> <p>6. Mean relative humidity across all cloudy air parcels</p> <p>7. Mean relative humidity across all clear air parcels</p> <p>8. Mean equilibrium supersaturation</p> <p>9. Mean threshold relative humidity for homogeneous nucleation</p> <p>10. Mean deposition rate across all cloudy air parcels</p> <p>11. Mean updraught velocity</p> <p> </p> <p>The columns in the "adj" files represent from left to right:</p> <p>1. Time since the simulation start in s</p> <p>2. Cloud fraction</p> <p>3. Mean relative humidity</p> <p>4. Mean specific humidity</p> <p>5. Mean specific ice content</p> <p>6. In-cloud Humidity</p> <p>7. Clear sky humidity</p> <p> </p> <p>The columns in the "par" files represent from left to right:</p> <p>1. Time since the simulation start in s</p> <p>2. Cloud fraction</p> <p>3. Mean relative humidity</p> <p>4. Mean specific humidity</p> <p>5. Mean specific ice content</p> <p>6. In-cloud Humidity</p> <p>7. Clear sky humidity</p> <p>8. Obsolete</p> <p>9. Equilibrium supersaturation</p>
Cloud droplet growth due to supersaturation fluctuations in stratiform clouds
<p>Datasets with numerical setups and direct numerical simulation (DNS) data for "Cloud-droplet growth due to supersaturation fluctuations in stratiform clouds", <em>Atmosph. Chem. Phys.</em>, DOI: https://doi.org/10.5194/acp-2018-644</p>
Non-equilibrium fractionation during ice cloud formation in iCAM5: evaluating the common parameterization of supersaturation as a linear function of temperature
<p>This archive includes data and python scripts to create the figures in</p> <p>Duetsch, M., Blossey, P. N., Steig, E. J., and Nusbaumer, J. M. (2019). Non-equilibrium fractionation during ice cloud formation in iCAM5: evaluating the common parameterization of supersaturation as a linear function of temperature. Submitted to J. Adv. Model. Earth Sy.</p> <p><strong>Model</strong></p> <p>Model output data are saved in model.tar.gz</p> <p>For all simulations, monthly averages of the following variables are saved in h0 files (used for Figures 3, 6, 8, 9):<br> T: temperature (Si_real simulations only)<br> PS: surface pressure (Si_real simulations only)<br> U: zonal wind (Si_real simulations only)<br> V: meridional wind (Si_real simulations only)<br> LANDFRAC: fraction of surface area covered by land<br> PRECT_H2O: total precipitation rate for H2O<br> PRECT_HDO: total precipitation rate for HDO<br> PRECT_H218O: total precipitation rate for H218O<br> H2OV: H2O mixing ratio for vapor<br> HDOV: HDO mixing ratio for vapor<br> H218OV: H218O mixing ratio for vapor<br> H2OI: H2O mixing ratio for cloud ice<br> HDOI: HDO mixing ratio for cloud ice<br> H218OI: H218O mixing ratio for cloud ice<br> H2OL: H2O mixing ratio for cloud liquid<br> HDOL: HDO mixing ratio for cloud liquid<br> H218OL: H218O mixing ratio for cloud liquid</p> <p>For the control and Si_real microphysical sensitivity simulations, 6-hourly averages of the following variables are saved in h1 files (used for Figures 4, 5, 7):<br> PRECL_H2O: Large-scale precipitation rate for H2O (Si_real simulations only)<br> PRECL_HDO: Large-scale precipitation rate for HDO<br> PRECL_H218O: Large-scale precipitation rate for H218O<br> PRECL_SAT: Large-scale precipitation rate for Si tracer (Si_real simulations only)<br> PRECL_TMP: Large-scale precipitation rate for T tracer (Si_real simulations only)<br> PRECL_RVD: Large-scale precipitation rate for R^D tracer<br> PRECL_RVO: Large-scale precipitation rate for R^18O tracer</p> <p>To limit the size of the data set, only the first 5 years of the simulations are saved.</p> <p><strong>Measurements</strong></p> <p>Ice core measurements for present-day climate and last glacial maximum are saved in icecores_PD.txt and icecores_LGM.txt, respectively.</p> <p>LGM values are averaged from 19000 BCE to 16000 BCE, PD values are averaged from 1000 BCE to 2000 CE.</p> <p>Antarctic surface snow measurements from Masson-Delmotte et al. (2008) are available at https://doi.org/10.1594/PANGAEA.681697</p> <p><strong>Python scripts</strong></p> <p>Scripts run with python 3.6</p> <p>Required packages:<br> - numpy<br> - matplotlib<br> - netCDF4<br> - datetime<br> - Basemap<br> - copy<br> - intergrid<br> - cmocean</p>
FIGURE 6 in Fish die-off in river and reservoir: A review on anoxia and gas supersaturation
FIGURE 6 | (A) Number of fish mortality events at different sites, considering the cause of death and (B) Number of fish mortality events at different sites, by cause of death attributed considering the Brazilian hydrographic basins. PHB = Parnaiba, WNA = Western Northeast Atlantic, PY = Paraguay, UY = Uruguay, EA = East Atlantic, SEA = Southeast Atlantic, TO-A = Tocantins-Araguaia Basin, SF = San Francisco, ENA = Eastern Northeast Atlantic, SA = Southeast Atlantic, AM = Amazon, PR = Paraná. See codes for causes of death in Tab. 1.
FIGURE 3 in Fish die-off in river and reservoir: A review on anoxia and gas supersaturation
FIGURE 3 | Saturations of dissolved oxygen (DO, red) and total dissolved gases (TDG, blue), for periods with intense (left) and sporadic (right) dam spillway activities. The black dotted line indicates the threshold of 110% TDG saturation.
FIGURE 5 in Fish die-off in river and reservoir: A review on anoxia and gas supersaturation
FIGURE 5 | Fish mortality events between 2010 and 2020 (A) and in different Brazilian hydrographic basins (B), as reported by news (Google searches). PHB = Parnaiba, WNA = Western Northeast Atlantic, PY = Paraguay, UY = Uruguay, EA = East Atlantic, SEA = Southeast Atlantic, TO-A = Tocantins-Araguaia Basin, SF = San Francisco, ENA = Eastern Northeast Atlantic, SA = Southeast Atlantic, AM = Amazon, PR = Paraná.
FIGURE 2 in Fish die-off in river and reservoir: A review on anoxia and gas supersaturation
FIGURE 2 | Indication of gas bubble disease in fish downstream from spillways, with bubbles in integument (A), eyeball (B), exophthalmos (A, B), along with the rays of the pectoral (C, D) and dorsal fins (E) and in the gill filament (F).
FIGURE 4 in Fish die-off in river and reservoir: A review on anoxia and gas supersaturation
FIGURE 4 | TDG and DO saturations for periods with intense (red) and sporadic (blue) dam spillway activities. Four models were fitted to these data, all with TDG as a response and DO as a predictor. The models were: i) simple linear regression (Linear); ii) linear regression with quadratic effect of DO (Quadratic); iii) segmented linear regression, with 1 breakpoint and 2 segments (Segmented (1)); iv) segmented linear regression, with 2 breakpoints and 3 segments (Segmented (2)). The most parsimonious model with the lowest AIC was segmented regression (2).
FIGURE 1 in Fish die-off in river and reservoir: A review on anoxia and gas supersaturation
FIGURE 1 | Processes involved in gas supersaturation at downstream a spillway. PTG = Total gas pressure, PC = Compensation Pressure (barometric + hydrostatic) – (modified from Abernethy et al., 2001).
Data from: Supersaturation and critical size of cloud condensation nuclei in marine stratus clouds
<p>Observations of marine stratus clouds in clean air off the Californian coast reveal a functional relationship between the number of cloud condensation nuclei (CCN) and supersaturation. Satellite-derived liquid droplet density estimates the number density of CCN. Combining the estimated supersaturation using Kohler theory, global maps of supersaturation and the critical activation size of CCN are estimated. Here, we show that high supersaturation >0.5% persists over the oceans with a critical CCN size of 25-30 nm, which is smaller than the conventional wisdom of 60 nm. Independent support for such high supersaturation in the marine cloud is obtained from CCN measurements provided by the "Atmospheric Tomography Mission". Higher supersaturation implies smaller activation size for CCN making cloud formation more sensitive to changes in aerosol nucleation.</p>
Data for "Kinetic limitations affect cloud condensation nuclei activity measurements under low supersaturation"
<p>Data for the manuscript "Kinetic limitations affect cloud condensation nuclei activity measurements under low supersaturation" by Tao et al.</p>
Supersaturated Calcium Phosphate Rinse in Preventing Oral Mucositis in Young Patients Undergoing Autologous or Donor Stem Cell Transplant
ClinicalTrials.gov study NCT01305200. IPD Sharing: Not stated. Countries: 3. Publications: 1.
Data from: Oxygen supersaturation could protect reef-building corals against acute thermal stress
Open the record for dataset details and reuse information.
Data from: Supersaturation and critical size of cloud condensation nuclei in marine stratus clouds
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Dataset for Supersaturation in the Wake of a Precipitating Hydrometeor and its Impact on Aerosol Activation
<p>******************Dataset Details******************</p> <p>1. Article Title: Supersaturation in the Wake of a Precipitating Hydrometeor and its Impact on Aerosol Activation<br> 2. Journal: Geophysical Research Letters, 47(22), e2020GL091179, <a href="https://doi.org/10.1029/2020GL091179">https://doi.org/10.1029/2020GL091179</a>, 2020<br> 3. Name and contact information:<br> Eberhard Bodenschatz,<br> Laboratory for Fluid Physics, Pattern Formation and Biocomplexity,<br> Max Planck Institute for Dynamics and Self-Organization,<br> Am Faßberg 17,<br> 37077 Göttingen, Germany<br> 4. Email: eberhard.bodenschatz@ds.mpg.de</p> <p>5. Funding: This research was funded by the Marie - Sk lodowska Curie Actions (MSCA) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 675675), and an extension to programme COMPLETE by Department of Applied Science and Technology, Politecnico di Torino.</p> <p>6. Abstract: The activation of aerosols impacts the life cycle of a cloud. A detailed understanding is necessary for reliable climate prediction. Recent laboratory experiments demonstrate that aerosols can be activated in the wake of precipitating hydrometeors. However, many quantitative aspects of this wake‐induced activation of aerosols remain unclear. Here, we report a detailed numerical investigation of the activation potential of wake‐induced supersaturation. By Lagrangian tracking of aerosols, we show that a significant fraction of aerosols are activated in the supersaturated wake. These “lucky aerosols” are entrained in the wake's vortices and reside in the supersaturated environment sufficiently long to be activated. Our analyses show that this wake‐induced activation of aerosols can contribute to the life cycle of the clouds.</p> <p>******************Dataset Organization******************</p> <p>1. Datasets are organized in folders which correspond to Figures in the Letter or in the Supporting Information (e.g., Figure 1 or Figure S2).<br> 2. Details of the entries can be found in the header of each dataset.<br> 3. Details of the simulation setup can be found in the text of each Figure.</p> <p>******************Software Details******************</p> <p>1. Open-source LBM library Palabos (Latt et al., 2020). Link: https://palabos.unige.ch/<br> 2. Data visualization is achieved using open-source library ParaView version 5.7 and Gnuplot Version 5.2.</p>
Data for the publication: Effects of turbulence on upper tropospheric ice supersaturation
<p>This repository contains the data sets shown in the above research study to appear in the Journal of Atmospheric Sciences. The Grace plot files contain ASCII data shown in figures 2-5 of that publication.</p>
Theoretical Framework for Measuring Cloud Effective Supersaturation Fluctuations using an Advanced Optical System
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Datasets for 'High Arctic aerosol hygroscopicity at sub- and supersaturated conditions during spring and summer'
<p>For a description of the datasets, please see the readme file included in the zip folder</p>
Data for the publication: Ice supersaturation variability in cirrus clouds: Role of vertical wind speeds and deposition coefficients
<p>The files contain the datasets shown in the publication "Ice supersaturation variability in cirrus clouds: Role of vertical wind speeds and deposition coefficients" to appear in J. Geophys. Res. Atmos. (revised manuscript submitted). The files are xmgrace plot files containing the research data (ASCII) shown in all figures in the main text and Appendix A.</p>
Indapamide and Chlorthalidone to Reduce Urine Supersaturation for Kidney Stone Prevention
ClinicalTrials.gov study NCT06111885. IPD Sharing: NO. Countries: 1. Publications: 1.
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