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16 results for “Cold Pools”
Seefeld Cold-Air Pool Experiment (SEECAP): WRF Simulation Output without snow cover January 16 2020 0000 UTC to January 17 2020 1200 UTC
<p>The Seefeld Cold-Air Pool Experiment (SEECAP) focused on the cross-country skiing area Olympiaregion Seefeld and in particular the topographic setting in the Nordic ski arena which favors the formation of cold-air pools and took place between December 2019 and March 2020. The measurement data are described in Rudolph (2022) and Rauchöcker et al. (2024d) and meteorological measurement data associated with SEECAP are published in Rauchöcker et al. (2024c). This upload contains WRF simulation output data for the night between January 16 and January 17 2020 without snow cover and the plotting routines to reproduce the figures in Rauchöcker et al. (2024d). The night between January 16 and January 17 2020 initially featured an ideal cold-air pool formation followed by a interuption by a wind disturbance around midnight. Simulation output for the same night, but with snow cover is also available (Rauchöcker et al., 2024a). The temperature evolution of the measurements agreed much better with the simulation with snow cover and otherwise the same model setting compared to the simulation without snow cover (Rauchöcker et al. 2024d). Also available in a different dataset is output from a simulation with snow cover for the night between January 12 and January 13 2020 (Rauchöcker et al., 2024b), which featured an undisturbed cold-air pool for almost the entire night. This case was considered to feature in Rauchöcker et al. (2024d), but a different case was chosen because some measurement data was not available during this period.</p> <h3><strong>WRF Simulation Output</strong></h3> <p>This Dataset includes data generated with WRFlux v1.4.1 (Göbel et al., 2022), a fork of the Weather Research and Forecasting model WRF (Skamarock et al. 2021). WRFlux allows to calculate the contribution of different processes to the potential temperature tendency at each grid point. The data published here is from the innermost simulation domain with 40m horizontal resolution and 10m vertical resolution close to the surface. The simulations were initialized at 00:00 UTC January 16 2020 and run until 12:00 UTC January 17 2020.</p> <p>Three different simulations were performed: two simulations with modified snow cover as described in Rauchöcker (2022), one each with the MYNN 2.5-order and the SMS-3DTKE PBL parameterizations (a scheme that blends a PBL scheme and a LES subgrid parameteriztion in the greyzone of turbulence), and one without snow cover with the MYNN 2.5-order PBL parameterization. Otherwise the simulations were identical. This dataset includes the simulation without snow cover. A detailed description of the model setup can be found in Rauchöcker et al (2024d) and in the file <em>namelist.input</em> that was used to generate the simulation results.</p> <p>Standard WRF output can be found in <em>wrfout_40m_jan16_nosnow</em>. The mean wind speed components, which were necessary to rotate the tendencies in a coordinate system that is aligned with the valley orientation, are contained in <em>windout_40m_jan16_nosnow</em>. These variables were contained in the unprocessed<em> </em>output files produced by WRFlux; the full files were unfortunately too large to be included here. The postprocessed tendencies are stored in <em>tend_40m_jan16_nosnow.nc</em>.</p>
Seefeld Cold-Air Pool Experiment (SEECAP): WRF Simulation Output with snow cover January 12 2020 0000 UTC to January 13 2020 1200 UTC
<p>The Seefeld Cold-Air Pool Experiment (SEECAP) focused on the cross-country skiing area Olympiaregion Seefeld and in particular the topographic setting in the Nordic ski arena which favors the formation of cold-air pools and took place between December 2019 and March 2020. The measurement data are described in Rudolph (2022) and Rauchöcker et al. (2024d) and meteorological measurement data associated with SEECAP are published in Rauchöcker et al. (2024c). This upload contains WRF simulation output data for the night between January 12 and January 13 2020 with snow cover. The night between January 12 and January 13 2020 featured an undisturbed cold-air pool for almost the entire night. This case was considered to feature in Rauchöcker et al. (2024d), but a different case was chosen because some measurement data was not available during this period. Also available in a different dataset are data from simulations of the night between January 16 and January 17 2020, which initially featured ideal condition for cold-air pool formation followed by a disturbance around midnight, with snow cover (Rauchöcker et al., 2024a) and also without snow cover (Rauchöcker et al., 2024b).</p> <h3><strong>WRF Simulation Output</strong></h3> <h3><strong> </strong></h3> <p>This Dataset includes data generated with WRFlux v1.4.1 (Göbel et al., 2022), a fork of the Weather Research and Forecasting model WRF (Skamarock et al. 2021). WRFlux allows to calculate the contribution of different processes to the potential temperature tendency at each grid point. The data published here is from the innermost simulation domain with 40m horizontal resolution and 10m vertical resolution close to the surface. The simulations were initialized at 00:00 UTC January 12 2020 and run until 12:00 UTC January 13 2020 and results for the same night but a coarser grid spacing are described in Rauchöcker (2022). Compared to the simulation with 200m grid spacing presented there, this simulation offers a significantly improved resolution. As input, we used ERA5 reanalysis data, 1-arc second SRTM terrain data and Corine 2018 land cover classification. The simulation was performed with modified snow cover as described in Rauchöcker (2022) and the MYNN 2.5-order PBL parameterization. A detailed description of the model setup can be found in Rauchöcker et al (2024d) and in the file <em>namelist.input</em> that was used to generate the simulation results.</p> <p>Standard WRF output can be found in <em>wrfout_40m_jan12</em>. The mean wind speed components, which were necessary to rotate the tendencies in a coordinate system that is aligned with the valley orientation, are contained in <em>windout_40m_jan12</em>. These variables were contained in the unprocessed output files produced by WRFlux; the full files were unfortunately too large to be included here. The postprocessed tendencies are stored in <em>tend_40m_jan12</em>.</p>
Fiber-optic Distributed Temperature Sensing and Wind Profiler Data during the Shallow Cold Pool Experiment
<p>The <a href="https://www.eol.ucar.edu/field_projects/scp">Shallow Cold Pool (SCP) experiment</a> was an <a href="https://www.eol.ucar.edu/observing_facilities/isfs">Integrated Surface Flux System (ISFS)</a> deployment conducted by the <a href="https://ncar.ucar.edu/">National Center for Atmospheric Research (NCAR)</a>, the <a href="https://ceoas.oregonstate.edu/">College of Earth, Ocean and Atmospheres (CEOAS)</a>, the <a href="https://bee.oregonstate.edu/">Department of Biological & Ecological Engineering (BEE)</a>, and the <a href="https://ctemps.org/">Center for Transformative Environmental Monitoring Programs (CTEMPS)</a> of <a href="https://oregonstate.edu/">Oregon State University</a>, in a shallow gully within the Pawnee Grasslands, Coloradp, USA. The primary goal of SCP was to examine the formation and maintenance of common shallow cold pools. These cold pools had not been previously examined with turbulence measurements and very little was known about their dynamics and interaction with gravity waves and other submesoscale motions.</p> <p>SCP consisted of a dense network of ultrasonic anemometers with 19 units being installed at 1m above ground level (agl) and 8 being mounted at different heights on a 20m high tower. In addition, air temperature, humidity, and carbon dioxide concentrations measurements were taken. This data can be found on <a href="https://data.eol.ucar.edu/project/SCP">https://data.eol.ucar.edu/project/SCP</a>.</p> <p>The unique observational technique featured in SCP was a cross-valley transect of the innovative active and passive fiber-optic distributed sensing technique (FODS) using a Distributed Temperature Sensing (DTS) unit (Model Ultima SR, Silixa, London, UK) as well as a ground-based acoustic wind profiler (SODAR, PCS2000-24, Metek GmbH, Elmshorn, Germany) in addition to the classical sonic anemometer network. The data archived in this submission publishes the FODS data and contains data for nine (9) nights between 16th November until 27th November between the hours of 19:00 and 05:00 MST (Local time). Details of the FODS setup are contained in <a href="https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3508?af=R">Pfister et al. (2019)</a> and <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2015GL066729">Sayde et al. (2015</a>).<br> The fiber-optic cross-valley transect was 240m long and stretched from the North to the South shoulder of the gully and contained FODS observations at three heights (0.5m, 1m, 2m agl). By combining passive and active FODS, air temperatures and wind speeds were measured spatially continuously with a temporal and spatial resolution of 5s and 25cm, respectively. Air temperatures were measured with an unheated white-PVC jacketed optical glass fiber cable with an outer diameter of 0.9mm, while for the wind speed measurements an additional actively heated stainless-steel uncoated fiber-optic cable (1.3mm outer diameter) was deployed. Wind speeds were derived from the difference between the heated and unheated fiber-optic pair similar to a hotwire anemometer (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2015GL066729">Sayde et al. 2015</a>).<br> The acoustic wind profiler (Sound Detection and Ranging, SODAR) was installed at the gully bottom about 200m down the gully from the fiber-optic transect (between station A18 and A19) and measured with a 5-min resolution, a 10-m gate range, and 17000 Hz, see map in <a href="https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3508?af=R">Pfister et al. (2019)</a>. The observational range was between 10m to 320m agl. The data provided is the cluster data output of the wind profiler, which is quality-controlled by the internal data processing software. The published data include horizontal wind speed (speed), wind direction (direction), unrotated along-wind component (u_unrot), unrotated cross-wind component (v_unrot), and unrotated vertical-wind component (w_unrot).</p> <p>By combining the fiber-optic distributed sensing, the sensor network, and the wind profiler, we were able to investigate specific class of submeso-scale motions in detail. The submeso-scale motion occurred frequently during SCP, significantly impacted air temperature, wind speed and direction, as well as the near-surface turbulence within less than a few minutes. These motions are not described or categorized by existing boundary layer regimes or concepts. Consequently, further research on submeso-scale motions using continuous FODS measurements is necessary to better understand the stable boundary layer.</p> <p> </p> <p>Pfister, L., Sayde, C., Selker, J., Mahrt, L., & Thomas, C. K. (2019). Classifying the Nocturnal Atmospheric Boundary Layer into Temperature and Flow Regimes. <em>Quart. J. Roy. Meteorol. Soc.</em>, <em>145</em>(721), 1515–1534. <a href="https://doi.org/10.1002/qj.3508">https://doi.org/10.1002/qj.3508</a></p> <p> </p> <p>Sayde, C., Thomas, C. K., Wagner, J., & Selker, J. S. (2015). High-resolution wind speed measurements using actively heated fiber optics. <em>Geophys. Res. Lett.</em>, <em>42</em>(22), 10,064–10,073. <a href="https://doi.org/10.1002/2015GL066729">https://doi.org/10.1002/2015GL066729</a></p>
Seefeld Cold-Air Pool Experiment (SEECAP): WRF Simulation Output with snow cover January 16 2020 0000 UTC to January 17 2020 1200 UTC
<p>The Seefeld Cold-Air Pool Experiment (SEECAP) focused on the cross-country skiing area Olympiaregion Seefeld and in particular the topographic setting in the Nordic ski arena which favors the formation of cold-air pools and took place between December 2019 and March 2020. The measurement data are described in Rudolph (2022) and Rauchöcker et al. (2024d) and meteorological measurement data associated with SEECAP are published in Rauchöcker et al. (2024c). This upload contains WRF simulation output data for the night between January 16 and January 17 2020 with snow cover and the plotting routines to reproduce the figures in Rauchöcker et al. (2024d). The night between January 16 and January 17 2020 initially featured ideal condition for cold-air pool formation followed by a disturbance around midnight. There is also an upload with simulation output for the same night, but without snow cover (Rauchöcker et al., 2024a). Also available in a different dataset are data from a simulation with snow cover for the night between January 12 and January 13 2020 (Rauchöcker et al., 2024b), which featured an undisturbed cold-air pool for almost the entire night. This case was considered to feature in Rauchöcker et al. (2024d), but a different case was chosen because some measurement data was not available during this period.</p> <h3><strong>WRF Simulation Output</strong></h3> <p>This Dataset includes data generated with WRFlux v1.4.1 (Göbel et al., 2022), a fork of the Weather Research and Forecasting model WRF (Skamarock et al. 2021). WRFlux allows to calculate the contribution of different processes to the potential temperature tendency at each grid point. The data published here is from the innermost simulation domain with 40m horizontal resolution and 10m vertical resolution close to the surface. The simulations were initialized at 00:00 UTC January 16 2020 and run until 12:00 UTC January 17 2020.</p> <p>Three different simulations were performed: two simulations with modified snow cover as described in Rauchöcker (2022), one each with the MYNN 2.5-order and the SMS-3DTKE PBL parameterizations (a scheme that blends a PBL scheme and a LES subgrid parameteriztion in the greyzone of turbulence), and one without snow cover with the MYNN 2.5-order PBL parameterization. Otherwise the simulations were identical. This dataset includes the two simulation with snow cover, where <em>jan126_sms.zip</em> contains the files relating to the simulations with the SMS-3DTKE scheme and <em>jan16.zip</em> those for the simulation with the MYNN 2.5-order PBL parameterization. A detailed description of the model setup can be found in Rauchöcker et al (2024d) and in the files <em>namelist.input</em> and <em>namelist_sms.input</em> that were used for the simulations. </p> <p>Standard WRF output can be found in <em>wrfout_40m_jan16</em> and <em>wrfout_40m_jan16_sms</em>. The mean wind speed components, which were necessary to rotate the tendencies in a coordinate system that is aligned with the valley orientation, are contained in <em>windout_40m_jan16</em> and <em>windout_40m_jan16_sms</em>. These variables were contained in the unprocessed output files produced by WRFlux; the full files were unfortunately too large to be included here. The postprocessed tendencies are stored in <em>tend_40m_jan16.nc</em> and <em>tend_40m_jan16_sms.nc</em>.</p> <h3><strong>Plotting routines</strong></h3> <p>Python scripts and environment files to reproduce most figures in Rauchoecker et al. (2024d) are included in <em>code.zip</em>. To reproduce plots involving measurement data, which is available in Rauchöcker et al. (2024c), is also needed.</p> <p>Due to conflicts between some packages, two different environment were needed. To reproduce Figure 2, install the <em>orthoplot</em> environment by running "<em>conda env create orthoplot.yml</em>" in a terminal window, activate it ("<em>conda activate orthoplot</em>") and then run <em>ortho_plot.py</em>. All other plots require the wrfstuff environent (installed by running "<em>conda env create wrfstuff.yml</em>" and activated by "<em>conda activate wrfstuff</em>") and are produced by <em>paper_plots.py</em>. Functions used to load data and plot the figures are included in <em>dataload.py</em> and <em>plotting_routines.py</em>, respectively<em>.</em></p> <p>Two variables decide which figures are plotted for which dataset: <em>dataname</em> and <em>doplot</em>. The variable <em>dataname</em> defines the path to the <em>dataset</em> that should be used to produce the figures, while the value <em>doplot</em> defines which figure to reproduce. By setting doplot="fig1", Figure 1 is reproduced, while doplot="fig7" and doplot="fig9" reproduce Figures 7 and 9, respectively. For all other values for <em>doplot</em>, Figures 4, 5, 6, 8 and 11 are reproduced. We decided not to include a script to plot Figure 3 because the data the climatology is based on is owned by GeoSphere Austria - the agency operating the permanent weather station. Further, no script for reproducing Figure 10 is included because it was not created within the framework of Python.</p> <h3><strong>Geofiles</strong></h3> <p>The files included in <em>g</em><em>eofiles.zip</em> are needed to plot Figure 1 and Figure 2, although not of particular interest on their own. Included are output files from <em>geogrid.exe</em>, whicih are necessary to plot the domains overview (Figure 1), as well as an orthophoto (<em>orthophoto.tif</em>) and high-resolution digital elevation model (<em>topo_hr.tif</em>) which are both based on data from Land Tirol.</p>
Dataset for the "a parameterization for cloud organization and propagation by evaporation-driven cold pools edges"
<p>When the negatively buoyant air in the cloud downdrafts reaches the surface, it spreads out horizontally, producing cold pools. A cold pool can trigger new convective cells. However, when combined with the ambient vertical wind shear, it can also connect and upscale them into large mesoscale convective systems (MCS). Given the broad spectrum of scales of the atmospheric phenomenon involving the interaction between cold pools and the MCS, a parameterization was designed here. Then, it is coupled with a classical convection parameterization to be applied in an atmospheric model with an insufficient spatial resolution to explicitly resolve convection and the sub-cloud layer. A new scalar quantity related to the deficit of moist static energy detrained by the downdrafts mass flux is proposed. This quantity is subject to grid-scale advection, mixing, and a sink term representing dissipation processes. The model is then applied to simulate moist convection development over a large portion of tropical land in the Amazon Basin in a wet and dry-to-wet 10-days period. Our results show that the cold pool edge parameterization improves the organization, longevity, propagation, and severity of simulated MCS over the Amazon and other different continental areas.</p><p> </p>
Cold-air pooling characterization and forest composition, New England, USA
This dataset corresponds to a project investigating whether cold-air pooling influences forest composition and function. The data include hourly sub-canopy air temperatures (measured continuously via ibuttons) and forest forest composition data for 48 plots along 9 transects in 3 sites across New England, USA. The temperature data also include surface lapse rates and temperature gradients across transects, as well as a designation indicating the presence or absence of a temperature inversion. We found that sites with the most frequent temperature inversions also displayed vegetation inversions across slopes, with more cold-adapted species at low instead of high elevations.
Seefeld Cold-Air Pool Experiment (SEECAP): Meteorological Measurement Data
<p>The Seefeld Cold-Air Pool Experiment (SEECAP) focused on the cross-country skiing area Olympiaregion Seefeld and in particular the topographic setting in the Nordic ski arena which favors the formation of cold-air pools and took place between December 2019 and March 2020. Six automatic weather stations and 41 unventilated temperature sensors were distributed within the valley to gain insight into the spatial structure of the cold-air pool in Seefeld. The study site as well as locations and instrumentation of each station are described in Rudolph (2022) and Rauchöcker et al. (2024d). This upload contains meteorological measurement data associated with SEECAP. WRF simulations were performed for two nights, representing an ideal evolution of the cold-air pool (January 12 and January 13 2020) and a disrupted evolution (January 16 and January 17 2020), respectively. The output data of simulations with snow cover for the night between January 16 and January 17 2020 are published in Rauchöcker et al. (2024a) and in Rauchöcker et al. (2024c) for the night between January 12 and January 13 2020. Simulation output without snow cover is available for the night between January 16 and January 17 2020 in Rauchöcker et al. (2024b).</p> <h3><strong>Automatic Weather Stations</strong></h3> <p>Measurement data of the six automatic weather stations can be found in <em>momaa.zip</em>. The folder includes one file for each station (<em>MOMAA02.dat, MOMAA03.dat, MOMAA04.dat, MOMAA07.dat, MOMAA08.dat</em> and <em>MOMAA10.dat</em>). These stations measured temperature, pressure, humidity, net radiation, wind speed and wind direction at 1-min intervals. A figure showing the location of the different stations is included as well (<em>Stations.pdf</em>); the station names of the automatic weather stations are abreviated in the legend of that figure (e.g. M04 instead of MOMAA04). Incoming and outgoing longwave and shortwave radiation, latent heat flux and sensible heat flux were measured at MOMAA04 and MOMAA08. The radiation data can be found in <em>MOMAA04_rad.dat</em> and <em>MOMAA08_rad.dat</em> and eddy covariance data in <em>MOMAA04_turb.csv</em> and <em>MOMAA08_turb.csv</em>, respectively.</p> <h3><strong>Temperature Sensors</strong></h3> <p>Data from the unventilated temperature sensors can be found in <em>hobos.zip</em>, which contains a file for each sensor and the file names refer to the naming convention in <em>Stations.pdf</em>. Most sensors were located along the valley floor and along a ski jump on its southeastern slope. At nine locations, temperature sensors were mounted at two heights (1 m and 2 m above the ground). File names reflect that height by adding <em>_1m</em> or <em>_2m</em> to the file name (e.g. <em>A_1m.txt</em> and <em>A_2m</em>.txt). Locations that had only one sensor were named according to the station name (e.g. <em>M.txt</em>). The remaining sensors were used for vertical profiles at 3 different levels of a walk-up tower (<em>TOWER_2m.txt, TOWER_2ndfloor.txt</em> and <em>TOWER_top.txt</em>) and at a bridge (VP; labeled from <em>VP_050.txt</em> at 0.5m above the ground to <em>VP_630.txt</em> at 6.3m). A pseudo-vertical profile for the sensors along the slope of the valley, at the walk-up tower down to the lowest station in the upper basin (top to bottom, M03, S4, S3, S2, S1, M10, G, H, M04) can be found in <em>PseudoProfile_basin.mat</em>. </p>
Outputs from new LBA simulations. Part III: Suppressed cold pools
<p>MIMICA model outputs for new LBA simulations. The case, model configuration and output files are all described in the attached TRANSITION_setup.pdf file. This is the third of a three part dataset containing results from simulations in which cold pools have been suppressed (see below for a very quick overview).</p> <p>Three sets of data outputs are available: One corresponding to the original, idealized LBA case, one in which cold pools have been suppressed by nudging temperature and moisture tendencies below cloud base(_nocp extension), and one in which the fixed surface fluxes have been replaced by interactive fluxes (_sst extension). For each set of simulations, five horizontal grid spacings were employed to test the sensitivity of the results to resolution (specified as file name extensions): 1.6 km (64 grid points), 800 m (128 grid points), 400 m (256 grid points), 200 m (512 grid points) and 100 m (1024 grid points). Finally, for each case, a series of various output files are available, including time series of domain averaged quantities (T_S), time series of horizontally averaged quantities (profiles_tot.nc), and two-dimensional horizontal slices extracted at various altitudes (slice_z_XXXX.nc). Note that all files present except T_S follow the NetCDF standard.</p>
Properties of cold pools from PERiLS 2022-2023
Open the record for dataset details and reuse information.
Hubbard Brook Experimental Forest: Nocturnal Cold-Air Pool Study, 2015
This dataset presents numerous observations from a tethered balloon taken around HBEF on November 4-5, 2015. All measurements (unless specified otherwise) were taken at 30 second intervals. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Mean Sea Level Pressure - Hurricane Beryl Analysis with and without a cold pool scheme
<p>This video compares the cold pool scheme, the control scheme, and ERA5 data. For more information, please contact the author.</p>
Selected HRRRv4 model output and plotting scripts for 'Evaluation of a cloudy cold-air pool in the Columbia River Basin in different versions of the HRRR model'
<p>This zip file contains selected model output data from HRRRv4 and plotting scripts used for the paper 'Evaluation of a cloudy cold-air pool in the Columbia River Basin in different versions of the HRRR model' which is submitted for publication to the journal Geoscientific Model Development (GMD).</p> <p>The individual files are:</p> <p>d01_d02_lwd_allsurfacestations_v4fp1_v4fp2.nc: longwave downward radiation flux at the locations of surface stations in the investigation area for domains d01 and d02 and runs v4fp1 and v4fp2<br> d01_d02_lwp_Wasco_v4fp1_v4fp2.nc: liquid water path at Wasco for domains d01 and d02 and runs v4fp1 and v4fp2<br> d01_d02_lwp_allsurfacestations_v4fp1_v4fp2.nc: liquid water path at the locations of surface stations in the investigation area for domains d01 and d02 and runs v4fp1 and v4fp2<br> d01_d02_lwp_gridpointsbelow500mMSL_v4fp1_v4fp2.nc: liquid water path at all grid points in the Columbia River Basin with a terrain height of less than 500 m for domains d01 and d02 and runs v4fp1 and v4fp2<br> d01_d02_lwp_numbergridpointsbelow500mMSL_v4fp1_v4fp2.nc: Number of gridpoints in the Columbia River Basin with a terrain height of less than 500 m MSL and a LWP larger than 10 g/m2 for domains d01 and d02 and runs v4fp1 and v4fp2<br> d01_d02_swd_allsurfacestations_v4fp1_v4fp2.nc: shortwave downward radiation flux at the locations of surface stations in the investigation area for domains d01 and d02 and runs v4fp1 and v4fp2<br> d01_d02_t2m_allsurfacestations_v4fp1_v4fp2.nc: 2-m temperature at the locations of surface stations in the investigation area for domains d01 and d02 and runs v4fp1 and v4fp2<br> d01_d02_windspeed_7sites_v4fp1_v4fp2.nc: Wind speed profiles at 7 sites in the investigation area for domains d01 and d02 and runs v4fp1 and v4fp2<br> d01_temperatureprofiles_Wasco_v4fp1.nc: temperature profiles at Wasco for domain d01 and run v4fp1<br> d01_temperatureprofiles_Wasco_v4fp2.nc: temperature profiles at Wasco for domain d01 and run v4fp2<br> d02_swd_spatial_v4fp1.nc: spatial distribution of surface shortwave downward radiation for domain d02 and run v4fp1<br> d02_temperatureprofiles_Wasco_v4fp1.nc: temperature profiles at Wasco for domain d02 and run v4fp1<br> d02_temperatureprofiles_Wasco_v4fp2.nc: temperature profiles at Wasco for domain d02 and run v4fp2<br> d02_terrain.nc: terrain height for domain d02<br> plot_miscs_hrrr_longforecasts.py: master script for plotting<br> plot_routines_wfip2_lf.py: plot routines for various type of plots<br> read_routines_wfip2_lf.py: read routines for all kinds of data<br> sites_locations_wfip2.txt: latitude, longitude and height of used stations<br> basic_functions.py: helper functions for plotting</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>
Datasets and the movie for the manuscript "Cold pools mediate mesoscale adjustments of trade-cumulus fields to changes in cloud-droplet number concentration"
<p>This file contains the basic profiles and time series of cloud-field properties, cloud organization metrics, and cold-pool properties associated with the project titled "Cold pools mediate mesoscale adjustments of trade-cumulus fields to changes in cloud-droplet number concentration".</p> <p>The numbers of simulations 104, 105, 1, 106, 107, and 108 are associated with the cloud-droplet number concentrations Nc of 20, 50, 70, 100, 200, and 1000 /cm3, respectively.</p> <p>The character "-cnstsun" at the end of a file name is related to simulations without the diurnal cycle of solar radiation.</p> <p>The movie (Nc20_Nc1000.mp4) shows the evolution of cloud albedo (1st column), mixed-layer height hmix (2nd column), total moisture anomaly qt' at the 200-m level (3rd column), and the vertical velocity w at the 200-m level (4th column) for simulations with Nc of 20 (1st row) and 1000 (2nd row) /cm3 and without the the diurnal cycle. </p>
ICON-LES for FESSTVaL: Jogi cold pool 2021-06-29
<p>Dataset used in</p> <p>How Variable are Cold Pools? </p> <p>Leah D. Grant, Bastian Kirsch, Jennie Bukowski, Nicholas M. Falk, Christine A. Neumaier, Mirjana Sakradzija Susan C. van den Heever, and Felix Ament</p> <p>submitted to Geophysical Research Letters</p> <p> </p>
Cold pool collisions
<p>This dataset contains data used in a publication regarding cold pool collisions.</p>
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