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143 results for “Boundary layer”
Quantifying the Impact of Vertical Resolution on the Representation of Marine Boundary Layer Physics for Global-Scale Models
<p>Data supporting the findings of DOI: 10.1175/MWR-D-23-0078.1, a 2023 Monthly Weather Review publication with the same title as this dataset.</p>
Data & code for 'BoundaryLayerDynamics.jl v1.0: a modern codebase for atmospheric boundary-layer simulations'
<p>Code and data required to reproduce the manuscript ‘BoundaryLayerDynamics.jl v1.0: a modern codebase for atmospheric boundary-layer simulations’, submitted for publication in Geoscientific Model Development.</p>
Data from: Physical mechanisms of deep convective boundary layer leading to dust emission in the Taklimakan desert
<p>Deserts play an important role in the climate system, which is closely associated with the emission and transport of dust aerosols. Based on the intensive observation experiment in the Taklimakan Desert, the potential physical processes between the deep convective boundary layer (CBL) and dust emission are revealed in this study. Deep CBL enables the formation of clouds in the late afternoon, leading to significant cooling of surface. Large-scale buoyant coherent structures thereby transform into the mechanical coherent structures confined near the surface. The responses promote the earlier occurrence of low-level jet (LLJ) than in cloudless conditions, which allows the downward transport of LLJ momentum and substantially increases surface wind. Therefore, dust emission is initiated by strong wind at dusk and lasts for several hours. The results are useful to predict dust emissions and improve our understanding of distinctive boundary-layer processes in desert regions.</p>
Archived Model Output and Code for "Marine Boundary Layer Cloud Condensation Nuclei Bias over the Southern Ocean: Comparisons between the Community Atmosphere Model 6 and Field Observations "
<div> <p>This is an archive of CAM6 simulation output used in the paper Marine Boundary Layer Cloud Condensation Nuclei Bias over the Southern Ocean: Comparisons between the Community Atmosphere Model 6 and Field Observations, submitted to the AGU Journal. Codes used to read the nc file is also attached.</p> </div>
Supporting data for boundary layer water vapour statistics from high-spatial-resolution spaceborne imaging spectroscopy
<p>This dataset includes the properties necessary to reproduce the analysis of water vapour statistics derived from imaging spectroscopy as in:</p> <p>Richardson et al. (2021a) DOI: 10.5194/amt-14-5555-2021<br> Richardson et al. (2021b) DOI: 10.5194/amt-2021-163 (pre-acceptance DOI, follow links to published version)</p> <p>Files include the retrieval emulator parameters, atmospheric profiles used in the emulator development, column-mean water vapour and cloud water both for the total column water vapour (TCWV) and "effective" TCWV, which accounts for the water vapour integrated along the direct solar path at a range of solar zenith angles, see Richardson 2021b, Eq. (7).</p>
Dynamics in a stellar convective layer and at its boundary: Comparison of five 3D hydrodynamics codes
<p>Supplementary materials for the paper "Dynamics in a stellar convective layer and at its boundary: Comparison of five 3D hydrodynamics codes". The data contained in the .tar.gz archives can be read and visualised using the Jupyter notebooks available on the CoCoPy repository (<a href="https://github.com/robert-andrassy/CoCoPy">https://github.com/robert-andrassy/CoCoPy</a>) and on the CoCo Hub (<a href="https://www.ppmstar.org/coco">https://www.ppmstar.org/coco</a>). The four parts of the archive 2D-slices.tar.gz need to be concatenated using the standard Unix tool "cat" before decompression.</p> <p>Two minor bugs affecting the 1D profiles are corrected in this version. Everything else is the same as in Version 1.</p>
Dataset: Large-eddy simulation of the ice shelf-ocean boundary layer model output
<p>This repository contains large-eddy simulation output from the CFD model <em>Diablo</em>. The simulations are of the boundary layer beneath a melting ice shelf. This model output underpins the submitted manuscript <em>Regimes and transitions in the basal melting of Antarctic ice shelves</em> submitted to the <em>Journal of Physical Oceanography</em> (December 2021).</p>
Variations of Subgrid-scale Turbulent Fluxes in the Convective Boundary Layer at Gray Zone Resolutions
<p>This dataset contains the data used in the submitted manuscript of Liu and Zhou 2022 JAS. Please refer to the manuscript for the detailed description of the dataset.</p>
Data of the Air-Sea Momentum Flux of the Coastal Marine Boundary Layer During Typhoons
<p>Contains data on the disturbance intensity of windspeed, friction velocity, wind speed and wind direction of Typhoon Chanthu, Hato and Koppu.</p>
A seasonal analysis of aerosol NO3- sources and NOx oxidation pathways in the Southern Ocean marine boundary layer
<p>This dataset includes coarse mode atmopsheric nitrate concentration and isotopic composition from the Southern Ocean marine boundary layer in summer (2018/19), winter (2019) and spring (2019). Environmental data (temperature, SLP and relative humidity) are also included.</p>
Planetary Boundary Layer Height Retrievals from Micropulse-lidar at four Multiple ARM Sites Around the World
<p><span>Planetary Boundary Layer Height Retrievals from Micropulse-lidar at the following ARM campaigns: GOAMAZON (MAO), COPS (FKB), CACTI (COR), and BAECC (TMP).</span><span> These retrievals </span><span>were computed</span><span> using the Different Thermo-Dynamic Stabilities (DTDS) algorithm </span><span>(Su et al., 2020; Su et al., 2022)</span><span>. The quality-control flag is provided in the dataset file, where zero (0) indicates a high-quality flag. </span></p> <p>Su, T., Zheng, Y. and Li, Z., 2022. Methodology to determine the coupling of continental clouds with surface and boundary layer height under cloudy conditions from lidar and meteorological data. Atmospheric Chemistry and Physics, 22(2), pp.1453-1466.</p> <p>Su, T., Li, Z. and Kahn, R., 2020. A new method to retrieve the diurnal variability of planetary boundary layer height from lidar under different thermodynamic stability conditions. Remote Sensing of Environment, 237, p.111519.</p> <p><span>Roldán-Henao, N., Su, T., and Li, Z. (2024, under review). Refining Planetary Boundary Layer Height Retrievals from Micropulse-lidar at Multiple ARM Sites Around the World. Submitted to <em><span>Journal of Geophysical Research: Atmospheres. </span></em></span></p>
Simulation results with the EULAG research model for the publication: "Large eddy simulations of the interaction between the Atmospheric Boundary Layer and degrading Arctic permafrost"
<p>Supplementary material for the publication</p> <ul> <li>Mark Schlutow, Tobias Stacke, Tom Doerffel, et al. Large eddy simulations of the interaction between the Atmospheric Boundary Layer and degrading Arctic permafrost. ESS Open Archive . January 24, 2024. <a href="https://doi.org/10.22541/essoar.170612558.81370785/v1">https://doi.org/10.22541/essoar.170612558.81370785/v1</a></li> </ul> <p>The material contains all simulation results and raw outputs that are necessary to reproduce the figures and statistics of the publication. </p>
The unsteady shock boundary layer interaction in a compressor cascade - Part 3: Mechanisms of shock oscillation - Promotional video 2
<p>Second promotional video with animation of mechanisms of shock oscillation for the ASME Turbo Expo 2024 open access publication with identifier GT2024-128197 and title "The unsteady shock boundary layer interaction in a compressor cascade - Part 3: Mechanisms of shock oscillation."</p>
Effects of surface fluxes on the moist potential vorticity distribution in the tropical cyclone boundary layer
<p>Hourly model outputs (t=150-240 hrs) from five axisymmetric simulations of tropical cyclones are provided as follows :</p> <ol> <li>cm1_test40_ver2 (referred to as CONTROL in the manuscript)</li> <li>cm1_test41_ver2 (referred to as H2.0 in the manuscript)</li> <li>cm1_test42_ver2 (referred to as H0.5 in the manuscript)</li> <li>cm1_test43_ver2 (referred to as M2.0 in the manuscript)</li> <li>cm1_test44_ver2 (referred to as M0.5 in the manuscript)</li> </ol> <p>Model outputs from the 3D simulation are interpolated to cylindrical coordinates and saved individually for each variable in binary format, and these are provided for t=150-240 hrs as follows:</p> <ol> <li>cm1_test13_ver2 (referred to as 3D-TC in the manuscript)</li> </ol> <p>Jupyter notebooks are also provided to read and analyze processed outputs from the datasets described above and plot the figures included in the manuscript. The datasets used in "<em>figure_05.ipynb</em>", "<em>figure_06.ipynb</em>", and "<em>figure_07.ipynb</em>" are large and can be made available by the authors upon request.</p>
On the cell broadening of mesoscale cellular convection in a well-mixed boundary layer
Open the record for dataset details and reuse information.
Varying partitioning of surface turbulent fluxes regulates temperature-humidity dissimilarity in the convective atmospheric boundary layer
<p>This dataset contains the data used in the submitted manuscript of Liu, Liu, Huang, and Xiao 2021. Please refer to the manuscript for the detailed description of the dataset.</p>
Storm-scale and fine-scale boundary layer structures of tropical cyclones simulated with the WRF-LES framework
<p>The numerical simulations were carried out on the Tianhe Supercomputer, China. The TCBL data used in this study are uploaded here. Due to the large number, the original simulation data are available on request (liuqy@cma.gov.cn or liguangwu@fudan.edu.cn).</p>
Data accompanying the paper titled: Evaluation of a forest parameterization to improve boundary layer flow simulations over complex terrain.
<p>The present repository contains the namelists, output data from the Weather Research and Forecasting (WRF) numerical simulations and observations described in the article "Evaluation of a forest parameterization to improve boundary layer flow simulations over complex terrain" submitted to the Geosciences Model Development journal.</p> <p>The numerical simulations were developed to evaluate the influence of a forest parametrization on the simulation of the boundary layer flow over moderate complex terrain in the context of the Perdigão 2017 field campaign. The numerical simulations used WRF large eddy simulation mode (WRF-LES). The short-term high resolution (40 m horizontal grid spacing) and long-term (200 m horizontal grid spacing) WRF-LES were run for an integration time of 12 hours and 1.5 months, respectively, with and without forest parameterization. The short-term simulations focus on low-level jet events over the valley, while the long-term simulations cover the whole intensive observation period (IOP) of the field campaign. The results are validated using lidar and meteorological tower observations.</p> <p>The files in the repository are organized as follows:</p> <p>Namelists used to run the WRF model are located in the 'namelists' sub-directory.</p> <p>Data slices of domains d03 and d04 covering the location of the three meteorological towers and LIDAR for the whole integration time are located in the 'reduced_domain' sub-directory.</p> <p>Direct WRF output for selected periods (representing the low-level-jet episodes observed) for domains d03 and d04 are located in the 'files_select_time' sub-directory.</p> <p>The observations used to validate the model are located in the 'observations' sub-directory. The complete dataset of observations from the field campaign can be found in the official campaign data repository: re3data.org: Perdigao Field Experiment; editing status 2020-03-20; re3data.org - Registry of Research Data Repositories. http://doi.org/10.17616/R31NJMN4 last accessed: 2021-10-13. The WRF model can be downloaded from https://www2.mmm.ucar.edu/wrf/users/download/get_sources_new.php.</p>
Late summer transition from a free-tropospheric to boundary layer source of Aitken mode aerosol in the high Arctic, model data
<p>This dataset contains model output that was used in the publication "Late summer transition from a free-tropospheric to boundary layer source of Aitken mode aerosol in the high Arctic" (Price et al., 2022, in prep).</p> <p> </p> <p>The output is from the UK Earth System Model run in atmosphere-only configuration at N96 resolution. Output is between September 2016 - December 2018 and is given variously as 3D monthly means, surface 3-hourly means, and 3D 2-hourly instantaneous values, depending on the variable in question.</p>
Data of Energy mechanism of atmospheric boundary layer development over the Tibetan Plateau
<p>Processed data of Energy mechanism of atmospheric boundary layer development over the Tibetan Plateau</p>
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