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Dataset results
13 results for “convection-permitting modeling”
A convection-permitting and limited-area model hindcast driven by ERA5 data: MOLOCH precipitation monthly data for the period 1979-2019
<p>This dataset represents a hindcast of monthly total precipitation for the period 1979-2019. Data were obtained using the convection-permitting MOLOCH model fed by BOLAM and ERA5 data as initial and boundary conditions. For additional details, see the reference below.</p> <p>Citation = "Capecchi V, et al 'A convection-permitting and limited-area model hindcast driven by ERA5 data: precipitation performances in Italy.' Climate Dynamics 61.3 (2023): 1411-1437";</p> <p>Creator_name = "Valerio Capecchi";</p> <p>Contact = "capecchi@lamma.toscana.it";</p> <p>Institute = "LaMMA - Laboratorio di Meteorologia e Modellistica Ambientale per lo sviluppo sostenibile";</p> <p>Geospatial bounds = "longitude: 2.4 to 19.873; latitude: 34.21235 to 49.64985 (Italy and nearby areas)";</p> <p>Grid spacing = "2.5 km";</p> <p>Grid = "506x626"</p>
A convection-permitting and limited-area model hindcast driven by ERA5 data: BOLAM precipitation monthly data for the period 1979-2019
<p>This dataset represents a hindcast of monthly total precipitation for the period 1979-2019. Data were obtained using the BOLAM model fed by ERA5 data as initial and boundary conditions. For additional details, see the reference below.</p> <p>Citation = "Capecchi V, et al 'A convection-permitting and limited-area model hindcast driven by ERA5 data: precipitation performances in Italy.' Climate Dynamics 61.3 (2023): 1411-1437";</p> <p>Creator_name = "Valerio Capecchi";</p> <p>Contact = "capecchi@lamma.toscana.it";</p> <p>Institute = "LaMMA - Laboratorio di Meteorologia e Modellistica Ambientale per lo sviluppo sostenibile";</p> <p>Geospatial bounds = "longitude: -26 to 53.121 by 0.089 degrees_east; latitude: 25.035 to 58.705 by 0.07 degrees_north (the Mediterranean Sea and nearby areas)";</p> <p>Grid spacing = "7 km";</p> <p>Grid = "890x482"</p>
A convection-permitting and limited-area model hindcast driven by ERA5 data: MOLOCH precipitation daily data for the period 1979-2019
<p>This dataset represents a hindcast of daily total precipitation for the period 1979-2019. Data were obtained using the convection-permitting MOLOCH model fed by BOLAM and ERA5 data as initial and boundary conditions. For additional details, see the reference below.</p> <p>Citation = "Capecchi V, et al 'A convection-permitting and limited-area model hindcast driven by ERA5 data: precipitation performances in Italy.' Climate Dynamics 61.3 (2023): 1411-1437";</p> <p>Creator_name = "Valerio Capecchi";</p> <p>Contact = "capecchi@lamma.toscana.it";</p> <p>Institute = "LaMMA - Laboratorio di Meteorologia e Modellistica Ambientale per lo sviluppo sostenibile";</p> <p>Geospatial bounds = "longitude: 2.4 to 19.873; latitude: 34.21235 to 49.64985 (Italy and nearby areas)";</p> <p>Grid spacing = "2.5 km";</p> <p>Grid = "506x626"</p>
A convection-permitting and limited-area model hindcast driven by ERA5 data: BOLAM precipitation daily data for the period 1979-2019
<p>This dataset represents a hindcast of daily total precipitation for the period 1979-2019. Data were obtained using the BOLAM model fed by ERA5 data as initial and boundary conditions. For additional details, see the reference below.</p> <p>Citation = "Capecchi V, et al 'A convection-permitting and limited-area model hindcast driven by ERA5 data: precipitation performances in Italy.' Climate Dynamics 61.3 (2023): 1411-1437";</p> <p>Creator_name = "Valerio Capecchi";</p> <p>Contact = "capecchi@lamma.toscana.it";</p> <p>Institute = "LaMMA - Laboratorio di Meteorologia e Modellistica Ambientale per lo sviluppo sostenibile";</p> <p>Geospatial bounds = "longitude: -26 to 53.121 by 0.089 degrees_east; latitude: 25.035 to 58.705 by 0.07 degrees_north (the Mediterranean Sea and nearby areas)";</p> <p>Grid spacing = "7 km";</p> <p>Grid = "890x482"</p>
A convection-permitting hindcast based on the MOLOCH model and driven by ERA5: hourly precipitation data for years 1994 and 2011 (sample data)
<p>Hourly estimates of rainfall accumulations were produced within the framework of the SPITBRAN Special project, which received computational resources from ECMWF (https://www.ecmwf.int/en/research/special-projects/spitbran-2018).</p> <p>Numerical gridded data at 2.5 km grid spacing were obtained with the MOLOCH model set in a convection-permitting mode and fed by ERA5 data as initial and boundary conditions for the period 1979-2019 and over the Italian domain.</p> <p>Hourly rainfall accumulations of such long-term hindcast are provided for the years 1994 and 2011. File format is Grib2.</p>
Datasets for Quantifying the impact of land use and land cover change on moisture recycling with convection-permitting WRF-tagging modeling in the agro-pastoral ecotone of northern China
<p>These datasets are the processed and refined data that support and lead to the described results and allow other readers to assess the conclusions in the paper, entitled “<strong>Quantifying the impact of land use and land cover change on moisture recycling with convection-permitting WRF-tagging modeling in the agro-pastoral ecotone of northern China </strong> ”.</p>
Impact of Convection-permitting and Model Resolution on the Simulation of Mesoscale Convective System Properties over East Asia: companion dataset
<p>This folder includes the intermediate data for the following manuscript:</p><p>Ding et al., Impact of Convection-permitting and Model Resolution on the Simulation of Mesoscale Convective System Properties over East Asia</p><p>The simulations were done using ICON-NWP (ICON Numerical Weather Prediction) model, version 2.6.1, over Asian monsoon region (62E–150E, 5.5N–54.5N) for 2020 summer. At the moment, we upload the intermediate data for MCS tracking. For more data, please contact the authors.</p>
Code, Data, and Technical Note for SCREAM Beijing flood Convection-Permitting Regionally Refined Model 1.0 version
<p><a href="https://zenodo.org/api/records/15126670/draft/files/BeijingRRM-v0.1-SCREAM_push.tar.gz/content" target="_blank" rel="noopener noreferrer">BeijingRRM-v0.1-SCREAM_push.tar.gz</a> :</p> <p>The code used to generate all simulations for the paper entitled "Through the lens of a kilometer-scale climate model: 2023 Jing-Jin-Ji flood under climate change" submitted to Geophysical Research Letters. The SCREAM Beijing RRM source code is also available on GitHub at https://github.com/E3SM-Project/scream/tree/jzhang/RRM_tmp (last access: 28 Aug 2024) and a maint branch (BeijingRRM-v0.1; https://github.com/jsbamboo/scream/releases/tag/BeijingRRM-v0.1, last access: 28 Aug 2024). </p> <p><a href="https://zenodo.org/api/records/15126670/draft/files/files_scream-BeijingRRM-v1.0_storylines.zenodo.tar.gz/content" target="_blank" rel="noopener noreferrer">files_scream-BeijingRRM-v1.0_storylines.zenodo.tar.gz</a> : </p> <p>The runscripts, mapping files, masks used for analysis and figures in the paper. The simulation outputs and processed data are too large (3.3T) to upload to zenodo, and are available on the NERSC portal: https://portal.nersc.gov/archive/home/z/zhang73/www/files_scream-BeijingRRM-v1.0_storylines</p> <p><a href="https://zenodo.org/uploads/15126670" target="_blank" rel="noopener noreferrer">BeijingFlood_Doc.pdf</a> :</p> <p>The technical note documenting our practice in generating the SCREAM Beijing flood RRM configurations. Source page: https://acme-climate.atlassian.net/wiki/spaces/DOC/pages/4056318237/SCREAM+Beijing+Flood+RRM+Technical+Note</p>
Lagrangian Heavy Precipitation Events in convection-permitting Regional Climate Models over the Alps and in the Mediterranean
<p>The csv datafile contains a set of heavy precipitation events identified in cpRCMs.</p> <p>Each of the entries represents an event and is described with detailed properties:</p> <p>'Start Date [YYYYMMDD.HOUR/24]', 'Latitude [°]', 'Longitude [°]',<br> 'Duration [h]', 'Volume [km² h]', 'P99(pr) [mm h-1]',<br> 'P90(pr) [mm h-1]', 'P75(pr) [mm h-1]',<br> 'P50(pr) [mm h-1]', 'P25(pr) [mm h-1]',<br> 'P10(pr) [mm h-1]', 'Total Precipitation [m3]',<br> 'Maximum Precipitation [mm h-1$]',<br> 'Mean Precipitation [mm h-1$]', 'Direction [°]',<br> 'Distance Traveled [km]', 'Eccentricity [-]', 'Track Eccentricity [-]',<br> 'Mean Ellipsicity [-]', 'Track Ellipsicity [-]', 'Mean Major Angle [°]',<br> 'Track Major Angle [°]', 'Mean Major Axis [-]', 'Track Major Axis [-]',<br> 'My Orientation [°]', 'My Track Orientation [°]', 'max(Elevation) [m]',<br> 'min(Elevation) [m]', 'Start Year [YYYY]', 'Start Month [MM]',<br> 'LandFallSea [-]', 'Scenarios', 'Models', 'situations', 'Ensemble',<br> 'Speed [km h$^{-1}$]', 'Mean(Area) [km²]', 'orographic [-]',<br> 'Severity [-]', 'I/O OBS [-]', 'Region [-]', 'orographic1500 [-]',<br> 'orographic2000 [-]', 'orographic2500 [-]', 'orographic3000 [-]']</p>
How well does a convection-permitting climate model represent the reverse orographic effect of extreme hourly precipitation? - Observed precipitation data
<p>The dataset contains the rain gauge hourly rainfall series used in the paper "How well does a convection-permitting climate model represent the reverse orographic effect of extreme hourly precipitation?". Each rain gauge series is saved in one Matlab variable, organized as a structure S with five fields:</p> <p>S.name: the identification name of the rain gauge station</p> <p>S.vals_mm: series of hourly rainfall in millimeter</p> <p>S.time_utc: time steps series, in UTC time</p> <p>S.elev_m: elevation of the station, in m a.s.l.</p> <p>S.xy_utm: station coordinates X and Y in meter in the Reference system WGS84/UTM zone 32N</p>
Climate model output from a study of tropical cyclones over the Shanghai region under climate change based on a convection-permitting modelling
Open the record for dataset details and reuse information.
Stable Machine-Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection-Permitting Simulations: Data and Visualization Notebooks
<p>The data, jupyter notebooks, and saved model weights for the "Stable Machine-Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection-Permitting Simulations" Hu et al. (2025) arxiv preprint: <a href="https://arxiv.org/abs/2407.00124">arXiv:2407.00124</a>. This updated version contains more analysis notebooks together with related data/model.</p>
Data for Global Convection-Permitting Model Improves Subseasonal Forecast of Plum Rain around Japan
<p>Data and plot scripts for this manuscript <strong>"<span>Global Convection-Permitting Model Improves Subseasonal Forecast of</span><span><span> </span></span><span><span>Plum Rain around Japan".</span></span></strong></p> <blockquote> <p><span><span>The md5 value is 7bf40160a0f97e94f048ff68048dee02</span></span></p> </blockquote>
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