Find research datasets worth reusing

opencc-by-4.0May 2020View details →

Supplementary data for the manuscript "Technical note: Estimating aqueous solubilities and activity coefficients of mono- and α,ω-dicarboxylic acids using COSMO-RS-DARE"

.cosmo files (BP-TZVPD-FINE) of dicarboxylic acids (C2-C8), dimers and monohydrates of mono- (C1-C6) and dicarboxylic acids, and water dimer.

opencc-by-4.0Jun 2024View details →

Cosmo Batista Realli (r2497)

-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> -- Name: Cosmo Batista Realli musiXplora-ID: r2497 musiXplora-URI: <a href="https://musixplora.de/mxp/r2497">https://musixplora.de/mxp/r2497</a> Gender: m First Mentioned: 1667 Last Mentioned: 1700 Sectors: Geigenbau Professions (Musical): Geigenbauer Other Places of Activity: Parma Portfolio: <table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>Sortimente</td><td>Sortiment</td><td>Pochette</td><td><a href="https://musixplora.de/mxp/2001476">2001476</a></td></tr></tbody></table> Titel/Medien: <table><tbody><tr><th>Role</th><th>Sigel</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Related</td><td>Lütgendorff 1922</td><td>Die Geigen- und Lautenmacher vom Mittelalter bis zur Gegenwart. 2 Bände. Lüt2</td><td><a href="https://musixplora.de/mxp/5002059">5002059</a></td></tr></tbody></table> Changelog:   - v0.0.1: Initial Upload.

opencc-by-4.0May 2018View details →

Regional climate simulations of surface precipitation and temperature for West Africa using COSMO-CLM based on MPI-LR (ECHAM6) and RCP4.5

Regional climate model COSMO-CLM (CCLM) simulations with a horizontal resolution of 0.11° (approx. 12 km) for sub-Saharan West Africa under current and future climate conditions. The CCLM is driven by initial and lateral boundary conditions from the MPI-LR (ECHAM6), based on the emission scenario RCP4.5. The downscaled MPI-LR (ECHAM6) data for surface precipitation (P) and surface temperature (Tmin, Tmax) are provided for the baseline period (1981-2010) and two future time slices, i.e. the 2021–2050 and the 2071–2100 period.

opencc-by-4.0Sep 2021View details →

Rutford Ice Stream, Antarctica M_sf tidal velocity components derived from COSMO-SkyMED SAR data

This repository provides rasters for velocity components of a tidal (periodic) model for Rutford Ice Stream (RIS), Antarctica. The tidal model consists of a secular (constant) term and a single sinusoidal component corresponding to the M_sf tidal cycle (14.76529 days). The tidal model is fit to time-dependent velocity fields over RIS derived from speckle tracking of COSMO-SkyMed SAR data, collected over 9 months beginning in August 2013. The original methodology and source dataset are described in the publication: Minchew, B. M., Simons, M., Riel, B., & Milillo, P. (2017). Tidally induced variations in vertical and horizontal motion on Rutford Ice Stream, West Antarctica, inferred from remotely sensed observations. Journal of Geophysical Research: Earth Surface, 122(1), 167-190. doi: <a href="https://doi.org/10.1002/2016JF003971">10.1002/2016JF003971</a> The rasters are provided in GeoTIFF format in the Polar Stereographic South (EPSG: 3031) coordinate system. The velocity components are also referenced to Polar Stereographic South coordinates. The pixel spacing is 400 meters (in both the X- and Y-directions). The individual files are: <ol> <li>vx_secular.tif: secular velocity in X-direction in meters/day.</li> <li>vy_secular.tif: secular velocity in Y-direction in meters/day.</li> <li>vx_amp.tif: M_sf velocity amplitude in X-direction in meters/day.</li> <li>vy_amp.tif: M_sf velocity amplitude in Y-direction in meters/day.</li> <li>vx_phase.tif: M_sf velocity phase delay in X-direction in days.</li> <li>vy_phase.tif: M_sf velocity phase delay in Y-direction in days.</li> </ol>

opencc-by-4.0Jul 2020View details →

Pre-eruption InSAR time-series at Kīlauea (Hawai`i, USA): COSMO-SkyMed Descending 2018

InSAR time-series data for Kīlauea (Hawai`i, USA), between Jan 2010 and Sep 2011 . Data were obtained by processing COSMO-SkyMed descending SAR data (track 165). Data were processed using the JPL-developed InSAR Scientific Computing Environment (<code>ISCE</code>) open-source software package, and further time-series analysis was performed using the <code>MintPy</code> software toolbox (<a href="https://github.com/insarlab/MintPy">Miami INsar Time-series software in PYthon</a>), developed at the University of Miami.  The following file is available in Hierarchical Data Format: <code>geo_timeseries_tropHgt_demErr_cskDT165.h5</code>: Descending Track timeseries file. Dates available: <code>['timeseries-20101001', 'timeseries-20101009', 'timeseries-20101017', 'timeseries-20101025', 'timeseries-20101102', 'timeseries-20101110', 'timeseries-20101118', 'timeseries-20101126', 'timeseries-20101204', 'timeseries-20101212', 'timeseries-20101220', 'timeseries-20110129', 'timeseries-20110206', 'timeseries-20110214', 'timeseries-20110222', 'timeseries-20110302', 'timeseries-20110303', 'timeseries-20110310', 'timeseries-20110318', 'timeseries-20110319', 'timeseries-20110322', 'timeseries-20110326', 'timeseries-20110403', 'timeseries-20110404', 'timeseries-20110407', 'timeseries-20110411', 'timeseries-20110419', 'timeseries-20110420', 'timeseries-20110423', 'timeseries-20110505', 'timeseries-20110506', 'timeseries-20110509', 'timeseries-20110513', 'timeseries-20110521', 'timeseries-20110522', 'timeseries-20110525', 'timeseries-20110529', 'timeseries-20110606', 'timeseries-20110607', 'timeseries-20110614', 'timeseries-20110622', 'timeseries-20110630', 'timeseries-20110708', 'timeseries-20110709', 'timeseries-20110716', 'timeseries-20110724', 'timeseries-20110725', 'timeseries-20110801', 'timeseries-20110809', 'timeseries-20110810', 'timeseries-20110817', 'timeseries-20110825', 'timeseries-20110826', 'timeseries-20110902', 'timeseries-20110910', 'timeseries-20110918']</code>   These data are supplemental to: Farquharson, J. I. and Amelung, F. [2020], "Extreme rainfall triggered the 2018 rift eruption at Kīlauea Volcano." <a href="https://doi.org/10.1038/s41586-020-2172-5">https://doi.org/10.1038/s41586-020-2172-5</a>

opencc-by-4.0Dec 2020View details →

Cyclone tracks from 1901 to 2010 in dynamically downscaled ERA-20C reanalysis (COSMO-CLM+NEMO)

The database contains two files: one with all cyclone trajectories from 1901 to 2010, and another one only with the so-called Vb-cyclones that propagate from the Mediterranean Sea north-eastward to Central Europe. We detected the cyclone trajectories with the method of Wernli and Schwierz (2006) and Sprenger et al. (2017) and classified all cyclone trajectories that crossed the 47°N latitude between 12°E and 22°E as Vb-cyclones following Hofstätter and Blöschl (2019). The cyclone tracking was based on mean sea level pressure data of dynamically downscaled ERA-20C reanalysis. The downscaling was performed over Europe [including MED-CORDEX (Somot et al. 2018) and EURO-CORDEX (Giorgi et al. 2009)] from 1901 to 2010 with an interactively coupled high-resolution atmosphere-ocean model (COSMO-CLM+NEMO) by Cristina Primo. More details on the data basis can be found in Primo et al. (2019) and Krug et al. (2020).   Giorgi, F., Jones, C. & Asrar, G. Addressing climate information needs at the regional level: the CORDEX framework. WMO Bulletin 58, 175–183 (2009). Hofstätter, M. & Blöschl, G. Vb Cyclones Synchronized With the Arctic-/North Atlantic Oscillation. J. Geophys. Res. Atmos. 124, 3259–3278 (2019). Krug, A., Primo, C., Fischer, S., Schumann, A. & Ahrens, B. On the temporal variability of widespread rain-on-snow floods. Meteorol. Zeitschrift 29, 147–163 (2020). Primo, C., Kelemen, F. D., Feldmann, H., Akhtar, N. & Ahrens, B. A regional atmosphere-ocean climate system model (CCLMv5.0clm7-NEMOv3.3-NEMOv3.6) over Europe including three marginal seas: on its stability and performance. Geosci. Model Dev. 12, 5077–5095 (2019). Somot, S. et al. Editorial for the Med-CORDEX special issue. Clim. Dyn. 51, 771–777 (2018). doi: 10.1007/s00382-018-4325-x Sprenger, M. et al. Global climatologies of Eulerian and Lagrangian flow features based on ERA-Interim. Bull. Am. Meteorol. Soc. (2017). doi:10.1175/BAMS-D-15-00299.1 Wernli, H. & Schwierz, C. Surface Cyclones in the ERA-40 Dataset (1958–2001). Part I: Novel Identification Method and Global Climatology. J. Atmos. Sci. 63, 2486–2507 (2006).

opencc-by-4.0Jan 2021View details →

Model intercomparison of COSMO 5.0 and IFS 45r1 at kilometer-scale grid spacing

Simulation output data from COSMO and IFS used to produce the figures in the model intercomparison paper (https://doi.org/10.5194/gmd-2021-31), as well as the data used for initializing the soil in COSMO. The output data is partitioned into different parts: <ol> <li>cosmo_hordiff.tar: Model output from COSMO for the horizontal diffusion experiment.</li> <li>cosmo_standard.tar: Model output from COSMO for the standard experiment.</li> <li>ifs_standard.tar: Model output from IFS for the standard experiment.</li> <li>soil_cosmo_ini_vergara2021_avg_mayjune_12km.nc: Initial conditions for the soil model used in COSMO.</li> </ol>

opencc-by-4.0Apr 2022View details →

Raw Data to "Prediction of acid pKa values in the solvent acetone based on COSMO-RS"

This data is a supplement to the publication entitled "Prediction of Acid pKa Values in the Solvent Acetone based on COSMO-RS" in the Journal of Computational Chemistry (DOI:10.1002/jcc.26864). The data includes initial starting structures as inputs for conformer searches using COSMOconf (versions 2020 and 2021) in combination with TURBOMOLE (version 7.3). The corresponding output files serve as inputs for the calculation of Gibbs free energies using COSMO-RS as provided by COSMOtherm. Additional information on the file structure is given in the README file.

opencc-by-sa-4.0Nov 2018View details →

Secondary ice production parameterization output - COSMO model

Secondary ice production via processes like rime splintering, frozen droplet shattering, and breakup upon ice hydrometeor collision have been proposed to explain discrepancies between in-cloud ice crystal and ice-nucleating particle numbers. To understand the impact of this kind of additional ice number generation on surface precipitation, we present one of the first studies to implement frozen droplet shattering and ice-ice collisional breakup parameterizations in a larger-scale model. We simulate a cold frontal rainband from the Aerosol Properties, PRocesses, And InfluenceS on the Earth's Climate campaign and investigate the impact of the new parameterizations on the simulated ice crystal number concentrations (ICNC) and precipitation. Near the convective regions of the rainband, contributions to ICNC can be as large from secondary production as from primary nucleation, but ICNCs greater than 50 L-1 remain underestimated by the model. Addition of the secondary production parameterizations also clearly intensifies the differences in both accumulated precipitation and precipitation rate between the convective towers and non-convective gap regions. We suggest, then, that secondary ice production parameterizations be included in large-scale models on the basis of large hydrometeor concentration and convective activity criteria.

opencc-by-4.0Jul 2019View details →

Data for "COSMO-BEP-Tree v1.0: a coupled urban climate model with explicit representation of street trees"

In order to represent the interactions between street trees, urban elements and the atmosphere in realistic regional weather and climate simulations, we coupled the vegetated urban canopy model BEPTree and the mesoscale weather and climate model COSMO. The performance and applicability of the coupled model, named COSMO-BEP-Tree, are demonstrated over the urban area of Basel, Switzerland, during the heatwave event of June-July 2015. The data includes: 1. datasets Datasets of building geometries (Shapefile, WGS84), trees (GeoTiff, WGS84), Landsat 7 scene (GeoTIFF, WGS84) and imperviousness (GeoTIFF, WGS84). 2. model outputs The processed model outputs (.npy files, generated with Python v3) are provided for all the simulations, in terms of time series at the observation sites and spatial distributions. The full 3D model outputs, 1 TB) can be provided by request by contacting the author (<a href="mailto:mussetti.gianluca@gmail.com">mussetti.gianluca@gmail.com</a>). 3. model inputs Input namelists for the COSMO-BEP-Tree model and initial/static conditions. The full 3D boundary conditions (60 GB) can be provided by request (<a href="mailto:mussetti.gianluca@gmail.com">mussetti.gianluca@gmail.com</a>). 4. observations Measurement data (.txt). 5. post-processing scripts Jupyter (Python 3) Notebook files used to generate the figures and to analyse model results. Tested in Python 3.6.5.

opencc-by-4.0Mar 2023View details →

QM and COSMO-RS calculation results and experimental data for: Computing kinetic solvent effects and liquid phase rate constants using quantum chemistry and COSMO-RS methods

This dataset contains the calculation results and the experimental data compiled from literature for the manuscript "Computing kinetic solvent effects and liquid phase rate constants using quantum chemistry and COSMO-RS methods". Citations should refer directly to the manuscript (Chung, Y.; Green, W. H. Computing kinetic solvent effects and liquid phase rate constants using quantum chemistry and COSMO-RS methods. J. Phys. Chem. A 2023, 127, 27, 5637–5651. doi: <a href="https://doi.org/10.1021/acs.jpca.3c01825">10.1021/acs.jpca.3c01825</a>).This includes: <ul> <li>expt_data_collected.xlsx: Experimental rate constants of various liquid phase reactions collected from various sources</li> <li>For each levels of theory used for gas-phase quantum chemical calculations and COSMO-RS calculations: <ul> <li>Gas-phase quantum chemical calculation results (output log files) and computed gas phase rate constants</li> <li>COSMO-RS calculation results and computed solvation free energies</li> <li>Predicted liquid phase rate constants and relative rate constants </li> </ul> </li> </ul>

zenodo36/100

PollyXT and COSMO-MUSCAT data for "Investigating the link between mineral dust hematite content and intensive optical properties by means of lidar measurements and aerosol modelling"

The dataset contains 4 different files:  <ul> <li>For the single case example on the 24 August 2021 between 2:45 to 5:27 UTC in Mndelo, Cabo Verde: <ul> <li>-Mindelo-PollyXT_CPV-20210824_0245-0527-77smooth-info.txt : contains the information of the vertically retrieved optical properties from PollyXT lidar measurements. The information contained refers to the chosen retrieval times, vertical smoothing, and reference heights</li> <li>-Mindelo-PollyXT_CPV-20210824_0245-0527-77smooth.txt : vertically retrieved optical properties per height.</li> <li>-Mindelo-model_24aug.csv : COSMO-MUSCAT vertical results of dust and mineral mass concentrations per height. The columns that end with "int mass" correspond to the integrated mass per dust layer and columns that end with numbers correspond to different size bins. For reference to the size bins see Table 1 in Gómez Maqueo Anaya et al., 2024</li> </ul> </li> <li>Mutiple case studies: <ul> <li>-Mindelo-lidar-uvvisdiff_model.csv : Twenty-two case studies with the following order: first, the mean values of the lidar-derived optical properties, along with their corresponding retrieval times and heights that define the dust plume. This is followed by the POLIPHON (Mamouri and Ansmann, 2014, 2017) data. The mean values from dust and mineral mass concentrations from the model start with the model heights where the dust plumes were calculated. At the end of the dataset rows, the times from which the modeled mean values are calculated can be found.</li> </ul> </li> </ul>

opencc-by-4.0Oct 2024View details →

zenodo36/100

Model output data for compressible EULAG dynamical core in COSMO: convective-scale Alpine weather forecasts

The archive contains model output data for article "Compressible EULAG dynamical core in COSMO: convective-scale Alpine weather forecasts" to be published in Monthly Weather Review. The article presents the semi-implicit compressible EULAGas a newdynamical core for convective-scale numerical weather prediction. The core is implemented within the infrastructure of the operational model of the Consortium for Small Scale Modeling (COSMO), forming the NWP COSMO-EULAG model (CE). This regional high-resolution implementation of the dynamical core complements its global implementation in the Finite-Volume Module of ECMWF’s Integrated Forecasting System. The paper documents the first operational-like application of the dynamical core for realistic weather forecasts. After discussing the formulation of the core and its coupling with the host model, the paper considers several high-resolution prognostic experiments over complex Alpine orography. Standard verification experiments examine the sensitivity of the CE forecast to the choice of the advection routine and assess the forecast skills against those of the default COSMO Runge-Kutta dynamical core at the 2.2 km grid showing a general improvement. The skills are also compared using satellite observations for a weak-flow convective Alpine weather case-study, showing favorable results. Additional validation of the new CE framework for partly convection-resolving forecasts using 1.1 km, 0.55 km, 0.22 km, and 0.1 km grids, designed to challenge its numerics and test the dynamics-physics coupling, demonstrates its high robustness in simulating multi-phase flows over complex mountain terrain, with slopes reaching 85 degrees, and the flow’s realistic representation.

opencc-by-4.0Apr 2021View details →

zenodo36/100

Raw Data to "Performance of the COSMO solvation model for photoacidity and basicity in water"

This data is a supplement to the publication entitled "Performance of the COSMO solvation model for photoacidity and basicity in water" in the the Journal of Computational Chemistry A (DOI: 10.1002/jcc.27173). It contains: Additional information on the file structure is given in the README file.

opencc-by-4.0May 2023View details →

opencc-by-4.0Jan 2022View details →

Predicting the execution time of COSMO weather forecast models

This data set is the work of E. Di Giacomo Master Thesis at the University of Bologna. Predicting the execution time of a numerical weather forecast model is a complex task. Generally, these models simulate the evolution of atmospheric weather and they are typically used for the production of weather forecasts, one or multiple times a day. Given their computational complexity, they require large computing capabilities, such as High Performance Computing systems. In these systems, job scheduling and resource allocation are carefully managed to optimize the usage of the finite and expensive hardware resources; in particular, several allocation and related pricing decisions are based on estimates of the duration of the application submitted, such as the execution time of weather forecast models. A reliable prediction of execution time allows for a better management of the overall system, an improved planning of the model execution, as well as the identification of possible anomalies during the execution, thus providing great benefits to both system administrators and users. This data set regards a particular weather forecast model, namely the COSMO model, the weather forecasting model used at the the Hydro-Meteo-Climate Structure of Arpae Emilia-Romagna. The data set contains many execution times of the COSMO meteorological model run under a variety of different scientific parameters and parallelization levels.

opencc-by-4.0May 2024View details →

Automated grounding line delineation using deep learning and phase gradient-based approaches on COSMO-SkyMed DInSAR data

This repository contains the file 'Natalya_Maslennikova_data_2024.zip,' which includes 171 DInSAR interferograms (both phase and coherence of a DInSAR signal) and three versions of the grounding lines: manually mapped ('gl_manual_mapping.shp'), mapped by the neural network ('gl_neural_network.shp'), and mapped using the phase gradient-based approach ('gl_phase_gradient.shp'). The X-band DInSAR data were acquired by the ASI’s COSMO-SkyMed mission between 2020 and 2022 over five East Antarctica glaciers: Stancomb-Wills (STA: 32 interferograms), Veststraumen (VES: 35 interferograms), Jutulstraumen (JUT: 42 interferograms), Moscow University (MOS: 27 interferograms), and Rennick (REN: 35 interferograms). The 'gl_neural_network.shp' and 'gl_phase_gradient.shp' shapefiles contain the following fields: the name of the glacier (the 'Glacier' column), four dates in YYYYMMDD format when the corresponding DInSAR interferogram was acquired (the 'Primary1', 'Secondary1', 'Primary2', 'Secondary2' columns), and the double difference name in 'Primary1_Secondary1-Primary2_Secondary2' format (the 'DD' column). The 'gl_manual_mapping.shp' shapefile contains 'Glacier', 'Primary1', 'Secondary1', 'Primary2', 'Secondary2', and 'DD' columns as well, along with the 'Revisit' column (difference in days between the 'Primary1' and 'Primary2' images were acquired), the 'Time' column (acquisition time in 'HHMMSS' format), the 'Coherence' column (average coherence of the corresponding DInSAR interferogram), and the 'Train_1', 'Train_2', 'Train_3' columns, which contain indicators 'Y' for 'yes' and 'N' for 'no', showing whether or not the DInSAR interferogram was used in the corresponding training session.

opencc-by-4.0Jul 2024View details →

2000-2002 Dataset [1/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'

This repository contains part 1/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy".  This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2000-2002. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper. This part 1/7 of the dataset also includes files related to metadata, static data, normalization, and plotting. To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.

opencc-by-4.0Jul 2024View details →

2009-2011 Dataset [4/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'

This repository contains part 4/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy".  This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2009-2011. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper. To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.