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40 results for “COSMO”
2015-2017 Dataset [6/7] 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'
<p>This repository contains part 6/7 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". </p> <p>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 2015-2017. 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.</p> <p>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.</p>
2006-2008 Dataset [3/7] 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'
<p>This repository contains part 3/7 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". </p> <p>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 2006-2008. 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.</p> <p>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.</p>
2003-2005 Dataset [2/7] 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'
<p>This repository contains part 2/7 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". </p> <p>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 2003-2005. 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.</p> <p>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.</p>
COSMO-CLM outputs Central Asia Russo et al. 2019
<p>Monthly values of 2-meter temperature (T2M), total precipitation (TOT_PREC) and diurnal temperature range (DTR) derived from the simulations performed with the Regional Climate Model COSMO-CLM (Roeckel et al. 2008) for the CORDEX Central Asia Domain, presented in Russo et al. 2019 ( https://doi.org/10.5194/gmd-2019-22, 2019).</p> <p>The data have a spatial resolution of ~25 km. The data cover the period 1996-2005. For some realization, a file covering the period 2005-2015 is also present. All simulations are initialized on the 01.01.1991. The only exception are 4 simulations used to investigate the model internal variability, whose initial time has been shifted of +1,-1,+3 and -3 with respect to the default initial date.</p>
COSMO-MUSCAT simulation data for 2p- and semi-explicit gasSOA approach for 20.05.2014
<p>Model data supporting the findings of the paper: " <strong><strong>URMELL - part II: semi-explicit isoprene and aromatics gasSOA modelling</strong>" </strong> by M. L. Luttkus, E. H. Hoffmann, A. Tilgner, J. Wackermann, H. Herrmann and R. Wolke. with DOI: 10.1039/d4ea00075g<strong><br></strong></p>
Additional data simulations COSMO-CLM Russo et al. 2021, Climate of the Past
<p>The data presented here are the additional data used for the performance of the COSMO-CLM Mid-Holocene and Pre-Industrial simulations, used for the analysis of the manuscript of Russo et al. 2021, submitted to the journal Climate of the Past and entitled: "The long-standing dilemma of European summer temperatures at the Mid-Holocene and other considerations on learning from the past for the future using a regional climate model".</p> <p>The data available here are:</p> <p>-ext_data_044.nc: The external parameters used for the simulations, including information on soil type, albedo, land mask, etc. <br> -W_SO_ref_half_rel_soil_moisture_18650401.nc: Soil Moisture at 50% saturation level on the first of April of the 15th year of the simulations. The data are used as reference for the simulation with different soil moisture.</p> <p> </p>
Data PI simulations COSMO-CLM Russo et al. 2021, Climate of the Past
<p>Postprocessed data of the PRE-Industrial (PI) simulations performed for the manuscript of Russo et al. 2021, submitted to the journal Climate of the Past and entitled: "The long-standing dilemma of European summer temperatures at the Mid-Holocene and other considerations on learning from the past for the future using a regional climate model".</p> <p>The structure of the directories of the data is as follow:</p> <p>--PPE_exp: data of the Physically Perturbed Ensembles<br> Inside this directory there are 3 subfolders:<br> -day_anm: daily mean anomalies<br> -mon_bias: decadal monthly means<br> -mon_fld: spatial averages of monthly means<br> In each of these subfolders, there are data for each of the considered variables:<br> -T_2M; CLCT; TOT_PREC<br> <br> For more specific Information on the presented data, please refer to the corresponding paper:</p>
Data MH simulations COSMO-CLM Russo et al. 2021, Climate of the Past
<p>Postprocessed data of the Mid-Holocene (MH) simulations performed for the manuscript of Russo et al. 2021, submitted to the journal Climate of the Past and entitled: "The long-standing dilemma of European summer temperatures at the Mid-Holocene and other considerations on learning from the past for the future using a regional climate model".</p> <p>The structure of the directories of the data is as follow:</p> <p>--PPE_exp: data of the Physically Perturbed Ensembles<br> Inside this directory there are 3 subfolders:<br> -day_anm: daily mean anomalies<br> -mon_bias: decadal monthly means<br> -mon_fld: spatial averages of monthly means<br> In each of these subfolders, there are data for each of the considered variables:<br> -T_2M; CLCT; TOT_PREC<br> --soil_pert: sensitivity tests with different initial soil moisture on 1st of April at MH<br> soil_pert only has data for MH for T_2M<br> <br> For more specific Information on the presented data, [please refer to the corresponding paper.</p> <p> </p> <p> </p> <p> </p>
Spatially averaged test results comparing SP and DP COSMO simulations
<p>This Dataset is related to the manuscript "Reduced floating-point precision in regional climate simulations: An<br> ensemble-based statistical verification" (Banderier et al., submitted).</p> <p>It contains spatially averaged test results for all five tests described in the manuscript : Identical, Single Precision, and all three Modified Diffusion experiments, for all ten years and 100 random selections.</p> <p>This Dataset also contains the final decisions, step d of the methodology, based on thresholding at the 95th percentile of the control rejections vs. the mean of the test rejections (see article for details).</p>
2018-2020 Dataset [7/7] 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'
<p>This repository contains part 7/7 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". </p> <p>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 2018-2019. 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.</p> <p>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.</p>
Sample dataset 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'
<p>This repository contains a sample of the input data 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". It allows the user to test and train the models on a reduced dataset (45GB).</p> <p>This sample dataset comprises ~3 years of normalized hourly data for both low-resolution predictors and high-resolution target variables. Data has been randomly picked from the whole dataset, from 2000 to 2020, with 70% of data coming from the original training dataset, 15% from the original validation dataset, and 15% from the original test dataset. 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.</p> <p>This sample dataset also includes files relative to metadata, static data, normalization, and plotting.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a> and unzip this zip file in the data folder.</p>
Supplementary data for the manuscript "Estimating the saturation vapor pressures of isoprene oxidation products C5H12O6 and C5H10O6 using COSMO-RS", submitted to Atmospheric Chemistry and Physics (Discussions)
<p>.cosmo and .energy files corresponding to two isoprene oxidation products</p>
COSMO Post Approval Registry: Corox OTW Steroid LV Lead Monitoring
ClinicalTrials.gov study NCT00396136. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Head-to-Head Evaluation of the Cancer Ontology Supervised Multimodal Orchestration (COSMO) AI System Versus Pathologist-Only Review
ClinicalTrials.gov study NCT07307157. IPD Sharing: NO. Countries: 1. Publications: 0.
COntinue the SaMe Systemic Therapy After Local Ablative Therapy for Oligo Progression in Metastatic Breast Cancer - the COSMO Study
ClinicalTrials.gov study NCT05301881. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
NanoSilk Cosmo: Evaluation of a Novel Silk Complex on Biophysical Parameters Related to Skin Aging
ClinicalTrials.gov study NCT04630418. IPD Sharing: NO. Countries: 1. Publications: 0.
Data for paper "Effects of Selective Simulated Seeding on the Lightning Potential Index in Northern Switzerland using the COSMO Model"
<p>This data is used in the paper "Effects of Selective Simulated Seeding on the Lightning Potential Index in Northern Switzerland using the COSMO Model". This data is supplemented by software, i.e. the code which was used to conduct the data analysis as well as plot the data, which is separately available on Zenodo. "LINET.nc" is the data for the observed lightning activity on 2019-07-06, provided by Nowcast GmbH. Additionally, there are 10 files "ens**_ctrl_lpi_fullcosmodomain.nc" which contain the simulated lightning activtiy corresponding to that date. These aforementioned files are used in the jupyter notebook "LINET_LPI_comparison.ipynb". The remaining 40 files "ens**_[ctrl or seedlow or seedmed or seedhigh].nc" are used in "data_analysis.ipynb".</p>
Condition-Specific Mapping of Operons (COSMO) Using Dynamic and Static Genome Data
GEO Series GSE203032. Mycobacterium tuberculosis. 64 samples. Type: Expression profiling by high throughput sequencing.
SPT16(Cosmo).S2
GEO Series GSE45093. Drosophila melanogaster. 4 samples. Type: Genome binding/occupancy profiling by genome tiling array.
Cosmo_rea6_processed
<p>Cosmo_rea6_processed</p>
ScienceDex guides
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