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60 results for “Unified Model”
Datasets for "A unified framework to estimate the origins of atmospheric moisture and heat using Lagrangian models"
<p>This repository contains the post-processed model outputs from HAMSTER v1.2.0 as used in the following paper: </p> <p>Keune, J., Schumacher, D. L., and Miralles, D. G.: A unified framework to estimate the origins of atmospheric moisture and heat using Lagrangian models, Geosci. Model Dev., 15, 1875–1898, https://doi.org/10.5194/gmd-15-1875-2022, 2022.<br> <br> The data set contains (1) global validation statistics for the three fluxes (evaporation, precipitation, sensible heat), and (2) the climatological source regions of precipitation and heat for Denver, Beijing and Windhoek. The former are found in the directory 'validation/global', and the latter are found in the directories '1001' (Denver), '3001' (Beijing) and '5002' (Windhoek). Multiple experiments were performed to assess the uncertainty of the source regions. Thus, multiple files exist, that show the same variables but for multiple experiments (indicated by the names "ALLPBL", "RH-10-20", "SOD08-SCH19", "SCH20", "FAS19" in the file name). For the moisture source regions, the uncertainty of the attribution methodology was assessed; these are indicated by the different folders, i.e. 'linear_upscaled' and 'random2_upscaled'. For each city and each experiment, the climatologically averaged source regions ('_mean.nc') and the climatologically averaged individual backward day contributions ('_bwmean.nc') are provided. Data sets are in the netCDF format and contain metadata following the CF convention.</p>
Impact of urban and shipping emissions on NASA-Unified Weather Research and Forecasting model results
<p>This dataset supports Huang et al. (2019, JGR-Atmospheres): "Impact of aerosols from urban and shipping emission sources on terrestrial carbon uptake and evapotranspiration: a case study in East Asia". The file named "NUWRFout.tar.gz" contains NUWRF base and sensitivity simulation results on 31 May 2016. The file named "LIS_soil_LAI.zip" contains model grid information, soil conditions and leaf area index (LAI) at NUWRF initialization times in late May 2016.</p>
The third Met Office Unified Model-JULES Regional Atmosphere and Land configuration, RAL3
<p>Supporting data for figures in GMD draft paper: The third Met Office Unified Model-JULES Regional Atmosphere and Land configuration, RAL3</p>
Met Office Unified Model outputs for simulations of Proxima Centauri b and TRAPPIST-1e
<p>Five datasets generated by the Met Office Unified Model configured for exoplanets Proxima Centauri b and TRAPPIST-1e. </p> <p>1. Control Prox: A simulation of Proxima Centauri b with a moist N2 atmosphere using observed planetary data (radius, stellar constant) as model parameters</p> <p>2. Warm Prox: A simulation of Proxima Centauri b with a moist N2 atmosphere as if moved to the inner edge of its habitable zone</p> <p>3. Control Trap: A simulation of TRAPPIST-1e with a moist N2 atmosphere using observed planetary data</p> <p>4. Warm Trap: A simulation of TRAPPIST-1e with a moist N2 atmosphere as if moved to the inner edge of its habitable zone</p> <p>5. Dry Trap: A simulation of TRAPPIST-1e with a dry atmosphere</p> <p>The data is used in Cohen et al. (2023). ""Traveling planetary-scale waves cause cloud variability on tidally locked aquaplanets." Submitted to The Planetary Science Journal.</p> <p>Abstract:</p> <p>"Cloud cover at the planetary limb of water-rich Earth-like planets is likely to weaken chemical<br> signatures in transmission spectra, impeding attempts to characterize these atmospheres. However,<br> based on observations of Earth and solar system worlds, exoplanets with atmospheres should have both<br> short-term weather and long-term climate variability, implying that cloud cover may be less during<br> some observing periods. We identify and describe a mechanism driving periodic clear sky events at<br> the terminators in simulations of tidally locked Earth-like planets. A feedback between dayside cloud<br> radiative effects, incoming stellar radiation and heating, and the dynamical state of the atmosphere,<br> especially the zonal wavenumber-1 Rossby wave identified in past work on tidally locked planets, leads<br> to oscillations in Rossby wave phase speeds and in the position of Rossby gyres and results in advection<br> of clouds to or away from the planet’s eastern terminator. We study this oscillation in simulations of<br> Proxima Centauri b, TRAPPIST 1-e, and rapidly rotating versions of these worlds located at the inner<br> edge of their stars’ habitable zones. We simulate time series of the transit depths of the 1.4 μm water<br> feature and 2.7 μm carbon dioxide feature. The impact of atmospheric variability on the transmission<br> spectra is sensitive to the structure of the dayside cloud cover and the location of the Rossby gyres,<br> but none of our simulations have variability significant enough to be detectable with current methods."</p>
Scripts and datas for "A unified energy-constrained mesoscale parameterisation for ocean climate models".
<p>Scripts and datasets used for creating the results of a submitted work :</p> <p><strong>R. Torres, R. Waldman, G. Madec, C. de Lavergne, R. Séférian and J. Mak</strong>: <em>A unified energy-constrained mesoscale parameterisation for ocean climate models. </em>(submitted in JAMES).<em><br></em></p> <p>Datas include eORCA1 mesh files (directory "mesh") and simulations output (direcotories "runs/*/output"). However, to avoid heavy archive, only 2D simulations output are provided. The post-processed 3D variables are first pre-processed for each simulations (directories "runs/*/post/post/post_averag_1995-2017").</p> <p>The reference EKE of <a href="https://doi.org/10.1029/2023gl104688">Torres et al. (2023)</a> is provided (directory "obs/postprocessed_kinetic_energy") while other observational reference datasets have to be download by the user (e.g. <a href="https://www.ncei.noaa.gov/archive/accession/NCEI-WOA18">World Ocean Atlas 2018</a>, <a href="https://gmd.copernicus.org/articles/13/3643/2020/">Tsujino et al. (2020)</a> and <a href="https://www.bodc.ac.uk/data/published_data_library/catalogue/10.5285/04c79ece-3186-349a-e063-6c86abc0158c/">RAPID</a>)</p> <p>IPython notebooks for computing and plotting metrics are provided :</p> <ul> <li><em>james-eke-heat_budget.ipynb</em> : plots for heat transport and global heat storage (section 4.1)</li> <li><em>james-eke-southern_ocean.ipynb</em> : plots for Southern Ocean (section 4.2) analysis</li> <li><em>james-eke-north_atlantic.ipynb</em> : plots for North Atlantic and Labrador Sea (section 4.3) analysis</li> <li><em>james-eke-timeseries.ipynb</em> : plot 0D metric timeseries for simulations (including spin-up)</li> </ul> <p>Note however that these scripts use the author python library XOCE availbale on GitHub: https://github.com/torresr-cnrm/xoce. All the scripts have been runned using the version 0.2 of XOCE. Feel free to contact (romain.torres@meteo.fr) for any help in installing and using this library.</p>
Archive of NASA-Unified WRF model daily forecasting simulations for DOE TRACER IOP
<pre># Copyright 2022 NASA GSFC All rights reserved. # Creative commons attribution 4.0 international license NASA-Unified WRF model daily simulations for DOE TRACER IOP Document updated: 22 June 2022 Point of contact: Takamichi Iguchi (ESSIC UMD, Code612 NASA GSFC), takamichi.iguchi@nasa.gov Toshi Matsui (ESSIC UMD, Code612 NASA GSFC), toshihisa.matsui-1@nasa.gov Contents: ./READMEtracer.txt # this file ./namelist.wps.tracer_iop_31.template # namelist.wps file to configure WRF Pre-Processing System (WPS) ./namelist.input.real.tracer_iop_31.template # namelist.input file for NU-WRF model real.exe ./namelist.input.wrf.tracer_iop_31.template # namelist.input file for NU-WRF model wrf.exe ./${YYYY}${MM}${DD} # these directories contain files produced from 48-hours NU-WRF forecasting from 00UTC on ${YYYY}${MM}${DD}: pyplot_${YYYY}-${MM}-${DD}_${HH}${MN}${SC}.png # Plot for Composite radar reflectivity (dBZ) # PBL height (m) + 10-m horizontal wind (850hPa-level wind in plots before 06/02/2022), # OLR TOA (W m-2), and 5-mins-accumulated IC+CG lighting flash extent density (flash km-2) # Note that this composite dBZ is calculated from NSSL 2-moment microphysics for S-band, # not from POLARRIS radar simulator pyplot.gif # Gif annimation file combining the png plot files for 1~48 hours in the forecasting accprecip_${YYYY}-${MM}-${DD}_${HH}${MN}${SC}.png # Plot for 1, 3, 6-hours, and total accumulated surface precipitation (mm) accprecip.gif # Gif annimation file combining the png plot files for 1~48 hours in the forecasting polarris_zh_zdr_rh_vr_${YYYY}_${MM}${DD}_${HH}${MN}${SC}.png # Plot from POLARRIS radar simulator in NU-WRF for QCed Reflectivity (dBZ), # differential reflectivity (dB), cross-polar correlation (-), and # radial velocity (m s-1) at 0.5 degree elevation angle polarris_zh_zdr_rh_vr.gif # Gif annimation file combining the png plot files roughly every hour # for 1~48 hours in the forecasting polarris_zh_4sweeps_${YYYY}_${MM}${DD}_${HH}${MN}${SC}.png # Plot from POLARRIS radar simulator in NU-WRF for QCed Reflectivity (dBZ) # at 0.5, 1.8, 4.0 and 8.0 degree elevation angles polarris_zh_4sweeps.gif # Gif annimation file combining the png plot files roughly every hour # for 1~48 hours in the forecasting # following files are produced 3 days late # day1 represent the first 0-24hr forecast, day2 represents the 24-48hr forecast. CFAD_con_day?.png # Convective part of Contoured Frequency of Altitude Diagrams CFAD_str_day?.png # Stratiform part of Contoured Frequency of Altitude Diagrams QVP_con_day?.png # Convective part of QVP-like domain-mean radar profiles QVP_str_day?.png # Stratiform part of QVP-like domain-mean radar profiles RadarFrac_day?.png # Composite Radar Horizontal Fraction (0-1) by different minimum reflectivity thresholds</pre>
SIMULATION of: A Unified Model for WaveParticle Duality and Quantum Entanglement.pdf
<p>This Google Colab notebook contains the simulations used to validate the Fasano Symbiosis model, as presented in the paper "A Unified Model for Wave-Particle Duality and Quantum Entanglement." The simulations explore how entangled particles interact and propagate information through their combined wave function. The notebook provides an interactive environment where users can run the simulations and explore the impact of different energy interactions on particle wavelengths, quantum entanglement, and wave function collapse. The results from these simulations align with the theoretical predictions discussed in the paper and offer insights into quantum communication and cryptography.</p>
scMARK an 'MNIST' like benchmark to evaluate and optimize models for unifying scRNA data
<p>Here we present a novel benchmark dataset (scMARK.v2), that consists of 11 published cancer scRNA-seq studies, for which we standardized cell-type author labels and gene identifiers. scMARK.v2 can be used to ask how well models integrate data from different scRNA studies. We also provide a 12th standardized study (Wu et al 2021) that we held-out for evaluation of alignment of data "never seen" before, and a 13th study of newly generated in-vitro scRNA-seq data from cancer and fibroblast cells.</p> <ul> <li>Data is provided as aData *h5ad files that can be read with Python's library <a href="https://scanpy.readthedocs.io/en/stable/">Scanpy</a>.</li> <li>Studies inclided in scMARK.v2 were downsampled to 10,000 cells per study.</li> <li>The difference between <a href="https://zenodo.org/record/5765804">scMARK.v1</a> and scMARK.v2, is that in v2, we provide at least two studies for each cancer type and each cell type; whereas in v1 a handfull of cell types were present only in one study.</li> </ul>
NUMAC: Description of the Nested Unified Model with Aerosols and Chemistry, and evaluation with KORUS-AQ data: supporting data
<p>Simulated data presented in manuscript with title above, generated with the Met Office Unified Model, together with observations from KORUS-AQ and MODIS that are used in the model evaluation.</p> <p>The Terra/MODIS aerosol datasets were acquired from the Level-1 and Atmosphere Archive & Distribution<br> System (LAADS) Distributed Active Archive Center (DAAC), located in the Goddard Space Flight Center in Greenbelt, Maryland https://ladsweb.nascom.nasa.gov/. All surface and aircraft observation data is freely and publicly available at https://www-air.larc.nasa.gov/cgi-bin/ArcView/korusaq, last access 10 June 2022.</p>
Dataset: A unified modelling framework for projecting sectoral greenhouse gas emissions
<p>Data provided includes results from the unified framework described in "A unified modelling framework for projecting sectoral greenhouse gas emissions". Contains posterior draws of emission intensities and resulting emissions for 173 countries, five main sectors up to the year 2050. Historical GHG emissions data based on <a href="https://doi.org/10.5194/essd-13-5213-2021">Minx et al (2021)</a>.</p> <p>Code for the processing of results can be found on <a href="https://github.com/oDNAudio/GHG_sector_projections">Github</a>.</p>
Experimental Results for "A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models"
<p>This package contains the raw data / logs (fetched from WandB) for the experiments of the following publication:</p> <p>O. Arenz, P. Dahlinger, Z. Ye, M. Volpp, and G. Neumann. A unified perspective on natural gradient variational inference with gaussian mixture models. Transactions on Machine Learning Research, 2023. URL: <a href="https://openreview.net/forum?id=tLBjsX4tjs">https://openreview.net/forum?id=tLBjsX4tjs</a>.</p> <p> </p>
Scupa: Single-cell unified polarization assessment of immune cells using the single-cell foundation model
<p>This dataset contains the processed scRNA-seq data and code for single-cell unified polarization assessment. Please refer to the article "Scupa: Single-cell unified polarization assessment of immune cells using the single-cell foundation model" for the detailed data and method description.</p> <p>*.rds: the processed scRNA-seq datasets with Universal Cell Embeddings saved as a Seurat object. The Universal Cell Embeddings are saved in the assay 'uce'.</p> <ul> <li>immune_dict_uce.rds: Immune Dictionary.</li> <li>ifnb_treatment_uce.rds: human PBMCs treated with IFN-beta.</li> <li>macrophage_stimulation_uce.rds: human macrophages treated with one or two of IFN-beta, IFN-gamma, TNF-alpha and IL-4.</li> <li>il2_treatment_uce.rds: mouse spleen CD8+ T cells treated with IL-2 or anti-PD-L1.</li> <li>pan_cancer_myeloid_uce.rds: tumor-infiltrating myeloid cells across seven cancer types.</li> </ul> <p>notebooks.zip: Jupyter notebooks containing code for training models and applications to multiple datasets.</p>
Unified Model Atmospheric Forecast Model Data for Machine Learning Cloud-Base Height
<p>Unified Model data, in pp format, for machine learning of cloud-base height based on profiles of temperature, humidity, pressure and cloud fraction. The model configuration is Global Atmosphere 6, running with a resolution of N320 (which is coarser than what was running operationally at the time). Each simulation is run for 24 hours, re-initialising every 24 hours. A separate data file is provided every 6 hours. Data points are on a latitude-longitude grid in the horizontal and on a stretched grid in the vertical. See https://gmd.copernicus.org/articles/10/1487/2017/ for details of the model configuration.</p> <p>Data from January 2016 is for training.</p> <p>Data from July 2017 is for development/validation</p> <p>Data from October 2017 is for final testing.</p> <p> </p>
Unified Model GA 7.0 Nudging Experiments used in JGR-A publication "Investigating Tropical versus Extratropical Influences on the Southern Hemisphere Tropical Edge in the Unified Model", Freisen et al. (2022)
<p><strong>UK Met Office Unified Model (UM) Global Atmosphere version 7.0 (GA7.0) Nudging Experiments </strong><strong>used in </strong><strong>"Investigating Tropical versus Extratropical Influences on the Southern Hemisphere Tropical Edge in the Unified Model" published in Journal of Geophysical Research: Atmospheres (doi.org/10.1029/2021JD036106).</strong></p> <p>Please refer to UM_nudging_experiments_readme.txt.</p>
Data for publication "A unified surface tension model for multi-component salt, organic and surfactant solutions"
<p>This repository contains the data of the publication:</p> <p>Title: "A unified surface tension model for multi-component<br>salt, organic and surfactant solutions"<br>Authors: Judith Kleinheins, Claudia Marcolli, Cari Dutcher, Nadia Shardt<br>Date: 2024</p>
Unified Clumpy AGN Torus Model and X-ray spectrum
<p>In Buchner et al. (in prep) we develop a geometry for the X-ray obscurer of AGN. Its cloud population is consistent with existing CLUMPY (Nenkova+02) infrared models. However, we find that a inner torus ring component is required to fit local Compton-thick AGN.</p> <p>More information at <a href="https://github.com/JohannesBuchner/xars/blob/master/doc/uxclumpy.rst">https://github.com/JohannesBuchner/xars/blob/master/doc/uxclumpy.rst</a></p> <p>Our model has two geometry parameter:<br> 1) The opening of the torus, or the vertical extent of the clouds (sigma)<br> 2) The covering of the inner Compton-thick wall.<br> Further parameters are:<br> 3) Photon index and energy cut-off of the corona<br> 4) Column density in the line of sight (up to 10^26)<br> 5) Viewing angle.</p> <p>We release XSPEC table spectra here.</p>
Spectral models for binary products: Unifying subdwarfs and Wolf-Rayet stars as a sequence of stripped-envelope stars
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2018A&A...615A..78G/abstract">Götberg et al. (2018)</a>. MESA version 8118.</p> <p>Publication DOI: <a href="https://doi.org/10.1051/0004-6361/201732274">10.1051/0004-6361/201732274</a></p>
A unified meta-ecosystem dynamics model: Integrating herbivore-plant subwebs with the intermittent upwelling hypothesis
<p>Determining the relative influence of biotic and abiotic processes in structuring communities at local to large spatial scales is best understood using a biogeographic comparative-experimental approach. Using this approach, previous work suggests that intertidal community dynamics (top-down and bottom-up effects) vary unimodally along an upwelling-based productivity gradient, termed the Intermittent Upwelling Hypothesis (IUH). Evidence consistent with the IUH comes from the sessile invertebrate/predator (SIP) subweb in certain rocky intertidal communities, but whether this pattern extends to macrophyte/herbivore (MH) subwebs is unknown. Here we ask: Are MH subwebs also structured as predicted by the IUH? What is the relative importance of herbivory and predation in structuring these communities? Under what conditions do ecological subsidies like nutrients or propagule production drive community dynamics? And are omnivorous interactions important? We hypothesize that MH subwebs are driven by a new construct, the Grazing-Weakening Hypothesis (GWH), which states that MH interactions weaken monotonically with increasing nutrients, with strong (weak) herbivory and low (high) macrophyte productivity at low (high) nutrients. We explored local-to-large spatial scale dynamics of both subwebs using a biogeographic comparative-experimental factorial field experiment testing joint and separate effects of herbivores and predators between two continents. Experiments at ten sites ranging across from persistent upwelling to persistent downwelling regimes ran for 26-29 months in Oregon and California, and New Zealand South Island. For the MH subweb, results were consistent with the GWH: herbivory declined and macrophytes increased with increasing nutrients. As expected, results for the SIP subweb were consistent with the IUH: predator effect size was unimodally related to upwelling. Overall, herbivory explained more variation in community structure than did predation, especially in New Zealand. Omnivory was weak, sessile invertebrates outcompeted macrophytes, and ocean-driven subsidies provided the basic template driving ecosystem dynamics. We propose a unified Meta-Ecosystem Dynamics Model (MEcoDynaMo) combining MH and SIP results: with increased upwelling, sessile invertebrates and underlying dynamics vary unimodally (as in the IUH), while herbivory decreases and macrophytes generally increase. While this model was based on research in temperate ecosystems varying in upwelling regime, its wider applicability remains to be tested.</p>
A unified meta-ecosystem dynamics model: Integrating herbivore-plant subwebs with the intermittent upwelling hypothesis
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A unified model explaining the unimodal relationship between productivity and species richness in fish communities
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.