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147 results for “data assimilation”
Data & code repository for "A re-appraisal of the ENSO response to volcanism with paleoclimate data assimilation"
<p>This repository includes the data and code that can be used to reproduce the figures for the paper entitled <em>A re-appraisal of the ENSO response to volcanism with paleoclimate data assimilation</em>.</p>
Data for "Impact of Gaussian transformation on cloud cover data assimilation for historical weather reconstruction"
<p>This dataset contains the simulation results in "Impact of Gaussian transformation on cloud cover data assimilation for historical weather reconstruction".</p>
Data assimilation-based surface temperature reconstructions over the last two millennia over Antarctica
<p>This dataset contains data assimilation-based temperature and δ<sup>18</sup>O reconstructions in 10 Antarctic regions over the last two millennia, presented in :</p> <blockquote> <p><a href="https://www.clim-past-discuss.net/cp-2018-90/">Klein, F., Abram, N. J., Curran, M. A. J., Goosse, H., Goursaud, S., Masson-Delmotte, V., Moy, A., Neukom, R., Orsi, A., Sjolte, J., Steiger, N., Stenni, B., and Werner, M.: Assessing the robustness of Antarctic temperature reconstructions over the past two millennia using pseudoproxy and data assimilation experiments, Clim. Past Discuss., https://doi.org/10.5194/cp-2018-90, in review, 2018. </a></p> </blockquote> <p>We use a new database of stable oxygen isotopes in ice cores compiled in the framework of Antarctica2k (Stenni et al., 2017) to constrain model ensembles derived from two simulations: one performed using ECHAM5-MPI-OM that covers the period 800-1999 CE with a horizontal resolution of 3.75° by 3.75° (Sjolte et al., 2018), and the other performed with ECHAM5-wiso, spanning 1871-2011 CE at 1.125° spatial resolution (Steiger et al., 2017). This latter simulation is available <a href="https://zenodo.org/record/1249604#.XHa824Uo_RY">here</a>.</p> <p>Four netCDF files are available:</p> <ol> <li>d18O_DA_ECHAM5-MPI-OM_1-2015.nc: data assimilation-based δ<sup>18</sup>O reconstructions using the model ensemble derived from ECHAM5-MPI-OM</li> <li>ts_DA_ECHAM5-MPI-OM_1-2015.nc: data assimilation-based surface temperature reconstructions using the model ensemble derived from ECHAM5-MPI-OM</li> <li>d18O_DA_ECHAM5-wiso_1-2015.nc: data assimilation-based δ<sup>18</sup>O reconstructions using the model ensemble derived from ECHAM5-wiso</li> <li>ts_DA_ECHAM5-wiso_1-2015.nc: data assimilation-based surface temperature reconstructions using the model ensemble derived from ECHAM5-wiso</li> </ol> <p>The variables included in the NetCDF files are:</p> <ul> <li>region: integers from 1 to 10 corresponding to the ID of the ten reconstructions targets, that were defined in Stenni et al. (2017): <ul> <li>1: East Antarctic Plateau</li> <li>2: Wilkes Land Coast</li> <li>3: Weddell Sea Coast</li> <li>4: Antarctic Peninsula</li> <li>5: West Antarctic Ice Sheet</li> <li>6: Victoria Land Coast-Ross Sea</li> <li>7: Dronning Maud Land Coast</li> <li>8: West Antarctica</li> <li>9: East Antarctica</li> <li>10: Antarctica</li> </ul> </li> <li>time: integers from 1 to 2015, corresponding to the years CE covered by the reconstructions</li> <li>DA_ts (or DA_d18O): data assimilation-based reconstructed surface temperature (or δ<sup>18</sup>O). The values are annual means and are given in anomalies computed over full period. The units are degrees celsius (or permil). </li> <li>DA_ts_std (or DA_d18O_std): Weighted standard deviation of the particles used for reconstructing temperature (or δ<sup>18</sup>O). The units are degrees celsius (or permil).</li> </ul> <p>For a detailed description of the experimental design, please see the associated publication (Klein et al., 2018). Don't hesitate to contact <a href="mailto:francois.klein@uclouvain.be">François Klein</a> for more information.</p> <p>References</p> <p>Klein, F., Abram, N. J., Curran, M. A. J., Goosse, H., Goursaud, S., Masson-Delmotte, V., Moy, A., Neukom, R., Orsi, A., Sjolte, J., Steiger, N., Stenni, B., and Werner, M.: Assessing the robustness of Antarctic temperature reconstructions over the past two millennia using pseudoproxy and data assimilation experiments, Clim. Past Discuss., https://doi.org/10.5194/cp-2018-90, in review, 2018.</p> <p>Sjolte, J., Sturm, C., Adolphi, F., Vinther, B. M., Werner, M., Lohmann, G., and Muscheler, R.: Solar and volcanic forcing of North Atlantic climate inferred from a process-based reconstruction, Climate of the Past, 14, 1179–1194, https://doi.org/10.5194/cp-14-1179-2018, 2018.</p> <p>Steiger, N. J., Steig, E. J., Dee, S. G., Roe, G. H., and Hakim, G. J.: Climate reconstruction using data assimilation of water isotope ratios from ice cores, Journal of Geophysical Research: Atmospheres, 122, 1545–1568, https://doi.org/10.1002/2016JD026011, 2017.</p> <p>Stenni, B., Curran, M. A. J., Abram, N. J., Orsi, A., Goursaud, S., Masson-Delmotte, V., Neukom, R., Goosse, H., Divine, D., van Ommen, T., Steig, E. J., Dixon, D. A., Thomas, E. R., Bertler, N. A. N., Isaksson, E., Ekaykin, A., Werner, M., and Frezzotti, M.: Antarctic climate variability on regional and continental scales over the last 2000 years, Climate of the Past, 13, 1609–1634, https://doi.org/10.5194/cp-13-1609-2017, 2017.</p>
Inputs (forcing, observations and config file) for the experiments included in "Spatio-temporal snow data assimilation with the ICESat-2 laser altimeter".
<p>Inputs or the experiments included in the manuscript <a href="https://doi.org/10.5194/egusphere-2024-1404">Spatio-temporal snow data assimilation with the ICESat-2 laser altimeter</a>. </p> <p>Three experiment's inputs (forcing, observations and config file) for the Multiple Snow data Assimilation system (<a href="https://doi.org/10.5281/zenodo.11147258">MuSA</a>, v2.1) for the experimental catchment of Izas in the Spanish Pyrenees. All the experiments use ERA5 data downscaled to 20 m spatial resolution with the statistical downscaling tool <a href="https://doi.org/10.21105/joss.05059">TopoPySCALE</a>. The experiments assimilate different variables. </p> <p> C) assimilation of fSCA retrieved from Sentinel-2;</p> <p> D) assimilation of snow depth profiles retrieved with ICESat-2;</p> <p> J) joint assimilation of variables in C) and D).</p> <p> </p> <p>All the experiments assimilate the observations with the deterministic ensemble smoother with multiple data assimilation (DES-MDA) scheme.</p>
Southern Hemisphere winds, pressure, and temperature over the 20th century from proxy-data assimilation
<p>This archive contains four reconstructions of annually resolved zonal surface wind (us), sea level pressure (psl), and surface temperature (tas) anomalies in the Southern Hemisphere over the period 1900 to 2005 CE. The anomaly reference period is 1961-1990. </p> <p>The reconstructions are generated using the Last Millennium Reanalysis data assimilation framework (Hakim et al., 2016; Tardif et al., 2019). The proxies assimilated come from a global database comprising the PAGES2k database (PAGES2k Consortium, 2017), additional ice core accumulation records (Thomas et al., 2017), and additional coral records (Sanchez et al., 2021). We use four climate models to produce the four reconstructions:</p> <ol> <li>the iCESM Last Millennium Ensemble (“CESM LM”, Brady et al., 2019, Stevenson et al., 2019)</li> <li>the HadCM3 Last Millennium Ensemble (“HadCM3 LM”, Collins et al., 2001)</li> <li>the CESM1 Large Ensemble (“LENS”; Kay et al., 2015)</li> <li>the CESM1 Pacific Pacemaker Ensemble (“PACE”; Schneider and Deser, 2018).</li> </ol> <p>The four reconstructions are named after the prior that is used. The last millennium ensemble of simulations include natural forcings only and the LENS and PACE ensemble of simulations include historical external forcings. For each reconstruction, there are three netCDF files containing the ensemble mean (mean of 100 ensemble members) for each climate field. More details can be found in O'Connor et al. (2021).</p> <p>Please cite O'Connor et al. (2021) when using these datasets. <a href="https://doi.org/10.1029/2021GL095999">https://doi.org/10.1029/2021GL095999</a></p>
Spatiotemporally-completed reconstruction of precipitation during the Holocene over the Northern Hemisphere using paleoclimate data assimilation
<div>(1) <strong>Data Content</strong>: Spatiotemporally complete reconstruction of annual precipitation during the Holocene (i.e., 12-0 ka BP) over the Northern Hemisphere. Based on the sources of the prior ensembles, the dataset comprises three distinct reconstructions, namely PDA (TraCE), PDA (HadCM) and PDA (Mixed), each of which contains: 1) 200 precipitation reconstructions derived from the Monte Carlo realizations for each experimental group, and 2) the corresponding mean and ±1 standard deviation calculated from each set of 200 reconstructions. <strong>(2) Data Production Method</strong>: We reconstructed annual precipitation fields for the Northern Hemisphere during the Holocene using a paleoclimate data assimilation system. This involved assimilating 2,421 Holocene precipitation records from the LegacyClimate 1.0 dataset. In our experiment, we utilized the time-averaged Ensemble Optimal Interpolation (EnIO) data assimilation algorithm. The static prior ensemble of states was constructed from either the TraCE 21 ka BP or the HadCM 23 ka transient climate simulations, or a combination of both in a mixed approach. The data have a temporal resolution of 100 years and a spatial resolution of 3.75°. </div> <div> </div> <div>All prerequisite materials for conducting the PDA-based reconstruction experiments - including prior model simulations, Holocene precipitation records, and Matlab codes- are publicly available on Zenodo repository (<span lang="EN-US">https://doi.org/10.5281/zenodo.17354887</span>). Please contact the author Miao Fang (E-mail: mfang@lzb.ac.cn) for more information about the details of the reconstructions.</div>
Data for: Triose phosphate utilization stress during photosynthesis addressed with dynamic assimilation measurements
<p>Oscillations in CO2 assimilation rate and associated fluorescence parameters have been observed alongside the triose phosphate utilization (TPU) limitation of photosynthesis for nearly 50 years. However, the mechanics of these oscillations are poorly understood. Here we utilize the recently developed Dynamic Assimilation Techniques (DAT) for measuring the rate of CO2 assimilation to increase our understanding of what physiological condition is required to cause oscillations. We found that TPU limiting conditions alone were insufficient, and that plants must enter TPU limitation quickly to cause oscillations. We found that ramps of CO2 caused oscillations proportional in strength to the speed of the ramp, and that ramps induce oscillations with worse outcomes than oscillations induced by step change of CO2 concentration. An initial overshoot is caused due to a temporary excess of available phosphate. During the overshoot, the plant out-performs steady state TPU and ribulose 1,5-bisphosphate regeneration limitations of photosynthesis but cannot exceed the rubisco limitation. We performed additional optical measurements which support the role of photosystem I reduction and oscillations in availability of NADP+ and ATP in supporting oscillations.</p>
WRF output for the Geophysical Research Letters publication "Potential Impacts of Radio Occultation Data Assimilation on Forecast Skill of Tropical Cyclone Formation in the Western North Pacific"
<p>This dataset is the Weather Research and Forecasting (WRF) model output for the <em>Geophysical Research Letters</em> publication entitled "Potential Impacts of Radio Occultation Data Assimilation on Forecast Skill of Tropical Cyclone Formation in the Western North Pacific". The dataset includes the azimuthal-averaged parameters with 0.2°resolution, three-day forecast, and two experiments for all cases analyzed in the publication. Due to the data size, only the variables used in the figures are uploaded (i.e., relative humidity, relative vorticity, temperature, and water vapor mixing ratio). Detailed information, composite calculation, and model settings can be found in the publication.</p>
(Dataset) Evaluating tomotectonic plate reconstructions using geodynamic models with data assimilation, the case for North America
<p>Dataset for the paper:</p> <p>Evaluating tomotectonic plate reconstructions using geodynamic models with data assimilation, the case for North America</p> <p>For more infomation, please look into the README file or contact ljliu@illinois.edu, thank you!</p>
MESMAR v1: A new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region. Article data
<p>Regional coupled and Earth System models are fundamental numerical tools for climate investigations, downscaling of predictions and projections, process-oriented understanding of regional extreme events, and many more applications. Here we introduce a newly developed coupled regional modeling framework for the Mediterranean region, called MESMAR (Mediterranean Earth System model at ISMAR) version 1, which is composed of the WRF atmospheric model, the NEMO oceanic 15 model, and the HD hydrological discharge model, coupled via the OASIS coupler. The model is implemented at moderate resolution (about 1/12° for the ocean and river routing, while twice coarser for the atmosphere) for long-term investigations.</p> <p>The gzipped tarball contains data files contained in the manuscript associated with the MESMARv1 description and submitted to Geoscientific Model Developments:</p> <p>MESMAR v1: A new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region</p> <p>by Andrea Storto, Yassmin Hesham Essa, Vincenzo de Toma, Alessandro Anav, Gianmaria Sannino,<br> Rosalia Santoleri, Chunxue Yang</p>
Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 4)
<p>The dataset contains:</p> <p>i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input</p> <p>ii. the model output and skill produced</p> <p>for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 4)</p>
Dataset for the paper submitted for peer-review with the title "Quantifying heterotrophic bacteria parameters and dissolved organic carbon biodegradability through oxygen data assimilation in a river water quality model"
<p>The proposed dataset is related to the following article submitted for peer review:</p> <p>Hasanyar, M., Flipo, N., Romary, T., Wang, S. (2023), Quantifying heterotrophic bacteria parameters and dissolved organic carbon biodegradability through oxygen data assimilation in a river water quality model, UNDER PEER-REVIEW</p> <p>It consists of command files for the prose-pa0.74 software available here: https://gitlab.com/prose-pa/prose-pa </p> <p>To run the model :</p> <p>1. Compile prose-pa0.74</p> <p>2. Copy the executable in the current directory</p> <p>3. In a terminal launch</p> <p>> ./prose-pa0.74 simulation.COMM test.log</p> <p>The “simulation.COMM” holds the settings for the ProSe-PA simulation related to the paper mentioned in the front head of the current file. </p> <p>The information on different parameters of “simulation.COMM” are included in “bathymetrie”, “Cmds”, “Inflows”, “layers”, “meteo”, “o2_obs”, “param_bio”, “Reaches” and “Singularities” folders.</p> <p>The “bathymetrie” folder holds the geometric information of several cross-sections along the river. </p> <p>The Cmds folder holds the “simulation.COMM” file. </p> <p>The “Inflows” folder the information about the boundary condition inflows to the river such as discharge, concentration of organic carbon, etc.</p> <p>The layer folder holds data of the initial conditions of the model (Table 2 in the article).</p> <p>The “meteo” folder holds the meteorological information.</p> <p>The “o2_obs” folder holds the observed oxygen data needed to do data assimilation. </p> <p>The “param_bio” folder holds information on the physiology of bacteria, phytoplankton, and other model species.</p> <p>The “Reaches” folder holds information about river reaches and their manning coefficient. </p> <p>The “param_range” file holds the variation range of model parameters considered in data assimilation together with their perturbation percentage.</p> <p>The output files are written in $HOME/Outputs folder. It is possible to change it directly in simulation.COMM, last entry “Output_folder”.</p>
Outputs of the Jupyter Notebook - Variational data assimilation with deep prior (CIRC23)
<p>The repository contains the outputs of the notebook "Variational data assimilation with deep prior (CIRC23)" published in The Environmental Data Science Book.</p>
Data assimilation experiments inform monitoring needs for near-term ecological forecasts in a eutrophic reservoir: data, forecasts, and scores
<p>This data publication contains zipped parquet from the Beaverdam Reservoir forecasting data assimilation experiments using the FLARE (Forecasting Lake And Reservoir Ecosystems) system: drivers.zip contains NOAA driver forecast files, targets.zip contains in-situ water temperature observations and meteorological data, forecasts.zip contains forecast parquet files generated from the BVR FLARE DA experiment workflow, and scores.zip contains forecast skill metrics required for analysis. Within the forecasts and scores folders, there are four runs that were conducted with different parameter tuning and uncertainty quantification. The "all_UC" folder includes forecasts run with process, driver, parameter, and initial condition uncertainty quantification. The "IC_off" folder includes forecasts run without initial conditions uncertainty included (i.e., only process, driver, and parameter uncertainty). The "constant_bad_pars" folder includes forecasts run with constant parameters (but daily updating of initial conditions) that were not tuned for Beaverdam Reservoir before forecasts were generated. Finally, the "tuned_bad_pars" folder includes forecasts that were run with daily updating of initial conditions and parameters, but the parameters started out at random values that were not tuned for Beaverdam Reservoir.</p>
A high-resolution regional data-assimilative ocean modeling output near Cape Hatteras in 2017
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High-resolution 4DVAR-based Gulf Stream data-assimilative model product
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A high-resolution regional data-assimilative ocean modeling output near Cape Hatteras in 2018
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Data from: Is phenotypic plasticity use-it-or-lose-it? Exploring genetic assimilation of salinity-plastic traits across threespine stickleback (<em>Gasterosteus aculeatus</em>) populations
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Data for: Triose phosphate utilization stress during photosynthesis addressed with dynamic assimilation measurements
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Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: Example of AWI-CM - output files and plot scripts
<p>This archive outputs_plotting.zip contains the raw output files (STDOUT) from the scaling runs performed for the paper "Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: Example of AWI-CM" submitted to GMD (gmd-2019-167). Further the scripts to extract timing information from the raw output files and plot scripts are included.</p> <p>The archive SST-DA_plotting.zip contains the scripts to compute RMS errors for the free ensemble run (output file in gmd_N46_free.zip) and the SST assimilation run (gmd_N46_sst.zip) and to plot these. The two output files contain each a Netcdf file with the ensemble mean state information and the stdout file from the model run.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.