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147 results for “Data Assimilation”
Enhancing accuracy of air quality and temperature forecasts during paddy crop-residue burning season in Delhi via chemical data assimilation
<p>This paper examines the accuracy of Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) generated 72 h fine particulate matter (PM<sub>2.5</sub>) forecasts in Delhi during the crop residue burning season of Oct-Nov 2017 with respect to assimilation of the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrievals, persistent fire emission assumption, and aerosol-radiation interactions. The assimilation significantly pushes the model AOD and PM<sub>2.5</sub> towards the observations with the largest changes below 5 km altitude in the fire source regions (northeastern Pakistan, Punjab, and Haryana) as well as the receptor New Delhi. WRF-Chem forecast with MODIS AOD assimilation, aerosol-radiation feedback turned on, and real-time fire emissions reduce the mean bias by 88-195 µg/m<sup>3</sup> (70-86%) with the largest improvement during the peak air pollution episode of 6-13 November 2017. Aerosol-radiation feedback contributes ~21%, ~25%, and ~24% to reduction in mean bias of the first, second, and third day of PM<sub>2.5 </sub>forecast. Persistence fire emission assumption is found to work really well, as the accuracy of PM<sub>2.5</sub> forecasts driven by persistent fire emissions was only 6% lower compared to those driven by real fire emissions. Aerosol-radiation feedback extends the benefits of assimilating satellite AOD beyond PM<sub>2.5</sub> forecasts to surface temperature forecast with a reduction in the mean bias of 0.9<sup>o</sup>C - 1.5<sup>o</sup>C (17-30%). These results demonstrate that air quality forecasting can benefit substantially from satellite AOD observations particularly in developing countries that lack resources to rapidly build dense air quality monitoring networks.</p>
Global state outputs from GRACE-CLSM data assimilation product from Girotto et al. 2016
<p>Mean and standard deviation of ensemble average state variables and terrestrial water storage (TWS) for GRACE-CLSM data assimilation product from Girotto et al. 2016. The dataset contains both the data assimilation (DA) output and the open-loop (OL), pre-assimilation states used in Scheliga et al. <em>In review.</em></p> <p># GENERAL INFORMATION</p> <p>Title of Dataset: Global state outputs from GRACE-CLSM data assimilation product from Girotto et al. 2016</p> <p><br>## Principal Investigator Information<br>* Name: Manuela Girotto<br>* ORCID: 0000-0003-2795-2063<br>* Institution: University of California, Berkeley<br>* Email: mgirotto@berkeley.edu</p> <p>## Co-Author Information<br>* Name: Ann Scheliga<br>* ORCID: 0000-0002-0217-6451<br>* Institution: University of California, Berkeley<br>* Email: ann_scheliga@berkeley.edu</p> <p>Date of data generation: approximately 2021-09-09</p> <p>Funding sources that supported the collection of the data: NASA Sea Level Change Team (Grant 80NSSC21K0321).</p> <p><br># SHARING/ACCESS INFORMATION</p> <p>* Licenses/restrictions placed on the data: CC-BY-NC<br>* Links to publications that describes the data: Scheliga, A., Girotto, M., Gerlein-Safdi C. Anthropogenic processes and sea-level rise contributions with data assimilation of GRACE and GRACE-FO terrestrial water storage. In review.<br>* Recommended citation for this dataset: Girotto, M. et al. Assimilation of gridded terrestrial water storage observations from GRACE into a land surface model. WRR. https://doi.org/10.1002/2015WR018417</p> <p> </p>
WACCMX DART Ionosphere Data Assimilation Observing System Simulation Experiment
This dataset contains output from the data assimilation version of the Whole Atmosphere Community Climate Model with thermosphere-ionosphere eXtension (WACCMX DART). The dataset includes model output fields of electron column density, temperature, and zonal wind as well as DART observation space diagnostics. The results are based on a series of Observing System Simulation Experiments that are aimed at evaluating the assimilation of ionospheric observations in WACCMX DART. Included are files from the OSSE truth simulation, as well as from three experiments: Experiment 1: No ionosphere data assimilation. Experiment 2: Ionosphere data assimilation without thermosphere constraint. Experiment 3: Ionosphere data assimilation with thermosphere constraint. The dataset is in support of the publication "Assimilation of Ionosphere Observations in the Whole Atmosphere Community Climate Model with thermosphere-ionosphere eXtension (WACCMX)".
Supplementary Data for "Comparison of Ensemble-Based Data Assimilation Methods for Sparse Oceanographic Data"
<p>This data set represents the supplementary data for the paper Comparison of Ensemble-Based Data Assimilation Methods for Sparse Oceanographic Data (Section 4) by Florian Beiser, Håvard Heitlo Holm, and Jo Eidsvik.</p><p>It contains the data that is plotted in the manuscript. The code for plotting is provided in the supplementary software.</p>
Data for simultaneous inference of sea ice state and surface emissivity model using machine learning and data assimilation
<h2>Overview</h2> <p>This dataset supports the draft manuscript "Simultaneous inference of sea ice state and surface emissivity model using machine learning and data assimilation" which describes a way to infer the daily maps of the sea ice concentration and empirical properties of the sea ice (relating to its snow cover and its physical properties, such as air inclusions) along with the creation of a new empirical model for the sea ice surface emissivity. This is done using knowledge of the atmosphere state, skin temperature and ocean water emissivity from the European Centre for Medium-range Weather Forecasts (ECMWF) weather forecasting model and the observed radiances at microwave frequencies from the Advanced Microwave Scanning Radiometer 2 (AMSR2). The inverse modelling and state estimation is achieved by combining empirical machine learning elements in a Bayesian-inspired network along with a number of physical components. The work also introduces the idea of an "empirical state", in this case describing the aspects of the sea ice physical state which affect the observations, and which is defined by the inputs to the new empirical model component (in machine learning terms, it is defined by the latent input state of a neural network). This dataset includes the data used in training the model and inferring the sea ice parameters, as well as the outputs from that training process. The software used to perform the training is in Python and uses the Keras and Tensorflow software. See the draft manuscript for full details of this data.</p> <p>The code used in the draft manuscript is archived at <a href="https://doi.org/10.5281/zenodo.10013542">https://doi.org/10.5281/zenodo.10013542</a></p> <p>The data used in the draft manuscript is archived at <a href="https://doi.org/10.5281/zenodo.10033377">https://doi.org/10.5281/zenodo.10033377</a></p> <h2>Training data </h2> <h3>Observation space training and ancillary data</h3> <p>Training is done at the location of AMSR2 superobservations (superobs) over ocean with less than 1% land contamination and polewards of 45 degrees latitude, between 1st July 2020 and 30th June 2021. There are 64,184,021 superobs used. A superob is the average of all raw JAXA level 1B observations from one orbit falling into a grid box on an approximately constant area (reduced Gaussian) grid at approximately 40 km by 40 km resolution (noting that polar regions can thus have up to around 7 superobs per day). The superobs have been computed using the field of view central locations for each channel as derived from the JAXA level 1B data. A subset of 10 of the AMSR2 channels is used, from 10 GHz, V polarised, to 89 GHz, H polarised.</p> <p>At each superob location, the relevant fields from the ECMWF 12 hour 'background' forecast are interpolated to the observation time and location. The atmosphere is represented indirectly by the relevant radiative transfer terms from a scattering radiative transfer model. The sea ice concentration from the ECMWF OCEAN5 analysis is included as a validation reference but is not used in the training itself, except to provide a monthly mean first guess to speed up the training. Each field is provided in a separate netCDF file:</p> <ul> <li>field_v2_JULIAN_DAY.nc - superob time in days since 12 UTC on Nov 24th 4714 BC on the proleptic Gregorian calendar</li> <li>field_v2_LAT.nc - superob central latitude in degrees</li> <li>field_v2_LON.nc - superob central longitude in degrees</li> <li>field_v2_IGRID.nc - corresponding grid number on the map grid used in this work (see below)</li> <li>field_v2_OBSVALUE.nc - observed superob brightness temperature at each of 10 AMSR2 channels.</li> <li>field_v2_TSFC.nc - skin temperature computed by the ECMWF forecast model</li> <li>field_v2_WINDSPEED10M.nc - 10m wind speed computed by the ECMWF forecast model</li> <li>field_v2_EMIS_WATER.nc - Ocean water surface emissivity at 10 AMSR2 channels, simulated from the ECMWF forecast fields using the FASTEM-6 model</li> <li>field_v2_CLOUD_FRACTION.nc - Effective cloud fraction used in the atmospheric radiative transfer model at each of 10 AMSR2 channels</li> <li>field_v2_TAUSFC_CLD.nc - Surface to space transmittance in the cloudy column at each of 10 AMSR2 channels</li> <li>field_v2_TUP_CLD.nc - Upwelling brightness temperature from the atmosphere in the cloudy column at each of 10 AMSR2 channels</li> <li>field_v2_TDOWN_CLD.nc - Downwelling brightness temperature from the atmosphere in the cloudy column at each of 10 AMSR2 channels</li> <li>field_v2_TAUSFC.nc - Equivalently for the clear column</li> <li>field_v2_TUP.nc - Equivalently for the clear column</li> <li>field_v2_TDOWN.nc - Equivalently for the clear column</li> <li>field_v2_SEAICE.nc - Sea ice concentration from the ECMWF OCEAN5 analysis, for validation only (not used in training)</li> </ul> <h3>Grid space data: initial data for training; validation sea ice data</h3> <p>A number of properties are provided to the hybrid physical-empirical model that is being trained, on a special map grid defined in this project, including all 62,499 of the reduced Gaussian 40km grid points that have at least one superob at some point during the year of training data. These are:</p> <ul> <li>ifs_seaice_initials_year.nc - sea ice concentration from OCEAN5, monthly averaged on the grid, and then provided on all days of the relevant month as initial conditions (technically, first guess) for the training. This includes an additional day before the beginning of the training, used for time-lagging (see draft paper).</li> <li>ifs_tsfc_year_dailyx.nc - skin temperature from ECMWF forecast fields at observation locations, averaged onto the daily grid, to help provide constraints on the likelihood of sea ice as part of a sea ice loss function.</li> </ul> <p>For diagnostic and validation purposes, the ECMWF OCEAN5 analysis is also provided on the grid:</p> <ul> <li>ifs_seaice_year.nc - sea ice concentration from OCEAN5 at observation locations, averaged onto the daily grid</li> </ul> <p>All these fields are provided on the following dimensions:</p> <ul> <li>LON - the longitude of the grid point in degrees</li> <li>DAY - the day through the training year (0-364, 1st July 2020 to 30th June 2021) or through the training year extended forward by one day (30th June 2020) for the sea ice (0-365). In practice the days are offset by 3 hours from the UTC day to match the ECMWF data assimilation windows, which start at 21 UTC the day before.</li> </ul> <p>The latitude is also provided</p> <ul> <li>LAT - the latitude of the grid point in degrees</li> </ul> <p>Note that the observation location IGRID is on the custom grid of the ML model that is defined implicitly in these gridded files. The LON and LAT vectors in these files are the longitude and latitude points of the grid and are of 62499 in length. The IGRID number for an observation is the index into these arrays from 0-62498.</p> <h2>Outputs from training</h2> <p>The following files are the output and diagnostics from the year-long training. The python code and the draft paper are the primary documentation for these:</p> <ul> <li>models_year.nc - settings of the model are recorded here, along with the trained values of the smaller empirical components/layers within the hybrid model. For example, the layer weights of the wind speed bias correction, the observation space bias correction, and the empirical surface emissivity model are recorded here. The values of the loss function at each epoch are also recorded here.</li> <li>properties_year.nc - trained values of each of 3 empirical properties of sea ice on the map grid (3 properties by 62499 locations by 365 days from 1st July 2020)</li> <li>seaice_year.nc - inferred values of sea ice fraction on the map grid (62499 locations by 365 days from 1st July 2020, discarding the additional day at the start)</li> <li>tbsim_year.nc - simulated AMSR2 brightness temperatures from the trained network</li> <li>tbsim_initial_year.nc - simulated AMSR2 brightness temperatures using the untrained network</li> </ul> <p>The longitude and latitude of the map grid is found in any of the initial data files described in the previous section. The days are 0-364 corresponding to 1st July 2020 to 30th June 2021.</p> <h3>Sea ice surface emissivity at grid locations</h3> <p>A packaged version of the sea ice surface emissivity is provided at grid locations, alongside the surface emissivity model, the sea ice concentration and the four inputs to the model, i.e. the normalised skin temperature and the three empirical variables:</p> <ul> <li>emissivity_grid_year.nc</li> </ul> <p>Note that in the training, the surface emissivity is computed at observation locations and has not been stored due to memory limitations. For easier comparison to other datasets, the surface emissivity has been recomputed on grid locations in this package, using the year-long trained emissivity model and its trained inputs. The sea ice surface emissivity is only physically meaningful for sea ice concentrations above around 0.25. Also be aware of the "hole at the pole" which is the small region of the Arctic ocean that is sometimes not covered by an AMSR2 overpass, and which is found from 88 degrees N. On days where the hole or part of the hole exists, the sea ice emissivity on the grid is not valid at these locations. These locations can be identified by having all values of the empirical properties zero (because the empirical properties were never constrained by any observations on that day, and remain at their initial values before training).</p> <h2>Sensitivity tests</h2> <p>Extensive sensitivity tests were carried out, as described in the appendices of the draft paper and as documented in the Python code, using the month of August 2020 as an example. These required equivalent month-long training and initial data similar to those described above, but all observation space fields are contained within the same file in this case. Output files follow similar principles to those described above. The full package is provided as a tar file:</p> <ul> <li>sensitivity.tar</li> </ul> <p>This contains the training and initial files:</p> <ul> <li>amsr2_v2_202008.nc</li> <li>ifs_tsfc_dailyx_202008.nc</li> <li>ifs_seaice_202008.nc</li> </ul> <p>as well as directories containing the trained model outputs and diagnostics at each of the sensitivity tests, using the same formats as described for the yearly training, with these names:</p> <ul> <li>nprop - number of empirical properties</li> <li>epoch - number of epochs</li> <li>deep - configuration of the empirical sea ice emissivity model, including multiple layers of nonlinear dense neural network</li> <li>bseaice - background error for the sea ice physical bounds background error (loss) term</li> <li>bemis - background error for the sea ice emissivity background error (loss) term</li> <li>bbias - background error for the bias correction background error (loss) term</li> <li>batchsize - batch size used in training</li> <li>bbatchsize - extended epochs testing of batch size used in training</li> </ul> <h2>Licensing</h2> <p>This data product is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). To view a copy of this licence, visit <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></p> <p>You are free to:</p> <ul> <li>Share — copy and redistribute the material in any medium or format</li> <li>Adapt — remix, transform, and build upon the material for any purpose, even commercially.</li> </ul> <p>Under the following terms:</p> <ul> <li>You must give appropriate credit (attribution) to ECMWF as outlined below, provide a link to the licence, and indicate if changes were made.</li> <li>No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the licence permits.</li> </ul> <p>The following wording shall be attached to the use of this ECMWF data product: </p> <ol> <li>Copyright statement: Copyright "© 2023 European Centre for Medium-Range Weather Forecasts (ECMWF)".</li> <li>Source <a href="http://www.ecmwf.int/">www.ecmwf.int </a>and <a href="https://doi.org/10.5281/zenodo.10009497">https://doi.org/10.5281/zenodo.10009497</a></li> <li>Licence Statement: This data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></li> <li>Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.</li> <li>Where applicable, an indication if the material has been modified and an indication of previous modifications.</li> <li>DOI: 10.5281/zenodo.10009498</li> </ol> <p>Original data for this value-added product was provided by Japan Aerospace Exploitation Agency (JAXA). Specifically, this dataset builds on the Advanced Microwave Scanning Radiometer 2 (AMSR2) level 1B data available from the JAXA G-Portal, https://gportal.jaxa.jp/gpr/, which has the following attribution and licensing:</p> <ol> <li>Give credit for the original data to JAXA, i.e. "Original data for this value added data product was provided by Japan Aerospace Exploration Agency"</li> <li>DOI for original JAXA data is L1B-Brightness temperature (TB) GCOM-W/AMSR2 L1B Brightness Temperature: <a href="https://doi.org/10.57746/EO.01gs73ans548qghaknzdjyxd2h">https://doi.org/10.57746/EO.01gs73ans548qghaknzdjyxd2h</a></li> <li>Original terms of data service from JAXA, with highlighted extracts: <ul> <li><a href="https://gportal.jaxa.jp/gpr/index/eula?lang=en">https://gportal.jaxa.jp/gpr/index/eula</a> <ol> <li>The user is entitled to use G-Portal data free of charge without any restrictions (including commercial use) except for the condition about acknowledgement of data credit as stipulated in Article 7.(2). (see above)</li> <li>JAXA is collecting results (papers, theses, reports, etc.) using G-Portal data. If you have any results using G-Portal data, please mail/e-mail a copy of the result to G-Portal Support Desk (Contact Information written at the end of the Terms of Use). We appreciate your cooperation very much.</li> </ol> </li> </ul> </li> </ol>
Bottom up Isoprene emissions (assimilated data)
<p>BVOC gas emissions from vegetation represent a hugely important contributor to the total emission of volatile organic compounds at the global scale. BVOCs in turn play a key role in tropospheric chemistry and impact both the formation of ozone photochemical smog and secondary organic aerosols, which both impact air quality. MEGAN is an open-sourced software package that has been adapted by researchers at NILU to operate with SURFEX model outputs and meteorological data from ECMWF. The MEGAN software calculates emissions of a wide range of BVOCs emitted by plants and aggregates these chemical components into representative chemical species defined in an external chemical mechanism. In our case, this done specifically is for the BVOCs defined (see Table 2 for full list) in the Regional Atmospheric Chemistry Mechanism (Stockwell et al., 1997) that is used in the MOCAGE (Guth et al., 2016) chemical transport model. MOCAGE is being used as the test model the SEEDS project since it is one of the CAMS regional atmospheric chemistry models.</p> <p><a href="https://www.seedsproject.eu/data/isop-assim">https://www.seedsproject.eu/data/isop-assim</a></p>
Data assimilation products by using multiple climate model simulations and different combinations of proxies
<p>This dataset of the climate reconstruction by data assimilation using isotope ratios provides annual surface air temperature, precipitation amount, and other climate variables during 850–2000.</p> <p>Two isotopes-incorporated atmospheric general circulation models and 129 isotopic proxy data (65 corals, 43 ice cores, and 21 tree-ring cellulose) were used in this study. There are nine experiments using three type of simulations and three combinations of proxies.</p> <p>The associated publication: Shoji, S., Okazaki, A., & Yoshimura, K. (2020). Impact of proxies and prior estimates on data assimilation using isotope ratios for the climate reconstruction of the last millennium. (submitted to Earth and Space Science)</p> <p>[Data structure]<br> X(lon) x Y(lat) x Z(2) x Variables(8) x Year(1151)<br> Z(1): analyses<br> Z(2): priors</p>
An Improved Coupled Data Assimilation System with a CGCM Using Multi-Timescale High Efficiency EnOI-Like Filtering
<p>Coupled data assimilation (CDA) combining coupled models and observations plays a critical role in climate studies by producing a four-dimensional estimation of Earth system states. However, traditional CDA algorithms while being expensive lack sufficient representation of multi-scale background flows. Here, a Multi-timeScale High-Efficiency Approximate EnKF (MSHea-EnKF) has been implemented in the global fully coupled climate model of the Geophysical Fluid Dynamics Laboratory. It consists of stationary, low-frequency, and high-frequency filters constructed from the timeseries of a single model solution, with improved representation for low-frequency background error statistics and enhanced computational efficiency. The MSHea-EnKF is evaluated in a biased twin experiment framework with synthetic “observations” produced by the other coupled model, Community Earth System Model, and a three-decade coupled analysis experiment with real observations. Results show that while computationally costing only a small fraction of traditional ensemble CDA, the MSHea-EnKF significantly improves the assimilation quality due to better representation of the slow-varying background flows in the filtering. The coupled analysis of MSHea-EnKF also improves the estimation of the Atlantic meridional overturning circulation, including better standard deviation distribution and mass transport at the Rapid section. These results of MSHea-EnKF on prevalent resolution coupled model with high computational efficiency promises its further applications to high-resolution coupled model data assimilation and reanalysis which will greatly advance our understanding for seamless weather-climate analysis and predictions.Coupled data assimilation (CDA) combining coupled models and observations plays a critical role in climate studies by producing a four-dimensional estimation of Earth system states. However, traditional CDA algorithms while being expensive lack sufficient representation of multi-scale background flows. Here, a Multi-timeScale High-Efficiency Approximate EnKF (MSHea-EnKF) has been implemented in the global fully coupled climate model of the Geophysical Fluid Dynamics Laboratory. It consists of stationary, low-frequency, and high-frequency filters constructed from the timeseries of a single model solution, with improved representation for low-frequency background error statistics and enhanced computational efficiency. The MSHea-EnKF is evaluated in a biased twin experiment framework with synthetic “observations” produced by the other coupled model, Community Earth System Model, and a three-decade coupled analysis experiment with real observations. Results show that while computationally costing only a small fraction of traditional ensemble CDA, the MSHea-EnKF significantly improves the assimilation quality due to better representation of the slow-varying background flows in the filtering. The coupled analysis of MSHea-EnKF also improves the estimation of the Atlantic meridional overturning circulation, including better standard deviation distribution and mass transport at the Rapid section. These results of MSHea-EnKF on prevalent resolution coupled model with high computational efficiency promises its further applications to high-resolution coupled model data assimilation and reanalysis which will greatly advance our understanding for seamless weather-climate analysis and predictions.</p>
4D-Var data assimilation experiment of the Lorenz 96 model using an adjoint model of a neural network surrogate model
<p>These data are the output of the 4D-Var data assimilation experiment of the Lorenz96 model using an adjoint model of a neural network surrogate model.<br> The details are described in Nishizawa (2022).<br> </p>
Data corresponding to "The Impact of Multi-sensor Land Data Assimilation on River Discharge Estimation"
<p>This dataset is corresponding to the input and output files that were used in this study:</p> <p>Wu, W.-Y., Z.-L. Yang, L. Zhao, P. Lin (2022), Joint Multi-sensor Data Assimilation for Constraining Water Storages and its Impact on Global Discharge Estimation (<em>in revision, RSE</em>)</p>
Winter Temperature Eurasian Data Assimilation Reconstruction (WinTEDA)
<p>A winter, Eurasian-focused paleoclimate temperature reconstruction over the past 1000 years. The reconstruction is over a seasonal average of December to February. The reconstruction includes Eurasia and Greenland paleoclimate data that have a correlation with winter temperature (p < 0.05, accounting for proxy autocorrelation). The reconstruction also includes long observational temperature records from the Northern Hemisphere.</p>
Data archive for: Chain forming diatoms use different strategies to avoid diffusion limited N assimilation
<p>Data archive for: “Chain forming diatoms use different strategies to avoid diffusion limited N assimilation” published in <em><strong>Limnology and Oceanography (L&O), <a href="https://doi.org/10.1002/lno.12677">https://doi.org/10.1002/lno.12677</a></strong></em>.</p> <p> </p> <p>Dataset of single cell assimilation of DIC and NO<sub>3</sub><sup>-</sup> by <em>Skeletonema marinoi</em> in the exponential and stationary growth phase captured using secondary ion mass spectrometry (SIMS) and stable isotopic tracers. The data set contains the data used in the study and the outliers excluded from further analysis.</p> <p> </p> <p>Two strains (Strain 1 and Strain 2) were incubated during either the exponential or stationary growth phase over 24h with <sup>15</sup>N enriched NO<sub>3</sub><sup>-</sup> and <sup>13</sup>C enriched DIC. The cell specific DIC and NO<sub>3</sub><sup>-</sup> assimilation was measured using SIMS.</p> <p>See the main manuscript for a extensive experimental setup and more details.</p> <p><strong>Each file is uploaded as both a .CSV and .XLSX, so that you can choose which you prefer.</strong></p> <p> </p> <p><strong>Row description:</strong></p> <p>Each row represents one <em>Skeletonema marinoi cell</em>.</p> <p><strong>Column description: </strong></p> <p><em>Single.cell:</em> if the cell was a solitary cell (y) or not (n)</p> <p><em>End.cell:</em> if the cell was located at the end of a chain (y) or not (n)</p> <p><em>Chain_position:</em> a number assigned to identify every cell in a chain starting with 1 at one end of the chain</p> <p><em>Chain_length_numeric:</em> the total number of cells in the chain</p> <p><em>Chain_length_max_6: </em>the total number of cell in the chain, all numbers larger than 6 are pooled together and labelled >6</p> <p><em>Two_chain:</em> if the cell was in a chain consisting of only 2 cells (y) or not (n)</p> <p><em>Cell_information:</em> whether the cell was a solitary cell (Single cell), found in a two cell chain (Two cell chain), found at the end of a chain longer than 2 cell (End cell), or in the middle of a chain longer than 3 cells (Middle cell).</p> <p><em>Chain:</em> a identifying number assigned to differentiate the different chains</p> <p><em>C_fmol_per_cell_day:</em> DIC assimilated in fmol cell<sup>-1</sup> day<sup>-1</sup></p> <p><em>N_fmol_per_cell_day:</em> NO<sub>3</sub><sup>-</sup> assimilated in fmol cell<sup>-1</sup> day<sup>-1</sup></p> <p> </p> <p> </p>
Plotting code and data for figures in ''Data Assimilation of Ion Drift Measurements for Estimation of Ionospheric Plasma Drivers''
<h2>2024_Hu_SpaceWeather_Data Assimilation of Ion Drift Measurements for Estimation of Ionospheric Plasma Drivers</h2> <p>This package includes the scripts and files for reproducing the plots (or subplots) in the paper, J. Hu, S. McDonald, A. Chartier, A. L. Rubio, S. Datta-Barua, Data Assimilation of Ion Drift Measurements for Estimation of Ionospheric Plasma Drivers, Space Weather.</p> <p> </p> <p> </p> <p>plotting_code_fig6.m : MATLAB code plotting the figure 6, SAMI3/IDA4D TEC global map</p> <p>---> calc_noon.m : MATLAB function calculating noon time location for the specific UT</p> <p>---> plot_tec_map.m : MATLAB function inside the plotting_code_fig.m</p> <p>plotting_code_fig8.m : MATLAB code plotting the figure 8, validation results compared to Millstone Hill Incoherent Scatter Radar measurements</p> <p>plotting_code_fig9.m : MATLAB code plotting the figure 9, validation results compared to SuperDARN measurements</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Paleo Hydrodynamics Data Assimilation product (PHYDA)
<p>See the associated publication: Steiger, N. J., Smerdon, J. E., Cook, E. R., and Cook, B. I., <a href="https://www.nature.com/articles/sdata201886">A reconstruction of global hydroclimate and dynamical variables over the Common Era</a>, <em>Scientific Data</em>, 5:180086, doi: 10.1038/sdata.2018.86.</p> <p>This database provides the first truly global reconstructions of hydroclimate along with associated climate dynamical variables over the past two thousand years. The reconstructions were made with an improved data assimilation reconstruction approach that optimally combines 2,978 paleoclimate proxy-data time series with the physical constraints of an atmosphere--ocean climate model. Three separate global reconstructions were created from the years 1 to 2000 CE, targeting annual means (defined as April to the next calendar year March), the boreal growing season of June, July, and August (JJA), and the austral growing season of December, January, and February (DJF); each of these reconstructions is contained in a separate NetCDF file. The reconstructions include three global variables gridded at about 2 degree resolution: surface temperature at 2 m, the Palmer drought severity index (PDSI), and the standardized precipitation evapotranspiration index (SPEI). Dynamical variables also included for each reconstruction: the global mean temperature, the North Atlantic sea surface temperature index which is the non-detrended and non-smoothed version of the Atlantic multidecadal oscillation (AMO), the monthly Nino SST indices (Nino 1+2, 3, 3.4, 4), the monthly equatorial Pacific zonal SST gradient, and the location of the intertropical convergence zone (ITCZ) in 11 longitudinal zones.</p> <p><strong>A 100 member ensemble (randomly drawn from the original reconstruction ensemble) is available externally due to Zenodo file size restrictions: <a href="http://clifford.ldeo.columbia.edu/nsteiger/recon_output/phyda_ens/">http://clifford.ldeo.columbia.edu/nsteiger/recon_output/phyda_ens/</a></strong></p> <p>Please cite the associated publication if you use this data set. Contact the author Nathan Steiger (<a href="http://www.ldeo.columbia.edu/~nsteiger/contact.html">http://www.ldeo.columbia.edu/~nsteiger/contact.html</a>) for more information about the details of the reconstructions.</p>
juliacarbajal/bilingual_assimilation: First release of data & analysis scripts for bilingual assimilation project.
<p>This release contains all the app data and vocabulary questionnaires collected for Carbajal et al. (in preparation)'s project on bilingual assimilation (OSF project <a href="https://osf.io/52z9g/">https://osf.io/52z9g/</a>). It also includes analysis scripts as of the 22nd of March, 2018.</p>
The prediction data analyzed in "Seasonal Arctic sea ice prediction using a newly developed fully coupled regional model with the assimilation of satellite sea ice observations"
<p>The outputs of seasonal predictions with the new modeling system analyzed in the article including:</p> <p>Sea ice concentration (SIC)</p> <p>Sea ice thickness (SIT)</p> <p>Sea surface temperature (SST)</p> <p>Near surface air temperature (T2) </p>
Implementation of an adaptive bias-aware extended Kalman filter for sea-ice data assimilation in the HARMONIE-AROME numerical weather prediction system: numerical experiments
<p>This data set provides the post-processed output of the numerical experiments performed to assess the possible effects of applying sea ice data assimilation within the surface analysis procedure of the HARMONIE-AROME NWP system. Results of five numerical experiments are provided:</p> <ul> <li>HA-REF – reference experiment <em>without</em> sea ice data assimilation applied, and with blending for upper-air initialization</li> <li>HA-EKF – sensitivity experiment with sea ice data assimilation applied, and with blending for upper-air initialization</li> <li>3DVAR-REF – reference experiment <em>without</em> sea ice data assimilation applied, and with 3DVAR for the upper-air analysis</li> <li>3DVAR-EKF – sensitivity experiment with sea ice data assimilation applied, and with 3DVAR for the upper-air analysis</li> <li>3DVAR-EKF-TS – sensitivity experiment with sea ice data assimilation, and with 3DVAR for the upper-air analysis using coupled surface and atmospheric data assimilation procedures</li> </ul> <p>For the HA-REF and HA-EKF experiments a subset of the gridded model output is provided; for the 3DVAR-REF, 3DVAR-EKF and 3DVAR-EKF-TS experiments a subset of the gridded model output and model data extracted at the positions of the SYNOP and TEMP stations within the model domain are provided. Additionally, in-situ observations, covering the same time period as the 3DVAR-REF, 3DVAR-EKF, 3DVAR-EKF-TS experiments, are provided.</p>
Pseudoproxies for the paper "A pseudoproxy assessment of data assimilation for reconstructing the atmosphere–ocean dynamics of hydroclimate extremes"
<p>Pseudoproxies for the paper “A pseudoproxy assessment of data assimilation for reconstructing the atmosphere–ocean dynamics of hydroclimate extremes” by Steiger and Smerdon 2017.</p> <p>If you have further questions, address them to the author Nathan J. Steiger.</p>
Data for: Drought impairs herbivore-induced volatiles, but not through constraints on newly assimilated carbon
<p>Volatile terpenes serve multiple biological roles including tree resistance against herbivores. The increased frequency and severity of drought stress observed in forests across the globe may hinder trees from producing defense-related volatiles in response to biotic stress. To assess how drought-induced physiological stress alters volatile emissions alone and in combination with a biotic challenge, we monitored pre-dawn water potential, gas exchange, needle terpene concentrations, and terpene volatile emissions of ponderosa pine (<em>Pinus ponderosa</em>) saplings during three periods of drought and in response to simulated herbivory via methyl jasmonate application. Although three-, six-, and seven-week drought treatments reduced net photosynthetic rates by 20%, 89%, and 105%, respectively, the magnitude of volatile fluxes remained generally resistant to drought. Herbivore-induced emissions, however, exhibited threshold-like behavior; saplings were unable to induce emissions above constitutive levels when pre-dawn water potentials were below the approximate zero-assimilation point. By comparing compositional shifts in emissions to needle terpene concentrations, we found evidence that drought effects on constitutive and herbivore-induced volatile flux and composition are primarily via constraints on the <em>de novo</em> fraction, suggesting that reduced photosynthesis during drought limits the carbon substrate available for <em>de novo</em> volatile synthesis. However, results from a subsequent <sup>13</sup>CO<sub>2</sub> pulse-chase labeling experiment then confirmed that both constitutive (<3% labeled) and herbivore-induced (<8% labeled) <em>de novo</em> emissions from ponderosa pine are synthesized predominantly from older carbon sources with little contribution from new photosynthates. Taken together, we provide evidence that in ponderosa pine, drought does not constrain herbivore-induced <em>de novo</em> emissions through substrate limitation via reduced photosynthesis, but rather, through more sophisticated molecular and/or biophysical mechanisms that manifest as saplings reach the zero-assimilation point. These results highlight the importance of considering drought severity when assessing impacts on the herbivore-induced response and suggest that drought-altered volatile metabolism constrains induced emissions once a physiological threshold is surpassed.</p>
Replication Data for: CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model
<p>This dataset includes three files necessary for understanding CHEEREIO model output in the demo section of my initial submission to GMD for the paper: <em>CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model.</em> Detailed guides for how to handle these datasets are provided in the <a href="https://cheereio.readthedocs.io/en/latest/Postprocess-workflow.html">CHEEREIO documentation postprocessing page</a>.</p> <ul> <li>control_hemco_diagnostics.nc contains the source-separated prior methane emissions.</li> <li>combined_hemco_diagnostics.nc contains the source-separated and ensemble member separated posterior methane emissions.</li> <li>bigY.pkl contains a Python dictionary which aligns TROPOMI XCH4 with simulated prior and posterior GEOS-Chem XCH4.</li> </ul>
ScienceDex guides
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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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.