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855 results for “model system”
Bern3D model output related to: Hysteresis of the Earth system under positive and negative CO2 emissions
<p>The data below is output from the Bern3D intermediate complexity model and idealized CO2 increase-decrease simulations used in Jeltsch-Thömmes et al., Environ. Res. Lett. 15 (2020) 124026, https://doi.org/10.1088/1748-9326/abc4af</p> <p><br> The data are provided as .csv and .nc files<br> There are different types of data</p> <p><br> 1) TIMESERIES DATA (Fig. 1 and 2)<br> =================================<br> The name of the files indicates the variable:<br> co2_ts.csv change in atm. co2 [ppm]<br> cumulativeEmissions_ts.csv cumulative emissions [GtC]<br> cumulativeAOflux_ts.csv cumulative atm-ocean C flux [GtC]<br> cumulativeABflux_ts.csv cumulative atm-land C flux [GtC]<br> sat_ts.csv change in surface air temperature [degC]<br> ohc_ts.csv change in ocean heat content [10^24 J]<br> amoc_ts.csv change in Atlantic meridional overturning circulation strength [Sv]<br> seaice_ts.csv fraction of pre-industrial sea-ice area remaining [fraction of PI]<br> <br> The first row in the .csv files contains the header, which indicates the experiment. The naming convention is as follows:<br> c4k#_###</p> <p>c4 indicates the maximum co2 as times pre-industrial (4 times)<br> k# indicates the equilibrium climate sensitivity of the respective simulation in degrees C (k2 to k5)<br> ### indicates the rate of CDR:<br> 010: 0.1% yr^-1<br> 010: 0.3% yr^-1<br> 010: 0.5% yr^-1<br> 010: 0.7% yr^-1<br> 100: 1% yr^-1<br> 200: 2% yr^-1<br> 400: 4% yr^-1<br> 600: 6% yr^-1</p> <p><br> 2) HYSTERESIS DATA (Fig. 3)<br> ===========================<br> The name of the files indicates the variables:<br> cumulativeEmissions_sat.csv cumulative emissions and change in surface air temperature [degC]<br> cumulativeEmissions_OHCsurf.csv cumulative emissions and change in upper ocean heat content (0-700 m) [10^24 J]<br> cumulativeEmissions_o2thermo.csv cumulative emissions and change in thermocline (200-600 m) o2 [mmol m^-3]<br> cumulativeEmissions_OM_arag.csv cumulative emissions and fraction of water in the uppermost 175 m with omegar_aragonite saturation state >3 [fraction]</p> <p>each file contains the time (simulation year) as well as cumulative emissions (cumuEmis) and the respective variable (same naming as in filename) for all the experiments (see timeseries data for naming convention)</p> <p><br> 3) SPATIAL DATA (Fig. 4 and 5)<br> ==============================<br> All data for Fig. 4 and 5 are contained in one single .nc file (fig4_5_data.nc) with a varibale for each map shown in Fig. 4 and 5:<br> c4k2_100_sat hysteresis (down-path minus up-path) in surface air temperature at cumulative emissions of 1000 GtC, ECS=2 degC, in [degC]<br> c4k3_100_sat hysteresis (down-path minus up-path) in surface air temperature at cumulative emissions of 1000 GtC, ECS=3 degC, in [degC]<br> c4k5_100_sat hysteresis (down-path minus up-path) in surface air temperature at cumulative emissions of 1000 GtC, ECS=5 degC, in [degC]<br> <br> c4k2_100_o2thermo hysteresis (down-path minus up-path) in thermocline (200-600 m) o2 at cumulative emissions of 1000 GtC, ECS=2 degC, in [mmol m^-3]<br> c4k3_100_o2thermo hysteresis (down-path minus up-path) in thermocline (200-600 m) o2 at cumulative emissions of 1000 GtC, ECS=3 degC, in [mmol m^-3]<br> c4k5_100_o2thermo hysteresis (down-path minus up-path) in thermocline (200-600 m) o2 at cumulative emissions of 1000 GtC, ECS=5 degC, in [mmol m^-3]<br> <br> c4k3_100_Om_arag_up mean aragonite saturation state of the uppermost 175 m at cumulative emissions of 1000 GtC on the up-path, ECS=3 degC, [unitless]<br> c4k3_100_Om_arag_do mean aragonite saturation state of the uppermost 175 m at cumulative emissions of 1000 GtC on the down-path, ECS=3 degC, [unitless]</p> <p> </p> <p> </p> <p><br> The files can be readily importet in python, for example, by:<br> import pandas as pd<br> import xarray as xr<br> <br> # for the .csv files<br> df = pd.read_csv('path+filename', sep=',', header=0, index_col=None)<br> <br> # for the .nc files<br> ds = xr.open_dataset('path+filename')</p> <p><br> For additional information or in case of questions please contact:<br> Aurich Jeltsch-Thömmes<br> aurich.jeltsch-thoemmes@unibe.ch</p>
Database on the Performance of Current Agro-Ecological Farming Systems (AEFS) as an Input to the Modelling in WP4
<p>This database contains farm data (e.g. yields) and results (indicators) from assessments with the three decision support tools in the UNISECO case studies: SMART (www.fibl.org/en/themes/smart-en.html), Cool Farm Tool (coolfarmtool.org) and COMPAS (www.thuenen.de). This version (2.0) was developed as a benchmark for the assessment of exemplary cases of how the core dilemmas of agro-ecological transitions may be overcome at farm level and as an input to the modelling at territorial level in WP4.</p> <p>This database was created in the course of the H2020 project UNISECO. The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 773901.</p>
Modelica Models and Jupyter Notebooks for System Analysis of Glucose Insulin Regulation
<p>This dataset contains source code of Modelica models of Glucose-Insulin regulation using different techniques.</p> <p>Accompanying Jupyter notebook is demo for system analysis (parameter estimation) of artificial data and to match model simulation able to be used in Teaching class.</p> <ul> <li><strong>ModelicaIdentification.ipynb</strong> - default notebook - code contains ellipsis which needs to be replaced as per instruction in text</li> <li><strong>ModelicaIdentificationResolution.ipynb - </strong>notebook - code with exemplar solution to default notebook</li> <li><strong>glucoseinsulin.mo - </strong>Modelica source code</li> <li><strong>PatientInsulinConcentration.csv</strong> - sample data to be fitted against model</li> <li><strong>seminar11hw.GIExperiment.fmu</strong> - FMU exported from Modelica in order to run simulation in Python and PyFMI library</li> </ul> <p>Thanks to the MYBINDER service, the Jupyter notebook can be viewed and executed as</p> <ul> <li><a href="https://mybinder.org/v2/zenodo/10.5281/zenodo.3633324/">https://mybinder.org/v2/zenodo/10.5281/zenodo.3633324/</a> note that you need to launch terminal first in Jupyter -> New -> Terminal and install pyfmi and matplotlib by:</li> </ul> <pre><code class="language-bash">conda install -c conda-forge pyfmi matplotlib</code></pre> <ul> <li>Most recent version with other models and notebooks <a href="https://mybinder.org/v2/gh/creative-connections/Bodylight-notebooks/master?filepath=Seminar11GlucoseInsulinIdentification/">https://mybinder.org/v2/gh/creative-connections/Bodylight-notebooks/master?filepath=Seminar11GlucoseInsulinIdentification/</a></li> </ul>
Broad-Coverage German Sentiment Classification Model and Dataset for Dialog Systems
<p><a href="http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.202.pdf"><strong>Training a Broad-Coverage German Sentiment Classification Model for Dialog Systems</strong></a></p> <p>This paper describes the training of a general-purpose German sentiment classification model. Sentiment classification is an important aspect of general text analytics. Furthermore, it plays a vital role in dialogue systems and voice interfaces that depend on the ability of the system to pick up and understand emotional signals from user utterances. The presented study outlines how we have collected a new German sentiment corpus and then combined this corpus with existing resources to train a broad-coverage German sentiment model. The resulting data set contains 5.4 million labelled samples. We have used the data to train both, a simple convolutional and a transformer-based classification model and compared the results achieved on various training configurations. The model and the data set will be published along with this paper.</p> <p>You can find the code for training testing the models, that was published along with the paper in this <a href="https://github.com/oliverguhr/german-sentiment">repository</a>.</p> <p>The <a href="https://github.com/oliverguhr/german-sentiment-lib"><em>germansentiment</em></a> Python package contains a easy to use interface for the model that was published with this paper.</p> <p> </p> <p> </p>
Thermal model of the Los Humeros super-hot geothermal system, Mexico
<p>The dataset contains 3D thermal model (format - .vtk and .h5) of Los Humeros geothermal system at a local scale (extent defined in Calcagno et al., 2018). The boundary conditions used for this thermal model are obtained from Scenario 3b of regional model discussed in<strong> </strong>EU Deliverable D6.3<strong> </strong>(<a href="http://doi.org/10.5281/zenodo.3723039">10.5281/zenodo.3723039</a>) and D6.6 (<a href="https://doi.org/10.5281/zenodo.3723224">10.5281/zenodo.3723224</a>).</p> <p>Before using the results of the model, the user is advised to carefully read the model parameters, assumptions and uncertainties associated with the model as reported in Deliverable D6.3 and Deliverable D6.6.</p> <ol> <li>The .h5 file contains data and attributes (quantity, unit)</li> <li>The .vtk files contains the following information <ul> <li>x, y, z UTM coordinates (m)</li> <li>temp Temperature (°C)</li> <li>head Hydraulic head (m)</li> <li>pres Pressure (MPa)</li> <li>por Porosity (-)</li> <li>q Heat flow (W m<sup>-2</sup>)</li> <li>kx, ky, kz Permeability (m<sup>2</sup>)</li> <li>vx, vy, vz Specific discharge or Darcy velocity (m s<sup>-1</sup>)</li> <li>lx, ly, lz Thermal conductivity (W m<sup>-1</sup> K<sup>-1</sup>)</li> </ul> </li> </ol> <p>Additional information regarding the model is presented in the PDF document.</p>
R code and data to reproduce figures from the "Multivariate autoregressive modelling and conditional simulation for temporal uncertainty analysis of an urban water system in Luxembourg" paper
<p>This repository contains the R code and data to reproduce figures from the "Multivariate autoregressive modelling and conditional simulation for temporal uncertainty analysis of an urban water system in Luxembourg" paper.</p>
A 3-km model configuration of the southern Benguela Current upwelling system: ROMS model data and Pyticles Lagrangian data
<p>This dataset contains model output data from the Regional Ocean Modelling System (ROMS) configuration of the southern Benguela upwelling system (SBUS) to study the interannual variability of Lagrangian transport in the SBUS. This is a 3-km model resolution that ran for 22 years from 1989-2011 period with the first 3 years considered as spin-up. The model outputs were archived at a daily frequency. The 3-km model was nested in a 7.5 km model resolution described by Ragoasha et.al., 2019.</p> <p>The model output data provided here is a monthly climatology (1995-2011) NetCDF file of the surface temperature, salinity, the velocity fields (<em>u,v & w</em>), and sea surface height (SSH). The file that contains the model grid is also provided.</p> <p>An eddy detection and tracking algorithm were also performed on the daily 3-km SSH model outputs to study mean eddy characteristics of the region for the 1992-2011 period. The file contains identifications of the Eddies detected and tracked in out model domain, their position (longitude and latitude), vorticity, amplitude, propagation and rotational speed.</p> <p> </p> <p>An example of a Pyticles (Gula et al., 2014; Ragoasha et.al., 2019) Lagrangian output subset for 3000 Lagrangian drifters tracked for 60 days. The drifters were released in the upper 100 m depth at an across-shore transect off Cape Point (34<sup>o</sup>S). A Matlab file is also provided for monthly (1992-2011) percentage of drifters that reach St Helena Bay (32<sup>o</sup>S) from Cape Point. </p> <p> </p> <p> </p> <p><strong>Dataset provided:</strong></p> <p>Monthly climatology file: “<em>roms_avg_Y1995M1-Y2011M12.nc”</em></p> <p>Model grid file: “<em>grid_roms_avg_r3km.nc”</em></p> <p>Eddy tracking file: “<em>TRA02_SEL01_DET02_eddies_r3km_1992M1_2011M12.nc”</em></p> <p>Pyticles Lagrangian experiment output example file: “<em>Pyticles_Y2010M10.nc”</em></p> <p>Monthly transport success Matlab file: <em>"R3km_monthly_transport_1992_2011.mat"</em></p> <p> </p> <p> </p> <p><strong>Citations:</strong></p> <p> </p> <p><strong>Ragoasha, N</strong>., Herbette, S., Cambon, G., Reason, C., Roy, C., 2019. Lagrangian pathways in the southern Benguela upwelling system. <em>Journal of Marine Systems</em>, 195: 50-66.</p> <p> </p> <p>Gula, J., Molemaker, M. J., & McWilliams, J. C., 2014. Submesoscale Cold Filaments in the Gulf Stream. <em>Journal of Physical Oceanography.,</em> 44 (10), 2617–2643. DOI: 10.1175/JPO-D-14-0029.1</p> <p> </p> <p><strong>Corresponding author:</strong></p> <p>M.N. Ragoasha, ORCID identifier: 0000-0002-1500-6259. Email: moagaboragoasha@gmail.com</p> <p> </p> <p><strong>Acknowledgements:</strong></p> <p>The authors acknowledge the funding of N. Ragoasha’s PhD by the South-Africa’s National Research Foundation (NRF, South Africa) and the French Institute for Research and Sustainable Development (IRD, France). This work was also supported by the French National Program LEFE/INSU under the project’s name Benguela Upwelling Innershelf</p> <p>647 Circulation (BUIC). This work was granted access to the HPC resources of [TGCC/CINES/IDRIS] under the allocation 2017- [DARI n<sup>◦</sup>A0020107443] attributed by GENCI (Grand Equipement National de Calcul Intensif).</p>
Supplementary data to *Benchmarking of numerical integration methods for ODE models of biological systems*
<p>This archive contains supplementary data and code for the manuscript <strong>Benchmarking of numerical integration methods for ODE models of biological systems </strong>by<strong> Städter et al. 2020</strong>. It contains</p> <ul> <li>scripts to automatically download and install all required packages and models,</li> <li>scripts to compile the models and to perform the study,</li> <li>value files containing all data underlying the analyses in the manuscript,</li> <li>scripts to generate the manuscript figures.</li> </ul> <p>There is a <strong>README.md </strong>file with further information, in particular on what scripts to execute to reproduce the study.</p>
Asymmetry in kinematic generalization between visual and passive lead-in movements are consistent with a forward model in the sensorimotor system
<p><span><span>In our daily life we often make complex actions comprised of linked movements, such as reaching for a cup of coffee and bringing it to our mouth to drink. Recent work has highlighted the role of such linked movements in the formation of independent motor memories, affecting the learning rate and ability to learn opposing force fields. In these studies, distinct prior movements (lead-in movements) allow adaptation of opposing dynamics on the following movement. Purely visual or purely passive lead-in movements exhibit different angular generalization functions of this motor memory as the lead-in movements are modified, suggesting different neural representations. However, we currently have no understanding of how different movement kinematics (distance, speed or duration) affect this recall process and the formation of independent motor memories. Here we investigate such kinematic generalization for both passive and visual lead-in movements to probe their individual characteristics. After participants adapted to opposing force fields using training lead-in movements, the lead-in kinematics were modified on random trials to test generalization. For both visual and passive modalities, recalled compensation was sensitive to lead-in duration and peak speed, falling off away from the training condition. However, little reduction in force was found with increasing lead-in distance. Interestingly, asymmetric transfer between lead-in movement modalities was also observed, with partial transfer from passive to visual, but very little vice versa. Overall these tuning effects were stronger for passive compared to visual lead-ins demonstrating the difference in these sensory inputs in regulating motor memories. Our results suggest these effects are a consequence of state estimation, with differences across modalities reflecting their different levels of sensory uncertainty arising as a consequence of dissimilar feedback delays. </span></span></p>
The Coastal Streamflow Flux in the Regional Arctic System Model
<p>The Arctic coastal streamflow flux is an important driver of dynamics in the coupled ice-ocean system. We have developed a new streamflow routing model (RVIC), coupled within the Regional Arctic System Model (RASM), to simulate the coastal freshwater flux. RASM is a high-resolution regional Earth system model applied over a Pan-Arctic model domain. This dataset includes distributed daily coastal streamflows between 1979 and 2014 for the RASM domain. In Hamman et al. (2017) we demonstrate the performance of RASM and RVIC-simulated streamflow in fully coupled model simulations and discuss the improvements this derived dataset has, relative to existing distributed datasets in the Arctic.</p> <p>See the following references for further details on this dataset:</p> <p>Hamman, J., B. Nijssen, A. Roberts, A. Craig, W. Maslowski, and R. Osinski, 2017: The Coastal Streamflow Flux in the Regional Arctic System Model. Journal of Geophysical Research: Oceans, doi:10.1002/2016JC012323.</p> <p>Hamman, J., B. Nijssen, M. Brunke, J. Cassano, A. Craig, A. DuVivier, M. Hughes, D.P. Lettenmaier, W. Maslowski, R. Osinski, A. Roberts, and X. Zeng, 2016: Land surface climate in the Regional Arctic System Model. Journal of Climate, doi:10.1175/JCLI-D-15-0415.1.</p>
Linear Time Varying System Examples for Model Order Reduction
<p>Three linear time varying system benchmarks implemented in MATLAB. </p> <p>1.) a time varying version of the Oberwolfach Steel Cooling Benchmark</p> <p>2.) a one dimensional heat equation with a moving point heat source</p> <p>3.) a linearized Burgers equation</p> <p>Model 1 comes in the same 5 resolutions as the original time-invariant version. the other two are freely scalable.</p> <p> </p>
Dataset of Optimization Methods for Model-Implemented Fault Injection in Cyber-Physical Systems: A Systematic Literature Review
<p>Data set for the paper entitled “<strong>Optimization Methods for Model-Implemented Fault Injection in Cyber-Physical Systems: a Systematic Literature Review</strong>”</p> <p>In this repo, we have some pictures and Excel files.</p> <ul> <li>Pictures are screenshots from the Parsifal tool (https://parsif.al/) which we use for performing the SLR.</li> <li>Excel files are as follows:</li> </ul> <table style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 21.8789%;"><col style="width: 78.1211%;"></colgroup> <tbody> <tr> <td><strong>Excel’s file name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Keyword_analysis </td> <td>In this file, you can see the evolution of our keyword selection.</td> </tr> <tr> <td>Articles_InclusionExclusion_QA </td> <td>In this file, you can find all found papers until Feb. 27, 2025. In the last column of this excel file, we can see the status of each paper, if it has been included, or excluded by authors. For the included paper (their status is “Accepted”) you can see their quality score in the last column.</td> </tr> <tr> <td>Extracted_data </td> <td>In this file, we logged the result of data extraction from qualified paper. In the first sheet “Articles”, you can see a list of the read papers with corresponding data. Other sheets in this Excel file are driven from the “Article” sheet for data visualization. So, they are not important.</td> </tr> </tbody> </table> <p> <br>If you have any questions, you can read the corresponding paper and contact the authors.</p>
In situ dataset for initialization and validation of the Copernicus Med-MFC biogeochemical model system (MedBGCins)
<p>The biogeochemical model system in use by the Mediterranean Monitoring Forecasting Centre (Med-MFC) of the EU Copernicus Marine Service requires several observational datasets for data assimilation and model initialization and validation (Coppini et al., 2023; Cossarini et al., 2021; Salon et al., 2019). The present MedBGCins dataset consists of the in situ measurements, coming from selected platforms, on which the initialization and validation of the biogeochemical model system are built. The MedBGCins dataset collects in situ measurements along the Mediterranean Sea water column and during the 1995-2023 time period for nutrients (i.e., nitrate, nitrite, phosphate, silicate, ammonium), dissolved oxygen, dissolved inorganic carbon, total alkalinity, total scale pH at 25°C. The dataset also provides pCO2 and total scale pH at in situ conditions, reconstructed by using the PyCO2SYS Python toolbox (Humpreys et al., 2024). The complete list of variables is indicated in Table 1. The largest subset of the original data are from EMODnet Chemistry Mediterranean Sea - Eutrophication and Acidity aggregated datasets 1911/2022 v2023 (reference in Table 2), including both profiles and time series, plus other documented cruises (same table).</p> <p>Additional information and references are included in the UserGuide file.</p> <p> </p>
Data and code for "Assessment of the accuracy in UV index modelling using the UVIOS2 system during the UVC-III campaign"
<p>Datasets nd codes that have been used for the paper with tittle "Assessment of the accuracy in UV index modelling using the UVIOS2 system during the UVC-III campaign"</p>
Global sensitivity analysis to enhance the transparency and rigour of energy system optimisation modelling - Supplementary Material
<p>Supplementary material for the manuscript "Global sensitivity analysis to enhance the transparency and rigour of energy system optimisation modelling".</p> <p>This deposit contains all data and visualization scripts needed to replicate results in the manuscript.This includes user created figures, model input files, model output files, configuration files for running the workflow, and all scripts needed to process results.</p> <p>In addition to the European Commission, we acknowledge that Trevor Barnes' contribution to this paper was funded via a Mitacs Globalink Research Award, grant number IT2569</p>
Data for: Implementing detailed nucleation predictions in the Earth system model EC-Earth3.3.4: sulfuric acid-ammonia nucleation
<p>Model dataset variables produced from the IFS and TM5 modules in EC-Earth3 version 3.3.4. which contains the control case and three experiments with the NPF lookup table. This paper is published at EGUshpere by journal: Geoscientific Model Development.</p> <p>The files contain:</p> <p>Compressed tar file of NetCDF data from IFS output for all four simulations. All IFS data have been averaged to monthly means from 6-hourly grib datasets. The post-process bash script which contains the function for the CDN and cloud effective radius weighted average towards cloud_time is found in the supplemented zendo link.</p> <p>NetCDF files from TM5 general output for each simulation. </p>
Structure of the Canadian Forest Fire Weather Index System: the model and its components
<p>This material is part of:</p> <p>de Rigo, D. 2018. <strong>The Canadian Forest Fire Weather Index System: a synopsis of computational semantics</strong>. https://doi.org/10.6084/m9.figshare.4046673<br><br><strong>Structure of the Canadian Forest Fire Weather Index system: the model and its components</strong> — The <a href="../record/10806780#preview-iframe">figure below</a> (formats: <a href="../record/10806780/files/FWI-sys_simple_diagram.png?download=1">PNG</a> or <a href="../record/10806780/files/FWI-sys_simple_diagram.pdf?download=1">PDF</a>) shows the logical subdivision of the Canadian Forest Fire Weather Index system (FWI-sys) in components.</p> <p> </p> <p>The Canadian FWI-sys (De Groot,1987; Van Wagner,1987) is an index of fire danger by weather designed to consider the effects on vegetation fuels of the sequence of weather conditions. It is designed to estimate a uniform numerical rating for the relative fire potential accounting for the local sequence of temperature, wind speed, relative humidity, and precipitation, for the day in which the rating is estimated but also modelling the dynamics of the previous days. In addition, the variable amount of possible drying due to the varying solar irradiation in different seasons is taken into account by adjusting the parameters per each month of the year.<br><br>The system is standardised to consider the behaviour of a reference typology of vegetation fuel (mature pine stand) regardless of other non-weather factors which may locally influence the fire danger, such as the specific topography or the pattern, composition, and structure of vegetation assemblages. Therefore, FWI-sys is suitable to support the harmonised comparison among variable weather conditions, either spatially (comparing different spatial regions) or temporally (comparing the same region over time).<br><br>The FWI-sys components are organised in three layers, processing at the daily frequency weather information (either from observations, reanalysis, forecast, or climate scenarios) and estimating from it a final standard aggregated numerical rating of fire intensity.<br><br>The required input variables are</p> <ul> <li>Temperature T (nominally, FWI-sys requires T at noon)</li> <li>Wind speed W (nominally, FWI-sys requires T at noon)</li> <li>Relative humidity</li> <li>Precipitation (24-hour rainfall)</li> <li>Month of the year</li> </ul> <p>The FWI-sys was originally designed to fit the Candian conditions. Following its success, adaptations of the system were studied for different areas of the globe. This implies that the parameters used inside the FWI-sys globally also depend on the latitude (Alexander, 2008).</p> <p>The first layer of components (the <em>fuel moisture codes</em>: Fine Fuel Moisture Content, FFMC; Duff Moisture Code, DMC; Drought Code, DC) is composed by dynamic variables. This means that the value of each component for a given day depends also on the value of the same component the day before. The dynamic components with longer memory of their past history also approximate the seasonal changes in solar radiation, by considering the month of the year (see Figure, bottom left).</p> <ul> <li><strong>Fine Fuel Moisture Code (FFMC)</strong> : provides a numerical rating of the moisture content of the top litter and other cured fine fuels, indicating the relative ease of ignition and flammability of fine fuel.</li> <li><strong>Duff Moisture Code (DMC)</strong> : models a standard moisture content of loosely-compacted organic layers of moderate depth (duff layers and medium-sized woody material). This component of the FWI-sys represents wooden fuels of intermediate thickness.</li> <li><strong>Drought Code (DC)</strong> : models a standard moisture content of deeper, compact, organic layers. This component of the FWI-sys is able to track seasonal drought effects on coarse wooden fuels.</li> </ul> <p> </p> <p>The second layer of components (the<em> fire behaviour indices</em>: Initial Spread Index, ISI; Buildup Index, BUI; Fire Weather Index, FWI) mathematically is composed by stateless D-TM components. This means that these components do not have an internal memory of the past conditions, while instead they rely on the combined information offered by the different temporal inertia of the fuel moisture codes, which they process as input information.</p> <ul> <li><strong>Initial Spread Index (ISI)</strong> : represents the expected rate of fire spread. It considers the combined effects of wind and the FFMC on the rate of spread. However, it excludes the influence of fuel moisture and availabity for the coarser wooden fuels.</li> <li><strong>Buildup Index (BUI)</strong> : combines DMC and DC to model the total amount of fuel available for combustion to the spreading fire.</li> <li><strong>Fire Weather Index (FWI)</strong> : offers a standard aggregated numerical rating of fire intensity which combines ISI and BUI.</li> </ul> <p><br>Given its structure, the model can also be interpreted as a recurrent neural network (RNN) where the input variables are transformed into the final aggregated numerical rating (FWI) by means of two hidden layers: the <em>fuel moisture codes</em> (three nodes/neurons); and the <em>fire behaviour indices</em> (two nodes/neurons).</p> <p>Note that this structure is not a simple feedforward network, as the first hidden layer is made by dynamic components (FFMC, DMC, DC, see highlighted feedack loops in <a href="../record/10806780/files/FWI-sys_simple_diagram_recurrent.png?download=1">PNG</a> format). The activation functions are complex, and the D-TM components (either dynamic or stateless) generally mix physically-based and empirical aspects. A consequence of the complexity of the FWI-sys activation functions is that a neural network with standard (e.g. sigmoidal) activation functions would need to exploit disproportionally many more additional neurons for the same FWI-sys D-TM complexity to be reasonably approximated.</p> <p> </p> <p>An additional FWI-sys component is a simple transfromation of the aggregated FWI values to better account for the nonlinear increase of fire control effort with increasing FWI values (Van Wagner, 1987):</p> <ul> <li><strong>Daily Severity Rating (DSR)</strong>: this transformation of FWI is meant to provide a measure of control difficulty:<br> DSR = 0.0272 ⋅ FWI <sup>1.77</sup><br>which easily invertible:<br> FWI = ( DSR / 0.0272 ) <sup>1 / 1.77</sup></li> </ul> <p><br><br>To cite the Figure, please refer to:<br><br>de Rigo, 2016. <strong>Structure of the Canadian Forest Fire Weather Index System: the model and its components</strong>. https://doi.org/10.5281/zenodo.6558576</p> <p>which is part of</p> <p>de Rigo, D. 2018. <strong>The Canadian Forest Fire Weather Index System: a synopsis of computational semantics</strong>. https://doi.org/10.6084/m9.figshare.4046673<br><br> </p> <p> </p> <p><strong>References</strong></p> <p>De Groot, W.J., 1987. <strong>Interpreting the Canadian Forest Fire Weather Index (FWI) System</strong>. In: <em>Fourth Central Regional Fire Weather Committee Scientific and Technical Seminar, Proceedings</em>. Winnipeg, Manitoba, Canada, pp. 3-14. <a href="https://purl.org/INRMM-MiD/c-14176512">https://purl.org/INRMM-MiD/c-14176512</a> </p> <p>Van Wagner, C.E., 1987. <strong>Development and structure of the Canadian Forest Fire Weather Index System</strong>. <em>Forestry Technical Report</em>. Canadian Forestry Service, Ottawa, Canada. <a href="https://purl.org/INRMM-MiD/c-14168337">https://purl.org/INRMM-MiD/c-14168337</a> </p> <p>Alexander, M.E., 2008. <strong>Latitude considerations in adapting the Canadian Forest Fire Weather Index System for use in other countries</strong>. In: Lawson, B.D., Armitage, O.B. (Eds.), <em>Weather Guide for the Canadian Forest Fire Danger Rating System</em>. Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, Edmonton, Alberta, Canada, pp. 67–73. ISBN:978-1-100-11565-8 <a href="https://purl.org/INRMM-MiD/z-MBDA6A6I">https://purl.org/INRMM-MiD/z-MBDA6A6I</a></p> <p> </p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2006_2010)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2011_2015)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2016_2020)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
ScienceDex guides
Understand access before you commit
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.