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1,121 results for “weathering”
Space Weather Modeling Framework ensemble simulations
<p><strong>Space Weather Modeling Framework ensemble simulations</strong></p> <p>This archive contains folders for 41 simulations with the operational geospace configuration of the University of Michigan's Space Weather Modeling Framework[1]. The operational configuration uses the University of Michigan's BATS-R-US magnetohydrodynamics code[2], the Ridley ionospheric electrodynamics solver[3], and the Rice Convection Model[4] inner magnetosphere model. More details of the operational geospace configuration are given by [5].</p> <p>These simulations were performed for, and used in, the paper</p> <p>> "Perturbed Input Ensemble Modeling with the Space Weather Modeling Framework",<br> > S.K. Morley, D.T. Welling and J.R. Woodroffe,<br> > Space Weather, 2018. doi: <a href="https://doi.org/10.1029/2018SW002000">10.1029/2018SW002000</a></p> <p><em>Directory Structure</em><br> All numbered directories are members of a perturbed input ensemble. The directory labeled "orig" is the reference (unperturbed) simulation. Each directory is structured identically.</p> <p>Each run directory contains `PARAM.in`, `LAYOUT.in` and `magin_GEM.dat` files. This set of files consistutes the required inputs for each run that are invariant. That is, these files are identical between runs and control the setup of the model and the types of outputs generated. Each run directory also contains an `IMF.dat` file that sets the upstream boundary condition. This file differs between each simulation. The values in each ensemble member have been perturbed from the values given in the reference simulation using a block resampling of measurement errors between an L1 solar wind monitor and a near-Earth monitor.</p> <p>Each run directory also contains `GM` and `GM\IO2` subdirectories. The `GM\IO2` subdirectory contains simulation output from the global magnetosphere module. Three files are present for each simulation: `geoindex_e20100404-190000.log`, `magnetometers_e20100404-190000.mag`, and `log_e20100404-190000.log`. These are standard SWMF log files that can be parsed and analyzed using, for example, the `pybats` module in the SpacePy[6] software package[7]. The simulation ouput includes ground magnetic perturbations at a set of magnetic observatory locations, local K indices, an estimated Kp index, a 1 minute resolution Sym-H/Dst index equivalent and simulated auroral electrojet indices.</p> <p><br> <em>Basic Analysis</em><br> To derive the time derivative of the horizontal ground magnetic perturbation (dB/dt) the magnetometer log file can be loaded using SpacePy<br> </p> <pre><code class="language-python">>>> import spacepy.pybats.bats >>> magdata = spacepy.pybats.bats.MagFile('run_001/GM/IO2/magnetometers_e20100404-190000.mag') >>> magdata.calc_h() #calculates horizontal from North and East components >>> magdata.calc_dbdt() #calculates time derivatives</code></pre> <p>To then calculate binned maxima in the dB/dt time series, e.g., for the Yellowknife (YKC) station<br> </p> <pre><code class="language-python">>>> import datetime as dt >>> import numpy as np >>> import spacepy.toolbox as tb >>> dBdt_max20, bintimes = tb.windowMean(magdata['YKC'], time=subset['time'], winsize=dt.timedelta(minutes=20), overlap=dt.timedelta(0), st_time=dt.datetime(2010,4,5), op=np.max)</code></pre> <p><br> and to turn this into a binary event series indicating a threshold crossing<br> </p> <pre><code class="language-python">>>> threshold = 1.1 #nT/s >>> predicted_event = np.asarray(dBdt_max20) >= threshold</code></pre> <p><br> Assuming that the observational data are obtained from NASA's CCMC and similarly processed, the event validation statistics can be calculated and displayed using the PyForecastTools package[8].<br> </p> <pre><code class="language-python">>>> import verify >>> c_table = verify.Contingency2x2.fromBoolean(predicted_event, observed_event) >>> ctable.summary(ci='bootstrap', verbose=True)</code></pre> <p> </p> <p><em>Footnotes</em><br> [1] Tóth, G., I. V. Sokolov, T. I. Gombosi, D. R. Chesney, C. R. Clauer, D. L. D. Zeeuw, K. C. Hansen, K. J. Kane, W. B. Manchester, R. C. Oehmke, K. G. Powell, A. J. Ridley, I. I. Roussev, Q. F. Stout, O. Volberg, R. A. Wolf, S. Sazykin, A. Chan, B. Yu, and J. KÃşta (2005), Space weather modeling framework: A new tool for the space science community, Journal of Geophysical Research: Space Physics, 110(A12), doi:10.1029/2005JA011126.</p> <p>[2] de Zeeuw, D. L., T. I. Gombosi, C. P. T. Groth, K. G. Powell, and Q. F. Stout (2000), An adaptive MHD method for global space weather simulations, IEEE Transactions on Plasma Science, 28(6), 1956–1965, doi:10.1109/27.902224.</p> <p>[3] Ridley, A. J., T. I. Gombosi, and D. L. DeZeeuw (2004), Ionospheric control of the magnetosphere: conductance, Annales Geophysicae, 22(2), 567–584, doi:10.5194/angeo-22-567-2004.</p> <p>[4] Toffoletto, F., S. Sazykin, R. Spiro, and R. Wolf (2003), Inner magnetospheric modeling with the Rice convection model, Space Science Reviews, 107(1), 175–196, doi: 10.1023/A:1025532008047.</p> <p>[5] Haiducek, J. D., D. T. Welling, N. Y. Ganushkina, S. K. Morley, and D. S. Ozturk (2017), SWMF global magnetosphere simulations of January 2005: Geomagnetic indices and cross-polar cap potential, Space Weather, 15(12), 1567–1587, doi: 10.1002/2017SW001695.</p> <p>[6] Morley, S. K., J. Koller, D. T. Welling, B. A. Larsen, M. G. Henderson, and J. T. Niehof (2011), Spacepy - a Python-based library of tools for the space sciences, in Proceedings of the 9th Python in science conference (SciPy 2010), Austin, TX.</p> <p>[7] SpacePy is packaged on PyPI, with the official git repository on SourceForge and an unofficial mirror on github.</p> <p>[8] PyForecastTools is packaged on PyPI and the repository is on github. The latest release is archived on Zenodo with doi: 10.5281/zenodo.1256921. The citation for v1.0.1 is Steve Morley. (2018, June 28). drsteve/PyForecastTools: PyForecastTools: Version 1.0.1 (Version v1.0.1). Zenodo. http://doi.org/10.5281/zenodo.1299389</p> <p> </p>
Data for "Multi-instrument studies of thermospheric weather above Alaska"
<p>These files contain the original data used to generate figures presented in "Multi-instrument studies of thermospheric weather above Alaska" submitted to the Journal of Geophysical Research - Space Physics in 2018 by authors M. G. Conde, W. A. Bristow, D. L. Hampton, and J. Elliott.</p> <p>The main figures in this work were generated by the IDL program named "sdi3k_newmapper.pro", which has also been uploaded here.</p> <p>Data providers were:</p> <p>Scanning Doppler Imager data (files with names ending in "NZ0115": M. G. Conde, University of Alaska Fairbanks</p> <p>SuperDARN ion convection (".map.vec.geo" or ".geo.dat" files): W. A. Bristow, University of Alaska Fairbanks</p> <p>PFISR ion convection (.h5 files): Roger Varney, SRI International</p> <p>SSUSI auroral images (".nc"files with the word "SSUSI" in the filename): Larry Paxton, John's Hopkins University Applied Physics Lab</p> <p>All-sky camera images (".FIT" or ".jpg" files): D. L. Hampton, University of Alaska Fairbanks</p> <p>Magnetometer data (".csv files): D. L. Hampton, University of Alaska Fairbanks</p> <p> </p> <p> </p>
National Weather Service Coded Surface Bulletins, 2003-
<p>The Coded Surface Bulletin dataset is a collection of ASCII files containing the locations of weather fronts, troughs, high pressure centers, and low pressure centers as determined by National Weather Service meteorologists at the Weather Prediction Center (WPC) during the surface analysis they do every three hours. Each bulletin is broadcast on the NOAAPort service, and has been available since 2003. The latitude/longitude precision in the initial version of the bulletin is 1°. Beginning in 2009, a high-resolution version began to be distributed alongside the original version. The high-resolution version of the bulletin has a latitude/longitude precision of 0.1°. The format and content of the original bulletin can be found at <a href="https://www.wpc.ncep.noaa.gov/html/read_coded_bull.shtml">this link</a>. The format and content of the high-resolution bulletin can be found at <a href="https://www.wpc.ncep.noaa.gov/html/read_coded_bull_hr.shtml">this link</a>.</p> <p>The bulletins within a rolling 2-week window can be obtained from the WPC at <a href="https://ftp.wpc.ncep.noaa.gov/coded_sfc/">this link</a>. The files in this dataset were provided by WPC personnel from an internal archive.</p>
DL-FRONT MERRA-2 weather front probability maps over North America, 1980-
<p>DL-FRONT is a Deep Learning Neural Network (DLNN) that was trained to detect weather fronts using spatial grids of near-surface atmospheric variables. The dataset is composed of hourly spatial grids containing probability maps for each of five front-type categories—cold front, warm front, stationary front, occluded front, and no front.</p> <p>This dataset is the product of processing data from the National Aeronautics and Space Administration (NASA) <a href="https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/">Modern-Era Retrospective analysis for Research and Applications, Version 2</a> (MERRA-2). DL-FRONT processed MERRA-2 hourly data grids of instantaneous measures of air pressure reduced to mean sea level, air temperature at 2 meters, specific humidity at 2 meters, and wind velocity at 10 meters over the time span 1980 - 2018 to produce this dataset. The original MERRA-2 data were resampled at 1 degree resolution over the spatial range 31W - 171W x 10N - 77N using bicubic interpolation.</p> <p>At each hourly time step the network produced a set of spatial grids with the same resolution and spatial range as the input, one for each of the five categories mentioned above. Each cell in a spatial grid for a given category records the network-assigned probability (from 0.0 to 1.0) that the cell is in a weather front boundary region of that category (or, for the "no front" category, the probability that the cell is not in any weather front boundary region).</p> <p>The DLNN was trained using MERRA-2 data and human-identified fronts from the NOAA National Weather Service (NWS) Weather Prediction Center (WPC) <a href="https://www.wpc.ncep.noaa.gov/html/sfc2.shtml">Coded Surface Bulletin</a> dataset. The training datasets covered the years 2003-2007.</p> <p>The dataset contains two sets of files. The first set contains the original front probability maps. The second set contains "one hot" versions of the front probability maps. In the one hot version the five front-type probabilities for a spatial grid cell for a given time step are replaced by the value 1 for the largest front-type probability, and by 0 for the others.</p> <p>The front probability files have names that follow the form merra2_merra2-1deg_fronts_<year>.nc. The one hot files have names that follow the form merra2_merra2-1deg_onehot_<year>.nc. Each file contains one year of hourly spatial data grids.</p>
DWD European Weather
<p><a href="https://opendata.dwd.de">opendata.dwd.de</a> - OpenData by Deutscher Wetter Dienst</p> <p>Conditions: <a href="https://www.dwd.de/EN/service/copyright/copyright_node.html">https://www.dwd.de/EN/service/copyright/copyright_node.html</a></p> <p>dates and times are UTC.</p>
Dataset for solar energy intake correlated with weather forecast data
<p>This is the first release.</p>
Data used in "Fire Weather Compromises Forestation-reliant Climate Mitigation Pathways" (Jaeger et al. 2024a)
Open the record for dataset details and reuse information.
Decoding the pedogenic weathering signals from the red clay sequence on the Chinese Loess Plateau
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Augmented-BDD: Diverse Adverse Weather and Lighting Conditions through Data Augmentations
<h1>The Augmented-BDD (A-BDD) dataset</h1>
Space Weathering and Compositional Stratigraphy of Apollo 17 Double Drive Tube 73001/2
<p>This dataset contains OMAT, FeO and TiO2 content and depth of Apollo 17 double drive tube 73001/2.</p>
Predicting Weather Disruptions for the ICC Champions Trophy 2025 in Pakistan Using Machine Learning and Data Analytics
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University of Queensland portable weather radar (UQ-XPOL) dataset for 20231025 and 20231031
<p>Two packages are included: 20231025_scan includes the raw scan files generated by the UQ-XPOL Furuno WR2100 for observations made near Tara, South-East Queensland, Australia. 20231031_scan includes the raw scan files generated by the UQ-XPOL Furuno WR2100 for observations made near Wallangarra at the border between Queensland and NSW, Australia. Both packages include observations of clouds and fire plumes of large fires happening during these two days. Observations were made with the help of Dr Jordan Brook from the Australian Bureau of Meteorology and Kathryn Turner, enrolled as PhD student at The University of Queensland at the time of the observations.</p> <p>This work was funded by Google.org, the charity branch of the company Google.</p>
Data for "Mitigation strategies can alleviate power system vulnerability to climate change and extreme weather: A case study on the Italian grid"
<p>Data employed for the paper "Mitigation strategies can alleviate power system vulnerability to climate change and extreme weather: A case study on the Italian grid"<br><br>Abstract<br>This study explores compounding impacts of climate change on power system's load and generation, emphasising the need to integrate adaptation and mitigation strategies into investment planning. We combine existing and novel empirical evidence to model impacts on: i) air-conditioning demand; ii) thermal power outages; iii) hydro-power generation shortages. Using a power dispatch and capacity expansion model, we analyse the Italian power system's response to these climate impacts in 2030, integrating mitigation targets and optimising for cost-efficiency at an hourly resolution. We outline different meteorological scenarios to explore the impacts of both average climatic changes and the intensification of extreme weather events. We find that addressing extreme weather in power system planning will require an extra 5-8 GW of photovoltaic (PV) capacity, on top of the 50 GW of the additional solar PV capacity required by the mitigation target alone. Despite the higher initial investments, we find that the adoption of renewable technologies, especially PV, alleviates the power system's vulnerability to climate change and extreme weather events. In fact, renewable energy sources are generally less vulnerable to the impacts of climate change, such as rising temperatures and shifting precipitation patterns, compared to thermal power and hydropower generation. Furthermore, enhancing short-term storage with lithium-ion batteries is crucial to counterbalance the reduced availability of dispatchable hydro generation.</p>
Raman spectral data for ''The evaluation of space weathering effects on lunar samples from a Raman spectroscopic perspective''
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Dataset and code for A Multidimensional Error Verification Method for Weather Forecast Based on the Spatial Distribution Structural Similarity of Meteorological Elements
<p>The dataset includes rainfall observations and mesoscale numerical prediction data for 16 July 2019, 13 June 2020 and 2024 rainy season.<br>The code is the algorithm procedure of this paper. Among them, meiyutest.py is the rainfall scoring procedure for the Meiyu period, and 2019case is the forecast scoring and plotting procedure for two individual cases.</p>
Forecasting Capability Verification of the Pangu-Weather and ECMWF Models for the 2022 Summer Weather in Jiangsu Province, China
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Weather- and climate-driven power supply and demand time series for European countries
<p>This repository contains times series of wind power generation, solar power generation, hydropower inflow, heating demand, and cooling demand that use both historical and projected climate variables. Historical climate data are from the ERA5 reanalysis (1940-2023), while projected climate variables are from three climate models of the CMIP5 EURO-CORDEX (2006-2100) and three representative concentration pathways (RCP 2.6, RCP 4.5 and RCP 8.5). The time series are at country level for the EU27 countries, Balkan countries, the United Kingdom, Norway, and Switzerland.</p> <p>Each country has the following files for the historical period, where ** is the country’s ISO Alpha-2 code:</p> <p>1) **__ERA5__wind__capacity_factor_time_series__onshore.nc<br>2) **__ERA5__wind__capacity_factor_time_series__offshore.nc<br>3) **__ERA5__solar__capacity_factor_time_series.nc<br>4) **__ERA5__hydropower__inflow_time_series__conventional_and_pumped_storage.nc<br>5) **__ERA5__hydropower__inflow_time_series__run_of_river.nc<br>6) **__ERA5__heating__demand_time_series__residential_space.nc<br>7) **__ERA5__heating__demand_time_series__services_space.nc<br>8) **__ERA5__cooling__demand_time_series.nc</p> <p>Each country has also the following files for the climate projections, where ** is the country’s ISO Alpha-2 code, #_# is the value of the RCP scenario (e.g., 2_6 is RCP 2.6), ££££__&&&& is the name of the combination of global-regional climate models (e.g., CNRM_CERFACS_CM5__CNRM_ALADIN63):</p> <p>9) **__CORDEX__RCP_#_#__££££__&&&&__wind__capacity_factor_time_series__onshore.nc<br>10) **__CORDEX__RCP_#_#__££££__&&&&__wind__capacity_factor_time_series__offshore.nc<br>11) **__CORDEX__RCP_#_#__££££__&&&&__solar__capacity_factor_time_series.nc<br>12) **__CORDEX__RCP_#_#__££££__&&&&__hydropower__inflow_time_series__conventional_and_pumped_storage.nc<br>13) **__CORDEX__RCP_#_#__££££__&&&&__hydropower__inflow_time_series__run_of_river.nc<br>14) **__CORDEX__RCP_#_#__££££__&&&&__heating__demand_time_series__residential_space.nc<br>15) **__CORDEX__RCP_#_#__££££__&&&&__heating__demand_time_series__services_space.nc<br>16) **__CORDEX__RCP_#_#__££££__&&&&__cooling__demand_time_series.nc</p> <p>The repository also contains the caliration coefficients that resulted from the calibration procedure for the countries in which data were available.</p> <p> </p>
Data for the manuscript "The impact of dealiasing biases on bird and insect data products of C-band weather radars and consequences for aeroecological applications" by Weisshaupt et al.
<p>Netcdf files of the analyses in the manuscript "The impact of dealiasing biases on bird and insect data products of C-band weather radars and consequences for aeroecological applications" by Weisshaupt et al. containing flight directions, flight speeds and animal densities in 200-m height layers between 0-1 km from four single PRF and one dual-PRF scans from 10 Finnish polarimetric C-band weather radars between 1-30 Sept 2022 and from the Kankaanpää weather radar between 10 Sept – 30 Oct 2023 and 10 April - 10 June 2024. Bird and insect echoes were identified by the classifier developed by Mäkinen, T., J. Ritvanen, S. Pulkkinen, N. Weisshaupt, and J. Koistinen, 2022: Bayesian Classification of Nonmeteorological Targets in Polarimetric Doppler Radar Measurements. <em>J. Atmos. Oceanic Technol.</em>, <strong>39</strong>, 1561–1578, <a href="https://doi.org/10.1175/JTECH-D-21-0177.1" target="_blank" rel="noopener">https://doi.org/10.1175/JTECH-D-21-0177.1</a>.</p> <p>File names indicate the PRF mode, processing mode (either untreated ("raw") or dealiased ("no_classif")), date and a 5-letter radar id.</p>
Revealing Key Dynamical Mechanisms of a Severe Supercell within a QLCS using Rapid Update 4DVar Assimilation of C-band Phased Array Weather Radar Data
<div> <p>These are data supporting the conclusions of the paper. The observation dataset including sounding data, automatic weather station data and radar data. And Vdras outputs are also provided here. The corresponding scripts are based on python(version 3.9) and NCL (version 6.6.2).</p> <p> </p> </div> <h2>Files</h2>
Contrasting sensitivity of nestling and fledgling Barn Swallow Hirundo rustica body mass to local weather conditions
<p>Local weather can influence the growth and development of young birds, either indirectly, by modifying prey availability, or directly, by affecting energetic trade‐offs. Such effects can have lasting implications for life history traits, but the nature of these effects may vary with the developmental stage of the birds, and over timescales from days to weeks. We examined the interactive effects of temperature, rainfall and wind speed on the mass of nestling and fledgling Barn Swallows <i>Hirundo rustica</i>, both on the day of capture and averaging weather across the time since hatching. At the daily timescale, nestling mass was negatively correlated with temperature, but the strength of this association depended on the level of rainfall and wind speed; nestlings were typically heavier on dry or windy days, and the negative effect of temperature was strongest under calm or wet conditions. At the early lifetime timescale (i.e. from hatching to post‐fledging), nestling mass was negatively correlated with temperature at low wind speed. Fledgling body mass was less sensitive to weather; the only weather effects evident were a negative correlation with temperature at the daily scale under high rainfall that became slightly positive under low rainfall. These changes are consistent with weather effects on availability and distribution of insects within the landscape (e.g. causing high concentrations of flying insects), and with the effects of weather variation on nest microclimate. These results together demonstrate the impacts of weather on chick growth, over immediate (daily) and longer term (nestling/fledgling lifetime) timescales. This shows that sensitivity to local weather conditions varies across the early lifetime of young birds (nestling‐fledgling stages) and illustrates the mechanisms by which larger scale (climate) variations influence the body condition of individuals.</p>
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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.