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50 results for “atmospheric river”
A Global database of methane concentrations and atmospheric fluxes for streams and rivers
This dataset, referred to as MethDB, is a collation of publicly available values of methane (CH4) concentrations and atmospheric fluxes for world streams and rivers, along with supporting information on location, geographic, physical, and chemical conditions of the study sites. The data set is composed of four linked tables, corresponding to the data sources (Papers_MethDB), the study sites (Sites_MethDB), concentrations (Concentrations_MethDB), and influx/efflux rates (Fluxes_MethDB). Information was extracted from journal articles, government reports, book chapters, and similar sources that were acquired before 15 September 2015. Concentrations and fluxes were converted to a standard unit (micromoles per liter for concentration and millimoles per square meter per day for flux) and both the author-reported and converted data are included in the database. MethDB was assembled as part of a larger synthesis effort on stream and river CH4 dynamics, and assembled data were used to identify large-scale patterns and potential drivers of fluvial CH4 and to generate an updated global-scale estimate of CH4 emissions from world rivers.
Atmospheric River Database for the Himalayas
<p>Atmospheric Rivers (ARs) are long and narrow regions of intense moisture transport in the lower troposphere. The dataset comprises of Atmospheric Rivers that have happened over the Himalayan Basins from 1982 to 2018. It includes the dates and times, duration, intensity/magnitude, tracks, and categories of the ARs.</p> <p> </p> <p><strong>File Names and description:</strong></p> <p><strong>1. </strong><strong>ERA5_Persistant_Database2000km:</strong> This file includes the date, times, average Integrated Water Vapor Transport (IVT) magnitude (kg.m^-1s^-1), starting IVT, maximum IVT, and duration of ARs. These terms are explained below in greater details.</p> <p><strong>Column “Date”:</strong></p> <p>Gives the date and time (in Coordinated Universal Time UTC) of each AR timestep. The IVT data used to identify ARs is 6-hourly (00UTC, 06UTC, 12UTC and 18UTC).</p> <p><strong>Column “AR_ID”:</strong></p> <p>Each identified persistent AR, lasting for at least 18 hours, is given a unique ID, which remains same for all timesteps of the AR. This column gives the ID of ARs. The ID of an AR is based on the year in which the AR occurred, the letters “AR”, and the occurrence serial of the AR in the year. For example, the first AR in 1990 has ID 1980AR1. If the AR lasted for 10 timesteps, all 10 timesteps will have the same ID.</p> <p><strong>Column “Ind”:</strong></p> <p>This column gives the python index of IVT data in 6-hour yearly data, giving the date and time of each AR timestep. This column can be ignored since the same information is more directly available in “Date” column.</p> <p><strong>Column “AvgIVT”:</strong></p> <p>This column gives the average IVT magnitude (kg.m^-1s^-1) along the AR major axis, i.e., the gridcells that have maximum IVT along the AR track. For example, the first value corresponds to the average of all values from column <em>“0”</em> to column <em>“88”,</em> which give the IVT magnitude at each gridcell of the major axis of the first timestep.</p> <p> </p> <p><strong>Column “StartIVT”:</strong></p> <p>This column gives the IVT magnitude (kg.m^-1s^-1) at the initial gridcell on the first timestep when AR condition was identified.</p> <p><strong>Column “ARDuration”:</strong></p> <p>This column gives duration of the AR in hours; for example, an AR lasting for three timesteps will have the duration of 18 hours, an AR lasting for four timesteps will have duration of 24 hours.</p> <p><strong>Column “MaxIVT”:</strong></p> <p>This column gives the maximum of all IVT values (kg.m^-1s^-1) at the starting gridcells on each timestep of an AR.</p> <p><strong>Column “ARCat”:</strong></p> <p>This column gives category of the AR, based on IVT magnitude and duration of the ARs. Six categories have been defined, Cat0 denoting the weakest AR and Cat5 denoting the strongest AR. More details on this can be found in the accompanying paper.</p> <p><strong>Column “0” to the end.</strong></p> <p>These columns give the IVT magnitude (kg.m^-1s^-1) at each gridcell of the major axis of each AR timestep.</p> <p> </p> <p><em>Note that the cyclone dates were not available before 1982, so AR dates for 1979 to 1981 includes cyclonic IVT structures.</em></p> <p><strong>2. </strong><strong>ERA5_Persistant_Database_lats_2000km:</strong> The file gives the latitudes of grid points of maximum IVT, i.e., the latitude of major axes of ARs throughout their duration.</p> <p><strong><em>Columns “Date”, “AR_ID”, “Ind”, “AvgIVT”, “StartIVT”, “ARDuration”, “MaxIVT”, “ARCat” are the same as given above for “ERA5_Persistant_Database2000km.csv” file.</em></strong></p> <p><strong>Column “0” to end.</strong></p> <p>These columns give the latitude ( in degrees North) at each gridcell of the major axis of each AR timestep.</p> <p><strong>3. </strong><strong>ERA5_Persistant_Database_lons_2000km:</strong> The file gives the longitudes of grid points of maximum IVT, i.e., the longitudes of major axes of ARs throughout their duration</p> <p>Columns “Date”, “AR_ID”, “Ind”, “AvgIVT”, “StartIVT”, “ARDuration”, “MaxIVT”, “ARCat” are the same as given above for “ERA5_Persistant_Database2000km.csv” file.</p> <p><strong>Column “0” to end.</strong></p> <p>These columns give the longitude (in degrees East) at each gridcell of the major axis of each AR timestep</p>
Marquette, Michigan Ground Observations and Atmospheric River Dataset
<p>This dataset contains ground-based meteorological observations and accompanying atmospheric river data used in "The influence of atmospheric rivers on cold-season precipitation in the Upper Great Lakes region", Mateling, Pettersen, Kulie, Mattingly, Henderson, and L'Ecuyer, submitted to GRL, in review.</p> <p>The ground-based data contains meteorological data including temperature, wind speed and direction, radar reflectivity and Doppler velocity from a Micro-Rain Radar2 (MRR) and precipitation data from a Precipitation Imaging Package (PIP) hosted at the National Weather Service in Marquette, Michigan (Pettersen, Kulie, et al., 2020; Pettersen, Bliven, et al., 2020; Kulie et al., 2021).</p> <p>The atmospheric river (AR) data contains a flag to identify when an AR is within 100 km of Marquette during a deep cold-season precipitation event. Additionally, the associated integrated water vapor transport (IVT) and motion vectors are within this file. The AR database was created and analyzed in Mattingly et al. (2018). </p>
Atmospheric_river_land_hydrology_western_US_HUC8_datasets
<p>Dataset for the manuscript entitled "Impact of Atmospheric Rivers on Surface Hydrological Processes in Western U.S. Watersheds".</p> <p> </p> <p>It includes daily meteorological and surface hydrological data from western U.S. WRF simulation. Data is aggregated to 8-digit Hydrological Unit (HUC8) watersheds.</p> <p> </p> <p>To use this dataset, please cite the following two publications:</p> <p> </p> <p>Chen, X., Leung, L. R., Gao, Y., Liu, Y., Wigmosta, M., & Richmond, M. (2018). Predictability of extreme precipitation in western U.S. watersheds based on atmospheric river occurrence, intensity, and duration. <em>Geophysical Research Letters</em>, 45, 11,693–11,701. <a href="https://doi.org/10.1029/2018GL079831">https://doi.org/10.1029/2018GL079831</a></p> <p> </p> <p>Chen, X., Leung, L. R., Wigmosta, M., & Richmond, M. (2019). Impact of Atmospheric Rivers on Surface Hydrological Processes in Western U.S. Watersheds. <em>Journal of Geophysical Research: Atmospheres</em>, <a href="http://doi.org/10.1029/2019JD03468">https://doi.org/10.1029/2019JD03468</a></p>
Atmospheric, hydrodynamic and water quality observations from environmental-quality stations, water level sensors, acoustic Doppler velocimeters, and meteorological stations located at the Guadalquivir river estuary (2008 - 2010)
<p>The dataset included in this repository was obtained during the project entitled “Propuesta metodológica para diagnósticar las consecuencias de las actuaciones humanas en el estuario del Guadalquivir” funded by the Autoridad Portuaria de Sevilla (APS), by the Consejería de Innovación, Ciencia y Empresa (Junta de Andalucía), CTM2011-22580, MedEX (CTM2008-04036-E) and PR11-RNM-7722. The data were collected in real time from 2008 until 2010 with a remote monitoring system installed by the Institute of Marine Sciences of Andalusia (ICMAN-CSIC) (Navarro et al., 2011).</p> <p> </p> <p>The environmental quality station recorded turbidity, temperature, conductivity, normalized turbidity, dissolved oxygen, oxygen, oxygen saturation, percentage of oxygen saturation, fluorescence, normalized fluorescence, and salinity every thirty minutes. Current data were measured every 15 minutes by means of acoustic current profilers. The former datasets were obtained at several depths and different locations along the Guadalquivir estuary. Water level sensors recorded the position of the free water surface every 10 minutes at several locations along the Guadalquivir estuary. Wind velocity and direction and solar radiation were measured every 10 minutes in a meteorological station at the mouth of the Guadalquivir estuary.</p> <p>Brief description of dataset.</p> <ul> <li> <p>velocities.csv (in m/s)</p> </li> <li> <p>Turbidity.csv (in Volts), temperature (in Celsius), conductivity (in Siemens/m), normalized turbidity (in FNU), dissolved oxygen (mg/L), oxygen (in Volts), fluorescence (in Volts), normalized fluorescence (in Volts), oxygen saturation (mg/L), percentage of oxygen saturation (%), salinity (in PSU).</p> </li> <li> <p>qual_Salmedina.csv, R_mean (mean radiative flux in W/m²), R_max (max radiative flux in W/m²), Rel_humidity (relative humidity in %), D_mean (wind mean direction in degrees), D_max (wind maximum direction in degrees), D_sig (standard deviation of the wind direction in degrees), V_mean (mean wind velocity in m/s), V_max (maximum wind velocity in m/s), V_sig (standard deviation of the wind velocity in m/s), P_atm_mean (mean atmospheric pressure in mbar), T_mean (mean air temperature in Celsius), T_max (maximum air temperature in Celsius), T_sig (standard deviation of the air temperature in Celsius).</p> </li> <li> <p>Sealevel.csv (in meters)</p> </li> </ul> <p>A wide description of the datasets can be found in Navarro et al (2011).</p> <p>Contact person: infogdfa@ugr.es (or mcobosb@ugr.es)</p>
Using variable-resolution grids to model precipitation from atmospheric rivers around the Greenland ice sheet
<p>This dataset can be used to reproduce the figures created in Waling et al. 2024, "Using variable-resolution grids to model precipitation from atmospheric rivers around the Greenland ice sheet." Each figure has its own script which can be executed.<br><br></p>
Data set of detected atmospheric rivers, cyclones, and fronts within the region of 75°N – 82.5°N, 0°E – 30°E and at Ny-Ålesund (Svalbard) for 2017 – 2021
<p>This data set contains times when atmospheric rivers, cyclones, or fronts have been detected within the broader region of 75°N – 82.5°N, 0°E – 30°E and specifically at Ny-Ålesund, Svalbard (78.92308 °N, 11.92108 °E) for the years 2017 to 2021. To this end, the detection methods, as described in Lauer et al. (2023), have been applied to the hourly-resolved ERA5 reanalysis (Hersbach et al., 2020) data. </p> <p>Data set overview</p> <p>Each file contains the times (year, month, day, hour in UTC) when the corresponding weather system, i.e. atmospheric river, cyclone and front, has been detected within the region of 75°N – 82.5°N, 0°E – 30°E. The last column indicates if the weather system was located also over Ny-Ålesund Svalbard (78.92308 °N, 11.92108 °E). </p>
Atmospheric Rivers Contribute to Summer Surface Buoyancy Forcing in the Atlantic Sector of the Southern Ocean
<p>These are the Wave glider data used in the analysis and creation of figures in Edholm et al. 2022: <em>Atmospheric Rivers Contribute to Summer Surface Buoyancy Forcing in the Atlantic Sector of the Southern Ocean</em> in support of open-code, transparency, and repeatability.</p> <p>Abstract:</p> <p>Atmospheric rivers (ARs) dominate moisture transport globally; however, it is unknown what impact ARs have on surface ocean buoyancy. This study explores the surface buoyancy gained by ARs using high-resolution surface observations from a Wave Glider deployed in the subpolar Southern Ocean (54°S, 0°E) between 19 December 2018 and 12 February 2019 (55 days). When ARs combine with storms, the associated precipitation is significantly enhanced (189%). In addition, the daily accumulation of AR-induced precipitation provides a buoyancy gain to the surface ocean equivalent to warming by surface heat fluxes. Over the 55 days, ARs accounted for 47% of the total precipitation equating to 10% of the summer surface ocean buoyancy gain. This study indicates that ARs play an important role in the summer precipitation over the subpolar Southern Ocean and that they can alter the upper-ocean buoyancy budget from synoptic to seasonal timescales.</p>
Polar atmospheric and aerosol river detection catalogs
<p>These detection catalogs of atmospheric and aerosol rivers were created for the publication <em>Lapere et al., "Polar aerosol atmospheric rivers: detection, characteristics and potential applications", Journal of Geophysical Research, Submitted</em>. The associated methodology is described in this publication.</p> <p>They provide binary detection of Atmospheric river (AR), Black carbon aerosol atmospheric river (BC_AER), Dust aerosol atmospheric river (DU_AER), Sea salt aerosol atmospheric river (SS_AER) and Organic carbon aerosol atmospheric river (OC_AER), in NetCDF format, for the period 1980-2022, with a 3-hour time resolution and 1x1° spatial resolution, for the regions 30°-90°N (indicated by the suffix "NH") and 30°-90°S (indicated by the suffix "SH").</p> <p>The code for pre-processing raw MERRA2 data, along with the detection algorithm are also provided here as Python Jupyter notebooks.</p>
Atmospheric_river_sensitivity_to_local_SST_investigation_datasets
<p>This repository includes all the data used to plot the figures in the main texts and supplements (if applicable) of the following paper:</p> <p> </p> <p>Chen, X. and L. R. Leung, 2020: Response of landfalling atmospheric rivers on the U.S. west coast to local sea surface temperature perturbations, <em>Geophys. Res. Lett.</em></p> <p> </p> <p>The related plotting scripts is available on GitHub: lucas-uw/Chen-2020-GRL</p> <p> </p> <p><em>Should you choose to use this dataset, please cite the above paper where appropriate.</em></p>
ARtracks - a Global Atmospheric River Catalogue Based on ERA5 and IPART
<p>The <strong>ARtracks Atmospheric River Catalogue</strong> is based on the ERA5 climate reanalysis dataset, specifically the output parameters "vertical integral of east-/northward water vapour flux". Most of the processing relies on<br>IPART (Image-Processing based Atmospheric River (AR) Tracking, https://github.com/ihesp/IPART), a Python package for automated AR detection, axis finding and AR tracking. The catalogue is provided as a pickled pandas.DataFrame as well as a CSV file.</p> <p>For detailed information, please see <a href="https://github.com/dominiktraxl/artracks">https://github.com/dominiktraxl/artracks</a>.</p> <p>The ARtracks catalogue covers the years from 1979 to the end of the year 2019.</p>
Atmospheric rivers dataset for machine learning training
<p>A thorough description of the data and how it was created can be found: <a title="http://climate-cms.org/CNN-Atmospheric-Rivers/" href="http://climate-cms.org/CNN-Atmospheric-Rivers/" target="_blank" rel="noopener">http://climate-cms.org/CNN-Atmospheric-Rivers/</a></p> <p>A Jupyter notebook has also been created where we'll show you how to use this data to train a deep learning model to identify whether an Integrated Vapor Transport map contains an atmosperhic river. it can be found: <a title="CNN_AR_tutorial.ipynb" href="https://github.com/coecms/CNN-Atmospheric-Rivers/blob/main/CNN_AR_tutorial.ipynb" target="_blank" rel="noopener">CNN_AR_tutorial.ipynb</a> and had been published here: </p> <p>Mesto, M., Hobeichi, S., & Green, S. (2024). CNN-Atmospheric-Rivers (v1.0.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.12538779" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12538779</a></p> <p>The data is organised in three folders:</p> <ul> <li>IVT_ERA5_2Deg: Contains global IVT data.</li> <li>AR_Global: Contains polygons representing AR objects identified and hand-labelled in each IVT map.</li> <li>Training_Testing_tiles: Contains tiles of IVT data with an annotation file that classifies each tile as one of the following: ‘Atmospheric River’, ‘Ambiguous’. The ‘Ambiguous’ class refers to objects that are not clearly identifiable as atmospheric rivers.</li> </ul> <p><strong>Integrated Vapor Transport maps:</strong></p> <p>These maps were computed using the magnitude of the vertical integral of northward and eastward water vapour flux variables from ERA5. The IVT values are expressed in units of kg m^-1 s^-1. All the IVT TIFF files were loaded into ArcGIS software and displayed using a colour scheme that allows for the visual identification of atmospheric rivers. Data details:</p> <ul> <li>Folder: <strong><em>IVT_ERA5_2Deg</em></strong></li> <li>File format: TIFF</li> <li>Spatial resolution: 2 degrees</li> <li>Spatial coverage: Global (longitude: -180 to 180 , latitude: -90 to 90)</li> <li>Geographic Coordinate System: GCS_WGS_1984</li> <li>Temporal coverage: 1<sup>st</sup> – 5<sup>th</sup> day of January, April, July, October for 2010, 2013, 2015; these years correspond to La Niña, neutral, and El Niño year respectively</li> <li>Temporal resolution: Daily</li> <li>Naming of files: ivt_2deg_ddmmyyyy.tif</li> <li>Number of files: 60 (5 days × 4 months × 3 years)</li> <li>Number of channels in each file: 1</li> </ul> <p> </p> <p><strong>Atmospheric Rivers in IVT maps:</strong></p> <p>The annotation tool ‘Label Objects for Deep Learning’ was used to draw polygons to cover the shape of atmospheric rivers on each IVT map. Each polygon was assigned one of two labels: 'Atmospheric Rivers' or 'Ambiguous'. The polygons were drawn based on visual identification of the shape of atmospheric rivers, guided by IVT values close to 500kg m^-1 s^-1 as in Reid et al (2020). The 'Ambiguous' label was assigned to objects that were unclear in their classification as ARs. This ambiguity arose from objects that were shorter, wider, had slightly lower IVT values, or it was hard to tell if they were ARs of tropical cyclones during the early stages of their formation. Data details:</p> <ul> <li>Folder: <strong><em>AR_Global</em></strong></li> <li>File format: SHP (shapefile)</li> <li>Spatial coverage: Global</li> <li>Geographic Coordinate System: GCS_WGS_1984</li> <li>Temporal coverage: 1<sup>st</sup> – 5<sup>th</sup> day of January, April, July, October for 2010, 2013, 2015 (corresponding to La Niña, neutral, and El Niño year respectively)</li> <li>Temporal resolution: Daily</li> <li>Naming of files: ivt_2deg_ddmmyyyy_labelled.shp</li> <li>Number of files: 60 (5 days × 4 months × 3 years)</li> </ul> <p> </p> <p><strong>Dataset for deep learning training:</strong></p> <p>The tool 'Export Training Data for Deep Learning' uses the IVT maps in the 'AR_Global' folder and the shapefiles in the 'IVT_ERA5_2Deg' folder to create labelled tiles for deep learning training. Each tile in the map is assigned a label: 'Atmospheric River', 'Ambiguous', or no label if it doesn’t contain any AR or ambiguous shape. The generated map chips are stored in folder <em>Training_Testing_tiles/ RCNN_Masks_All_Tiles (no 3-10-15)/images</em>, and the labels are provided in the textfile 'map.txt' file in folder Training_Testing_tiles/ RCNN_Masks_All_Tiles (no 3-10-15)/. Data details:</p> <ul> <li>Folder: <strong><em>Training_Testing_tiles/</em> RCNN_Masks_All_Tiles (no 3-10-15)/images</strong></li> <li>File format: TIFF</li> <li>Spatial resolution: 2 degrees</li> <li>Spatial coverage: varies. Width of tile = 40 gridcells. Height of tile = 20grid cells</li> <li>Geographic Coordinate System: GCS_WGS_1984</li> <li>Temporal coverage: 1<sup>st</sup> – 5<sup>th</sup> day of January, April, July, October for 2010, 2013, 2015. These years correspond to La Niña, neutral, and El Niño year respectively. Please note that data for certain days are missing; these omissions correspond to days with no or only a single atmospheric river detected.</li> <li>Temporal resolution: Daily</li> <li>Number of files: varies</li> <li>Number of channels in each file: 1</li> </ul>
Atmospheric_river_precipitation_predictability_data
<p>Data for the manuscript entitled "Predictability of Extreme Precipitation Associated With Atmospheric Rivers in Western U.S. Watersheds".</p> <p> </p> <p>It includes daily precipitation data from WRF and PRISM. Also includes atmospheric river information derived from ARTMIP Tier 1 archive.</p> <p> </p> <p>The tools used to generate the figures in the paper is at: <a href="https://github.com/lucas-uw/Chen-2018-GRL">https://github.com/lucas-uw/Chen-2018-GRL</a></p> <p> </p> <p>If you use this dataset, please cite the following paper:</p> <p> </p> <p>Chen, X., Leung, L. R., Gao, Y., Liu, Y., Wigmosta, M., & Richmond, M. (2018). Predictability of extreme precipitation in western U.S. watersheds based on atmospheric river occurrence, intensity, and duration. Geophysical Research Letters, 45, 11,693–11,701. <a href="http://doi.org/10.1029/2018GL079831">https://doi.org/10.1029/2018GL079831</a></p> <p> </p> <p>Chen, X., Leung, L. R., Wigmosta, M., & Richmond, M. (2019). Impact of Atmospheric Rivers on Surface Hydrological Processes in Western U.S. Watersheds. Journal of Geophysical Research: Atmospheres, <a href="http://doi.org/10.1029/2019JD03468">https://doi.org/10.1029/2019JD03468</a></p>
Detection of Atmospheric Rivers in the Northern Hemisphere based on ERA5 reanalysis data and the IPART algorithm, 1979-2020
<p># 1. Overview</p> <p>This is a catalogue of atmospheric river (AR) detections over the Northern Hemisphere, based on 6-hourly ERA5 reanalysis dataset and the Image-Processing based Atmospheric River Tracking (IPART) algorithm.</p> <p>Time domain of the data:</p> <ul> <li>From 1979-Jan-01 to 2020-Dec-31</li> <li>Temporal resolution is 6-hourly</li> </ul> <p>Spatial domain of the data:</p> <ul> <li>Northern Hemisphere, land and ocean</li> <li>Spatial resolution is 0.25 * 0.25 degrees latitude/longitude</li> </ul> <p>Input data from ERA5 include:</p> <ul> <li>Vertical integral of northward water vapour flux, in kg/(m s).</li> <li>Vertical integral of eastward water vapour flux, in kg/(m s).</li> </ul> <p>Data in the Northern Hemisphere domain (0 - 90 N), at 0.25 * 0.25 degrees latitude/longitude resolution are obtained from https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5.</p> <p>Version v3.0.8 of the IPART Python module used for detection and tracking of atmospheric rivers is preserved at <strong>10.5281/zenodo.4164826</strong>, available via Creative Commons Attribution 4.0 International license and developed openly at the Github repository https://github.com/ihesp/IPART.</p> <p># 2. File naming convention</p> <p>The data files are named using the following convention:</p> <p>ar_YYYYMM.nc</p> <p>where:</p> <ul> <li>YYYY: 4-digit year number</li> <li>MM: 2-digit month number</li> </ul> <p>E.g. `ar_199902.nc` means detections in Feb of 1999.</p> <p>Months are calendar months, including Feb-29th in leap-years.</p> <p># 3. Data format</p> <p>Data are saved in netCDF format.</p> <p>Each data file contains one 3-dimensional array, of a shape `(t, 360, 1440)`, where:</p> <ul> <li>`t`: length of the time dimension. Since data are 6-hourly, t equals 4 * num_of_days_in_month.</li> <li>`360`: latitude dimension, from 0 - 90N, with a 0.25-degree step.</li> <li>`1440`: longitude dimension, from 80 - 440 E (shifted eastward by 80 degrees to put both the Pacific and Atlantic oceans within the domain), with a 0.25-degree step.</li> </ul> <p>Each time slice of the data contains maps of the Northern Hemisphere, with integer values in grid cells. Possible values are:</p> <ul> <li>0: meaning no AR is detected in the grid cell.</li> <li>1, 2, ... ,n: integer labels, each corresponding to the region of an AR entity.</li> </ul> <p># 4. Important parameters in the IPART algorithm</p> <p>Here are the most important parameters used when detecting ARs from ERA5 data using the IPART python module:</p> <ul> <li> THR filtering kernel: `[16, 13, 13]`. `16` means 16 time slices, or equivalently 4 days given 6-hourly input data. `13` means 13 grid cells, or equivalently ~325 km, given 0.25 degrees latitude/longitude input data. Note that both of these temporal and spacial lengths are half of the sizes of the filtering kernel.</li> <li> minimum area: `50 * 1e4`, in km^2, minimum size of AR region candidates.</li> <li> maximum area: `1800 * 1e4`, in km^2, maximum size of AR region candidates.</li> <li> minimum L/W: `2.0`, minimum length/width ratio of AR region candiates.</li> <li> minimum length: `2000`, in km, minimum length of AR region candidates.</li> <li> minimum latitude: `20`, minimum latitude of the geometrical centroid of an AR region candidate.</li> <li> maximum latitude: `80`, maximum latitude of the geometrical centroid of an AR region candidate.</li> </ul> <p>For more details regarding these parameters, as well as the IPART algorithm, please refer to our published works:</p> <ul> <li>Xu, G., Ma, X., Chang, P., and Wang, L.: Image-processing-based atmospheric river tracking method version 1 (IPART-1), Geosci. Model Dev., 13, 4639–4662, https://doi.org/10.5194/gmd-13-4639-2020, 2020.</li> </ul> <p>Or the Github repository that houses the IPART module:</p> <ul> <li>https://github.com/ihesp/IPART</li> </ul>
20th Century Atmospheric River Archive for Western North America and Europe
<p><strong>General Description</strong></p> <p>This datasets provides 6-hourly instantaneous atmospheric river absence-presence time series for 13 sub-regions along the coastlines of Western North America and Europe, as well the corresponding Integrated Water Vapor (IVT) values and exceeded climatological quantiles. These data were retrieved from 3 distinct reanalyses:</p> <p>1. ERA-20C, 1900-2010, 1.125 degrees resolution, here termed "era20c"</p> <p>2. NOAA-CIRES 20th Century Reanalysis version 2, 1900-2012, 2 degrees resolution, here termed "c20", ARs were retrieved from instantaneous ensemble-mean data.</p> <p>3. ECMWF ERA-Interim, 1979-2014, 0.75 degrees resolution, here termed "interim"</p> <p>The file structure is as in this example:</p> <p>ar_Brands_v0_interim_scalifornia_JFMAOND_1979_2014.nc</p> <p>translates to:</p> <p>ar_<algorithm name>_<version>_<underlying dataset>_<target region as illustrated in fig_studyregions.pdf>_<considered months>_<start year>_<end_year>.nc</p> <p>The 13 study regions are indicated in <fig_studyregions.pdf> attached below and described in Brands et al. (2017). The lat-lon coordinates of each region are provided in the netCDF files.</p> <p>For western North America and Europe the October-through-April and October-through-March season is covered, respectively. The compressed netCDF4 files offered here come with detailed metadata information. For generating the present dataset, the initial version of the AR detection and tracking algorithm developed in my PhD thesis was used (here referred to as version 0, see Brands et al. 2017 for a full description). Although newer algorithm versions have become available in the framework of the Atmospheric River Method Intercomparison Project (ARTMIP, see Rutz et al. 2019), the initial version 0 was specifically developed for detecting landfalling ARs along the coastlines of Western North America and Europe. The correct functioning was supervised by eye for hundreds, if not thousands of cases.</p> <p>The 9 distinct AR detection and tracking methods contained in each netCDF file (coined "method 0,1...8" in there) use distinct climatological percentile thresholds to 1) detect ARs along the coastline (the detection percentile, termed "prct_detect") and then "crawl" upwards the flow guided by the strongest IVT above the tracking percentile ("prct_track") and by the respective U and V components until a minimum length of 2000 km is reached. The results obtained from the 9 methods thus differ in AR intensity.</p> <p>The netCDF files of the present dataset have been recompiled from the non-standard .mat files generated in my PhD thesis during the years 2013-2017. For the target regions in Europe, the content of the present dataset partly overlaps with the non-standard dataset previously published at http://dx.doi.org/10.13140/RG.2.2.14711.32160. The target regions in western North America have been newly included and are only available from the present dataset.</p> <p>Contact: Swen Brands, brandssf@ifca.unican.es</p> <p> </p> <p><strong>References</strong></p> <p>Brands, S., Gutiérrez, J.M. & San-Martín, D. (2017). Twentieth-century atmospheric river activity along the west coasts of Europe and North America: algorithm formulation, reanalysis uncertainty and links to atmospheric circulation patterns. <em>Climate Dynamics</em> 48, 2771–2795. https://doi.org/10.1007/s00382-016-3095-6</p> <p>Compo, G.P., Whitaker, J.S., Sardeshmukh, P.D., Matsui, N., Allan, R.J., Yin, X., Gleason, B.E., Vose, R.S., Rutledge, G., Bessemoulin, P., Brönnimann, S., Brunet, M., Crouthamel, R.I., Grant, A.N., Groisman, P.Y., Jones, P.D., Kruk, M.C., Kruger, A.C., Marshall, G.J., Maugeri, M., Mok, H.Y., Nordli, Ø., Ross, T.F., Trigo, R.M., Wang, X.L., Woodruff, S.D. and Worley, S.J. (2011), The Twentieth Century Reanalysis Project. <em>Q.J.R. Meteorol. Soc.</em>, 137: 1-28, https://doi.org/10.1002/qj.776</p> <p>Dee, D.P., Uppala, S.M., Simmons, A.J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M.A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A.C.M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A.J., Haimberger, L., Healy, S.B., Hersbach, H., Hólm, E.V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A.P., Monge-Sanz, B.M., Morcrette, J.-.-J., Park, B.-.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-.-N. and Vitart, F. (2011), The ERA-Interim reanalysis: configuration and performance of the data assimilation system. <em>Q.J.R. Meteorol. Soc.</em>, 137: 553-597, https://doi.org/10.1002/qj.828</p> <p>Poli, P., and Coauthors, 2016: ERA-20C: An Atmospheric Reanalysis of the Twentieth Century. <em>J. Climate</em>, 29, 4083–4097, https://doi.org/10.1175/JCLI-D-15-0556.1</p> <p>Rutz, J. J., Shields, C. A., Lora, J. M., Payne, A. E., Guan, B., Ullrich, P., et al. (2019). The Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Quantifying uncertainties in atmospheric river climatology. <em>Journal of Geophysical Research: Atmospheres</em>, 2019; 124: 13777– 13802. https://doi.org/10.1029/2019JD030936</p>
Data from: River interlinking alters land-atmosphere feedback and changes the Indian summer monsoon.
<p>The dataset contains post-processed output from two experiments performed for Indian Summer Monsoon (June-September) from 1991-2012 using WRF-CLM4: CTL and IRR. Here, CTL represents WRF-CLM4 simulation with irrigation currently practiced in India. We use a modified irrigation module in CLM4 that better represents the Indian practices of irrigation by incorporating groundwater withdrawal and flood irrigation practiced over paddy fields. The module can be found at <a href="https://github.com/IMMM-SFA/WRF_CLM4_Irrigation">https://github.com/IMMM-SFA/WRF_CLM4_Irrigation</a> and <a href="https://doi.org/10.1029/2019GL083875">https://doi.org/10.1029/2019GL083875</a>. IRR simulation adds additional irrigation to CTL by increasing the percentage of irrigated area to 80% in regions where India's river-interlinking projects target an increase in the culturable command area.</p> <p>The post-processed output contains the following variables:</p> <ol> <li>Mean Daily Temperature</li> <li>Daily Maximum Temperature</li> <li>Latent Heat Flux</li> <li>Sensible Heat Flux</li> <li>Relative Humidity</li> <li>U-Wind at Pressure levels</li> <li>V-Wind at Pressure levels</li> <li>Net-Solar Radiation on Land</li> <li>Soil Moisture</li> </ol> <p>Irrigation input files for CTL and IRR simulations of WRF-CLM4 are also included.</p>
"I was the class teacher at that time. It was a class trip, usually organized near the end of the schoolterm in summer. The pupils went there by bike to have a barbecue at the sandy banks of the river Rhine near Dusseldorf. The landscape around is mostly dominated by agriculture and glasshouse cultures. You find a mixture of former villages nowadays completely suburbanized. The population finds jobs in the nearby urban centers like Dusseldorf, Neuss and other big cities. The reason why Irecorded the scene is simply because Iam interested in collecting sounds in general by doing recordings in different surroundings like nature, cities and everything between. My memories about the event are that it was a relaxing and funny atmosphere, which is not always the case while teaching in a classroom" [Reinhard/reinsamba]15 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"I was the class teacher at that time. It was a class trip, usually organized near the end of the schoolterm in summer. The pupils went there by bike to have a barbecue at the sandy banks of the river Rhine near Dusseldorf. The landscape around is mostly dominated by agriculture and glasshouse cultures. You find a mixture of former villages nowadays completely suburbanized. The population finds jobs in the nearby urban centers like Dusseldorf, Neuss and other big cities. The reason why Irecorded the scene is simply because Iam interested in collecting sounds in general by doing recordings in different surroundings like nature, cities and everything between. My memories about the event are that it was a relaxing and funny atmosphere, which is not always the case while teaching in a classroom" [Reinhard/reinsamba]15
Atmospheric Rivers Antacrtica IPSL-CM6
<p>Netcdf file of detected Atmospheric Rivers (AR) in Antarctica using an vIVT based algorithm.</p> <p>Two time periods:</p> <ul> <li>"hist": 1995 to 2015, calculated from historical simulations from IPSL-CM6. r*i1p1f1 represent the diferente ensemble members</li> <li>"ssp245": calculated from scenarioMIP simulations from IPSL-CM6 with the ssp245 scenariop. 2015 to 2055 for folowing ensemble members: r2i1p1f1, r3i1p1f1, r4i1p1f1, r6i1p1f1, r14i1p1f1 r22i1p1f1 and 2015 to 2100 for r1i1p1f1, r5i1p1f1, r10i1p1f1, r11i1p1f1.</li> </ul> <p>Two threshods used during the detection:</p> <ul> <li>"hist_thr": Threshold based on the 98th percentile of Historical vIVT values (it is a fixed threshold).</li> <li>"adp_thr": Adaptive threshold scaled on the increase of humidity in the southern hemisphere.</li> </ul>
Dataset of the paper "Response of Sea Surface Temperature to Atmospheric Rivers"
<p>Dataset of the paper "Response of Sea Surface Temperature to Atmospheric Rivers", whose manuscript will be submitted by 10/25/2023</p> <p><br>The dataset contains the necessary data to generate the figures in the paper with the code in the link <a href="https://doi.org/10.5281/zenodo.10958491">https://doi.org/10.5281/zenodo.10958491</a> whose Github reference is <a href="https://github.com/meteorologytoday/paperfigures-2023-AR-SST-response">https://github.com/meteorologytoday/paperfigures-2024-AR-SST-response</a></p> <p> </p>
Historical Data for paper "When will humanity notice its impacts on atmospheric rivers?"
<p>The repository contains the historical scenario simulation (one ensemble member) of GFDL SPEAR large ensemble data. The data is used to support the finding in the paper entitled "When will humanity notice its impacts on atmospheric rivers?" by Tseng et al. </p>
ScienceDex guides
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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.