{ "cells": [ { "cell_type": "markdown", "id": "4de9387c", "metadata": { "papermill": { "duration": 0.006197, "end_time": "2024-07-01T13:29:44.877492", "exception": false, "start_time": "2024-07-01T13:29:44.871295", "status": "completed" }, "tags": [] }, "source": [ "# Metadata table" ] }, { "cell_type": "code", "execution_count": 1, "id": "96ce8164", "metadata": { "execution": { "iopub.execute_input": "2024-07-01T13:29:44.893580Z", "iopub.status.busy": "2024-07-01T13:29:44.892465Z", "iopub.status.idle": "2024-07-01T13:29:44.907924Z", "shell.execute_reply": "2024-07-01T13:29:44.906570Z" }, "papermill": { "duration": 0.027707, "end_time": "2024-07-01T13:29:44.910948", "exception": false, "start_time": "2024-07-01T13:29:44.883241", "status": "completed" }, "tags": [ "remove-input" ] }, "outputs": [], "source": [ "# NO CODE\n", "from json2html import json2html\n", "import json, re\n", "from IPython.display import HTML" ] }, { "cell_type": "code", "execution_count": 2, "id": "c032db2b", "metadata": { "execution": { "iopub.execute_input": "2024-07-01T13:29:44.927218Z", "iopub.status.busy": "2024-07-01T13:29:44.926453Z", "iopub.status.idle": "2024-07-01T13:29:44.934075Z", "shell.execute_reply": "2024-07-01T13:29:44.932709Z" }, "papermill": { "duration": 0.018307, "end_time": "2024-07-01T13:29:44.937587", "exception": false, "start_time": "2024-07-01T13:29:44.919280", "status": "completed" }, "tags": [ "parameters", "remove-input" ] }, "outputs": [], "source": [ "# NO CODE\n", "# PARAMETERS\n", "path = './meta/environment/meteocat_xema'" ] }, { "cell_type": "code", "execution_count": 3, "id": "d0df0e5c", "metadata": { "execution": { "iopub.execute_input": "2024-07-01T13:29:44.956232Z", "iopub.status.busy": "2024-07-01T13:29:44.954579Z", "iopub.status.idle": "2024-07-01T13:29:44.963416Z", "shell.execute_reply": "2024-07-01T13:29:44.961653Z" }, "papermill": { "duration": 0.018244, "end_time": "2024-07-01T13:29:44.965892", "exception": false, "start_time": "2024-07-01T13:29:44.947648", "status": "completed" }, "tags": [ "injected-parameters" ] }, "outputs": [], "source": [ "# Parameters\n", "path = \"./meta/environment/era5h\"\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "4b275e2d", "metadata": { "execution": { "iopub.execute_input": "2024-07-01T13:29:44.981247Z", "iopub.status.busy": "2024-07-01T13:29:44.980653Z", "iopub.status.idle": "2024-07-01T13:29:45.004119Z", "shell.execute_reply": "2024-07-01T13:29:45.002791Z" }, "papermill": { "duration": 0.034354, "end_time": "2024-07-01T13:29:45.007659", "exception": false, "start_time": "2024-07-01T13:29:44.973305", "status": "completed" }, "tags": [ "remove-input", "full-width" ] }, "outputs": [ { "data": { "text/html": [ "
$schema../schema.json
@context
@vocabhttps://schema.org/
qudthttp://qudt.org/schema/qudt/
xsdhttp://www.w3.org/2001/XMLSchema#
@typeDataset
nameera5h
conditionsOfAccessPrivate
descriptionERA5 hourly gridded data on single levels from 1979 to present. ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate. This dataset is not intended for general public access since the original row dataset is freely distributed by ECMWF
licenseFree of charge, worldwide, non-exclusive, royalty free and perpetual. See https://apps.ecmwf.int/datasets/licences/copernicus/
citation
  • ERA5 hourly data on single levels from 1979 to present [DOI: 10.24381/cds.adbb2d47]
temporalCoverage1979-01-01/..
spatialCoverage
@typePlace
nameWorldwide
sameAshttps://www.wikidata.org/wiki/Q13780930
distribution
  • @typeDataDownload
    namecds_api
    descriptionDistribution by Climate Data Store API
    encodingFormatnetCDF
    workExample./notebooks/era5.py
    contentUrl
    • https://cds.climate.copernicus.eu/api/v2
    contentSize
  • @typeDataDownload
    namecluster_ceab
    descriptionDistribution by CSIC-CEAB cluster
    encodingFormatnetCDF
    workExample./notebooks/era5.py
    contentUrl
    • sftp://{USER}@cluster-ceab:/leov1/era5/daily_chunks/masked_{VARIABLE_NAME}_t_{YEAR-MONTH-DAY}.nc
    contentSize10MB
variableMeasured
  • @typePropertyValue
    name2m_temperature
    description2 m above ground air temperature
    unitTextDegree Kelvin
    qudt:dataTypexsd:int
  • @typePropertyValue
    name2m_dewpoint_temperature
    description2 m above ground dew point temperature
    unitTextDegree Kelvin
    qudt:dataTypexsd:int
  • @typePropertyValue
    name10m_u_component_of_wind
    description10 m above ground wind velocity component u
    unitTextm s^-1
    qudt:dataTypexsd:int
  • @typePropertyValue
    name10m_v_component_of_wind
    description10m above ground wind velocity component v
    unitTextm s^-1
    qudt:dataTypexsd:int
  • @typePropertyValue
    nametotal_precipitation
    descriptionAccumulated total precipitation during 1 hour
    unitTextmm h^-1
    qudt:dataTypexsd:int
creator
  • @typeOrganization
    @idECMWF
    nameEuropean Centre for Medium-Range Weather Forecasts (ECMWF)
    contactPoint
    @typeContactPoint
    emailcopernicus-support@ecmwf.int
    urlhttps://www.ecmwf.int/
  • @typePerson
    @idZJ
    nameJužnič Zonta, Živko
    identifierhttps://orcid.org/0000-0002-2362-9771
    contactPoint
    @typeContactPoint
    roleNamedata engineer
    emailjzivko@gmail.com
    urlhttps://es.linkedin.com/in/zivko
measurementTechnique
  • Automatic weather station
  • Data assimilation (reanalysis) that combines first principle models with observations
" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# NO CODE\n", "# FULL WIDTH\n", "with open(f'{path}.json') as f:\n", " data = json.load(f)\n", "\n", "table = json2html.convert(json=data, clubbing=False)\n", "table_sub = re.sub('', '', table)\n", "\n", "HTML(table_sub)\n", "\n", "# Run the following in the command line to build a html table\n", "# $ jupyter nbconvert --to html --no-input --no-prompt build_tables.ipynb\n" ] }, { "cell_type": "code", "execution_count": null, "id": "cb7986c9", "metadata": { "papermill": { "duration": 0.006367, "end_time": "2024-07-01T13:29:45.021736", "exception": false, "start_time": "2024-07-01T13:29:45.015369", "status": "completed" }, "tags": [] }, "outputs": [], "source": [] } ], "metadata": { "interpreter": { "hash": "524d3359b179f3b444361f48db8ae048bd8c237924fac1cd48a4c6f8144f6452" }, "kernelspec": { "display_name": "Python 3.7.10 64-bit ('scidb': conda)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.4" }, "metadata": { "interpreter": { "hash": "e3961729dbf4ff77740ff872c9a3eef08621b5b434e3d8d81026af4505918c74" } }, "papermill": { "default_parameters": {}, "duration": 1.515135, "end_time": "2024-07-01T13:29:45.351497", "environment_variables": {}, "exception": null, "input_path": "build_info.ipynb", "output_path": "./meta_ipynb/era5h.ipynb", "parameters": { "path": "./meta/environment/era5h" }, "start_time": "2024-07-01T13:29:43.836362", "version": "2.3.4" } }, "nbformat": 4, "nbformat_minor": 5 }