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Change","sub_category":"Climate Data Access and Visualization","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"# Analysis-Ready, Cloud Optimized ERA5\n\nRecipes for reproducing Analysis-Ready \u0026 Cloud Optimized (ARCO) ERA5 datasets.\n\n[Introduction](#introduction) • [Overview](#overview) • [Get Started with Colab Notebooks](#get-started-with-colab-notebooks) • [Analysis Ready Data](#analysis-ready-data)\n• [Raw Cloud Optimized Data](#raw-cloud-optimized-data) • [Project roadmap](#project-roadmap) • [How to reproduce](#how-to-reproduce)\n• [FAQs](#faqs) • [How to cite this work](#how-to-cite-this-work) • [License](#license)\n\n## Introduction\n\nOur goal is to make a global history of the climate highly accessible in the cloud. To that end, we present a curated\ncopy of the ERA5 corpus in [Google Cloud Public Datasets](https://cloud.google.com/storage/docs/public-datasets/era5).\n\n\u003cdetails\u003e\n\u003csummary\u003eWhat is ERA5?\u003c/summary\u003e\n\nERA5 is the fifth generation of [ECMWF's](https://www.ecmwf.int/) Atmospheric Reanalysis. It spans atmospheric, land,\nand ocean variables. ERA5 is an hourly dataset with global coverage at 30km resolution (~0.28° x 0.28°), ranging from\n1979 to the present. The total ERA5 dataset is about 5 petabytes in size.\n\nCheck out [ECMWF's documentation on ERA5](https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5) for\nmore.\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003eWhat is a reanalysis?\u003c/summary\u003e\n\nA reanalysis is the \"most complete picture currently possible of past weather and climate.\" Reanalyses are created from\nassimilation of a wide range of data sources via numerical weather prediction (NWP) models.\n\nRead [ECMWF's introduction to reanalysis](https://www.ecmwf.int/en/about/media-centre/focus/2020/fact-sheet-reanalysis)\nfor more.\n\n\u003c/details\u003e\n\nSo far, we have ingested meteorologically valuable variables for the land and atmosphere. From this, we have produced a\ncloud-optimized version of ERA5, in which we have converted [grib data](https://en.wikipedia.org/wiki/GRIB)\nto [Zarr](https://zarr.readthedocs.io/) with no other modifications. In addition, we have created \"analysis-ready\"\nversions on regular lat-lon grids, oriented towards common research \u0026 ML workflows.\n\nThis two-pronged approach for the data serves different user needs. Some researchers need full control over the\ninterpolation of data for their analysis. Most will want a batteries-included dataset, where standard pre-processing and\nchunk optimization is already applied. In general, we ensure that every step in this pipeline is open and reproducible,\nto provide transparency in the provenance of all data.\n\n## Overview\n\n| Location | Type | Description |\n|----------------|-----------------|-------------------------------------------------------------------------------|\n| `$BUCKET/ar/` | Analysis Ready | An ML-ready, unified (surface \u0026 atmospheric) version of the data in Zarr. |\n| `$BUCKET/co/` | Cloud Optimized | A port of gaussian-gridded ERA5 data to Zarr. |\n| `$BUCKET/raw/` | Raw Data | All raw grib \u0026 NetCDF data. | \n\n- The `gcp-public-data-arco-era5` bucket is stored in the `us-central1` (Iowa) Google Cloud region.\n- The stable version of ERA5 is updated on a **monthly cadence** (on roughly the 9th of each month) with a 3 month delay. The preliminary version of ERA5, known as ERA5T is available with approximately 1 week delay (where 5-6 days delay are due to processing at ECWMF).\n- The most recent data available can be found by examining the metadata associated with each Zarr store. The metadata encompasses three essential attributes: `valid_time_start`, `valid_time_stop` (for ERA5), and `last_updated`. For ERA5T, use `valid_time_stop_era5t` instead. These attributes specify the start date, stop date, and most recent time of update for the dataset's data, respectively. Please note that both start and end times are inclusive, and all times are given in UTC.\n\n## Get Started with Colab Notebooks\n\nExplore the ERA5 dataset interactively with these notebooks:\n\n| Notebook | Description | Open in Colab |\n|----------|-------------|---------------|\n| [Surface Reanalysis Walkthrough](https://github.com/google-research/arco-era5/blob/main/docs/0-Surface-Reanalysis-Walkthrough.ipynb) | Learn how to work with surface-level ERA5 data | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-research/arco-era5/blob/main/docs/0-Surface-Reanalysis-Walkthrough.ipynb) |\n| [Model Levels Walkthrough](https://github.com/google-research/arco-era5/blob/main/docs/1-Model-Levels-Walkthrough.ipynb) | Explore atmospheric model levels data | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-research/arco-era5/blob/main/docs/1-Model-Levels-Walkthrough.ipynb) |\n\n## Analysis Ready Data\n\nThese datasets have been regridded to a uniform 0.25° equiangular horizontal resolution to facilitate downstream analyses, e.g., with [WeatherBench2](https://github.com/google-research/weatherbench2).\n\n### 0.25° Pressure and Surface Level Data\n\nThis dataset contains most pressure-level fields and all surface-level field regridded to a uniform 0.25° resolution.\nIt is a superset of the data used to train [GraphCast](https://github.com/google-deepmind/graphcast) and\n[NeuralGCM](https://github.com/google-research/neuralgcm).\n\n```python\nimport xarray\n\nds = xarray.open_zarr(\n 'gs://gcp-public-data-arco-era5/ar/full_37-1h-0p25deg-chunk-1.zarr-v3',\n chunks=None,\n storage_options=dict(token='anon'),\n)\nar_full_37_1h = ds.sel(time=slice(ds.attrs['valid_time_start'], ds.attrs['valid_time_stop']))\n```\n\n* _Times_: `00/to/23`\n* _Levels_: `1/2/3/5/7/10/20/30/50/70/100/125/150/175/200/225/250/300/350/400/450/500/550/600/650/700/750/775/800/825/850/875/900/925/950/975/1000`\n* _Grid_: equiangular lat-lon\n* _Size_: 2.05 PB\n* _Chunking_: `{'time': 1, 'latitude': 721, 'longitude': 1440, 'level': 37}`\n* _Chunk size (per variable)_: 154 MB\n\n\n\u003cdetails\u003e\n\u003csummary\u003eData summary table\u003c/summary\u003e\n\n|name |short name |units |docs |config |\n|---------------------------------------------------------------|--------------|---------------------|-------------------------------------------------|--------------------------------------------------|\n|100m_u_component_of_wind |100u |m s**-1 |https://codes.ecmwf.int/grib/param-db/228246 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|100m_v_component_of_wind |100v |m s**-1 |https://codes.ecmwf.int/grib/param-db/228247 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|10m_u_component_of_neutral_wind |u10n |m s**-1 |https://codes.ecmwf.int/grib/param-db/228131 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|10m_u_component_of_wind |10u |m s**-1 |https://codes.ecmwf.int/grib/param-db/165 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|10m_v_component_of_neutral_wind |v10n |m s**-1 |https://codes.ecmwf.int/grib/param-db/228132 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|10m_v_component_of_wind |10v |m s**-1 |https://codes.ecmwf.int/grib/param-db/166 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|10m_wind_gust_since_previous_post_processing |10fg |m s**-1 |https://codes.ecmwf.int/grib/param-db/49 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|2m_dewpoint_temperature |2d |K |https://codes.ecmwf.int/grib/param-db/168 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|2m_temperature |2t |K |https://codes.ecmwf.int/grib/param-db/167 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|air_density_over_the_oceans |rhoao |kg m**-3 |https://codes.ecmwf.int/grib/param-db/140209 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|angle_of_sub_gridscale_orography |anor |radians |https://codes.ecmwf.int/grib/param-db/162 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|anisotropy_of_sub_gridscale_orography |isor |~ |https://codes.ecmwf.int/grib/param-db/161 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|benjamin_feir_index |bfi |dimensionless |https://codes.ecmwf.int/grib/param-db/140253 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|boundary_layer_dissipation |bld |J m**-2 |https://codes.ecmwf.int/grib/param-db/145 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|boundary_layer_height |blh |m |https://codes.ecmwf.int/grib/param-db/159 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|charnock |chnk |~ |https://codes.ecmwf.int/grib/param-db/148 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|clear_sky_direct_solar_radiation_at_surface |cdir |J m**-2 |https://codes.ecmwf.int/grib/param-db/228022 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|cloud_base_height |cbh |m |https://codes.ecmwf.int/grib/param-db/228023 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|coefficient_of_drag_with_waves |cdww |dimensionless |https://codes.ecmwf.int/grib/param-db/140233 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|convective_available_potential_energy |cape |J kg**-1 |https://codes.ecmwf.int/grib/param-db/59 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|convective_inhibition |cin |J kg**-1 |https://codes.ecmwf.int/grib/param-db/228001 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|convective_precipitation |cp |m |https://codes.ecmwf.int/grib/param-db/228143 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|convective_rain_rate |crr |kg m**-2 s**-1 |https://codes.ecmwf.int/grib/param-db/228218 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|convective_snowfall |csf |m of water equivalent|https://codes.ecmwf.int/grib/param-db/239 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|convective_snowfall_rate_water_equivalent |csfr |kg m**-2 s**-1 |https://codes.ecmwf.int/grib/param-db/228220 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|downward_uv_radiation_at_the_surface |uvb |J m**-2 |https://codes.ecmwf.int/grib/param-db/57 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|duct_base_height |dctb |m |https://codes.ecmwf.int/grib/param-db/228017 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|eastward_gravity_wave_surface_stress |lgws |N m**-2 s |https://codes.ecmwf.int/grib/param-db/195 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|eastward_turbulent_surface_stress |ewss |N m**-2 s |https://codes.ecmwf.int/grib/param-db/180 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|evaporation |e |m of water equivalent|https://codes.ecmwf.int/grib/param-db/182 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|forecast_albedo |fal |(0 - 1) |https://codes.ecmwf.int/grib/param-db/243 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|forecast_logarithm_of_surface_roughness_for_heat |flsr |~ |https://codes.ecmwf.int/grib/param-db/245 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|forecast_surface_roughness |fsr |m |https://codes.ecmwf.int/grib/param-db/244 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|fraction_of_cloud_cover |cc |(0 - 1) |https://codes.ecmwf.int/grib/param-db/248 |[era5_pl_hourly.cfg](raw/daily/era5_pl_hourly.cfg)|\n|free_convective_velocity_over_the_oceans |wstar |m s**-1 |https://codes.ecmwf.int/grib/param-db/140208 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|friction_velocity |zust |m s**-1 |https://codes.ecmwf.int/grib/param-db/228003 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|geopotential_at_surface |z |m2 s-2 |https://codes.ecmwf.int/grib/param-db/129 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|gravity_wave_dissipation |gwd |J m**-2 |https://codes.ecmwf.int/grib/param-db/197 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|high_cloud_cover |hcc |(0 - 1) |https://codes.ecmwf.int/grib/param-db/3075 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|high_vegetation_cover |cvh |(0 - 1) |https://codes.ecmwf.int/grib/param-db/28 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|ice_temperature_layer_1 |istl1 |K |https://codes.ecmwf.int/grib/param-db/35 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|ice_temperature_layer_2 |istl2 |K |https://codes.ecmwf.int/grib/param-db/36 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|ice_temperature_layer_3 |istl3 |K |https://codes.ecmwf.int/grib/param-db/37 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|ice_temperature_layer_4 |istl4 |K |https://codes.ecmwf.int/grib/param-db/38 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|instantaneous_10m_wind_gust |i10fg |m s**-1 |https://codes.ecmwf.int/grib/param-db/228029 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|instantaneous_eastward_turbulent_surface_stress |iews |N m**-2 |https://codes.ecmwf.int/grib/param-db/229 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|instantaneous_large_scale_surface_precipitation_fraction |ilspf |(0 - 1) |https://codes.ecmwf.int/grib/param-db/228217 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|instantaneous_moisture_flux |ie |kg m**-2 s**-1 |https://codes.ecmwf.int/grib/param-db/232 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|instantaneous_northward_turbulent_surface_stress |inss |N m**-2 |https://codes.ecmwf.int/grib/param-db/230 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|instantaneous_surface_sensible_heat_flux |ishf |W m**-2 |https://codes.ecmwf.int/grib/param-db/231 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|k_index |kx |K |https://codes.ecmwf.int/grib/param-db/260121 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|lake_bottom_temperature |lblt |K |https://codes.ecmwf.int/grib/param-db/228010 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|lake_cover |cl |(0 - 1) |https://codes.ecmwf.int/grib/param-db/26 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|lake_depth |dl |m |https://codes.ecmwf.int/grib/param-db/228007 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|lake_ice_depth |licd |m |https://codes.ecmwf.int/grib/param-db/228014 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|lake_ice_temperature |lict |K |https://codes.ecmwf.int/grib/param-db/228013 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|lake_mix_layer_depth |lmld |m |https://codes.ecmwf.int/grib/param-db/228009 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|lake_mix_layer_temperature |lmlt |K |https://codes.ecmwf.int/grib/param-db/228008 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|lake_shape_factor |lshf |dimensionless |https://codes.ecmwf.int/grib/param-db/228012 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|lake_total_layer_temperature |ltlt |K |https://codes.ecmwf.int/grib/param-db/228011 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|land_sea_mask |lsm |(0 - 1) |https://codes.ecmwf.int/grib/param-db/172 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|large_scale_precipitation |lsp |m |https://codes.ecmwf.int/grib/param-db/3062 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|large_scale_precipitation_fraction |lspf |s |https://codes.ecmwf.int/grib/param-db/50 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|large_scale_rain_rate |lsrr |kg m**-2 s**-1 |https://codes.ecmwf.int/grib/param-db/228219 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|large_scale_snowfall |lsf |m of water equivalent|https://codes.ecmwf.int/grib/param-db/240 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|large_scale_snowfall_rate_water_equivalent |lssfr |kg m**-2 s**-1 |https://codes.ecmwf.int/grib/param-db/228221 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|leaf_area_index_high_vegetation |lai_hv |m2 m-2 |https://codes.ecmwf.int/grib/param-db/67 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|leaf_area_index_low_vegetation |lai_lv |m2 m-2 |https://codes.ecmwf.int/grib/param-db/66 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|low_cloud_cover |lcc |(0 - 1) |https://codes.ecmwf.int/grib/param-db/3073 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|low_vegetation_cover |cvl |(0 - 1) |https://codes.ecmwf.int/grib/param-db/27 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|maximum_2m_temperature_since_previous_post_processing |mx2t |K |https://codes.ecmwf.int/grib/param-db/201 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|maximum_individual_wave_height |hmax |m |https://codes.ecmwf.int/grib/param-db/140218 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|maximum_total_precipitation_rate_since_previous_post_processing|mxtpr |kg m**-2 s**-1 |https://codes.ecmwf.int/grib/param-db/228226 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_boundary_layer_dissipation |avg_ibld |W m**-2 |https://codes.ecmwf.int/grib/param-db/235032 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_convective_precipitation_rate |avg_cpr |kg m**-2 s**-1 |https://codes.ecmwf.int/grib/param-db/235030 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_convective_snowfall_rate |avg_csfr |kg m**-2 s**-1 |https://codes.ecmwf.int/grib/param-db/235056 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_direction_of_total_swell |mdts |degrees |https://codes.ecmwf.int/grib/param-db/140238 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_direction_of_wind_waves |mdww |degrees |https://codes.ecmwf.int/grib/param-db/500072 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_eastward_gravity_wave_surface_stress |avg_iegwss |N m**-2 |https://codes.ecmwf.int/grib/param-db/235045 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_eastward_turbulent_surface_stress |avg_iews |N m**-2 |https://codes.ecmwf.int/grib/param-db/235041 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_evaporation_rate |erate |m*s**-1 |https://codes.ecmwf.int/grib/param-db/172182 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_gravity_wave_dissipation |avg_igwd |W m**-2 |https://codes.ecmwf.int/grib/param-db/235047 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_large_scale_precipitation_fraction |avg_ilspf |Proportion |https://codes.ecmwf.int/grib/param-db/235026 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_large_scale_precipitation_rate |avg_lsprate |kg m**-2 s**-1 |https://codes.ecmwf.int/grib/param-db/235029 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_large_scale_snowfall_rate |avg_lssfr |kg m**-2 s**-1 |https://codes.ecmwf.int/grib/param-db/235057 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_northward_gravity_wave_surface_stress |avg_ingwss |N m**-2 |https://codes.ecmwf.int/grib/param-db/235046 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_northward_turbulent_surface_stress |avg_inss |N m**-2 |https://codes.ecmwf.int/grib/param-db/235042 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_period_of_total_swell |mpts |s |https://codes.ecmwf.int/grib/param-db/140239 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_period_of_wind_waves |mpww |s |https://codes.ecmwf.int/grib/param-db/500074 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_potential_evaporation_rate |avg_pevr |kg m**-2 s**-1 |https://codes.ecmwf.int/grib/param-db/235070 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_runoff_rate |avg_rorwe |kg m**-2 s**-1 |https://codes.ecmwf.int/grib/param-db/235048 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_sea_level_pressure |msl |Pa |https://codes.ecmwf.int/grib/param-db/151 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_snow_evaporation_rate |avg_esrwe |kg m**-2 s**-1 |https://codes.ecmwf.int/grib/param-db/235023 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_snowfall_rate |avg_tsrwe |kg m**-2 s**-1 |https://codes.ecmwf.int/grib/param-db/235031 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_snowmelt_rate |avg_smr |kg m**-2 s**-1 |https://codes.ecmwf.int/grib/param-db/235024 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_square_slope_of_waves |msqs |dimensionless |https://codes.ecmwf.int/grib/param-db/140244 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_sub_surface_runoff_rate |avg_ssurfror |kg m**-2 s**-1 |https://codes.ecmwf.int/grib/param-db/235021 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_surface_direct_short_wave_radiation_flux |avg_sdirswrf |W m**-2 |https://codes.ecmwf.int/grib/param-db/235058 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_surface_direct_short_wave_radiation_flux_clear_sky |avg_sdirswrfcs|W m**-2 |https://codes.ecmwf.int/grib/param-db/235059 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_surface_downward_long_wave_radiation_flux |avg_sdlwrf |W m**-2 |https://codes.ecmwf.int/grib/param-db/235036 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_surface_downward_long_wave_radiation_flux_clear_sky |avg_sdlwrfcs |W m**-2 |https://codes.ecmwf.int/grib/param-db/235069 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_surface_downward_short_wave_radiation_flux |avg_sdswrf |W m**-2 |https://codes.ecmwf.int/grib/param-db/235035 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_surface_downward_short_wave_radiation_flux_clear_sky |avg_sdswrfcs |W m**-2 |https://codes.ecmwf.int/grib/param-db/235068 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_surface_downward_uv_radiation_flux |avg_sduvrf |W m**-2 |https://codes.ecmwf.int/grib/param-db/235027 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_surface_latent_heat_flux |avg_slhtf |W m**-2 |https://codes.ecmwf.int/grib/param-db/235034 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_surface_net_long_wave_radiation_flux |avg_snlwrf |W m**-2 |https://codes.ecmwf.int/grib/param-db/235038 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_surface_net_long_wave_radiation_flux_clear_sky |avg_snlwrfcs |W m**-2 |https://codes.ecmwf.int/grib/param-db/235052 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_surface_net_short_wave_radiation_flux |avg_snswrf |W m**-2 |https://codes.ecmwf.int/grib/param-db/235037 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_surface_net_short_wave_radiation_flux_clear_sky |avg_snswrfcs |W m**-2 |https://codes.ecmwf.int/grib/param-db/235051 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_surface_runoff_rate |avg_surfror |kg m**-2 s**-1 |https://codes.ecmwf.int/grib/param-db/235020 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_surface_sensible_heat_flux |avg_ishf |W m**-2 |https://codes.ecmwf.int/grib/param-db/235033 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_top_downward_short_wave_radiation_flux |avg_tdswrf |W m**-2 |https://codes.ecmwf.int/grib/param-db/235053 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_top_net_long_wave_radiation_flux |avg_tnlwrf |W m**-2 |https://codes.ecmwf.int/grib/param-db/235040 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_top_net_long_wave_radiation_flux_clear_sky |avg_tnlwrfcs |W m**-2 |https://codes.ecmwf.int/grib/param-db/235050 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_top_net_short_wave_radiation_flux |avg_tnswrf |W m**-2 |https://codes.ecmwf.int/grib/param-db/235039 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_top_net_short_wave_radiation_flux_clear_sky |avg_tnswrfcs |W m**-2 |https://codes.ecmwf.int/grib/param-db/235049 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_total_precipitation_rate |avg_tprate |kg m**-2 s**-1 |https://codes.ecmwf.int/grib/param-db/235055 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_vertical_gradient_of_refractivity_inside_trapping_layer |dndza |m**-1 |https://codes.ecmwf.int/grib/param-db/228016 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_vertically_integrated_moisture_divergence |avg_vimdf |kg m**-2 s**-1 |https://codes.ecmwf.int/grib/param-db/235054 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_wave_direction |mwd |Degree true |https://codes.ecmwf.int/grib/param-db/500185 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_wave_direction_of_first_swell_partition |mwd1 |degrees |https://codes.ecmwf.int/grib/param-db/140122 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_wave_direction_of_second_swell_partition |mwd2 |degrees |https://codes.ecmwf.int/grib/param-db/140125 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_wave_direction_of_third_swell_partition |mwd3 |degrees |https://codes.ecmwf.int/grib/param-db/140128 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_wave_period |mwp |s |https://codes.ecmwf.int/grib/param-db/140232 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_wave_period_based_on_first_moment |mp1 |s |https://codes.ecmwf.int/grib/param-db/140220 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_wave_period_based_on_first_moment_for_swell |p1ps |s |https://codes.ecmwf.int/grib/param-db/140226 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_wave_period_based_on_first_moment_for_wind_waves |p1ww |s |https://codes.ecmwf.int/grib/param-db/140223 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_wave_period_based_on_second_moment_for_swell |p2ps |s |https://codes.ecmwf.int/grib/param-db/140227 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_wave_period_based_on_second_moment_for_wind_waves |p2ww |s |https://codes.ecmwf.int/grib/param-db/140224 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_wave_period_of_first_swell_partition |mwp1 |s |https://codes.ecmwf.int/grib/param-db/140123 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_wave_period_of_second_swell_partition |mwp2 |s |https://codes.ecmwf.int/grib/param-db/140126 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_wave_period_of_third_swell_partition |mwp3 |s |https://codes.ecmwf.int/grib/param-db/140129 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|mean_zero_crossing_wave_period |mp2 |s |https://codes.ecmwf.int/grib/param-db/140221 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|medium_cloud_cover |mcc |(0 - 1) |https://codes.ecmwf.int/grib/param-db/3074 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|minimum_2m_temperature_since_previous_post_processing |mn2t |K |https://codes.ecmwf.int/grib/param-db/202 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|minimum_total_precipitation_rate_since_previous_post_processing|mntpr |kg m**-2 s**-1 |https://codes.ecmwf.int/grib/param-db/228227 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|minimum_vertical_gradient_of_refractivity_inside_trapping_layer|dndzn |m**-1 |https://codes.ecmwf.int/grib/param-db/228015 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|model_bathymetry |wmb |m |https://codes.ecmwf.int/grib/param-db/140219 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|near_ir_albedo_for_diffuse_radiation |alnid |(0 - 1) |https://codes.ecmwf.int/grib/param-db/18 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|near_ir_albedo_for_direct_radiation |alnip |(0 - 1) |https://codes.ecmwf.int/grib/param-db/17 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|normalized_energy_flux_into_ocean |phioc |dimensionless |https://codes.ecmwf.int/grib/param-db/140212 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|normalized_energy_flux_into_waves |phiaw |dimensionless |https://codes.ecmwf.int/grib/param-db/140211 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|normalized_stress_into_ocean |tauoc |dimensionless |https://codes.ecmwf.int/grib/param-db/140214 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|northward_gravity_wave_surface_stress |mgws |N m**-2 s |https://codes.ecmwf.int/grib/param-db/196 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|northward_turbulent_surface_stress |nsss |N m**-2 s |https://codes.ecmwf.int/grib/param-db/181 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|ocean_surface_stress_equivalent_10m_neutral_wind_direction |dwi |degrees |https://codes.ecmwf.int/grib/param-db/140249 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|ocean_surface_stress_equivalent_10m_neutral_wind_speed |wind |m s**-1 |https://codes.ecmwf.int/grib/param-db/140245 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|ozone_mass_mixing_ratio |o3 |kg kg**-1 |https://codes.ecmwf.int/grib/param-db/500242 |[era5_pl_hourly.cfg](raw/daily/era5_pl_hourly.cfg)|\n|peak_wave_period |pp1d |s |https://codes.ecmwf.int/grib/param-db/500190 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|period_corresponding_to_maximum_individual_wave_height |tmax |s |https://codes.ecmwf.int/grib/param-db/140217 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|potential_evaporation |pev |m |https://codes.ecmwf.int/grib/param-db/228251 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|potential_vorticity |pv |K m2 kg-1 s**-1 |https://codes.ecmwf.int/grib/param-db/60 |[era5_pl_hourly.cfg](raw/daily/era5_pl_hourly.cfg)|\n|precipitation_type |ptype |code table (4.201) |https://codes.ecmwf.int/grib/param-db/260015 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|runoff |ro |m |https://codes.ecmwf.int/grib/param-db/228205 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|sea_ice_cover |siconc |(0 - 1) |https://codes.ecmwf.int/grib/param-db/262001 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|sea_surface_temperature |sst |K |https://codes.ecmwf.int/grib/param-db/151159 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|significant_height_of_combined_wind_waves_and_swell |swh |m |https://codes.ecmwf.int/grib/param-db/500071 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|significant_height_of_total_swell |shts |m |https://codes.ecmwf.int/grib/param-db/140237 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|significant_height_of_wind_waves |shww |m |https://codes.ecmwf.int/grib/param-db/500073 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|significant_wave_height_of_first_swell_partition |swh1 |m |https://codes.ecmwf.int/grib/param-db/140121 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|significant_wave_height_of_second_swell_partition |swh2 |m |https://codes.ecmwf.int/grib/param-db/140124 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|significant_wave_height_of_third_swell_partition |swh3 |m |https://codes.ecmwf.int/grib/param-db/140127 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|skin_reservoir_content |src |m of water equivalent|https://codes.ecmwf.int/grib/param-db/198 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|skin_temperature |skt |K |https://codes.ecmwf.int/grib/param-db/235 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|slope_of_sub_gridscale_orography |slor |~ |https://codes.ecmwf.int/grib/param-db/163 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|snow_albedo |asn |(0 - 1) |https://codes.ecmwf.int/grib/param-db/228032 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|snow_density |rsn |kg m**-3 |https://codes.ecmwf.int/grib/param-db/33 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|snow_depth |sd |m of water equivalent|https://codes.ecmwf.int/grib/param-db/228141 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|snow_evaporation |es |m of water equivalent|https://codes.ecmwf.int/grib/param-db/44 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|snowfall |sf |m of water equivalent|https://codes.ecmwf.int/grib/param-db/228144 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|snowmelt |smlt |m of water equivalent|https://codes.ecmwf.int/grib/param-db/45 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|soil_temperature_level_1 |stl1 |K |https://codes.ecmwf.int/grib/param-db/139 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|soil_temperature_level_2 |stl2 |K |https://codes.ecmwf.int/grib/param-db/170 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|soil_temperature_level_3 |stl3 |K |https://codes.ecmwf.int/grib/param-db/183 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|soil_temperature_level_4 |stl4 |K |https://codes.ecmwf.int/grib/param-db/236 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|soil_type |slt |~ |https://codes.ecmwf.int/grib/param-db/43 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|specific_cloud_ice_water_content |ciwc |kg kg**-1 |https://codes.ecmwf.int/grib/param-db/247 |[era5_pl_hourly.cfg](raw/daily/era5_pl_hourly.cfg)|\n|specific_cloud_liquid_water_content |clwc |kg kg**-1 |https://codes.ecmwf.int/grib/param-db/246 |[era5_pl_hourly.cfg](raw/daily/era5_pl_hourly.cfg)|\n|specific_humidity |q |kg kg**-1 |https://codes.ecmwf.int/grib/param-db/133 |[era5_pl_hourly.cfg](raw/daily/era5_pl_hourly.cfg)|\n|standard_deviation_of_filtered_subgrid_orography |sdfor |m |https://codes.ecmwf.int/grib/param-db/74 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|standard_deviation_of_orography |sdor |m |https://codes.ecmwf.int/grib/param-db/160 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|sub_surface_runoff |ssro |m |https://codes.ecmwf.int/grib/param-db/9 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|surface_latent_heat_flux |slhf |J m**-2 |https://codes.ecmwf.int/grib/param-db/147 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|surface_net_solar_radiation |ssr |J m**-2 |https://codes.ecmwf.int/grib/param-db/180176 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|surface_net_solar_radiation_clear_sky |ssrc |J m**-2 |https://codes.ecmwf.int/grib/param-db/210 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|surface_net_thermal_radiation |str |J m**-2 |https://codes.ecmwf.int/grib/param-db/180177 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|surface_net_thermal_radiation_clear_sky |strc |J m**-2 |https://codes.ecmwf.int/grib/param-db/211 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|surface_pressure |sp |Pa |https://codes.ecmwf.int/grib/param-db/500026 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|surface_runoff |sro |m |https://codes.ecmwf.int/grib/param-db/174008 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|surface_sensible_heat_flux |sshf |J m**-2 |https://codes.ecmwf.int/grib/param-db/146 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|surface_solar_radiation_downward_clear_sky |ssrdc |J m**-2 |https://codes.ecmwf.int/grib/param-db/228129 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|surface_solar_radiation_downwards |ssrd |J m**-2 |https://codes.ecmwf.int/grib/param-db/169 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|surface_thermal_radiation_downward_clear_sky |strdc |J m**-2 |https://codes.ecmwf.int/grib/param-db/228130 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|surface_thermal_radiation_downwards |strd |J m**-2 |https://codes.ecmwf.int/grib/param-db/175 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|temperature |t |K |https://codes.ecmwf.int/grib/param-db/500014 |[era5_pl_hourly.cfg](raw/daily/era5_pl_hourly.cfg)|\n|temperature_of_snow_layer |tsn |K |https://codes.ecmwf.int/grib/param-db/238 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|toa_incident_solar_radiation |tisr |J m**-2 |https://codes.ecmwf.int/grib/param-db/212 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|top_net_solar_radiation |tsr |J m**-2 |https://codes.ecmwf.int/grib/param-db/180178 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|top_net_solar_radiation_clear_sky |tsrc |J m**-2 |https://codes.ecmwf.int/grib/param-db/208 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|top_net_thermal_radiation |ttr |J m**-2 |https://codes.ecmwf.int/grib/param-db/180179 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|top_net_thermal_radiation_clear_sky |ttrc |J m**-2 |https://codes.ecmwf.int/grib/param-db/209 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|total_cloud_cover |tcc |(0 - 1) |https://codes.ecmwf.int/grib/param-db/228164 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|total_column_cloud_ice_water |tciw |kg m**-2 |https://codes.ecmwf.int/grib/param-db/79 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|total_column_cloud_liquid_water |tclw |kg m**-2 |https://codes.ecmwf.int/grib/param-db/78 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|total_column_ozone |tco3 |kg m**-2 |https://codes.ecmwf.int/grib/param-db/206 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|total_column_rain_water |tcrw |kg m**-2 |https://codes.ecmwf.int/grib/param-db/228089 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|total_column_snow_water |tcsw |kg m**-2 |https://codes.ecmwf.int/grib/param-db/228090 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|total_column_supercooled_liquid_water |tcslw |kg m**-2 |https://codes.ecmwf.int/grib/param-db/228088 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|total_column_water |tcw |kg m**-2 |https://codes.ecmwf.int/grib/param-db/136 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|total_column_water_vapour |tcwv |kg m**-2 |https://codes.ecmwf.int/grib/param-db/137 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|total_precipitation |tp |m |https://codes.ecmwf.int/grib/param-db/228228 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|total_totals_index |totalx |K |https://codes.ecmwf.int/grib/param-db/260123 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|trapping_layer_base_height |tplb |m |https://codes.ecmwf.int/grib/param-db/228018 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|trapping_layer_top_height |tplt |m |https://codes.ecmwf.int/grib/param-db/228019 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|type_of_high_vegetation |tvh |~ |https://codes.ecmwf.int/grib/param-db/30 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|type_of_low_vegetation |tvl |~ |https://codes.ecmwf.int/grib/param-db/29 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|u_component_of_wind |u |m s**-1 |https://codes.ecmwf.int/grib/param-db/500028 |[era5_pl_hourly.cfg](raw/daily/era5_pl_hourly.cfg)|\n|u_component_stokes_drift |ust |m s**-1 |https://codes.ecmwf.int/grib/param-db/140215 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|uv_visible_albedo_for_diffuse_radiation |aluvd |(0 - 1) |https://codes.ecmwf.int/grib/param-db/16 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|uv_visible_albedo_for_direct_radiation |aluvp |(0 - 1) |https://codes.ecmwf.int/grib/param-db/15 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|v_component_of_wind |v |m s**-1 |https://codes.ecmwf.int/grib/param-db/500030 |[era5_pl_hourly.cfg](raw/daily/era5_pl_hourly.cfg)|\n|v_component_stokes_drift |vst |m s**-1 |https://codes.ecmwf.int/grib/param-db/140216 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_divergence_of_cloud_frozen_water_flux |viiwd |kg m**-2 s**-1 |https://codes.ecmwf.int/grib/param-db/162057 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_divergence_of_cloud_liquid_water_flux |vilwd |kg m**-2 s**-1 |https://codes.ecmwf.int/grib/param-db/162056 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_divergence_of_geopotential_flux |vigd |W m**-2 |https://codes.ecmwf.int/grib/param-db/162085 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_divergence_of_kinetic_energy_flux |viked |W m**-2 |https://codes.ecmwf.int/grib/param-db/162082 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_divergence_of_mass_flux |vimad |kg m**-2 s**-1 |https://codes.ecmwf.int/grib/param-db/162081 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_divergence_of_moisture_flux |viwvd |kg m**-2 s**-1 |https://codes.ecmwf.int/grib/param-db/162084 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_divergence_of_ozone_flux |viozd |kg m**-2 s**-1 |https://apps.ecmwf.int/codes/grib/param-db/162087|[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_divergence_of_thermal_energy_flux |vithed |W m**-2 |https://apps.ecmwf.int/codes/grib/param-db/162083|[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_divergence_of_total_energy_flux |vitoed |W m**-2 |https://apps.ecmwf.int/codes/grib/param-db/162086|[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_eastward_cloud_frozen_water_flux |viiwe |kg m**-1 s**-1 |https://apps.ecmwf.int/codes/grib/param-db/162090|[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_eastward_cloud_liquid_water_flux |vilwe |kg m**-1 s**-1 |https://apps.ecmwf.int/codes/grib/param-db/162088|[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_eastward_geopotential_flux |vige |W m**-1 |https://apps.ecmwf.int/codes/grib/param-db/162073|[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_eastward_heat_flux |vithee |W m**-1 |https://codes.ecmwf.int/grib/param-db/162069 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_eastward_kinetic_energy_flux |vikee |W m**-1 |https://codes.ecmwf.int/grib/param-db/162067 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_eastward_mass_flux |vimae |kg m**-1 s**-1 |https://codes.ecmwf.int/grib/param-db/162065 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_eastward_ozone_flux |vioze |kg m**-1 s**-1 |https://codes.ecmwf.int/grib/param-db/162077 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_eastward_total_energy_flux |vitoee |W m**-1 |https://codes.ecmwf.int/grib/param-db/162075 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_eastward_water_vapour_flux |viwve |kg m**-1 s**-1 |https://codes.ecmwf.int/grib/param-db/162071 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_energy_conversion |viec |W m**-2 |https://codes.ecmwf.int/grib/param-db/162064 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_kinetic_energy |vike |J m**-2 |https://apps.ecmwf.int/codes/grib/param-db/162059|[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_mass_of_atmosphere |vima |kg m**-2 |https://apps.ecmwf.int/codes/grib/param-db/162053|[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_mass_tendency |vimat |kg m**-2 s**-1 |https://apps.ecmwf.int/codes/grib/param-db/162092|[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_northward_cloud_frozen_water_flux |viiwn |kg m**-1 s**-1 |https://apps.ecmwf.int/codes/grib/param-db/162091|[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_northward_cloud_liquid_water_flux |vilwn |kg m**-1 s**-1 |https://apps.ecmwf.int/codes/grib/param-db/162089|[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_northward_geopotential_flux |vign |W m**-1 |https://apps.ecmwf.int/codes/grib/param-db/162074|[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_northward_heat_flux |vithen |W m**-1 |https://codes.ecmwf.int/grib/param-db/162070 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_northward_kinetic_energy_flux |viken |W m**-1 |https://codes.ecmwf.int/grib/param-db/162068 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_northward_mass_flux |viman |kg m**-1 s**-1 |https://codes.ecmwf.int/grib/param-db/162066 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_northward_ozone_flux |viozn |kg m**-1 s**-1 |https://codes.ecmwf.int/grib/param-db/162078 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_northward_total_energy_flux |vitoen |W m**-1 |https://codes.ecmwf.int/grib/param-db/162076 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_northward_water_vapour_flux |viwvn |kg m**-1 s**-1 |https://codes.ecmwf.int/grib/param-db/162072 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_potential_and_internal_energy |vipie |J m**-2 |https://apps.ecmwf.int/codes/grib/param-db/162061|[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_potential_internal_and_latent_energy |vipile |J m**-2 |https://codes.ecmwf.int/grib/param-db/162062 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_temperature |vit |K kg m**-2 |https://codes.ecmwf.int/grib/param-db/162054 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_thermal_energy |vithe |J m**-2 |https://codes.ecmwf.int/grib/param-db/162060 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_integral_of_total_energy |vitoe |J m**-2 |https://codes.ecmwf.int/grib/param-db/162063 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|vertical_velocity |w |Pa s**-1 |https://codes.ecmwf.int/grib/param-db/500032 |[era5_pl_hourly.cfg](raw/daily/era5_pl_hourly.cfg)|\n|vertically_integrated_moisture_divergence |vimd |kg m**-2 |https://codes.ecmwf.int/grib/param-db/213 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|volumetric_soil_water_layer_1 |swvl1 |m3 m-3 |https://codes.ecmwf.int/grib/param-db/39 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|volumetric_soil_water_layer_2 |swvl2 |m3 m-3 |https://codes.ecmwf.int/grib/param-db/40 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|volumetric_soil_water_layer_3 |swvl3 |m3 m-3 |https://codes.ecmwf.int/grib/param-db/41 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|volumetric_soil_water_layer_4 |swvl4 |m3 m-3 |https://codes.ecmwf.int/grib/param-db/42 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|wave_spectral_directional_width |wdw |radians |https://codes.ecmwf.int/grib/param-db/140222 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|wave_spectral_directional_width_for_swell |dwps |radians |https://codes.ecmwf.int/grib/param-db/140228 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|wave_spectral_directional_width_for_wind_waves |dwww |radians |https://codes.ecmwf.int/grib/param-db/140225 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|wave_spectral_kurtosis |wsk |dimensionless |https://codes.ecmwf.int/grib/param-db/140252 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|wave_spectral_peakedness |wsp |dimensionless |https://codes.ecmwf.int/grib/param-db/140254 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|wave_spectral_skewness |wss |dimensionless |https://codes.ecmwf.int/grib/param-db/140207 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n|zero_degree_level |deg0l |m |https://codes.ecmwf.int/grib/param-db/228024 |[era5_sl_hourly.cfg](raw/daily/era5_sl_hourly.cfg)|\n\n\u003c/details\u003e\n\n### 0.25° Model Level Data\n\nThis dataset contains 3D fields at 0.25° resolution with ERA5's [native vertical coordinates](https://confluence.ecmwf.int/display/UDOC/L137+model+level+definitions)\n(hybrid pressure/sigma coordinates).\n\n```python\nimport xarray\n\nds = xarray.open_zarr(\n 'gs://gcp-public-data-arco-era5/ar/model-level-1h-0p25deg.zarr-v1',\n chunks=None,\n storage_options=dict(token='anon'),\n)\nar_native_vertical_grid_data = ds.sel(time=slice(ds.attrs['valid_time_start'], ds.attrs['valid_time_stop']))\n```\n\nIt can combined with surface-level variables from the 0.25° pressure- and surface-level dataset:\n```python\nds = xarray.open_zarr(\n 'gs://gcp-public-data-arco-era5/ar/full_37-1h-0p25deg-chunk-1.zarr-v3',\n chunks=None,\n storage_options=dict(token='anon'),\n)\nar_full_37_1h = ds.sel(time=slice(ds.attrs['valid_time_start'], ds.attrs['valid_time_stop']))\n\nar_model_level_and_surface_data = xarray.merge([\n ar_native_vertical_grid_data, ar_full_37_1h.drop_dims('level')\n])\n```\n\n* _Times_: `00/to/23`\n* _Levels_: `1/to/137`\n* _Grid_: equiangular lat-lon\n* _Size_: 5.88 PB\n* _Chunking_: `{'time': 1, 'hybrid': 18, 'latitude': 721, 'longitude': 1440}`\n* _Chunk size (per variable)_: 74.8 MB\n\n\n\u003cdetails\u003e\n\u003csummary\u003eData summary table\u003c/summary\u003e\n\n|name |short name|units |docs |config |\n|-----------------------------------|----------|--------|-------------------------------------------------|--------------------------------------------------|\n|vorticity (relative) |vo |s^-1 |https://apps.ecmwf.int/codes/grib/param-db?id=138|[era5_ml_dve.cfg](raw/daily/era5_ml_dve.cfg) |\n|divergence |d |s^-1 |https://apps.ecmwf.int/codes/grib/param-db?id=155|[era5_ml_dve.cfg](raw/daily/era5_ml_dve.cfg) |\n|geopotential |z |m^2 s^-2|https://apps.ecmwf.int/codes/grib/param-dbid=129 |[era5_sfc.cfg](raw/monthly/era5_sfc.cfg) |\n|temperature |t |K |https://apps.ecmwf.int/codes/grib/param-db?id=130|[era5_ml_tw.cfg](raw/daily/era5_ml_tw.cfg) |\n|vertical velocity |w |Pa s^-1 |https://apps.ecmwf.int/codes/grib/param-db?id=135|[era5_ml_tw.cfg](raw/daily/era5_ml_tw.cfg) |\n|specific humidity |q |kg kg^-1|https://apps.ecmwf.int/codes/grib/param-db?id=133|[era5_ml_o3q.cfg](raw/daily/era5_ml_o3q.cfg) |\n|ozone mass mixing ratio |o3 |kg kg^-1|https://apps.ecmwf.int/codes/grib/param-db?id=203|[era5_ml_o3q.cfg](raw/daily/era5_ml_o3q.cfg) |\n|specific cloud liquid water content|clwc |kg kg^-1|https://apps.ecmwf.int/codes/grib/param-db?id=246|[era5_ml_o3q.cfg](raw/daily/era5_ml_o3q.cfg) |\n|specific cloud ice water content |ciwc |kg kg^-1|https://apps.ecmwf.int/codes/grib/param-db?id=247|[era5_ml_o3q.cfg](raw/daily/era5_ml_o3q.cfg) |\n|fraction of cloud cover |cc |(0 - 1) |https://apps.ecmwf.int/codes/grib/param-db?id=248|[era5_ml_o3q.cfg](raw/daily/era5_ml_o3q.cfg) |\n|specific rain water content |crwc |kg kg^-1|https://apps.ecmwf.int/codes/grib/param-db?id=75 |[era5_ml_qrqs.cfg](raw/daily/era5_ml_qrqs.cfg) |\n|specific snow water content |cswc |kg kg^-1|https://apps.ecmwf.int/codes/grib/param-db?id=76 |[era5_ml_qrqs.cfg](raw/daily/era5_ml_qrqs.cfg) |\n|u component of wind |u |m s**-1 |https://codes.ecmwf.int/grib/param-db/500028 |[era5_pl_hourly.cfg](raw/daily/era5_pl_hourly.cfg)|\n|v component of wind |v |m s**-1 |https://codes.ecmwf.int/grib/param-db/500030 |[era5_pl_hourly.cfg](raw/daily/era5_pl_hourly.cfg)|\n\n\u003c/details\u003e\n\n## Raw Cloud Optimized Data\n\nThese datasets contain the raw data used to produce the Analysis Ready data. Whenever possible, parameters are represented by their native grid resolution\nSee [this ECMWF documentation](https://confluence.ecmwf.int/display/CKB/ERA5%3A+What+is+the+spatial+reference) for more.\n\n**Please view our walkthrough notebook for demos of these cloud-optimized datasets:** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-research/arco-era5/blob/main/docs/0-Surface-Reanalysis-Walkthrough.ipynb)\n\n### Model Level Wind\n\nThis dataset contains model-level wind fields on ERA5's native grid, as spherical harmonic coefficients. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-research/arco-era5/blob/main/docs/1-Model-Levels-Walkthrough.ipynb)\n\n```python\nimport xarray\n\nds = xarray.open_zarr(\n 'gs://gcp-public-data-arco-era5/co/model-level-wind.zarr-v2',\n chunks=None,\n storage_options=dict(token='anon'),\n)\nmodel_level_wind = ds.sel(time=slice(ds.attrs['valid_time_start'], ds.attrs['valid_time_stop']))\n```\n\n* _Levels_: `1/to/137`\n* _Times_: `00/to/23`\n* _Grid_: `T639` spherical harmonic coefficients\n ([docs](https://confluence.ecmwf.int/display/UDOC/How+to+access+the+data+values+of+a+spherical+harmonic+field+in+GRIB+-+ecCodes+GRIB+FAQ))\n* _Size_: 664 TB\n* _Chunking_: `{'time': 1, 'hybrid': 1, 'values': 410240}`\n* _Chunk size (per variable)_: 1.64 MB\n\n\n\u003cdetails\u003e\n\u003csummary\u003eData summary table\u003c/summary\u003e\n\n|name |short name|units |docs |config |\n|--------------------|----------|-------|-------------------------------------------------|--------------------------------------------------|\n|vorticity (relative)|vo |s^-1 |https://apps.ecmwf.int/codes/grib/param-db?id=138|[era5_ml_dve.cfg](raw/daily/era5_ml_dve.cfg) |\n|divergence |d |s^-1 |https://apps.ecmwf.int/codes/grib/param-db?id=155|[era5_ml_dve.cfg](raw/daily/era5_ml_dve.cfg) |\n|temperature |t |K |https://apps.ecmwf.int/codes/grib/param-db?id=130|[era5_pl_hourly.cfg](raw/daily/era5_pl_hourly.cfg)|\n|vertical velocity |w |Pa s^-1|https://apps.ecmwf.int/codes/grib/param-db?id=135|[era5_ml_tw.cfg](raw/daily/era5_ml_tw.cfg) |\n\n\u003c/details\u003e\n\n### Model Level Moisture\n\nThis dataset contains model-level moisture fields on ERA5's native reduced Gaussian grid.\n\n```python\nimport xarray\n\nds = xr.open_zarr(\n 'gs://gcp-public-data-arco-era5/co/model-level-moisture.zarr-v2/',\n chunks=None,\n storage_options=dict(token='anon'),\n)\nmodel_level_moisture = ds.sel(time=slice(ds.attrs['valid_time_start'], ds.attrs['valid_time_stop']))\n```\n\n* _Levels_: `1/to/137`\n* _Times_: `00/to/23`\n* _Grid_: `N320`,\n a [Reduced Gaussian Grid](https://confluence.ecmwf.int/display/EMOS/Reduced+Gaussian+Grids) ([docs](https://www.ecmwf.int/en/forecasts/documentation-and-support/gaussian_n320))\n* _Size_: 1.54 PB\n* _Chunking_: `{'time': 1, 'hybrid': 1, 'values': 542080}`\n* _Chunk size (per variable)_: 2.17 MB\n\n\n\u003cdetails\u003e\n\u003csummary\u003eData summary table\u003c/summary\u003e\n\n|name |short name|units |docs |config |\n|-----------------------------------|----------|--------|-------------------------------------------------|----------------------------------------------|\n|specific humidity |q |kg kg^-1|https://apps.ecmwf.int/codes/grib/param-db?id=133|[era5_ml_o3q.cfg](raw/daily/era5_ml_o3q.cfg) |\n|ozone mass mixing ratio |o3 |kg kg^-1|https://apps.ecmwf.int/codes/grib/param-db?id=203|[era5_ml_o3q.cfg](raw/daily/era5_ml_o3q.cfg) |\n|specific cloud liquid water content|clwc |kg kg^-1|https://apps.ecmwf.int/codes/grib/param-db?id=246|[era5_ml_o3q.cfg](raw/daily/era5_ml_o3q.cfg) |\n|specific cloud ice water content |ciwc |kg kg^-1|https://apps.ecmwf.int/codes/grib/param-db?id=247|[era5_ml_o3q.cfg](raw/daily/era5_ml_o3q.cfg) |\n|fraction of cloud cover |cc |(0 - 1) |https://apps.ecmwf.int/codes/grib/param-db?id=248|[era5_ml_o3q.cfg](raw/daily/era5_ml_o3q.cfg) |\n|specific rain water content |crwc |kg kg^-1|https://apps.ecmwf.int/codes/grib/param-db?id=75 |[era5_ml_qrqs.cfg](raw/daily/era5_ml_qrqs.cfg)|\n|specific snow water content |cswc |kg kg^-1|https://apps.ecmwf.int/codes/grib/param-db?id=76 |[era5_ml_qrqs.cfg](raw/daily/era5_ml_qrqs.cfg)|\n\n\u003c/details\u003e\n\n\n### Single Level Surface\n\nThis dataset contains single-level renanalysis fields on ERA5's native grid, as spherical harmonic coefficients.\n\n```python\nimport xarray\n\nds = xarray.open_zarr(\n 'gs://gcp-public-data-arco-era5/co/single-level-surface.zarr-v2/',\n chunks=None,\n storage_options=dict(token='anon'),\n)\nsingle_level_surface = ds.sel(time=slice(ds.attrs['valid_time_start'], ds.attrs['valid_time_stop']))\n```\n\n* _Times_: `00/to/23`\n* _Grid_: `TL639` spherical harmonic coefficients\n ([docs](https://confluence.ecmwf.int/display/UDOC/How+to+access+the+data+values+of+a+spherical+harmonic+field+in+GRIB+-+ecCodes+GRIB+FAQ))\n* _Size_: 2.42 TB\n* _Chunking_: `{'time': 1, 'values': 410240}`\n* _Chunk size (per variable)_: 1.64 MB\n\n\n\u003cdetails\u003e\n\u003csummary\u003eData summary table\u003c/summary\u003e\n\n|name |short name|units |docs |config |\n|-----------------------------|----------|--------|----------------------------------------------------|------------------------------------------------|\n|logarithm of surface pressure|lnsp |Numeric |https://apps.ecmwf.int/codes/grib/param-db?id=152 |[era5_ml_lnsp.cfg](raw/monthly/era5_ml_lnsp.cfg)|\n|surface geopotential |zs |m^2 s^-2|https://apps.ecmwf.int/codes/grib/param-db?id=162051|[era5_ml_zs.cfg](raw/monthly/era5_ml_zs.cfg) |\n\n\u003c/details\u003e\n\n\n### Single Level Reanalysis\n\nThis dataset contains single-level renanalysis fields on ERA5's native reduced Gaussian grid.\n\n```python\nimport xarray\n\nds = xarray.open_zarr(\n 'gs://gcp-public-data-arco-era5/co/single-level-reanalysis.zarr-v2',\n chunks=None,\n storage_options=dict(token='anon'),\n)\nsingle_level_reanalysis = ds.sel(time=slice(ds.attrs['valid_time_start'], ds.attrs['valid_time_stop']))\n```\n\n* _Times_: `00/to/23`\n* _Grid_: `N320`,\n a [Reduced Gaussian Grid](https://confluence.ecmwf.int/display/EMOS/Reduced+Gaussian+Grids) ([docs](https://www.ecmwf.int/en/forecasts/documentation-and-support/gaussian_n320))\n* _Size_: 60.9 TB\n* _Chunking_: `{'time': 1, 'values': 542080}`\n* _Chunk size (per variable)_: 2.17 MB\n\n\n\u003cdetails\u003e\n\u003csummary\u003eData summary table\u003c/summary\u003e\n\n|name |short name|units |docs |config |\n|----------------------------------------------------------|----------|------------|----------------------------------------------------|----------------------------------------------------|\n|convective available potential energy |cape |J kg^-1 |https://apps.ecmwf.int/codes/grib/param-db?id=59 |[era5_sfc_cape.cfg](raw/monthly/era5_sfc_cape.cfg) |\n|total column cloud ice water |tciw |kg m^-2 |https://apps.ecmwf.int/codes/grib/param-db?id=79 |[era5_sfc_cape.cfg](raw/monthly/era5_sfc_cape.cfg) |\n|vertical integral of divergence of cloud frozen water flux|wiiwd |kg m^-2 s^-1|https://apps.ecmwf.int/codes/grib/param-db?id=162080|[era5_sfc_cape.cfg](raw/monthly/era5_sfc_cape.cfg) |\n|100 metre U wind component |100u |m s^-1 |https://apps.ecmwf.int/codes/grib/param-db?id=228246|[era5_sfc_cape.cfg](raw/monthly/era5_sfc_cape.cfg) |\n|100 metre V wind component |100v |m s^-1 |https://apps.ecmwf.int/codes/grib/param-db?id=228247|[era5_sfc_cape.cfg](raw/monthly/era5_sfc_cape.cfg) |\n|sea ice area fraction |ci |(0 - 1) |https://apps.ecmwf.int/codes/grib/param-db?id=31 |[era5_sfc_cisst.cfg](raw/monthly/era5_sfc_cisst.cfg)|\n|sea surface temperature |sst |Pa |https://apps.ecmwf.int/codes/grib/param-db?id=34 |[era5_sfc_cisst.cfg](raw/monthly/era5_sfc_cisst.cfg)|\n|skin temperature |skt |K |https://apps.ecmwf.int/codes/grib/param-db?id=235 |[era5_sfc_cisst.cfg](raw/monthly/era5_sfc_cisst.cfg)|\n|soil temperature level 1 |stl1 |K |https://apps.ecmwf.int/codes/grib/param-db?id=139 |[era5_sfc_soil.cfg](raw/monthly/era5_sfc_soil.cfg) |\n|soil temperature level 2 |stl2 |K |https://apps.ecmwf.int/codes/grib/param-db?id=170 |[era5_sfc_soil.cfg](raw/monthly/era5_sfc_soil.cfg) |\n|soil temperature level 3 |stl3 |K |https://apps.ecmwf.int/codes/grib/param-db?id=183 |[era5_sfc_soil.cfg](raw/monthly/era5_sfc_soil.cfg) |\n|soil temperature level 4 |stl4 |K |https://apps.ecmwf.int/codes/grib/param-db?id=236 |[era5_sfc_soil.cfg](raw/monthly/era5_sfc_soil.cfg) |\n|temperature of snow layer |tsn |K |https://apps.ecmwf.int/codes/grib/param-db?id=238 |[era5_sfc_soil.cfg](raw/monthly/era5_sfc_soil.cfg) |\n|volumetric soil water layer 1 |swvl1 |m^3 m^-3 |https://apps.ecmwf.int/codes/grib/param-db?id=39 |[era5_sfc_soil.cfg](raw/monthly/era5_sfc_soil.cfg) |\n|volumetric soil water layer 2 |swvl2 |m^3 m^-3 |https://apps.ecmwf.int/codes/grib/param-db?id=40 |[era5_sfc_soil.cfg](raw/monthly/era5_sfc_soil.cfg) |\n|volumetric soil water layer 3 |swvl3 |m^3 m^-3 |https://apps.ecmwf.int/codes/grib/param-db?id=41 |[era5_sfc_soil.cfg](raw/monthly/era5_sfc_soil.cfg) |\n|volumetric soil water layer 4 |swvl4 |m^3 m^-3 |https://apps.ecmwf.int/codes/grib/param-db?id=42 |[era5_sfc_soil.cfg](raw/monthly/era5_sfc_soil.cfg) |\n|ice temperature layer 1 |istl1 |K |https://apps.ecmwf.int/codes/grib/param-db?id=35 |[era5_sfc_soil.cfg](raw/monthly/era5_sfc_soil.cfg) |\n|ice temperature layer 2 |istl2 |K |https://apps.ecmwf.int/codes/grib/param-db?id=36 |[era5_sfc_soil.cfg](raw/monthly/era5_sfc_soil.cfg) |\n|ice temperature layer 3 |istl3 |K |https://apps.ecmwf.int/codes/grib/param-db?id=37 |[era5_sfc_soil.cfg](raw/monthly/era5_sfc_soil.cfg) |\n|ice temperature layer 4 |istl4 |K |https://apps.ecmwf.int/codes/grib/param-db?id=38 |[era5_sfc_soil.cfg](raw/monthly/era5_sfc_soil.cfg) |\n|total column cloud liquid water |tclw |kg m^-2 |https://apps.ecmwf.int/codes/grib/param-db?id=78 |[era5_sfc_tcol.cfg](raw/monthly/era5_sfc_tcol.cfg) |\n|total column rain water |tcrw |kg m^-2 |https://apps.ecmwf.int/codes/grib/param-db?id=228089|[era5_sfc_tcol.cfg](raw/monthly/era5_sfc_tcol.cfg) |\n|total column snow water |tcsw |kg m^-2 |https://apps.ecmwf.int/codes/grib/param-db?id=228090|[era5_sfc_tcol.cfg](raw/monthly/era5_sfc_tcol.cfg) |\n|total column water |tcw |kg m^-2 |https://apps.ecmwf.int/codes/grib/param-db?id=136 |[era5_sfc_tcol.cfg](raw/monthly/era5_sfc_tcol.cfg) |\n|total column vertically-integrated water vapour |tcwv |kg m^-2 |https://apps.ecmwf.int/codes/grib/param-db?id=137 |[era5_sfc_tcol.cfg](raw/monthly/era5_sfc_tcol.cfg) |\n|Geopotential |z |m^2 s^-2 |https://apps.ecmwf.int/codes/grib/param-dbid=129 |[era5_sfc.cfg](raw/monthly/era5_sfc.cfg) |\n|Surface pressure |sp |Pa |https://apps.ecmwf.int/codes/grib/param-db?id=134 |[era5_sfc.cfg](raw/monthly/era5_sfc.cfg) |\n|Total column vertically-integrated water vapour |tcwv |kg m^-2 |https://apps.ecmwf.int/codes/grib/param-db?id=137 |[era5_sfc.cfg](raw/monthly/era5_sfc.cfg) |\n|Mean sea level pressure |msl |Pa |https://apps.ecmwf.int/codes/grib/param-db?id=151 |[era5_sfc.cfg](raw/monthly/era5_sfc.cfg) |\n|Total cloud cover |tcc |(0 - 1) |https://apps.ecmwf.int/codes/grib/param-db?id=164 |[era5_sfc.cfg](raw/monthly/era5_sfc.cfg) |\n|10 metre U wind component |10u |m s^-1 |https://apps.ecmwf.int/codes/grib/param-db?id=165 |[era5_sfc.cfg](raw/monthly/era5_sfc.cfg) |\n|10 metre V wind component |10v |m s^-1 |https://apps.ecmwf.int/codes/grib/param-db?id=166 |[era5_sfc.cfg](raw/monthly/era5_sfc.cfg) |\n|2 metre temperature |2t |K |https://apps.ecmwf.int/codes/grib/param-db?id=167 |[era5_sfc.cfg](raw/monthly/era5_sfc.cfg) |\n|2 metre dewpoint temperature |2d |K |https://apps.ecmwf.int/codes/grib/param-db?id=168 |[era5_sfc.cfg](raw/monthly/era5_sfc.cfg) |\n|Low cloud cover |lcc |(0 - 1) |https://apps.ecmwf.int/codes/grib/param-db?id=186 |[era5_sfc.cfg](raw/monthly/era5_sfc.cfg) |\n|Medium cloud cover |mcc |(0 - 1) |https://apps.ecmwf.int/codes/grib/param-db?id=187 |[era5_sfc.cfg](raw/monthly/era5_sfc.cfg) |\n|High cloud cover |hcc |(0 - 1) |https://apps.ecmwf.int/codes/grib/param-db?id=188 |[era5_sfc.cfg](raw/monthly/era5_sfc.cfg) |\n|100 metre U wind component |100u |m s^-1 |https://apps.ecmwf.int/codes/grib/param-db?id=228246|[era5_sfc.cfg](raw/monthly/era5_sfc.cfg) |\n|100 metre V wind component |100v |m s^-1 |https://apps.ecmwf.int/codes/grib/param-db?id=228247|[era5_sfc.cfg](raw/monthly/era5_sfc.cfg) |\n\u003c/details\u003e\n\n### Single Level Forecast\n\nThis dataset contains single-level forecast fields on ERA5's native reduced Gaussian grid.\n\n```python\nimport xarray\n\nds = xarray.open_zarr(\n 'gs://gcp-public-data-arco-era5/co/single-level-forecast.zarr-v2/', \n chunks=None,\n storage_options=dict(token='anon'),\n)\nsingle_level_forecasts = ds.sel(time=slice(ds.attrs['valid_time_start'], ds.attrs['valid_time_stop']))\n```\n\n* _Times_: `06:00/18:00`\n* _Steps_: `0/1/2/3/4/5/6/7/8/9/10/11/12/13/14/15/16/17/18`\n* _Grid_: `N320`,\n a [Reduced Gaussian Grid](https://confluence.ecmwf.int/display/EMOS/Reduced+Gaussian+Grids) ([docs](https://www.ecmwf.int/en/forecasts/documentation-and-support/gaussian_n320))\n* _Size_: 53.2 TB\n* _Chunking_: `{'time': 1, 'step': 1, 'values': 542080}`\n* _Chunk size (per variable)_: 2.17 MB\n \n\u003cdetails\u003e\n\u003csummary\u003eData summary table\u003c/summary\u003e\n\n|name |short name|units |docs |config |\n|------------------------------------------|----------|---------------------|----------------------------------------------------|------------------------------------------------|\n|snow density |rsn |kg m^-3 |https://apps.ecmwf.int/codes/grib/param-db?id=33 |[era5_sfc_pcp.cfg](raw/monthly/era5_sfc_pcp.cfg)|\n|snow evaporation |es |m of water equivalent|https://apps.ecmwf.int/codes/grib/param-db?id=44 |[era5_sfc_pcp.cfg](raw/monthly/era5_sfc_pcp.cfg)|\n|snow melt |smlt |m of water equivalent|https://apps.ecmwf.int/codes/grib/param-db?id=45 |[era5_sfc_pcp.cfg](raw/monthly/era5_sfc_pcp.cfg)|\n|large-scale precipitation fraction |lspf |s |https://apps.ecmwf.int/codes/grib/param-db?id=50 |[era5_sfc_pcp.cfg](raw/monthly/era5_sfc_pcp.cfg)|\n|snow depth |sd |m of water equivalent|https://apps.ecmwf.int/codes/grib/param-db?id=141 |[era5_sfc_pcp.cfg](raw/monthly/era5_sfc_pcp.cfg)|\n|large-scale precipitation |lsp |m |https://apps.ecmwf.int/codes/grib/param-db?id=142 |[era5_sfc_pcp.cfg](raw/monthly/era5_sfc_pcp.cfg)|\n|convective precipitation |cp |m |https://apps.ecmwf.int/codes/grib/param-db?id=143 |[era5_sfc_pcp.cfg](raw/monthly/era5_sfc_pcp.cfg)|\n|snowfall |sf |m of water equivalent|https://apps.ecmwf.int/codes/grib/param-db?id=144 |[era5_sfc_pcp.cfg](raw/monthly/era5_sfc_pcp.cfg)|\n|convective rain rate |crr |kg m^-2 s^-1 |https://apps.ecmwf.int/codes/grib/param-db?id=228218|[era5_sfc_pcp.cfg](raw/monthly/era5_sfc_pcp.cfg)|\n|large scale rain rate |lsrr |kg m^-2 s^-1 |https://apps.ecmwf.int/codes/grib/param-db?id=228219|[era5_sfc_pcp.cfg](raw/monthly/era5_sfc_pcp.cfg)|\n|convective snowfall rate water equivalent |csfr |kg m^-2 s^-1 |https://apps.ecmwf.int/codes/grib/param-db?id=228220|[era5_sfc_pcp.cfg](raw/monthly/era5_sfc_pcp.cfg)|\n|large scale snowfall rate water equivalent|lssfr |kg m^-2 s^-1 |https://apps.ecmwf.int/codes/grib/param-db?id=228221|[era5_sfc_pcp.cfg](raw/monthly/era5_sfc_pcp.cfg)|\n|total precipitation |tp |m |https://apps.ecmwf.int/codes/grib/param-db?id=228 |[era5_sfc_pcp.cfg](raw/monthly/era5_sfc_pcp.cfg)|\n|convective snowfall |csf |m of water equivalent|https://apps.ecmwf.int/codes/grib/param-db?id=239 |[era5_sfc_pcp.cfg](raw/monthly/era5_sfc_pcp.cfg)|\n|large-scale snowfall |lsf |m of water equivalent|https://apps.ecmwf.int/codes/grib/param-db?id=240 |[era5_sfc_pcp.cfg](raw/monthly/era5_sfc_pcp.cfg)|\n|precipitation type |ptype |code table (4.201) |https://apps.ecmwf.int/codes/grib/param-db?id=260015|[era5_sfc_pcp.cfg](raw/monthly/era5_sfc_pcp.cfg)|\n|surface solar radiation downwards |ssrd |J m^-2 |https://apps.ecmwf.int/codes/grib/param-db?id=169 |[era5_sfc_rad.cfg](raw/monthly/era5_sfc_pcp.cfg)|\n|top net thermal radiation |ttr |J m^-2 |https://apps.ecmwf.int/codes/grib/param-db?id=179 |[era5_sfc_rad.cfg](raw/monthly/era5_sfc_rad.cfg)|\n|gravity wave dissipation |gwd |J m^-2 |https://apps.ecmwf.int/codes/grib/param-db?id=197 |[era5_sfc_rad.cfg](raw/monthly/era5_sfc_rad.cfg)|\n|surface thermal radiation downwards |strd |J m^-2 |https://apps.ecmwf.int/codes/grib/param-db?id=175 |[era5_sfc_rad.cfg](raw/monthly/era5_sfc_rad.cfg)|\n|surface net thermal radiation |str |J m^-2 |https://apps.ecmwf.int/codes/grib/param-db?id=177 |[era5_sfc_rad.cfg](raw/monthly/era5_sfc_rad.cfg)|\n\n\u003c/details\u003e\n\n## Project roadmap\n\n_Updated on 2024-06-25_\n\n1. [x] **Phase 0**: Ingest raw ERA5\n2. [x] **Phase 1**: Cloud-Optimize to Zarr, without data modifications\n 1. [x] Use [Pangeo-Forge](https://pangeo-forge.readthedocs.io/) to convert the data from grib to Zarr.\n 2. [x] Create example notebooks for common workflows, including regridding and variable derivation.\n3. [x] **Phase 2**: Produce an Analysis-Ready corpus\n 1. [ ] Update GCP CPDs documentation.\n 2. [ ] Create walkthrough notebooks.\n4. [x] **Phase 3**: Automatic dataset updates, data is back-fillable.\n5. WIP **Phase 4**: Mirror ERA5 data in Google BigQuery.\n6. [ ] **Phase 5**: Derive a high-resolution version of ERA5\n 1. [x] Regrid datasets to lat/long grids.\n 2. [x] Convert model levels to pressure levels (at high resolution).\n 3. [x] Compute derived variables.\n 4. [ ] Expand on example notebooks.\n\n\n## How to reproduce\n\nAll phases of this dataset can be reproduced with scripts found here. To run them, please clone the repo and install the\nproject.\n\n```shell\ngit clone https://github.com/google-research/arco-era5.git\n```\n\nOr, via SSH:\n\n```shell\ngit clone git@github.com:google-research/arco-era5.git\n```\n\nThen, install with `pip`:\n\n```shell\ncd arco-era5\npip install -e .\n```\n\n### Acquire \u0026 preprocess raw data\n\nPlease consult the [instructions described in `raw/`](raw).\n\n### Cloud-Optimization\n\nAll our tools make use of Apache Beam, and thus\nare [portable to any cloud (or Runner)](https://beam.apache.org/documentation/runners/capability-matrix/). We use GCP's\n[Dataflow](https://cloud.google.com/dataflow) to produce this dataset. If you would like to reproduce the project this\nway, we recommend the following:\n\n1. Ensure you have access to a GCP project with GCS read \u0026 write access, as well as full Dataflow permissions (see these\n [\"Before you begin\" instructions](https://cloud.google.com/dataflow/docs/quickstarts/create-pipeline-python)).\n2. Export the following variables:\n ```shell\n export PROJECT=\u003cyour-gcp-project\u003e\n export REGION=us-central1\n export BUCKET=\u003cyour-beam-runner-bucket\u003e\n ```\n\nFrom here, we provide examples of how to run the recipes at the top of each script.\n\n```shell\npydoc src/single-levels-to-zarr.py\npydoc src/ar-to-zarr.py\n```\n\nYou can also discover available command line options by invoking the script with `-h/--help`:\n\n```shell\npython src/model-levels-to-zarr.py --help\n```\n\n### Automating dataset Updates in zarr and BigQuery\nThis feature is works in 4 parts.\n 1. Acquiring raw data from CDS, facilitated by [`weather-dl`](https://weather-tools.readthedocs.io/en/latest/weather_dl/README.html) tool.\n 2. Splitting raw data using [`weather-sp`](https://weather-tools.readthedocs.io/en/latest/weather_sp/README.html).\n 3. Ingest this splitted data into a zarr file.\n\n#### How to Run.\n1. Set up a Cloud project with sufficient permissions to use cloud storage (such as [GCS](https://cloud.google.com/storage)) and a Beam runner (such as [Dataflow](https://cloud.google.com/dataflow)).\n \u003e Note: Other cloud systems should work too, such as S3 and Elastic Map Reduce. However, these are untested. If you\n \u003e experience an error here, please let us know by [filing an issue](https://github.com/google/weather-tools/issues).\n2. Acquire one or more licenses from [Copernicus](https://cds.climate.copernicus.eu/user/register?destination=/api-how-to).\n3. Add the all `Copernicus` licenses into the [secret-manager](https://cloud.google.com/secret-manager) with value likes this: {\"api_url\": \"URL\", \"api_key\": \"KEY\"}\n \u003e NOTE: for every API_KEY there must be unique secret-key.\n\n4. Update all of these variable in [docker-file](deployment/constants.py).\n * `PROJECT` \n * `REGION`\n * `BUCKET`\n * `MANIFEST_LOCATION`\n * `API_KEY_*`\n * In case of multiple API keys, API_KEY must follow this format: `API_KEY_*`. here * can be numeric value i.e. 1, 2. \n * API_KEY_* value is the resource name of [secret-manager key](https://cloud.google.com/secret-manager) and it's value looks like this :: ```projects/PROJECT_NAME/secrets/SECRET_KEY_NAME/versions/1``` \n * `WEATHER_TOOLS_SDK_CONTAINER_IMAGE`\n * Is made using this [dockerfile](https://github.com/google/weather-tools/blob/setuptools/Dockerfile) and is stored in a docker registry.\n * `ARCO_ERA5_SDK_CONTAINER_IMAGE`\n * Is made using this [dockerfile](https://github.com/google-research/arco-era5/blob/main/Dockerfile) and is stored in a registry.\n\n\n5. Create docker image.\n\n```\nexport PROJECT_ID=\u003cyour-project-here\u003e\nexport REPO=\u003crepo\u003e eg:arco-era5-raw-to-zarr-to-bq\n\ngcloud builds submit . --tag \"gcr.io/$PROJECT_ID/$REPO:latest\" \n```\n\n6. Run script to create cloud run jobs. [create_job](deployment/create_job.py)\n```\npython deployment/create_job.py\n```\n\n7. There will be 5 different cloud run jobs.\n - `arco-era5-zarr-ingestion` - For zarr data ingestion.\n - `arco-era5t-daily-executor` - Triggers daily to process era5t-daily data.\n - `arco-era5t-monthly-executor` - Triggers monthly to process era5t-monthly data.\n - `arco-era5-sanity` - Sanity job to validate the data era5 vs era5t and replace in case of difference.\n - `arco-era5-executor` - Triggers every month to run a sanity job for every zarr available.\n8. Set up cloud schedulers to trigger above jobs on specified frequencies.\n - `arco-era5t-daily-executor` - Schedule a daily trigger for `era5t-daily` data.\n - `arco-era5t-monthly-executor` - Schedule a monthly trigger for `era5t-monthly` data.\n - `arco-era5-executor` - Schedule a monthly trigger to do era5 vs era5t sanity for `current_month - 3` month.\n### Making the dataset \"High Resolution\" \u0026 beyond...\n\nThis phase of the project is under active development! If you would like to lend a hand in any way, please check out our\n[contributing guide](CONTRIBUTING.md).\n\n## FAQs\n\n### How did you pick these variables?\n\nThis dataset originated in [Loon](https://x.company/projects/loon/), Alphabet’s project to deliver internet service\nusing stratospheric balloons, and is now curated by Google Research \u0026 Google Cloud Platform. Loon’s Planning, Simulation\nand Control team needed accurate data on how the stratospheric winds have behaved in the past to evaluate the\neffectiveness of different balloon steering algorithms over a range of weather. This led us to download the model-level\ndata. But Loon also needed information about the atmospheric radiation to model balloon gas temperatures, so we\ndownloaded that. And then we downloaded the most commonly used meteorological variables to support different product\nplanning needs (RF propagation models, etc)...\n\nEventually, we found ourselves with a comprehensive history of weather for the world.\n\n### Where are the U/V components of wind? Where is geopotential height? Why isn’t `X` variable in this dataset?\n\nWe intentionally did not include many variables that can be derived from other variables. For example, U/V components of\nwind can be computed from divergence and vorticity; geopotential is a vertical integral of temperature.\n\nIn the second phase of our roadmap (towards \"Analysis Ready\" data), we aim to compute all of these variables ourselves.\nIf you’d like to make use of these parameters sooner, please check out our example notebooks where we demo common\ncalculations. If you notice non-derived missing data, such as surface variables, please let us know of your needs\nby [filing an issue](https://github.com/google-research/arco-era5/issues), and we will be happy to incorporate them into\nour roadmap.\n\n### Do you have plans to get _all_ of ERA5?\n\nWe aim to support hosting data that serves general meteorological use cases, rather than aim for total completeness.\nWave variables are missing from this corpus, and are a priority on our roadmap. If there is a variable or dataset that\nyou think should be included here, please file a [Github issue](https://github.com/google-research/arco-era5/issues).\n\nFor a complete ERA5 mirror, we recommend consulting with\nthe [Pangeo Forge project](https://pangeo-forge.readthedocs.io/)\n(especially [staged-recipes#92](https://github.com/pangeo-forge/staged-recipes/issues/92)).\n\n### Why are there two model-level datasets and not one?\n\nIt definitely is possible for all model level data to be represented in one grid, and thus one dataset. However, we\nopted to preserve the native representation for variables in ECMWF's models. A handful of core model variables (wind,\ntemperature and surface pressure) are represented\nas [spectral harmonic coefficients](https://confluence.ecmwf.int/display/UDOC/How+to+access+the+data+values+of+a+spherical+harmonic+field+in+GRIB+-+ecCodes+GRIB+FAQ)\n, while everything else is stored on a Gaussian grid. This avoids introducing numerical error by interpolating these\nvariables to physical space. For a more in depth review of this topic, please consult these references:\n\n* [_Fundamentals of Numerical Weather Prediction_](https://doi.org/10.1017/CBO9780511734458) by Jean Coiffier\n* [_Atmospheric modeling, data assimilation, and predictability_](https://doi.org/10.1017/CBO9780511802270) by Eugenia\n Kalnay\n\nPlease note: in a future releases, we intend to create a dataset version where all model levels are in one grid and\nZarr.\n\n### Why doesn’t this project make use of Pangeo Forge Cloud?\n\nWe are big fans of the [Pangeo Forge project](https://pangeo-forge.readthedocs.io/), and of [Pangeo](https://pangeo.io/)\nin general. While this project does make use of [their Recipes](https://github.com/pangeo-forge/pangeo-forge-recipes),\nwe have a few reasons to not use their cloud. First, we would prefer to use internal rather than community resources for\ncomputations of this scale. In addition, there are several technical reasons why Pangeo Forge as it is today would not\nbe able to handle this case ([0](https://github.com/pangeo-forge/staged-recipes/issues/92#issuecomment-959481038),\n[1](https://github.com/pangeo-forge/pangeo-forge-recipes/issues/244),\n[2](https://github.com/pangeo-forge/pangeo-forge-recipes/pull/245#issuecomment-997070261),\n[3](https://github.com/pangeo-forge/pangeo-forge-recipes/issues/256)). To work around this, we opted to combine\nfamiliar-to-us infrastructure with Pangeo-Forge's core and to use\nthe [right tool](https://github.com/google/weather-tools/) for the right job.\n\n### Why use this dataset? What uses are there for the data?\n\nERA5 can be used in many applications. It can be used to train ML models that predict the impact of weather on different\nphenomena. ERA5 data could also be used to train and evaluate ML models that forecast the weather. The data could be\nused to compute climatologies, or the average weather for a region over a given period of time. ERA5 data can be used to\nvisualize and study historical weather events, such as Hurricane Sandy.\n\n### Where should I be cautious? What are the limitations of the dataset?\n\n| | |\n|:-----------------------------------------------------------------------:|:-----------------------------------------------------------------------------:|\n| ![Mumbai, India](docs/assets/table0-fig0-mumbai.png) \u003cbr\u003e Mumbai, India | ![San Francisco, USA](docs/assets/table0-fig1-sf.png) \u003cbr\u003e San Francisco, USA |\n| ![Tokyo, Japan](docs/assets/table0-fig2-tokyo.png) \u003cbr\u003e Tokyo, Japan | ![Singapore](docs/assets/table0-fig3-singapore.png) \u003cbr\u003e Singapore |\n\n| | |\n|:------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------:|\n| ![ERA5 Topography](docs/assets/table1-fig0-topo-era5.png) \u003cbr\u003e ERA5 Topography | ![GMTED2010 Topography](docs/assets/table1-fig1-topo-GMTED2010.png) \u003cbr\u003e GMTED2010 Topography |\n\nIt is important to remember that a reanalysis is an estimate of what the weather was, it is not guaranteed to be an\nerror-free estimate. There are several areas where the novice reanalysis user should be careful.\n\nFirst, the user should be careful using reanalysis data at locations near coastlines. The first figure shows the\nfraction of land (1 for land, 0 for ocean) of ERA5 grid points at different coastal locations. This is important because\nthe land-surface model used in ERA5 tries to blend in the influence of water with the influence of land based on this\nfraction. The most visible effect of this blending is that as the fraction of land decreases, the daily variation in\ntemperature will also decrease. Looking at the first figure, there are sharp changes in the fraction of land between\nneighboring grid cells so there could be differences in daily temperature range that might not be reflected in actual\nweather observations.\n\nThe user should also be careful when using reanalysis data in areas with large variations in topography. The second\nfigure is a plot of ERA5 topography around Mount Everest compared with GMTED2010 topography. The ERA5 topography is\ncompletely missing the high peaks of the Everest region and missing most of the structure of the mountain valleys.\nTopography strongly influences temperature and precipitation rate, so it is possible that ERA5’s temperature is too warm\nand ERA5’s precipitation patterns could be wrong as well.\n\nERA5’s precipitation variables aren’t directly constrained by any observations, so we strongly encourage the user to\ncheck ERA5 against observed precipitation (for example, [Wu et al., 2022](https://doi.org/10.1175/JHM-D-21-0195.1)). A\nstudy comparing reanalyses (not including ERA5) against gridded precipitation observations showed striking differences\nbetween reanalyses and observation [Lisa V Alexander et al 2020 Environ. Res. Lett. 15 055002](\nhttps://iopscience.iop.org/article/10.1088/1748-9326/ab79e2).\n\n### Can I use the data for {research,commercial} purposes?\n\nYes, you can use our ERA5 data according to the terms of\nthe [Copernicus license](https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf).\n\nResearchers, see the [next section](#how-to-cite-this-work) for how to cite this work.\n\nCommercial users, please be sure to provide acknowledgement to the Copernicus Climate Change Service according to\nthe [Copernicus Licence](https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf)\nterms.\n\n## How to cite this work\n\nPlease cite our presentation at the 22nd Conference on Artificial Intelligence for Environmental Science describing ARCO-ERA5.\n```\nCarver, Robert W, and Merose, Alex. (2023):\nARCO-ERA5: An Analysis-Ready Cloud-Optimized Reanalysis Dataset.\n22nd Conf. on AI for Env. Science, Denver, CO, Amer. Meteo. Soc, 4A.1,\nhttps://ams.confex.com/ams/103ANNUAL/meetingapp.cgi/Paper/415842\n```\n\nIn addition, please cite the ERA5 dataset accordingly:\n\n```\nHersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., \nMuñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., \nSimmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., \nBechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., \nDahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., \nForbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., \nHogan, R.J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., \nLopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F.,\nVillaume, S., Thépaut, J-N. (2017): Complete ERA5: Fifth generation of \nECMWF atmospheric reanalyses of the global climate. Copernicus Climate \nChange Service (C3S) Data Store (CDS). (Accessed on DD-MM-YYYY)\n\nHersbach et al, (2017) was downloaded from the Copernicus Climate Change \nService (C3S) Climate Data Store. We thank C3S for allowing us to \nredistribute the data.\n\nThe results contain modified Copernicus Climate Change Service \ninformation 2022. Neither the European Commission nor ECMWF is \nresponsible for any use that may be made of the Copernicus information \nor data it contains.\n```\n\n## License\n\nThis is not an official Google product.\n\n```\nCopyright 2022 Google LLC\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n https://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the 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