Analysis-Ready, Cloud Optimized ERA5

Recipes for reproducing Analysis-Ready & Cloud Optimized (ARCO) ERA5 datasets.

Introduction • Overview • Analysis Ready Data • Raw Cloud Optimized Data • Project roadmap • How to reproduce • FAQs • How to cite this work • License

Introduction

Our goal is to make a global history of the climate highly accessible in the cloud. To that end, we present a curated copy of the ERA5 corpus in Google Cloud Public Datasets.

So far, we have ingested meteorologically valuable variables for the land and atmosphere. From this, we have produced a cloud-optimized version of ERA5, in which we have converted grib data to Zarr with no other modifications. In addition, we have created "analysis-ready" versions on regular lat-lon grids, oriented towards common research & ML workflows.

This two-pronged approach for the data serves different user needs. Some researchers need full control over the interpolation of data for their analysis. Most will want a batteries-included dataset, where standard pre-processing and chunk optimization is already applied. In general, we ensure that every step in this pipeline is open and reproducible, to provide transparency in the provenance of all data.

Overview

• The gcp-public-data-arco-era5 bucket is stored in the us-central1 (Iowa) Google Cloud region.
• Files are updated from ECMWF on a monthly cadence (on roughly the 9th of each month) with a 3 month delay, which avoids including preliminary versions of ERA5.
• 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, and last_updated. 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.

Analysis Ready Data

These datasets have been regridded to a uniform 0.25° equiangular horizontal resolution to facilitate downstream analyses, e.g., with WeatherBench2.

0.25° Pressure and Surface Level Data

This dataset contains most pressure-level fields and all surface-level field regridded to a uniform 0.25° resolution. It is a superset of the data used to train GraphCast and NeuralGCM.

import xarray

ds = xarray.open_zarr(
    'gs://gcp-public-data-arco-era5/ar/full_37-1h-0p25deg-chunk-1.zarr-v3',
    chunks=None,
    storage_options=dict(token='anon'),
)
ar_full_37_1h = ds.sel(time=slice(ds.attrs['valid_time_start'], ds.attrs['valid_time_stop']))

• Times: 00/to/23
• 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
• Grid: equiangular lat-lon
• Size: 2.05 PB
• Chunking: {'time': 1, 'latitude': 721, 'longitude': 1440, 'level': 37}
• Chunk size (per variable): 154 MB

0.25° Model Level Data

This dataset contains 3D fields at 0.25° resolution with ERA5's native vertical coordinates (hybrid pressure/sigma coordinates).

import xarray

ds = xarray.open_zarr(
    'gs://gcp-public-data-arco-era5/ar/model-level-1h-0p25deg.zarr-v1',
    chunks=None,
    storage_options=dict(token='anon'),
)
ar_native_vertical_grid_data = ds.sel(time=slice(ds.attrs['valid_time_start'], ds.attrs['valid_time_stop']))

It can combined with surface-level variables from the 0.25° pressure- and surface-level dataset:

ds = xarray.open_zarr(
    'gs://gcp-public-data-arco-era5/ar/full_37-1h-0p25deg-chunk-1.zarr-v3',
    chunks=None,
    storage_options=dict(token='anon'),
)
ar_full_37_1h = ds.sel(time=slice(ds.attrs['valid_time_start'], ds.attrs['valid_time_stop']))

ar_model_level_and_surface_data = xarray.merge([
    ar_native_vertical_grid_data, ar_full_37_1h.drop_dims('level')
])

• Times: 00/to/23
• Levels: 1/to/137
• Grid: equiangular lat-lon
• Size: 5.88 PB
• Chunking: {'time': 1, 'hybrid': 18, 'latitude': 721, 'longitude': 1440}
• Chunk size (per variable): 74.8 MB

Raw Cloud Optimized Data

These datasets contain the raw data used to produce the Analysis Ready data. Whenever possible, parameters are represented by their native grid resolution See this ECMWF documentation for more.

Please view out our walkthrough notebook for a demo of these cloud-optimized datasets.

Model Level Wind

This dataset contains model-level wind fields on ERA5's native grid, as spherical harmonic coefficients.

import xarray

ds = xarray.open_zarr(
    'gs://gcp-public-data-arco-era5/co/model-level-wind.zarr-v2',
    chunks=None,
    storage_options=dict(token='anon'),
)
model_level_wind = ds.sel(time=slice(ds.attrs['valid_time_start'], ds.attrs['valid_time_stop']))

• Levels: 1/to/137
• Times: 00/to/23
• Grid: T639 spherical harmonic coefficients (docs)
• Size: 664 TB
• Chunking: {'time': 1, 'hybrid': 1, 'values': 410240}
• Chunk size (per variable): 1.64 MB

Model Level Moisture

This dataset contains model-level moisture fields on ERA5's native reduced Gaussian grid.

import xarray

ds = xr.open_zarr(
    'gs://gcp-public-data-arco-era5/co/model-level-moisture.zarr-v2/',
    chunks=None,
    storage_options=dict(token='anon'),
)
model_level_moisture = ds.sel(time=slice(ds.attrs['valid_time_start'], ds.attrs['valid_time_stop']))

• Levels: 1/to/137
• Times: 00/to/23
• Grid: N320, a Reduced Gaussian Grid (docs)
• Size: 1.54 PB
• Chunking: {'time': 1, 'hybrid': 1, 'values': 542080}
• Chunk size (per variable): 2.17 MB

Single Level Surface

This dataset contains single-level renanalysis fields on ERA5's native grid, as spherical harmonic coefficients.

import xarray

ds = xarray.open_zarr(
    'gs://gcp-public-data-arco-era5/co/single-level-surface.zarr-v2/',
    chunks=None,
    storage_options=dict(token='anon'),
)
single_level_surface = ds.sel(time=slice(ds.attrs['valid_time_start'], ds.attrs['valid_time_stop']))

• Times: 00/to/23
• Grid: TL639 spherical harmonic coefficients (docs)
• Size: 2.42 TB
• Chunking: {'time': 1, 'values': 410240}
• Chunk size (per variable): 1.64 MB

Single Level Reanalysis

This dataset contains single-level renanalysis fields on ERA5's native reduced Gaussian grid.

import xarray

ds = xarray.open_zarr(
    'gs://gcp-public-data-arco-era5/co/single-level-reanalysis.zarr-v2',
    chunks=None,
    storage_options=dict(token='anon'),
)
single_level_reanalysis = ds.sel(time=slice(ds.attrs['valid_time_start'], ds.attrs['valid_time_stop']))

• Times: 00/to/23
• Grid: N320, a Reduced Gaussian Grid (docs)
• Size: 60.9 TB
• Chunking: {'time': 1, 'values': 542080}
• Chunk size (per variable): 2.17 MB

Single Level Forecast

This dataset contains single-level forecast fields on ERA5's native reduced Gaussian grid.

import xarray

ds = xarray.open_zarr(
    'gs://gcp-public-data-arco-era5/co/single-level-forecast.zarr-v2/', 
    chunks=None,
    storage_options=dict(token='anon'),
)
single_level_forecasts = ds.sel(time=slice(ds.attrs['valid_time_start'], ds.attrs['valid_time_stop']))

• Times: 06:00/18:00
• Steps: 0/1/2/3/4/5/6/7/8/9/10/11/12/13/14/15/16/17/18
• Grid: N320, a Reduced Gaussian Grid (docs)
• Size: 53.2 TB
• Chunking: {'time': 1, 'step': 1, 'values': 542080}
• Chunk size (per variable): 2.17 MB

Project roadmap

Updated on 2024-06-25

1. [x] Phase 0: Ingest raw ERA5
2. [x] Phase 1: Cloud-Optimize to Zarr, without data modifications
   1. [x] Use Pangeo-Forge to convert the data from grib to Zarr.
   2. [x] Create example notebooks for common workflows, including regridding and variable derivation.
3. [x] Phase 2: Produce an Analysis-Ready corpus
   1. [ ] Update GCP CPDs documentation.
   2. [ ] Create walkthrough notebooks.
4. [x] Phase 3: Automatic dataset updates, data is back-fillable.
5. WIP Phase 4: Mirror ERA5 data in Google BigQuery.
6. [ ] Phase 5: Derive a high-resolution version of ERA5
   1. [x] Regrid datasets to lat/long grids.
   2. [x] Convert model levels to pressure levels (at high resolution).
   3. [x] Compute derived variables.
   4. [ ] Expand on example notebooks.

How to reproduce

All phases of this dataset can be reproduced with scripts found here. To run them, please clone the repo and install the project.

git clone https://github.com/google-research/arco-era5.git

Or, via SSH:

git clone git@github.com:google-research/arco-era5.git

Then, install with pip:

cd arco-era5
pip install -e .

Acquire & preprocess raw data

Please consult the instructions described in raw/.

Cloud-Optimization

All our tools make use of Apache Beam, and thus are portable to any cloud (or Runner). We use GCP's Dataflow to produce this dataset. If you would like to reproduce the project this way, we recommend the following:

1. Ensure you have access to a GCP project with GCS read & write access, as well as full Dataflow permissions (see these "Before you begin" instructions).
2. Export the following variables:

   export PROJECT=<your-gcp-project>
   export REGION=us-central1
   export BUCKET=<your-beam-runner-bucket>

From here, we provide examples of how to run the recipes at the top of each script.

pydoc src/single-levels-to-zarr.py
pydoc src/ar-to-zarr.py

You can also discover available command line options by invoking the script with -h/--help:

python src/model-levels-to-zarr.py --help

Automating dataset Updates in zarr and BigQuery

This feature is works in 4 parts.

1. Acquiring raw data from CDS, facilitated by weather-dl tool.
2. Splitting raw data using weather-sp.
3. Ingest this splitted data into a zarr file.
4. [WIP] Ingest AR data into BigQuery with the assistance of the weather-mv.

How to Run.

1. Set up a Cloud project with sufficient permissions to use cloud storage (such as GCS) and a Beam runner (such as Dataflow).

   Note: Other cloud systems should work too, such as S3 and Elastic Map Reduce. However, these are untested. If you experience an error here, please let us know by filing an issue.

2. Acquire one or more licenses from Copernicus.

3. Add the all Copernicus licenses into the secret-manager with value likes this: {"api_url": "URL", "api_key": "KEY"}

   NOTE: for every API_KEY there must be unique secret-key.

4. Update all of these variable in docker-file.

   • PROJECT
   • REGION
   • BUCKET
   • MANIFEST_LOCATION
   • API_KEY_*
   • WEATHER_TOOLS_SDK_CONTAINER_IMAGE
   • ARCO_ERA5_SDK_CONTAINER_IMAGE
   • BQ_TABLES_LIST

   • REGION_LIST

     • In case of multiple API keys, API_KEY must follow this format: API_KEY_*. here * can be numeric value i.e. 1, 2.
     • APIKEY* value is the resource name of secret-manager key and it's value looks like this :: projects/PROJECT_NAME/secrets/SECRET_KEY_NAME/versions/1
     • BQ_TABLES_LIST is list of the BigQuery table in which data is ingested and it's value is like this :: '["PROJECT.DATASET.TABLE1", "PROJECT.DATASET.TABLE2", ..., "PROJECT.DATASET.TABLE6"]'.
     • REGION_LIST is list of the GCP_region in which the job of ingestion will run :: '["us-east1", "us-west4",..., "us-west2"]'.
     • Size of BQ_TABLES_LIST and REGION_LIST must be 6 as total 6 zarr file processed in the current pipeline and also, data ingestion in Bigquery are corresponding to ZARR_FILES_LIST of raw-to-zarr-to-bq.py so add table name in BQ_TABLES_LIST accordingly.
     • WEATHER_TOOLS_SDK_CONTAINER_IMAGE is made using this dockerfile and is stored in a docker registry.
     • ARCO_ERA5_SDK_CONTAINER_IMAGE is made using this dockerfile and is stored in a registry.

5. Create docker image.

export PROJECT_ID=<your-project-here>
export REPO=<repo> eg:arco-era5-raw-to-zarr-to-bq

gcloud builds submit . --tag "gcr.io/$PROJECT_ID/$REPO:latest" 

7. Create a VM using above created docker-image

   
   export ZONE=<zone> eg: us-central1-a
   export SERVICE_ACCOUNT=<service account> # Let's keep this as Compute Engine Default Service Account
   export IMAGE_PATH=<container-image-path> # The above created image-path

gcloud compute instances create-with-container arco-era5-raw-to-zarr-to-bq \ --project=$PROJECT_ID \ --zone=$ZONE \ --machine-type=n2-standard-4 \ --network-interface=network-tier=PREMIUM,subnet=default \ --maintenance-policy=MIGRATE \ --provisioning-model=STANDARD \ --service-account=$SERVICE_ACCOUNT \ --scopes=https://www.googleapis.com/auth/cloud-platform \ --image=projects/cos-cloud/global/images/cos-stable-109-17800-0-45 \ --boot-disk-size=200GB \ --boot-disk-type=pd-balanced \ --boot-disk-device-name=arco-era5-raw-to-zarr-to-bq \ --container-image=$IMAGE_PATH \ --container-restart-policy=on-failure \ --container-tty \ --no-shielded-secure-boot \ --shielded-vtpm \ --shielded-integrity-monitoring \ --labels=goog-ec-src=vm_add-gcloud,container-vm=cos-stable-109-17800-0-45 \ --metadata-from-file=startup-script=start-up.sh

8. Once VM is created, the script will execute on `7th day of every month` as this is default set in the [cron-file](data_automate/cron-file).Also you can see the logs after connecting to VM through SSH.
> Log will be shown at this(`/var/log/cron.log`) file.
> Better if we SSH after 5-10 minutes of VM creation. 
### Making the dataset "High Resolution" & beyond...

This phase of the project is under active development! If you would like to lend a hand in any way, please check out our
[contributing guide](CONTRIBUTING.md).

## FAQs

### How did you pick these variables?

This dataset originated in [Loon](https://x.company/projects/loon/), Alphabet’s project to deliver internet service
using stratospheric balloons, and is now curated by Google Research & Google Cloud Platform. Loon’s Planning, Simulation
and Control team needed accurate data on how the stratospheric winds have behaved in the past to evaluate the
effectiveness of different balloon steering algorithms over a range of weather. This led us to download the model-level
data. But Loon also needed information about the atmospheric radiation to model balloon gas temperatures, so we
downloaded that. And then we downloaded the most commonly used meteorological variables to support different product
planning needs (RF propagation models, etc)...

Eventually, we found ourselves with a comprehensive history of weather for the world.

### Where are the U/V components of wind? Where is geopotential height? Why isn’t `X` variable in this dataset?

We intentionally did not include many variables that can be derived from other variables. For example, U/V components of
wind can be computed from divergence and vorticity; geopotential is a vertical integral of temperature.

In the second phase of our roadmap (towards "Analysis Ready" data), we aim to compute all of these variables ourselves.
If you’d like to make use of these parameters sooner, please check out our example notebooks where we demo common
calculations. If you notice non-derived missing data, such as surface variables, please let us know of your needs
by [filing an issue](https://github.com/google-research/arco-era5/issues), and we will be happy to incorporate them into
our roadmap.

### Do you have plans to get _all_ of ERA5?

We aim to support hosting data that serves general meteorological use cases, rather than aim for total completeness.
Wave variables are missing from this corpus, and are a priority on our roadmap. If there is a variable or dataset that
you think should be included here, please file a [Github issue](https://github.com/google-research/arco-era5/issues).

For a complete ERA5 mirror, we recommend consulting with
the [Pangeo Forge project](https://pangeo-forge.readthedocs.io/)
(especially [staged-recipes#92](https://github.com/pangeo-forge/staged-recipes/issues/92)).

### Why are there two model-level datasets and not one?

It definitely is possible for all model level data to be represented in one grid, and thus one dataset. However, we
opted to preserve the native representation for variables in ECMWF's models. A handful of core model variables (wind,
temperature and surface pressure) are represented
as [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)
, while everything else is stored on a Gaussian grid. This avoids introducing numerical error by interpolating these
variables to physical space. For a more in depth review of this topic, please consult these references:

* [_Fundamentals of Numerical Weather Prediction_](https://doi.org/10.1017/CBO9780511734458) by Jean Coiffier
* [_Atmospheric modeling, data assimilation, and predictability_](https://doi.org/10.1017/CBO9780511802270) by Eugenia
  Kalnay

Please note: in a future releases, we intend to create a dataset version where all model levels are in one grid and
Zarr.

### Why doesn’t this project make use of Pangeo Forge Cloud?

We are big fans of the [Pangeo Forge project](https://pangeo-forge.readthedocs.io/), and of [Pangeo](https://pangeo.io/)
in general. While this project does make use of [their Recipes](https://github.com/pangeo-forge/pangeo-forge-recipes),
we have a few reasons to not use their cloud. First, we would prefer to use internal rather than community resources for
computations of this scale. In addition, there are several technical reasons why Pangeo Forge as it is today would not
be able to handle this case ([0](https://github.com/pangeo-forge/staged-recipes/issues/92#issuecomment-959481038),
[1](https://github.com/pangeo-forge/pangeo-forge-recipes/issues/244),
[2](https://github.com/pangeo-forge/pangeo-forge-recipes/pull/245#issuecomment-997070261),
[3](https://github.com/pangeo-forge/pangeo-forge-recipes/issues/256)). To work around this, we opted to combine
familiar-to-us infrastructure with Pangeo-Forge's core and to use
the [right tool](https://github.com/google/weather-tools/) for the right job.

### Why use this dataset? What uses are there for the data?

ERA5 can be used in many applications. It can be used to train ML models that predict the impact of weather on different
phenomena. ERA5 data could also be used to train and evaluate ML models that forecast the weather. The data could be
used to compute climatologies, or the average weather for a region over a given period of time. ERA5 data can be used to
visualize and study historical weather events, such as Hurricane Sandy.

### Where should I be cautious? What are the limitations of the dataset?

|                                                                         |                                                                               |
|:-----------------------------------------------------------------------:|:-----------------------------------------------------------------------------:|
| ![Mumbai, India](docs/assets/table0-fig0-mumbai.png) <br> Mumbai, India | ![San Francisco, USA](docs/assets/table0-fig1-sf.png) <br> San Francisco, USA |
|  ![Tokyo, Japan](docs/assets/table0-fig2-tokyo.png) <br> Tokyo, Japan   |      ![Singapore](docs/assets/table0-fig3-singapore.png) <br> Singapore       |

|                                                                                |                                                                                               |
|:------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------:|
| ![ERA5 Topography](docs/assets/table1-fig0-topo-era5.png) <br> ERA5 Topography | ![GMTED2010 Topography](docs/assets/table1-fig1-topo-GMTED2010.png) <br> GMTED2010 Topography |

It is important to remember that a reanalysis is an estimate of what the weather was, it is not guaranteed to be an
error-free estimate. There are several areas where the novice reanalysis user should be careful.

First, the user should be careful using reanalysis data at locations near coastlines. The first figure shows the
fraction of land (1 for land, 0 for ocean) of ERA5 grid points at different coastal locations. This is important because
the land-surface model used in ERA5 tries to blend in the influence of water with the influence of land based on this
fraction. The most visible effect of this blending is that as the fraction of land decreases, the daily variation in
temperature will also decrease. Looking at the first figure, there are sharp changes in the fraction of land between
neighboring grid cells so there could be differences in daily temperature range that might not be reflected in actual
weather observations.

The user should also be careful when using reanalysis data in areas with large variations in topography. The second
figure is a plot of ERA5 topography around Mount Everest compared with GMTED2010 topography. The ERA5 topography is
completely missing the high peaks of the Everest region and missing most of the structure of the mountain valleys.
Topography strongly influences temperature and precipitation rate, so it is possible that ERA5’s temperature is too warm
and ERA5’s precipitation patterns could be wrong as well.

ERA5’s precipitation variables aren’t directly constrained by any observations, so we strongly encourage the user to
check ERA5 against observed precipitation (for example, [Wu et al., 2022](https://doi.org/10.1175/JHM-D-21-0195.1)). A
study comparing reanalyses (not including ERA5) against gridded precipitation observations showed striking differences
between reanalyses and observation [Lisa V Alexander et al 2020 Environ. Res. Lett. 15 055002](
https://iopscience.iop.org/article/10.1088/1748-9326/ab79e2).

### Can I use the data for {research,commercial} purposes?

Yes, you can use our ERA5 data according to the terms of
the [Copernicus license](https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf).

Researchers, see the [next section](#how-to-cite-this-work) for how to cite this work.

Commercial users, please be sure to provide acknowledgement to the Copernicus Climate Change Service according to
the [Copernicus Licence](https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf)
terms.

## How to cite this work

Please cite our presentation at the 22nd Conference on Artificial Intelligence for Environmental Science describing ARCO-ERA5.

Carver, Robert W, and Merose, Alex. (2023): ARCO-ERA5: An Analysis-Ready Cloud-Optimized Reanalysis Dataset. 22nd Conf. on AI for Env. Science, Denver, CO, Amer. Meteo. Soc, 4A.1, https://ams.confex.com/ams/103ANNUAL/meetingapp.cgi/Paper/415842

In addition, please cite the ERA5 dataset accordingly:

Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R.J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., Thépaut, J-N. (2017): Complete ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service (C3S) Data Store (CDS). (Accessed on DD-MM-YYYY)

Hersbach et al, (2017) was downloaded from the Copernicus Climate Change Service (C3S) Climate Data Store. We thank C3S for allowing us to redistribute the data.

The results contain modified Copernicus Climate Change Service information 2022. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.

## License

This is not an official Google product.

Copyright 2022 Google LLC

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

https://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.