era5_in_gee VS arco-era5

Yeah - I work with Python for my main work dealing with weather & climate data but things still throw me off. It feels like every example I run into is an easy to follow "20-steps + 10 packages + 64GB cloud server instance" process when all I want to do is to download some historical temperature data to CSV.

Example: https://github.com/jwagemann/era5_in_gee#workflow-overview

Why?

Most weather and climate datasets - including ERA5 - are highly structured on regular latitude-longitude grids. Even if you were solely doing timeseries analyses for specific locations plucked from this grid, the strength of this sort of dataset is its intrinsic spatiotemporal structure and context, and it makes very little sense to completely destroy the dataset's structure unless you were solely and exclusively to extract point timeseries. And even then, you'd probably want to decimate the data pretty dramatically, since there is very little use case for, say, a point timeseries of surface temperature in the middle of the ocean!

The vast majority of research and operational applications of datasets like ERA5 are probably better suited by leveraging cloud-optimized replicas of the original dataset, such as ARCO-ERA5 published on the Google Public Datasets program [1]. These versions of the dataset preserve the original structure, and chunk it in ways that are amenable to massively parallel access via cloud storage. In almost any case I've encountered in my career, a generically chunked Zarr-based archive of a dataset like this will be more than performant enough for the majority of use cases that one might care about.

[1]: https://cloud.google.com/storage/docs/public-datasets/era5

You can get some of the historical data also from here: https://cloud.google.com/storage/docs/public-datasets/era5 (if the official API is too slow. )

To use the data in live fashion I think you would need to get license from ECMWF...

This article misses one of the coolest things about the Zarr format - that it's flexible enough that it's also becoming widely used in climate science.

In particular the Pangeo project (https://pangeo.io/architecture.html) uses large Zarr stores as a performant format in the cloud which we can analyse in parallel at scale using distributed computing frameworks like dask.

More and more climate science data is being made publicly available as Zarr in the cloud, often through open data partnerships with cloud providers (e.g. on AWS (https://aws.amazon.com/blogs/publicsector/decrease-geospatia...) ERA-5 on GCP(https://cloud.google.com/storage/docs/public-datasets/era5)).

I personally think that the more that common tooling can be shared between scientific disciplines the better.