Earth2Studio Data Fetch Skill

Purpose

Guide a user through downloading weather/climate data via Earth2Studio data source APIs. Identifies compatible sources by checking the lexicon, verifies variable support, and produces a working fetch script outputting an xarray DataArray.

Prerequisites

Earth2Studio installed (
```
uv pip install earth2studio
```
or equivalent)
Network access to remote data stores (GCS, S3, CDS API, etc.)
For CDS-based sources: valid CDS API key configured (
```
~/.cdsapirc
```
)
Python 3.10+

Instructions

You are helping a user download specific weather/climate data using Earth2Studio's data source APIs. Your job is to identify which data source(s) can provide the requested variables, verify compatibility via the lexicon system, and produce a working fetch script.

Core principle: live docs and lexicon are the source of truth

Data source APIs, available variables, and the lexicon evolve between releases. Before recommending a data source or writing a fetch script:

Fetch the relevant data source doc page to confirm the API signature and constructor arguments.
Check the lexicon to verify the requested variable is supported by that data source.

Live doc references (fetch only what the user's request requires):

Analysis data sources: https://nvidia.github.io/earth2studio/modules/datasources_analysis.html
Forecast data sources: https://nvidia.github.io/earth2studio/modules/datasources_forecast.html
DataFrame data sources: https://nvidia.github.io/earth2studio/modules/datasources_dataframe.html
Lexicon base: https://github.com/NVIDIA/earth2studio/blob/main/earth2studio/lexicon/base.py
Lexicon per-source: https://github.com/NVIDIA/earth2studio/tree/main/earth2studio/lexicon

Interaction protocol

Step 1. Understand the user's request

Extract from what the user has said (ask follow-ups if needed, cap at 3 questions):

Variables — what do they want? Use Earth2Studio variable names (e.g.
```
t2m
```
,
```
u500
```
,
```
z850
```
,
```
tp
```
,
```
msl
```
). If the user uses plain language ("500 hPa geopotential height"), map it to the E2Studio name by checking the live
```
base.py
```
E2STUDIO_VOCAB.
Time — what date/time range? A single timestamp, a range, or multiple discrete times?
Data type — analysis/reanalysis (historical state) or forecast (lead-time based)?
Lead time (forecast only) — how far ahead? Which initialization time?
Region — global or regional (e.g. North America for HRRR)?
Output format — xarray DataArray (default), save to file (NetCDF/Zarr)?

Step 2. Identify candidate data sources

Based on the request type, narrow candidates:

Analysis/reanalysis (historical state at a specific time):

Use analysis data source page to identify options
Common choices: GFS (operational, recent), HRRR (NA, hourly), IFS/IFS_ENS (ECMWF), ARCO/CDS/WB2ERA5/NCAR_ERA5 (ERA5 reanalysis), GOES/MRMS/JPSS (observational)

Forecast (predictions from an initialization time with lead times):

Use forecast data source page to identify options
Common choices: GFS_FX, GEFS_FX, HRRR_FX, IFS_FX, IFS_ENS_FX, AIFS_FX, CFS_FX

Key differentiators to surface:

Temporal coverage — operational sources (GFS, HRRR) have limited history; reanalysis (ERA5 via ARCO/CDS/WB2) goes back decades
Spatial resolution — HRRR is 3km NA-only; GFS is 0.25° global; WB2ERA5_32x64 is 5.625° global
Update frequency — some are real-time, some have multi-day lag

Step 3. Verify variable support via lexicon

This is critical. Each data source has a lexicon file that defines which E2Studio variables it can provide.

To verify:

Fetch the source's lexicon file from

https://github.com/NVIDIA/earth2studio/blob/main/earth2studio/lexicon/<source>.py

(e.g.

gfs.py

hrrr.py

cds.py

arco.py

wb2.py

)

Check that the user's requested variable(s) appear as keys in the source's
```
VOCAB
```
dict
If a variable is NOT in a source's lexicon, that source cannot provide it — try another

The lexicon VOCAB maps Earth2Studio variable names → source-specific identifiers. If a variable key exists in the VOCAB, the source supports it.

Present the results clearly: "GFS supports
t2m
,
u500
,
z850
. HRRR also supports these but is limited to North America. ARCO (ERA5) supports all three and has data back to 1959."

Step 4. Confirm data source selection with user

Present the viable options with tradeoffs:

Source	Variables	Coverage	Resolution	Time Range
...	...	...	...	...

Let the user pick. If there's one obvious choice, recommend it and ask for confirmation.

Step 5. Generate fetch script

Write a Python script that uses the selected data source to fetch the requested data. The script structure depends on whether it's an analysis or forecast source.

Analysis source pattern:

python

import datetime
from earth2studio.data import <SourceClass>

# Initialize data source
ds = <SourceClass>()

# Fetch data
# Analysis sources use: ds(time, variable) -> xr.DataArray
time = [datetime.datetime(YYYY, M, D, H)]  # or array of times
variable = ["var1", "var2"]  # E2Studio variable names

data = ds(time, variable)

Forecast source pattern:

python

import datetime
from earth2studio.data import <SourceClass>

# Initialize data source
ds = <SourceClass>()

# Forecast sources use: ds(time, lead_time, variable) -> xr.DataArray
time = [datetime.datetime(YYYY, M, D, H)]  # initialization time
lead_time = [datetime.timedelta(hours=H)]   # or array of lead times
variable = ["var1", "var2"]

data = ds(time, lead_time, variable)

Always fetch the specific data source's API doc page to confirm the exact constructor arguments and call signature before writing the script — they can vary (some need auth tokens, cache paths, specific parameters).

Include in the script:

Appropriate imports
Clear comments explaining each step
How to inspect the result (
```
print(data)
```
,
```
data.shape
```
,
```
data.coords
```
)
Optional: saving to file if the user requested it

Step 6. Offer next steps

After delivering the script, mention:

How to change variables/times without rewriting the whole thing
If they might want to feed this into a model, point them to the discover skill
Cache behavior (data is cached locally after first fetch via
```
EARTH2STUDIO_CACHE
```
)

Ownership and out-of-scope

Owns: identifying data sources for a user's variable/time request, verifying variable support via lexicon, generating data fetch scripts, explaining analysis vs. forecast source differences.

Does not own: installation (earth2studio-install), model selection (earth2studio-discover), inference pipelines, custom data source creation (point to extend examples), data source authentication setup beyond what the docs describe.

Examples

Typical invocation:

"I need 500 hPa geopotential height and 2m temperature from ERA5 for January 1, 2020 at 00Z."

The skill would:

Map plain language →
```
z500
```
,
```
t2m
```
Check ARCO/CDS/WB2ERA5 lexicons for support
Recommend ARCO (free, no API key) or CDS (official, needs key)
Generate a fetch script using the selected source

Limitations

Network required — all data sources fetch from remote stores (GCS, S3, CDS API)
No local file loading — for local NetCDF/Zarr, use
```
DataArrayFile
```
/
```
DataSetFile
```
directly
One source type per script — cannot mix analysis and forecast sources in a single call
Variable availability varies — not all sources provide all variables; always verify via lexicon
Rate limits — CDS API has queue-based throttling; GCS/S3 sources are generally faster

Troubleshooting

Error	Cause	Solution
`KeyError: '<var>'`	Not in lexicon	Check lexicon; try another source
`FileNotFoundError` / 404	Time not available	Verify temporal coverage
`CDS API timeout`	Queue congestion	Retry or use ARCO for ERA5
`ModuleNotFoundError`	Not installed	`uv pip install earth2studio`
Empty DataArray	Time/var mismatch	Check datetime and variable name

earth2studio-data-fetch

NPX Install

Tags

SKILL.md Content