Earth2studio Data Fetch

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:

1. Fetch the relevant data source doc page to confirm the API signature and constructor arguments.
2. 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_ERA5/CDS_ERA5/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_ERA5/CDS_ERA5/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:

1. 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)
2. Check that the user's requested variable(s) appear as keys in the source's VOCAB dict
3. 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:

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:

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:

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:

1. Map plain language → z500, t2m
2. Check ARCO_ERA5/CDS_ERA5/WB2ERA5 lexicons for support
3. Recommend ARCO_ERA5 (free, no API key) or CDS_ERA5 (official, needs key)
4. 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