Earth2studio Data Fetch
NVIDIAOfficialCreated by NVIDIA Earth-2 Team
- Skill
- Tool integration
- Data and AI
- Apache-2.0
- Medium risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The Earth2Studio Data Fetch skill assists users in retrieving weather and climate data by identifying appropriate data sources and generating functional Python scripts. It emphasizes verifying variable support through the Earth2Studio lexicon system and ensures compatibility with the user's requested timeframes and data types, whether for historical analysis or forecast predictions.
What it does
- Maps user-requested weather variables to Earth2Studio-compatible names.
- Identifies suitable data sources based on temporal and spatial requirements.
- Verifies variable availability using the official lexicon system.
- Generates Python scripts to fetch data as xarray DataArrays.
- Provides guidance on selecting between analysis and forecast data sources.
- Explains trade-offs between different data sources regarding resolution and coverage.
When to use it
- When you need to programmatically access weather or climate data for research or analysis.
- When you are unsure which Earth2Studio data source supports specific climate variables.
- When you need a template script to fetch historical reanalysis or operational forecast data.
- When you need to verify if a specific data source supports your required variables.
Advantages
- Ensures data retrieval accuracy by checking live lexicon definitions.
- Reduces manual effort in writing boilerplate code for data source APIs.
- Provides clear guidance on the differences between analysis and forecast data patterns.
- Helps navigate complex data source requirements like API keys or regional limitations.
Usage examples
- “I need 500 hPa geopotential height and 2m temperature from ERA5 for January 1, 2020 at 00Z.”
- The agent maps the request to z500 and t2m, checks the ARCO/CDS lexicons, recommends a source, and provides a Python script to fetch the data.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/earth2studio-data-fetch/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip earth2studio-data-fetch.zip -d .agents/skills
Documentation by NVIDIA Earth-2 Team
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 earth2studioor 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 livebase.pyE2STUDIO_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
VOCABdictIf 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:
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:
Map plain language →
z500,t2mCheck 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/DataSetFiledirectlyOne 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 |
Files
- ATTRIBUTION.mdAdded by AutoGineer940 B
- BENCHMARK.md3.6 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md8.5 KiB
- evals/evals.json2.8 KiB
- evals/targets/eval_1_target.pyScript1.2 KiB
- evals/targets/eval_4_target.pyScript1.3 KiB
- evals/targets/eval_7_target.pyScript1.3 KiB
- skill-card.md3.9 KiB
- skill.oms.sig5.2 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs an API key, Needs Python
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Attribution and licence
- Created by
- NVIDIA Earth-2 Team
- Publisher
- NVIDIA (Official)
- Source
- NVIDIA/skills · skills/earth2studio-data-fetch · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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