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aerostratospheric/uogw-weather-forecast
uogw-weather-forecast is a tabular regression model from aerostratospheric. Use it for the tabular regression task on the model card, and read the license before you ship it in a product. It is set up for sklearn. The card lists the license as cc-by-4.0.
Publisher: Aerostratospheric / Midwest Stratospheric Data Systems Library: scikit-learn (.joblib heads) · helper: inference.py Site focus: Casey, Illinois + UOGW surface context Sibling data: aerostratospheric/uogw ·…
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Updated Oct 23, 2026
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From the Hugging Face model README
Publisher: Aerostratospheric / Midwest Stratospheric Data Systems
Library: scikit-learn (.joblib heads) · helper: inference.py
Site focus: Casey, Illinois + UOGW surface context
Sibling data: aerostratospheric/uogw · casey-openmeteo-hourly
Multi-horizon research forecast heads (temperature, precip occurrence, gust, MSLP, RH, surface regime).
| Is | Is not |
|---|---|
| Open tabular forecast screening for Casey / UOGW | Official NWS / NOAA forecast product |
| Trained on Open-Meteo hourly archive (~60 days) + lag/roll features | A live operational forecast desk |
| sklearn joblib artifacts + small loader | A foundation / transformers weather model |
Trained 2026-09-18T15:34:31Z. Hub re-touch 2026-09-23 — weights not re-trained that day. Intended retrain cadence: ~12:15 UTC after UOGW package sync.
| Head | Horizon | Task |
|---|---|---|
temp_1h / temp_3h / temp_6h / temp_24h | 1–24 h | Temperature regression (°C) |
precip_1h / precip_6h | 1 / 6 h | Precip occurrence classifier |
gust_1h | 1 h | Wind gust regression |
mslp_3h / rh_3h | 3 h | Pressure / RH regression |
surface_regime | — | 5-cluster surface regime |
Feature lists and holdout MAE / R² / accuracy: metrics.json.
import json
from pathlib import Path
import joblib
from huggingface_hub import snapshot_download
root = Path(snapshot_download("aerostratospheric/uogw-weather-forecast"))
# Option A — config-driven
cfg = json.loads((root / "config.json").read_text())
heads = {h: joblib.load(root / "models" / f"{h}.joblib") for h in cfg["heads"]}
# Option B — packaged helper (same directory layout)
# import importlib.util
# spec = importlib.util.spec_from_file_location("inf", root / "inference.py")
# inf = importlib.util.module_from_spec(spec); spec.loader.exec_module(inf)
# heads = inf.load_all()
# Predict with a row matching metrics.json features for that head
# yhat = heads["temp_1h"].predict([feature_row])
Requires scikit-learn, joblib, huggingface_hub.
metrics.json → source)aerostratospheric/uogw 2026-09-23T18:29:53Zgmc-800/latest.json: still 2026-09-16 (stale — not used as a live forecast input claim here)Details: FRESHNESS.md.
| Artifact | URL |
|---|---|
| UOGW scientific suite | https://huggingface.co/aerostratospheric/uogw-scientific-suite |
| Tornado trends | https://huggingface.co/aerostratospheric/uogw-tornado-trends |
| Open-models index | https://huggingface.co/aerostratospheric/msds-open-models |
| UOGW Git tip | https://github.com/Midwest-Stratospheric/Unified-Open-Global-Weather |
| Site | https://midwestsds.com |
| Intro | https://calendly.com/aerostratospheric/15min |
Aerostratospheric / Midwest Stratospheric Data Systems (2026).
UOGW Weather Forecast Suite. Hugging Face.
https://huggingface.co/aerostratospheric/uogw-weather-forecast
License: CC BY 4.0. Attribute Open-Meteo and agency sources in derived work.
This forecast packaging sits beside the live open desk:
Collaboration / data partnership welcome. No in-kind asks.
Disclosure: Aerostratospheric owned model card.