metar
Datasets
All datasets matching “metar”global_sat_metar_v2
Global GMGSI + METAR Patches (v2)
Dense global satellite imagery (GMGSI, 4 channels, 0.1° / ~9 km, hourly) paired
with sparse global METAR station observations rasterized onto the same
3600×1800 grid, sliced into 128×128 spatial patches with a 7-frame
hourly temporal context. Designed as a self-supervised / supervised
pre-training corpus for weather foundation models that need to jointly
see geostationary satellite fields and ground-truth in-situ observations.
This dataset is… See the full description on the dataset page: https://huggingface.co/datasets/meteolibre-dev/global_sat_metar_v2.global_sat_metar_v3
global_sat_metar_v3
Corrected republish of meteolibre-dev/global_sat_metar_v2.
Same shards, schema, patch grid and satellite/radar/elevation bytes; only
the rasterized METAR/SYNOP channel was fixed.
What was fixed
The v2 rasterizer used a closed 1-hour window [t-1h, t], so every
observation stamped at exactly HH:00:00 (all SYNOP/FM-12 reports, and
any station with reporting gaps > 1h) was written into two consecutive
hourly frames. ~20-40% of consecutive obs pairs… See the full description on the dataset page: https://huggingface.co/datasets/meteolibre-dev/global_sat_metar_v3.asos-metar-archive
ASOS METAR daily archive
Daily Parquet snapshots of raw METARs from ~920 NWS/FAA/DOD AOMC ASOS
stations, harvested from the live O.W.L. REST API at
consgicody/asos-tools.
File layout: YYYY/MM/DD.parquet — one file per UTC day.
Quick start (Python)
import pandas as pd
df = pd.read_parquet(
"hf://datasets/consgicody/asos-metar-archive/2026/09/09.parquet"
)
print(df[df["station"] == "JFK"].head())
Or query directly with DuckDB:
import duckdb
duckdb.sql("""… See the full description on the dataset page: https://huggingface.co/datasets/consgicody/asos-metar-archive.MetaR-Metaphorical-Riddle
METAR: Metaphorical Riddle Dataset for Meta-Reasoning
This dataset is the official training data for the paper: "Metaphor Reasoning is Meta-reasoning" (ACL 2026).
Abstract
Metaphor reasoning is an essential cognitive ability that maps knowledge from familiar domains to more abstract domains. This study proposes METAR, an automated system for synthesizing metaphorical riddles that are diverse, balanced, reasoning-oriented, challenging, and verifiable.
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/Abbey4799/MetaR-Metaphorical-Riddle.Meta-rater-PRRC-Rater-dataset
PRRC Rater Training and Evaluation Dataset
Dataset Description
This dataset contains the full training and evaluation data for the PRRC rater models described in Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models. It is designed for training and benchmarking models that score text along four key quality dimensions: Professionalism, Readability, Reasoning, and Cleanliness.
Source: Subset of SlimPajama-627B, annotated for PRRC dimensions… See the full description on the dataset page: https://huggingface.co/datasets/opendatalab/Meta-rater-PRRC-Rater-dataset.OCR-MetaReasoning
OCR-MetaReasoning Benchmark: Evaluating the Meta-Reasoning Ability of MLLMs in Text-Rich Image Understanding
Gengxu Li1, Yuan Wu1*, Yi Chang1,2,3
1 School of Artificial Intelligence, Jilin University 2 Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, MOE, China
3 International Center of Future Science, Jilin University
Overview ·
Task ·
Dataset ·
Evaluation ·
Citation
OCR-MetaReasoning is a controlled benchmark for testing… See the full description on the dataset page: https://huggingface.co/datasets/GengxuLi123/OCR-MetaReasoning.