WIND-Bench: A Benchmark Dataset for In-Situ Near-Surface Wind Speed Across the Conterminous United States
Accurate wind forecasts are essential for operational decision-making and public safety, yet forecasts tend to miss near-surface high wind speeds in complex terrain. In response, recent advances in machine learning (ML) weather prediction methods have demonstrated the ability to improve forecast skill beyond traditional numerical weather prediction (NWP) models. However, the absence of a benchmark dataset to evaluate NWP and ML models with sufficient, quality-controlled wind speed observations in complex terrain poses challenges to the development and intercomparison of high-quality surface wind forecasts across the Coterminous United States (CONUS).
We develop Wind IN-situ Data Benchmark (WIND-Bench), a novel benchmark dataset from in-situ observations in the Meteorological Assimilation Data Ingest System (MADIS) observational network. WIND-Bench integrates multiple sensor networks with quality control that distinguishes sensor failures from high-wind conditions, using a framework that validates observations against forecasts from the National Oceanic and Atmospheric Administration (NOAA) High-Resolution Rapid Refresh (HRRR) model. WIND-Bench provides a standardized benchmark for evaluating ML and NWP models and for quantifying forecast skill, accelerating the development, evaluation, and operational deployment of skilled near-surface wind forecasts.
Note that this data is accompanied by a manuscript with comprehensive documentation that is being submitted to a journal in August 2026. The manuscript will be linked here when available.
Resources
5 resources available
-
wind_bench_conus_v1.0.0_2021.nc
NC -
wind_bench_conus_v1.0.0_2022.nc
NC -
wind_bench_conus_v1.0.0_2023.nc
NC -
wind_bench_conus_v1.0.0_2024.nc
NC -
wind_bench_conus_v1.0.0_2025.nc
NC
Find Related Datasets
Search by Tags
Click any tag below to search for similar datasets
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[ "019:20" ] |
| contactPoint |
{ "fn": "Grant Buster", "@type": "vcard:Contact", "hasEmail": "mailto:grant.buster@nlr.gov" } |
| dataQuality |
true
|
| description | Accurate wind forecasts are essential for operational decision-making and public safety, yet forecasts tend to miss near-surface high wind speeds in complex terrain. In response, recent advances in machine learning (ML) weather prediction methods have demonstrated the ability to improve forecast skill beyond traditional numerical weather prediction (NWP) models. However, the absence of a benchmark dataset to evaluate NWP and ML models with sufficient, quality-controlled wind speed observations in complex terrain poses challenges to the development and intercomparison of high-quality surface wind forecasts across the Coterminous United States (CONUS). We develop Wind IN-situ Data Benchmark (WIND-Bench), a novel benchmark dataset from in-situ observations in the Meteorological Assimilation Data Ingest System (MADIS) observational network. WIND-Bench integrates multiple sensor networks with quality control that distinguishes sensor failures from high-wind conditions, using a framework that validates observations against forecasts from the National Oceanic and Atmospheric Administration (NOAA) High-Resolution Rapid Refresh (HRRR) model. WIND-Bench provides a standardized benchmark for evaluating ML and NWP models and for quantifying forecast skill, accelerating the development, evaluation, and operational deployment of skilled near-surface wind forecasts. Note that this data is accompanied by a manuscript with comprehensive documentation that is being submitted to a journal in August 2026. The manuscript will be linked here when available. |
| distribution |
[ { "@type": "dcat:Distribution", "title": "wind_bench_conus_v1.0.0_2021.nc", "format": "nc", "mediaType": "application/octet-stream", "description": "Near surface weather observations organized and cleaned from the MADIS archive for the year 2021", "downloadURL": "https://data.openei.org/files/8729/wind_bench_conus_v1.0.0_2021.nc" }, { "@type": "dcat:Distribution", "title": "wind_bench_conus_v1.0.0_2022.nc", "format": "nc", "mediaType": "application/octet-stream", "description": "Near surface weather observations organized and cleaned from the MADIS archive for the year 2022", "downloadURL": "https://data.openei.org/files/8729/wind_bench_conus_v1.0.0_2022.nc" }, { "@type": "dcat:Distribution", "title": "wind_bench_conus_v1.0.0_2023.nc", "format": "nc", "mediaType": "application/octet-stream", "description": "Near surface weather observations organized and cleaned from the MADIS archive for the year 2023", "downloadURL": "https://data.openei.org/files/8729/wind_bench_conus_v1.0.0_2023.nc" }, { "@type": "dcat:Distribution", "title": "wind_bench_conus_v1.0.0_2024.nc", "format": "nc", "mediaType": "application/octet-stream", "description": "Near surface weather observations organized and cleaned from the MADIS archive for the year 2024", "downloadURL": "https://data.openei.org/files/8729/wind_bench_conus_v1.0.0_2024.nc" }, { "@type": "dcat:Distribution", "title": "wind_bench_conus_v1.0.0_2025.nc", "format": "nc", "mediaType": "application/octet-stream", "description": "Near surface weather observations organized and cleaned from the MADIS archive for the year 2025", "downloadURL": "https://data.openei.org/files/8729/wind_bench_conus_v1.0.0_2025.nc" } ] |
| identifier | https://data.openei.org/submissions/8729 |
| issued | 2026-07-16T06:00:00Z |
| keyword |
[ "CONUS", "HRRR", "MADIS", "ML", "NOAA", "NWP", "atmosphere", "benchmark", "data", "fire", "humidity", "machine learning", "near-surface", "observations", "processed data", "resilience", "surface observations", "temperature", "wildfire", "wind", "wind gust", "wind speed" ] |
| landingPage | https://data.openei.org/submissions/8729 |
| license | https://creativecommons.org/licenses/by/4.0/ |
| modified | 2026-08-26T22:51:35Z |
| programCode |
[ "019:000", "019:010", "019:023" ] |
| publisher |
{ "name": "National Laboratory of the Rockies (NLR)", "@type": "org:Organization" } |
| spatial |
"{"type":"Polygon","coordinates":[[[-124.637,24.55419],[-67.00917,24.55419],[-67.00917,49.32],[-124.637,49.32],[-124.637,24.55419]]]}"
|
| title | WIND-Bench: A Benchmark Dataset for In-Situ Near-Surface Wind Speed Across the Conterminous United States |
