The IEM maintains an ever growing archive of automated airport weather observations from around the world! These observations are typically called 'ASOS' or sometimes 'AWOS' sensors. A more generic term may be METAR data, which is a term that describes the format the data is transmitted as. If you don't get data for a request, please feel free to contact us for help. The IEM also has a one minute interval dataset for US ASOS (2000-) and Iowa AWOS (1995-2011) sites. This archive simply provides the as-is collection of historical observations, very little quality control is done. More details on this dataset are here.
Data Sources: The data made available on this page is sourced from a number of places including: Unidata IDD, NCEI ISD, and MADIS One Minute ASOS.
Tools/Libaries
Python Script Examples
fetch by network
selectively fetch
R Script Examples
A community user has contributed R language version of the python script.
There is also a riem R package
allowing for easy access to this archive.
This archive contains processed observations up until
2025-11-13T21:11:25Z. Data
is synced from the real-time ingest every 10 minutes.
Backend documentation exists for those that wish to script against this service.
Download Variable Description
ASOS User's Guide
has detailed information about these data variables. The value "M" represents
either value that was reported as missing or a value that was set to missing
after meeting some general quality control check, or a value that was never
reported by the sensor. The METAR format makes it difficult to determine
which of the three cases may have happened.
- station:
- three or four character site identifier
- valid:
- timestamp of the observation
- tmpf:
- Air Temperature in Fahrenheit, typically @ 2 meters
- dwpf:
- Dew Point Temperature in Fahrenheit, typically @ 2 meters
- relh:
- Relative Humidity in %
- drct:
- Wind Direction in degrees from *true* north
- sknt:
- Wind Speed in knots
- p01i:
- One hour precipitation for the period from the observation time to the time of the previous hourly precipitation reset. This varies slightly by site. Values are in inches. This value may or may not contain frozen precipitation melted by some device on the sensor or estimated by some other means. Unfortunately, we do not know of an authoritative database denoting which station has which sensor.
- alti:
- Pressure altimeter in inches
- mslp:
- Sea Level Pressure in millibar
- vsby:
- Visibility in miles
- gust:
- Wind Gust in knots
- skyc1:
- Sky Level 1 Coverage
- skyc2:
- Sky Level 2 Coverage
- skyc3:
- Sky Level 3 Coverage
- skyc4:
- Sky Level 4 Coverage
- skyl1:
- Sky Level 1 Altitude in feet
- skyl2:
- Sky Level 2 Altitude in feet
- skyl3:
- Sky Level 3 Altitude in feet
- skyl4:
- Sky Level 4 Altitude in feet
- wxcodes:
- Present Weather Codes (space seperated)
- feel:
- Apparent Temperature (Wind Chill or Heat Index) in Fahrenheit
- ice_accretion_1hr:
- Ice Accretion over 1 Hour (inches)
- ice_accretion_3hr:
- Ice Accretion over 3 Hours (inches)
- ice_accretion_6hr:
- Ice Accretion over 6 Hours (inches)
- peak_wind_gust:
- Peak Wind Gust (from PK WND METAR remark) (knots)
- peak_wind_drct:
- Peak Wind Gust Direction (from PK WND METAR remark) (deg)
- peak_wind_time:
- Peak Wind Gust Time (from PK WND METAR remark)
- metar:
- unprocessed reported observation in METAR format
Publications Citing IEM Data (View All)
These are the most recent 10 publications that have cited the usage of data from this page. This list is not exhaustive, so please let us know if you have a publication that should be added.
- Subedi, A., P. Singleton, et al. 2025, Right-Turn Safety for Pedestrians: Insights from Multilevel Models of Conflicts in Utah. Transportation Research Record: Journal of the Transportation Research Board, 0(0) https://doi.org/10.1177/03611981251378144
- Castillo, M. 2025, Geographic Transferability of Machine Learning Models for Short-Term Airport Fog Forecasting. arxiv. https://arxiv.org/pdf/2510.21819
- Adhikari, A. C. Wertz, et al. 2025, Quantifying Power Systems Resilience Using Statistical Analysis and Bayesian Learning. arXiv:2511.03043 [eess.SY] https://doi.org/10.48550/arXiv.2511.03043
- Lakshman, K., Y. Nekkali, et al. 2025, Evaluating the Impact of the Planetary Boundary Layer on Dynamics of Urban Thunderstorms Over the Eastern Indian Region. Royal Met Soc. Met Applications. https://doi.org/10.1002/met.70123
- Niu, H., S. Murray, et al. 2025, Tail-Aware Forecasting of Precipitation Extremes Using STL-GEV and LSTM Neural Networks. Hydrology 2025, 12(11), 284 https://doi.org/10.3390/hydrology12110284
- Roy, A., D. Heinemann, et al. 2025, Data-driven Combination of METAR Observations and CAMS Reanalysis Aerosols to Enhance Satellite Retrieval of Surface Solar Irradiance. https://doi.org/10.21203/rs.3.rs-7820256/v1
- Liu, J., S. Wang, et al. 2025, Do Aging Aircraft Run Late? Evidence from the United States Domestic Flights. Journal of the Air Transport Research Society https://doi.org/10.1016/j.jatrs.2025.100090
- Fazel-Rastgar, F. and S. Mthembu. 2025, Unprecedented abnormal cold weather with snowfall in eastern Southern Africa associated with a disturbed stratospheric south polar vortex: 21 September 2024 Storm. Journal of Atmospheric and Solar-Terrestrial Physics https://doi.org/10.1016/j.jastp.2025.106670
- Bromwich, D., S. Shuvo, et al. 2025, Simulating the 12 February 2020 North Dakota Blizzard With the PIEKTUK‐D Blowing Snow Algorithm Coupled With the Polar Weather Research and Forecasting Model. JGR Atmospheres https://doi.org/10.1029/2025JD043735
- Rahman, S. and H. Reza. 2025, Hybrid Deep Learning Approaches for Accurate Electricity Price Forecasting: A Day-Ahead US Energy Market Analysis with Renewable Energy. Mach. Learn. Knowl. Extr. 2025, 7(4), 120 https://doi.org/10.3390/make7040120