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
2026-11-09T23:41:31Z. 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.
- Elaji, A. 2026, Spatiotemporal Variability in Aerosol Loading and Precipitation During Severe Dust Storm Events: A Multi-Event Assessment Across Saudi Arabia. Atmosphere 2026, 17(11) https://doi.org/10.3390/atmos17111090
- Suriano, Z., J. Durkee, et al. 2026, Spatial variations in wet-bulb globe temperature during the Bank of America Chicago Marathon. International Journal of Biometeorology. Volume 70, article number 292 https://doi.org/10.1007/s00484-026-03361-8
- Bawa, G. 2026, Domain Knowledge-informed Machine Learning Model to Study Weather-induced Flight Delays. ERAU Masters Thesis https://commons.erau.edu/edt/1026
- Armas, F. 2026, Where You Sit Matters More Than Where You Play: Systemic Thermal Exposure Inequality Across the 2026 FIFA World Cup. https://doi.org/10.21203/rs.3.rs-11152012/v1
- Gao, Y., Z. Dou, et al. 2026, Factors associated with discrepancies between scheduled and actual flight block times: evidence from U.S. domestic airline operations. Transportation Research Part A: Policy and Practice. Vol 213 https://doi.org/10.1016/j.tra.2026.105227
- Alanazi, O., R. Boukhanouf, et al. 2026, Experimental Investigation of Air Cooling and Humidification by a Wetted Porous-Clay-Duct Earth–Air Heat Exchanger in a Hot-Arid Greenhouse. Energies 2026, 19(21) https://doi.org/10.3390/en19215190
- Reid, K., B. White, et al. 2026, Australian Fog Climatology, Synoptic Patterns and Trends. Journal of Hydrometeorology. https://doi.org/10.1175/JHM-D-26-0099.1
- Lipari, S., R. Kirshnamurthy, et al. 2026, A multi-instrument framework for boundary layer wind and turbulence profiling. EGU Sphere https://doi.org/10.5194/egusphere-2026-5984
- Huang, T., K. Tang, et al. 2026, Stability-Aware Constraint Programming for Emergency Rescheduling of Airport Apron Ground-Support Operations. Mathematics 2026, 14, 3778 https://doi.org/10.3390/math14203778
- Alblooshi, S., K. Nicoll, et al. 2026, Investigating dust electrification in arid environments using sea breeze systems. Aeolian Research https://doi.org/10.1016/j.aeolia.2026.101092