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
2024-12-25T06:51:30Z
. Data
is synced from the real-time ingest every 10 minutes.
Backend documentation exists for those that wish to script against this service.
1) Select Station/Network by clicking on location:
If you select no stations, you can download up to a 24 hour period of all data available from this archive!
2) Select From Available Data:
Note: Precipitation data is unavailable for non-US sites. The Heat Index/Wind Chill value retroactively use current NWS equations.
3) Select Date Range:
Note: These dates define timestamps starting at midnight of the selected timezone. The start date is inclusive and the end date is exclusive.
Start Date: | |
---|---|
End Date: |
4) Timezone of Observation Times:
The following options are available for how the observation time is presented.
5) Download Options:
Data Format:
Include Latitude + Longitude?
Include Elevation (meters)?
How to represent missing data?
How to represent Trace reports?
6) Limit Report Types
See news item on recent changes made for report types. When in doubt, pick both routine and specials.
7) Finally, get Data:
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
These are 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. Hopefully by early 2025, this listing can be more complete.
- Cigrand, C. 2024, Hydrologic and Hydraulic Analyses of Silver Creek and Selected Tributaries Associated with Scott Air Force Base, Illinois, 2022–24. Scientific Investigations Report 2024-5117 https://doi.org/10.3133/sir20245117
- Mubashir, A. 2024, Sensitive Study of Cosmic Ray Flux Variation with Space Weather and Geomagnetic Disturbances. Dissertation, Georgia State University https://scholarworks.gsu.edu/phy_astr_diss/172
- Robinson, M., K. Schueth, K. Ardon-Dryer. 2024. Spatial, temporal, and meteorological impact of the 26 February 2023 dust storm: increase in particulate matter concentrations across New Mexico and West Texas. Atmos. Chem. Phys., 24, 13733–13750. https://doi.org/10.5194/acp-24-13733-2024
- Painuli, S., S. Bhowmick, et al. 2024, Mitigation of Residential EV Charging Effects on Power Distribution Networks with Optimal Allocation of DGs Using Coati Optimization. Arab J Sci Eng. https://doi.org/10.1007/s13369-024-09782-0
- Alves, D., F. Mendonca, et al. 2024, Deep Learning Enhanced Wind Speed and Direction Forecasting for Airport Regions. Weather and Forecasting https://doi.org/10.1175/WAF-D-24-0069.1
- Berg, E.. 2024, Variability in the urban heat island of Madison, Wisconsin across spatial and temporal scales. PhD Dissertation, U of Wisconsin-Madison. https://proquest.com/docview/3144877488
- Mouat, A.P., Spinei E., et al. 2024, Informing Near-Airport Satellite NO2 Retrievals Using Pandora Sky-Scanning Observations. ACS ES&T Air Article ASAP https://pubs.acs.org/doi/10.1021/acsestair.4c00158
- Osterhaus, D.M., B.M. Van Doren, et al, 2024. Evaluation of methods to estimate nocturnal bird migration activity: C comparison of radar and nocturnal flight call monitoring in the American West. Ornithological Applications, 127:duae000. https://academic.oup.com/condor/advance-article-pdf/doi/10.1093/ornithapp/duae062/60304578/duae062.pdf
- Deshpandem P., S. Tripathi, et al. 2024, Bayesian Neural Networks for Satellite Fog Detection: Quantifying Epistemic and Aleatoric Uncertainties. Remote sensing in earth systems sciences. https://link.springer.com/article/10.1007/s41976-024-00155-7
- Pane, Melanie M., and Robert E. Davis. 2024. The association between short-term temperature variability and mortality in Virginia. Plos one 19.9 https://doi.org/10.1371/journal.pone.0310545
- Weiksnar, K.D. M.L Garcia, et al. 2024, Use of National Centers Environmental Prediction (NCEP) Data to Support Severe Accident Consequence Analysis at Locations Without Onsite Meteorological Data. Sandia Report. https://www.osti.gov/servlets/purl/2462945
- Neyra, Jesus S., and Robert E. Davis. 2024. The association between climate and emergency department visits for renal and urinary disease in Charlottesville, Virginia. Environmental Research 240 https://doi.org/10.1016/j.envres.2023.117525
- Davis, Robert E., et al. 2023. Climate and human mortality in Virginia, 2005–2020. Science of The Total Environment 894 https://doi.org/10.1016/j.scitotenv.2023.164825
- Davis, Robert E., Elizabeth K. Driskill, and Wendy M. Novicoff. 2022. The association between weather and emergency department visitation for diabetes in Roanoke, Virginia. International Journal of Biometeorology 66.8 https://link.springer.com/article/10.1007/s00484-022-02303-4
- Dutrieux, S.C.M. Predicting Floght Delay Distributions. A Machine Learning-Based Approach at a Regional Airport. Master of Science, Delft University. https://repository.tudelft.nl/file/File_c4259c3f-c7fc-4912-b7f4-9aac4c2c775f