Automated Weather Data Network

The Automated Weather Data Network (AWDN) represents a longstanding partnership among states in and around the High Plains Region with the purpose of gathering observational data and providing this information to stakeholders in agriculture and related fields. The AWDN provides valuable observation data, serving clients around the world.

Partnerships

The AWDN is a cooperative effort, which is made possible by partnerships with state-run mesonet programs across the High Plains region and the US. The primary function of the AWDN is to aggregate and quality control the data from these discrete networks in order to create regional products that can then be used for various sectors, such as agriculture and water resources. Current and/or archived observations from over 350 stations are available across the states of Colorado, Iowa, Kansas, Nebraska, and Wyoming. You can learn more about each participating network by clicking on the state names below


The Colorado Agricultural Meteorological Network (CoAgMET) is managed by the Colorado Climate Center at Colorado State University. It was originally started in the early 1990s through a collaboration between the USDA’s Agricultural Research Service and Colorado State University Extension. Over time, the network has grown to over 90 stations located across the state. Along with access to raw data, CoAgMET provides daily and monthly statistics, maps, and summaries. A selection of tools include:
  • Soil Monitor
  • Crop Water Use (ET)
  • Growing Degree Day Plots
  • Web Services for Data Access
  • Wind Summaries
Visit CoAgMet
Visit Colorado Climate Center

The ISU Soil Moisture Network is managed by Iowa State University (ISU). The network began in the 1980s as the ISU Ag Climate Network and was transitioned to its current configuration of stations and equipment in 2013 and 2014. The network now consists of over 20 stations, located across the state. Numerous tools are available through the Iowa Environmental Mesonet (IEM) page, which features the ISU Soil Moisture Network data, including:
  • Growing Season Maps
  • Monthly Summaries
  • Real-time and Historical Data Maps
  • Time Series Plots
  • Web Services for Data Access
Visit ISU Soil Moisture Network
Visit IEM

The Kansas Mesonet is managed by the Kansas State Weather Data Library at Kansas State University. The network was started in the 1980s with stations at K-State Research & Extension facilities across the state. Over time, the network has grown to over 75 stations and now includes collaborations with the Kansas Water Office, Big Bend Groundwater Management District, and the USDA Soil Climate Analysis Network (SCAN). A number of tools are available from the Kansas Mesonet, including:
  • Animal Comfort Index
  • Freeze Monitor
  • Fire Danger Forecast
  • Soil Moisture
  • Temperature Inversions
Visit Kansas Mesonet
Visit Kansas Weather Data Library

The Nebraska Mesonet is managed by the Nebraska State Climate Office at the University of Nebraska-Lincoln. The network was established in the early 1980s by the High Plains Regional Climate Center, as one of the first automated weather networks in the country. Since taking over in 2016, the Nebraska State Climate Office has grown the network to include over 70 stations across the state. Tools available from the Nebraska Mesonet include:
  • Vegetated Soil Moisture
  • Cattle Comfort Index
  • Interactive Station Maps
  • Current Conditions
  • Crop/Water Management
Visit Nebraska Mesonet
Visit Nebraska State Climate Office

The Wyoming Agricultural Climate Network (WACNet) is managed by the Wyoming State Engineer’s Office. Starting in the late 2000s, WACNet is one of the younger mesonets in the region. The network currently consists of over 20 automated weather stations, and data from the network flow to the Water Resources Data System at the University of Wyoming for quality control and housing. Some tools that are available from the WACNet include:
  • Data Tables
  • Maps and Graphs
  • Soil Moisture
  • Soil Temperature
  • Winter Precipitation (via heated rain gauges)
Visit WACNet
Visit Water Resources Data System & State Climate Office
COMING SOON
COMING SOON
COMING SOON
COMING SOON

Mesonet Directory

AWDN retains data from past partnerships


The North Dakota Agricultural Weather Network (NDAWN) is managed by the NDAWN Center at North Dakota State University. The network was established in the late 1980s with a grant from the HPRCC. Since that time, NDAWN has grown to be one of the largest networks in the region, with over 90 stations across North Dakota, Minnesota, and Montana. NDAWN provides a wide range of tools, including:
  • Deep Soil Temperature Measurements
  • Numerous Agricultural Tools (Crop and Insect GDD, Crop Water Use, etc.)
  • Soil Moisture
  • Temperature Inversion Data
  • Wind Chill
Visit NDAWN

AWDN Station Locations


Instrumentation

This graphic shows an example mesonet station configuration. Please note that station configurations can vary from state to state, or even within the same mesonet, especially as stations are upgraded. For information about a specific station, please contact the appropriate state mesonet program (see the Partners tab for more information).

Tools

Access Tool

Explore AWDN data using Web Services

Products

AWDN Maps

Check out 1-day and 7-day maps from the AWDN. Popular maps include soil temperature and evapotranspiration.

Station Data Explorer

Create static and/or interactive graphs for thousands of stations across the country. Precipitation and snowfall accumulation graphs are available within this tool.

Spatial Climate Analysis

View a GIS version of the ACIS Maps on a web map. Click on any station point to graph station data, or use the Grid Point Inspector to analyze any location in the lower-48 states.

Climate Summaries

View the Precipitation and Temperature Summaries

Heat Index and Wind Chill Chart

View chart generated from the Wind Chill or Heat Index products for Daily or Hourly AWDNdata.

Wind Roses

Obtain wind rose plots and hourly occurrence values from select AWDN stations.

Cattle Comfort Index

A graphic tool for the Cattle Comfort Index with Forecasts.

Access Data Through Web Services

AWDN data can be access through Web Services. This service is free and offers access to calculated products. You can use the Access Tool to help build a Web Service call.

Quick Start

To get started getting AWDN data you can simply enter the following in your browser's address bar:
https://awdn2.unl.edu/productdata/get?name=Holdrege 5N&productid=penet&days=7&&end=20250723&format=csv

This will get Penman-Monteith Evapotranspiration data (productid=penet) for Holdrege, NE (name=Holdrege 5N) for a week (days=7 prior to July 23, 2025 (end=20250723). The result will be in comma-separated values (format=csv).

The above call returns the daily reference ET in millimeters (ReferenceETmm) and inches (ReferenceETin). Additionally, the forecasted reference ET is returned based on National Weather Service's National Digital Forecast Database (NDFD).

Most product calls use there parameters but there are a few that require different parameters. If your interested in other products please see this table for options.

If you want to try and build other data calls please see the Access Tool. There you can build data calls and see their result.


Using Web Services

To use web services you have to build an URL with the parameters you need. The base URL is:
https://awdn2.unl.edu/productdata/get?
Several calculated products are available using state mesonet data:

What the temperature feels like to the human body when relative humidity is combined with the air temperature. Available in both hourly and daily values.

Parameter Name Description Options
name Desired station name to retrieve data
productid product to request productid=hi
end timestamp to stop data retrieval Takes the format YYYYmmddHH where: Y = year, m = month, d = day, H = hour. The hour can be omitted to get the full day
format Output format csv (comma delimited), json (Javascript Object Notation), geojson (Geographical Javascript Object Notation), PDF
tz Time zone offset to return data records The options are any US timezone abbreviation or UTC
days The number of days prior to the end date
units Units to return that data us (United States System), si (International System)

What the air temperature feels like to the human skin due to the combination of cold temperatures and winds blowing on exposed skin. Available in both hourly and daily values.

Parameter Name Description Options
name Desired station name to retrieve data
productid product to request productid=wc
end timestamp to stop data retrieval Takes the format YYYYmmddHH where: Y = year, m = month, d = day, H = hour. The hour can be omitted to get the full day
format Output format csv (comma delimited), json (Javascript Object Notation), geojson (Geographical Javascript Object Notation), PDF
tz Time zone offset to return data records The options are any US timezone abbreviation or UTC
days The number of days prior to the end date
units Units to return that data us (United States System), si (International System)

A summary that used a range of days to compare the high, low, and average air temperature to the NCEI Normals. The values reported include the high/low and average air temperature over the range as well as the departure from the average observed and the average Normals temperature.

Parameter Name Description Options
name Desired station name to retrieve data
productid product to request productid=tsummary
end timestamp to stop data retrieval Takes the format YYYYmmddHH where: Y = year, m = month, d = day, H = hour. The hour can be omitted to get the full day
format Output format csv (comma delimited), json (Javascript Object Notation), geojson (Geographical Javascript Object Notation), PDF
tz Time zone offset to return data records The options are any US timezone abbreviation or UTC
days The number of days prior to the end date
units Units to return that data us (United States System), si (International System)

A summary over a range of days showing the total rainfall as well as the percent of Normals.

Parameter Name Description Options
name Desired station name to retrieve data
productid product to request productid=psummary
end timestamp to stop data retrieval Takes the format YYYYmmddHH where: Y = year, m = month, d = day, H = hour. The hour can be omitted to get the full day
format Output format csv (comma delimited), json (Javascript Object Notation), geojson (Geographical Javascript Object Notation), PDF
tz Time zone offset to return data records The options are any US timezone abbreviation or UTC
days The number of days prior to the end date
units Units to return that data us (United States System), si (International System)

Calculations over a range of days including Cooling Degree Days, Heating Degree Days, and Growing Degree Days with 40 and 50 degree bounds. Departure from Normals is also reported.

Parameter Name Description Options
name Desired station name to retrieve data
productid product to request productid=psummary
end timestamp to stop data retrieval Takes the format YYYYmmddHH where: Y = year, m = month, d = day, H = hour. The hour can be omitted to get the full day
format Output format csv (comma delimited), json (Javascript Object Notation), geojson (Geographical Javascript Object Notation), PDF
tz Time zone offset to return data records The options are any US timezone abbreviation or UTC
days The number of days prior to the end date
units Units to return that data us (United States System), si (International System)

Shows the frequency of wind direction and speed over a range of time.

Parameter Name Description Options
name Desired station name to retrieve data
productid product to request productid=wrose
end timestamp to stop data retrieval Takes the format YYYYmmddHH where: Y = year, m = month, d = day, H = hour. The hour can be omitted to get the full day
format Output format csv (comma delimited), json (Javascript Object Notation), geojson (Geographical Javascript Object Notation), PDF
tz Time zone offset to return data records The options are any US timezone abbreviation or UTC
days The number of days prior to the end date
units Units to return that data us (United States System), si (International System)

Evapotranspiration calculated using the Penman-Monteith method. Available in both hourly and daily values.

Parameter Name Description Options
name Desired station name to retrieve data
productid product to request productid=penet
end timestamp to stop data retrieval Takes the format YYYYmmddHH where: Y = year, m = month, d = day, H = hour. The hour can be omitted to get the full day
format Output format csv (comma delimited), json (Javascript Object Notation), geojson (Geographical Javascript Object Notation), PDF
tz Time zone offset to return data records The options are any US timezone abbreviation or UTC
days The number of days prior to the end date
refcrop Change the reference crop used in calculation grass or alfalfa
units Units to return that data us (United States System), si (International System)

An index used to assess the stress level of livestock using several environmental factors. Available only as hourly values.

Parameter Name Description Options
name Desired station name to retrieve data
productid product to request productid=cci
end timestamp to stop data retrieval Takes the format YYYYmmddHH where: Y = year, m = month, d = day, H = hour. The hour can be omitted to get the full day
format Output format csv (comma delimited), json (Javascript Object Notation), geojson (Geographical Javascript Object Notation), PDF
tz Time zone offset to return data records The options are any US timezone abbreviation or UTC
days The number of days prior to the end date
units Units to return that data us (United States System), si (International System)

A report containing High, Low and Average Temperature, Average Relative Humidity, Average Dew Point, Average Soil Temperature at 10 cm (4 in), Total Solar Radiation, Total Precipitation, Reference Evapotraspiration, and Growing Degree Days with base 40 and 50.

Parameter Name Description Options
name Desired station name to retrieve data
productid product to request productid=agreport
end timestamp to stop data retrieval Takes the format YYYYmmddHH where: Y = year, m = month, d = day, H = hour. The hour can be omitted to get the full day
format Output format csv (comma delimited), json (Javascript Object Notation), geojson (Geographical Javascript Object Notation), PDF
tz Time zone offset to return data records The options are any US timezone abbreviation or UTC
days The number of days prior to the end date
units Units to return that data us (United States System), si (International System)

A report containing 7 day Total Precipitation, Percent of Normal Precipitation, and Accumulated Growing Degree Days. For each passed crop 3 days Previous and Forecast Potential Evapotranspiration, Previous Stage, Current Stage, and Next Stage of Growth. Multiple crops can be passed in the parameters by separating the crop and emergence dates with commas.

Parameter Name Description Options
name Desired station name to retrieve data
productid product to request productid=psummary
emerge Emergence date for crop Takes the format YYYYmmdd where: Y = year, m = month, d = day
Multiple emergence dates can be used with multiple crops by separating them with commas: crop=corp,soybeans&emerge=20250301,20250401
format Output format csv (comma delimited), json (Javascript Object Notation), geojson (Geographical Javascript Object Notation), PDF
tz Time zone offset to return data records The options are any US timezone abbreviation or UTC
crop crop name of interest Available crops are:
  • Sorghum
  • Sunflower
  • Wheat
  • Alfalfa
  • Sugar Beet
  • Corn
  • Soybeans
  • Grass
  • Potato
  • Bean
units Units to return that data us (United States System), si (International System)

To request data from web services you need to add parameters to the base URL. For example lets say we need to request Evapotranspiration for Alda, NE on May 12th, 2025 for 5 days.

First we add name=Alda 5NW for the station name then add productid=penet for the product.
https://awdn2.unl.edu/productdata/get?name=Alda 5NW&productid=penet
Next, we add the end date, end=20250512 and the number of days we are interested in, days=5. this gives us a URL of:
https://awdn2.unl.edu/productdata/get?name=Alda 5NW&productid=penet&end=20250512&days=5

A useful tool would be the list and active calls. These calls return all stations in the system or only stations that are currently operating, respectively. You must specify which productid (listed in above table) you want the list or active to return by setting the parameter equal to it. An example of making these data calls are as follows:
https://awdn2.unl.edu/productdata/get?list=scqc1440
https://awdn2.unl.edu/productdata/get?active=scqc1440
The list and active calls are an easy way to get the station names from the system so when making your data calls you can get it from the correct station.


Table of Variables

When calling the GeoJSON or JSON formats, you will get variables names. The definition for each variable can be found below:
Parameter Name Description
name Station name
state State station resides
county County station resides
coordinates Station longitude, latitude
generated The date the product was generated
7dayaccumprecipin 7 day accumulated Precip in inches
percentprecipnormal The percent of normal for the passed days
pcpnsummary Bins of Precip totals organized as:
  • jan-mar: January-March
  • apr-jun: April-June
  • jul-sep: July-September
  • oct-dec: October-December
  • ytd: Year-to_date
The psummary product includes several summarizing values as well.
gdd50bins Bins of accumulated GDD base 50 organized as:
  • jan-mar: January-March
  • apr-jun: April-June
  • jul-sep: July-September
  • oct-dec: October-December
  • ytd: Year-to_date
Each bin contains the departure and accumulated values.
referencecrop Reference crop, grass or alfalfa used in the ET calculations
crops The crop report that, for each crop, contains:
  • emergencedate: Y-d-m
  • accgdd: Accumulated GDD starting at emergence date
  • cropcoefficient: Crop coefficient
  • stages: The previous, current, and next crop stage
  • cropet: Potential ET for the prior 3 days from the current date
  • forecastcropet: The forecast potential ET
  • 3dayforecastettotal: 3 day total Forecast ET
validstart Valid start date
validend Valid end date
days Number of days
agreport Agricultural Report variables:
  • tMax: Max Temperature
  • tMin: Min Temperature
  • tAvg: Average Temperature
  • rhAvg: Average Relative Humidity
  • dpAvg: Average Dew Point
  • stAvg: Average Soil Temperature at 10 cm
  • SolarTotal: Total Solar Radiation
  • pTot: Precipitation Total
  • wsAvg: Average Wind Speed
  • penet: Penman-Monteith Evapotranspiration
  • dailydd: Report from Degree Days
dailycci Max CCI value and environment
hourlycci Hourly CCI value and environment
forecastcci Hourly forecast CCI with precip values
forecastdailycci Max forecast CCI value and environment
penet Reference ET
forecastpenet Forecast reference ET
windrose 16 bins for Wind Rose
dailydd Typical degree day variables includeing CDD, HDD, and GDD
hourlywc Hourly wind chill
dailywc Max wind chill
tempsummary Temperature summary values
hourlyhi Hourly heat index
dailyhi Max heat index

Network Identifiers

When using web services you can limit the returned data to a specific network in the system. To do this you would add the network parameter to the URL call:
https://awdn2.unl.edu/productdata/get?list=scqc60&network=iem
Name Parameter States Monitoring
The Colorado Agricultural Meteorological Network coagmet Colorado
Iowa Environmental Mesonet iem Iowa
Kansas Mesonet kstate Kansas
Nebraska Mesonet nemesonet Nebraska
North Dakota Agricultural Weather Network ndawn Minnesota,Montana, and North Dakota
Wyoming Agricultural Climate Network wacnet Wyoming
Upper Missouri River Basin Soil Moisture and Snow Depth Project umrb South Dakota
Montana Mesonet mtmesonet Montana
Wisconet wisconet Wisconsin
Arizona Meteorological Network azmet Arizona
Enviroweather enviro Michigan

Data Delivery

It is recommended that users interested in accessing AWDN data use Web Services. That said if you are interested in getting data but dont have programming skills there's the option to have data delivered through email. Please email the AWDN Manager your interest and you will be required to fill out a survey to evaluate youre usage. AWDN/HPRCC retains the right to reject any requests for Delivery for any reason.

Quality Control and Estimation Techniques

All incoming AWDN data are quality controlled using a two-step process that includes both automated and manual techniques. The quality control process not only helps to identify spurious values, but also helps to identify potential issues with the equipment. For instance, a station that is consistently recording identical hourly wind measurements may be iced over, or a station that suddenly seems to be missing precipitation events may have a clogged rain gauge.

When data are flagged as failing QC or are completely missing, these values are estimated using spatial and statistical methods in order to create a serially complete dataset. Serially complete datasets are required for a number of purposes, including climatological, agricultural, and watershed modeling. Users can easily identify estimated values within Classic Online, as each value has an appended “flag” that corresponds to a particular estimation technique. Flags are simply letters that appear at the end of the data value. Examples of data with flags include the letters E, R, and e, which are described below.

Inverse Distance Weighting

Inverse Distance Weighting (IDW) is a method of estimation that takes into account the distance a station is from a target station. The inverse of this distance is taken and multiplied by the variable’s value, to get an estimate for the station. This scheme uses the five closest stations surrounding the station in question to make the estimate. If all requirements are met, the value is replaced and is marked with an “E” flag. IDW is primarily used to fill missing observations in a station’s record.

Spatial Regression Test

The Spatial Regression Test (SRT) is a statistical model used to make estimates for mesonet data. This scheme looks at the observations of the five closest stations and uses data from all stations for a 24-day period to create a linear regression model. A value is then estimated for the station in question using the linear regression model. The value replaces the existing value and is marked with an “R” flag. The model is also used to create a confidence interval which is used to determine validity of a value.

Persistence

In rare instances when there is no data for nearby stations and the IDW or SRT methods cannot be used, data are estimated based on persistence. When persistence is used, the value from the previous day or hour replaces the existing value and that value is then marked with an “e” flag.

References

  • Hubbard, K. G., and J. You, 2005: Sensitivity Analysis of Quality Assurance Using the Spatial Regression Approach—A Case Study of the Maximum/Minimum Air Temperature. J. Atmos. Oceanic Technol., 22, 1520–1530, https://doi.org/10.1175/JTECH1790.1.
  • You, J, K. G. Hubbard, and S. Goddard, 2008: Comparison of methods for spatially estimating station temperatures in a quality control system, Int. J. Climatol., 28, 777–787, https://doi.org/10.1002/joc.1571.
  • Hubbard, K. G., N. B. Guttman, J. You, and Z. Chen, 2007: An Improved QC Process for Temperature in the Daily Cooperative Weather Observations. J. Atmos. Oceanic Technol., 24, 206–213, https://doi.org/10.1175/JTECH1963.1.
  • Hubbard, K. G., S. Goddard, W. D. Sorensen, N. Wells, and T. T. Osugi, 2005: Performance of Quality Assurance Procedures for an Applied Climate Information System. J. Atmos. Oceanic Technol., 22, 105–112, https://doi.org/10.1175/JTECH-1657.1.
  • You, J., K. G. Hubbard, S. Nadarajah, and K. E. Kunkel, 2007: Performance of Quality Assurance Procedures on Daily Precipitation. J. Atmos. Oceanic Technol., 24, 821–834, https://doi.org/10.1175/JTECH2002.1.