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.

Notification of Service Changes
Data sources for AWDN will be changing on January 1st. NDAWN (North Dakota, Montana, Minnesota) and UMRB (South Dakota) will be using Synoptic as their data source. Because of the terms of service established by Synoptic, processed data for these mesonets may become unavailable through Web Services. Gridded data and Products generated with this data will still be available. We appologize for this inconvenience as the landscape of mesonet data access continues to evolve.

AWDN Station Locations


Regional Partnerships

The AWDN is a cooperative effort, which is made possible by partnerships with six state-run mesonet programs across the High Plains region. 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 nine states of Colorado, Iowa, Kansas, Minnesota, Missouri, Montana, Nebraska, North Dakota, 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 80 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:
  • Crop Water Use (ET)
  • Growing Degree Day Plots
  • Text Message Service
  • Web Services
  • 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
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 60 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
  • Heat Index and Wind Chill
  • 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 nearly 70 stations across the state. Tools available from the Nebraska Mesonet include:
  • Interactive Station Maps
  • Current Conditions
  • Real-time and Daily Maps
Visit Nebraska Mesonet
Visit Nebraska State Climate Office

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

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 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

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

Grapher

Visualize AWDN data using the Graphing Tool

AWDN Maps

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

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.

Access Data Through Web Services

AWDN data can be access through Web Services. This service is free and offers limited access to Serial Complete, Quality Control products for hourly and daily observations. You can use the Access Tool to 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=scqc60&begin=20230123&end=20230123&units=us&format=csv

This will get hourly data (productid=scqc60) for Holdrege, NE (name=Holdrege 5N) on Dec 12, 2023 (begin=20230123 and end=20230123). The result will be in comma-separated values (format=csv) format as US units (units=us).

If you are looking for daily data, changing the productid parameter to scqc1440 (productid=scqc1440) will return that data. What if your not interested in Holdrege but a different station? You can use the Station Locations tab to find the name of your station. If I wanted to access daily Fargo data from May 10 to June 20, 2021, I would enter this into my browser address bar:

https://awdn2.unl.edu/productdata/get?name=Fargo&productid=scqc1440&begin=20210510&end=20210620&units=us&format=csv

Let say your intereseted in gathering Evapotraspiration data for Elmdale, KS for July, 2022. To get that data set you would run:
https://awdn2.unl.edu/productdata/get?name=Elmdale 1SE&productid=penet&end=20220731&units=us&format=csv&days=31

This call is very similar to the previous calls with a few differences. We want data for the whole month of July which is 31 days (days=31) ending on the last day of July (end=20220731). The result of the call will give us reference ET in both inches per day and millimeters per day. 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?
In general, the parameters to retrieve data from AWDN Web Services are in the following table:

Parameter Name Description Options
name Desired station name to retrieve data
productid product to request scqc60 (Serial Complete Hourly data), scqc1440 (Serial Complete Daily data)
network Network to limit data retrieved A table of networks is listed here
begin timestamp to start 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
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), geojson (Geographical Javascript Object Notation)
tz Time zone offset to return data records The options are any US timezone abbreviation or UTC
sensor Retrieve data for a specific sensor A list of sensors can be found here
state State to limit data calls by. This can help locate a station when just the name fails to retrieve the correct station. CO, IA, KS, MN, MT, ND, NE, SD, WY
units Units to return that data us (United States System), si (International System)

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.

Now, using the name of a station you want data from you can make a call:
https://awdn2.unl.edu/productdata/get?name=Fargo&productid=scqc1440&begin=20220501&end=20220531&tz=CST&units=us
The call returns Daily data for Fargo, ND from May 1st to May 31st, 2022. The observation times are reported in Central Standard Time, displayed as United States units, in CSV format.

Calculated Products

In addition to Hourly and Daily products there are also calculated products available through web services.

Product Name Description parameter
Heat Index 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. hi
Wind Chill 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. wc
Temperature Summary 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. tsummary
Precipitation Summary A summary over a range of days showing the total rainfall as well as the percent of Normals. psummary
Degree Days 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. dd
Wind Rose Shows the frequency of wind direction and speed over a range of time. wrose
Penman-Monteith Evapotranspiration Evapotranspiration calculated using the Penman-Monteith method. Available in both hourly and daily values. penet
Cattle Comfort Index An index used to assess the stress level of livestock using several environmental factors. Available only as hourly values. cci
Ag Report 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. agreport
Crop Report 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. Available crops:
  • Sorghum
  • Sunflower
  • Wheat
  • Alfalfa
  • Sugar Beet
  • Corn
  • Soybeans
  • Grass
  • Potato
  • Bean
 cropreport

Web service calls for calculated products have different parameters used to retrieve data:
Parameter Name Description Options
name Desired station name to retrieve data
productid product to request listed in the above table
end timestamp to stop data retrieval Takes the format YYYYmmdd where: Y = year, m = month, d = day
days The number of days prior to the end date
tz Time zone offset to return data records The options are any US timezone abbreviation or UTC
crop Crop name (cropreport only) List of crops above
emerge Emergence date for crop (cropreport only) Takes the format YYYYmmdd where: Y = year, m = month, d = day
format Output format csv (comma delimited), json (Javascript Object Notation geojson), geojson (Geographical Javascript Object Notation), pdf (Portable Document Format)
To get a precipitation summary for Fargo from May 1st to May 31st, 2022 we would make a call:
https://awdn2.unl.edu/productdata/get?name=Fargo&productid=psummary&end=20220531&days=31

Table of Variables

Users of the system can also limit calls to returning only a specific variable. To request data for Maximum Air Temperature at 10 Meters:
https://awdn2.unl.edu/productdata/get?name=Fargo&productid=scqc1440&begin=20220501&end=20220531&sensor=AirTempMax10m
A table of available variables follows:

Variable Name Description US Units SI Units
Precip Precipitation in mm
PrecipTotal 24 hour Precipitation Total in mm
RelHum2m Relative Humidity % %
RelHumMax2m Maximum Relative Humidity % %
RelHumMin2m Minimum Relative Humidity % %
Solar Solar Radiation Langley/hour W/m^2
SolarTotal Total Solar Radiation Langley MJ/m^2/day
AirTemp2m Air Temperature F C
AirTempMax2m Maximum Air Temperature F C
AirTempMin2m Minimum Air Temperature F C
SoilTemp5cm Soil Temperature at 5 cm depth F C
SoilTemp10cm Soil Temperature at 10 cm depth F C
SoilTemp15cm Soil Temperature at 15 cm depth F C
SoilTempMax5cm Maximum Soil Temperature at 5 cm depth F C
SoilTempMin5cm Minimum Soil Temperature at 5 cm depth F C
SoilTempMax10cm Maximum Soil Temperature at 10 cm depth F C
SoilTempMin10cm Minimum Soil Temperature at 10 cm depth F C
SoilTempMax15cm Maximum Soil Temperature at 15 cm depth F C
SoilTempMin15cm Minimum Soil Temperature at 15 cm depth F C
SoilTemp30cm Soil Temperature at 30 cm depth F C
SoilTempMax30cm Maximum Soil Temperature at 30 cm depth F C
SoilTempMin30cm Minimum Soil Temperature at 30 cm depth F C
SoilTemp60cm Soil Temperature at 60 cm depth F C
SoilTempMax60cm Maximum Soil Temperature at 60 cm depth F C
SoilTempMin60cm Minimum Soil Temperature at 60 cm depth F C
SoilTemp127cm Soil Temperature at 127 cm depth F C
SoilTempMax127cm Maximum Soil Temperature at 127 cm depth F C
SoilTempMin127cm Minimum Soil Temperature at 127 cm depth F C
WindDirection2m Wind Direction degrees degrees
WindDirectionAvg2m Average Wind Direction degrees degrees
WindSpeed2m Wind Speed mph m/s
WindSpeedAvg2m Average Wind Speed mph m/s
RelHum10m Relative Humidity at 10 meters % %
RelHumMax10m Maximum Relative Humidity at 10 meters % %
RelHumMin10m Minimum Relative Humidity at 10 meters % %
WindSpeed10m Wind Speed at 10 meter height mph m/s
WindSpeedAvg10m Average wind speed at 10 meters mph m/s
WindDirection10m Wind Direction at 10 meters degrees degrees
WindDirectionAvg10m Average Wind Direction at 10 meters degrees degrees
AirTemp10m Air Temperature at 10 meters F C
AirTempMax10m Maximum Air Temperature at 10 meters F C
AirTempMin10m Minimum Air Temperature at 10 meters F C
AtmPressure Atmospheric Pressure inHg mb
AtmPressureMax Maximum Atmospheric Pressure inHg mb
AtmPressureMin Minimum Atmospheric Pressure inHg mb
WindDirectionSD10m Wind Direction Standard Deviation at 10m degrees degrees
WindDirectionSD2m Wind Direction Standard Deviation degrees degrees
SoilTempBare10cm Soil Temperature At 10 Cm under Bare Soil F C
WindSpeedMag10m Wind Speed Magnitude at 10 m mph m/s
WindSpeedMag2m Wind Speed Magnitude mph m/s
WindSpeedMax10m Wind Speed 5 Sec Gust at 10m mph m/s
WindSpeedMax2m Wind Speed 5 Sec Gust mph m/s
WindSpeedSD10m Wind Speed Standard Deviation at 10m mph m/s
WindSpeedSD2m Wind Speed Standard Deviation mph m/s
WindDirectionSDAvg10m Average Wind Direction Standard Deviation at 10m degrees degrees
WindDirectionSDAvg2m Average Wind Direction Standard Deviation degrees degrees
WindSpeedMagAvg10m Average Wind Speed Magnitude at 10 m mph m/s
WindSpeedMagAvg2m Average Wind Speed Magnitude mph m/s
WindSpeedSDAvg10m Average Wind Speed Standard Deviation at 10m mph m/s
WindSpeedSDAvg2m Average Wind Speed Standard Deviation mph m/s
SoilTempBareMax10cm Maximum Soil Temperature At 10 Cm under Bare Soil F C
SoilTempBareMin10cm Minimum Soil Temperature At 10 Cm under Bare Soil F C
AirTempMaxTime2m Time Of Max Temperature Timestamp Timestamp
AirTempMinTime10m Time Of Min Temperature At 9 Meters Timestamp Timestamp
AirTempMinTime2m Time Of Min Temperature Timestamp Timestamp
WindSpeedMaxTime10m Time of Wind Speed 5 Sec Gust at 10m Timestamp Timestamp
WindSpeedMaxTime2m Time of Wind Speed 5 Sec Gust Timestamp Timestamp
AirTempMaxTime10m Time Of Max Temperature At 9 Meters Timestamp Timestamp

Description of Quality Control Flags

All variables have an associated flag that describes the procedure applied during processing. Flag variables have the suffix "SCQC_Flag" attached to the corrosponding variable.

Flag Description
R Estimation made using Spatial Regression Test
E Estimation made using Inverse Distance Weighting
e Estimate made using Persistence
U Value was changed manually
W Winter Precipitation Estimate
p Precipitation value set to 0.00
S Solar Radiation value other than zero during night time so changed to 0.00
M Value is Missing

Cooperating 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

Example Scripts

Followed are 2 exmple scripts that can be used:
PHP Example
Python Example

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.