(Editor’s note: The following story was provided by Jean Phillips of the University of Wisconsin-Madison and adapted for NOAA Research. Go online for the original UW story.)
A new weather forecasting tool could soon find itself part of the day-to-day operations of NOAA’s National Weather Service (NWS).
The instrument, called Atmospheric Emitted Radiance Interferometer, or AERI, measures temperature, water vapor and trace gases (like ozone, carbon monoxide and methane) in the lowest layer of Earth’s atmosphere, the troposphere. Now, an AERI project led by Tim Wagner, a scientist with NOAA’s Cooperative Institute for Meteorological Satellite Studies (CIMSS) at the University of Wisconsin–Madison, has received funding through NOAA’s Joint Technology Transfer Program.
The program, managed by NOAA’s Office of Weather and Air Quality, is an effort to accelerate technological advances into application and use by NOAA National Weather Service forecasters.
New forecast tool
An Atmospheric Emitted Radiance Interferometer (AERI) operating during NOAA's recent field campaign to study storms over the Plains in 2015. Credit: Jon Gero, University of Wisconsin-Madison
With AERI “we can monitor the evolution of the atmospheric boundary layer and see how stability is changing over time,” says Wagner. “But what we haven’t done, up to this point, is see what happens if we actually take those observations and put them into the forecast models—would that improve the accuracy of the models? We think it will.”
The boundary layer is that part of the troposphere that is directly influenced by Earth’s surface.
Currently, the standard for collecting atmospheric temperature and moisture information is with weather balloons, but they are typically launched twice a day, at scattered locations, and do not measure small-scale changes in the atmosphere that are necessary for accurate, location-based forecasts.
Wagner hypothesizes that assimilating precise AERI observations from the boundary layer into computer models will provide a much better picture of low-level moisture changes. That additional information could help NWS issue better forecasts about quantity and location of precipitation — information that could have a huge impact for aviation, agriculture, flooding, or anyone who relies on or needs water information.
Today, forecasters can accurately predict which days will be rainy, and, according to Wagner, “we can look at the total area to be affected. We’re not doing a bad job of forecasting precipitation for the general area. But when we drill down to individual locations, trying to pinpoint specific storms and their specific times, that’s where we’re not doing as well.”
To the farmer whose crops need rain, it doesn’t matter that a forecaster accurately predicted an inch of rain if it happened 10 miles to the east of his fields, adds Wagner.
During the first year of the two-year study, Wagner and co-investigators will assimilate data from the 2015 Plains Elevated Convection at Night (PECAN) field experiment where a network of AERIs was distributed across the Great Plains.
In the second year, the researchers will take advantage of a network of AERIs being deployed across the Southern Great Plains as part of the Department of Energy’s Atmospheric Radiation Measurement (ARM) field measurement program.
They will assimilate data into an experimental weather model that will be run at NOAA’s Hazardous Weather Testbed, a joint project of NOAA’s National Severe Storms Laboratory and NOAA NWS forecasters who will evaluate the model and provide feedback to help Wagner’s team improve it.
The turn-around time, from data collection to analysis to generating results is very short. However, the researchers are confident that AERI will provide valuable data for assimilation into forecasts and support the case for a much larger network of AERIs across the United States.
For more information, please contact Monica Allen, director of public affairs at NOAA Research, at 301-734-1123 or by email at firstname.lastname@example.org