Underused satellite, radar data may improve thunderstorm forecasts


Date:
March 9, 2023
Source:
Penn State
Summary:
Tens of thousands of thunderstorms may rumble around the world
each day, but accurately predicting the time and location where
they will form remains a grand challenge of computer weather
modeling. A new technique combining underused satellite and radar
data in weather models may improve these predictions, according
to a team of scientists.


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FULL STORY ==========================================================================
Tens of thousands of thunderstorms may rumble around the world each day,
but accurately predicting the time and location where they will form
remains a grand challenge of computer weather modeling. A new technique combining underused satellite and radar data in weather models may improve these predictions, according to a Penn State-led team of scientists.


========================================================================== "Thunderstorms are so ubiquitous it's hard to count how many you get in Pennsylvania, or the United States or globally every day," said Keenan
Eure, doctoral student in the Department of Meteorology and Atmospheric
Science at Penn State. "A lot of our challenges, even today, are figuring
out how to correctly predict the time and location of the initiation of thunderstorms." The scientists found that by combining data from the geostationary weather satellite GOES-16 and ground-based Doppler radar
they could capture a more accurate picture of initial conditions in the boundary layer, the lowest part of the atmosphere, where storms form.

"There's value in improving thunderstorm predictions from both Doppler
radar observations and satellite observations that are currently underused
and we showed that not only can they be used to improve predictions but
putting them together has lots of benefits," said Eure, lead author on
the study. "The sum is greater than the individual parts." The technique showed promise in improving forecasts of convection initiation, the
conditions that spawn storms, several hours before the thunderstorms
occurred in a case study from May 2018 in the Texas panhandle. The
scientists reported their findings in the journal Monthly Weather Review.

"Keenan focused on using satellite observations to better define the environment in which the storms would later form, and on using radar observations to improve the low-level wind fields that eventually
helped to create the storms," said David Stensrud, professor of
meteorology at Penn State and Eure's advisor and co-author on the
study. "This observation combination had not been studied previously and
ended up adding significant value to the model forecasts on this day."
The scientists used data assimilation, a statistical method that can paint
the most accurate possible picture of current weather conditions in the
weather model, important because even small changes in the atmosphere
can lead to large discrepancies in forecasts over time.

Understanding conditions in the boundary layer is particularly important because it strongly influences the ingredients for convection --
near-surface moisture, lift and instability -- a process that causes
warm air near the Earth's surface to rise and form clouds.

"We obviously can't model every molecule in the atmosphere, but we want to
get as close as possible," Eure said. We really believe this work adds a
lot of valuable information that models currently don't have and that we
can help the depiction of the lowest part of the atmosphere." The team assimilated satellite and radar data separately and simultaneously and
found the best results came from combining infrared brightness temperature observations from the satellite and radial wind velocity and boundary
height observations from the radar.

The work uses all-sky satellite data assimilation, developed by
Penn State's Center for Advanced Data Assimilation and Predictability Techniques, that assimilates satellite data from all weather conditions, including cloudy and clear skies. Forecasting previously relied on
clear-sky observations, due to challenges in diagnosing the complex
physical processes within clouds, the scientists said.

"While more cases need to be explored, these observations are currently available and could be used to improve thunderstorm prediction over the
coming decade as NOAA continues to advance its Warn-on-Forecast paradigm
in which computer model predictions help to make severe weather warnings
more accurate and timely," Stensrud said.

Other Penn State researchers on the project were Matthew Kumjian and
Steven Greybush, associate professors, Yunji Zhang, assistant professor
and Paul Mykolajtchuk, former graduate student, in the Department of Meteorology and Atmospheric Science.

This research builds on work by the late Fuqing Zhang, distinguished
professor of meteorology and atmospheric science.

NASA and the National Oceanic and Atmospheric Administration supported
this research.

* RELATED_TOPICS
o Space_&_Time
# Satellites # Sun # Solar_Flare # NASA
o Earth_&_Climate
# Weather # Severe_Weather # Storms # Atmosphere
* RELATED_TERMS
o Meteorology o Numerical_weather_prediction o
Weather_forecasting o Weather o Earth_science
o Severe_weather_terminology_(United_States) o
National_Hurricane_Center o Global_Positioning_System

========================================================================== Story Source: Materials provided by Penn_State. Original written by
Matthew Carroll. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Keenan C. Eure, Paul D. Mykolajtchuk, Yunji Zhang, David
J. Stensrud,
Fuqing Zhang, Steven J. Greybush, Matthew R. Kumjian. Simultaneous
Assimilation of Planetary Boundary Layer Observations from Radar
and All- Sky Satellite Observations to Improve Forecasts of
Convection Initiation.

Monthly Weather Review, 2023; DOI: 10.1175/MWR-D-22-0188.1 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2023/03/230309125034.htm

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