In 2020, a line of severe thunderstorms known as a derecho swept across the Midwest United States, unleashing powerful winds that caused extensive damage, running into billions of dollars.
This event underscored the need for more accurate weather forecasts to predict such severe weather phenomena.
A team of scientists from Penn State University has made a significant leap forward in this regard.
They have developed a new technique that utilizes satellite data to enhance weather forecast models, making it possible to predict where the most intense storms and strongest winds will occur with greater accuracy.
Yunji Zhang, an assistant professor in the Department of Meteorology and Atmospheric Science at Penn State and the lead author of the study, emphasizes the impact of this advancement.
By incorporating microwave data collected by satellites orbiting the Earth at low altitudes, their computer weather forecast models can now consistently highlight areas at risk of experiencing the most severe storms and wind damage.
This innovation is particularly crucial for regions that lack ground-based weather monitoring infrastructure, such as radars, which are traditionally relied upon for forecasting.
The technique is groundbreaking, especially for areas without surface observations or radar coverage, offering a promising solution for generating reliable forecasts of severe weather events. This approach could significantly benefit regions vulnerable to extreme weather but are currently underserved by modern meteorological infrastructure.
The team’s research, published in the journal Geophysical Research Letters, focuses on a case study of the 2020 Midwest Derecho.
Their findings demonstrate that adding satellite-derived microwave data to existing forecast models significantly improves the accuracy of surface gust predictions.
This method contrasts with previous practices that mainly utilized infrared brightness temperature data from satellites, which only provided information about the cloud tops.
Microwave sensors, however, can penetrate clouds to offer insights into the storm’s convection beneath them, thereby offering a more comprehensive view of developing weather conditions.
This novel use of satellite data is an extension of the team’s prior work on data assimilation—a statistical method that integrates various data sources to create the most accurate depiction of current weather conditions.
By assimilating both infrared and microwave data, the researchers can predict the locations and maximum values of surface gusts with unprecedented accuracy.
Looking to the future, Zhang and his colleagues plan to apply this method to other regions lacking in weather observation resources, such as parts of West Africa.
These areas, often hit by torrential rainfalls and significantly impacted by global warming, could greatly benefit from improved weather forecasts.
The Penn State team’s work not only represents a significant advancement in weather forecasting technology but also holds the potential to enhance preparedness and reduce the impact of severe weather events on vulnerable communities around the world.
The research findings can be found in Geophysical Research Letters.
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