Hurricane Harvey made landfall over coastal Texas in 2017, pouring down record rainfall, flooding neighborhoods, and becoming one of the wettest and most catastrophic hurricanes in US history. According to Penn State scientists, a novel approach based on freely accessible data decreases prediction errors and has the potential to enhance track, strength, and rainfall projections for future storms such as Hurricane Harvey.
“Our findings suggest that there are opportunities for developing more accurate predictions for tropical cyclones utilizing accessible but underused data,” said Yunji Zhang, assistant research professor in Penn State’s Department of Meteorology and Atmospheric Science. “In the future, this might lead to greater alerts and preparation for tropical cyclone-related threats.”
Using Hurricane Harvey as a case study, the scientists found that incorporating microwave data acquired by low-Earth orbiting satellites into current computer weather prediction models improved predicted storm course, intensity, and rainfall.
“Over the ocean, we don’t have other kinds of observations beneath the cloud tops to tell us where the eyewalls are, where the strongest convections are, and how many rain or snow particles there are in those regions, except for the occasional reconnaissance aircraft that fly into some of the hurricanes,” Zhang explained. “This is critical for future forecasts of how powerful storms will be and how much rainfall hurricanes will deliver.”
The study builds on the team’s previous work, which improved hurricane forecasts through data assimilation, a statistical method that aims to paint the most accurate picture of current weather conditions, which is important because even minor changes in the atmosphere can lead to large discrepancies in forecasts over time.
Scientists from Penn State’s Center for Advanced Data Assimilation and Predictability Techniques previously assimilated infrared brightness temperature data from the US Geostationary Operational Environmental Satellite, GOES-16. Brightness temperatures indicate how much radiation is released by things on Earth and in the atmosphere, and the scientists employed infrared brightness temperatures at various frequencies to build a more accurate image of atmospheric water vapor and cloud formation.
However, infrared sensors can only see what is occurring at the cloud tops. According to the scientists, microwave sensors can see a complete vertical column, providing unprecedented insight into what happens underneath clouds after storms begin.
“This is particularly critical when a storm evolves in later phases of development, when there are prominent and coherent cloud formations and you can’t see what’s going on below them,” Zhang said. “Hurricanes are most hazardous at this period because they are very powerful and, in some cases, are already nearing landfall and posing a threat to humanity. That is when microwave data will give the most useful information.”
The researchers reported in the journal Geophysical Research Letters that combining integrated infrared and microwave data decreased prediction errors in course, fast intensification, and peak strength for Hurricane Harvey compared to infrared radiation alone. They claimed that combining both sets of data led in a 24-hour increase in projected lead-time for the storm’s fast intensification, a vital phase when certain storms rapidly acquire power.
According to the experts, including the microwave data resulted in a better understanding of the number of water particles in the storm as well as more precise rainfall totals for Harvey.
“Rainfall forecasting is crucial for preparing the public for risks and evacuations,” Zhang added. “If we have a better grasp of how many rainfall particles are in the storm, we will be able to make more precise estimates of how much rain will fall. We will have more sophisticated instructions on how individuals should behave as a result of this.”
More work is required, according to the scientists, to enhance the model’s microphysics and replicate water and ice particles more accurately.
This research is based on the work of former Penn State Distinguished Professor Fuqing Zhang, who directed the project until his untimely death in July 2019.
“When our dear friend and colleague Fuqing Zhang passed away, the thread of ideas that had been weaving together our ongoing combined infrared and microwave radiance data assimilation experiments unraveled,” said Eugene Clothiaux, professor of meteorology and atmospheric science and a co-author of the paper. “We worked together for a long time to reconstruct the thread as best we could.”
Penn State also provided contributions from associate professor Steven Greybush, assistant professor Xingchao Chen, and graduate students Man-Yau Chan, Christopher Hartman, and Zhu Yao.
Several former Penn State PhD students, postdoctoral fellows, and faculty members also contributed: Scott Sieron, support scientist at I.M. Systems Group; Yinghui Lu, associate professor at Nanjing University in China; Robert Nystrom, postdoctoral researcher at the National Center for Atmospheric Research; Masashi Minamide, assistant professor at the University of Tokyo; James Ruppert, assistant professor at the University of Oklahoma; and Atsushi Okazaki, assistant professor at Hirosaki University in Japan
This research was funded by the National Science Foundation, NASA, the National Oceanic and Atmospheric Administration, and the Department of Energy’s Biological and Environmental Research program.
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Penn State donated the materials. Matthew Carroll wrote the original. Please keep in mind that content may be altered for style and length.