![\"\"](\"https:\/\/www.ssec.wisc.edu\/news\/wp-content\/uploads\/sites\/19\/2019\/09\/CIMSS_Scientist_Anthony_Wimmers_Credit_Eric_Verbeten.jpg\")
<\/a>CIMSS researcher Anthony Wimmers developed a deep learning AI program to estimate tropical cyclone intensity from satellite passive microwave imagery. Credit: Eric Verbeten<\/p><\/div>\n
In a 2019 paper published in the journal Monthly Weather Review, Wimmers and colleagues Chris Velden, of CIMSS, and Josh Cossuth, of the US Naval Research Lab, describe a way to harness deep learning to estimate hurricane strength based on data from particular weather satellites. As they pass overhead, these satellites collect key information about a hurricane\u2019s structure through measurements in the microwave part of the electromagnetic spectrum. With 30 years of microwave data, their paper demonstrates how the model can estimate \u2014 with increasing accuracy \u2014 a tropical cyclone\u2019s intensity.<\/p>\n
\u201cThe results from the study show a lot of promise, not just for the model’s nowcasting accuracy, but also because these results came from data not typically used to estimate hurricane intensity,\u201d Wimmers says.<\/p>\n
Rapid analyses like these can give forecasters vital information about a storm\u2019s behavior and what to expect, such as whether the storm will undergo eyewall replacements or rapid intensification.<\/p>\n
When predicting a hurricane\u2019s maximum sustained winds, DeepMicroNet\u2019s results differed from the historical record of forecaster-estimated values by about 16 miles per hour. DeepMicroNet’s results improved, however, when the datasets were limited to data measured directly by aircraft. Then, DeepMicroNet was off by less than 11.5 miles per hour. By comparison, estimates using state-of-the-art methods are typically off by around 10 miles per hour.<\/p>\n
Wimmers set out to answer three main questions. First, he wanted to determine how well the model performed compared to state-of-the-art methods for predicting hurricane intensity. Second, it was important to assess whether the results were meaningful and advanced the science of meteorology. Lastly, he wanted to demonstrate new ways to incorporate less-commonly-used data such as microwave imagery into prediction models, while offering valuable insight into a storm.<\/p>\n
\u201cThe reason Deep Learning systems have grown so much in satellite meteorology is because they are ready-made for these types of applications where you have tens of thousands of images available for training a model,” says Wimmers. “It also applies to situations where you need an answer rapidly.\u201d<\/p>\n
Wimmers designed his experiment to test how well an AI system could reproduce a hurricane\u2019s history. The historic hurricane intensities came from a combination of forecaster estimates based on other satellite data and aircraft observations. With no knowledge of the data types, DeepMicroNet’s job was to estimate intensities from a large, independent dataset of microwave imagery measured in the 37Ghz and 89Ghz frequencies.<\/p>\n
\u201cThose two frequencies are useful for revealing different hurricane structures,\u201d says Wimmers. \u201cTheir relatively coarse resolution also means they can be analyzed and processed in a computer quickly.\u201d<\/p>\n