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An innovative technique for predicting extreme weather events in South Florida has emerged from researchers at the FAMU-FSU College of Engineering. The innovation specifically addresses the challenges of forecasting extreme heat and heavy rainfall.

"Many of the techniques used in climate downscaling and bias correction research are limited in prediction of extreme weather events," said Ebrahim Ahmadisharaf, lead researcher at the joint college's Resilient Infrastructure & Disaster Response (RIDER) Center. "They use methods that give us the big picture but have limitations."

The research, in Earth's Future, introduces a hybrid statistical technique that promises more accurate climate predictions for local communities and infrastructure planning.

Advancing weather prediction models

The study revealed that while current bias correction techniques effectively predict light and moderate rainfall and average temperatures, they fall short when forecasting extreme weather events. To address this gap, researchers developed a technique called EQM-LIN (Empirical Quantile Mapping with Linear correction).

Using data from 20 weather stations across South Florida, the new method combines two statistical approaches to provide more precise climate projections than existing global climate models.

"We found the hybrid technique is especially good at predicting extreme climate variables, namely precipitation and air temperature," Ahmadisharaf said. "Our projection shows that in the future, South Florida will likely experience slight decreases in precipitation in the summer and an increase in the fall."

Practical applications

The research has immediate practical value for infrastructure planning and community protection. The technique helps stakeholders identify areas vulnerable to potential flooding and assess at-risk infrastructure.

"The results can bolster the resilience of our infrastructure and local communities against climate-related hazards," Ahmadisharaf said.

Future directions

The multi-station analysis approach offers a promising framework for understanding and preparing for future climatic challenges, but researchers acknowledge that ongoing refinement of the statistical bias correction technique is necessary. Future studies may incorporate regional climate models for even more precise local projections.

"Further improving the bias correction of extreme events and investigating the structure of compound climatic events under future climate remains a priority," said lead author and postdoctoral researcher Leila Rahimi.