Much of the heavy rains that hit the Philippines during the Amihan northeast monsoon season between November and March are triggered by “shear lines”: kilometers-long bands of converging warm and cold air that are constantly shifting and difficult to spot even via satellite.

A new physics-based algorithm developed by a Filipino-led team of international researchers facilitates not just identifying but also tracking shear lines. When further integrated with AI, it could advance weather forecasting by offering improved monitoring not just of shear lines but also other similarly ephemeral weather phenomena such as the Inter-Tropical Convergence Zone (ITCZ).

“Our study is the first to develop an objective index for monitoring and detecting shear lines over the Philippines,” said lead researcher Lyndon Mark P. Olaguera. “There are no universally-accepted thresholds or criteria for detecting shear lines, unlike cold fronts or Tropical Cyclones; these systems form from cold fronts that lose their well-defined structure when they pass over the warm waters of the ocean.”

Researchers from the Ateneo de Manila University, the Manila Observatory, Tokyo Metropolitan University, and the Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA) looked at decades’ worth of weather data, including a particularly extreme rainfall event in 2017 that was attributed to shear lines. They then looked at common weather characteristics—such as wind patterns, temperature, and moisture—that could be used to quickly spot these elusive weather phenomena, and serve as the bases for the detection algorithm.

They said that this detection method is useful for quantifying the contribution of shear lines to rainfall extremes during the northeast monsoon season in the Philippines; for identifying areas that are more likely to experience heavy to extreme rainfall events; and for improving scientists’ understanding of how this weather system evolves.

“The primary application of the algorithm is in weather forecasting and the development of early warning systems, but it can also be applied to climatological studies; the verification of numerical models, for example, assessing whether existing mathematical models can capture shear lines; the improvement of numerical weather prediction parameterizations, such as adjusting physical schemes if shear lines are not well represented; and the validation of forecast system performance,” Olaguera explained.

This work marks groundbreaking progress towards improving the Philippines’ weather monitoring and forecasting capabilities; the authors recommend further research to identify additional criteria for improving shear line detection.