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We developed regression methods to estimate meteorological changes during disruptive events like solar eclipses. These methods quantified a temperature drop of 1-2°C during the 2019 total solar eclipse in Chile.

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Area of Science:

  • Meteorology
  • Atmospheric Science
  • Geophysics

Background:

  • Disruptive events, such as solar eclipses, significantly impact Earth's surface meteorological variables.
  • Accurate quantification of these impacts requires estimating baseline conditions absent the event.

Purpose of the Study:

  • To design and compare methods for estimating the impact of singular disruptive events on meteorological variables.
  • To apply these methods to quantify the effect of the 2019 total solar eclipse on near-surface air temperature and winds in Chile.

Main Methods:

  • Development and comparison of regularized regression techniques, including a Bayesian variant.
  • Utilizing extended observational time series from individual meteorological surface stations.
  • Estimating both the magnitude and uncertainty of the event's impact.

Main Results:

  • Most stations within the solar eclipse's umbra showed a temperature decrease of 1-2°C.
  • The maximum estimated temperature drop exceeded 6°C.
  • The existence of an 'eclipse cyclone' could not be confirmed or denied.

Conclusions:

  • The developed regression methods effectively estimate the impact of disruptive meteorological events.
  • These methods can be applied to diverse scenarios, including volcanic eruptions, forest fires, and data gap filling.
  • The study provides quantitative insights into the meteorological effects of the 2019 total solar eclipse in Chile.