Forecasting Short-term Dynamics of Fair-Weather Cumuli using Dynamic Mode Decomposition

TL;DR

This paper introduces a Dynamic Mode Decomposition approach for short-term forecasting of fair-weather cumulus cloud dynamics, offering a simpler alternative to traditional fluid models for very short-term sky condition predictions.

Contribution

The paper demonstrates the application of Dynamic Mode Decomposition to cloud evolution modeling, improving short-term forecast accuracy over advection-only methods.

Findings

Effective for up to seven-minute horizons

Outperforms advection-only forecasts

Applicable to real cloud image sequences

Abstract

Application of Dynamic Mode Decomposition to clear-sky index forecasting of shadowing effects of convective fair-weather cumulus clouds is presented. Cloud dynamics are captured by sequences of visible-light photographic video frames. This method can be more easily applied to the modeling of cloud evolution than traditional fluid-based methods, and can enhance existing frozen-cloud advection methods. Its use is demonstrated for an actual fair-weather cumulus cloud image sequence and compared to an advection-only forecast. It is concluded that the method shows promise for very short-term clear-sky index forecasting for up to seven minute horizons.