Probabilistic and Deterministic Forecasting using Evolutionary Program Ensembles

Charles Darwin wrote: “Can it … be thought improbable … that other variations useful in some way to each being in the great and complex battle of life, should sometimes occur in the course of thousands of generations? If such do occur, can we doubt … that individuals having any advantage, however slight … would have the best chance of surviving and of procreating their kind?” This is the conceptual basis of evolutionary programming (EP), a process in which simulated evolution is used to find solutions to problems as diverse as the sorting of numbers and forecasting minimum temperature. Despite a history in computer sciences dating back to the 1960s, the application of this idea to meteorological studies is relatively new. Recently, EP has been adapted to the weather domain in order to generate large member ensemble forecasts for minimum temperature, maximum temperature, wind power, and heavy rainfall. These studies have shown that the method can provide greater probabilistic and deterministic skill, particularly at the extremes, than post-processed numerical weather prediction (NWP) ensembles. Further research has shown that this skill advantage persists out to longer ranges, where the forecast signal is presumably weaker.

In this talk, I will discuss the EP concept and its most recent meteorological forms, including examples from various applications of the method. I will provide an overview of modifications to the technique which incorporate various forms of genetic exchange, disease, mutation, and the training of solutions within ecological niches, and a successful effort to produce an adaptive form that can account for changing local conditions (such as changing flow regimes) as well as improved forecast inputs. I will then conclude with a discussion of outstanding questions regarding the method and future research directions.