The goal of this research is to use evolutionary programming (EP), a machine-learning method, to develop a statistical-dynamical model capable of provide improved tropical cyclone intensity forecasts to lead times of up to 120 h. An EP algorithm has a specified structure that represents a combination of coefficients, normalized variables, and mathematical operators. To begin, a set of initial algorithms is randomly generated and its performance on the testing data is evaluated. Based on the desired skill metric, a specified percentage of worst-performing algorithms is replaced by mutated versions of the best-performing algorithms. The process continues until the forecast skill gained between training cycles asymptotes to zero, at which time a specified number of best-performing algorithms (over the set of all cycles) is retained for further consideration.

For this research, two similar but separate models are developed: one for the north Atlantic basin and the other for the east/central Pacific basin. These models are developed using predictors sourced from the SHIPS developmental dataset from 2000-2016. The set of all observed tropical cyclones is stratified by lifetime maximum intensity (tropical cyclone, minimal hurricane, major hurricane) and assigned evenly and randomly to the training, cross-validation, and testing datasets. This ensures that an even distribution of storms by intensity is achieved in each dataset. Five sets of 2,000 algorithms are initialized, and 20% of the population of each tribe is killed off with each training cycle. The evolutionary process continues for 300 iterations per initialization, and a total of five initializations are used. The 100 best-performing models (as determined by performance on the cross-validation dataset) at each lead time for each basin are retained, from which a root-mean squared difference algorithm is used to identify a subset of five models with high skill and large differences from other algorithms. Bayesian model combination is then used to combine the five algorithms into a single model capable of providing skillful deterministic and probabilistic forecasts. This presentation will summarize model development and model performance in testing and in retrospective simulations of the 2017 hurricane season.