A new multimodel ensemble method using nonlinear genetic algorithm: An application to boreal winter surface air temperature and precipitation prediction

Abstract

A new multimodel ensemble (MME) method that uses a genetic algorithm (GA) is developed and applied to the prediction of winter surface air temperature (SAT) and precipitation. The GA based on the biological process of natural evolution is a nonlinear method which solves nonlinear optimization problems. Hindcast data of winter SAT and precipitation from the six coupled general circulation models participating in the seasonal MME prediction system of the Asia‐Pacific Economic Conference Climate Center are used. Three MME methods using GA (MME/GAs) are examined in comparison with a simple composite MME strategy (MS0): MS1 which applies GA to single‐model ensembles (SMEs), MS2 which applies GA to each ensemble member and then performs a simple composite method for MME, and MS3 which applies GA to both MME and SME. MS3 shows the highest predictability compared to MS0, MS1, and MS2 for both winter SAT and precipitation. These results indicate that biases of ensemble members of each model and model ensemble are more reduced with MS3 than with other MME/GAs and MS0. The predictability of the MME/GAs shows a greater improvement than that of MS0, particularly in higher‐latitude land areas. The reason for the more improved increase of predictability over the land area, particularly in MS3, seems to be the fact that GA is more efficient in finding an optimum solution in a complex region where nonlinear physical properties are evident.

Weighting of NMME temperature and precipitation forecasts across Europe

“…Overall, the skill of our multimodels is similar to that of other multi-model weighting techniques such as equal weights Hagedorn et al, 2005;Slater et al, 2017), multiple linear regression , other Bayesian-based approaches (Rajagopalan et al, 2002;Robertson et al, 2004;Weigel et al, 2008), optimal weights (Wanders and Wood, 2016;Weigel et al, 2008) or genetic algorithms (Ahn and Lee, 2016). However, it is difficult to compare these multi-models in detail as most have been applied over different spatial and temporal resolutions, and often verified using different evaluation metrics.…”

Improvement of daily precipitation estimations using PRISM with inverse-distance weighting

“…A dynamical method that uses a regional climate model has the advantage of producing physically and dynamically balanced data. However, the method has some limitations such as the fact that a regional climate model requires considerable integration time and significant storage to produce high-resolution data (Chen et al 2012), and that the model outputs may include systematic errors (Ahn et al 2012;Jo and Ahn 2014;Ahn and Lee 2016;Lee and Ahn 2018). Due to these drawbacks, a statistical method is also often used as an efficient method that produces meteorological and climate data at a high resolution (e.g., Daly 2006;Brunetti et al 2013).…”

Applying a Multi-Model Ensemble Method for Long-Term Runoff Prediction under Climate Change Scenarios for the Yellow River Basin, China

“…A recent application of combined output to be used as an alternative to the output of a single individual rainfall-runoff model. A recent application of this concept to future climate projections can be found in [17] and [18], where surface air temperature and precipitation are predicted.…”