Global Optimization of an Analog Method by Means of Genetic Algorithms

Abstract

Analog methods are based on a statistical relationship between synoptic meteorological variables (predictors) and local weather (predictand, to be predicted). This relationship is defined by several parameters, which are often calibrated by means of a semiautomatic sequential procedure. This calibration approach is fast, but has strong limitations. It proceeds through successive steps, and thus cannot handle all parameter dependencies. Furthermore, it cannot automatically optimize some parameters, such as the selection of pressure levels and temporal windows (hours of the day) at which the predictors are compared. To overcome these limitations, the global optimization technique of genetic algorithms is considered, which can jointly optimize all parameters of the method, and get closer to a global optimum, by taking into account the dependencies of the parameters. Moreover, it can objectively calibrate parameters that were previously assessed manually and can take into account new degrees of freedom. However, genetic algorithms must be tailored to the problem under consideration. Multiple combinations of algorithms were assessed, and new algorithms were developed (e.g., the chromosome of adaptive search radius, which is found to be very robust), in order to provide recommendations regarding the use of genetic algorithms for optimizing several variants of analog methods. A global optimization approach provides new perspectives for the improvement of analog methods, and for their application to new regions or new predictands.

AtmoSwing: Analog Technique Model for Statistical Weather forecastING and downscalING

“…This is particularly useful when working with a global optimization method, where nothing is fixed but the structure of the AM. This nomenclature has been used in Horton et al (2017aHorton et al ( , b, 2018 and Horton and Brönnimann (2018).…”

“…This allows for optimization of all parameters jointly in a fully automatic and objective way. The method is described in Horton et al (2017a) and an application is provided in .…”

“…Additionally, a module is available for calibrating the different parameters required for the method, namely the Optimizer. AtmoSwing is continuously evolving and has been used in Horton et al (2012Horton et al ( , 2017aHorton et al ( , b, 2018 and Horton and Brönnimann (2018). Some existing AMs designed for daily precipitation will first be described along with the required data (Sect.…”

Data-Driven Interpolation of Sea Level Anomalies Using Analog Data Assimilation

“…2: Split the case study region into overlapping P × P patches, here, 20 × 20 patches 3: For each patch position s, use the Analog Ensemble Kalman Smoother (AnEnKS) [13], for patch P s of field dX. As stated in (13), the assimilation is performed in the EOF space, i.e., for EOF decomposition Φ(P s , t), using the operator derived from EOF-based reconstruction (12) and decomposition (11) as observation model H in (8) and the patch-level training catalog described in the previous section. The assimilation is sequential and is performed each 3-days.…”

“…Several recent works involving applications of analog forecasting methods in geoscience fields contribute in the revival of these methods, recent applications comprise the prediction of soil moisture anomalies [4], the prediction of sea-ice anomalies [5], rainfall nowcasting [6], numerical weather prediction [7][8][9], etc. One may also cite methodological developments such as dynamically-adapted kernels [10] and novel parameter estimation schemes [11]. Importantly, analog strategies have recently been extended to address data assimilation issues within the so-called analog data assimilation (AnDA) [12,13], where the dynamical model is stated as an analog forecasting model and combined to state-of-the-art stochastic assimilation procedures such as Ensemble Kalman filters.…”