Texture-based classification of high-resolution precipitation forecasts with machine-learning methods

Object-based methods are commonly used for verification and postprocessing of high-resolution precipitation forecasts. They usually detect objects based on intensity criteria only, without considering the spatial organization of rainfall, known as texture. This article evaluates the performance of several machine-learning methods to detect "continuous" and "intermittent" rainfall patterns in the forecasts of the French convective-scale Arome model. A sliding-window segmentation algorithm, which applies a classification model at each grid point, is implemented. Several classifiers and input textural features are compared. Overall, intermittent precipitation is the most difficult to detect. The random forest classifier is shown to provide the best classification results independently of the predictor used, with a surprising ability to extract a relevant signal from a synthetic descriptor such as the rainfall cumulative distribution function, as well as from the large amount of unprocessed information provided by neighbouring grid points. On the other hand, the logistic regression classifier needs a texture-oriented predictor, such as the statistics derived from the grey-level co-occurrence matrix, to perform well. Global insight into model behaviour is then obtained by examining the importance of input features. Finally, we show that random forests trained on Arome deterministic forecasts can be applied successfully to discriminate between precipitation textures in different Arome configuration outputs and gridded observations.