PyNMME: A python toolkit to retrieve, calibrate and verify seasonal precipitation forecasts

Abstract

The North American Multi-Model Ensemble (NMME) experiment assembles valuable ensemble forecasts from more than ten global climate models (GCMs). Focusing on environmental applications of NMME forecasts, this paper develops the pyNMME, a Python-based toolkit to implement data retrieval, forecast calibration and forecast verification. Specifically, the toolkit is composed of three modules that streamline the processes of retrieving the NMME data for locations and seasons of interest from high-dimensional datasets, calibrating raw GCM forecasts by the linear scaling, quantile mapping and Bernoulli-Gamma-Gaussian models and verifying predictive performances by twenty verification metrics and six types of diagnostic plots. The results show that the characteristics of bias, reliability and skill of raw forecasts vary across the globe and that the three calibration models in the pyNMME effectively improve raw forecasts to different extents. Overall, the pyNMME can serve as a useful tool to exploit valuable NMME precipitation forecasts for environmental modelling and management.