Visualizing Uncertainty for Probabilistic Weather Forecasting based on Reforecast Analogs (Introductory Talk)

  We present “Albero”, a visual analytics solution that unfolds different aspects of probabilistic forecasts based on the Reforecast Analog Regression technique. This technique comprises the analysis of large series of past forecasts generated with a specific numerical model and observations. An analog regression can be used to obtain a better estimation of systematic model-biases and errors. Albero allows forecasters to study probabilistic forecasts, their inner analogs and observations, their associated spatial errors and additional statistical information by means of coordinated and linked views. Our solution presents a three nested-loops process of visual analysis, unveiling the cyclical mechanisms of the analytics discourse to convey new visual knowledge.