ML-based prediction of storm properties using KONRAD3D cell objects, lightning data, and environment conditions from NWP

Abstract

Object-based cell detection and tracking algorithms provide a useful tool for analyzing current and past storm properties and movement. KONRAD3D, an algorithm which recently became operational at DWD, uses radar scans of different elevation angles to derive 3-dimensional cell objects with specific properties.  Nowcasting of storm position by displacing it using the current cell movement seems to be straightforward, however, predicting the life cycle or specific storm properties such as lifetime, maximum future severity, and hail occurrence and size is difficult. We aim at analyzing the potential of machine learning techniques (ML), in particular random forest and gradient boosting, to provide these predictions using KONRAD3D cell properties in combination with NWP and lightning activity data. We perform a recalculation of KONRAD3D for a 6-year time period, where we considered several other data sources to compute specific storm environment and attributes. For instance, NWP data from the ICON-EU model is used to characterize the convective environment, while lightning data is used quantify electrical activity. Next, we filter resulting storm detections regarding our three prediction tasks using ML: (1) Maximum expected future cell severity: all storms are considered. (2) Longevity of quasi-stationary cells: only storms with a minimum speed below a threshold are considered. (3) Hail size: all storms are considered. For tasks 1 and 2, we analyze the prediction performance for different storm ages, i.e. cell attributes at initial detection, after 15 min, and 30 min during the life cycle are used as predictors for ML. Maximum hail size (task 3) is estimated using individual cell detections. In order to achieve a final comparison of methods, we perform a 10-fold cross validation Longevity of quasi-stationary storm events is difficult to predict since there many events with a short duration and only very few with a long duration. The distribution of maximum expected severity is less skew and therefore prediction scores are better. Random forest and gradient boosting are preferred over artificial neural networks, since decision tree methods are easier and faster to train and also provide better cross validation scores. In case of maximum future severity, a substantial improvement compared to a simple persistence forecast is achieved. ML-based prediction of maximum hail size delivers reasonable results and is able to outperform classical methods, such as MESH. However, prediction uncertainty remains high in most cases and needs to be quantified in order to generate meaningful predictions.