Exploring the use of 3D radar measurements in predicting the evolution of convective rain cells

Object-based radar rainfall nowcasting is a widely-used technique for convective storm prediction. Due to the data and algorithmic limitations, most existing object-based nowcasting methods focus on predicting the movements of each rain object (or cell). The evolution of rain cells’ properties (e.g. cell size, shape and intensity) themselves is often neglected. It is however critical to account for the temporal changes in cells’ properties in order to improve the predictability for convective storms. 

 

In the literature, three-dimensional (3D) radar images have been used for observing the vertical feature changes through the formation process of convective rain cells. This shows the potential of extracting useful information from 3D images to facilitate characterising the life cycle of rain cells. Most of these works however focused on analysing or reconstructing the life cycles of individual convective rain cells or storm events. It remains an open challenge to incorporate 3D radar rainfall information into object-based radar rainfall nowcasting. 

 

In this research, we would like to explore the use of deep learning techniques to predict the evolution of convective rain cells. The proposed work comprises two main parts. The first part is rain cell data preparation. An enhanced TITAN storm tracking algorithm proposed by Muñoz et al. (2018) is employed to identify 2D rain cells and their temporal associations (or tracks) across successive time steps. The information of 2D cells are then used to extract cell properties from 3D radar images. These include mean reflectivity, area, major and minor axis lengths and the convective core altitude of each rain cell. In the second part of the work, a LSTM-Encoder-Decoder model is developed, which uses cells’ properties from the past 15 min to predict the evolution of these properties in the next 15 min. 

 

A total of 4708 lifespans of rain cells extracted from high-resolution (5-min, 1 km, 24 levels) 3D radar images are used to train the model, and a total of 1177 extracted lifespans are used to validate the prediction result. The result suggests that the proposed LSTM-Encoder-Decoder model can well predict the evolution of cells’ properties, and, with the employed 3D information (core altitude), the prediction errors of mean reflectivity can be further reduced by 20-25% at 15-min forecast lead time.  

 

Reference

Muñoz, C., Wang, L.-P., and Willems, P. (2018). Enhanced object-based tracking algorithm for convective rain storms and cells. Atmospheric Research, 201:144–158.