Optical flow models as an open benchmark for radar-based precipitation nowcasting (rainymotion v0.1)

Abstract

Abstract. Quantitative precipitation nowcasting (QPN) has become an essential technique in various application contexts, such as early warning or urban sewage control. A common heuristic prediction approach is to track the motion of precipitation features from a sequence of weather radar images and then to displace the precipitation field to the imminent future (minutes to hours) based on that motion, assuming that the intensity of the features remains constant (“Lagrangian persistence”). In that context, “optical flow” has become one of the most popular tracking techniques. Yet the present landscape of computational QPN models still struggles with producing open software implementations. Focusing on this gap, we have developed and extensively benchmarked a stack of models based on different optical flow algorithms for the tracking step and a set of parsimonious extrapolation procedures based on image warping and advection. We demonstrate that these models provide skillful predictions comparable with or even superior to state-of-the-art operational software. Our software library (“rainymotion”) for precipitation nowcasting is written in the Python programming language and openly available at GitHub (https://github.com/hydrogo/rainymotion, Ayzel et al., 2019). That way, the library may serve as a tool for providing fast, free, and transparent solutions that could serve as a benchmark for further model development and hypothesis testing – a benchmark that is far more advanced than the conventional benchmark of Eulerian persistence commonly used in QPN verification experiments.

Pysteps: an open-source Python library for probabilistic precipitation nowcasting (v1.0)

“…The tools available in the noise and the time series modules can be used for stochastic simulation of design storms (e.g., Seed et al, 1999;Paschalis et al, 2013) and weather generators (Peleg et al, 2017), and also to understand and quantify the subpixel variability of radar rainfall (e.g., Gires et al, 2014;Benoit et al, 2018;Peleg et al, 2016). Other applications can include stochastic downscaling or emulation of climate model output (e.g., Raut et al, 2018;.…”

Convcast: An embedded convolutional LSTM based architecture for precipitation nowcasting using satellite data

“…Rivolta et al [14] used simple feed-forward neural network without any consideration for spatiotemporal aspects which leads to large error in nowcasting results. Optical flow based techniques in nowcasting have limited success results because the tracking of pixels and extrapolation of values during prediction are considered as two separate processes [17,18].…”

Precipitation Nowcasting with Orographic Enhanced Stacked Generalization: Improving Deep Learning Predictions on Extreme Events

“…More formally, given a reflectivity field at time T 0 , radar-based nowcasting methods aim to extrapolate m future time steps T 1 , T 2 , ..., T m in the sequence, using as input the current and n previous observations T −n , ..., T −1 , T 0 .Traditional nowcasting models are manly based on Lagrangian echo extrapolation [7,8], with recent modification that try to infer precipitation growth and decay [9,10] or integrate with Numerical Weather Predictions to extend the time horizon of the prediction [11,12]. In the last few years, Deep Learning (DL) models based on combination of Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) have shown substantial improvement over nowcasting methods based on Lagrangian extrapolations for quantitative precipitation forecasting (QPF) [13]. Shi et al [14] introduced the application of the Convolutional Long Short-Term Memory (Conv-LSTM) network architecture with the specific goal of improving precipitation nowcasting over extrapolation models, where LSTM is modified using a convolution operator in the state-to-state and input-to-state transitions.…”