Abstract
Abstract. Eastern China is one of the most economically developed and densely populated areas in the world. Due to its special geographical location and climate, eastern China is affected by different weather systems, such as monsoons, shear lines, typhoons, and extratropical cyclones. In the near future, the rainfall rate becomes difficult to predict precisely due to these systems. Traditional physics-based methods such as numerical weather prediction (NWP) tend to perform poorly on nowcasting problems due to the spin-up issue. Moreover, various meteorological stations are distributed in this region, generating a large amount of observation data every day, which have great potential for application to data-driven methods. Thus, it is important to train a data-driven model from scratch that is suitable for the specific weather situation of eastern China. However, due to the high degrees of freedom and nonlinearity of machine learning algorithms, it is difficult to add physical constraints. Therefore, with the intention of using various kinds of data as a proxy for physical constraints, we collected three kinds of data (radar, satellite, and precipitation data) in the flood season from 2017 to 2018 in this area and preprocessed them into tensors (256×256) that cover eastern China with a domain of 12.8×12.8∘. The developed multisource data model (MSDM) combines the optical flow, random forest, and convolutional neural network (CNN) algorithms. It treats the precipitation nowcasting task as an image-to-image problem, which takes radar and satellite data with an interval of 30 min as inputs and predicts radar echo intensity with a lead time of 30 min. To reduce the smoothing caused by convolutions, we use the optical flow algorithm to predict satellite data in the following 120 min. The predicted radar echoes from the MSDM together with satellite data from the optical flow algorithm are recursively implemented in the MSDM to achieve a 120 min lead time. The MSDM predictions are comparable to those of other baseline models with a high temporal resolution of 6 min. To solve blurry image problems, we applied a modified structural similarity (SSIM) index as a loss function. Furthermore, we use the random forest algorithm with predicted radar and satellite data to estimate the rainfall rate, and the results outperform those of the traditional, nonlinear radar reflectivity factor and rainfall rate (Z–R) relationships that use logarithmic functions. The experiments confirm that machine learning with multisource data provides more reasonable predictions and reveals a better nonlinear relationship between radar echo and precipitation rate. Apart from developing complicated machine learning algorithms, exploiting the potential of multisource data will yield more improvements.
Highlights
In recent years, deep learning (DL) and machine learning (ML) have achieved great advances with big data
The input of optical flow and ConvLSTM is a sequence of five frames before time 0, and the output is a sequence of five frames in the following 0.5 h
The input of U-Net is a single frame of the radar echo data at time 0, and the input of the multisource data model (MSDM) includes a frame of satellite data and a frame of the radar echo data
Summary
Deep learning (DL) and machine learning (ML) have achieved great advances with big data. Tremendous meteorological data are produced every day, which perfectly matches these novel data-driven artificial intelligence (AI) approaches. Quantitative precipitation nowcasting (QPN) using radar echo extrapolation (REE) has recently become popular (Tran and Song, 2019). Precipitation nowcasting predicts rainfall intensity in the following few hours. Based on various data with high spatiotemporal resolutions, AI precipitation prediction can be relatively accurate compared to traditional numerical weather prediction (NWP) methods. U-Net (Ronneberger et al, 2015) is a well-known network designed for image segmentation, and its core is upsampling, downsampling, and skip connection.
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