Accurate Precipitation Forecast by Efficiently Learning from Massive Atmospheric Variables and Unbalanced Distribution

Abstract

Short-term (0-24 hours) precipitation forecasting is highly valuable to socioeconomic activities and public safety. However, the highly complex evolution patterns of precipitation events, the extreme imbalance between precipitation and non-precipitation samples, and the inability of existing models to efficiently and effectively utilize large volumes of multi-source atmospheric observation data hinder improvements in precipitation forecasting accuracy and computational efficiency. To address the above challenges, this study developed a novel forecasting model capable of effectively and efficiently utilizing massive atmospheric observations by automatically extracting and iteratively predicting the latent features strongly associated with precipitation evolution. Furthermore, this study introduces a 'WMCE' loss function, designed to accurately discriminate extremely scarce precipitation events while precisely predicting their intensity values. Extensive experiments on two datasets demonstrate that our proposed model substantially and consistently outperforms all prevalent baselines in both accuracy and efficiency. Moreover, the proposed forecasting model substantially lowers the computational cost required to obtain valuable predictions compared to existing approaches, thereby positioning it as a milestone for efficient and practical precipitation forecasting.

Figures (18)

• Figure 1: Comparison between the precipitation prediction framework. ConvLSTM NIPS2015_07563a3f and PredRNN 9749915 primarily focus on ultra-short-term severe convection and thunderstorms nowcasting. Metnet models snderby2020metnetEspeholt2022andrychowicz2023 employs a dense interval-partitioned cross-entropy loss to train its single-lead-time network. In contrast, our novelly designed latent space iterative prediction framework integrates a ‘WMCE’ loss to train the network for short-term precipitation forecasting.
• Figure 2: Network structure and loss function of our proposed forecasting model. The ‘labels’ include the MRMS hourly cumulative QPE from ‘USA’ dataset or near-surface radar reflectivity from ‘Hubei’ dataset. And the ‘MAE’ and ‘CE’ losses for precipitation samples employ a weighting scheme, as presented in Eq. \ref{['a9990']} and Eq. \ref{['a9991']}. ‘LPM’ represents the latent feature iterative prediction model. It is worth noting that the encoder simultaneously encodes the latent features from two time steps.
• Figure 3: Network structure of the designed encoder. Note that the input for the ‘USA’ dataset does not include satellite imagery and the input for the ‘Hubei’ dataset does not include MRMS QPE.
• Figure 4: The network structure of ViT-based latent feature iterative prediction model ($LPM$). It takes the latent atmospheric features of the previous two time steps as input and outputs the predicted latent feature for the next time step. The input also includes the corresponding time embeddings and constant embeddings. After passing through the multi-scale residual blocks, 3D convolutional layer, ViT, and the multi-scale residual blocks again, the predicted results are output. Note that the ‘multi-scale residual blocks’ are identical to those in the encoder, as depicted in Fig. \ref{['fig:encoder']}.
• Figure 5: A schematic diagram of the inference process for the improved HTA algorithm, which takes two time steps as input for iterative prediction. Arrows of different colors represent predictions with different time step intervals.