MMINR: Multi-frame-to-Multi-frame Inference with Noise Resistance for Precipitation Nowcasting with Radar

TL;DR

This paper introduces MMINR, a novel multi-frame inference model with noise resistance for radar-based precipitation nowcasting, addressing error accumulation and noise issues with innovative modules, achieving competitive results.

Contribution

The paper proposes MMINR, a multi-frame-to-multi-frame inference model with noise resistance, featuring NDM and SRM modules to improve accuracy and robustness in radar precipitation prediction.

Findings

MMINR achieves competitive prediction scores.

NDM and SRM effectively reduce noise impact.

Model resists error accumulation in multi-frame nowcasting.

Abstract

Precipitation nowcasting based on radar echo maps is essential in meteorological research. Recently, Convolutional RNNs based methods dominate this field, but they cannot be solved by parallel computation resulting in longer inference time. FCN based methods adopt a multi-frame-to-single-frame inference (MSI) strategy to avoid this problem. They feedback into the model again to predict the next time step to get multi-frame nowcasting results in the prediction phase, which will lead to the accumulation of prediction errors. In addition, precipitation noise is a crucial factor contributing to high prediction errors because of its unpredictability. To address this problem, we propose a novel Multi-frame-to-Multi-frame Inference (MMI) model with Noise Resistance (NR) named MMINR. It avoids error accumulation and resists precipitation noise\'s negative effect in parallel computation. NR contains a Noise Dropout Module (NDM) and a Semantic Restore Module (SRM). NDM deliberately dropout noise simple yet efficient, and SRM supplements semantic information of features to alleviate the problem of semantic information mistakenly lost by NDM. Experimental results demonstrate that MMINR can attain competitive scores compared with other SOTAs. The ablation experiments show that the proposed NDM and SRM can solve the aforementioned problems.