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Precipitation nowcasting based on radar echo maps is essential inmeteorological research. Recently, Convolutional RNNs based methods dominatethis field, but they cannot be solved by parallel computation resulting inlonger inference time. FCN based methods adopt a multi-frame-to-single-frameinference (MSI) strategy to avoid this problem. They feedback into the modelagain to predict the next time step to get multi-frame nowcasting results inthe prediction phase, which will lead to the accumulation of prediction errors.In addition, precipitation noise is a crucial factor contributing to highprediction errors because of its unpredictability. To address this problem, wepropose a novel Multi-frame-to-Multi-frame Inference (MMI) model with NoiseResistance (NR) named MMINR. It avoids error accumulation and resistsprecipitation noise\'s negative effect in parallel computation. NR contains aNoise Dropout Module (NDM) and a Semantic Restore Module (SRM). NDMdeliberately dropout noise simple yet efficient, and SRM supplements semanticinformation of features to alleviate the problem of semantic informationmistakenly lost by NDM. Experimental results demonstrate that MMINR can attaincompetitive scores compared with other SOTAs. The ablation experiments showthat the proposed NDM and SRM can solve the aforementioned problems.

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