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Recently, flood damage has become a social problem owing to unexperiencedweather conditions arising from climate change. An immediate response to heavyrain is important for the mitigation of economic losses and also for rapidrecovery. Spatiotemporal precipitation forecasts may enhance the accuracy ofdam inflow prediction, more than 6 hours forward for flood damage mitigation.However, the ordinary ConvLSTM has the limitation of predictable range morethan 3-timesteps in real-world precipitation forecasting owing to theirreducible bias between target prediction and ground-truth value. This paperproposes a rain-code approach for spatiotemporal precipitation code-to-codeforecasting. We propose a novel rainy feature that represents a temporal rainyprocess using multi-frame fusion for the timestep reduction. We performrain-code studies with various term ranges based on the standard ConvLSTM. Weapplied to a dam region within the Japanese rainy term hourly precipitationdata, under 2006 to 2019 approximately 127 thousands hours, every year from Mayto October. We apply the radar analysis hourly data on the central broaderregion with an area of 136 x 148 km2 . Finally we have provided sensitivitystudies between the rain-code size and hourly accuracy within the severalforecasting range.

Rain-Code Fusion : Code-to-code ConvLSTM Forecasting Spatiotemporal Precipitation