Rain-Code Fusion : Code-to-code ConvLSTM Forecasting Spatiotemporal Precipitation
Takato Yasuno, Akira Ishii, Masazumi Amakata

TL;DR
This paper introduces a rain-code fusion method using ConvLSTM for improved spatiotemporal precipitation forecasting, enabling more accurate flood prediction over longer forecast horizons by reducing temporal bias.
Contribution
The paper presents a novel rain-code approach with multi-frame fusion to extend ConvLSTM forecast range for precipitation prediction, addressing its previous limitations.
Findings
Enhanced forecast accuracy over 6 hours for flood mitigation.
Effective rain-code representation reduces temporal bias.
Sensitivity analysis shows optimal rain-code size for accuracy.
Abstract
Recently, flood damage has become a social problem owing to unexperienced weather conditions arising from climate change. An immediate response to heavy rain is important for the mitigation of economic losses and also for rapid recovery. Spatiotemporal precipitation forecasts may enhance the accuracy of dam inflow prediction, more than 6 hours forward for flood damage mitigation. However, the ordinary ConvLSTM has the limitation of predictable range more than 3-timesteps in real-world precipitation forecasting owing to the irreducible bias between target prediction and ground-truth value. This paper proposes a rain-code approach for spatiotemporal precipitation code-to-code forecasting. We propose a novel rainy feature that represents a temporal rainy process using multi-frame fusion for the timestep reduction. We perform rain-code studies with various term ranges based on the standard…
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Taxonomy
TopicsPrecipitation Measurement and Analysis · Meteorological Phenomena and Simulations · Flood Risk Assessment and Management
MethodsSigmoid Activation · Convolution · Tanh Activation · ConvLSTM
