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

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 ConvLSTM. We applied to a dam region within the Japanese rainy term hourly precipitation data, under 2006 to 2019 approximately 127 thousands hours, every year from May to October. We apply the radar analysis hourly data on the central broader region with an area of 136 x 148 km2 . Finally we have provided sensitivity studies between the rain-code size and hourly accuracy within the several forecasting range.