INVESTIGATING EFFECTIVE RAIN GAUGE LOCATIONS THROUGH THE DATA-DRIVEN SPARSE SENSOR PLACEMENT METHOD

Abstract

 Rain gauges play an important role in estimating the spatiotemporal distribution of precipitation and should be installed in effective locations. This study investigates better rain gauge locations using the data-driven sparse sensor placement method (SSP). The SSP can also reconstruct the entire rain fields from a limited number of sparse observations. This study extends to use data assimilation for the reconstruction of the entire rain field (DA-based reconstruction). Using Radar-raingauge analyzed precipitation data from the Japan Meteorological Agency, we investigated the impacts of SSP-based placements of rain gauges in Hokkaido, Japan. We found that DA-based reconstruction yields better estimates on rain fields than the commonly-used nearest neighbor method when the present AMeDAS observation sites are used. The SSP reduced errors in estimated rain fields relative to the AMeDAS placements, suggesting that SSP can be used to place better rain gauge stations in Japan.