Time‐averaged areal mean of precipitation: Estimation and network design

Publisher Website
Google Scholar
Abstract
Rainfall is recognized as a random process in time and space. With this in mind, data collection is treated as an estimation problem in which discrete, noisy, and incomplete information is used to estimate the true rainfall process. The estimation of the unknown time‐averaged areal mean of precipitation is accomplished through a state augmentation procedure and the use of multivariate linear estimation concepts, in particular, the Kalman‐Bucy filter. A technique results which can be used to analyze existing data networks, design new networks, and process data from existing networks. The procedure can handle any network configuration and explicitly accounts for the number of stations, their particular locations, the duration of observations, and the measurement errors. Results are presented.