An Adaptive Neural Network Scheme for Radar Rainfall Estimation from WSR-88D Observations

Abstract

Recent research has shown that neural network techniques can be used successfully for ground rainfall estimation from radar measurements. The neural network is a nonparametric method for representing the relationship between radar measurements and rainfall rate. The relationship is derived directly from a dataset consisting of radar measurements and rain gauge measurements. The effectiveness of the rainfall estimation by using neural networks can be influenced by many factors such as the representativeness and sufficiency of the training dataset, the generalization capability of the network to new data, season change, location change, and so on. In this paper, a novel scheme of adaptively updating the structure and parameters of the neural network for rainfall estimation is presented. This adaptive neural network scheme enables the network to account for any variability in the relationship between radar measurements and precipitation estimation and also to incorporate new information to the network without retraining the complete network from the beginning. This precipitation estimation scheme is a good compromise between the competing demands of accuracy and generalization. Data collected by a Weather Surveillance Radar-1988 Doppler (WSR-88D) and a rain gauge network were used to evaluate the performance of the adaptive network for rainfall estimation. It is shown that the adaptive network can estimate rainfall fairly accurately. The implementation of the adaptive network is very efficient and convenient for real-time rainfall estimation to be used with WSR-88D.

Investigating rainfall estimation from radar measurements using neural networks

“…It has been demonstrated in prior work that RBF Neural Network is capable of learning the relation between ground radar measurements and rain gauge data (Liu et al, 2001;Orlandini and Morlini, 2000;Xu and Chandrasekar, 2005;Teschl et al, 2007). In this paper, an adaptive relation between ground radar measurements and rain gauge measurements will be developed in the training process, and studies are conducted to improve the performance of the network.…”

“…It gets its name from the use of the radial basis function as activation function in the hidden layer. Figure 1 shows the structure of an RBF network (Liu et al, 2001). It contains three layers which are the input layer, the hidden layer and the output layer.…”

“…Another concern is that the new data might not carry new information. Therefore, the idea of adaptively training the RBFNN on a daily basis is useful (Liu et al, 2001). To include the information from the new data, it is necessary to update the network not only by adding some neurons, but also by removing some neurons.…”

“…It is well established that an empirical Z-R relation is not sufficient to capture the variability and has large uncertainty and it needs to be adaptively adjusted based on validation (Cifelli and Chandrasekar, 2010). Prior research has shown that neural networks can be used to estimate ground rainfall from radar measurements (Xiao and Chandrasekar, 1997;Xiao et al, 1998;Liu et al, 2001;Orlandini and Morlini, 2000). The usefulness of the rainfall estimation using neural networks is subject to many factors such as the representativeness and sufficiency of the training dataset, the generalization capability of the network to new data, seasonal changes, regional changes, and so on.…”

Recent advances in data-driven modeling of remote sensing applications in hydrology

“…Even though the above Z-R equation is rather simple, the relationship between reflectivity and rainfall rate is indeed more complex and has been restricted within Previous work by Liu et al (2001) demonstrated that an adaptive radial basis function neural network was able to provide rainfall estimation fairly accurately. This study was intended for real-time implementation of ANN-based rainfall retrieval algorithm on WSR-88D radars.…”