This study develops a DNN framework

This study develops a DNN framework that is capable of extracting “deeper” information automatically and effectively from satellite IR imagery to reduce bias in satellite precipitation products. One significant difference between DNNs and traditional artificial neural networks is that DNNs aim to automatically extract information at multiple levels of abstraction to allow a system to learn a complicated functional mapping of the input to the output directly from the data while traditional neural networks tend to use manually designed features. It is achieved by applying the pretraining techniques to initialize weights to preserve information that better reconstructs the raw data (Bengio 2009). A more complete overview of the development of DNNs can be found in Bengio (2009).Figure 3 presents a four-layer, fully connected artificial neural network, as used in this study. The network consists of neurons (or nodes) organized in layers through connections between nodes. A node receives inputs from connections, sums them, and passes the summation through a transformation function (or activation function) to produce an output delivered to nodes in the next layer. In the network, nodes in the first (top) layer receive input data; nodes in the last layer send outputs. The layers between input and output layers are called hidden layer(s). Connections between nodes have different strength or weights to determine various input–output relationships. To possess a required functional mapping, a deep architecture must assign a specific value to each weight (parameters); this is accomplished automatically by training the network with available input and output data samples.