The Use of Artificial Neural Networks Within Boundary Layer Meteorology

Artificial Neural Networks (ANNs) are a class of supervised learning algorithms that have found widespread utilization in a broad variety of fields. ANNs also show promise within the field of boundary layer meteorology. In this work we show ANNs can be used to perform a range of useful tasks on experimental field data. Using data from a system of low-cost distributed sensors, we use ANNs to perform spatial gap filling and evaporative flux prediction.