Technical Note: A simple feedforward artificial neural network for high temporal resolution classification of wet and dry periods using signal attenuation from commercial microwave links

Two simple feedforward neural networks (MLPs) are trained to classify wet and dry periods using signal attenuation from commercial microwave links (CMLs) as predictors and high temporal resolution reference data as target. MLPGA is trained against nearby rain gauges and MLPRA is trained against gauge-adjusted weather radar. Both MLPs perform better than existing methods, showcasing their effectiveness in capturing the intermittent behaviour of rainfall. This study is the first using both radar and rain gauges for training and testing for CML wet-dry classification. Where previous studies has mainly focused on hourly reference data, our findings show that it is possible to predict wet and dry periods with a higher temporal precision.