Can Convolutuonal neural networks be used for weather prediction using different sensor data frequencies?

Let's say there are sensors that feed meteorological input in different intervals 1 minute, 5 minutes, 15 minutes, 20 minutes. Can a CNN be trained to take data from all these sensors and predict rain probability in the next 1 hour? Can it be able to make the probability more accurate as new data gets fed in different sensors?