Building a Rain Predictor. Discussion.

The index to the articles in this series is found here.

While I continue to download training data from Environment Canada, I’ll talk a bit about this project.

So, I’ve been calling this a rain predictor. What is it, and what is it not? This neural network project aims to look at a time series of radar images, specifically 6 images taken at 10 minute intervals before the present. These images are taken from the Franktown radar facility SW of Ottawa and show only active precipitation. It then attempts to predict how these rain patterns will move through the region, and whether and when to expect rain in Ottawa. The radar images have a coverage with a radius of 240 km.

Rain that forms directly overhead has no radar warning, and this network will always be surprised by that. This code is limited to making predictions based only on a short (one hour) time series of active precipitation images. It will never pretend to know the weather next week, or tomorrow.

This isn’t a forecasting application, the scientists at Environment Canada have their own sophisticated models for estimating weather patterns over different parts of the country on a time scale of hours to days.

The obvious next question is, does it work with snow? The training data I’m downloading now is a continuous record (barring station outages due to hardware maintenance or failure). It will have training data from snow events when this is done. It so happens that the data I collected in 2015 and again in 2019 were all for summer months, so the network has not been trained against winter weather patterns. I should have enough data downloaded in about a week to train a new network, and I expect it will happily incorporate winter conditions.

I started this project thinking I could just throw the pixels from the radar into a neural network. I hadn’t quite appreciated the scale of that, and the resulting network was unusably slow to train. I then came up with a simple manual data convolution that allowed me to discard two levels of the neural network, and reduce each 480×480 .gif images to a record 800 bytes long. Six of these, less than 5k in total, is the entire input to the neural network for one prediction.

I tried various regularizations, activations, and topologies, but so far haven’t found any places for obvious improvement. Examining the distribution of weights with TensorBoard shows very good behaviour, no pathologies that produce unphysical dependencies on subsets of the data. I’m downloading more training data now, and once I’ve trained a new network from that I’ll check again to see if there’s anything I can do to improve it.

Training time on my home machine is about 20-30 minutes to get a stable, but overtrained network in 200 epochs with the training set I’ve been using until now. The best validation network can appear as soon as 5 minutes in.

The resulting network is at least as good as I am at reading the radar image sequence and estimating rain in Ottawa.

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