Updated 6 October 2019 with newer ML models
NeuralMET has now been rolled-out to multiple airports: EGAC (Belfast City, UK), EGNH (Blackpool, UK), EGNS (Isle of Man, UK), EGPF (Glasgow, UK), EGPK (Prestwick, UK), KLAX (Los Angeles, US), and KSFO (San Francisco). More are on the way (the historical METAR data-gathering has commenced: forecasts available in a few months time i.e., when sufficient historical data has been captured for training the models). Reasons for choosing these specific airports: (i) EGAC, EGNH, EGNS, EGPF and EGPK are all geographically close to one another, so if I ever wanted to extend the models to look for correlations across locations, these would be a suitable starting point for such analyses; (ii) KLAX and KSFO are in very different global locations than the others (all in the UK), and exhibit very different weather patterns. I thought it would be interesting to compare how the models work across such variations.
The performance of the forecasts certainly varies across the different locations. A recent set of Error Curves can be found here (see previous posts for definitions and explanations). No yet wholly conclusive, but the forecasts are generally improving compared with naïve estimates and random guesses (!)
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