Hi @pswired Good question! Our “continuous learning” system is actually more than just one algorithm. It’s an ongoing process that performs a set of QC analyses and applies calibration corrections when necessary. Some of the QC steps do not require a reference source. But you’re correct that many steps in the process do need to compare data from your station to one or more reference sources (aka “trusted sources”). Trusted sources currently include gridded analysis products, physical models, mesoscale numerical models and aggregated weather station observations.
For a parameter like UV or RH, the basic step of creating a calibration curve doesn’t actually include any machine learning. For most parameters, that’s a simple line-fitting process (aka “fudge factor”). The AI/ML algorithms kick in when determining which data to use in building the calibration curve. That’s part of the secret sauce that’s continuously evolving and improving.
The haptic rain sensor is by far our biggest challenge as no home weather station before has had a sensor quite like this, and where AI/ML is playing a larger role. While every SKY out of the box is great at detecting rain and measuring relative intensities, we learned during the field test that it takes a lot more than a simple fudge factor (or simple curve fit) to translate this to a rain amount for an individual SKY. The relationship between rain rate and sensor signal is a function of many factors, and we’re still learning the nuances of how they all play together. While there is still a ways to go, we are very excited about the prospects for improving the process based on what we have already learned. So excited, in fact, that our staff will be presenting new findings at the next annual meeting of the American Meteorological Society in January 2019.
And remember, we’ve been looking at weather data for many years. We’re not just throwing numbers into a black box (although that works sometimes!). We’re leaning on our decades of meteorological expertise to inform the machine learning algorithms. It takes a lot of genius to create the first and only weather stations that get smarter over time.
In addition to ongoing automated calibration, our network systems will employ machine learning to better understand how nearly infinite weather variables affect each other. For example: the effects of temp or wind on rain accumulation. All of this will lead to improvements in both measurement and location-specific forecasting. More good things to come.