How to Simplify Weather Impact on Society with Jared Goldberg of WeatherOptics

[Audio]  Podcast: Play in new window | Download Subscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSS Jared Goldberg is the Head of Data Science at WeatherOptics. He owns a Bachelor of Science in Biopsychology, Cognition, and Neuroscience; Applied Statistics from University of Michigan. Episode Links:   Jared Goldberg’s LinkedIn: https://www.linkedin.com/in/jared-goldberg-427462103  Jared Goldberg’s Twitter: @weatheroptics Jared Goldberg’s Website:https://www.weatheroptics.co/ https://github.com/jaredbgo  Podcast Details:  Podcast website: https://www.humainpodcast.com  Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009  Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS  RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9  YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag  YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos  Support and Social Media:   – Check out the sponsors above, it’s the best way to support this podcast – Support on Patreon: https://www.patreon.com/humain/creators  – Twitter:  https://twitter.com/dyakobovitch  – Instagram: https://www.instagram.com/humainpodcast/  – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/  – Facebook: https://www.facebook.com/HumainPodcast/  – HumAIn Website Articles: https://www.humainpodcast.com/blog/  Outline:  Here’s the timestamps for the episode:  (00:00) – Introduction (01:36) – WeatherOptics started as a weather blog way back when, from our founder and CEO, Scott Pecoriello, he was a weather nut growing up. And he had this blog, it was on Facebook and social media. And his snowfall accuracies were crazy good. In November of 2017, he reached out to me to bring the business into this tech side of things and into the data side of things. And we have just been gaining momentum since then. (03:15) –  Quantitative forecasting was started in the 1920s by the Norwegians. The modern era of forecasting started in the 1980s and that's where we had global forecasting models based on a more complex system of observations, but still building off these physics concepts that were used originally. Since the 1980s things have just gotten more complex. These models have gotten better. And now there's this whole wild system that no one really realizes is happening where you have all of these different inputs from all these weather gauges, like airplanes and satellites, and they're all amalgamated and interpolated into these models. (09:52) –  To some extent, everyone has this inherent understanding that the weather changes our behavior. However, we need to keep in mind that our business and where we really understand the weather better is the short term weather events. It is these sorts of impacts that obviously are not as flashy as something like a hurricane or a tornado, but we feel understanding how weather impacts daily life at these smaller scales and these less major events actually can save people and companies a lot of money and can really improve their processes. (14:11) – While some industries have been excluded or cut off, and obviously a lot of people are losing jobs, there are other industries that we are leaning on much heavier. And one of these industries is logistics. And one major application of our weather data is building useful ways to understand, not just that it is going to rain or I guess in this case, it is not just going to snow in. It's how is that going to affect your route? We are aware that weather has an impact on sales. So we consider weather data, a viable source of alternative data in terms of quantitative investing and things like that. We think these weather signals can help explain variations in other datasets that help us understand the market.  (17:46) –We have a combination of meteorological expertise, as well as machine learning. We have been very thorough to truly understand how the raw weather data paired with these non weather variables, add up to these actual impacts and we feel by delivering impacts as opposed to raw weather data, we are going to allow businesses to make impactful decisions, that way they do not have to wrestle with the data itself. (23:39) – We expect that these self-driving cars will need to have an even better safeguard against these road conditions that could be disruptive to normal driving. It is those sorts of interactions between variables that we feel our impact indices would allow people to have the upper hand to understand that just because it is raining does not mean that the roads are not going to be dangerous. And perhaps these cars, these very smart and intelligent cars should know the level of danger and how prepared they need to be in order to uphold the safety of the people using them. (27:37) – Power outages can be in terms of how weather affects humans on a day-to-day level. California outages would be the perfect use case where if the emergency management companies and government groups that were preparing for these things, if they had a really accurate forecast of what was going to happen in the future, based on the weather, then they could have had a better response. (32:32) – The whole idea of our company is these impact indices and all of our forecasts allow these companies to have the heads up to say, we think something disruptive is going to happen. So you should change your behavior in order to mitigate loss.And  once a company has identified that they would like the heads up about this bad weather, and they would like to understand how weather is going to impact their day to day operations, the whole idea is we want to deliver that information in a format that makes the most sense. (34:45) – Our insight portal is for more of the non-technical audience. And this is for individuals who perhaps are managing a certain geographic area.The insight portal is our attempt at the most user-friendly nontechnical delivery of these same insights. Our most technical offerings you could argue are our APIs, which are delivering the raw weather data itself, such that we give you those impacts very granularly. And then your data science team would get a chance to play around with it and use it in the way that is best for them we are building this middle ground to deliver things like Excel templates that have this weather data aggregated up.  (39:16) – We cannot blame individual events, but we do know that these large term changes can be attributed or are more evident that things are happening.So it's important to know that as the climate changes and as these big term big level changes happen, it's going to result in these small level things that are going to start affecting our lives. That's why it is just going to become increasingly important to know when those individual bad weather events are going to happen in order to prepare for these bad things and mitigate loss as we've discussed, but also we need to keep track of them.  (42:48) – In some ways, the weather can pop up relatively randomly and be quite disruptive across industries.Moving forward is getting these crop indices up, testing their accuracy and deploying them across our product suite.  (45:28) – This could even feed into that fire in terms of technology and improving and people realizing how important prediction is going to be. Maybe it'll just make people more excited about technology. I certainly hope so.  (47:40) – If people can use weather as a framework for technology and artificial intelligence as a whole, it will allow people to understand how powerful prediction is. Advertising Inquiries: https://redcircle.com/brands Privacy & Opt-Out: https://redcircle.com/privacy