Category Archives: anomaly detection
Source: How to Describe Numbers from the Stats With Cats blog.
On Ed Hawkins’ blog. The Committee on Science, Space & Technology of the US House of Representatives conducts regular evidence hearings on various science topics. On Wednesday 29th March, there is a hearing on “Climate science: assumptions, policy implications, and … Continue reading
Zeke Hausfather at And Then There’s Physics regarding Baselines and Buoys.
(This blog post was updated 19th January 2017 with a correction to the interpretation of the leak data. The correction was offered by Professor Phillips. The blog author is responsible for the original misunderstanding. Apologies for any inconvenience.) The West … Continue reading
Hint: Climate change has somethin’ to do with it. Schematic diagram illustrating the component parts of the AMOC and the 26◦ N observing system. Black arrows represent the Ekman transport (predominantly northward). Red arrows illustrate the circulation of warm waters … Continue reading
Dr Schmidt is essentially betting that the trend, seen as a random variable, will regress towards the smooth mean. I have a post at Nate Silver’s 538 site on how we can predict annual surface temperature anomalies based on El … Continue reading
Famous climate denialist Joe Bastari of WeatherBELL Analytics LLC, formerly of Accuweather.com made a prediction on Arctic ice recovery back in 2010 (when at AccuWeather), and observations have since made his “studies” laughable. I have heard his colleague, Joseph D’Aleo … Continue reading
Originally posted on Climate Denial Crock of the Week:
Hermine still developing. Predictions are for it to hold in place off the East Coast for several days, due to a blocking pattern known as a “Rex Block”. This and many…
(See the major update at the bottom of this post as well.) (On “Less Science and More Social Science” at And Then There’s Physics) And Then There’s Physics is one of my favorite blogs discussing climate disruption and related policy … Continue reading
Many people seem to view the electrical grid of the future being much like the present one. I think a lot about networks, because of my job. And I especially think a lot about network topologies, although primarily concerning the … Continue reading
The Bayesian blocks algorithm of Scargle, Jackson, Norris, and Chiang has an enthusiastic user community in astrostatistics, in data mining, and among some in machine learning. It is a dynamic programming algorithm (see VanderPlas referenced below) and, so, exhibits optimality … Continue reading