One of the authors here. Thanks for your comment! While a lot of this research is theoretical and does not have immediate use cases, we have tried to summarize some of them in the last paragraph of the paper (VII. Applications of Non-Euclidean Geometry). See page 26. We present some in Chemistry and Drug Development, Structural Biology and Protein Engineering, Computer Vision, Biomedical Imaging, Recommender Systems and Social Networks and Physics.
Thanks for the feedback! This model will evolve over time, we are planning on pushing a much higher resolution soon. Skill wise we will publish a paper in the next few months.
We are currently integrating with Forecast Watch a 3rd party that analyses and compare various forecasting systems [1]. Please stay tuned until we integrate our APIs. I will be updating this thread when it is ready.
We will be adding the Celsius switch shortly. We started with F since we expected most users of the forecast to be in the bay area but it is great to see interest from non Fahrenheit users :)
Thanks for the feedback :) The highest resolution models you can currently find on windy is around 3x3 km (HRRR) and this resolution is unfortunately too big to capture fine terrain and water features. 300x300 gives you 100 times more data points to work with.
While a good set of initial conditions is indeed critical, having a smaller model is helpful for modeling micro climates such as the ones you see in the Bay Area. At this resolution you can have a much more detailed representation of relief and water, which are two of the biggest drivers behind the beautiful dynamics we observe here.
Kalman filtering is only one part of the process, and plays a critical role during the data assimilation part. Classical Kalman filtering is optimal for Gaussian-distributed linear dynamical systems, but needs tweaks for non Gaussian distributions and non linear systems.
Classical NWP models for instance will integrate the primitive partial differential equations in time and space and run various parameterizations (which can be in some cases even more expensive than integrating the primitive equations). ECMWF on their end use IFS, which is a spectral method for solving the PDEs.
The whole process of solving these models accurately has definitely been some of the most fascinating science and engineering I’ve had the pleasure to work with. It’s extremely humbling :)