Blog | Deciphering compound rain-on-snow flood pathways as observed from satellites

New {discretes} R package available

Tue, 31 Mar 2026 00:00:00 +0000

Launching the Probaverse packages last December has for sure streamlined my workflow around distributions. But one critical thing was missing: native support for discrete distributions more generally.

While continuous distributions get the most attention, my workflows encounter distributions with a discrete component all the time. Think: empirical distributions; zero-inflated models; poisson processes.

The difficult part of discrete distributions is keeping track of what the possible outcomes are, because sometimes there are infinitely many possibilities, as in the Poisson distribution. Start manipulating these distributions, and keeping track of these becomes much harder.

That’s what the new {discretes} package does. I’m happy to announce that this package is now available on CRAN as of today! I talk more about this package in the official blog post on the probaverse.com website. While it stands alone from the Probaverse ecosystem, it will become a critical component to support discrete distributions, and the distributional learning tasks faced by this rain-on-snow research project.

Launch of the Probaverse

Sun, 14 Dec 2025 00:00:00 +0000

One great thing about moving into an academic position has been the space that’s available for finishing up half-baked initiatives.

In this case, I’ve been working on the Probaverse R packages over the past 5 years as a workaround for dealing with my own frustrations about the lack of computational support around probability distributions. Since this rain-on-snow research initiative heavily involves probability distributions, I wanted to get the infrastructure published so that the heavy work could be streamlined.

Take a look at the website to learn more about the Probaverse project. https://probaverse.com

Project Beginnings: A New Website

Wed, 17 Sep 2025 00:00:00 +0000

I’m excited to launch this new project website as we begin the research project, “Deciphering Compound Rain-on-Snow Flood Pathways as Observed from Satellites.” This project, supported by the European Space Agency’s Open Space Innovation Platform and the Politecnico di Milano, aims to enhance our understanding of compound climate events by advancing the analysis of satellite data.

The research will focus on developing an extremes-informed model that integrates statistical and machine learning methods to predict flood scenarios and their probabilities. By leveraging satellite data, we aim to explore new combinations of flood hazards and their impacts, ultimately contributing to better disaster preparedness and climate resilience.

Click around to learn more about the project. And, come back to this blog to see more project updates throughout the 2-year duration of the project.