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This post has been originally published in July 2020. This is an updated version with the data available at the beginning of 2024 Although the Netherlands is a not a country that everyone would define “sunny”, solar power is becoming there more and more common.

(This article has been published on Medium) Assessing climate risk — the potential negative consequences associated with climate change — is a formidable task for many reasons. Among these lies the difficulty in dealing with uncertainty.

I live in the Netherlands in a terraced house (rijtjeshuis), one of those very common houses where - as far as I can see walking in my neighbourhood - solar panels on the roofs are extremely common.

I have recently published a Python notebook illustrating how to create a model for all the European power systems using only open data and tools. Basically, selecting a list of countries and a period (for example from 1st January 2021 to 1st February 2021), the code creates the inputs needed for the PyPSA open source model to simulate the hourly behaviour of the European power systems, based on the data from ENTSO-E (for electricity generation, infrastructure) and from Copernicus Climate Change Service (C3S) for the meteorological-derived variables (e.

The impacts of climate change on energy systems may be complex. And what about its impact on the energy systems we will have in Europe in 2050? This is the topic of the paper led by Hannah Bloomfield from the University of Reading, UK, recently published on Renewable Energy and that I co-authored.

I am the lead author of a report titled JRC Technical report on the impact of climate variability in the European power systems and released a few days ago. One of the main topics of the report is the co-variability of renewables generation in Europe.

Modelling (and thus understanding) the generation of renewable energy sources is very important, especially considering their link with meteorological variables. In this blog post I would like to focus on run-of-river hydropower, a common source of electricity in Europe.

I have recently shared a Python notebook using Google Colab (and GitHub). The code implements an entire workflow for climate science starting from data retrieving (seasonal forecasts and reanalysis) to the calculation of performance metrics (deterministic and probabilistic).

In the last years I needed many times to aggregate the data into a gridded dataset (for example ERA5 meteorological data) into a time-series, according to specific borders (for example administrative regions).

Girolamo Ruscelli, an Italian cartographer and writer, in 1561 drawed this map of West Africa. Today, after almost 500 years, we can access from literally everywhere satellite images with an incredibly high-resolution, like the following one produced by NASA/NOAA Suomi-NPP and download from Worldview.