This guest post is written by Daniel Rothenberg, one of our Google Summer of Code students.
As a meteorologist and student of climate science, I’m fascinated by the atmosphere and the weather it produces. Throughout my studies, I’ve learned to use equations and simplified models to explain how the atmosphere works and to predict its behavior. However, a key to understanding the atmosphere’s future lies in understanding its past; by studying past weather and climate, we can begin to learn how it has changed over time and might continue to change in the future.
Thankfully, such studies are made easier thanks to recorded observations about the weather and atmosphere, which extend back a century and cover much of the globe. These vast historical records provide a wealth of insight into how weather affects people and places, and also offer clues about the extent of natural climate variability. They also afford us a clear view on how weather and climate has changed over the industrial era.
Unfortunately, many weather observations recorded over the years are riddled with data quality issues. As time progresses, observation sites and observers come and go, and there are significant changes in methodology (migrating from analog to digital thermometers, for instance, or the time of day the observation is taken). Often, equipment can malfunction and drift from its initial calibrations.
These known biases complicate the process of reconstructing past weather, even for variables as innocuous as daily temperatures. To better understand observed climate variability, we try to identify and remove discontinuities and anomalies in the temperature record due to some of these biases. In fact, we automate this process with the help of “homogenization” algorithms, which compare temperature records against each other to identify potential data quality errors – even when we have no prior knowledge or record of them.
My project this summer is to translate several of these algorithms from the dense, difficult-to-understand FORTRAN codes used at the National Climactic Data Center into something simpler and easier to understand, and into something that can easily be used by people interested in studying the observational temperature record in more detail. By doing this, I hope to further the Climate Code Foundation’s goal of improving climate science software practices and opening up scientific software for public consumption and improvement. Ultimately, I hope this project will contribute positively to the public’s knowledge of and reception to the science behind climate change, as well as encourage my fellow scientists to adopt similar goals for their own codes and software.