July Edition: Climate Resources for Data Scientists

Author:Murphy | View: 21085 | Time: 2025-03-23 18:11:35

Monthly Edition

For many of us, the arrival of summer used to be a cause of uncomplicated excitement: school's out; work schedules often get a bit less hectic; the prospect of a lazy afternoon on the beach or at the nearest park beckons.

We don't mean to put a damper on your positive summer vibes (as a Canada-based team, we appreciate a nice, sunny day as much as anyone), but these days it's hard not to feel a more complex mix of emotions about the warm season. Many of our readers live in areas affected by wildfires (and their far-reaching, border-crossing smoke), drought, floods, and other extreme weather events, and it's all but certain we'll experience more of these Climate Change-related phenomena in years to come.

Unlike many other professions, though, data practitioners are in a position to play an important role in shaping climate discussions and effect real change—whether it's through helping communities and policymakers become more aware of the impact of their choices, or by modeling (and in some cases building) potential solutions.

We've gathered a strong lineup of climate-focused articles and resources to get you thinking about the ways we can harness data and Machine Learning tools to address our current (and future) challenges. We hope our reading recommendations inspire you, at the very least, to learn more and become more engaged in these conversations.

Before we dive in, we wanted to thank you, as always, for all your support. For those of you who'd like to make a meaningful contribution, consider becoming Medium members.

TDS Editors

TDS Editors Highlights

Five Free and Reliable Weather Data Sources ([Anthony Baum](https://towardsdatascience.com/july-edition-climate-resources-for-data-scientists-72a614607d43/None), May 2023, 6 minutes) and Top 5 Places to Find Climate Change Datasets ([Eugenia Anello](https://towardsdatascience.com/july-edition-climate-resources-for-data-scientists-72a614607d43/None), June 2023, 6 minutes) Before data scientists can begin working on climate issues in earnest, they first need to get their hands on robust, reliable, and up-to-date data. Anthony Baum and Eugenia Anello each compiled a handy list of resources that deliver on that front.
Flight Impact: Adding Carbon Emissions to the Itinerary (January 2022, 4 minutes) It's difficult to raise awareness about the link between our consumption habits and climate change when these two areas rarely converge in our day-to-day lives. Nina Sweeney aims to change that by creating an app that informs travelers of the emissions generated through their itineraries.
Time Series for Climate Change: Reducing Food Waste with Clustering (June 2023, 6 minuts) Vitor Cerqueira‘s excellent series on time-series analysis has approached climate issues from a wide range of angles. One recent installment looks into the crucial problem of food waste: "Reducing overproduction is an important milestone for decreasing greenhouse gas emissions. We can tackle this problem by better understanding how much we need."
Assessing Global Temperature Anomaly Using NASA's Space Studies: Part 1 (October 2022, 12 minutes) and Part 2 (June 2023, 10 minutes) How can we explain the frequency of recent extreme weather events? Himalaya Bir Shrestha looks at NASA data as the starting point for a hands-on exploration of global surface temperatures.
Accessing and Visualizing Digital Elevation Models with Python (March 2023, 7 minutes) Governments and other organizations will need to use geospatial data analysis to better prepare for a changing climate and to protect people and infrastructure from its potentially disastrous impact. Parvathy Krishnan (with coauthors Mahdi Fayazbakhsh and Kai Kaiser) take a close look at the role digital elevation models might play in this context.
Green AI: Methods and Solutions to Improve AI Sustainability (June 2023, 9 minutes) The environmental cost of training, deploying, and running compute-heavy models is becoming a major concern—especially as generative-AI tools go mainstream. Federico Peccia‘s recent overview of green AI initiatives and the research powering innovation in this field is a helpful primer for anyone who cares about ensuring AI's growing footprint is sustainable.

Original Features

Explore our latest selection of resources and reading recommendations.

[The Challenge of Seeing AI's Big Picture](http://The Challenge of Seeing AI's Big Picture)Take a step (or two) back to explore the bigger themes around recent developments in AI—we've selected some of our best recent articles on this ever-evolving field.
For Data Scientists, There's Always a New Python Skill to LearnFrom new packages to innovative workflows, don't miss our collection of standout programming-focused guides.