Clean Technology Meets Data Science: News and Articles

Last time, we looked at how models and data science are used in measuring, monitoring, predicting and responding to a changing climate in the latest IPCC reports. Today, let’s look at the results from the reports. First, we’ve now reached an average warming level of 1.1 0 C [0.95 0 C - 1.20 0 C] compared to pre-industrial levels (1850-1900) . This result is based on satellite and sensor observations taken from the land and the oceans. Further, when compared to paleoclimate data (for e.g. data from ice core samples existing millions of years ago), the key indicators of the climate system are increasingly at levels that have not been seen for centuries and are changing at rates that are unprecedented for the last 2000 years. Also, several studies have shown that the ocean absorbed a significant amount of heat between 1998-2012, a process that resulted in a smaller rate of increase in land surface temperatures. However, this effect appears to be temporary, with strong warming seen since 2

I t’s Earth Month and the Inter-governmental Panel on Climate Change (IPCC) just released the third installment in the series of reports on the state of the planet. The first report dealt with the science of climate change and the second one looked at its impacts on society and the planet. The third report was released a few days ago and looked at our response to climate change as societies and what we can expect as a result. IPCC reports have been released approximately every decade since 1990 - the current one is the 6th installment. Several hundred scientists collaborate on the technical and scientific assessments - reviewing the latest research published globally, combining multiple scientific areas and disciplines - in order to develop as comprehensive a picture as possible of the state of the planet. Just as an example, the second report on impacts to the planet draws from 34,000 studies and involved 270 authors from 67 countries.   The first report , released in December 2021, e

In the first of our two-part conversation on machine learning in climate science, we talked about the main challenges in using machine learning in earth and environmental science. Today, let’s talk about why we go through the effort of using these tools in clean technology when they require a significant investment in understanding and modifying them. Why use machine learning? Three main reasons - 1) Do it better 2) Do it faster 3) Find unexplained trends or patterns. 1) Do it better : Last time we talked about the challenges of using machine learning in solving problems in clean technology. And that’s still true for unmodified, off-the shelf models. However, there’s a huge opportunity for scientists and engineers who are interested in understanding and adapting these models to make them work effectively with all the other tools in the tool box!   Let’s look at one such adaptation where machine learning algorithms can be used in concert with physics based models to generate more accur

  As the impacts of climate change on the planet become clearer, scientists and professionals in climate science are looking at the latest tools and technologies in AI and machine learning to help understand and mitigate the effects. At the same time, career opportunities in the field are growing and we’re seeing increasing numbers of students and early career professionals interested in developing and using their skills in ways that can help the planet.   So, when and where can machine learning and AI be used in climate science? And what are the pitfalls? If you’re working in environmental and earth sciences, you probably already have a pretty big toolbox that has been developed over several decades! It consists of standard statistical techniques including spatial and temporal statistics, a range of physics-based or process based models, and several data collection and data integration technologies at different scales.   What can machine learning add to this? Does it replace all the o

If there’s one area in clean technology and data science that’s seen tremendous growth in the last three years, it’s the use of robots, drones and satellites to collect data and perform tasks.   Satellite data was used in the early 1970s for applications in clean technology through machine learning and statistics. Back then, LandSat were the earliest satellites and the data were used to understand land use and monitor environmental impacts from space. Of course, today we’ve got a wide range of satellite data available for all kinds of applications - from traditional government sources like the LandSat and Copernicus satellites to commercial satellites launched by companies like Planet.   Similarly, we’ve seen an explosion in the use of robots and drones in different clean technology areas - ranging from ocean monitoring to repairing infrastructure like oil and sewer pipes to maintaining solar arrays and wind turbines to cleaning up plastic pollution to harvesting fruits and vegetab

As we enter 2022, let’s take a look back at what happened in the clean technology and data science space during the last year. It was certainly an interesting year from a technology perspective - and not just because the pandemic upended industries all over the world!   Clean technology saw some remarkable growth during the past year. We saw the explosion in the market share of electric vehicles (EVs), significant increases in renewable energy production and use around the world, the growth of wastewater surveillance and startups and utilities focusing on water management tools, and finally with COP26, we saw climate and sustainability come to the forefront with major players asking for better monitoring and decision making tools.   As clean technology sectors grew, we also saw data science tools and technologies being deployed more frequently - not just by startups and other innovators, but also by larger companies and organizations. And data science was being used not just to develop