From the Descriptive to the Predictive – Analytics in Climate Science

With progress in the field of data science; climate experts, private players and the academia, are exploring the scope of information that can be extracted from climate trends and computer models. In recognition of the need for innovation in climate data analytics, the United Nations even unveiled a Big Data Climate Change Challenge that saw winning examples showing how Big Data can mitigate climate risks and strengthen resilience.

Climate Data gets Bigger

Data related to past and current climate observations across the world is being amassed every moment. However, without analysing this information or using it for predictions, all this voluminous data is of little relevance.

This gap has been closing over the past few decades. Peer-reviewed methods have applied easily available open-source software for research and proven analytical methodology to decode this Big Climate Data. Detailed analysis of the data has enabled climate trends to be forecast and predictive models to be built for decision making.

In an era of climate change and extreme weather patterns, policy makers need to understand weather and climate changes. This is made possible by various statistical models that create simulations of climate change at both the regional and global levels. As various Big Data analysis software emerge, comparison between multiple versions of complex datasets has helped to derive detailed insights for an integrated climate picture.

Making predictions and taking appropriate decisions on time ultimately help safeguard lives and property, cutting down on disaster response time and costs.

Big Data Analytics in climate science has already gained momentum in the U.S. with initiatives taken by the NASA, academia and policy makers. Can we hope to see students and analytics data science professionals in India too, take on the challenge of applying analytics to climate data?


  • High-resolution maps and datasets with confidence estimates for a location and point in time picture
  • Climate change simulations for patterns in extreme climate, heavy rainfall or heat waves, underlying factors that drive them
  • Sea level rise estimates in real-time
  • Inundation analysis tools
  • Social vulnerability index in vulnerable areas
  • On-site weather predictive modeling to assess potential delays of projects / undertake risk analysis

The possibilities are endless.

Already the prolific use of data analytics and Statistical software are evident across social and healthcare sectors, as climate analytics becomes core to epidemiology studies for deeper insights.

Application of Visual Analytics

Web-based visual analytics is leveraged for advanced visualisation and analysis capabilities for large-scale earth system simulations. Visual analytics helps minimize data movements and leverage HPC platforms through distributed framework with multi-scale statistical views. Besides interactive spatio-temporal data mining methods that preserve details for a time-series analysis, multivariate data analytics are also being applied to climate science.

The Role of Big Data

The rise of Big Data is helping make more accurate predictions of storms and extreme weather patterns. Advances in cloud computing are also allowing an integrated picture of massive weather-related data sets. Is it any wonder that various start-ups have entered this domain to not only support the DSS (Decision Support System) of governance, but also use such information in area like insurance?

Emergence of Climate Analytics-as-a-service

To meet the major Big Data challenges in climate science, Cloud Computing and machine learning is used for Climate Analytics-as-a-Service (CAaaS). Examples like the MERRA Analytic Services which orchestrates Big Data with Climate Analytics-as-a-Service are inspiring analytics professionals and developers. This Climate Analytics-as-a-Service model performs “analyses using the MapReduce parallel computing approach running on Hadoop technology”, with the Climate Model Data Services (CDS) API for web service access. What’s more, the “Climate Analytics-as-a-Service (CAaaS) technology stack can be deployed on local enterprise hardware or on the cloud”.

Bottomline – So whether you are a student who was always enamoured with earth or climate science, or an analytics professional looking for a way to leverage analytics innovatively, this is an area that you could look at.

The capabilities of data analytics in this area of climate science are unlimited. However, you need to have an understanding of huge datasets, the cloud environment and parallel computing as well as take advantage of analytics courses if you are working in government or climate research. And if you are a developer, you may choose to explore the location aspect and Big Data, both of which are critical to climate analytics. While ESRI’s ArcGIS has some weather analysis  capabilities, in my opinion the way to go is the Big Data way, to integrate climate analytics with location analytics for deep and sweeping insights.

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