In a world that is constantly evolving, weather and climate research is preparing us for today’s conditions and tomorrow’s trends. Weather data analytics not only helps scientists to identify and track weather patterns, but it also enables organizations to provide real-time updates, hone early warning systems, study climate change, and even predict the next natural disaster. In 2016, a team from Lawrence Berkeley National Laboratory used weather data insights to identify tropical cyclones, atmospheric rivers, and weather fronts. But despite recent advancements in computing technologies, weather forecasting remains an inexact science.

Some organizations claim that they can predict weather patterns as many as 30 to 45 days in advance. Yet these forecasts can be vague and largely inaccurate. Medium-range and short-range predictions have a higher probability of accuracy, yet only 80 percent of three-day forecasts are “accurate enough” for planning purposes. In order to improve these statistics, organizations must invest in the next generation of high performance computing (HPC) technologies.


Today’s research institutions are taking a real-time, data-centric approach to improve the accuracy of weather forecasting. Backed by HPC solutions, IT teams can rapidly collect, analyze, and act on a deluge of geospatial data—such as barometric pressure, temperature, cloud movement, wind speed, rainfall, dew points, and much more. From there, scientists utilize predictive analytics to uncover patterns in historical data merged with current observations in order to derive real-time insights for more accurate and informed predictions.

As HPC-driven analytics fuels progress in weather and climate research, many scientists are looking to artificial intelligence (AI) capabilities to analyze data even more quickly and accurately. Deep learning, a subset of AI, leverages a series of trained algorithms that learn to make predictions based on past insights. Deep learning tools are designed to process massive data sets in order to identify patterns, and because learning can be supervised or unsupervised (using algorithms to reach specific answers or learning without a specific answer in mind), scientists can extract critical insights without exhausting their IT resources.

According to a research paper on deep hybrid models for weather forecasting, IT architectures based on deep learning demonstrated an improved ability to predict the accumulated daily precipitation for the next day. Using supervised learning, the architecture was able to forecast the daily accumulated rainfall at a specific meteorological station, and outperformed all other analytical approaches.


Meteorologists still have a lot of ground to cover in the realm of weather forecasting, but thanks to HPC and AI capabilities, predictions are becoming faster and more reliable than ever.

To reap the full benefits of predictive data analytics, IT departments are investing in NVIDIA GPU-accelerated computing to derive actionable intelligence in real time. Combining the massively parallel processing power of NVIDIA GPUs with the optimized serial processing of CPUs, research institutions can streamline data analysis and harness intelligence to drive scientific exploration. And by implementing powerful and affordable server platforms, HPC users can utilize NVIDIA GPU accelerators to dramatically improve the speed and accuracy of predictions.

Together, Hewlett Packard Enterprise (HPE) and NVIDIA are empowering research institutions to improve weather and climate exploration by harnessing the power of AI. For more information on how data-driven insights are enhancing weather forecasting, follow me on Twitter at @pango. I also invite you to follow @HPE_HPC and @NvidiaAI for up-to-the-minute HPC news and updates.