When it comes to better understanding our world, researchers across the space, earth, and life sciences are striving to expand our knowledge and accelerate the next great discovery. To bolster these efforts, scientists are utilizing the latest technologies to help them efficiently study natural phenomena, collect and analyze troves of complex data, and derive what they hope is groundbreaking insight.
Of the physical sciences, weather and climate exploration is one of the most impactful yet also one of the most widely questioned fields. Climate trends effect people and businesses around the globe, and the ability to accurately and expediently predict the weather is the difference between being prepared and leaving the public at risk. Although meteorologists have significantly improved the accuracy of three-day forecasts over the past 20 years, there is still a great deal of uncertainty surrounding extended forecasts, climate change, and severe weather warnings.
Early warning systems have improved dramatically since the early 2000s, doubling the lead times for tornado warnings and providing at-risk populations an additional 40 minutes to escape flash floods. But meteorologists still have a lot of ground to cover.
IMPROVING WEATHER SIMULATION AND FORECASTING
Today, many organizations are leveraging high performance computing (HPC) to enhance the speed, quality, and performance of weather and climate research. HPC solutions are helping meteorologists in many ways:
- Effectively manage proliferating volumes of data
- Easily deploy and manipulate data-intensive weather models
- Utilize highly accurate spatial and temporal weather simulations
- Accelerate time to insight
- Reduce time to action (time to alert)
Data analysis is a fundamental tool for scientists, enabling them to extract actionable intelligence from a variety of data types—for meteorologists, these types include geospatial, atmospheric, climate, historical data, and more. First, scientists gather data from sources such as weather stations, satellites, and sensors. Once the information has been collected, scientists utilize visualization technologies to create complex models of past, present, and future weather conditions. Based on these dynamic models, scientists can apply predictive analytics to compute a prediction.
Also called “predictive models,” weather models and simulations are key to finding correlations between weather patterns and the compiled datasets. Machine learning techniques are turbocharging predictive analytics, helping researchers identify trends and patterns to anticipate climate trends and predict hazardous events long before they occur.
LEVERAGING PREDICTIVE ANALYTICS
The demand for a robust IT environment is skyrocketing as scientists attempt to virtualize and actualize data in real time. High performance compute platforms are empowering research organizations to accelerate data capture, analysis, and insights like never before, not only to increase our understanding of global climate trends but also to dramatically improve our ability to predict natural disasters.
Backed by powerful HPC servers, many scientists are also investing in GPU computing to enhance weather modeling and simulation with massively parallel processing capabilities. GPU-accelerated solutions are optimized for managing data-rich applications, delivering higher-resolution models that allow for more accurate forecasts.
Find out more about GPU-accelerated applications for weather, climate, and related applications go to nvidia.com.
To learn more about how HPC simulation and analytics are helping meteorologists improve weather forecasting and climate research, I invite you to follow me on Twitter at @wheatHPC. You can also visit @HPE_HPC and @NVIDIADC for the latest news in HPC innovation and GPU-accelerated computing.
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