As artificial intelligence (AI) and machine learning (ML) models become capable of making more accurate predictions, organizations must adopt data storage solutions to keep pace with the demands of these technologies.
Weather forecasting has long been a challenging endeavor, as the atmosphere is a complex and chaotic system. For centuries, forecasts were based on simple heuristics and extrapolations of past observations, but the advent of Doppler radar, computer modeling and weather satellites in the 20th century revolutionized the field. These technologies have allowed meteorologists to better understand the dynamics of the atmosphere and to make more accurate predictions of future weather conditions.
As technology advances, weather forecasting gets better and more accurate. Today, we have better computing power than ever before as well as improved data modeling and a proliferation of data points from devices across the world. We have powerful data analytics and machine learning. These technologies are all components that enable artificial intelligence (AI) solutions to identify complex patterns and make impressive predictions. Using the best technology available, AI has serious potential to drive great leaps forward in forecasting accuracy and precision.
Let’s take a closer look at how AI is transforming weather forecasting.
The role of AI in weather forecasting
AI is capable of processing large datasets and making inferences from them in a way different from standard computing. While computers can process data and make calculations based on that information, AI can go a step further and make inferences that are not already there in the data. This is because AI systems can learn from data without being explicitly programmed, which allows them to identify patterns and make predictions that would be hard for humans to detect. This ability to make sense of large amounts of data is what allows AI to achieve superhuman levels of performance.
Once an AI application is set up, it can continue to gather information and learn from it through machine learning. The AI can observe and analyze data around the clock, without any input from human workers. This continual analysis gives the AI vast amounts of data to learn from, which allows it to improve its predictions over time. As the AI learns more, it becomes smarter and more accurate in its predictions. This ability to learn and improve over time is one of the key advantages of AI over traditional methods of weather forecasting.
Because weather does truly have patterns, the AI gradually learns which current conditions lead to which weather events, while also incorporating information about anomalies from those patterns—when they occur and what happens afterwards. Through this machine learning process and data analytics, the AI system can make more precise and accurate predictions further in the future than humans can. That is immensely valuable because the sooner officials can alert the public to a potentially devastating storm or tornado, the better people can prepare themselves and their homes and leave the area to reduce injuries and property damage, or even death in some cases.
While the most exciting possibilities of AI in weather forecasting are largely in experimental stages, there is also a lot of promise and potential value in incorporating AI into existing numerical-based forecasting models. Some companies are testing the results of adding new, complex data points into existing models that the models couldn’t use before because of the data’s density. This information can include things measured by satellites such as water vapors, cloud cover and atmospheric winds. Using AI to parse that dense data and combine it with the results of predictions from standard models can make those predictions even more accurate and precise.
Another area where AI truly excels is in what the industry calls “nowcasting,” or predicting weather conditions over the next several hours. A Fast Company article recently reported that “the AI company DeepMind built a deep-learning tool that beat out existing physics-based models at predicting rain in the next hour and a half.”
Using AI in weather forecasting will only become more common as the applicable technologies evolve and mature and as researchers develop the right tools. If today’s five-day forecasts are accurate just 80% of the time, industry experts are estimating that AI will boost that percentage by quite a bit by 2030.
AI requires robust, high-performance data storage
As AI becomes more prevalent in the weather industry, it will affect a weather organization’s approach to data storage. AI can chew through enormous volumes of data and therefore AI applications require powerful storage solutions that can keep up. Organizations need data storage that offers:
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High performance – Low latency and high throughput are important factors here. Higher performance allows researchers to complete projects more quickly and efficiently.
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Simple scalability – AI projects can start small, such as during a development phase or proof of concept that features a small dataset that researchers can use to train and refine their algorithms. Once the AI application is ready, however, data volume will likely skyrocket. Scalability should be easy and fast.
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High availability – Many AI and machine learning projects run around the clock for days, weeks or months. Data storage must be highly available because those systems shouldn’t have to wait for the data they need.
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Strong security – Access to data storage should be controlled through authentication and authorization protocols. Data should be encrypted and features such as data lock and remote data erase are also smart in case a storage device falls into the wrong hands.
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Resiliency – Data storage for AI projects should never be vulnerable to a single point of failure. Fault-tolerant storage drives that have self-healing features are a good choice.
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Phison NAND flash storage supports the most intensive AI projects
At Phison, engineers are continually working to develop and refine the highest-performance NAND flash storage solutions for today’s advanced needs, including high-volume AI workloads.
Phison storage solutions address four primary issues prevalent in AI applications today:
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Massive datasets – AI needs fast, reliable access to large amounts of information
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CPU efficiency – The CPU is responsible for fetching data from storage and preprocessing it, so a high-performance CPU can lead to faster training times and better AI models
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High GPU demands – The computing power and stability of GPUs are crucial for most AI computations
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HDD limitations – The mechanical parts of hard disk drives make it difficult to keep pace with speed and consistency needs of GPUs and CPUs
With the company’s X1 SSD platform, you get a highly customizable storage solution that delivers higher storage density, lower power consumption and higher performance so you can reduce the total cost of ownership. Its architecture is optimized for low power consumption and it includes proprietary Phison fifth generation LDPC error detection and correction engine for a higher level of reliability.
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