< IMG SRC = "/Uploads/Blogs/92/C9/IB-FR5VLVC2F_240DF4A4. < H2 class = "entry-sub-title" > Aardvark Weather uses AI to predict weather without models, works faster than GFS systems and is suitable even for ordinary computer; Rsquo; < P > The new Aardvark Weather & Nbsp; Nbsp; Nbsp; Nbsp; Nbsp; Nbsp; Nbsp; Nbsp; Nbsp; Nbsp; Nbsp; Nbsp; Nbsp; < P > Traditional weather forecasting systems require considerable resources. They need a supercomp & Rsquo; Auters and many hours of calculations. Such systems have been developed for decades to provide accurate forecasts.
< P >< Strong > Aardvark Weather dramatically changes the approach to forecasting, offering comparable accuracy at much lower resource costs. ~ ~ ~& nbsp; researchers reported in Nature on March 20, 2025. Research results indicate a potential breakthrough in the industry.
< p > Richard Turner from Cambridge University emphasizes the scale of achievement:
& ldquo; in just 18 months, we were able to create something that competes with the best systems using only a tenth of data on a desktop computer; Rsquo; Uuteri & Rdquo;. < h2 > fundamental difference from traditional systems
< P > Modern meteorological forecasting is created by a multi -stage process. Initially, observations data are collected. They are then introduced into complex physical models of the atmosphere. This approach requires several hours of work of specialized supercomp & Rsquo; Uuter.
< p >Aardvark Weather works in a fundamentally different scheme. The machine learning model uses raw data directly from different sources. Among them are satellites, meteorological stations, ships and meteorological balloons.< P > The key innovation is the rejection of intermediate atmospheric models. SI-algorithm learns to recognize patterns directly from input. Particularly important for forecasts were satellite observations.
< P > In this approach, the system works much faster. Instead of hours of work Supercomp & Rsquo; Uuter Aardvark generates a forecast for a few minutes on a regular desktop computer; rsquo; under.
~ < h2 > Comparative efficiency and limitation
< P > Researchers have compared Aardvark with leading forecasting systems. They evaluated the accuracy of global weather forecasts. Results were impressive even for the developers' team.
< p >Using only 8% of the observations needed by traditional approaches, Aardvark exceeded the American National Global Forecast System (GFS). According to some parameters, it proved to be comparable to US meteorological service.
< P > However, the system has some restrictions. Aardvark spatial resolution is 1.5 degrees. This means that each cell in its grid covers an area of 1.5 ? 1.5 degrees. For comparison, GFS works with a grid of 0.25 degrees.< P > The lower resolution reduces accuracy for hyperlocular weather forecasting. However, researchers say that the system can adapt to specific needs. It is able to use the higher resolution regional data if available.
< H2 > Prospects and Opportunities < P > The potential of Aardvark Weather goes far beyond the base weather forecasting. Anna Allen's study co -author emphasizes:
& ldquo; this cross -cutting approach to learning can be easily applied to other weather forecasting problems, such as hurricanes, forest fires and tornado & RDquo;. < p > The system can be finely tuned for specific industry needs. It can focus on predicting temperature for agriculture. Or at wind speed for renewable energy. Targeted training increases accuracy for specific parameters.
< P >< Strong > Particularly promising is the use of Aardvark in regions with limited resources. & nbsp; developing countries, often do not have access to powerful supercomp. This limits their capabilities to accurate weather forecasting.
< P > Scott Hosting, a researcher of the Alan Triging Institute, emphasizes this aspect:
& ldquo; by moving the weather forecasting from the Supercomp & Rsquo; Rsquo on desktop computer & Rsquo; Rsquo; < h2 > wider context application of technology
< P > Researchers see Aardvark Weather only the first step to the wider application of similar approaches. This method can expand to other areas of Earth's system forecasting.
< P > Among the potential scope & mdash; Modeling of air quality. Forecasting the dynamics of oceans and sea ice. Early warning about extreme weather phenomena and natural disasters.
< p > The ability to generate forecasts quickly and with less resources is critical in climate change. Extreme weather phenomena become more frequent and more intense. Timely warning can save people's lives.
< p >< em > & ldquo; these results & mdash; Only the beginning of what Aardvark & amp; Rdquo; , & mdash; Anna Allen summarizes. Combination of accessibility, speed and sufficient accuracy makes technology a powerful tool for meteorologists worldwide.
< P > Despite the restriction of the first version, Aardvark Weather demonstrates how AI can transform industries that have traditionally been considered dependent on high -power computing resources. This opens up new opportunities for innovation in meteorology and related land sciences.