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A newartificial intelligence(AI)-driven atmospheric condition prediction system could transmute prognostication , researchers predict
The system , dubbed Aardvark Weather , generates forecasts tens of times faster than traditional forecasting systems using a fraction of the computing power , researchers report Thursday ( March 20 ) in the journalNature .
Aardvark Weather generates forecasts more quickly and with less computing power than existing forecasting systems.
" The weather forecasting systems we all rely on have been rise over tenner , but in just 18 month , we ’ve been capable to progress something that ’s competitive with the best of these systems , using just a tenth of the datum on a desktop computer,“Richard Turner , an locomotive engineer at the University of Cambridge in the United Kingdom , said in astatement .
Current atmospheric condition prognosis are generated by inputting datum into complex physics model , a multi - stage process that requires several hr on a dedicatedsupercomputer .
Aardvark Weather circumvents this demand process : the car learning manikin uses raw information from satellite , atmospheric condition station , ships and weather condition balloon to make its predictions without bank on atmospheric model . planet data are particularly significant for the model ’s prediction , the team observe .
Related : Google builds an AI model that can predict future conditions catastrophes
This new overture could extend major advantages in terms of cost , speed and accuracy of weather forecasts , the researcher claim . or else of requiring a supercomputer and a dedicated team , Aardvark Weather can mother a forecast on a desktop electronic computer in just a few minutes .
Replacing the weather prediction pipeline with AI
The squad compared Aardvark ’s performance to existing forecasting system that generate worldwide predictions . Using just 8 % of the observational data point that traditional prognostication system indigence , Aardvark outperform the U.S. nationalGlobal Forecast System(GFS)system and was like to forecasts made by the United States Weather Service .
However , Aardvark ’s spatial resolution is somewhat lower than those of current forecasting system , which could make its initial predictions less relevant for hyper - local weather condition forecasting . Aardvark Weather function at 1.5 - degree resolution , meaning each box in its grid covers 1.5 degrees of latitude and 1.5 degree of longitude . For comparability , the GFS uses a 0.25 - point storage-battery grid .
However , the researchers also read that because the AI learns from the data it is feed , it could be tailored to predict weather in specific arenas — such as temperatures for African agribusiness or wind speeds for renewable push in Europe . Aardvark can integrate high-pitched - resolution regional data , where they exist , to elaborate local prognosis .
" These results are just the start of what Aardvark can reach , " cogitation coauthorAnna Allen , of the University of Cambridge , enjoin in the affirmation . " This end - to - end learning approach can be easy applied to other weather forecasting problem , for example hurricanes , wildfires , and tornadoes . Beyond weather condition , its applications stretch to broader Earth system forecasting , including melodic phrase timbre , sea dynamics , and sea ice prediction . "
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Aardvark could also support forecasting centers in areas of the world that miss the resources to polish global forecasts into high - resolution regional prognostication , the researchers said .
" Aardvark ’s find is not just about amphetamine , it ’s about access,“Scott Hosking , an AI researcher at The Alan Turing Institute in the U.K. , articulate in the statement . " By shift weather prediction from supercomputer to desktop computer , we can democratise forecasting , making these powerful technologies available to prepare Nation and data - sparse regions around the globe . "
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