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AMSIMP
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An open-source solution which leverages machine learning to improve numerical weather prediction
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<img src="images/content_images/vars/t2m.png" alt="Example 2 metre temperature prediction from the AMSIMP GFM.">
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<img src="images/content_images/vars/t.png" alt="Example 850 hPa air temperature prediction from the AMSIMP GFM.">
<div class="casestudies-box-text">The second air temperature parameter is at a pressure surface of 850 hPa. The 850 hPa pressure surface is usually above the planetary boundary layer and therefore not affected by diurnal variations but provides information about broader temperature trends.</div>
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<img src="images/content_images/vars/z.png" alt="Example 500 hPa air temperature prediction from the AMSIMP GFM.">
<div class="casestudies-box-text">Geopotential at 500 hPa pressure is a commonly used variable that encodes the synoptic-scale pressure distribution. It is the standard verification variable for most medium-range numerical weather prediction models.</div>
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