# Machine-learning weather prediction

Whether machine-learning weather models such as GraphCast actually use lower-stratospheric and moisture information had lacked controlled input interventions. An audit over 72 ten-day ERA5 forecasts from 2016 to 2021 replaced the atmosphere above 150 hPa with its zonal mean, shifting Northern Hemisphere Z500 by 55.2 m at 120 hours, 1.49 times the response to random-phase noise with identical spectra; global humidity scaling moved 30–70°N Z500 eddy variance monotonically, down to 0.661 of control under complete drying.

Whether machine-learning weather models such as GraphCast actually use lower-stratospheric and moisture information had lacked controlled input interventions. An audit over 72 ten-day ERA5 forecasts from 2016 to 2021 replaced the atmosphere above 150 hPa with its zonal mean, shifting Northern Hemisphere Z500 by 55.2 m at 120 hours, 1.49 times the response to random-phase noise with identical spectra; global humidity scaling moved 30–70°N Z500 eddy variance monotonically, down to 0.661 of control under complete drying.