we want m(o)ore€¦ · technologically we want moore unsustainable with current technology: •...
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We Want M(o)ore
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Societally we want and need more
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Scientifically we want more
Skamarock et al 2014
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ECMWFs IFS global 2,5 km
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Global scale, effect of resolution on blocking
Schiemann et al 2016
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More complexity on large and small scales
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Technologically we want Moore
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Technologically we want MooreUnsustainable with current technology:
• Need millions of cores for global 1km simulations
• Too expensive in energy use
• Fault tolerance
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Increasing resolution
George Mozdzynski
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Power costs…and days to complete forecast
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Adjust to new hardware
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Vers van de pers …
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State of the art in global weather predictions
ECMWF: 9 km resolution (Tco1279), 137 layerscoupled to interactive ocean, sea ice, land, aerosol
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State of the art in long term global projections
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Domain 1: 12.5kmdefault setup
Domain 2: 2.5kmdefault setup
Domain 4: 100mRijkswaterstaat river
temperatures,TOP10NL, satellite
imagery, AHN2 (height map), CBS
data
Domain 3: 500mhi-res landuse, ec.
Rijkswaterstaat river temperatures
Daily forecastsWRF3.5 + urban module (SLUCM)
48 hour runs, 24 hour spin-up
State of the art in regional predictions
Attema et al, IEEE eScience, 2015
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Data integration and assimilation
https://www.esciencecenter.nl/project/summer-in-the-city
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Adding data assimilation
Amsterdam city center
Urban home weather stations
Randstad
https://www.esciencecenter.nl/project/era-urban
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Local simulations (LES, DNS)
Stevens et al JHU (FOM, XSEDE, SURF); Goncalvez (eScience, COMMIT)
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Physically constrained deep learning?
Grover et al, 2015 Microsoft Research
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• We want, need and can do more– Societal demand high for more detail and insight
in high impact weather and climate���� Resolution ���� Complexity���� Data assimilation and integration
• Meteorology has been at the forefront and needs to keep innovating
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Congratulations to NVBM