Benchmark Tests for Numerical Weather Forecasts on Inexact Hardware
A reduction of computational cost would allow higher resolution in numerical weather predictions within the same budget for computation. This paper investigates two approaches that promise significant savings in computational cost: the use of reduced precision hardware, which reduces floating point...
Váldodahkkit: | Dueben, P, Palmer, T |
---|---|
Materiálatiipa: | Journal article |
Giella: | English |
Almmustuhtton: |
American Meteorological Society
2014
|
Geahča maid
-
On the use of inexact, pruned hardware in atmospheric modelling.
Dahkki: Düben, P, et al.
Almmustuhtton: (2014) -
Machine learning emulation of gravity wave drag in numerical weather forecasting
Dahkki: Chantry, M, et al.
Almmustuhtton: (2021) -
WeatherBench: A Benchmark Data Set for Data‐Driven Weather Forecasting
Dahkki: Stephan Rasp, et al.
Almmustuhtton: (2020-11-01) -
Machine Learning Emulation of Gravity Wave Drag in Numerical Weather Forecasting
Dahkki: Matthew Chantry, et al.
Almmustuhtton: (2021-07-01) -
An approach to secure weather and climate models against hardware faults
Dahkki: Dueben, P, et al.
Almmustuhtton: (2017)