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Submission + - Same programs + Different computers = Different weather forecasts (ametsoc.org)

knorthern knight writes: Most major weather services (US NWS, Britain's Met Office, etc) have their own supercomputers, and their own weather models. But there are some models which are used globally. A new paper has been published, comparing outputs from one such program on different machines around the world. Apparently, the same code, running on different machines, can produce different outputs due to accumulation of differing round-off errors. The handling of floating-point numbers in computing is a field in its own right http://docs.oracle.com/cd/E19957-01/806-3568/ncg_goldberg.html

The paper apparently deals with 10-day weather forecasts. Weather forecasts are generally done in steps of 1 hour. I.e. the output from hour 1 is used as the starting condition for the hour 2 forecast. The output from hour 2 is used as the starting condition for hour 3, etc.

The paper is paywalled, but the abstract at http://journals.ametsoc.org/doi/abs/10.1175/MWR-D-12-00352.1 says...

The global model program (GMP) of the Global/Regional Integrated Model system (GRIMs) is tested on 10 different computer systems having different central processing unit (CPU) architectures or compilers. There exist differences in the results for different compilers, parallel libraries, and optimization levels, primarily due to the treatment of rounding errors by the different software systems. The system dependency, which is the standard deviation of the 500-hPa geopotential height averaged over the globe, increases with time. However, its fractional tendency, which is the change of the standard deviation relative to the value itself, remains nearly zero with time. In a seasonal prediction framework, the ensemble spread due to the differences in software system is comparable to the ensemble spread due to the differences in initial conditions that is used for the traditional ensemble forecasting.

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Same programs + Different computers = Different weather forecasts

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