A New Number Format for Ensemble Simulations

Dueben, Peter

doi:10.1029/2018ms001420

Cited by 3 publications

(3 citation statements)

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“…For weather prediction models, a natural and practical strategy is to focus on the quantitative and objective prediction skill metrics that are routinely used for model evaluation. In the studies of Ván a et al (2017), Thornes et al (2017), and Düben (2018), the skill metrics employed included the continuous ranked probability score (Hersbach, 2000), anomaly correlation coefficient (WMO, 2015), absolute error or root-mean-square error (RMSE) score (WMO, 2015), and ranked probability skill score (Christensen et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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An Objective and Efficient Method for Assessing the Impact of Reduced‐Precision Calculations On Solution Correctness

Zhang

Wan

Rasch

et al. 2019

J Adv Model Earth Syst

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Recent studies have shown that reducing the precision of floating‐point calculations in an atmospheric model can improve the model's computational performance without affecting model fidelity, but code changes are needed to accommodate lower precision or to prevent undue round‐off error. For complex and computationally expensive systems like the Energy Exascale Earth System Model, a method is needed to objectively and efficiently assess the quality of the lower‐precision simulations and to quickly identify problematic code pieces. This paper demonstrates that the accuracy of the computed solution can be evaluated through a simple and quantitative error metric based on time step convergence. The proposed test method can unambiguously detect the accumulation of precision error and objectively assess its impact on solution accuracy. Furthermore, since fast physical processes are known to affect key features of the multiyear mean climate in atmospheric models, we show that the convergence test applied to short simulations can provide useful information about the impact of reduced precision on a model's long‐term behavior. In contrast, the traditional method of climate model evaluation requires multiple years of simulations and involves inspecting many physical quantities for statistical significance in the presence of natural variability. The simplicity, computational efficiency, and objectivity of the proposed method makes it very attractive for high‐resolution model development. The method is also expected to be applicable to other models that numerically solve time evolution equations.

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Section: Introductionmentioning

confidence: 99%

“…(), Thornes et al. (), and Düben (), the skill metrics employed included the continuous ranked probability score (Hersbach, ), anomaly correlation coefficient (WMO, ), absolute error or root‐mean‐square error (RMSE) score (WMO, ), and ranked probability skill score (Christensen et al., ).…”

Section: Introductionmentioning

confidence: 99%

An Objective and Efficient Method for Assessing the Impact of Reduced‐Precision Calculations On Solution Correctness

Zhang

Wan

Rasch

et al. 2019

J Adv Model Earth Syst

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“…Using simplistic chaotic models, it was shown that the majority of 64 bits at double precision do not contain real information [19]. Running algorithms used for weather forecast models at precision lower than single, for example with half precision 16-bit floats, is an active field of research, but remains challenging [9,10,17,30]. Most research on reduced precision modelling for weather and climate applications makes use of software emulators [8] that provide other arithmetics than the widely supported single and double precision floats.…”

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confidence: 99%

Posits as an alternative to floats for weather and climate models

Klöwer

Dueben

Palmer

2019

Proceedings of the Conference for Next Generation Arithmetic 2019

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Posit numbers, a recently proposed alternative to floating-point numbers, claim to have smaller arithmetic rounding errors in many applications. By studying weather and climate models of low and medium complexity (the Lorenz system and a shallow water model) we present benefits of posits compared to floats at 16 bit. As a standardised posit processor does not exist yet, we emulate posit arithmetic on a conventional CPU. Using a shallow water model, forecasts based on 16-bit posits with 1 or 2 exponent bits are clearly more accurate than half precision floats. We therefore propose 16 bit with 2 exponent bits as a standard posit format, as its wide dynamic range of 32 orders of magnitude provides a great potential for many weather and climate models. Although the focus is on geophysical fluid simulations, the results are also meaningful and promising for reduced precision posit arithmetic in the wider field of computational fluid dynamics.

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