Development of the data assimilation scheme of the Hydrometcentre of Russia

Tsyrulnikov, Michael; Svirenko, P. I.; Gayfulin, Dmitry; Горбунов, М. Е.; Uspensky, A. B.

doi:10.37162/2618-9631-2019-4-112-126

Cited by 8 publications

(2 citation statements)

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“…The start date of the coupled model computation corresponded to November 11, 2021. Initial data was prepared in advance using 3dvar meteorological data assimilation system [67] and oceanographic data assimilation system [58]. The assimilation system for the ocean and sea ice is based on the relaxation procedure for ice concentration in SI3 model which is applied along with the 3DVAR analysis for temperature and salinity of sea water in NEMO model.…”

Section: Numerical Experiments Methodologymentioning

confidence: 99%

“…Both SLAV and NEMO models are applied over a wide range of time scales: medium-range weather forecasting, sub-seasonal (with lead times from 2 to 6 weeks) and longrange ensemble prediction. Initial conditions for these models are provided by the software developed at Hydrometcenter of Russia: 3dvar meteorological data assimilation system [67] is used for SLAV and oceanographic data assimilation system [58] is applied for NEMO. OASIS3-MCT [68] software is used to couple SLAV and NEMO.…”

Section: Introductionmentioning

confidence: 99%

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The Parallel Performance of SLNE Atmosphere-Ocean-Sea Ice Coupled Model

2023

JSFI

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The paper presents the first version of SLNE coupled model. SL and NE here are the first two letters from SLAV (Semi-Lagrangian, based on Absolute Vorticity equation) atmospheric model and NEMO (Nucleus for European Modelling of the Ocean) ocean model that have been coupled using OASIS3-MCT software. SLAV uses 0.9 • x0.72 • regular lat-lon grid with 96 vertical levels. NEMO incorporates SI3 sea ice model. Both of them use the same ORCA025 tripolar grid. Flux adjustments to correct inconsistencies at the interface between coupled atmosphereocean models have not been applied in SLNE. The model design and coupling particularities are described here in detail. A series of numerical experiments with SLNE model were performed to measure its parallel performance. We also investigated the scalability of SLNE model and its components in terms of simulation speed. Based on these results, an optimum configurations of SLNE were identified. It was found that the coupled model showed scaling efficiency of about 85% on 4000 computational cores of Cray XC40-LC in comparison to the SLNE configuration running on 224 cores. Simulations with lead times ranging from a few days to several years showed that there are no significant systematic errors in the coupled model.

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Section: Numerical Experiments Methodologymentioning

confidence: 99%