scholarly journals Compact Modeling Framework v3.0 for high-resolution global ocean-ice-atmosphere models

2018 ◽  
Author(s):  
Vladimir V. Kalmykov ◽  
Rashit A. Ibrayev ◽  
Maxim N. Kaurkin ◽  
Konstantin V. Ushakov
Keyword(s):  
High Resolution ◽  
Global Ocean ◽  
Optimal Interpolation ◽  
Compact Modeling ◽  
Software Environment ◽  
Modeling Framework ◽  
Coupled Models ◽  
High Resolution Models ◽  
High Level ◽  
Parallel Supercomputers

Abstract. We present new version of the Compact Modeling Framework (CMF3.0) developed for providing the software environment for stand-alone and coupled models of the Global geophysical fluids. The CMF3.0 designed for implementation high and ultra-high resolution models at massive-parallel supercomputers. The key features of the previous CMF version (2.0) are mentioned for reflecting progress in our researches. In the CMF3.0 pure MPI approach with high-level abstract driver, optimized coupler interpolation and I/O algorithms is replaced with PGAS paradigm communications scheme, while central hub architecture evolves to the set of simultaneously working services. Performance tests for both versions are carried out. As addition a parallel realisation of the EnOI (Ensemble Optimal Interpolation) data assimilation method as program service of CMF3.0 is presented.

scholarly journals Compact Modeling Framework v3.0 for high-resolution global ocean–ice–atmosphere models

2018 ◽  
Vol 11 (10) ◽  
pp. 3983-3997 ◽  
Author(s):  
Vladimir V. Kalmykov ◽  
Rashit A. Ibrayev ◽  
Maxim N. Kaurkin ◽  
Konstantin V. Ushakov
Keyword(s):  
Message Passing ◽  
Message Passing Interface ◽  
Global Ocean ◽  
Optimal Interpolation ◽  
Compact Modeling ◽  
Software Environment ◽  
Modeling Framework ◽  
Partitioned Global Address Space ◽  
Version 2.0 ◽  
High Level

Abstract. We present a new version of the Compact Modeling Framework (CMF3.0) developed for the software environment of stand-alone and coupled global geophysical fluid models. The CMF3.0 is designed for use on high- and ultrahigh-resolution models on massively parallel supercomputers.The key features of the previous CMF, version 2.0, are mentioned to reflect progress in our research. In CMF3.0, the message passing interface (MPI) approach with a high-level abstract driver, optimized coupler interpolation and I/O algorithms is replaced with the Partitioned Global Address Space (PGAS) paradigm communications scheme, while the central hub architecture evolves into a set of simultaneously working services. Performance tests for both versions are carried out. As an addition, some information about the parallel realization of the EnOI (Ensemble Optimal Interpolation) data assimilation method and the nesting technology, as program services of the CMF3.0, is presented.

scholarly journals Impact of horizontal resolution on global ocean-sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2)

2020 ◽  
Author(s):  
Eric P. Chassignet ◽  
Stephen G. Yeager ◽  
Baylor Fox-Kemper ◽  
Alexandra Bozec ◽  
Fred Castruccio ◽  
...  
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Global Climate ◽  
Horizontal Resolution ◽  
Ocean Model ◽  
Global Ocean ◽  
Western Boundary ◽  
Low Resolution ◽  
Circumpolar Current ◽  
High Resolution Models

Abstract. This paper presents global comparisons of fundamental global climate variables from a suite of four pairs of matched low- and high-resolution ocean and sea-ice simulations that are obtained following the OMIP-2 protocol (Griffies et al., 2016) and integrated for one cycle (1958–2018) of the JRA55-do atmospheric state and runoff dataset (Tsujino et al., 2018). Our goal is to assess the robustness of climate-relevant improvements in ocean simulations (mean and variability) associated with moving from coarse (~ 1º) to eddy-resolving (~ 0.1º) horizontal resolutions. The models are diverse in their numerics and parameterizations, but each low-resolution and high-resolution pair of models is matched so as to isolate, to the extent possible, the effects of horizontal resolution. A variety of observational datasets are used to assess the fidelity of simulated temperature and salinity, sea surface height, kinetic energy, heat and volume transports, and sea ice distribution. This paper provides a crucial benchmark for future studies comparing and improving different schemes in any of the models used in this study or similar ones. The biases in the low-resolution simulations are familiar and their gross features – position, strength, and variability of western boundary currents, equatorial currents, and Antarctic Circumpolar Current – are significantly improved in the high-resolution models. However, despite the fact that the high-resolution models "resolve" most of these features, the improvements in temperature or salinity are inconsistent among the different model families and some regions show increased bias over their low-resolution counterparts. Greatly enhanced horizontal resolution does not deliver unambiguous bias improvement in all regions for all models.

scholarly journals High resolution stochastic downscaling method for ocean forecasting models and its application to the Red Sea dynamics

2021 ◽  
Author(s):  
Georgy I. Shapiro ◽  
Jose M. Gonzalez-Ondina ◽  
Vladimir N. Belokopytov
Keyword(s):  
High Resolution ◽  
Red Sea ◽  
A Priori ◽  
Global Ocean ◽  
Efficient Tool ◽  
True Solution ◽  
Ocean Models ◽  
High Resolution Models ◽  
Ocean Forecasting ◽  
The Cost

Abstract. High-resolution modelling of a large ocean domain requires significant computational resources. The main purpose of this study is to develop an efficient tool for downscaling the lower resolution data such as available from Copernicus Marine Environment Monitoring Service (CMEMS). Common methods of downscaling CMEMS ocean models utilize their lower resolution output as boundary conditions for local, higher resolution hydrodynamic ocean models. Such methods reveal greater details of spatial distribution of ocean variables; however, they increase the cost of computations, and often reduce the model skill due to the so called double penalty effect. This effect is a common problem for many high-resolution models where predicted features are displaced in space or time. This paper presents a Stochastic Deterministic Downscaling (SDD) method, which is an efficient tool for downscaling of ocean models based on the combination of deterministic and stochastic approaches. The ability of the SDD method is first demonstrated in an idealised case when the true solution is known a priori. Then the method is applied to create an operational eddy-resolving Stochastic Model of the Red Sea (SMORS) with the parent model being the eddy-permitting Mercator Global Ocean Analysis and Forecast System. The stochastic component is data-driven rather than equation-driven and applied to the areas smaller than the Rossby radius, where distributions of ocean variables are more coherent. The method, based on objective analysis, is similar to what is used for data assimilation in ocean models, and stems from the philosophy of 2D turbulence. The SMORS model produces higher resolution (1/24th degree latitude mesh) oceanographic data using the output from a coarser resolution (1/12th degree mesh) parent model available from CMEMS. The values on the high-resolution mesh are computed under condition of minimisation of the cost function which represents the error between the model and true solution. The SMORS model has been validated against Sea Surface Temperature and ARGO floats observations. Comparisons show that the model and observations are in good agreement and SMORS is not subject to the ‘double penalty’ effect. SMORS is very fast to run on a typical desktop PC and can be relocated to another area of the ocean.

scholarly journals High-resolution stochastic downscaling method for ocean forecasting models and its application to the Red Sea dynamics

Ocean Science ◽  
2021 ◽  
Vol 17 (4) ◽  
pp. 891-907
Author(s):  
Georgy I. Shapiro ◽  
Jose M. Gonzalez-Ondina ◽  
Vladimir N. Belokopytov
Keyword(s):  
High Resolution ◽  
Red Sea ◽  
A Priori ◽  
Global Ocean ◽  
Efficient Tool ◽  
True Solution ◽  
Ocean Models ◽  
High Resolution Models ◽  
Ocean Forecasting ◽  
The Cost

Abstract. High-resolution modelling of a large ocean domain requires significant computational resources. The main purpose of this study is to develop an efficient tool for downscaling the lower-resolution data such as those available from Copernicus Marine Environment Monitoring Service (CMEMS). Common methods of downscaling CMEMS ocean models utilise their lower-resolution output as boundary conditions for local, higher-resolution hydrodynamic ocean models. Such methods reveal greater details of spatial distribution of ocean variables; however, they increase the cost of computations and often reduce the model skill due to the so called “double penalty” effect. This effect is a common problem for many high-resolution models where predicted features are displaced in space or time. This paper presents a stochastic–deterministic downscaling (SDD) method, which is an efficient tool for downscaling of ocean models based on the combination of deterministic and stochastic approaches. The ability of the SDD method is first demonstrated in an idealised case when the true solution is known a priori. Then the method is applied to create an operational Stochastic Model of the Red Sea (SMORS), with the parent model being the Mercator Global Ocean Analysis and Forecast System at 1/12∘ resolution. The stochastic component of the model is data-driven rather than equation-driven, and it is applied to the areas smaller than the Rossby radius, within which distributions of ocean variables are more coherent than over a larger distance. The method, based on objective analysis, is similar to what is used for data assimilation in ocean models and stems from the philosophy of 2-D turbulence. SMORS produces finer-resolution (1/24∘ latitude mesh) oceanographic data using the output from a coarser-resolution (1/12∘ mesh) parent model available from CMEMS. The values on the fine-resolution mesh are computed under conditions of minimisation of the cost function, which represents the error between the model and true solution. SMORS has been validated against sea surface temperature and ARGO float observations. Comparisons show that the model and observations are in good agreement and SMORS is not subject to the “double penalty” effect. SMORS is very fast to run on a typical desktop PC and can be relocated to another area of the ocean.

scholarly journals Impact of horizontal resolution on global ocean–sea ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2)

2020 ◽  
Vol 13 (9) ◽  
pp. 4595-4637 ◽  
Author(s):  
Eric P. Chassignet ◽  
Stephen G. Yeager ◽  
Baylor Fox-Kemper ◽  
Alexandra Bozec ◽  
Frederic Castruccio ◽  
...  
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Global Climate ◽  
Horizontal Resolution ◽  
Ocean Model ◽  
Global Ocean ◽  
Western Boundary ◽  
Low Resolution ◽  
Circumpolar Current ◽  
High Resolution Models

Abstract. This paper presents global comparisons of fundamental global climate variables from a suite of four pairs of matched low- and high-resolution ocean and sea ice simulations that are obtained following the OMIP-2 protocol (Griffies et al., 2016) and integrated for one cycle (1958–2018) of the JRA55-do atmospheric state and runoff dataset (Tsujino et al., 2018). Our goal is to assess the robustness of climate-relevant improvements in ocean simulations (mean and variability) associated with moving from coarse (∼ 1∘) to eddy-resolving (∼ 0.1∘) horizontal resolutions. The models are diverse in their numerics and parameterizations, but each low-resolution and high-resolution pair of models is matched so as to isolate, to the extent possible, the effects of horizontal resolution. A variety of observational datasets are used to assess the fidelity of simulated temperature and salinity, sea surface height, kinetic energy, heat and volume transports, and sea ice distribution. This paper provides a crucial benchmark for future studies comparing and improving different schemes in any of the models used in this study or similar ones. The biases in the low-resolution simulations are familiar, and their gross features – position, strength, and variability of western boundary currents, equatorial currents, and the Antarctic Circumpolar Current – are significantly improved in the high-resolution models. However, despite the fact that the high-resolution models “resolve” most of these features, the improvements in temperature and salinity are inconsistent among the different model families, and some regions show increased bias over their low-resolution counterparts. Greatly enhanced horizontal resolution does not deliver unambiguous bias improvement in all regions for all models.

scholarly journals New Gridded Product for the Total Columnar Atmospheric Water Vapor over Ocean Surface Constructed from Microwave Radiometer Satellite Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2402
Author(s):  
Weifu Sun ◽  
Jin Wang ◽  
Yuheng Li ◽  
Junmin Meng ◽  
Yujia Zhao ◽  
...  
Keyword(s):  
Water Vapor ◽  
Microwave Radiometer ◽  
Atmospheric Water Vapor ◽  
Global Ocean ◽  
Optimal Interpolation ◽  
Fusion Product ◽  
Mean Deviation ◽  
Atmospheric Water ◽  
Absolute Deviation ◽  
Better Than

Based on the optimal interpolation (OI) algorithm, a daily fusion product of high-resolution global ocean columnar atmospheric water vapor with a resolution of 0.25° was generated in this study from multisource remote sensing observations. The product covers the period from 2003 to 2018, and the data represent a fusion of microwave radiometer observations, including those from the Special Sensor Microwave Imager Sounder (SSMIS), WindSat, Advanced Microwave Scanning Radiometer for Earth Observing System sensor (AMSR-E), Advanced Microwave Scanning Radiometer 2 (AMSR2), and HY-2A microwave radiometer (MR). The accuracy of this water vapor fusion product was validated using radiosonde water vapor observations. The comparative results show that the overall mean deviation (Bias) is smaller than 0.6 mm; the root mean square error (RMSE) and standard deviation (SD) are better than 3 mm, and the mean absolute deviation (MAD) and correlation coefficient (R) are better than 2 mm and 0.98, respectively.

scholarly journals Order and stochasticity in the folding of individual Drosophila genomes

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Sergey V. Ulianov ◽  
Vlada V. Zakharova ◽  
Aleksandra A. Galitsyna ◽  
Pavel I. Kos ◽  
Kirill E. Polovnikov ◽  
...  
Keyword(s):  
Random Walk ◽  
High Resolution ◽  
3D Genome ◽  
Topologically Associating Domains ◽  
Chromatin Folding ◽  
A Cell ◽  
Single Nucleus ◽  
High Level ◽  
Cell To Cell Variability

AbstractMammalian and Drosophila genomes are partitioned into topologically associating domains (TADs). Although this partitioning has been reported to be functionally relevant, it is unclear whether TADs represent true physical units located at the same genomic positions in each cell nucleus or emerge as an average of numerous alternative chromatin folding patterns in a cell population. Here, we use a single-nucleus Hi-C technique to construct high-resolution Hi-C maps in individual Drosophila genomes. These maps demonstrate chromatin compartmentalization at the megabase scale and partitioning of the genome into non-hierarchical TADs at the scale of 100 kb, which closely resembles the TAD profile in the bulk in situ Hi-C data. Over 40% of TAD boundaries are conserved between individual nuclei and possess a high level of active epigenetic marks. Polymer simulations demonstrate that chromatin folding is best described by the random walk model within TADs and is most suitably approximated by a crumpled globule build of Gaussian blobs at longer distances. We observe prominent cell-to-cell variability in the long-range contacts between either active genome loci or between Polycomb-bound regions, suggesting an important contribution of stochastic processes to the formation of the Drosophila 3D genome.

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