scholarly journals Examination of Computational Performance and Potential Applications of a Global Numerical Weather Prediction Model MPAS Using KISTI Supercomputer NURION

2021 ◽  
Vol 9 (10) ◽  
pp. 1147
Author(s):  
Ji-Sun Kang ◽  
Hunjoo Myung ◽  
Jin-Hee Yuk
Keyword(s):  
Prediction Model ◽  
High Performance ◽  
Weather Prediction ◽  
Time Integration ◽  
Forecast Accuracy ◽  
Coastal Flooding ◽  
Extreme Weather Events ◽  
Variable Resolution ◽  
Computational Performance ◽  
Highly Nonlinear

To predict extreme weather events, we conducted high-resolution global atmosphere modeling and simulation using high-performance computing. Using a new-generation global weather/climate prediction model called MPAS (Model for Prediction Across Scales) with variable resolution, we tested strong scalability on the KISTI (Korea Institute of Science and Technology Information) supercomputer NURION. In addition to assessing computational performance, we simulated three typhoons that occurred in 2019 to analyze the forecast accuracy of MPAS. MPAS results were also applied to force an ADCIRC (The Advanced CIRCulation) + SWAN (Simulating Waves Nearshore) model to predict coastal flooding over southern Korea. The time-integration of MPAS showed excellent scalability up to 4096 cores of NURION KNL (KNight Landing) nodes, but a serious I/O bottleneck issue was still found after trying two additional I/O strategies (i.e., adjusting the stripe count and using a burst buffer). On the other hand, the forecast accuracy of MPAS showed very encouraging results for wind and pressure during typhoons. ADCIRC+SWAN also generated a good estimate of significant wave height for typhoon Mitag. The proposed variable-resolution MPAS model, under an efficient computational environment, could be utilized to predict and understand the highly nonlinear chaotic atmosphere and coastal flooding in typhoons.

scholarly journals Computational Efficiency Examination of a Regional Numerical Weather Prediction Model using KISTI Supercomputer NURION

2021 ◽  
Vol 12 (6) ◽  
pp. 743-749
Author(s):  
Ji-Sun Kang Et.al
Keyword(s):  
Prediction Model ◽  
Numerical Weather Prediction ◽  
Computational Efficiency ◽  
Prediction Models ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Extreme Weather Events ◽  
Numerical Weather ◽  
Computational Performance ◽  
The Impact

For well-resolving extreme weather events, running numerical weather prediction model with high resolution in time and space is essential. We explore how efficiently such modeling could be, using NURION. We have examined one of community numerical weather prediction models, WRF, and KISTI’s 5th supercomputer NURION of national HPC. Scalability of the model has been tested at first, and we have compared the computational efficiency of hybrid openMP + MPI runs with pure MPI runs. In addition to those parallel computing experiments, we have tested a new storage layer called burst buffer to see whether it can accelerate frequent I/O. We found that there are significant differences between the computational environments for running WRF model. First of all, we have tested a sensitivity of computational efficiency to the number of cores per node. The sensitivity experiments certainly tell us that using all cores per node does not guarantee the best results, rather leaving several cores per node could give more stable and efficient computation. For the current experimental configuration of WRF, moreover, pure MPI runs gives much better computational performance than any hybrid openMP + MPI runs. Lastly, we have tested burst buffer storage layer that is expected to accelerate frequent I/O. However, our experiments show that its impact is not consistently positive. We clearly confirm the positive impact with relatively smaller problem size experiments while the impact was not seen with bigger problem experiments. Significant sensitivity to the different computational configurations shown this paper strongly suggests that HPC users should find out the best computing environment before massive use of their applications

Impact of aerosols on the forecast accuracy of solar irradiance calculated by a numerical weather prediction model

2014 ◽  
Vol 223 (12) ◽  
pp. 2621-2630 ◽  
Author(s):  
Ken-ichi Shimose ◽  
Hideaki Ohtake ◽  
Joao Gari da Silva Fonseca ◽  
Takumi Takashima ◽  
Takashi Oozeki ◽  
...  
Keyword(s):  
Prediction Model ◽  
Numerical Weather Prediction ◽  
Solar Irradiance ◽  
Weather Prediction ◽  
Forecast Accuracy ◽  
Numerical Weather Prediction Model ◽  
Numerical Weather

scholarly journals Performance evaluation of a throughput-aware framework for ensemble data assimilation: the case of NICAM-LETKF

2016 ◽  
Vol 9 (7) ◽  
pp. 2293-2300 ◽  
Author(s):  
Hisashi Yashiro ◽  
Koji Terasaki ◽  
Takemasa Miyoshi ◽  
Hirofumi Tomita
Keyword(s):  
Data Assimilation ◽  
High Performance ◽  
Weather Prediction ◽  
File Systems ◽  
Atmospheric Model ◽  
Distributed File Systems ◽  
Ensemble Data Assimilation ◽  
Computational Performance ◽  
Ensemble Data ◽  
Data Throughput

Abstract. In this paper, we propose the design and implementation of an ensemble data assimilation (DA) framework for weather prediction at a high resolution and with a large ensemble size. We consider the deployment of this framework on the data throughput of file input/output (I/O) and multi-node communication. As an instance of the application of the proposed framework, a local ensemble transform Kalman filter (LETKF) was used with a Non-hydrostatic Icosahedral Atmospheric Model (NICAM) for the DA system. Benchmark tests were performed using the K computer, a massive parallel supercomputer with distributed file systems. The results showed an improvement in total time required for the workflow as well as satisfactory scalability of up to 10 K nodes (80 K cores). With regard to high-performance computing systems, where data throughput performance increases at a slower rate than computational performance, our new framework for ensemble DA systems promises drastic reduction of total execution time.

scholarly journals In-situ observational network for extreme weather events in India

MAUSAM ◽  
2021 ◽  
Vol 67 (1) ◽  
pp. 67-76
Author(s):  
GAJENDRA KUMAR ◽  
SURESH CHAND ◽  
R. R. MALI ◽  
S. K. KUNDU ◽  
A. K. BAXLA
Keyword(s):  
High Performance ◽  
Prediction Models ◽  
Weather Prediction ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Natural Systems ◽  
Observational Network ◽  
Weather Events ◽  
Vegetables And Fruits

Extreme weather events, interacting with vulnerable human and natural systems, can lead to disasters, especially in absence of responsive social system. Accurate and timely monitoring and forecast of heavy rains, tropical cyclones, thunderstorms, hailstorms, cloudburst, drought, heat and cold waves, etc. are required to respond effectively to such events. Due to extreme weather events, crops over large parts of the country are adversely affected reducing production of total food grains, fodder, cash crops, vegetables and fruits which in turn affect the earnings and livelihood of individual farmers as well as the economy of the country. In situ observational network are the vital component for skilful prediction of extreme weather events. Current observational requirements for extreme weather prediction are met, to varying degrees by a range of in-situ observing systems and space-based systems. The augmentation of in-situ observational network is continuously progressing. IMD now has a network of Doppler Weather Radars (DWRs), Automatic Weather Stations (AWSs), Agro AWSs, Automatic Rain Gauges (ARGs), GPS upper air systems etc. These observations along with non-conventional (satellite) data are now being used to run its global and regional numerical prediction models on High Performance Computing Systems (HPCS). This has improved monitoring and forecasting capabilities for extreme weather events like cyclones, severe thunderstorm, heavy rainfall and floods in a significant manner. This paper provides an overview of the role of in-situ observational network for extreme weather events in India, framework for further augmentation to the network and other requirements to further enhance capabilities for high impact & extreme weather events and natural hazards.

scholarly journals Performance evaluation of throughput-aware framework for ensemble data assimilation: The case of NICAM-LETKF

2016 ◽  
Author(s):  
H. Yashiro ◽  
K. Terasaki ◽  
T. Miyoshi ◽  
H. Tomita
Keyword(s):  
Data Assimilation ◽  
High Performance ◽  
Weather Prediction ◽  
File Systems ◽  
Atmospheric Model ◽  
Distributed File Systems ◽  
Ensemble Data Assimilation ◽  
Computational Performance ◽  
Ensemble Data ◽  
Data Throughput

Abstract. In this paper, we propose the design and implementation of an ensemble data assimilation (DA) framework for weather prediction at a high resolution and with a large ensemble size. We consider the deployment of this framework on the data throughput of file input/output (I/O) and multi-node communication. As an instance of the application of the proposed framework, a Local Ensemble Transform Kalman Filter (LETKF) was used with a Non-hydrostatic Icosahedral Atmospheric Model (NICAM) for the DA system. Benchmark tests were performed using the K computer, a massive parallel supercomputer with distributed file systems. The results showed an improvement in total time required for the workflow as well as satisfactory scalability of up to 10 K nodes (80 K cores). With regard to high-performance computing systems, where data throughput performance increases at a slower rate than computational performance, our new framework for ensemble DA systems promises drastic reduction of total execution time.

scholarly journals Implementation of a semi-Lagrangian fully-implicit time integration of the unified soundproof system of equations for numerical weather prediction

2021 ◽  
Author(s):  
Abdessamad Qaddouri ◽  
Claude Girard ◽  
Syed Zahid Husain ◽  
Rabah Aider
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Time Integration ◽  
Implicit Time ◽  
System Of Equations ◽  
Dynamical Core ◽  
Vertical Coordinate ◽  
Numerical Weather ◽  
Fully Implicit ◽  
Computational Performance

AbstractAn alternate dynamical core that employs the unified equations of A. Arakawa and C.S. Konor (2009) has been developed within Environment and Climate change Canada’s GEM (Global Environmental Multiscale) atmospheric model. As in the operational GEM dynamical core, the novel core utilizes the same fully-implicit two-time-level semi-Lagrangian scheme for time discretization while the log-pressure-based terrain-following vertical coordinate has been slightly adapted. Overall, the new dynamical core implementation required only minor changes to the existing informatics code of the GEM model and from a computational performance perspective, the new core does not incur any significant additional cost. A broad range of tests – that include both two-dimensional idealized theoretical cases and three-dimensional deterministic forecasts covering both hydrostatic and non-hydrostatic scales–have been carried out to evaluate the performance of the new dynamical core. For all the tested cases, when compared to the operational GEM model, the new dynamical core based on the unified equations has been found to produce statistically equivalent results. These results imply that the unified equations can be adopted for operational numerical weather prediction that would employ a single soundproof system of equations to produce reliable forecasts for all meteorological scales of interest with negligible changes for the computational overhead.

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