Managing Large-Scale Scientific Computing in Ensemble Prediction Using BPEL

2009 ◽  
Author(s):  
Cancan Liu ◽  
Weimin Zhang ◽  
Zhigang Luo ◽  
Xiaoqun Cao ◽  
Hai Liu
Keyword(s):  
Large Scale ◽  
Scientific Computing ◽  
Ensemble Prediction

scholarly journals Ensemble forecasting with a stochastic convective parametrization based on equilibrium statistics

2011 ◽  
Vol 11 (11) ◽  
pp. 30457-30485 ◽  
Author(s):  
P. Groenemeijer ◽  
G. C. Craig
Keyword(s):  
Large Scale ◽  
Mesoscale Model ◽  
Initial Conditions ◽  
Deep Convection ◽  
Horizontal Resolution ◽  
Ensemble Forecasting ◽  
Total Variance ◽  
Ensemble Prediction ◽  
Ensemble Prediction System ◽  
The Impact

Abstract. The stochastic Plant-Craig scheme for deep convection was implemented in the COSMO mesoscale model and used for ensemble forecasting. Ensembles consisting of 100 48 h forecasts at 7 km horizontal resolution were generated for a 2000 × 2000 km domain covering central Europe. Forecasts were made for seven case studies and characterized by different large-scale meteorological environments. Each 100 member ensemble consisted of 10 groups of 10 members, with each group driven by boundary and initial conditions from a selected member from the global ECMWF Ensemble Prediction System. The precipitation variability within and among these groups of members was computed, and it was found that the relative contribution to the ensemble variance introduced by the stochastic convection scheme was substantial, amounting to as much as 76% of the total variance in the ensemble in one of the studied cases. The impact of the scheme was not confined to the grid scale, and typically contributed 25–50% of the total variance even after the precipitation fields had been smoothed to a resolution of 35 km. The variability of precipitation introduced by the scheme was approximately proportional to the total amount of convection that occurred, while the variability due to large-scale conditions changed from case to case, being highest in cases exhibiting strong mid-tropospheric flow and pronounced meso- to synoptic scale vorticity extrema. The stochastic scheme was thus found to be an important source of variability in precipitation cases of weak large-scale flow lacking strong vorticity extrema, but high convective activity.

Exploiting resource profiling mechanism for large-scale scientific computing on grids

2016 ◽  
Vol 19 (3) ◽  
pp. 1527-1539 ◽  
Author(s):  
Md. Azam Hossain ◽  
Cao Ngoc Nguyen ◽  
Jik-Soo Kim ◽  
Soonwook Hwang
Keyword(s):  
Large Scale ◽  
Scientific Computing

Cloud in Science

2014 ◽  
pp. 165-191
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Scientific Computing ◽  
Scientific Discovery ◽  
Software Environment ◽  
Access To Resources ◽  
On Demand ◽  
Service Models ◽  
New Models ◽  
The Impact

Cloud computing technologies and service models are attractive to scientific computing users due to the ability to get on-demand access to resources as well as the ability to control the software environment. Scientific computing researchers and resource providers servicing these users are considering the impact of new models and technologies. SaaS solutions like Globus Online and IaaS solutions such as Nimbus Infrastructure and OpenNebula accelerate the discovery of science by helping scientists to conduct advanced and large-scale science. This chapter describes how cloud is helping researchers to accelerate scientific discovery by transforming manual and difficult tasks into the cloud.

scholarly journals Probabilistic Heat Wave Forecast Based on a Large-Scale Circulation Pattern Using the TIGGE Data

2020 ◽  
Vol 35 (2) ◽  
pp. 367-377
Author(s):  
Hyun-Ju Lee ◽  
Woo-Seop Lee ◽  
Jong Ahn Chun ◽  
Hwa Woon Lee
Keyword(s):  
South Korea ◽  
Heat Wave ◽  
Large Scale ◽  
Heat Waves ◽  
Ensemble Prediction ◽  
Multimodel Ensemble ◽  
Large Scale Circulation ◽  
Circulation Patterns ◽  
Wave Forecast ◽  
Prediction Systems

Abstract Forecasting extreme events is important for having more time to prepare and mitigate high-impact events because those are expected to become more frequent, intense, and persistent around the globe in the future under the warming atmosphere. This study evaluates the probabilistic predictability of the heat wave index (HWI) associated with large-scale circulation patterns for predicting heat waves over South Korea. The HWI, reflecting heat waves over South Korea, was defined as the vorticity difference at 200 hPa between the South China Sea and northeast Asia. The forecast of up to 15 days from five ensemble prediction systems and the multimodel ensemble has been used to predict the probabilistic HWI during the summers of 2011–15. The ensemble prediction systems consist of different five operational centers, and the forecast skill of the probability of heat waves occurrence was assessed using the Brier skill score (BSS), relative operating characteristics (ROC), and reliability diagram. It was found that the multimodel ensemble is capable of better predicting the large-scale circulation patterns leading to heat waves over South Korea than any other single ensemble system through all forecast lead times. We concluded that the probabilistic forecast of the HWI has promise as a tool to take appropriate and timely actions to minimize the loss of lives and properties from imminent heat waves.

Approximate weighted matching on emerging manycore and multithreaded architectures

2012 ◽  
Vol 26 (4) ◽  
pp. 413-430 ◽  
Author(s):  
Mahantesh Halappanavar ◽  
John Feo ◽  
Oreste Villa ◽  
Antonino Tumeo ◽  
Alex Pothen
Keyword(s):  
Approximation Algorithms ◽  
Large Scale ◽  
Scientific Computing ◽  
Graph Matching ◽  
Combinatorial Problem ◽  
Fine Tuning ◽  
Matching Problem ◽  
Weighted Matching ◽  
Wide Range ◽  
The Impact

Graph matching is a prototypical combinatorial problem with many applications in high-performance scientific computing. Optimal algorithms for computing matchings are challenging to parallelize. Approximation algorithms are amenable to parallelization and are therefore important to compute matchings for large-scale problems. Approximation algorithms also generate nearly optimal solutions that are sufficient for many applications. In this paper we present multithreaded algorithms for computing half-approximate weighted matching on state-of-the-art multicore (Intel Nehalem and AMD Magny-Cours), manycore (Nvidia Tesla and Nvidia Fermi), and massively multithreaded (Cray XMT) platforms. We provide two implementations: the first uses shared work queues and is suited for all platforms; and the second implementation, based on dataflow principles, exploits special features available on the Cray XMT. Using a carefully chosen dataset that exhibits characteristics from a wide range of applications, we show scalable performance across different platforms. In particular, for one instance of the input, an R-MAT graph (RMAT-G), we show speedups of about [Formula: see text] on [Formula: see text] cores of an AMD Magny-Cours, [Formula: see text] on [Formula: see text] cores of Intel Nehalem, [Formula: see text] on Nvidia Tesla and [Formula: see text] on Nvidia Fermi relative to one core of Intel Nehalem, and [Formula: see text] on [Formula: see text] processors of Cray XMT. We demonstrate strong as well as weak scaling for graphs with up to a billion edges using up to 12,800 threads. We avoid excessive fine-tuning for each platform and retain the basic structure of the algorithm uniformly across platforms. An exception is the dataflow algorithm designed specifically for the Cray XMT. To the best of the authors' knowledge, this is the first such large-scale study of the half-approximate weighted matching problem on multithreaded platforms. Driven by the critical enabling role of combinatorial algorithms such as matching in scientific computing and the emergence of informatics applications, there is a growing demand to support irregular computations on current and future computing platforms. In this context, we evaluate the capability of emerging multithreaded platforms to tolerate latency induced by irregular memory access patterns, and to support fine-grained parallelism via light-weight synchronization mechanisms. By contrasting the architectural features of these platforms against the Cray XMT, which is specifically designed to support irregular memory-intensive applications, we delineate the impact of these choices on performance.

scholarly journals On the Impact of the Choice of Global Ensemble in Forcing a Regional Ensemble System

2016 ◽  
Vol 31 (2) ◽  
pp. 515-530 ◽  
Author(s):  
Florian Weidle ◽  
Yong Wang ◽  
Geert Smet
Keyword(s):  
Large Scale ◽  
Ensemble Forecasting ◽  
Ensemble Prediction ◽  
Lateral Boundary Condition ◽  
Limited Area ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Driving Model ◽  
Environmental Prediction ◽  
The Impact

Abstract It is quite common that in a regional ensemble system the large-scale initial condition (IC) perturbations and the lateral boundary condition (LBC) perturbations are taken from a global ensemble prediction system (EPS). The choice of global EPS as a driving model can have a significant impact on the performance of the regional EPS. This study investigates the impact of large-scale IC/LBC perturbations obtained from different global EPSs on the forecast quality of a regional EPS. For this purpose several experiments are conducted where the Aire Limitée Adaption dynamique Développement International–Limited Area Ensemble Forecasting (ALADIN-LAEF) regional ensemble is forced by two of the world’s leading global ensembles, the European Centre for Medium-Range Weather Forecasts’ Ensemble Prediction System (ECMWF-EPS) and the Global Ensemble Forecasting System (GEFS) from the National Centers for Environmental Prediction (NCEP), which provide the IC and LBC perturbations. The investigation is carried out for a 51-day period during summer 2010 over central Europe. The results indicate that forcing of the regional ensemble with GEFS performs better for surface parameters, whereas at upper levels forcing with ECMWF-EPS is superior. Using perturbations from GEFS lead to a considerably higher spread in ALADIN-LAEF, which is beneficial near the surface where regional EPSs are usually underdispersive. At upper levels, forcing with GEFS leads to an overdispersion of ALADIN-LAEF as a result of the large spread of some parameters, where forcing ALADIN-LAEF with ECMWF-EPS provides statistically more reliable forecasts. The results indicate that the best global EPS might not always provide the best ICs and LBCs for a regional ensemble.

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