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Volume 270 - International Symposium on Grids and Clouds (ISGC) 2016 (ISGC 2016) - Earth & Environmental Sciences & Biodiversity
Seasonal Ensemble Forecasting Application On Dependable Sumegha Scientific Cloud Infrastructure
R.N. Laveti,* B.B.P. Rao, B. Arunachalam, V. Arackal
*corresponding author
Full text: pdf
Published on: 2017 January 11
Abstract
Despite several advances in understanding the behaviour of monsoon variability, innovations in
the numerical modeling and the availability of higher computational capabilities, accurate
prediction of Indian summer monsoon still remains a serious challenge. Seasonal Forecast
Model (SFM), developed for seasonal forecast and climate research, is used for forecasting the
Indian summer monsoon in advance of a season. Ensemble forecasting method helps us in
finding and minimizing the uncertainty inherent in seasonal forecast. The inherent parallel
nature and the bursty computational demands of the ensemble forecasting method allows it to
effectively utilize the Infrastructure-as-a-Service (IaaS) model on the cloud platform. However,
realizing huge scientific experiments is still a challenge to the cloud service providers as well as
to the climate modeling community.

To start with prototype experiments using SFM model were conducted at T-62 resolution (~ 200
km x 200 km grid). The experience gained from the prototype runs were used by the SuMegha
operational team to fine tune the configuration of SuMegha Cloud resources to improve the
quality of service. High resolution SFM at T-320 (~ 37 km x 37 km grid) was also configured
and experiments were conducted to understand the scalability, computational performance of the
application and the reliability of SuMegha Cloud.

In this paper, we use SFM as a case study to present the key problems found by climate
applications, and propose a framework to run the same on SuMegha Cloud infrastructure to
allow a climate model to take advantage of these cloud resources in a seamless and reliable way.
The framework uses classification and outlier detection techniques to classify the resources and
also to identify the faulty resources. It addresses the challenges such as unexpected hardware
failures, power outages, failed porting and software bugs. We share our experience in
conducting the ensemble forecasting experiments on SuMegha Cloud using the proposed
framework. We also attempt to provide a perspective on the desirable features of a scientific
cloud infrastructure, for easier adaptation by the climate modeling community to conduct large
scientific experiments.
DOI: https://doi.org/10.22323/1.270.0005
Open Access
Creative Commons LicenseCopyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.