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Reseach Article

Developing Cloud Computing Architecture for Modeling (Model as a Service) using Data Assimilation Techniques

by Dhanashree Kuthe
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 34
Year of Publication: 2018
Authors: Dhanashree Kuthe
10.5120/ijca2018917936

Dhanashree Kuthe . Developing Cloud Computing Architecture for Modeling (Model as a Service) using Data Assimilation Techniques. International Journal of Computer Applications. 181, 34 ( Dec 2018), 9-11. DOI=10.5120/ijca2018917936

@article{ 10.5120/ijca2018917936,
author = { Dhanashree Kuthe },
title = { Developing Cloud Computing Architecture for Modeling (Model as a Service) using Data Assimilation Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2018 },
volume = { 181 },
number = { 34 },
month = { Dec },
year = { 2018 },
issn = { 0975-8887 },
pages = { 9-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number34/30208-2018917936/ },
doi = { 10.5120/ijca2018917936 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:08:05.369859+05:30
%A Dhanashree Kuthe
%T Developing Cloud Computing Architecture for Modeling (Model as a Service) using Data Assimilation Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 34
%P 9-11
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data assimilation refers to any use of observational information to improve a model. To solve any problem like weather forecasting, traffic management, water management, agricultural management, urban planning modeling of that particular problem is important. For developing the perfect model real time observational information is necessary. To get the correct solution and forecasting incorporating the observational data in the model will definitely improve the results and the perfect model has been build. Data assimilation techniques like statistical interpolation, Kalman Filter, 4d-Var, Ensemble Kalman filter, Optimal Interpolation, Nudging, Analysis Correction and Successive correction can be used to improve the model. But the question is how to get the real time data and improve the model, since to develop any model and to incorporate the huge amount of real time data into the model huge amount of computing resources is necessary. Cloud Computing provide the resources as required in agile way with its characteristics like elasticity, broad network access and resource pooling. The integration of cloud computing and data assimilation will help to build new applications to solve the above problems and get the instant access to those applications on the internet so any common man or any researcher can use it.

References
  1. William Y.Y., Cheng Yubao, Liu Alfred, J. Bourgeois, Yonghui Wu Sue and Ellen Haupt. Short-term wind forecast of a data assimilation/weather forecasting system with wind turbine anemometer measurement assimilation. Renewable Energy Volume 107, July 2017, Pages 340-351.
  2. Philipp Schneider, Nuria Castell, Matthias Vogt Franck , R. Dauge William A.Lahoz Alena Bartonova. Mapping urban air quality in near real-time using observations from low-cost sensors and model information. Environment International Volume 106, September 2017, Pages 234-247.
  3. Carlos Roma et. al. Correcting satellite-based precipitation products through SMOS soil moisture data assimilation in two land-surface models of different complexity: API and SURFEX. Remote Sensing of Environment Volume 200, October 2017, Pages 295-310.
  4. Chaowei Yang Manzhu, Yu Fei Hu, Yongyao Jiang and Yun Li. Utilizing Cloud Computing to address big geospatial data challenges. Computers, Environment and Urban Systems, Volume 61, Part B, January 2017, Pages 120-128
  5. Amir Javaheri, Mohammad Nabatian, Ehsan Omranian, Meghna Babbar-Sebensand and Seong Jin Noh Merging Real-Time Channel Sensor Networks with Continental-Scale Hydrologic Models: A Data Assimilation Approach for Improving Accuracy in Flood Depth Predictions. Hydrology 2018, 5(1),9; doi:10.3390/hydrology5010009.
  6. Chong Chen Dan, ChenYingnanYan, Gaofeng Zhang Qingguo and Zhou Rui Zhou. Integration of numerical model and cloud computing. Future Generation Computer Systems Volume 79, Part 1, February 2018, Pages 396-407.
  7. Brian K.Blaylock John D.Horel and Samuel T.Liston, Cloud archiving and data mining of High-Resolution Rapid Refresh forecast model output. Computers & Geosciences Volume 109, December 2017, Pages 43-50.
  8. Jun Shen, Zuqin Ji, Yubin Zhu, and Jinjin Huang. An Analytical Method of Network Service Scalability. Special section on recent advances on radio access and security methods in 5g networks, Volume 6, 2018.
  9. Brynmor Lloyd Evans PhD, Bethan Paterson MSc, Steve Onyett PhD, Ellie Brown MSc, Hannah Istead MSc, D Clin Psych, Richard Gray PhD, Claire Henderson PhD and Sonia Johnson DM. National implementation of a mental health service model: A survey of Crisis Resolution Teams in England. International Journal of Mental Health, Volume 27, Issue I, February 2018 Pages 214-226.
  10. Community Corrections service delivery model: An evidence-based approach to reduce reoffending, R Caruana- Judicial Officers Bulletin, 2018.
  11. N Becker and M Fidler. A non-stationary service curve model for estimation of cellular sleep scheduling. IEEE Transactions on Mobile Computing 2018.
  12. R Singh, HS Sandhu, BA Metri and R Kaur. Supply Chain Management Practices, Competitive Advantage and Organizational Performance: A Confirmatory Factor Model. Operations and Service, 2018 –igi-global.com.
  13. M Potschin-Young, R Haines-Young and C Görg. ] Understanding the role of conceptual frameworks: Reading the ecosystem service cascade. Ecosystem Services, 2018 – Elsevier.
Index Terms

Computer Science
Information Sciences

Keywords

Model as a Service Cloud Computing Data Assimilation.