A Systematic Literature Review on Numerical Weather Prediction Models and Provenance Data

Abstract

The weather has been an important issue for mankind since the earliest times. The need to predict the weather accurately increases every day when considering the effects of natural disasters such as floods, hails, extreme winds, landslides, etc. on many sectors from transportation to agriculture, which all depend on weather conditions. Numerical weather prediction (NWP) models, today’s the de-facto tools used for weather forecasting, are scientific software that models atmospheric dynamics in accordance with the laws of physics. These models perform complex mathematical calculations on very large data (gridded) and require high computational power. For this reason, NWP models are scientific software that is usually run on distributed infrastructures and often takes hours to finish. On the other hand, provenance is another key concept as important as weather prediction. Provenance can be briefly defined as metadata that provides information about any kind of data, process, or workflow. In this SLR study, a comprehensive screening of literature was performed to discover primary studies that directly suggest systematic provenance structures for NWP models, or primary studies in which at least a case study was implemented on an NWP model even if considered in a broader perspective. Afterward, these primary studies were thoroughly examined in line with specific research questions, and the findings were presented in a systematic manner. An SLR study on primary studies which combines the two domains of NWP models and provenance research has never been done before. So we think that this work will fill an important gap in literature regarding studies combining the two domains and increase the interest in the subject.

A Appendix: Relevant Primary Studies

S01	Behrens, H. W., Candan, K. S., Chen, X., Gadkari, A., Garg, Y., Li, M. L., ..., Sapino, M. L. Datastorm-FE: A data-and decision-flow and coordination engine for coupled simulation ensembles. Proceedings of the VLDB Endowment, 11(12), 1906–1909 (2018)
S02	Cheah, Y. W., Plale, B. Provenance quality assessment methodology and framework. Journal of Data and Information Quality (JDIQ), 5(3), 1–20 (2014)
S03	Chen, P., Plale, B., Aktas, M. S. Temporal representation for scientific data provenance. In 2012 IEEE 8th International Conference on E-Science, IEEE, 1–8 (2012)
S04	Chen, P., Plale, B., Aktas, M. S. Temporal representation for mining scientific data provenance. Future generation computer systems, 36, 363–378 (2014)
S05	Cinquini, L., Crichton, D., Mattmann, C., Harney, J., Shipman, G., Wang, F., ..., Schweitzer, R. The Earth System Grid Federation: An open infrastructure for access to distributed geospatial data. Future Generation Computer Systems, 36, 400–417 (2014)
S06	Galizia, A., Roverelli, L., Zereik, G., Danovaro, E., Clematis, A., D’Agostino, D. Using Apache Airavata and EasyGateway for the creation of complex science gateway front-end. Future Generation Computer Systems, 94, 910–919 (2019)
S07	Liu, W., Ye, Q., Wu, C. Q., Liu, Y., Zhou, X., Shan, Y. Machine Learning-assisted Computational Steering of Large-scale Scientific Simulations. In 2021 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), 984–992 (2021)
S08	Lopez, J. L. A., Kalyuzhnaya, A. V., Kosukhin, S. S., Ivanov, S. V. Data quality control for St. Petersburg flood warning system. Procedia Computer Science, 80, 2128–2140 (2016)
S09	Puiu, D., Barnaghi, P., Tönjes, R., Kümper, D., Ali, M. I., Mileo, A., ..., Fernandes, J. Citypulse: Large scale data analytics framework for smart cities. IEEE Access, 4, 1086–1108 (2016)
S10	Tufek, A., Gurbuz, A., Ekuklu, O. F., Aktas, M. S. Provenance collection platform for the weather research and forecasting model. In 2018 14th International Conference on Semantics, Knowledge and Grids (SKG), IEEE, 17–24 (2018)
S11	Tufek, A., Aktas, M. S. On the provenance extraction techniques from large scale log files: a case study for the numerical weather prediction models. In European Conference on Parallel Processing, Springer, 249–260 (2020)
S12	Tufek, A, Aktas, MS. On the provenance extraction techniques from large scale log files. Concurrency and Computation: Practice and Experience (2021). https://doi.org/10.1002/cpe.6559
S13	Turuncoglu, U. U., Dalfes, N., Murphy, S., DeLuca, C. Toward self-describing and workflow integrated Earth system models: A coupled atmosphere-ocean modeling system application. Environmental modelling & software, 39, 247–262 (2013)
S14	Wu, C. Q., Lin, X., Yu, D., Xu, W., Li, L. End-to-end delay minimization for scientific workflows in clouds under budget constraint. IEEE Transactions on Cloud Computing, 3(2), 169–181 (2014)
S15	Xie, Y., Muniswamy-Reddy, K. K., Feng, D., Li, Y., Long, D. D. Evaluation of a hybrid approach for efficient provenance storage. ACM Transactions on Storage (TOS), 9(4), 1–29 (2013)
S16	Zhao, R., Atkinson, M., Papapanagiotou, P., Magnoni, F., Fleuriot, J. Dr. Aid: Supporting Data-governance Rule Compliance for Decentralized Collaboration in an Automated Way. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2), 1–43 (2021)