Weather/marine extreme event simulation with Galaxy-ES (Earth System) scientific workflow engine and cloud computing tools

  1. Montella°*

°Dpt. of Science and Technologies – University of Napoli Parthenope

*Computation Institute – University of Chicago (visiting)

The global climate changes have driven impressive local weather modifications with huge and reasonable impact on environmental, economic and social habits. Just focusing on the Italian context, the food, the wine and the tourism industries cover the most part of the national gross product. Extreme weather events (EWE) are difficult to predict with the accuracy needed because some main reasons:  EWEs are deeply local and influenced by the high-resolution orography and land use; the local data assimilation is a key modeling issue meaning a pervasive and geographically distributed sensor network is required; simulation and forecast model have to be coupled in a simple, data typed, consistent way in order to perform large size local scale ensembles; finally the computational resources (both pure computing and storage) are a limitation factor because the need of a huge amount of them, but elastically allocated.

The aim of the project presented in this document is to build an user friendly computation platform based on workflows dedicated to extreme weather/marine event simulation and prediction leveraging on the Face-IT project product Galaxy-ES (Earth System) where storage and computing is dynamically and elastically allocated using cloud computing tools as, but not limited to, Amazon Web Services.

The use cases are focused on some extreme weather events happened in the south of Italy in the years 2013 and 2014 that had a great impact on citizen security and economy.

This project is intended as a Face-IT spin-off/partnership open to be extended to other participants sharing the same intents and interests.

Acknowledgement: funded by the Dpt. of Science and Technologies – University of Napoli Parthenope, supported by the Face-IT project

 

The following scientific works have been carried out thanks to the program to strengthen departmental research lines:

  1. Montella R., G. Giunta, and G. Laccetti. “Virtualizing high-end GPGPUs on ARM clusters for the next generation of high performance cloud computing.” Cluster Computing (2014): 1-14.
  2. Laccetti G., Montella R., Palmieri C., Pelliccia V. The High Performance Internet of Things: using GVirtuS to share high-end GPUs with ARM based cluster computing nodes. PPAM 2014 (expected LNCS).In press.
  3. A. Riccio, E. Chianese, G. Agrillo, C. Esposito, L. Ferrara, and G. Tirimberio. Source apportion of atmospheric particulate matter: a joint Eulerian/Lagrangian approach.        Environmental Science & Pollution Research, 2014. In Press.
  4. S. Basu, A.A.M. Holtslag, L. Caporaso, A. Riccio, and G.J. Steeneveld. Observational support for the stability dependence of the bulk Richardson number across the stable boundary layer. Boundary-layer Meteorology, 150(3):515–523, 2014.
  5. G. Agrillo, E. Chianese, A. Riccio, and A. Zinzi. Modeling and characterization of air pollution: Perspectives and recent developments with a focus on the Campania region (Southern Italy). International Journal of Environmental Research, 7(4):909–916, 2013.
  6. E. Solazzo, A. Riccio, I. Kioutsioukis, and S. Galmarini. Pauci ex tanto numero: reduce redundancy in multi-model ensembles. Atmospheric Chemistry and Physics, 13(16):8315–8333, 2013.
  7. L. Caporaso, A. Riccio, F. Di Giuseppe, and F. Tampieri. Relating mean radio sounding profiles to surface fluxes for the very stable boundary layer. Boundary-layer Meteorology, 146(2):203–215, 2013.
  8. L. Tositti, A. Riccio, S. Sandrini, E. Brattich, D. Baldacci, S. Parmeggiani, P. Cristofanelli, and P. Bonasoni. Short-term climatology of PM10 at a high altitude background station in Southern Europe. Atmospheric Environment, 65:142–152, 2013.