Articles | Volume 20, issue 12
https://doi.org/10.5194/hess-20-4707-2016
https://doi.org/10.5194/hess-20-4707-2016
Research article
 | 
29 Nov 2016
Research article |  | 29 Nov 2016

Coupled hydro-meteorological modelling on a HPC platform for high-resolution extreme weather impact study

Dehua Zhu, Shirley Echendu, Yunqing Xuan, Mike Webster, and Ian Cluckie

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Cited articles

Cloke, H. L. and Pappenberger, F.: Ensemble flood forecasting: A review, J. Hydrol., 375, 613–626, 2009.
Dudhia, J.: A non-hydrostatic version of the Penn State/NCAR mesoscale model: validation tests and simulation of an Atlantic cyclone and cold front, Mon. Weather Rev., 121, 1493, https://doi.org/10.1175/1520-0493(1993)121<1493:ANVOTP>2.0.CO;2, 1993.
Golding B. W.: NIMROD: a system for generating automated very short range forecasts, Met. Appl., 5, 1–16, 1998.
Grell, G., Dudhia, J., and Stauffer, D.: A description of the fifth generation Penn State/NCAR Mesoscale Model (MM5), NCAR Technical Note, NCAR/TN-398CSTR, 117 pp., 1994.
Jasper, K., Gurtz, J., and Lang, H.: Advanced flood forecasting in Alpine watersheds by coupling meteorological observations and forecasts with a distributed hydrological model, J. Hydrol., 267, 40–52, 2002.
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Short summary
The study in the paper is utilizing and maximizing high-performance computing (HPC) power resources to support the study on extreme weather impact due to climate change, which for the first time allows modellers to simulate the entire system, ranging from the global circulation to a target catchment for impact study on a single platform, where both NWP and the hydrological model are executed so that more effective interaction and communication can be achieved and maintained between the model.