Atef O. Sherif - CIMAP

15 May 2008 A. O. Sherif
NFS-AST Workshop on Supercomputing Applications in Climate and Remote Sensing
Cairo Egypt, 13-16 May 2008
Cairo University
FVLab
� Availability of suitable observational data limits model evaluation. It is much
more attractive to evaluate a model in terms of physical processes such as
energy and water budgets and fluxes as well as state variables (such as
temperature). Observational data on the former is, unfortunately, quite
limited.
� Finally, even though RMs are run at relatively high resolution, they still fall
short of the scale and nature of such point data as is collected on
meteorological stations, ocean buoys, and such.
� This is a complication in particular for the evaluation of many kinds of
extremes, as data generated with RCMs (gridded data) are more homogenous
in space compared to observations (station data). For example, in the former,
extremes are typically attenuated compared to point values observed at
stations. *
� Model evaluation on the process level (e.g., fluxes) or by means of
integrative metrics (e.g., river run-off) rather than in terms of state variables
(e.g., temperature) is an avenue that deserves more exploration.**
� Another important limitation is the relatively high demand of computational
resources which can put a limit on the number, resolution, or length of RCM
runs.
* Haylock MR, Hofstra N, Klein Tank AMG, Klok EJ, Jones PD, et al. A European daily high-resolution gridded dataset of surface temperature and precipitation.J
Geophys Res 2008, 113:D20119. Doi:10.1029/2008JD10201.
** Lind P, Kjellstro¨m E. Water budget in the Baltic Sea drainage basin: valuation of simulated fluxes in a regional climate model. Boreal Env Res 2009, 14:56–67.
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