Estimation of the systematic error of precipitation and humidity in the MM5 model

International audience To comprehensively diagnose model capabilities in simulating atmospheric flow including the relevant microphysical processes, the main prognostic fields of the MM5 model are compared with ERA40 reanalysis data. This approach allows to identify and compare meaningful features o...

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Bibliographic Details
Main Authors: Ivanov, S., Simmer, C., Palamarchuk, J., Bachner, S.
Other Authors: Odessa State Environmental University, Odessa National I.I.Mechnikov University, Meteorological Institute Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn
Format: Article in Journal/Newspaper
Language:English
Published: HAL CCSD 2008
Subjects:
Online Access:https://hal.science/hal-00297092
https://hal.science/hal-00297092/document
https://hal.science/hal-00297092/file/adgeo-16-97-2008.pdf
Description
Summary:International audience To comprehensively diagnose model capabilities in simulating atmospheric flow including the relevant microphysical processes, the main prognostic fields of the MM5 model are compared with ERA40 reanalysis data. This approach allows to identify and compare meaningful features of model parameterization schemes and to quantify model errors. Various combinations of schemes for cumulus convection, planetary boundary layer (PBL), microphysics and radiative transfer are used in order to identify those combinations which produce the closest resemblance between model state and reanalysis. The spatial structure of systematic errors, both horizontal and vertical will be described and geographical regions and synoptic situations will be identified, which are associated with pronounced systematic model deviations. The study focused on precipitation and humidity fields as well as on the main thermodynamic atmospheric variables on a coarse resolution grid (about 80 km) over the North Atlantic - Europe region. Our results identify advantages and shortcomings of the various parameterization schemes. They also indicate that, in general, the combination of best schemes does not result in optimal simulations of a particular variable.