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Forecasting mesoscale precipitation using the MM5 model with the four-dimensional data assimilation (FDDA) technique

  • Authors
    Yamazaki Y.
    Orgaz M.

The two basic forms of multi-scale data assimilation procedures (FDDA), based on
Newtonian relaxation, of analysis and observations nudging have been applied for
precipitation event period occurred over Portugal during summer season, using the Fifth
Generation Mesoscale Model (MM5) developed and maintained by the Pennsylvania
State University and National Center for Atmospheric Research (PSU/NCAR). The model
has been configured for three nested grid domains covering part of the Eastern part of
North Atlantic region evolving the Portugal, with 35 vertical levels, from surface up to 100
hPa top level. The model forecasting have been conducted employing daily available data
from surface observational network, radio-sounding from Lisbon/Portugal and NOAA-16
polar orbiting satellite retrieved vertical profiles data. The three integration domains of
MM5 model have been processed using, as boundary and first guess fields, the global
atmospheric forecast NCEP-NWS/AVN model data gathered through the Unidata Local
Data Manager (LDM)/Unidata Internet Data Distribution (IDD) system. All daily
forecasting, with FDDA and with no FDDA, have been run for 60 hours forecast, with 30
minutes interval model data output to provide enough timely detailed results. The FDDA
analysis presented a quite reasonable data ingesting volume of almost all available
satellite data, with the exception of humidity data retrieved for high levels, above around
500 hPa. The obtained results indicate that, even using weak FDDA constraint coefficient
values, presents a significant improvement in the numerical prognosis in the precipitation
field, on both space and time integration levels. The results also presented an
enhancement of the physics of the convective mesoscale system development,
particularly over mountain region, indicating that it would be interesting to conduct an
experiment with a dense data collecting platform coverage focused on events which occur
in some prevailing mountain region of Portugal.

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