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The Impact of Assimilating Surface Pressure Observations on Severe Weather Events in a WRF Mesoscale Ensemble System
Author(s) -
Dustan M. Wheatley,
David J. Stensrud
Publication year - 2009
Publication title -
monthly weather review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.862
H-Index - 179
eISSN - 1520-0493
pISSN - 0027-0644
DOI - 10.1175/2009mwr3042.1
Subject(s) - mesoscale meteorology , weather research and forecasting model , data assimilation , surface pressure , environmental science , climatology , ensemble kalman filter , meteorology , altimeter , convection , north american mesoscale model , numerical weather prediction , atmospheric sciences , geology , global forecast system , kalman filter , geography , computer science , artificial intelligence , extended kalman filter
Surface pressure observations are assimilated into a Weather Research and Forecast ensemble using an ensemble Kalman filter (EnKF) approach and the results are compared with observations for two severe weather events. Several EnKF experiments are performed to evaluate the relative impacts of two very different pressure observations: altimeter setting (a total pressure field) and 1-h surface pressure tendency. The primary objective of this study is to determine the surface pressure observation that is most successful in producing realistic mesoscale features, such as convectively driven cold pools, which often play an important role in future convective development. Results show that ensemble-mean pressure analyses produced from the assimilation of surface temperature, moisture, and winds possess significant errors in regard to mesohigh strength and location. The addition of surface pressure tendency observations within the assimilation yields limited ability to constrain such errors, while the as...

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