A Numerical Study of Mesoscale Model Initialization with Data Assimilation

서지정보

ㆍ발행기관 : 한국지구과학회 ㆍ수록지정보 : 한국지구과학회지 / 35권 / 5호
ㆍ저자명 : Min, Ki-Hong

목차

Abstract
Introduction
Numerical Modeling and Data Assimilation Systems
The Purdue Mesoscale Model (PMM)
Goddard Earth Observing System (GEOS) finite volume GCM (fvGCM)
Land Data Assimilation System (LDAS)
Methodology
Discussion of Results
Surface state and atmospheric circulation
Moisture budget calculation
Summary and Conclusion
Acknowledgments
References

영어 초록

Data for model analysis derived from the finite volume (fv) GCM (Goddard Earth Observing System Ver. 4, GEOS-4) and the Land Data Assimilation System (LDAS) have been utilized in a mesoscale model. These data are tested to provide initial conditions and lateral boundary forcings to the Purdue Mesoscale Model (PMM) for a case study of the Midwestern flood that took place from 21-23 May 1998. The simulated results with fvGCM and LDAS soil moisture and temperature data are compared with that of ECMWF reanalysis. The initial conditions of the land surface provided by fvGCM/LDAS show significant differences in both soil moisture and ground temperature when compared to ECMWF control run, which results in a much different atmospheric state in the Planetary Boundary Layer (PBL). The simulation result shows that significant changes to the forecasted weather system occur due to the surface initial conditions, especially for the precipitation and temperature over the land. In comparing precipitation, moisture budgets, and surface energy, not only do the intensity and the location of precipitation over the Midwestern U.S. coincide better when running fvGCM/LDAS, but also the temperature forecast agrees better when compared to ECMWF reanalysis data. However, the precipitation over the Rocky Mountains is too large due to the cumulus parameterization scheme used in the PMM. The RMS errors and biases of fvGCM/LDAS are smaller than the control run and show statistical significance supporting the conclusion that the use of LDAS improves the precipitation and temperature forecast in the case of the Midwestern flood. The same method can be applied to Korea and simulations will be carried out as more LDAS data becomes available.

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