Remote Sensing Data Assimilation In
WRF-UCM Mesoscale Model:
Madrid Case Study
Author(s)
R. San José, J. L. Pérez, J. L. Morant & R. M. González
Abstract
Data assimilation is a powerful numerical technique that is used to substantially
improve numerical meteorological simulations. In this contribution we have used
the WRF mesoscale meteorological model (NCAR, US) to show the importance
of using remote sensing data (satellite and tower data), the sensitivity of the
results and the improvement when compared with observational surface data
(wind and temperature). We have used CLC100 m instead of GTOPO 30’’, 10 m
spatial resolution GIS data of Madrid (Spain) city to produce urban land use
types according to the Urban Canopy Model (UCM) (NCAR) approach: airborne
temperature (4 m spatial resolution), albedo, anthropogenic heat flux, shadowing
in UCM and tower data (wind and temperature). The results show a high
sensitivity to all of these parameters. For historical simulations - where in-situ
meteorological data is available - data assimilation is a crucial tool to improve
the results. The sensitivity of the results to the different high resolution input
data is also crucial for the results of the simulation. The correlation coefficient
for temperature is improved up to 0.960.
Keywords: remote sensing, data assimilation, mesoscale models.
1 Introduction
Data assimilation methods have been used in meteorological simulations for the
last 50 years. The first methods included the so-called Cressman algorithm or
Keywords
remote sensing, data assimilation, mesoscale models
Related Book
Other papers in this volume
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