Abstract:
Land surface heterogeneities are important for accurate estimation of land-atmosphere interactions and their feedbacks
on water and energy budgets. To physically introduce
existing land surface heterogeneities into a mesoscale model, a
land data assimilation system was coupled with a mesoscale model
(LDAS-A) to assimilate low-frequency satellite microwave observations
for soil moisture and the combined system was applied in
the Tibetan Plateau. Though the assimilated soil moisture distribution
showed high correlation with Advanced Microwave Scanning
Radiometer on the Earth Observing System soil moisture
retrievals, the assimilated land surface conditions suffered substantial
errors and drifts owing to predicted model forcings (i.e.,
solar radiation and rainfall). To overcome this operational pitfall,
the Coupled Land and Atmosphere Data Assimilation System
(CALDAS) was developed by coupling the LDAS-A with a cloud
microphysics data assimilation. CALDAS assimilated lower frequency
microwave data to improve the representation of land surface
conditions, and merged them with higher frequency microwave
data to improve the representation of atmospheric conditions over
land surfaces. The simulation results showed that CALDAS effectively
assimilated atmospheric information contained in higher
frequency microwave data and significantly improved correlation
of cloud, distribution compared with the satellite observation. CALDAS
also improved biases in cloud conditions and associated
rainfall events, which contaminated land surface conditions in
LDAS-A. Improvements in predicted clouds resulted in better
land surface model forcings (i.e., solar radiation and rainfall),
which maintained assimilated surface conditions in accordance
with observed conditions during the model forecast. Improvements
in both atmospheric forcings and land surface conditions
enhanced land-atmosphere interactions in the CALDAS model, as
confirmed by radiosonde observations