Abstract:
Retrieval of land surface variables and atmospheric variables over land from passive
microwave remote-sensing data sets has been a challenge for many years. A lot
of progress has been made in these quests such as using cloud-resolving models and
data assimilation. Data assimilation allows the integration of observations (including
observation errors) into imperfect models, thereby yielding more improved
model forecasts. In this work, a coupled data assimilation framework (CDAF) is
proposed and applied to predict the evolution of land surface and atmospheric conditions.
CDAF comprises a coupling of two data assimilation schemes, namely a
land data assimilation scheme (LDAS) and an ice microphysics data assimilation
scheme (IMDAS). This system has been developed and evaluated using data for
the Tibetan Plateau. In this framework, both low-frequency and high-frequency
passive microwave brightness temperatures (TBs) are assimilated. Low-frequency
TBs are assimilated in the LDAS subsystem and used to obtain land surface conditions,
which are subsequently used as improved initial conditions together with
high-frequency TBs and assimilated in the IMDAS subsystem to obtain atmospheric
conditions. The retrieved land surface variables and integrated atmospheric
variables are demonstrated to show good agreement with observed land and atmosphere
conditions such as those derived from point measurements of temperature
and soil moisture (using the Soil Moisture and Temperature Measurement System
(SMTMS)), sonde, Advanced Infrared Sounder (AIRS) and Global Precipitation
Climatology Project (GPCP) products. The distribution of integrated cloud liquid
water and cloud ice is shown to follow the observed cloud distribution over the
study area. It is shown that by using IMDAS with modifications to account for precipitation
and a good description of land surface emission, it is possible to obtain
precipitation information of high fidelity over the land surface. Retrieved integrated
water vapour using IMDAS shows correspondence with ‘corrected’ AIRS
total precipitable water product. It is also shown that the relative humidity profile
obtained from IMDAS agrees with the corresponding sonde profile. From the
simulations, it is clear that by using the CDAF, there is marked improvement in
the forecast conditions compared with the non-assimilation scenario for all of the variables considered. Comparisons with observed land surface conditions and inferences of atmosphere state from the Geostationally Operational Environmental
Satellite Series 9 (GOES-9) InfraRed Channel 1 (IR1) brightness temperatures and the GCPC’s cumulative daily precipitation indicate that the CDAF is able to
generate reliable forecasts that agree with observation-derived products.
