Abstract:
Soil moisture retrievals from satellites often contain large
time-variant and time-invariant errors because the physical processes
that relate brightness temperature to soil moisture are difficult to
parameterize, and because the necessary parameters are difficult to
obtain on the global scale. For the design of new satellite sensors it
is important to understand just how uncertain satellite retrievals can
be and still add useful information to a land data assimilation
system. In this paper, we address this question with a fraternal twin
experiment that is based on high-resolution (1 km) true soil
moisture fields and associated passive microwave brightness
temperatures from a long-term integration of the TOPLATS land surface
model over the Red-Arkansas river basin. From the true fields, we
simulate many different retrieval data sets at a typical satellite
footprint scale (36 km). The different retrieval data sets reflect
various realistic sources of uncertainty with different error
structure and magnitude. After scaling the satellite data to the model
soil moisture climatology for bias removal, the simulated retrieval
data sets are then assimilated into the NASA Catchment land surface
model with an Ensemble Kalman filter (EnKF). Finally, the quality of
the assimilation estimates (with respect to the synthetic truth) is
compared with that of a baseline integration of the Catchment model
without assimilation. This procedure permits us to quantify explicitly
the maximum level of uncertainty in the satellite retrievals for which
information is still added in the assimilation. Performance measures
include the traditional absolute (RMS) error, which is important for
water cycle studies, and the time series correlation coefficient. The
latter measures the quality of (scaled) anomaly estimates that can be
used, for example, for forecast initialization.