Publications in Scientific Journals:

K. Scipal, M. Drusch, W. Wagner:
"Assimilation of a ERS scatterometer derived soil moisture index in the ECMWF numerical weather prediction system";
Advances in Water Resources, 31 (2008), 8; 1101 - 1112.

English abstract:
The European Centre for Medium-Range Weather Forecasts (ECMWF) currently prepares the assimilation
of soil moisture data derived from advanced scatterometer (ASCAT) measurements. ASCAT is part of the
MetOp satellite payload launched in November 2006 and will ensure the operational provision of soil
moisture information until at least 2020. Several studies showed that soil moisture derived from scatterometer
data contain skillful information. Based on data from its predecessor instruments, the ERS-1/
2 scatterometers we examine the potential of future ASCAT soil moisture data for numerical weather prediction
(NWP). In a first step, we compare nine years of the ERS scatterometer derived surface soil moisture
index (HS) against soil moisture from the ECMWF re-analysis (ERA40) data set (HE) to (i) identify
systematic differences and (ii) derive a transfer function which minimises these differences and transformsHS
into model equivalent volumetric soil moisture H S.We then use a nudging scheme to assimilate
H S in the soil moisture analysis of the ECMWF numerical weather prediction model. In this scheme the
difference between H S and the model first guess HFG, calculated at 1200 UTC, is added in 1/4 fractions
throughout a 24 h window to the model resulting in analysed soil moisture HNDG. We compare results
from this experiment against those from a control experiment where soil moisture evolved freely and
against those from the operational ECMWF forecast system, which uses an optimum interpolation
scheme to analyse soil moisture. Validation against field observations from the Oklahoma Mesonet,
shows that the assimilation of H S increases the correlation from 0.39 to 0.66 and decreases the RMSE
from 0.055 m3 m 3 to 0.041 m3 m 3 compared against the control experiment. The corresponding forecasts
for low level temperature and humidity improve only marginally compared to the control experiment
and deteriorate compared to the operational system. In addition, the results suggest that an
advanced data assimilation system, like the Extended Kalman Filter, could use the satellite observations
more effectively.

Soil moisture, Scatterometer, Data assimilation, Numerical weather prediction

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Created from the Publication Database of the Vienna University of Technology.