Session 5: Surface parameter retrieval: assimilation experiments
Session Chair: Xiaolei Zou
29. Preparation activities for the assimilation of SMOS data in the
ECMWF land surface analysis system
J. Munoz Sabater, P. de Rosnay, M. Drusch, A. Fouilloux, M. Dahoui, S.
Author：
Mecklenburg
Affiliation： ECMWF
The 2 November 2009 the Soil Moisture and Ocean Salinity (SMOS) mission of the
European Space Agency (ESA) was successfully put into a polar orbit around the
Earth. Since then SMOS has continuously been providing global multi-angular and
multi-polarized maps of brightness temperatures at the L-band. Although the
technical and operational challenges of this mission are multiple, SMOS data has
already proved its potential to deliver valuable information about the water content
of a shallow surface.
Since the beginning of the mission, the European Centre for Medium-Range Weather
Forecasts (ECMWF) has been actively involved in this mission. At ECMWF the
main objective is to study the potential impact that the assimilation of SMOS
brightness temperatures has on the weather forecast skill, by improving the
initialization of the global soil moisture state before a forecast run. However, prior to
assimilation, SMOS data needs to go through a series of steps which guarantee a
good quality dataset ready to be assimilated. Firstly, an operational chain was
developed which makes it possible to monitor SMOS data in Near Real Time. As a
consequence, global statistical maps of the observations, a model equivalent of the
observations and the difference between both sources of information (also called
first-guess departures), are systematically being produced just a few hours
observation time, among others. These statistics are an excellent tool to localize
systematic bias or drifts in the observations or in the model. They also provide
support to calibration and validation teams. Secondly, SMOS data needs to be
significantly thinned as the data volume contained in a single orbit is too large for
the current operational capabilities in Numerical Weather Prediction systems.
Different thinning strategies were analysed and tested. Thirdly, SMOS observed
brightness temperatures are significantly noised, due the different complex geometry
of the multi-angular observations. In this respect, a simple but efficient noise
reduction scheme is currently being tested, which not only reduce random
observational noise from the observations, but also thin the data before assimilation.
Finally, a bias correction scheme based on a future available re-processed dataset
will be applied to the observed brightness temperatures as to ensure the assimilation
of an unbiased dataset.
This paper will show the current status of these activities aiming to prepare SMOS
data for the assimilation in the ECMWF land surface analysis system.