Fourth Study Conference on BALTEX Scala Cinema Gudhjem

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Assimilation of New Land Surface Data Sets in Weather Prediction Models
Matthias Drusch
ECMWF, Shinfield Park, Reading RG2 9AX, UK, dar@ecmwf.int
1. Introduction
A well-posed analysis is a better estimate of the true state
than either the background (a-priori) information coming
from a numerical weather prediction model or the
observation data sets available. Consequently, any analysis
is a useful product in itself as a comprehensive diagnostic
part of the state of the atmosphere, the ocean or the land
surface. Analysis or re-analysis products are used for
scientific applications comprising the development of
parameterizations, retrieval algorithms and calibration /
validation studies. For numerical modelling applications and
weather forecasting, the analysis products are used as the
initial state and for quality checks for other observations.
2. Motivation
In atmospheric sciences and numerical weather prediction
data assimilation has been a major research topic for the last
20 years and the current schemes are well developed. The
leading operational weather prediction centres either run 3
or 4 dimensional variational data assimilation systems and
analysis increments are typically small compared with the
change made by the background forecast from the preceding
analysis. Simmons (2003) compared mean-square changes in
500 hPa height produced by the background forecasts from
06UTC to 12UTC and by the 12UTC analysis for the year
2001 from the 3D-Var ERA-40 data assimilation. Meansquare
analysis increments were found to be smaller than the
background –forecast changes by an order of magnitude or
more. In contrast, analysis increments of snow water
equivalent (SWE) and soil moisture are a sizeable part of the
land surfaces’ water budget.
3. Soil Moisture
Current operational soil moisture analyses methods use
screen level parameters, namely 2m air temperature and
relative humidity, only. For July 1988 mean soil moisture
analysis increments varying from -5 to 5 mm/day were
obtained for the BALTEX area (nudging, land surface
scheme without tiles). Slightly smaller increments could be
obtained through a more advanced analysis (optimal
interpolation) and the use of the tiled ECMWF land surface
scheme (Viterbo, personal communication). However,
atmospheric screen level parameters are proxy data for root
zone soil moisture and it is desirable to include
hyperspectral satellite data in the soil moisture analysis.
Due to penetration depths ranging from several millimetres
(X-band) to centimetres (L-band) the frequency range from
11 to 1.4 GHz can be used for surface soil moisture
retrievals and data assimilation applications. Although it is
well known that L-band sensors are the most promising
instruments current research also addresses higher frequency
applications, since space-borne observations at C- and Xband
have been available since the late 70’s (SMMR, TMI,
AMSR-E, AMSR). Satellite-borne passive microwave
observations at L-band will be available from 2007 on
through the SMOS (Soil Moisture / Ocean Salinity) mission.
Within the framework of the various Land Data
Assimilation Studies (LDAS) for Europe (ELDAS), North
America (NLDAS) and the Globe (GLDAS) new
assimilation techniques for satellite observations have
been developed and tested.
In order to investigate potential benefits of L-band data,
brightness temperatures and screen level parameters were
assimilated in different combinations using a simplified
Extended Kalman Filter (sEKF) and the single column
version of the ECMWF forecast model. When compared
to observations it could be shown that the sEKF
assimilation based on the synergy of the different
observations gives more consistent results with regard to
the prediction of net radiation, heat fluxes and nearsurface
soil moisture (Seuffert et al., 2003).
4. Snow Water Equivalent
The current snow depth analysis at ECMWF has been
operational since 1987 with modifications made in 2001.
The spatial interpolation scheme (Cressman analysis)
relies on in-situ observations, which are available in realtime,
the 6 hour short range forecast of the Integrated
Forecast System (IFS) and the snow climatology
published by Foster and Davy (1988). When compared to
satellite derived snow extent, it was found that the analysis
overestimates snow extent as defined through model grid
boxes characterized by SWE exceeding 0 mm. In addition,
interannual variability was underestimated through the
impact of the climatology. Significant differences between
the analysis and the satellite data were present in large
parts of central and south-east Europe and the southern
part of Sweden. Daily maps of NOAA NESDIS snow
extent have been incorporated in the Cresman analysis
scheme (Drusch et al., 2003). The revised analysis results
in larger negative SWE increments and exhibits a better
agreement with independent high resolution snow cover
observations derived from the Moderate Resolution
Imaging Spectroradiometer (MODIS).
References
Drusch, M., D. Vasiljevic, and P. Viterbo, 2003:
ECMWFs global snow analysis: Assessment and
revision based on satellite observation, submitted to J.
Applied Meteorology
Foster, D.J. and R.D. Davy, Global snow depth
climatology. U.S. Air Force Environmental Tech.
Applications Center/TN-88/006, 48 pp, 1988
Simmons, A., Observations, assimilation and the
improvement of global weather prediction – Some
results from operational forecasting and ERA-40,
Seminar Proceedings Recent developments in data
assimilation for atmosphere and ocean, ECMWF, pp.
1-28, 2003
Seuffert, G., Wilker, H., Viterbo, P., Drusch, M., and J.-F.
Mahfouf, The usage of screen level parameters and
microwave brightness temperature for soil moisture
analysis, accepted for publication in J. Hydromet.,
2003