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Initialisation of Land Surface Variables for Numerical
Weather Prediction
Patricia de Rosnay
·
Gianpaolo Balsamo
· Clément Albergel
·
Joaquín Muñoz-Sabater · Lars Isaksen
Received: 1 June 2012 / Accepted: 8 October 2012 / Published online: 30 October 2012
©
Springer Science+Business Media Dordrecht 2012
Abstract Land surface processes and their initialisation are of crucial importance for
Numerical Weather Prediction (NWP). Current land data assimilation systems used to
initialise NWP models include snow depth analysis, soil moisture analysis, soil tempera-
ture and snow temperature analysis. This paper gives a review of different approaches used
in NWP to initialise land surface variables. It discusses the observation availability and
quality, and it addresses the combined use of conventional observations and satellite data.
Based on results from the European Centre for Medium-Range Weather Forecasts (EC-
MWF), results from different soil moisture and snow depth data assimilation schemes are
shown. Both surface fields and low-level atmospheric variables are highly sensitive to the
soil moisture and snow initialisation methods. Recent developments of ECMWF in soil
moisture and snow data assimilation improved surface and atmospheric forecast
performance.
Keywords Land surface · Data assimilation · Numerical weather prediction ·
Soil moisture · Snow
1 Introduction
Land surface processes determine the lower boundary conditions of the atmosphere, and
they represent a crucial component of the hydrological cycle (Mueller and Seneviratne
2012 ; Entekhabi et al 1999 ; Koster and Suarez 1992 ; Shukla and Mintz 1982 ). In
Numerical Weather Prediction (NWP) and climate models, surface-atmosphere interaction
processes are represented by Land Surface Models (LSMs). These models have been
improved considerably during the last two decades and, nowadays, they represent
exchanges of water and energy through the soil-plant-atmosphere continuum with a good
 
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