Abstract
For more than a decade, the European Centre for Medium-Range Weather Forecasts (ECMWF) has used in-situ observations of 2 m temperature and 2 m relative humidity to operationally constrain the temporal evolution of model soil moisture. These observations are not available everywhere and they are indirectly linked to the state of the surface, so under various circumstances, such as weak radiative forcing or strong advection, they cannot be used as a proxy for soil moisture reinitialization in numerical weather prediction. Recently, the ECMWF soil moisture analysis has been updated to be able to account for the information provided by microwave brightness temperatures from the Soil Moisture and Ocean Salinity (SMOS) mission of the European Space Agency (ESA). This is the first time that ECMWF uses direct information of the soil emission from passive microwave data to globally adjust the estimation of soil moisture by a land-surface model. This paper presents a novel version of the ECMWF Extended Kalman Filter soil moisture analysis to account for remotely sensed passive microwave data. It also discusses the advantages of assimilating direct satellite radiances compared to current soil moisture products, with a view to an operational implementation. A simple assimilation case study at global scale highlights the potential benefits and obstacles of using this new type of information in a global coupled land-atmospheric model.
Highlights
The importance of accurately initialisating land-surface variables for numerical weather and climatic prediction is widely accepted
It is of wide interest to put substantial effort into using all the available sources of soil moisture information in systems which are able to merge this information and produce estimates that are more accurate than only model-based simulations
In contrast to most studies found in the literature with passive microwaves, the system presented in this paper makes it possible to assimilate direct information about soil moisture from passive microwave TB to correct a model estimation
Summary
The importance of accurately initialisating land-surface variables for numerical weather and climatic prediction is widely accepted. [1,2,3,4,5,6] perturbed the initial value of soil moisture in various experiments and showed impact in the forecast skill of air temperature and humidity at short and medium range, whereas [7,8,9]. Soil moisture initialization is crucial in seasonal forecasting studies ([10,11,12]), since anomalies may persist at monthly to seasonal time scales ([13,14,15]). Soil moisture information is relevant for weather prediction studies. Other areas, such as drought monitoring, agricultural and hydrological processes are increasing the demand for more accurate knowledge of the available water resources. It is of wide interest to put substantial effort into using all the available sources of soil moisture information in systems which are able to merge this information and produce estimates that are more accurate than only model-based simulations
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