Abstract
Abstract. This paper introduces an ensemble square root filter (EnSRF) in the context of jointly assimilating observations of surface soil moisture (SSM) and the leaf area index (LAI) in the Land Data Assimilation System LDAS-Monde. By ingesting those satellite-derived products, LDAS-Monde constrains the Interaction between Soil, Biosphere and Atmosphere (ISBA) land surface model (LSM), coupled with the CNRM (Centre National de Recherches Météorologiques) version of the Total Runoff Integrating Pathways (CTRIP) model to improve the reanalysis of land surface variables (LSVs). To evaluate its ability to produce improved LSVs reanalyses, the EnSRF is compared with the simplified extended Kalman filter (SEKF), which has been well studied within the LDAS-Monde framework. The comparison is carried out over the Euro-Mediterranean region at a 0.25∘ spatial resolution between 2008 and 2017. Both data assimilation approaches provide a positive impact on SSM and LAI estimates with respect to the model alone, putting them closer to assimilated observations. The SEKF and the EnSRF have a similar behaviour for LAI showing performance levels that are influenced by the vegetation type. For SSM, EnSRF estimates tend to be closer to observations than SEKF values. The comparison between the two data assimilation approaches is also carried out on unobserved soil moisture in the other layers of soil. Unobserved control variables are updated in the EnSRF through covariances and correlations sampled from the ensemble linking them to observed control variables. In our context, a strong correlation between SSM and soil moisture in deeper soil layers is found, as expected, showing seasonal patterns that vary geographically. Moderate correlation and anti-correlations are also noticed between LAI and soil moisture, varying in space and time. Their absolute value, reaching their maximum in summer and their minimum in winter, tends to be larger for soil moisture in root-zone areas, showing that assimilating LAI can have an influence on soil moisture. Finally an independent evaluation of both assimilation approaches is conducted using satellite estimates of evapotranspiration (ET) and gross primary production (GPP) as well as measures of river discharges from gauging stations. The EnSRF shows a systematic albeit moderate improvement of root mean square differences (RMSDs) and correlations for ET and GPP products, but its main improvement is observed on river discharges with a high positive impact on Nash–Sutcliffe efficiency scores. Compared to the EnSRF, the SEKF displays a more contrasting performance.
Highlights
Land surface variables (LSVs) are key components of the Earth’s water, vegetation and carbon cycles
The validity of our approach has be assessed over the Euro-Mediterranean region for the period 2008–2017 and compared to a simplified extended Kalman filter that is routinely used in Land Data Assimilation Systems (LDASs)-Monde
We have examined the impact of ensemble square root filter (EnSRF) on controlled soil moisture for deeper soil layers
Summary
Land surface variables (LSVs) are key components of the Earth’s water, vegetation and carbon cycles. Understanding their behaviour and simulating their evolution is a challenging task that has significant implications on various topics, from vegetation monitoring to weather prediction. Forced by atmospheric data and coupled with river routing models, they aim to simulate LSVs such as soil moisture (SM), biomass and the leaf area index (LAI). LSMs are prone to errors owing to inaccurate initialization, misspecified parameters, flawed forcing or inadequate model physics. Another way to monitor LSVs is to use observations either from in situ networks or satellites. Passive microwave satellite sensors used traditionally to estimate soil moisture are sensible only to the near-surface (0–2 cm depth) moisture content (Schmugge, 1983), leading to the development of indirect approaches to estimate root-zone soil moisture from satellite data (see e.g. Albergel et al, 2008)
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