An Evaluation of the EnKF vs. EnOI and the Assimilation of SMAP, SMOS and ESA CCI Soil Moisture Data over the Contiguous US

Abstract

A number of studies have shown that assimilation of satellite derived soil moisture using the ensemble Kalman Filter (EnKF) can improve soil moisture estimates, particularly for the surface zone. However, the EnKF is computationally expensive since an ensemble of model integrations have to be propagated forward in time. Here, assimilating satellite soil moisture data from the Soil Moisture Active Passive (SMAP) mission, we compare the EnKF with the computationally cheaper ensemble Optimal Interpolation (EnOI) method over the contiguous United States (CONUS). The background error–covariance in the EnOI is sampled in two ways: (i) by using the stochastic spread from an ensemble open-loop run, and (ii) sampling from the model spinup climatology. Our results indicate that the EnKF is only marginally superior to one version of the EnOI. Furthermore, the assimilation of SMAP data using the EnKF and EnOI is found to improve the surface zone correlation with in situ observations at a 95 % significance level. The EnKF assimilation of SMAP data is also found to improve root-zone correlation with independent in situ data at the same significance level; however this improvement is dependent on which in situ network we are validating against. We evaluate how the quality of the atmospheric forcing affects the analysis results by prescribing the land surface data assimilation system with either observation corrected or model derived precipitation. Surface zone correlation skill increases for the analysis using both the corrected and model derived precipitation, but only the latter shows an improvement at the 95 % significance level. The study also suggests that assimilation of satellite derived surface soil moisture using the EnOI can correct random errors in the atmospheric forcing and give an analysed surface soil moisture close to that of an open-loop run using observation derived precipitation. Importantly, this shows that estimates of soil moisture could be improved using a combination of assimilating SMAP using the computationally cheap EnOI while using model derived precipitation as forcing. Finally, we assimilate three different Level-2 satellite derived soil moisture products from the European Space Agency Climate Change Initiative (ESA CCI), SMAP and SMOS (Soil Moisture and Ocean Salinity) using the EnOI, and then compare the relative performance of the three resulting analyses against in situ soil moisture observations. In this comparison, we find that all three analyses offer improvements over an open-loop run when comparing to in situ observations. The assimilation of SMAP data is found to perform marginally better than the assimilation of SMOS data, while assimilation of the ESA CCI data shows the smallest improvement of the three analysis products.

Monitoring Soil Moisture Drought over Northern High Latitudes from Space

“…This is justified by the fact that it incorporates both model and observation data in a data assimilation system. Other studies have shown that land data assimilation systems are able to correct for errors in precipitation datasets, and as a result, improve the representation of SSM (see for example [45]). Another example is provided by [46], where the authors show that the LDAS-Monde improves the representation of the 2012 US corn belt drought.…”

An ensemble square root filter for the joint assimilation of surface soil moisture and leaf area index within the Land Data Assimilation System LDAS-Monde: application over the Euro-Mediterranean region

“…The latter study has notably shown that jointly assimilating observations of SSM and LAI can improve the quality of root-zone SM estimates for one location in southwestern France. This work has been carried out with the CO 2 -responsive version of the Interactions between Soil, Biosphere and Atmosphere (ISBA) LSM (Calvet et al, 1998(Calvet et al, , 2004Gibelin et al, 2006), developed by CNRM (Centre National de Recherches Météorologiques). This version of ISBA allows for the simulation of vegetation dynamics including biomass and LAI.…”

Application of soil moisture information for operational water management

“…Albergel et al, 2008;Carranza et al, 2018) or hydrological modelling (e.g. Sabater et al, 2007;Renzullo et al, 2014;Dumedah et al, 2015;Blyverket et al, 2019) can help in extrapolating surface soil information to deeper layers.…”

“…We use the SMAP (Soil Moisture Active Passive) L3 Enhanced Radiometer-only daily gridded soil moisture product for the data assimilation scheme (Entekhabi et al, 2010;Chan et al, 2018;O'Neill et al, 2018). The value of SMAP data for hydrological data assimilation has been shown in several studies (Kolassa et al, 2017;Lievens et al, 2017;Koster et al, 2018;Blyverket et al, 2019). The delivery of the enhanced SMAP soil moisture products was motivated by the gap that emerged after failure of the SMAP radar Das et al, 2018).…”

“…Satellite-based soil moisture products provide valuable information for hydrological models if they are used in combination with data assimilation schemes (Houser et al, 1998;Moradkhani, 2008;Reichle, 2008;Liu et al, 2012). Several studies investigated the applicability of remotely sensed soil moisture data for data assimilation by using data products from satellites such as AMSR-E (Sahoo et al, 2013;Wanders et al, 2014a,b), ASCAT (Gruber et al, 2015;Loizu et al, 2018), SMOS (Lievens et al, 2015;Srivastava et al, 2015), H-SAF (Laiolo et al, 2015), SMAP (Koster et al, 2018;Blyverket et al, 2019), a combination of AMSR-2 and SMOS (Gevaert et al, 2018) and a combination of Sentinel-1 and SMAP (Lievens et al, 2017). Ratto et al (2012) state that integrating metamodelling with data assimilation schemes could signi cantly contribute to the operational use of metamodels and remotely sensed soil moisture products for decision-making in operational water resources management.…”