Next Article in Journal
Comprehensive In Situ Validation of Five Satellite Land Surface Temperature Data Sets over Multiple Stations and Years
Next Article in Special Issue
Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation
Previous Article in Journal
Object-Based Classification of Forest Disturbance Types in the Conterminous United States
Previous Article in Special Issue
Improving the Informational Value of MODIS Fractional Snow Cover Area Using Fuzzy Logic Based Ensemble Smoother Data Assimilation Frameworks
Open AccessArticle

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

1
NILU—Norwegian Institute for Air Research, INBY, Instituttveien 18, 2007 Kjeller, Norway
2
Geophysical Institute, University of Bergen UiB, Allegaten 70, 5020 Bergen, Norway
3
Nansen Environmental and Remote Sensing Center, 5020 Bergen, Norway
4
CNRM—Université de Toulouse, Météo-France, CNRS, 31057 Toulouse, France
5
European Centre for Medium-range Weather Forecasts (ECMWF), Reading RG2 9AX, UK
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(5), 478; https://doi.org/10.3390/rs11050478
Received: 21 January 2019 / Revised: 14 February 2019 / Accepted: 20 February 2019 / Published: 26 February 2019
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. View Full-Text
Keywords: land data assimilation; EnKF; EnOI; SMAP; SMOS; ESA CCI for soil moisture land data assimilation; EnKF; EnOI; SMAP; SMOS; ESA CCI for soil moisture
Show Figures

Graphical abstract

MDPI and ACS Style

Blyverket, J.; Hamer, P.D.; Bertino, L.; Albergel, C.; Fairbairn, D.; Lahoz, W.A. An Evaluation of the EnKF vs. EnOI and the Assimilation of SMAP, SMOS and ESA CCI Soil Moisture Data over the Contiguous US. Remote Sens. 2019, 11, 478.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop