Preprint Article Version 1 This version is not peer-reviewed

Improving Soil Moisture Estimates Over the Contiguous US Using Satellite Retrievals and Ensemble Based Data Assimilation Techniques

Version 1 : Received: 21 January 2019 / Approved: 22 January 2019 / Online: 22 January 2019 (12:33:58 CET)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: Remote Sens. 2019, 11, 478
DOI: 10.3390/rs11050478

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 suggest that the EnOI can be used for bias-correction of the atmospheric fields where post-processed data are not available. Finally, we assimilate three different Level-2 satellite derived soil moisture products from ESA Climate Change Initiative (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.

Subject Areas

land data assimilation; EnKF; EnOI; SMAP; SMOS; ESA CCI

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