SMOS brightness temperature assimilation into the Community Land Model

Abstract

Abstract. SMOS (Soil Moisture and Ocean Salinity mission) brightness temperatures at a single incident angle are assimilated into the Community Land Model (CLM) across Australia to improve soil moisture simulations. Therefore, the data assimilation system DasPy is coupled to the local ensemble transform Kalman filter (LETKF) as well as to the Community Microwave Emission Model (CMEM). Brightness temperature climatologies are precomputed to enable the assimilation of brightness temperature anomalies, making use of 6 years of SMOS data (2010)(2011)(2012)(2013)(2014)(2015). Mean correlation R with in situ measurements increases moderately from 0.61 to 0.68 (11 %) for upper soil layers if the root zone is included in the updates. A reduced improvement of 5 % is achieved if the assimilation is restricted to the upper soil layers. Root-zone simulations improve by 7 % when updating both the top layers and root zone, and by 4 % when only updating the top layers. Mean increments and increment standard deviations are compared for the experiments. The longterm assimilation impact is analysed by looking at a set of quantiles computed for soil moisture at each grid cell. Within hydrological monitoring systems, extreme dry or wet conditions are often defined via their relative occurrence, adding great importance to assimilation-induced quantile changes. Although still being limited now, longer L-band radiometer time series will become available and make model output improved by assimilating such data that are more usable for extreme event statistics.

Improving soil moisture and runoff simulations at 3 km over Europe using land surface data assimilation

“…PDAF provides data assimilation methods such as the ensemble Kalman filter (EnKF) (Burgers et al, 1998;Evensen, 2003) and the local ensemble transform Kalman filter (LETKF) (Hunt et al, 2007). In this study, the EnKF algorithm was used for data assimilation, which is a relatively efficient and robust technique for assimilating satellite data into land surface models (e.g., Brocca et al, 2012;Crow et al, 2017;Draper et al, 2011;Matgen et al, 2012;Mohanty et al, 2013;Pauwels et al, 2001Pauwels et al, , 2002. It uses ensembles of model simulations to approximate the model state and parameter error covariance matrix in order to optimally merge model predictions with observations.…”

“…Furthermore, in land surface modeling systematic differences between the model climatology and the observation data climatology are commonly corrected before assimilation, to ensure that data assimilation is applied under conditions of no systematic bias. Previous studies used different procedures to correct for biases, such as the estimation of a single constant bias value, seasonal dependent bias or CDF matching (e.g., Drusch et al, 2005;Reichle and Koster, 2004). The procedure has some important limitations: (i) the polynomial fit during CDF matching cannot provide perfect agreement because the introduced noise changes the random difference between both datasets, (ii) the bias is only partially corrected or overcorrected; (iii) the bias in the DA-procedure is not assigned to the model or measurement data, but after the assimilation it is implicitly assumed that the systematic bias is related to the bias in the measurements (model states are not corrected for a systematic bias).…”

“…In order to improve model predictions and simultaneously honor observation and model uncertainties, remotely sensed soil moisture has been merged with model predication using data assimilation (DA) (Chen et al, 2013;De Lannoy and Reichle, 2016;Kumar et al, 2008;Lahoz and De Lannoy, 2014;Lievens et al, 2016;Liu and Gupta, 2007;Moradkhani et al, 2005;Nie et al, 2011). Using DA approaches, previous studies also investigated the impact of uncertainties in both parameters and state variables of a hydrologic model based on a joint state-parameter estimation approach (Cammalleri and Ciraolo, 2012;DeChant and Moradkhani, 2012;Gharamti et al, 2015;Pathiraja et al, 2016;Rafieeinasab et al, 2014;Xie and Zhang, 2010). For example, Han et al (2014) evaluated the joint state and parameter estimation method at catch-ment scale for the coupled CLM and Community Microwave Emission Model (CMEM) (De Rosnay et al, 2009) through assimilation of synthetic microwave brightness temperature data.…”

“…Fewer studies demonstrated the potential of assimilating satellite observations into land surface models to improve soil moisture and runoff estimates at regional and global scales (e.g., Crow and Ryu, 2009;De Lannoy and Reichle, 2016;Lievens et al, 2015;Liu and Mishra, 2017;López López et al, 2016;Pan et al, 2008;Rains et al, 2017;Reichle and Koster, 2005;Renzullo et al, 2014). For instance, Rains et al (2017) assimilated SMOS data into CLM over Australia for drought monitoring purposes. Similarly, Liu and Mishra (2017) also assimilated satellite SM data at the global scale to evaluate the performance of the community land surface model (CLM4.5) in simulating hydrologic fluxes such as SM, ET and runoff at 0.5 • (∼ 50 km) spatial resolution.…”

Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversity—Part I: Soil Characteristics

“…Root zone soil moisture, which to a larger extent effects the biodiversity of a habitat rather than the water content of the top layer, can be estimated by additional methods. Examples are: Direct retrieval by longer wavelengths such as P-band [304], surface soil moisture assimilation into a hydrological model [305][306][307][308][309][310], or data-driven methods such as neural networks [311] to improve root zone soil moisture estimates. Moreover, indirect methods use the plants as "sensors" of root-zone properties.…”