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), improving soil moisture simulations over the Australian continent. 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 (2010–2015). Mean correlation R increases moderately from 0.61 to 0.68 when the root-zone is included in the updates. A slightly reduced improvement is achieved when restricting the assimilation to the upper soil layers. Furthermore, the long-term assimilation impact is analysed by looking at a set of quantiles computed 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 now still limited, longer L-band radiometer time series will become available and make model output improved by assimilating such data more usable for extreme event statistics.

Ground, Proximal, and Satellite Remote Sensing of Soil Moisture

“…A priori bias mitigation strategies include cumulative distribution function matching ) and least squares regression rescaling (Crow & Zhan, 2007), or TC (Stoffelen, 1998). Applications of near-surface SM assimilation are discussed in Lievens et al (2015), Parrens et al (2014), Rains et al (2017), andXu et al (2015).…”