Optimization of a Radiative Transfer Forward Operator for Simulating SMOS Brightness Temperatures over the Upper Mississippi Basin

Abstract

The Soil Moisture Ocean Salinity (SMOS) satellite mission routinely provides global multiangular observations of brightness temperature TB at both horizontal and vertical polarization with a 3-day repeat period. The assimilation of such data into a land surface model (LSM) may improve the skill of operational flood forecasts through an improved estimation of soil moisture SM. To accommodate for the direct assimilation of the SMOS TB data, the LSM needs to be coupled with a radiative transfer model (RTM), serving as a forward operator for the simulation of multiangular and multipolarization top of the atmosphere TBs. This study investigates the use of the Variable Infiltration Capacity model coupled with the Community Microwave Emission Modelling Platform for simulating SMOS TB observations over the upper Mississippi basin, United States. For a period of 2 years (2010-11), a comparison between SMOS TBs and simulations with literature-based RTM parameters reveals a basin-averaged bias of 30 K. Therefore, time series of SMOS TB observations are used to investigate ways for mitigating these large biases. Specifically, the study demonstrates the impact of the LSM soil moisture climatology in the magnitude of TB biases. After cumulative distribution function matching the SM climatology of the LSM to SMOS retrievals, the average bias decreases from 30 K to less than 5 K. Further improvements can be made through calibration of RTM parameters related to the modeling of surface roughness and vegetation. Consequently, it can be concluded that SM rescaling and RTM optimization are efficient means for mitigating biases and form a necessary preparatory step for data assimilation.

Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX_v8.0: LDAS-Monde assessment over the Euro-Mediterranean area

“…Despite the proven record of assimilating retrieved soil moisture from point scale to regional and continental scale (e.g. Albergel et al, 2010;Draper et al, 2012;Matgen et al, 2012;De Rosnay et al, 2013;Barbu et al, 2014;Wanders et al, 2014;Ridler et al, 2014), there is an increasing tendency towards the direct assimilation of T b and σ o observations (De Lannoy et al, 2013;Han et al, 2014;Lievens et al, 2015Lievens et al, , 2016. Retrieval methods usually make use of land surface parameters and auxiliary information, such as vegetation, texture and temperature, possibly proving inconsistencies with specific model simulations (which also include these parameters but potentially from different sources).…”

SMOS soil moisture assimilation for improved hydrologic simulation in the Murray Darling Basin, Australia

“…Note that ascending and descending orbits are treated separately, as they often reveal different statistics (e.g. due to differences in ionospheric or surface conditions at 6 am/6 pm, or tilt-and azimuth-dependent RFI (Bircher, Skou, Jensen, Walker, & Rasmussen, 2012;Leroux et al, 2014;Lievens et al, 2015;Verhoest et al, 2015)). The minimum number of data pairs (simulations and observations) for application of the bias correction was set to 30.…”

SMOS brightness temperature assimilation into the Community Land Model

“…Calibrating the radiative transfer model to closely match the observed time series is a possible solution, as shown by ), De Lannoy et al (2013 and Lievens et al (2015a), with the alternative being the rescaling of the measurements to mimic more closely the forward simulations (Lievens et al, 2015b), as mentioned above. The details of preparing the observations prior to assimilation are given here.…”