A dual‐pass variational data assimilation framework for estimating soil moisture profiles from AMSR‐E microwave brightness temperature

Abstract

[1] To overcome the difficulties in determining the optimal parameters needed for a radiative transfer model (RTM), which acts as the observational operator in a land data assimilation system, we have designed a dual-pass assimilation (DP-En4DVar) framework to optimize the model state (volumetric soil moisture content) and model parameters simultaneously using the gridded Advanced Microwave Scanning Radiometer-EOS (AMSR-E) satellite brightness temperature data. This algorithm embeds a dual-pass (the state assimilation pass and the parameter optimization pass) optimization technique based on an ensemble-based four-dimensional variational assimilation method and a shuffled complex evolution approach (SCE-UA). The SCE-UA method optimizes the parameters using observational information, thereby leading to improved simulations. The RTM is used to estimate brightness temperature from surface temperature and soil moisture. This algorithm is implemented differently in two phases: the parameter calibration phase and the pure assimilation phase. Both passes are applied in each assimilation time window during the parameter calibration phase. However, only the state assimilation pass is used in the pure assimilation phase after the parameters are determined during the parameter calibration phase. Several experiments conducted using this framework coupled partially with a land surface model (the NCAR CLM3) show that volumetric soil moisture content can be significantly improved to be comparable with in situ observations by assimilating only daily satellite brightness temperature. Furthermore, the improvement in surface soil moisture also propagates to lower layers where no observations are available.Citation: Tian, X., Z. Xie, A. Dai, C. Shi, B. Jia, F. Chen, and K. Yang (2009), A dual-pass variational data assimilation framework for estimating soil moisture profiles from AMSR-E microwave brightness temperature,

The Tibetan Plateau observatory of plateau scale soil moisture and soil temperature (Tibet-Obs) for quantifying uncertainties in coarse resolution satellite and model products

“…Rain falling on the surface runs off rapidly and accumulates in local depressions forming lakes and wetlands. The land cover in the higher parts of the study area can be characterized as grasslands consisting of prairie grasses and mosses (van der Velde et al, 2008;van der Velde and Su, 2009;van der Velde, 2010). In the winter period, very little precipitation occurs either in liquid or frozen state as snow resulting in spatially and temporally stable soil moisture dynamics in a frozen soil.…”

“…wind speed, humidity and temperature), incoming and outgoing (shortwave and longwave) radiation, turbulent heat fluxes, soil moisture at depths of 0.05 and 0.20 m below surface, and soil temperatures profile down to a depth of 0.40 m below surface (e.g. Ma et al 2003Ma et al , 2006Ma et al , 2007van der Velde et al, 2009).…”

“…Another promising method of obtaining consistent global soil moisture data sets is through land data assimilation (e.g. Yang et al, 2007Yang et al, , 2009Drusch et al, 2008;Qin et al, 2009;Tian et al, 2009), in which continuous data sets are produced by integrating remote sensing data sets in a land surface model. At present, several such systems produce operational spatiotemporally continuous soil moisture estimates and examples are the Global Land Data Assimilation System (GLDAS) (Rodell et al, 2004) and the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) (http: //www.ecmwf.int/research/era/do/get/era-interim).…”

A non-linear least squares enhanced POD-4DVar algorithm for data assimilation

“…For example, the proper orthogonal decomposition (POD)-based ensemble 4/3DVar (referred as POD-4/ 3DVar, Tian et al, 2011) has been successfully applied to real carbon cycle data assimilation and radar assimilation (Pan et al, 2012), which shows a robust performance in the atmospheric transport data assimilation. Furthermore, we have also built a POD-4DVar based land data assimilation system on the community land model platform (Tian et al, 2009(Tian et al, , 2010. Very recently results from the real data assimilation experiments with the Weather Research and Forecasting (WRF) model show that significant improvements in predicting the convective system and thus precipitation are achieved due to improved initial conditions for the storm's dynamics and microphysics through POD-4DVar-based assimilation of the radial velocity and reflectivity data (Zhang et al, 2014).…”

Assimilation of Remotely-Sensed Leaf Area Index into a Dynamic Vegetation Model for Gross Primary Productivity Estimation

“…The PODEn4DVar method has the superiority that: (1) the dynamic constraint can use complete simulation equations; (2) it is capable of simultaneously assimilating observations at multiple times; (3) it can reflect the spatial structure of the 4D variables and characteristics of time evolution by using POD-transformed base vectors; and (4) this method does not require an integral adjoint model during the computational process, and it is easy to realize. The PODEn4DVar method can simplify the process of data assimilation and maintain most advantages of the traditional 4DVar method, which has shown considerable satisfactory performance within land data assimilation [22,23], Tan-Tracker joint data assimilation [24], and radar data assimilation [25]. Considering that EnKF and PODEn4DVar each have unique features, each method was applied into LPJ-DGVM separately, and their respective performances were compared in this study.…”