An Integrated Framework for a Joint Assimilation of Brightness Temperature and Soil Moisture Using the Nondominated Sorting Genetic Algorithm II

Abstract

This study has applied the Nondominated Sorting Genetic Algorithm II (NSGA-II) in a two-step assimilation procedure to jointly assimilate brightness temperature into a radiative transfer model and soil moisture into a land surface model. The first assimilation procedure generates a time series of soil moisture by assimilating brightness temperature from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) into the Land Parameter Retrieval Model (LPRM). The second procedure generates assimilated soil moisture by assimilating the soil moisture from LPRM into the Canadian Land Surface Scheme (CLASS). Note that the assimilated soil moisture was generated by merging two soil moisture estimates: one from LPRM and the other from the CLASS simulation. The assimilated soil moisture is better than using the soil moisture determined either from the satellite observation or the land surface scheme alone. This method provides improved model state and parameterizations for both LPRM and CLASS with the aim to facilitate real-time forecasts when satellite information becomes available. Application of this framework to the Brightwater Creek watershed in southern Saskatchewan illustrates the utility of the joint assimilation framework to improve a time series of soil moisture estimates. The estimated soil moisture datasets were evaluated over an agricultural site in southern Saskatchewan using in situ monitoring networks. These results demonstrate that soil moisture generated from assimilation of brightness temperature could be improved by incorporating it into a land surface model. A comparison between the assimilated soil moisture and in situ dataset demonstrates an improvement in accuracy and temporal pattern that is accomplished through the assimilation framework.

Hybridizing Bayesian and variational data assimilation for robust high-resolution hydrologic forecasting

“…Such method has been used before 5 (Dumedah et al, 2011) and is based on the employment of non-dominated sorting (Deb, 2014), another technique from the multi-objective optimization literature, which is used to balance the potential tensions between various objectives. This sorting approach is centred on the concept of "dominance" instead of organizing all particles from the "best" to the "worst."…”

“…The first computation can be used in the evaluation step as an objective function to preserve the consistency with the state history. It should be noted that other such metrics exist, from very simple (Dumedah et al, 2011) to quite complex (Ning et al, 2014). The probability density of a weighted kernel density 5 distribution at a given point can be computed using Eq.…”

Integration of evolutionary based assimilation into Kalman-type methods for streamflow simulations in ungauged watersheds

“…Data assimilation (DA) continue to facilitate and improve hydrological forecasts (Dumedah et al, 2011;Thirel et al, 2010;Xie and Zhang, 2010;Van Leeuwen, 2009;Clark et al, 2008). The forecasting improvements are usually illustrated for a single catchment but there is a growing demand to transfer forecast skill and its associated state and model parameter values from gauged watersheds to ungauged ones -a process usually called regionalization (Zhang and Chiew, 2009).…”

“…Recent studies using evolutionary based data assimilation have shown that the dynamics of large number of members could be approximated by a small number of continuously evolved members (Dumedah et al, 2011;Dumedah, 2012c;Dumedah and Coulibaly, 2012b). In particular, the updated members are tightly linked to these evolved members which represent specific values for states and model parameterizations.…”

Hybridizing sequential and variational data assimilation for robust high-resolution hydrologic forecasting

“…Moreover, we consider that including an additional objective which favours particles that are more consistent with previous time steps, and thus enable better enforcing of the mass and energy conservation laws, would ultimately contribute to reduce overfitting by counterbalancing the traditional single-minded focus on observations. An array of measures of consistency, from very simple (Dumedah et al, 2011) to very complex (Ning et al, 2014) ones, has been 15 proposed. Here we suggest taking advantage of the probabilistic approach by attempting to maximize the likelihood of source states given the current distribution .…”