Development of subgrid parameterizations using ensemble-based data assimilation

PULIDO M

Buenos Aires

Workshop; Workshop on Big Data and Environment; 2015

UMI-IFAECI

Oceanic and atmospheric global numerical models represent directly the large-scale dynamics while the smaller-scale dynamic features are not resolved in the model so that their effects in the large-scale dynamics are included through subgrid-scale parameterizations. These parameterizations model small-scale effects as a function of the resolved variables. Two big challenges for modellers are parameterization development and the estimation of the unknown free parameters. In this talk,  I will show that data assimilation principles can in principle be used not only to estimate the parameters of subgrid-scale parameterizations that model the small-scale variables but also  to recover the functional dependences of the subgrid-scale processes.  Two data assimilation techniques based on the ensemble transform Kalman filter (ETKF) are evaluated, online and offline estimations. Some of the nonlinear dependencies may be detected by data assimilation these techniques.  The stochastic characteristics of the  subgrid-scale processes are not well represented by them.