Abstract
Data assimilation methods provide a means to handle the modeling errors and uncertainties in sophisticated ocean models. In this study, we have created an OpenDA-NEMO framework unlocking the data assimilation tools available in OpenDA for use with NEMO models. This includes data assimilation methods, automatic parallelization, and a recently implemented automatic localization algorithm that removes spurious correlations in the model based on uncertainties in the computed Kalman gain matrix. We have set up a twin experiment where we assimilate sea surface height (SSH) satellite measurements. From the experiments, we can conclude that the OpenDA-NEMO framework performs as expected and that the automatic localization significantly improves the performance of the data assimilation algorithm by successfully removing spurious correlations. Based on these results, it looks promising to extend the framework with new kinds of observations and work on improving the computational speed of the automatic localization technique such that it becomes feasible to include large number of observations.
Highlights
Due to present day computational resources, it is possible to build a sophisticated ocean models
The results demonstrate that the root mean square errors (RMSE) are lower for 100 ensemble case specially for later part of the assimilation
A set of twin experiments was performed for the NEMO model where we assimilate satellite sea surface height (SSH) values using Ensemble Kalman filter (EnKF)
Summary
Due to present day computational resources, it is possible to build a sophisticated ocean models. Several synthetic experiments, assimilating the simulated altimetry into a double-gyre NEMO ocean model, are performed with the objective to investigate the impact of different EnKF setups in the quality of analysis These setups mainly focused on the observation distribution, the ensemble size, and the localization length scale. The ensemble members can propagate the model state forward in time in parallel but still due to limited computational resources, we often need to use relatively small ensemble sizes. This method often needs quite some tuning in order to find the optimal distances
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
