NN Technique for Producing Consistent Ocean Color Data for Assimilation in Ocean Models

Satellite remote-sensing of ocean color (OC) parameters provides the only means for broadly observing the biological component of the world’s oceans. Consequently, this capability must be exploited for analysis and prediction of ocean bio-physical processes and establishing a linkage to biological components of ocean ecological forecasts. Operational integration/assimilation of ocean OC field (chlorophyll, Kd₄₉₀, Kd_PAR) into NOAA’s operational ocean models has three fundamental requirements/conditions: 1) gaps in the observations need to be addressed, both in the current instance and for extended gaps; 2) the data being assimilated must have a long data record to facilitate compilation of a robust statistical database spanning multiple seasons; and 3) the data being assimilated must be for a predicted parameter.

In our previous work [1] we demonstrated that neural network (NN) technique [1] can be successfully used to fill both short and small (several days and several grid points), and extended (several months and global) gaps in OC satellite measurements. In this work, we show that the other two principal requirements can also be satisfied using NN technique.

[1] Krasnopolsky V., S. Nadiga, A. Mehra, E. Bayler, and D. Behringer, 2015. “Neural Network Technique for Filling Gaps in Satellite Measurements: Application to Satellite Ocean Color Observations”, Computational Intelligence and Neuroscience, Article ID 923230, December, http://www.hindawi.com/journals/cin/aa/923230/