Instantaneous photosynthetically available radiation models for ocean waters using neural networks

Author:

Aryal KamalORCID,Zhai Peng-Wang,Gao Meng12,Franz Bryan A.2

Affiliation:

1. Science Systems and Applications, Inc.

2. NASA Goddard Space Flight Center

Abstract

Instantaneous photosynthetically available radiation (IPAR) at the ocean surface and its vertical profile below the surface play a critical role in models to calculate net primary productivity of marine phytoplankton. In this work, we report two IPAR prediction models based on the neural network (NN) approach, one for open ocean and the other for coastal waters. These models are trained, validated, and tested using a large volume of synthetic datasets for open ocean and coastal waters simulated by a radiative transfer model. Our NN models are designed to predict IPAR under a large range of atmospheric and oceanic conditions. The NN models can compute the subsurface IPAR profile very accurately up to the euphotic zone depth. The root mean square errors associated with the diffuse attenuation coefficient of IPAR are less than 0.011 m 1 and 0.036 m 1 for open ocean and coastal waters, respectively. The performance of the NN models is better than presently available semi-analytical models, with significant superiority in coastal waters.

Funder

National Aeronautics and Space Administration

Publisher

Optica Publishing Group

Subject

Atomic and Molecular Physics, and Optics,Engineering (miscellaneous),Electrical and Electronic Engineering

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