Aryal’s first article published as an Editors’s Pick
UMBC Physics PhD student Kamal Aryal, his PhD advisor Pengwang Zhai and their collaborators from NASA Goddard Space Flight center has recently published their work in Applied Optics. In this work, they have developed machine learning models based on Neural Networks which can be used to get the vertical profile of Instantaneous Photosynthetically Available Radiation (IPAR, and corresponding diffuse attenuation coefficients) from surface to euphotic zone depths in global oceans under a wide range of atmospheric and oceanic conditions. These models have high accuracy compared to existing semi-analytical models with significant superiority especially in coastal oceans.
IPAR regulates the amount of photosynthesis by phytoplanktons which plays a major role in fixing atmospheric carbon dioxide inside oceans. Their work has been highlighted in Applied Optics as an Editor’s pick.
Kamal Aryal, Peng-Wang Zhai, Meng Gao, and Bryan A. Franz, "Instantaneous photosynthetically available radiation models for ocean waters using neural networks," Appl. Opt. 61, 9985-9995 (2022)
URL: https://doi.org/10.1364/AO.474914
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.
Kamal Aryal, Peng-Wang Zhai, Meng Gao, and Bryan A. Franz, "Instantaneous photosynthetically available radiation models for ocean waters using neural networks," Appl. Opt. 61, 9985-9995 (2022)
URL: https://doi.org/10.1364/AO.474914
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.
Posted: November 23, 2022, 1:27 PM
