Two regional bio-optical algorithms are combined to retrieve the Chlorophyll-a($\text {Chl-}\textit {a}$) concentration in the Black Sea. The first is a band-ratio algorithm that computes$\text {Chl-}\textit {a}$as a function of the slope of Remote Sensing Reflectance ($\mathit {R_{\text {RS}}}$) values at two wavelengths using a polynomial regression that captures the overall data trend, enhancing extrapolation results. The second algorithm is a Multilayer Perceptron neural net based on$\mathit {R_{\text {RS}}}$values at three individual wavelengths that features interpolation capabilities helpful to fit data non-linearities. A new merging scheme is then designed to benefit from the complementarity of the two approaches. Remote sensing data employed to demonstrate the merging of regional results for the Black Sea are those acquired by the Ocean and Land Color Instrument on board Sentinel-3A to acknowledge the need for data products of higher accuracy within the long-term Copernicus program.

Summary This paper presents a high‐performance computing (HPC) framework for Monte Carlo (MC) simulations in the ocean color (OC) application domain. The objective is to optimize a parallel MC radiative transfer code named MOX, developed by the authors to create a virtual marine environment for investigating the quality of OC data products derived fromin situmeasurements of in‐water radiometric quantities. A consolidated set of solutions for performance modeling, prediction, and optimization is implemented to enhance the efficiency of MC OC simulations on HPC run‐time infrastructures. HPC, machine learning, and adaptive computing techniques are applied taking into account a clear separation and systematic treatment of accuracy and precision requirements for large‐scale MC OC simulations. The added value of the work is the integration of computational methods and tools for MC OC simulations in the form of an HPC‐oriented problem‐solving environment specifically tailored to investigate data acquisition and reduction methods for OC field measurements. Study results highlight the benefit of close collaboration between HPC and application domain researchers to improve the efficiency and flexibility of computer simulations in the marine optics application domain. © 2016 The Authors.Concurrency and Computation: Practice and ExperiencePublished by John Wiley & Sons, Ltd.

This study investigates standard and regional algal pigment index! (API!) estimates in the Atlantic off the southwestern Iberian Peninsula. Standard API! data are those delivered by the Medium Resolution Imaging Spectrometer (MERIS) orbiting sensor. Equivalent quantities are computed by applying a regional inversion scheme using as input both MERIS and in situ remote sensing reflectances (Rrs). Reference data for the development of the regional algorithm and for the analysis of tested products include field measurements of total concentration of chlorophyll a (TChla) and coincident Rrsvalues collected at different distances from the coast. Validation results, based on matchup analysis, identifies a systematic overestimation of standard API1 versus the reference TChla values. The additional comparison of product maps in selected regions of interest confirms this tendency and demonstrates the feasibility and relevance of using regional algorithms for investigating spaceborne products. Analogous applications are hence devised for the early-stage evaluation of the forthcoming Sentinel-3/OLCI data products.

This study investigates how the number of data collected for algorithm development in typical case-1 waters and optically complex cases can affect the remote sensing of ocean color (OC) products. Marine conditions dominated by the colored dissolved organic matter (CDOM) and nonalgal particles are considered. The applied OC inversion schemes are based on multilayer perceptron (MLP) neural nets for data classification and regression. Simulated data for MLP training are generated with a forward OC model. Results show that a disproportion of samples representing different marine optical cases influences the MLP learning and hence also the data product retrieval, mostly in mixed case-1 and CDOM-dominated environments. A postclassification correction is then employed for performance improvements. Methodological developments are presented, acknowledging the coastal water monitoring prioritized by the Copernicus Earth Observation program.

This letter discusses the normalized water-leaving reflectance obtained from two atmospheric corrections built in the MEGS 8.0 processor applied to the Medium Resolution Imaging Spectrometer (MERIS) marine data. In situ reference data for intercomparisons with satellite-derived reflectance products are from the Ocean Color component of the Aerosol Robotic Network (AERONET-OC). Case of study is the northern Adriatic Sea AERONET-OC site characterized by both Case-1 and Case-2 waters. The accuracy of MERIS pigment indices is also discussed in relation to uncertainties and biases affecting atmospherically corrected data.

Advances in satellite ocean color technologies and methodologies are expected to lead to the generation of coastal water bio-optical products with accuracies close to those targeted for oceanic regions. In view of contributing to such a progress, multilayer perceptron neural networks complying with standard Medium Resolution Imaging Spectrometer (MERIS) pigment indices were developed relying on regional highly accurate in situ data. This work illustrates and discusses the application to sample MERIS imagery of those neural networks trained to produce pigment indices in seas characterized by increased levels of bio-optical complexity: the Baltic, the Northern Adriatic, and the Western Black Seas.

Statistical performance tuning of a parallel Monte Carlo (MC) radiative transfer code for ocean color (OC) applications is presented. A low observed-to-peak performance ratio due to highly sparse computations is compensated by online and offline tuning techniques based on a statistical indicator of products accuracy. Run-time adaptive control employs the accuracy indicator to set up two complementary tuning criteria: one general to MC computations and the other specific to OC applications. The same accuracy indicator is also used for pre-execution tuning of a threshold parameter. Numerical simulations of real case scenarios showed that the proposed methods consistently led to faster runs, while satisfying application accuracy requirements. Specifically, speed-ups range from 2.17 to 7.44 times when compared with the un-optimized version of the MC code. The applied techniques are orthogonal to parallelization, so that the reported performance gains are further amplified by parallel speed-ups.

Recent progress of science and technology has made numerical simulation an important approach for studies in various fields. Although scalable and high performance numerical libraries on large scale computing resources are indispensable tools for handling various multiscale phenomena, few projects for integrating these numerical libraries have been reported. The object of this project is the development of a basic library of solutions and algorithms required for large scale scientific simulations, which have been developed separately in each fields, and its integration into a scalable software infrastructure. The components include a scalable iterative solvers library Lis, having a number of solvers, preconditioners, and matrix storage formats that are flexibly combinable, a fast Fourier transform library FFTSS for various superscalar architectures with SIMD instructions, which outperforms some vendor-provided FFT libraries, and a language- and computing environment-independent matrix computation framework SILC. We show some highlights of our achievements on leading high performance computers.