Neural Networks Technique for Filling Gaps in Satellite Measurements: Application to Ocean Color Observations.

A neural network (NN) technique to fill gaps in satellite data is introduced, linking satellite-derived fields of interest with other satellites and in situ physical observations. Satellite-derived "ocean color" (OC) data are used in this study because OC variability is primarily driven by biological processes related and correlated in complex, nonlinear relationships with the physical processes of the upper ocean. Specifically, ocean color chlorophyll-a fields from NOAA's operational Visible Imaging Infrared Radiometer Suite (VIIRS) are used, as well as NOAA and NASA ocean surface and upper-ocean observations employed--signatures of upper-ocean dynamics.

Efficacy of vitamin C vaginal tablets as prophylaxis for recurrent bacterial vaginosis: a randomised, double-blind, placebo-controlled clinical trial.

Background: The aetiology of bacterial vaginosis (BV) is still unclear but it is currently considered to be a synergistic polymicrobial syndrome. BV can often arise as a chronic or recurrent disease. The reason for such recurrences is not well elucidated.

Neural network approach for robust and fast calculation of physical processes in numerical environmental models: compound parameterization with a quality control of larger errors.

Development of neural network (NN) emulations for fast calculations of physical processes in numerical climate and weather prediction models depends significantly on our ability to generate a representative training set. Owing to the high dimensionality of the NN input vector which is of the order of several hundreds or more, it is rather difficult to cover the entire domain, especially its "far corners" associated with rare events, even when we use model simulated data for the NN training. Moreover the domain may evolve (e.

Reducing uncertainties in neural network Jacobians and improving accuracy of neural network emulations with NN ensemble approaches.

A new application of the NN ensemble technique to improve the accuracy (reduce uncertainty) of NN emulation Jacobians is presented. It is shown that the introduced ensemble technique can be successfully applied to significantly reduce uncertainties in NN emulation Jacobians and to reach the accuracy of NN Jacobian calculations that is sufficient for the use in data assimilation systems. An NN ensemble approach is also applied to improve the accuracy of NN emulations themselves.