The aim of the present study is to investigate the potential for building a common platform to support direct end-user application of machine learning algorithms across diverse scientific areas, emphasizing not only the suitability of appropriate tools, but also how results can be disseminated and utilised in a shared data environment.
Decision makers need accessible robust evidence to introduce new policies to mitigate and adapt to climate change. There is an increasing amount of environmental information available to policy makers concerning observations and trends relating to the climate.
This study focuses on the assessment of surface solar radiation (SSR) based on operational neural network (NN) and multi-regression function (MRF) modelling techniques that produce instantaneous (in less than 1?min) outputs.
This study assesses the impact of dust on surface solar radiation focussing on an extreme dust event. For this purpose, we exploited the synergy of AERONET measurements and passive and active satellite remote sensing (MODIS and CALIPSO) observations, in conjunction with radiative transfer model (RTM) and chemical transport model (CTM) simulations and the 1-day forecasts from the Copernicus Atmosphere Monitoring Service (CAMS).
This study aims to cross-validate ground-based and satellite-based models of three photobiological UV effective dose products - the Commission Internationale de l'Eclairage (CIE) erythemal UV, the production of vitamin D in the skin, and DNA damage, using high-temporal-resolution surface-based measurements of solar UV spectral irradiances from a synergy of instruments and models.
We report on the construction of generic models to calculate photosynthetically active radiation (PAR) from global horizontal irradiance (GHI), and vice versa. We show how multi-linear regression and nonlinear neural network (NN) models, trained at a calibration site (Thessaloniki) can be made generic provided that the input-output time series are processed with multi-channel singular spectrum analysis (M-SSA).
We report on the development and validation of a neural network (NN) model of PM10 concentrations in terms of photochemical measurements of NO, NO2 and O3 and temporal parameters that include the day of the week and the day of the year with its sinusoidal variation.