This paper reports on the development of a neural network (NN) model for instantaneous and accurate estimation of solar radiation spectra and budgets geared toward satellite cloud data using a ~ 2.4 M record, high-spectral resolution look up table (LUT) generated with the radiative transfer model libRadtran.
Cluster analysis is applied to seven years of three-hourly gridded aerosol optical depth data from the Goddard Chemistry Aerosol Radiation and Transport model, one of the most-studied global simulations of aerosol type currently available, to construct a spatial partition of the globe into a finite number of aerosol mixtures.
Here, a methodology based on neural networks is developed to retrieve such parameters from satellite inputs and to validate them with ground-based remote sensing data. For key combinations of input variables available from the MODerate resolution Imaging Spectroradiometer (MODIS) and the Ozone Measuring Instrument (OMI) Level 3 data sets, a grid of 100 feed-forward neural network architectures is produced, each having a different number of neurons and training proportion.
We present a method for determining the mode separation point for equivalent-volume bi-lognormal distributions based on optimization of the root mean squared error and the coefficient of determination. The extracted secondary parameters are compared with those provided by AERONET's Level 2.0 Version 2 inversion algorithm for a set of benchmark dominant aerosol types, including desert dust, biomass burning aerosol, urban sulphate and sea salt.
We demonstrate improvements in CALIPSO (Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations) dust extinction retrievals over northern Africa and Europe when corrections are applied regarding the Saharan dust lidar ratio assumption, the separation of the dust portion in detected dust mixtures, and the averaging scheme introduced in the Level 3 CALIPSO product.
In this work, we have developed a generalisation of the second order NESM model (Tsallis et al, 2003) to higher orders and we have fit the complete spectrum including the ankle with third order NESM. We find that, towards the GDZ limit, a new mechanism comes into play.
This tutorial presents a review of the analytical approach to obtain exact solutions for the populations of n-level ions, and summarizes the ideas behind detailed balance and the statistical physics of collisionally-excited ions. Spectrophotometric observations of a real ionized gas (the planetary nebula A39) are then used to obtain empirical values of forbidden line ratios and level populations for the 5-level [O III] ion.
Here, we report on a new artificial intelligence-based approach to determine metallicity indicators that shows promise for the provision of improved empirical fits. The method hinges on the application of an evolutionary neural network to observational emission line data.