Modeling of Surface UV Radiation Using Satellite Data

Solar UV radiation is harmful for life on planet Earth, but fortunately the atmospheric oxygen and ozone absorb almost entirely the most energetic UVC radiation photons. However, part of the UVB radiation and much of the UVA radiation reaches the surface of the Earth, and affect human health, environment, materials and drive atmospheric and aquatic photochemical processes. In order to quantify these effects and processes there is a need for ground-based UV measurements and radiative transfer modeling to estimate the amounts of UV radiation reaching the biosphere. Satellite measurements with their near-global spatial coverage and long-term data conti-nuity offer an attractive option for estimation of the surface UV radiation. This work focuses on radiative transfer theory based methods used for estimation of the UV radiation reaching the surface of the Earth. The objectives of the thesis were to implement the surface UV algorithm originally developed at NASA Goddard Space Flight Center for estimation of the surface UV irradiance from the meas-urements of the Dutch-Finnish built Ozone Monitoring Instrument (OMI), to improve the original surface UV algorithm especially in relation with snow cover, to validate the OMI-derived daily surface UV doses against ground-based measurements, and to demonstrate how the satellite-derived surface UV data can be used to study the effects of the UV radiation. The thesis consists of seven original papers and a summary. The summary includes an introduction of the OMI instrument, a review of the methods used for modeling of the surface UV using satellite data as well as the con-clusions of the main results of the original papers. The first two papers describe the algorithm used for estimation of the surface UV amounts from the OMI measurements as well as the unique Very Fast Delivery processing system developed for processing of the OMI data received at the Sodankylä satellite data centre. The third and the fourth papers present algorithm improvements related to the surface UV albedo of the snow-covered land. Fifth paper presents the results of the comparison of the OMI-derived daily erythemal doses with those calculated from the ground-based measurement data. It gives an estimate of the expected accuracy of the OMI-derived sur-face UV doses for various atmospheric and other conditions, and discusses the causes of the differences between the satellite-derived and ground-based data. The last two papers demonstrate the use of the satellite-derived sur-face UV data. Sixth paper presents an assessment of the photochemical decomposition rates in aquatic environment. Seventh paper presents use of satellite-derived daily surface UV doses for planning of the outdoor material weathering tests.

Computationally Efficient Methods for MDL-Optimal Density Estimation and Data Clustering

The Minimum Description Length (MDL) principle is a general, well-founded theoretical formalization of statistical modeling. The most important notion of MDL is the stochastic complexity, which can be interpreted as the shortest description length of a given sample of data relative to a model class. The exact definition of the stochastic complexity has gone through several evolutionary steps. The latest instantation is based on the so-called Normalized Maximum Likelihood (NML) distribution which has been shown to possess several important theoretical properties. However, the applications of this modern version of the MDL have been quite rare because of computational complexity problems, i.e., for discrete data, the definition of NML involves an exponential sum, and in the case of continuous data, a multi-dimensional integral usually infeasible to evaluate or even approximate accurately. In this doctoral dissertation, we present mathematical techniques for computing NML efficiently for some model families involving discrete data. We also show how these techniques can be used to apply MDL in two practical applications: histogram density estimation and clustering of multi-dimensional data.