The topic of the paper is modelling and prediction of atmospheric variables that are further used for prediction of the consequences of radioactive-material release to the atmosphere. Physics-based models of atmospheric dynamics provide an approximate description of the true nature of a dynamic system. However, the accuracy of the model’s short-term predictions and long-term forecasts, especially over complex terrain, decreases when the information at a micro-location is sought. Integration of a physics-based model with a statistical model for enhancing the prediction power is proposed in the paper. Gaussian Processes models can be used to identify the mapping between the system input and output measured values. With the given mapping function, we can provide one-step ahead prediction of the system output values together with its uncertainty, which can be used advantageously. In this paper, we combine a physics-based model with a Gaussian process model to identify air temperature from measurements at different atmospheric surface layers as a dynamic system and to make short-term predictions as well as long-term forecasts.
COBISS.SI-ID: 32875815
The aim of this paper is to study, from an algebraic point of view, the properties of interdependencies between sets of elements (i.e., pieces of secrets, atmospheric variables, etc.) that appear in various natural models, by using the algebraic hyperstructure theory. Starting from specific examples, we first define the relation of dependence and study its properties, and then, we construct various hyperoperations based on this relation. We prove that two of the associated hypergroupoids are Hv-groups, while the other two are, in some particular cases, only partial hypergroupoids. Besides, the extensivity and idempotence property are studied and related to the cyclicity. The second goal of our paper is to provide a new interpretation of the dependence relation by using elements of the theory of algebraic hyperstructures.
COBISS.SI-ID: 5453307
This paper highlights the problem of step-length selection for the one-step-ahead prediction of ozone called the data time interval. The problem elaborated is relevant for the step-length selection for various pollutants and atmospheric variables that are considered in the project, but was in this paper demonstrated on ozone prediction. This is done using a case-study based comparison of two approaches for predicting the maximum daily values of tropospheric ozone. The first approach is the 1-day-ahead prediction and the second is the prediction of the maximum values based on a multistep-ahead iteration of 1-h predictions. Gaussian process modelling is utilised for this comparison. In particular, evolving Gaussian-process models are used that update online with the incoming measurement data. These sorts of models have been successfully used in the past for the prediction of ozone pollution. This paper contributes an assessment of the way that the maximum ozone values are predicted. The forecast results are in favour of the on-line model based on hourly predictions when approaching closer to the real maximum values of ozone, and in favour of the daily predictions when they are made on a daily basis. This is the conclusion worth transferring to the analysis of other pollutants and atmospheric variables.
COBISS.SI-ID: 31210023
From a temporal viewpoint, air pollution has significant daily patterns/cycles of behaviour. These cycles are conditioned by anthropogenic and natural phenomena. In both cases, a detailed observation and an understanding of the daily cycles rules or daily patterns of air pollution can be significant and at the same time can contribute to more effective measures to reduce the harmful impact of air pollution on human health. In this paper the new sunflower diagram is presented. The key advantage of the sunflower diagram is the ease of understanding the result and the ability to present information in the form of a graphic pattern, allowing the user to quickly understand the content. Using the sunflower diagram, we will present an analysis of the meteorological parameters that are important for understanding air pollution and air-pollution data for different locations in Slovenia.
COBISS.SI-ID: 31160359
In conditions of complex terrain, modelling of air pollutant dispersion still has a number of scientific challenges. Ideally, appropriate meteorological data should be available for modelling. Unfortunately, for many purposes, there is no time to carry out suitable measuring campaigns. Therefore the results of prognostic weather forecasts (NWP models) are being widely used. However, these models still have quite a few disadvantages when their results are used as input for dispersion models over complex terrain. This study presents the validation of the quality of the weather forecasts in the surroundings of the Nuclear Power Plant Krško in Slovenia, an area with highly complex terrain and the resulting complex meteorological characteristics. The forecast is available for a horizontal resolution of 2 km and half hour temporal interval and seven days in advance. The predicted meteorological parameters are validated using the measured meteorological parameters.
COBISS.SI-ID: 31160103