Applying Airspace Capacity Estimation Models to the Airspace of Hungary

Estimation of airspace capacity in order to keep air traffic controller workload at an optimal level is essential for the safety of air traffic. In the past, several different methods were developed for airspace capacity estimation with different benefits and drawbacks. In our research we studied the applicability of one of these methods (based on a neural network model) in the airspace of Hungary. This paper presents a possible way of gathering and processing data and validating the results given by the model

Tropospheric ozone columns retrieval from SCIAMACHY limb-nadir-matching observations

Tropospheric ozone (O3 ), has two main sources: transport from the stratosphere and photochem- ical production in the troposphere. It plays important roles in atmospheric chemistry and cli- mate change. Its amount and destruction are being modified by anthropogenic activity. Global measurements are needed to test our understanding of its sources and sinks. This dissertation presents a unique method for retrieving tropospheric O3 columns (TOCs) from a global satellite data set. The method involves the combination of limb and nadir observations (hereinafter referred to as limb-nadir-matching (LNM)) of the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument, which flew onboard the European Space Agency (ESA) satellite Envisat (2002 - 2012). The LNM technique involve the subtraction of the stratospheric O3 columns (SOCs), retrieved from the limb observations, from the total O3 columns (TOZs), derived from the nadir observations to yield the TOC