Vol. 119, No. 3 * Pages 307–423 * July - September 2015

At present, Unmanned Aircraft Systems (UAS) are playing more and more significant role in military and civil operations in Hungary. A well-used meteorological support system is essential during the planning and executing phases of different UAS missions. In the present work, the structure of an applied analog statistical and a WRF-based numerical forecast system is to be introduced with special regards to aviation meteorological factors, such as visibility, ceiling, turbulence, icing, etc. Within such a system, it is very important to generate an accurate short-time visibility prediction. In order to develop such forecasts, we combined an analogy based statistical approach to a high-resolution numerical model for visibility prediction, which are currently available as a hybrid visibility prediction for the regions of four main airports in Hungary. On the other hand, we also present the first Hungarian Unmanned Meteorological Aircraft System (HUMAS). In our case study, the HUMAS measurements are compared to dynamical weather prediction data during the international planetary boundary layer (PBL) campaign in Szeged, Hungary.

In this study, the infrastructure development and operation of an urban human comfort monitoring network and information system in Szeged and the related preliminary research results are discussed. The selection of the representative sites of the network is based primarily on the pattern of the local climate zones in and around the city. After the processing of the incoming data (air temperature and relative humidity, as well as global radiation and wind speed), a human comfort index (PET) is calculated from the four meteorological parameters are with a neural network method (MLP), then the measured and calculated parameters interpolated linearly into a regular grid with 500 m resolution. As public information, maps and graphs about the thermal and human comfort conditions appear in 10-minute time steps as a real-time visualisation on the internet. As the preliminary case studies show, the largest intra-urban thermal differences between the LCZ areas in a two-day period occurred in the nocturnal hours reaching even 5 ºC in early spring. In the spatial distribution of human comfort conditions, there are distinct differences in the strength of the loading or favorable environmental conditions between the neighborhoods during the daytime. Finally, the utilization possibilities of the results in the future are detailed.

This paper investigates regional air quality model performance and the regulation of atmospheric emissions. Although evaluation of regional models cannot be reduced to a set of rules, the paper shows ways of developing better understanding of model performance. It draws on studies in recent years by the Environment Agency to quantify the uncertainty in predictions of regional air quality models. It is argued that a decision by a regulator on how to use a regional air quality model should be based on both operational evaluation (involving comparison with observation) and diagnostic evaluation (for developing understanding of the model), using operational and diagnostic metrics. Operational and diagnostic evaluations were undertaken, using a 'constructor' (CMAQ) and a 'seer' type (TRACK-ADMS) regional air quality model, for the secondary pollutants PM₁₀ , PM_2.5 and ozone, though for episodic ozone it was not possible to define an appropriate performance metric.
Neither type of model showed clearly better performance when applied to long-term average concentrations. There was not enough information to set a minimum margin of error in operational evaluations but margins of 20% or more are to be expected. Unlike operational metrics there is no obvious way of deriving diagnostic metrics. However a footprint diagnostic metric was shown to be a way to reveal the behaviour of PM₁₀  and PM_2.5 in both types of model. It is therefore suggested that seer models are used to reveal the structure of a model's underlying mathematical equations from which diagnostic metrics can be formed.
In the absence of an objective basis for setting acceptance criteria for models, it is proposed that the underlying pragmatic principle should be to use whatever has comparable accuracy with the best existing international practice. For regulatory applications, the error expected in current types of air quality models should be a consideration in any decision made on the basis of models.

This paper presents a development of the first order combined closure (local and nonlocal) single-column atmospheric boundary layer model. The model simulates the turbulent mixing of sensible heat, moisture, and momentum as a split between small scale (subgrid) and large scale (supergrid) processes according to the estimated ratio between local and nonlocal mixing. To verify the validity of the model, an evaluation process was conducted. The evaluation process included controlled offline numerical experiments and tests using the Wangara Experiment database. The obtained vertical profiles and estimated boundary layer heights are in good agreement with the Wangara observation data. Furthermore, uncertainty range affected by the choice of profile functions when estimating the ratio between local and nonlocal mixing processes was analyzed.

People spend a significant part of their days in buildings or in a box within some kind of means of transport. It is one of the main issues in our fast-paced world that typifies the twentieth and twenty-first centuries. Consequently, suitable environment creation plays an increasingly important part, which has significant influence on human comfort, health, and productivity as well. It turns out in pursuance of developments in various heating, ventilation and air conditioning (HVAC) systems that human comfort is affected not only by air temperature, humidity, and draught but also by meteorological, physiological, and psychological parameters. Airborne particles, so-called atmospheric ions assessments have been carried out in the course of these investigations. This paper presents effects of atmospheric ions on indoor environment and the occupants.

The aim of the research presented in this study is to elaborate a multivariate linear regression model that describes the spatial structure of the mean maximal development urban heat island (UHI) formed under favorable synoptic conditions on the basis of surface parameters. Temperature data were gathered in a small town, Beregszász, Zakarpattia, Ukraine. As a first step, a one-year-long UHI measurement campaign has been carried out using mobile techniques in order to obtain data for the description of the UHI in the study area. Two surface parameters (ratio of non-evaporating surfaces in the environment of the measurement sites and distance of measurement sites from the center of the settlement) have been selected first. The two surface parameters had to be quantified next. On this basis, relationships between surface parameters as independent variables and UHI intensities as dependent variables could be traced by performing a multivariate linear regression. Results have showed that the two chosen parameters have strong impact on UHI development in our study area. Spatial structure and intensity of UHI can be estimated with an accuracy of 0.4 °C within the built-up area of the town using our MLR model. The high resolution surface parameter database and the UHI estimating model enable the prediction of heat load of smaller spaces and town parts. This procedure helps the reduction of heat load and the determination of the location of green areas important for urban planning as well.