Vol. 123, No. 2 * Pages 135–264 * April - June 2019

Richardson extrapolation is a numerical procedure which enables us to enhance the accuracy of any convergent numerical method in a simple and powerful way.
In this paper we overview the theoretical background of Richardson extrapolation in space and time, where two numerical solutions, obtained on a coarse and a fine space-time grid are combined by a suitable weighted average. We show that when the Crank-Nicolson method is appropriately combined with this extrapolation technique for the solution of the one-dimensional advection equation, then the order of accuracy increases by two both in time and space. The theoretically derived consistency order and the necessity of the smoothness conditions for the exact solution and for the advection velocity are illustrated by numerical experiments, performed by the advection module of the Danish Eulerian Model (DEM).

The strong interaction between the radiation, cloud microphysics, and cloud dynamics requires more advanced radiation schemes in numerical calculations. Detailed (bin) microphysical schemes, which categorize the cloud particles into bin intervals according to their sizes, are useful tools for more accurate simulation of evolution of the hydrometeors. Our research aimed at the development of a new bin radiation scheme based on a commonly used bin microphysical scheme and the implementation of this new scheme into the RRTMG LW longwave radiation-transfer model. We have applied the MADT approximation method to evaluate radiation interaction. The absorption coefficients are calculated by using bin resolved size distribution of water droplets, which is the output of a bin microphysical scheme.
The longwave absorption coefficients applied in this new method are in tune with those of a bulk radiation scheme, which is currently used in operational numerical weather prediction models. However, the two schemes gave reasonably different results for longwave radiation cooling rates at stratocumulus cloud tops and fog layers. Unfortunately, only few observation data are available to check our results directly. Indirect evaluation can be based on outputs of numerical radiative transfer models published in various studies since the nineties. Achievements of our research enable more precise calculation of longwave radiation profiles, and better prediction of dynamic and thermodynamic processes in water clouds (e.g., lifetime of stratocumulus clouds, fog evolution, and precipitation formation).

Fine-tuning of a weather model requires immense computational resources, however, such capacities are usually available on non-homogeneous IT platforms. In addition, development and operational application are typically performed on different, heterogeneous systems (from laptops to dedicated HPC servers or cloud computing environments). To manage scalability and platform independent portability, a new layer – supporting state-of-the-art software container technology and batch processing – has been introduced. Encouraged by prior successful benchmark tests of the WRF model, the effect of model setup has been investigated over 10 different cases, tested on 30 different configurations. Including different parameterizations, the results of 300 different runs can be compared in a uniform database, yielding a sufficiently wide pool of samples in order to obtain the configuration of the modeling system optimal to the scope of our research, based on a relatively objective selection method. Continuously expanding database of near real-time preliminary outputs gives the opportunity for run-time steering of the experiments. This research currently benefits the development of an aviation meteorological support system, in the meanwhile, our contributions could be applied in an even wider aspect, either from the applicability of big data technology point of view, or with respect to the given best practice model setup.

In this study, the performance of a high resolution numerical weather prediction (NWP) model is investigated in a particular weather situation, namely, in winter anticyclonic cases over land with low level clouds and fog. Most NWP models tend to underestimate low level cloudiness during these events which causes the overestimation of daytime temperature. Several sensitivity tests are performed to trace the cause of the erroneous model performance, and it is shown that model microphysics and, in particular, the autoconversion of cloud ice to snow is responsible for the underestimation of cloud cover. A modification is proposed which significantly reduces ice autoconversion and consequently keeps the low level clouds for situations with temperatures below freezing level. The modification is tested on several case studies and also on longer time intervals and proves to be applicable for operational model runs.

WRF-Chem is a numerical Eulerian non-hydrostatic mesoscale weather prediction model online coupled with the atmospheric chemistry model, developed mainly by the National Center for Atmospheric Research (NCAR) and the National Oceanic and Atmospheric Administration (NOAA). This model system is a frequently used tool for creating high resolution air quality simulations at different spatial and temporal scales for various air pollutants. In this study, the technical backgrounds of the WRF-Chem model applied for high resolution urban air quality forecasts in Budapest are presented. The meteorological module of the system uses the WRF-ARW (Weather Research and Forecasting – Advanced Research WRF) dynamical solver, and obtains its initial and boundary conditions from the GFS (Global Forecast System) using a horizontal resolution of 0.25 × 0.25 degree. By applying two nested model domains (with 15 × 5 km horizontal resolution), fine resolution meteorological fields can be achieved. In the chemical module, the National Emission Inventories created by the Hungarian Meteorological Service were applied, different chemical reaction sets were used and tested, and constant deposition rates were assumed. In this work, a case study for different pollutants (O₃, NO, NO₂, and CO) is presented for an early summer period of 2015.

Present study evaluates the ability of the ERA-Interim-driven regional climate model (RCM) simulations conducted in the framework of the Coordinated Regional Climate Downscaling Experiment (CORDEX) in describing precipitation and temperature climatic conditions over the Carpathian Region. In total, nine RCM simulations were assessed from EURO-CORDEX and Med-CORDEX (at 0.44° and 0.11° nominal resolutions) against the CARPATCLIM high resolution gridded observational database. Present work focuses on the mean, minimum, and maximum near-surface air temperature and precipitation. The study shows the performance of the members of RCM ensembles in representing the basic spatiotemporal patterns of the climate over the Carpathian Region for the period of 1989–2008. Different metrics covering from daily to monthly and from seasonal to annual time scales are analyzed over the region of interest: spatial patterns of seasonal mean temperature and precipitation, annual cycle of precipitation, monthly mean temperature bias, as well as climate indices, including CDD (consecutive dry days), R95, FD (frost days, when T_minmax>25 °C). The results confirm the distinct capabilities of RCMs in capturing the local features of the climatic conditions of the Carpathian Region. This work is in favor to select RCMs with reasonable performance over the Carpathian Region, based on which a high-resolution bias-adjusted climatic database can be established for future risk assessment and impact studies.

The fog not only makes the traffic more difficult, but it is frequently accompanied by increased air pollution. A research program has been started recently to improve our knowledge about fog both in macro and micro scales. In the first part of the research project, analysis of the data collected in the last 60 years has been performed. This database contains information about the visibility and the duration of the reduced visibility at 8 different cities in different regions of Hungary. The climatology of fog in Hungary has been studied in only few research programs, and no comprehensive analysis of the data has been performed. The first results of the data analysis show that the frequency and duration of the mist significantly reduced between the 1980s and 2000s, and the most dramatic reduction occurred in the northeast region of the country. Furthermore, the frequency of fog also dropped in this time period. The most dramatic reduction of the fog and mist events was found in northeastern Hungary, which was one of the most polluted regions in the country until the 90s of the last century. The coincidence of the significant reduction of duration of fog and that of the sulfate emission in NE Hungary supports the hypothesis that there is a strong correlation between the air pollution and the formation of the mist and fog.