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Fodor N.,Hungarian Academy of Sciences | Mika J.,Hungarian Meteorological Service | Mika J.,Eszterházy Károly College
Agricultural and Forest Meteorology | Year: 2011

Using analogies from soil science a new global radiation estimation method was developed and tested on a large North-American meteorological database. The newly developed procedure was compared to the well-known, Donatelli-Campbell method. Both the overall indicators and the more detailed analysis of the model performance confirmed that the new method is more efficient than the Donatelli-Campbell method. The improved performance is due to the greater number of parameters, as well as to the more adequate function type it uses during the calculation. Results suggest that it is sufficient to have 2 year-long data series for parameterizing the new method to provide at least as accurate radiation estimates as provided by the Donatelli-Campbell method. The new method can be efficiently used for locations with no radiation measurement by combining it with a simple spatial interpolation technique. © 2010 Elsevier B.V.

Baran S.,Debrecen University | Horanyi A.,Hungarian Meteorological Service | Nemoda D.,Debrecen University
Meteorology and Atmospheric Physics | Year: 2014

Weather forecasting is based on the outputs of deterministic numerical weather forecasting models. Multiple runs of these models with different initial conditions result in forecast ensembles which are used for estimating the distribution of future atmospheric variables. However, these ensembles are usually under-dispersive and uncalibrated, so post-processing is required. In the present work, Bayesian model averaging (BMA) is applied for calibrating ensembles of temperature forecasts produced by the operational limited area model ensemble prediction system of the Hungarian Meteorological Service (HMS). We describe two possible BMA models for temperature data of the HMS and show that BMA post-processing significantly improves calibration and probabilistic forecasts although the accuracy of point forecasts is rather unchanged. © 2014 Springer-Verlag Wien.

Horanyi A.,Hungarian Meteorological Service | Mile M.,Hungarian Meteorological Service | SzuCs M.,Hungarian Meteorological Service
Tellus, Series A: Dynamic Meteorology and Oceanography | Year: 2011

Ensemble prediction systems (EPSs) are an essential part of numerical weather prediction for the provision of probabilistic forecast guidance. The Hungarian Meteorological Service has implemented a limited area EPS (called ALADIN HUNEPS) based on the ALADIN mesoscale limited area model coupled to the French global ARPEGE EPS (PEARP). The dynamical downscaling method is assessed in terms of ensemble verification scores taking also into account the recent upgrade of the PEARP global system. The verification results point towards some weaknesses of the ALADIN HUNEPS, mainly with respect to the near-surface parameters. Therefore, some improvements are needed so as to provide better and more reliable ensemble predictions for the Carpathian Basin. The application of near-surface perturbations into the surface data assimilation scheme is implemented and tested. The first results show that the surface perturbation method slightly improves the ALADIN HUNEPS, however, further experiments should be made to find out the optimal settings of the method for definite robust improvements of ALADIN HUNEPS. © 2011 The Authors Tellus A © 2011 John Wiley & Sons A/S.

Hagel E.,Hungarian Meteorological Service
Idojaras | Year: 2010

Dynamical downscaling of the global ARPEGE based ensemble prediction system PEARP is running quasi-operationally at the Hungarian Meteorological Service since February 2008. For the downscaling of the PEARP members, the ALADIN limited area model is used with 12 km horizontal resolution. Both systems have 10+1 ensemble members (one control member that starts from the unperturbed analysis and 10 members which start from perturbed initial conditions). At present, both the initial and lateral boundary conditions of the ALADIN runs are provided by the PEARP ensemble members, however, it is planned to generate the initial condition perturbations locally at a later stage. In the article this quasi-operational short-range limited area ensemble prediction system, as well as its performance through verification results will be presented.

Haszpra L.,Hungarian Meteorological Service | Barcza Z.,Eötvös Loránd University
Tellus, Series B: Chemical and Physical Meteorology | Year: 2010

This paper analyses a 15-year long atmospheric CO2 mixing ratio record measured at a mid-continental, low-elevation station (Hegyhátsál, Hungary) to reveal the effect of regional climate variability. While the long-term trend and the temporal fluctuation of the growth rate of CO2 mixing ratio follow the global tendencies to a large extent, the shorter-term variations show special features. We present the distorted seasonal cycle caused by the seasonality in the atmospheric vertical mixing and the tendentious change in its shape, which can be attributed to the gradual warming and to the resulted prolongation of the growing season. The decreasing summer diurnal amplitude and the decreasing seasonal amplitude in the mixing ratio, furthermore the higher than average summer CO2 mixing ratio growth rate in the first period of the measurements (1994-2003) with generally rising temperature and decreasing precipitation are explained as the consequence of the reduced activity of the biosphere in the influence area of the station and that of the reduced biomass under environmental conditions getting increasingly unfavourable. The explanation is supported by the co-located tall tower surface-atmosphere CO2 exchange measurements and by the crop yield statistics of the dominantly agricultural region around the station. © 2010 The Authors Tellus B © 2010 International Meteorological Institute in Stockholm.

Szentimrey T.,Hungarian Meteorological Service
Idojaras | Year: 2013

The so-called variable correction methods form a special type of methods developed for daily data homogenization. Their common assumption is that in case of daily data series, the corrections for inhomogeneity have to vary according to the meteorological situation of each day in order to represent the extremes. In this paper we express our objections to these variable correction methods, especially to their underlying principles. Since the exact theoretical mathematical formulation of the question of daily data homogenization is generally neglected, we also try to formulate and analyze this problem in accordance with mathematical conventions.

Baran S.,Debrecen University | Horanyi A.,Hungarian Meteorological Service | Nemoda D.,Debrecen University
Meteorologische Zeitschrift | Year: 2013

Weather forecasting is mostly based on the outputs of deterministic numerical weather forecasting models. Multiple runs of these models with different initial conditions result in a forecast ensemble which is applied for estimating the future distribution of atmospheric variables. However, as these ensembles are usually under-dispersive and uncalibrated, post-processing is required. In the present work, Bayesian Model Averaging (BMA) is applied for calibrating ensembles of wind speed forecasts produced by the operational Limited Area Model Ensemble Prediction System of the Hungarian Meteorological Service (HMS). We describe two possible BMA models for wind speed data of the HMS and show that BMA post-processing significantly improves the calibration and accuracy of point forecasts. © by Gebrüder Borntraeger 2013.

Nygaard B.E.K.,Norwegian Meteorological Institute | Agustsson H.,University of Iceland | Somfalvi-Toth K.,Hungarian Meteorological Service
Journal of Applied Meteorology and Climatology | Year: 2013

Methods to model wet snow accretion on structures are developed and improved, based on unique records of wet snow icing events as well as large datasets of observed and simulated weather. Hundreds of observed wet snow icing events are logged in detail in an icing database, most of which include an estimate of the mean and maximum diameter of observed icing on overhead power conductors. Observations of weather are furthermore available from a dense network of weather stations. The existing models for wet snow accretion on a standard cylinder are updated with realistic values for the terminal fall speed of wet snowflakes together with a snowflake liquid fraction-based criterion to identify wet snow. The widely used parameterization of the sticking efficiency is found to strongly underestimate the accretion rate. A calibrated parameterization of the sticking efficiency is suggested on the basis of long-term statistics of observed and modeled wet snow loads. Application of the improved method is demonstrated in a high-resolution simulation for a case of observed widespread and intensive wet snow icing in south Iceland. The results form a basis for mapping the climatology of wet snow icing in the complex terrain of Iceland as well as for preparing operational forecasts of wet snow icing and severe weather for overhead power transmission lines in complex terrain. © 2013 American Meteorological Society.

Many methods have been worked out to estimate precipitation rainfall from meteorological satellite radiances sensed by microwave (on-board low-orbiting satellites) and infrared (on-board geostationary satellites) sensors. Validation of such estimated precipitation products have become more and more important.This paper describes the validation study limited to the geographical area of Hungary of three satellite-based rain estimates for hydrology purposes. These estimates were recently developed in the frame of the Hydrology SAF project, launched by EUMETSAT (European Organization for the Exploitation of Meteorological Satellites) in 2005. While two of these products were estimated using microwave-based method, one of them was estimated using a combination of infrared (IR) and microwave (MW) radiances.The validation studies carried out at the Hungarian Meteorological Service covered statistical analyses of instantaneous values over monthly periods and case-by-case analyses. Statistics were evaluated for every month, and focus was given to the differences between summer and winter to depict the seasonal features of the rainfall estimates. All three products gave best results in summer months (probability of detection was between 0.5 and 0.7 compared to 0.1 and 0.2 in winter; and false alarm rate was 0.3-0.6 in summer compared to 0.8-0.9 in winter), due to the much easier detection of deep convective clouds by satellites. Correlation was between 0.2 and 0.4 in summer months for every product, whereas in the winter it was under 0.1. In case studies, the location of convective cells formed in the summer was reflected well by MW observation; the combined products showed overestimated area of low precipitation.Significant underestimation of heavy rainfall (mean error of -15 to 20. mm/h) was found in the case of the infrared-microwave (IR-MW) mixed product. According to case studies, the MW-based retrievals overestimated high precipitation intensities of the convective cells. However, in the monthly statistics the mean error was negative (-5 to 10. mm/h) which demonstrated that the overestimation was not systematic.Winter results revealed that light rainfall had low probability of detection both by MW and IR-MW combined measurements. However, the case study presented for a winter day showed for all the three products well detection of liquid precipitation. Overall results exposed more reliable detection of convective than stratiform precipitation. © 2012 Elsevier B.V.

Aims of the spectral radiation measurements can be devided to two wider areas: one is to get information about the radiation source, and the other is to get information about the properties of the space between the radiation source and the detector if output signal from the radiation source is known. In the latter case either the optical properties of the certain space or some optical parameter of an object placed in there is to be studied. The sun can be the object of the study or it can be used as natural radiation source to investigate some important properties of the atmosphere. The term 'solar spectrophotometry' refers to this. Although detection of spectral distribution of the solar radiation is considered a special area that is relatively rarely used even today in atmospheric physical measurements, it still has big significance. In addition to the 'mere' knowledge of spectal solar irradiance, the measured data can be used in a considerably wide range. In special cases, the narrow spectral range informations about the radiation can be very useful. Typical example is the erythemally weighted UV radiation. Though it does not give spectral information, the spectral range that is characterized by it, is considerably narrower than that of the classical radiation components. So this type of measurements is also discussed here. Main applied physical and technical principles of solar spectrophotometry, as well as spectrophotometers working in the UV, visible, and near infrared spectral range used at the Hungarian Meteorological Service (HMS), are shown in this paper. Measurement results and results from studies and researches using these data are also shown and analyzed. Also some special studies performed occasionally are shown. Today the primary base for operation of high accuracy measurement systems is the calibration. Since we have reference instruments, QA/QC procedures are of crucial importance in our measuring practice. Our activitiy as we operate WMO Regional Center for Solar Radiation in Region VI gives even bigger emphasis to that.

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