Friday 22 September 2023
Időjárás - Quarterly Journal of the Hungarian Meteorological Service (OMSZ)

Vol. 127, No. 2 * Pages 143–265 * April - June 2023

Journal of the Hungarian Meteorological Service

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Drought events in Hungary and farmers’ attitudes towards sustainable irrigation
Márta Gaál and Enikő Becsákné Tornay
DOI:10.28974/idojaras.2023.2.1 (pp. 143–165)
 PDF (5631 KB)   |   Abstract

Among the different forms of agricultural damage in Hungary, drought poses a remarkably high risk according to the reported drought events, the area affected, and the level of mitigation payments. This study explores drought damage based on the 2018–2020 data of the Hungarian Agricultural Risk Management System. Owing to eligibility criteria, slightly more than half of the members of the scheme who reported drought damage received mitigation payments, but for them, the value of compensation significantly exceeded the mitigation contribution. According to our results, most of the damaged areas were outside the impact areas of water supply systems or were within the impact area but on non-irrigated fields, which proved that irrigation could be an effective drought mitigation tool. To avoid drought damage, irrigation development is essential, and special attention should be paid to the territory of Szabolcs-Szatmár-Bereg county. This area suffered significant drought damage in the years examined, and currently the impact area of the surface water-based water supply systems is small, while the groundwater resources are already overexploited. At the same time, the risk management system should be modified to transform it into a preventive system which encourages farmers to use water retentive soil cultivation methods, appropriate cropping systems, sustainable water management, and efficient and reasonable levels of irrigation. Accordingly, fewer mitigation benefits would be paid through less drought damage. Based on questionnaire surveys, farmers are open to using water retention practices and sustainable irrigation management.

Retrieval of atmospheric trace gases from satellite infrared limb sounding data
Shaomin Cai, Weijia Zhang,and Yizhou Zhao
DOI:10.28974/idojaras.2023.2.2 (pp. 167–197)
 PDF (7402 KB)   |   Abstract

The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) instrument which operated in the near-to-mid infrared on the Envisat satellite from 2002–2012 is a Fourier-transform spectrometer for the measurement of high-resolution atmospheric emission spectra at the Earth’s limb. The initial operational products are profiles of temperature, H2O, O3, CH4, N2O, HNO3, and NO2, and this list is extended to include the important GHG (greenhouse gases) and ODS (ozone depleting substances) species using MORSE (orbital retrieval processor).
This paper discusses retrievals of minor trace species of HCFC-22, HOCl, OCS, C2H6, COF2, HCN, CF4, SF6, and CCl4. Preliminary zonal mean results of these retrievals are satisfactory except for HCN and CF4 species. We then use MIPAS to estimate the total F and Cl budget in the atmosphere. Comparisons with SLIMCAT are also discussed, with the results consistent.
Finally, we focus on improving the retrieval method for minor trace species, COF2, by adjusting correlation length and a priori constraint, as well as the chi-square method introduced to analyze noise. Tests have shown that our improvement could get more information from the measurements.

Wind speed estimation for the correction of wind-caused errors in historical precipitation data
Tibor Rácz
DOI:10.28974/idojaras.2023.2.3 (pp. 199–216)
 PDF (4631 KB)   |   Abstract

The wind has a significant impact on the accuracy of precipitation measurement in the case of collecting gauges. As widely known, the velocity field of wind suffers a deformation over and around the precipitation gauges, which causes deviations in the measured quantities. This error must be corrected if it is possible. Thanks to numerous researches, correction formulas give tools for adjusting precipitation data in the function of the wind speed and raindrop distribution (DSD) relationship, gauge parameters, and for the case of snow and temperature. The measured intensity of precipitation in historical data allows estimating the DSD, but in most cases, there are no simultaneously measured wind speed data coupled to the historical precipitation data.
Characteristic data of wind speed can be estimated based on the wind speed statistics, and these data can be utilized for the statistical correction of the precipitation measurements. The statistical correction means that the rainfall data can be adjusted with the expected value of the wind speed for a more extended observation period, assuming a stationarity of wind speed statistics for the given location. After the statistical correction, the unique data will not be unbiased, but statistically they will be closer to the actual value, and the correction will be statistically correct in inherited perecipitation cheracteristics, as for example the IDF curves. For this correction, an investigation is necessary to find the adequate wind statistics for the rainfall correction. This paper shows the results of a study about the relation of statistics of wind speeds during precipitation, based on a 10-minute sampling period. The wind speed data were independent of the rain depth (or intensity) data. The result of the study shows that the distribution of wind speeds differs of the wind speed distribution measured in the precipitation events. This difference can be treated easily using the stable rate of the means of these distributions. This result gives a step toward correcting the wind-affected error of historical precipitation data.

Analysis of the correlation between the incidence of food-borne diseases and climate change in Hungary
Tímea Kocsis, Kinga Magyar-Horváth, Zita Bihari, and Ilona Kovács-Székely
DOI:10.28974/idojaras.2023.2.4 (pp. 217–231)
 PDF (1710 KB)   |   Abstract

It is increasingly accepted globally, that many food-borne diseases are associated with climate change. The goal of the present research is to investigate whether changes in the annual number of the registered food-borne diseases in Hungary can be correlated to any climate parameter, as it is reasonable to suppose that it can be linked to climate change. Ten climate parameters and indices were examined as potential influencing
factors. A multiple linear regression model was employed, using the backward elimination method to find the climate factors that have a significant effect on the annual number of food-borne diseases. It was found that the annual mean temperature was the only significant predictor of the annual number of registered food-borne diseases, and that 22.0% of the total variance in the annual number of food-borne diseases can be explained by the annual mean temperature. It should be noted that this relationship is negative, given that they are derived from time series with opposite trends. This phenomenon may be explained by the process of evolution and adaptation of the infecting fauna.

Graupel mixing ratio forecast from a cloud resolving numerical weather prediction model as a tool for lightning activity prediction
Boryana Tsenova, Konstantin Mladenov, and Milen Tsankov
DOI:10.28974/idojaras.2023.2.5 (pp. 233–251)
 PDF (2782 KB)   |   Abstract

Graupel mixing ratio over Bulgaria for the warm half year of 2021 based on the AROME-BG numerical weather prediction (NWP) model, is evaluated and connected lightning data detected by the ATDnet lightning location network. Lightning data and forecasted graupel mixing ratios were considered on resolutions of 5×5 km and 10×10 km with flash rate for one and three hours, as well on a daily base using upscaling neighborhood method. Two daily model runs are considered – at 06 and 18 UTC. Commonly used skill-scores in meteorological forecasts are used as evaluation metrics – probability of detection (POD), false alarm rate (F), proportion correct index (PC), and frequency bias index (FBI).  Lightning probability forecast (based on graupel mixing ratio) is evaluated at diurnal, monthly, and spatial bases. Results show that graupel mixing ratio taken from the cloud resolving NWP model AROME-BG could be used as a tool to forecast lightning probability with a relatively high performance. Decreases of forecast spatial resolution and time frequency lead to improvement of forecast probability of detection (POD) and frequency bias index (FBI) and to a slight deterioration of its false alarm rate (F) and its percent correct (PC), and the impact of forecast time frequency is more pronounced.

The role of temperature on the germination activity of leguminous crops exposed to saline conditions
Noriza Khalid, Asma H. Sghaier, Márton Jolánkai, and Ákos Tarnawa
DOI:10.28974/idojaras.2023.2.6 (pp. 253–265)
 PDF (670 KB)   |   Abstract

Germination is an important starting point of plant life. Abiotic stresses during the germination stage in seeds can threaten the development process of a plant species. Abiotic factors such as temperature and salt concentration influence the germination process of various crop seeds, including leguminous species. The aim of this study is to determine the germination rate and seedling growth of leguminous cover crops under two different temperatures and four levels of salt stress. Alfalfa (Medicago sativa), red clover (Trifolium pratense), and chickpea (Cicer arietinum) were studied in this in vitro trial. The study results showed that the increase in sodium chloride (NaCl) concentration suppressed the growth of the germinated seedlings. At the same time, the increase in temperature reduced the germination rate of red clover and chickpea at higher salt concentrations. The data also showed a significant relationship between salt concentration and temperature on shoot and radicle growth in all three leguminous species. These data may benefit farmers and growers trying to cultivate these crops in unfavorable conditions.

IDŐJÁRÁS - Quarterly Journal