TY - JOUR AU - Kern, Anikó AU - Dobor, Laura AU - Hollós, Roland AU - Marjanović, Hrvoje AU - Torma, Csaba AU - Kis, Anna AU - Fodor, Nándor AU - Barcza, Zoltán TI - Seamlessly combined historical and projected daily meteorological datasets for impact studies in Central Europe: The FORESEE v4.0 and the FORESEE-HUN v1.0 JF - CLIMATE SERVICES J2 - CLIM SERV VL - 33 PY - 2024 SN - 2405-8807 DO - 10.1016/j.cliser.2023.100443 UR - https://m2.mtmt.hu/api/publication/34472868 ID - 34472868 N1 - ELTE Eötvös Loránd University, Institute of Geography and Earth Sciences, Department of Geophysics and Space Science, Space Research Group, Budapest H-1117, Pázmány P. st. 1/A, Hungary Czech University of Life Sciences Prague, Faculty of Forestry and Wood Sciences, 165 21 Prague 6, Kamýcká 129, Czech Republic ELTE Eötvös Loránd University, Institute of Geography and Earth Sciences, Department of Meteorology, Budapest H-1117, Pázmány P. st. 1/A, Hungary ELTE Eötvös Loránd University, Excellence Center, Faculty of Science, H-2462 Martonvásár, Brunszvik u. 2., Hungary Agricultural Institute, Centre for Agricultural Research, H-2462 Martonvásár, Brunszvik u. 2, Hungary Croatian Forest Research Institute, Department of Forest Management and Forestry Economics, Jastrebarsko HR-10450, Cvjetno naselje 41, Croatia Cited By :1 Export Date: 28 March 2024 Correspondence Address: Kern, A.; ELTE Eötvös Loránd University, Budapest H-1117, Pázmány P. st. 1/A, Hungary; email: aniko.kern@ttk.elte.hu LA - English DB - MTMT ER - TY - CHAP AU - Fodor, Nándor AU - Barcza, Zoltán AU - Hollós, Roland AU - Bónis, Péter AU - Sugár, Eszter AU - Árendás, Tamás ED - Széles, Adrienn [Ványiné] TI - A mezőgazdaság jövője az AGROMO modellrendszer eredményei alapján T2 - Növény és környezet: a debreceni tartamkísérletek 40 éve PB - Debreceni Egyetem-MÉK Fölhasznosítási, Műszaki és Precíziós Technológiai Intézet CY - Debrecen SN - 9789634905400 PY - 2023 SP - 17 PG - 1 UR - https://m2.mtmt.hu/api/publication/34397478 ID - 34397478 LA - Hungarian DB - MTMT ER - TY - JOUR AU - Bellocchi, G. AU - Barcza, Zoltán AU - Hollós, Roland AU - Acutis, M. AU - Vidnyánszky-Bottyán, Emese AU - Doro, L. AU - Hidy, Dóra AU - Lelleiné Kovács, Eszter AU - Ma, S. AU - Minet, J. AU - Pacskó, Vivien AU - Perego, A. AU - Ruget, F. AU - Seddaiu, G. AU - Wu, L. AU - Sándor, Renáta TI - Sensitivity of simulated soil water content, evapotranspiration, gross primary production and biomass to climate change factors in Euro-Mediterranean grasslands JF - AGRICULTURAL AND FOREST METEOROLOGY J2 - AGR FOREST METEOROL VL - 343 PY - 2023 PG - 22 SN - 0168-1923 DO - 10.1016/j.agrformet.2023.109778 UR - https://m2.mtmt.hu/api/publication/34231901 ID - 34231901 LA - English DB - MTMT ER - TY - JOUR AU - Yan, Yuping AU - Albujeer, Mohammed B. M. Kamel AU - Zoltay, Marcell AU - Gál, Marcell AU - Hollós, Roland AU - Jin, Yaochu AU - Ligeti, Péter AU - Tényi, Ákos TI - Fedlabx: a practical and privacy-preserving framework for federated learning JF - COMPLEX & INTELLIGENT SYSTEMS J2 - COMPLEX INTELL SYST VL - 10 PY - 2023 IS - 1 SP - 677 EP - 690 PG - 14 SN - 2199-4536 DO - 10.1007/s40747-023-01184-3 UR - https://m2.mtmt.hu/api/publication/34087599 ID - 34087599 AB - Federated learning (FL) draws attention in academia and industry due to its privacy-preserving capability in training machine learning models. However, there are still some critical security attacks and vulnerabilities, including gradients leakage and interference attacks. Meanwhile, communication is another bottleneck in basic FL schemes since large-scale FL parameter transmission leads to inefficient communication, latency, and slower learning processes. To overcome these shortcomings, different communication efficiency strategies and privacy-preserving cryptographic techniques have been proposed. However, a single method can only partially resist privacy attacks. This paper presents a practical, privacy-preserving scheme combining cryptographic techniques and communication networking solutions. We implement Kafka for message distribution, the Diffie–Hellman scheme for secure server aggregation, and gradient differential privacy for interference attack prevention. The proposed approach maintains training efficiency while being able to addressing gradients leakage problems and interference attacks. Meanwhile, the implementation of Kafka and Zookeeper provides asynchronous communication and anonymous authenticated computation with role-based access controls. Finally, we prove the privacy-preserving properties of the proposed solution via security analysis and empirically demonstrate its efficiency and practicality. LA - English DB - MTMT ER - TY - JOUR AU - Kalmár, Tímea AU - Kristóf, Erzsébet AU - Hollós, Roland AU - Pieczka, Ildikó AU - Pongrácz, Rita TI - Quantifying uncertainties related to observational datasets used as reference for regional climate model evaluation over complex topography — a case study for the wettest year 2010 in the Carpathian region JF - THEORETICAL AND APPLIED CLIMATOLOGY J2 - THEORET APPL CLIMAT VL - 153 PY - 2023 IS - 1 SP - 807 EP - 828 PG - 22 SN - 0177-798X DO - 10.1007/s00704-023-04491-4 UR - https://m2.mtmt.hu/api/publication/33989177 ID - 33989177 N1 - Department of Meteorology, Institute of Geography and Earth Sciences, Eötvös Loránd University, Pázmány Péter sétány 1/A, Budapest, H-1117, Hungary Doctoral School of Earth Sciences, Eötvös Loránd University, Pázmány P. sétány 1/A, Budapest, H-1117, Hungary Agricultural Institute, Centre for Agricultural Research, Brunszvik u. 2, Budapest, H-2462, Hungary Doctoral School of Environmental Sciences, Eötvös Loránd University, Pázmány Péter sétány 1/A, Budapest, H-1117, Hungary Export Date: 5 July 2023 Correspondence Address: Kalmár, T.; Department of Meteorology, Pázmány Péter sétány 1/A, Hungary; email: timea.kalmar@ttk.elte.hu AB - Gridded observational datasets are often used for the evaluation of regional climate model (RCM) simulations. However, the uncertainty of observations affects the evaluation. This work introduces a novel method to quantify the uncertainties in the observational datasets and how these uncertainties affect the evaluation of RCM simulations. Besides precipitation and temperature, our method uses geographic variables (e.g. elevation, variability of elevation, effect of station), which are considered as uncertainty sources. To assess these uncertainties, a complex analysis based on various statistical tools, e.g. correlation analysis and permutation test, was carried out. Furthermore, we used a special metric, the reduction of error ( RE ) to identify where the RCM shows improvement compared to the lateral boundary conditions (LBCs). We focused on the Carpathian region, because of its unique orographic and climatic conditions. The method is applied to two observational datasets (CarpatClim and E-OBS) and to RegCM simulations for 2010, the wettest year in this region since 1901. LA - English DB - MTMT ER - TY - JOUR AU - Fodor, Nándor AU - Árendás, Tamás AU - Bónis, Péter AU - Sugár, Eszter AU - Hollós, Roland TI - A kukorica jövőbeli sorsa JF - AGROFÓRUM EXTRA J2 - AGROFÓRUM EXTRA VL - 33 PY - 2022 IS - 97 SP - 32 EP - 38 PG - 7 SN - 1788-7380 UR - https://m2.mtmt.hu/api/publication/33331257 ID - 33331257 LA - Hungarian DB - MTMT ER - TY - JOUR AU - Kern, Anikó AU - Barcza, Zoltán AU - Hollós, Roland AU - Birinyi, Edina AU - Marjanović, Hrvoje TI - Critical Climate Periods Explain a Large Fraction of the Observed Variability in Vegetation State JF - REMOTE SENSING J2 - REMOTE SENS-BASEL VL - 14 PY - 2022 IS - 21 SN - 2072-4292 DO - 10.3390/rs14215621 UR - https://m2.mtmt.hu/api/publication/33239320 ID - 33239320 N1 - Funding Agency and Grant Number: Hungarian Scientific Research Fund [OTKA FK128709]; Croatian Science Foundation [HRZZ IP-2019-04-6325]; Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences [BO/00254/20/10]; National Multidisciplinary Laboratory for Climate Change [RRF-2.3.1-21-2022-00014]; Hungarian National Research, Development and Innovation Office [TKP2021NVA.29]; OP RDE [CZ.02.1.01/0.0/0.0/16_019/0000803]; Ministry of Innovation and Technology, financed from the National Research, Development and Innovation Fund Funding text: The research has been supported by the Hungarian Scientific Research Fund (OTKA FK128709); by the Croatian Science Foundation project MODFLUX (HRZZ IP-2019-04-6325); by the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences (grant no. BO/00254/20/10); by the National Multidisciplinary Laboratory for Climate Change, RRF-2.3.1-21-2022-00014 project; by the Hungarian National Research, Development and Innovation Office under Grant number TKP2021NVA.29; and by the grant "Advanced research supporting the forestry and wood-processing sector's adaptation to global change and the 4th industrial revolution", No. CZ.02.1.01/0.0/0.0/16_019/0000803 financed by OP RDE". The research was prepared with the professional support of the Doctoral Student Scholarship Program of the Co-Operative Doctoral Program of The Ministry of Innovation and Technology, financed from the National Research, Development and Innovation Fund. AB - Previous studies have suggested that a major part of the observed variability in vegetation state might be associated with variability in climatic drivers during relatively short periods within the year. Identification of such critical climate periods, when a particular climate variable most likely has a pronounced influence on the vegetation state of a particular ecosystem, becomes increasingly important in the light of climate change. In this study, we present a method to identify critical climate periods for eight different semi-natural ecosystem categories in Hungary, in Central Europe. The analysis was based on the moving-window correlation between MODIS NDVI/LAI and six climate variables with different time lags during the period 2000–2020. Distinct differences between the important climate variables, critical period lengths, and direction (positive or negative correlations) have been found for different ecosystem categories. Multiple linear models for NDVI and LAI were constructed to quantify the multivariate influence of the environmental conditions on the vegetation state during the late summer. For grasslands, the best models for NDVI explained 65–87% variance, while for broad-leaved forests, the highest explained variance for LAI was up to 50%. The proposed method can be easily implemented in other geographical locations and can provide essential insight into the functioning of different ecosystem types. LA - English DB - MTMT ER - TY - JOUR AU - Hollós, Roland AU - Fodor, Nándor AU - Merganičová, K. AU - Hidy, D. AU - Árendás, Tamás AU - Grünwald, T. AU - Barcza, Zoltán TI - Conditional interval reduction method: A possible new direction for the optimization of process based models JF - ENVIRONMENTAL MODELLING & SOFTWARE J2 - ENVIRON MODELL SOFTW VL - 158 PY - 2022 PG - 17 SN - 1364-8152 DO - 10.1016/j.envsoft.2022.105556 UR - https://m2.mtmt.hu/api/publication/33157280 ID - 33157280 N1 - Export Date: 13 December 2022 CODEN: EMSOF Correspondence Address: Fodor, N.; Agricultural Institute, Brunszvik u. 2, Hungary; email: fodor.nandor@atk.hu LA - English DB - MTMT ER - TY - CONF AU - Kern, Anikó AU - Dobor, Laura AU - Hollós, Roland AU - Marjanović, H. AU - Torma, Csaba AU - Kis, Anna AU - Fodor, N. AU - Barcza, Zoltán TI - Climate data for 1951–2100 for scientific, societal and policy purposes in Central Europe: the FORESEE database T2 - Book of abstracts: Natural resources, green technology and sustainable development PY - 2022 SP - 32 UR - https://m2.mtmt.hu/api/publication/33107531 ID - 33107531 LA - English DB - MTMT ER - TY - JOUR AU - Haszpra, László AU - Barcza, Zoltán AU - Pappné Ferenczi, Zita AU - Kern, Anikó AU - Hollós, Roland AU - Kljun, N TI - Real-world wintertime CO, N2O and CO2 emissions of a Central European village JF - ATMOSPHERIC MEASUREMENT TECHNIQUES J2 - ATMOS MEAS TECH VL - 15 PY - 2022 IS - 17 SP - 5019 EP - 5031 PG - 13 SN - 1867-1381 DO - 10.5194/amt-15-5019-2022 UR - https://m2.mtmt.hu/api/publication/32784533 ID - 32784533 AB - Abstract. Although small rural settlements are only minor individual sources of greenhouse gases and air pollution, their high overalloccurrence can significantly contribute to the total emissions of a regionor country. Emissions from a rural lifestyle may be remarkably differentthan those of urban and industrialized regions, but nevertheless they havehardly been studied so far. Here, flux measurements at a tall-tower eddycovariance monitoring site and the footprint model FFP are used to determine the real-world wintertime CO, N2O, and CO2 emissions of a small village in western Hungary. The recorded emission densities, dominantly resulting from residential heating, are 3.5,0.043, and 72 µg m−2 s−1 for CO, N2O, and CO2, respectively. While the measured CO and CO2emissions are comparable to those calculated using the assumed energyconsumption and applying the according emission factors, the nitrous oxideemissions exceed the expected value by a magnitude. This may indicate thatthe nitrous oxide emissions are significantly underestimated in the emissioninventories, and modifications in the methodology of emission calculationsare necessary. Using a three-dimensional forward transport model, we furthershow that, in contrast to the flux measurements, the concentrationmeasurements at the regional background monitoring site are onlyinsignificantly influenced by the emissions of the nearby village. LA - English DB - MTMT ER -