L. (Lőrinc) Mészáros
- Mészáros, L., & El Serafy, G. (2018). Setting up a water quality ensemble forecast for coastal ecosystems: a case study of the southern North Sea. Journal of Hydroinformatics, jh2018027. doi.org/10.2166/hydro.2018.027
- Lőrinc Mészáros, Frank van der Meulen, Geurt Jongbloed, and Ghada El Serafy (2018). Use of multiway Partial Least Squares Regression (N-PLS) as model emulator to quantify climate change induced uncertainty in future marine chlorophyll-a concentrations. EGU General Assembly 2018. (Oral presentation)
- Lorinc Meszaros and Ghada El Serafy (2017). Use of ensemble prediction technique to estimate the inherent uncertainty in the simulated chlorophyll-a concentration in coastal ecosystems. EGU General Assembly 2017. (Poster)
In 2014 Lőrinc Mészáros graduated from the Budapest University of Technology and Economics, Budapest, Hungary, Faculty of Civil Engineering, with distinction, specialised in infrastructural engineering. From 2014 he followed a Master of Science program in Hydroinformatics and Water Management in several consortium member countries such as France, United Kingdom, Germany, and the Netherlands. He conducted his Master of Science research at Deltares, Delft, The Netherlands with the title “Ensemble forecasting system for Chlorophyll-a concentration prediction in the southern North Sea”. In 2016 he obtained the Erasmus Mundus Joint Degree of Master of Science with distinction.
Currently, Lőrinc Mészáros is a PhD candidate at Deltares and the Statistics group of the Delft Institute of Applied Mathematics (DIAM) at the Delft University of Technology, Delft, The Netherlands. His PhD research is mainly dedicated to statistical quantification of climate change induced uncertainty in future coastal ecosystem state and ecosystem services. His previous publications covered the topics of “Use of ensemble prediction technique to estimate the inherent uncertainty in the simulated chlorophyll-a concentration in coastal ecosystems” and “Setting up a water quality ensemble forecast for coastal ecosystems: A case study of the southern North Sea”.
GREEN (2017 – present)
Designation: ECHO/SUB/2016/740172/PREV18, Grant No. 740172
Main project features: To improve the resilience of society, both structural and non-structural measures and green infrastructure will be needed. In particular, a greater deployment of nature-based solutions and green infrastructures (GIs) is being increasingly advocated by European institutions as a part of flexible, effective and efficient, and no-regret measures for disaster risk reduction and adaptation to climate. Despite this recognized potential grey solutions have often prevailed over green solutions; primarily because grey infrastructure is often perceived to be more effective, efficient and easier to implement. GREEN addresses these shortcomings and provides the necessary innovation in methods, tools, and solutions to appropriately promote the role of Gl for DRR, climate change adaptation (CCA) and sustainable.
Activities performed: research related to data science for ecosystem modelling and Green Infrastructure, integrated monitoring and assessment methods for Green Infrastructures.
ODYSSEA (2017 – present)
Designation: H2020-MSCA-RISE-2015, Grant No. 691053
Main project features: The ODYSSEA project develops and operates a cost-effective platform that fully integrates networks of observing and forecasting systems across the Mediterranean basin, addressing both the open sea and the coastal zone. The developed platform will collect its data from the many databases maintained by agencies, public authorities, research institutions and universities of Mediterranean EU and non-EU countries, integrating existing earth observation facilities and networks in the Mediterranean Sea building on key initiatives such as Copernicus, GEOSS, GOOS, EMODNet, ESFRI, Lifewatch, Med-OBIS, GBIF, AquaMaps, Marine IBA e-atlas, MAPAMED and others with marine and maritime links.Activities performed : research related to marine environmental quality and ecosystem health in the Mediterranean, integrated monitoring and modelling of marine water quality.
2017 - Present: Contract PhD in statistics, Delft University of Technology & Deltares, Delft, The Netherlands
2014 - 2016: Msc in Hydroinformatics and Water Management, Erasmus Mundus Joint Degree (France, United Kingdom, Germany)
2009 - 2014: Bsc in Civil Engineering, Budapest University of Technology and Economics, Budapest, Hungary
Applied statistics, Uncertainty estimation, Stochastic modelling, Ecosystem modelling