DSpace Collection:https://repository.cyi.ac.cy/handle/123456789/8602026-04-22T18:44:17Z2026-04-22T18:44:17ZGlobal high-resolution ultrafine particle number concentrations through data fusion with machine learning CreatorsGeorgiades, PantelisPozzer, AndreaDovrolis, ConstantineLelieveld, JosChristoudias, TheodorosNicolaou, Mihalishttps://repository.cyi.ac.cy/handle/CyI/25772026-01-15T12:20:39Z2025-02-10T00:00:00ZTitle: Global high-resolution ultrafine particle number concentrations through data fusion with machine learning Creators
Authors: Georgiades, Pantelis; Pozzer, Andrea; Dovrolis, Constantine; Lelieveld, Jos; Christoudias, Theodoros; Nicolaou, Mihalis2025-02-10T00:00:00ZEffect of planetary boundary layer evolution on new particle formation events over CyprusDeot, NehaKanawade, VijayPapetta, AlkistisPikridas, MichaelMarenco, FrancoSciare, JeanJokinen, Tuijahttps://repository.cyi.ac.cy/handle/CyI/24432025-02-21T07:34:27Z2024-10-22T00:00:00ZTitle: Effect of planetary boundary layer evolution on new particle formation events over Cyprus
Authors: Deot, Neha; Kanawade, Vijay; Papetta, Alkistis; Pikridas, Michael; Marenco, Franco; Sciare, Jean; Jokinen, Tuija
Description: The data set is related to the article:
Effect of planetary boundary layer evolution on new particle formation events over Cyprus.
Neha Deot1, Vijay P. Kanawade1,2, Alkistis Papetta1, Rima Baalbaki3,1, Michael Pikridas1, Franco Marenco1, Markku Kulmala3, Jean Sciare1, K. Lehtipalo3,4, Tuija Jokinen12024-10-22T00:00:00ZTemperature Humidity Index GDDP-NEX-CMIP6 ML projectionsGeorgiades, Pantelishttps://repository.cyi.ac.cy/handle/CyI/24422025-02-21T07:46:33Z2024-07-21T00:00:00ZTitle: Temperature Humidity Index GDDP-NEX-CMIP6 ML projections
Authors: Georgiades, Pantelis
Description: The experiment conducted aimed to enhance the temporal resolution of climate projections for agricultural applications by using machine learning to downscale daily NEX-GDDP-CMIP6 climate data (https://doi.org/10.7917/OFSG3345) to hourly Temperature Humidity Index (THI) values. The THI is a critical metric for assessing heat stress in dairy cattle, which is a significant concern under changing climatic conditions. We utilized the Extreme Gradient Boost (XGBoost Chen et al. 2016) algorithm, chosen for its efficiency and capability to handle large datasets, to train models using historical hourly data from the ERA5 reanalysis dataset (Hersbach et al. 2020). The trained models were then applied to generate hourly THI projections from 2020 to 2100 across 12 climate models under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5). The focus was exclusively on land areas, with a spatial grid resolution of 0.25 degrees, ensuring the relevance and applicability of the data for agricultural purposes. The result is a comprehensive, high-resolution dataset that provides detailed insights into the future impacts of heat stress on dairy cattle, facilitating better planning and mitigation strategies in the agricultural sector.2024-07-21T00:00:00ZPollution mask for the continuous corrected particle number concentration data in 1 min resolution, measured in the Swiss aerosol container during MOSAiC 2019/2020 [dataset]Jokinen, Tuijahttps://repository.cyi.ac.cy/handle/CyI/22882024-11-14T13:57:51Z2022-01-01T00:00:00ZTitle: Pollution mask for the continuous corrected particle number concentration data in 1 min resolution, measured in the Swiss aerosol container during MOSAiC 2019/2020 [dataset]
Authors: Jokinen, Tuija
Description: Pollution mask for the continuous corrected particle number concentration data in 1 min resolution, measured in the Swiss aerosol container during MOSAiC 2019/20202022-01-01T00:00:00Z