Journal papers
Alejandro Cervantes, Álvaro Reviriego, Manuel Soler. Machine learning for local nowcasting of lightning from a network of weather and GNSS stations. Pending submission.
Sandy Chkeir, Aikaterini Anesiadou, Riccardo Biondi, Alejandro Cervantes, Álvaro Reviriego, Manuel Soler. Severe weather nowcasting using recurrent neural networks: Single and Multi-stations approaches. Pending submission.
Mascitelli, A. et al., Multi-sensors data analysis of intense weather events – Como Lake case study, Water, in final revision.
Sandy Chkeir, Aikaterini Anesiadou, Alessandra Mascitelli, Riccardo Biondi.
Nowcasting extreme rain and extreme wind speed with machine learning techniques applied to different input datasets, Atmospheric Research, 2022, 106548, ISSN 0169-8095, https://doi.org/10.1016/j.atmosres.2022.106548
Mascitelli A, Petracca M, Puca S, Realini E, Gatti A, Biondi R, Anesiadou A, Brocca L, Vulpiani G, Torcasio RC, Federico S, Oriente A, Dietrich S. Multi-Sensor Data Analysis of an Intense Weather Event: The July 2021 Lake Como Case Study. Water. 2022; 14(23):3916. https://doi.org/10.3390/w14233916
Theys, N., Lerot, C., Brenot, H., van Gent, J., De Smedt, I., Clarisse, L., Burton, M., Varnam, M., and Van Roozendael, M.: Improved retrieval of SO2 plume height from TROPOMI using an iterative Covariance-Based Retrieval Algorithm, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2022-148, © Author(s) 2022. This work is distributed under the Creative Commons Attribution 4.0 License.
Dietmüller, S. Matthes, S., Dahlmann, K., Yamashita, H., Soler, M., Simorgh, A., Linke, F., Lührs, B., Mendiguchia Meuser, M. , Weder, C., Yin, F., Castino, F., Grewe, V. (2022): A python library for computing individual and merged non-CO2 algorithmic climate change functions: CLIMaCCF V1.0, Geoscientific Model Development (GMD). Under Review. In pre-print. https://gmd.copernicus.org/preprints/gmd-2022-203/
Chkeir, S., Anesiadou, A., Mascitelli, A., Biondi, R., Nowcasting Extreme Weather with Machine Learning Techniques Applied to Different Input Datasets, Atmospheric Research, in final review, Available at SSRN: https://ssrn.com/abstract=4144317 or http://dx.doi.org/10.2139/ssrn.4144317
Hugues Brenot et al. EUNADICS-AV early warning system dedicated to supporting aviation in the case of a crisis from natural airborne hazards and radionuclide clouds. Nat. Hazards Earth Syst. Sci., 21, 3367–3405, 2021 https://doi.org/10.5194/nhess-21-3367-2021 © Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License.
Proceedings
R. Biondi, S. Chkeir, A. Anesiadou, A. Mascitelli, E. Realini, L. Nisi, C. Cimarelli, Multivariate Multi-Step Convection Nowcasting with Deep Neural Networks: The Novara Case Study, IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium (pp. 6598-6601) Proceedings, IEEE, https://doi.org/10.1109/IGARSS46834.2022.9883665
Conference Papers
Hugues Brenot, Nicolas Theys, Erwin de Donder, Jeroen Van Gent, Michel Van Roozendael, Manuel Soler, Javier Garcia-Heras, Alejandro Cervantes, Abolfazl Simorgh, Virginia Villaplana, Sigrun Matthes, Simone Dietmüller, Volker Grewe, Riccardo Biondi, Sandy Chkeir, Katerina Anesiadou, Tim Winter, Adam Durant, Tanja Bolić, Francesco Urdih, Lieven Clarisse, Pierre de Buyl, Nicolas Clerbaux and Klaus Sievers. Early warning system of natural hazards and decrease of climate impact from aviation. SESAR Innovation Days’22. Budapest 5-8 Dec. 2022.
Chkeir, S., Anesiadou, A., and Biondi, R.: Spatio-temporal nowcasting of local severe weather events with deep neural networks, 17th Plinius Conference on Mediterranean Risks, Frascati, Rome, Italy, 18–21 Oct 2022, Plinius17-12, https://doi.org/10.5194/egusphere-plinius17-12, 2022.
Anesiadou, A., Chkeir, S., and Biondi, R.: Characterization and nowcasting of severe weather events over Milano Malpensa, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9357, https://doi.org/10.5194/egusphere-egu22-9357, 2022.
Chkeir, S., Anesiadou, A., and Biondi, R.: Multi-station Multivariate Multi-step Convection Nowcasting with Deep Neural Networks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8719, https://doi.org/10.5194/egusphere-egu22-8719, 2022.
Brenot, H., Theys, N., de Donder, E., Clarisse, L., de Buyl, P., Clerbaux, N., Dietmüller, S., Matthes, S., Grewe, V., Chkeir, S., Mascitell, A., Anesiadou, A., Biondi, R., Mahorčič, I., Bolić, T., Bhattacharya, R., Winter, T., Durant, A., Van Roozendael, M., and Soler, M.: Decrease of anthropogenic emission from aviation and detection of natural hazards with potential application in geosciences using satellite sensors, ground-based networks and model forecasts in the context of the SACS/ALARM early warning system, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8948, https://doi.org/10.5194/egusphere-egu22-8948, 2022.
Sievers, K., Brenot, H., Theys, N., and Kessinger, C.: Investigation on flight level contamination using volcanic SO2 plume and cloud top height satellite products, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10309, https://doi.org/10.5194/egusphere-egu21-10309, 2021. © Author(s) 2022. This work is distributed under the Creative Commons Attribution 4.0 License.
Soler M., Brenot H., Biondi R., Bannister D., Grewe V., Bolic T. and García-Heras J. ALARM Project -multi-hAzard monitoring andearLy wARning systeM. EGU General Assembly 2021. EGU21-7664 https://doi.org/10.5194/egusphere-egu21-7664, © Author(s) 2022. This work is distributed under the Creative Commons Attribution 4.0 License.