Unit:
Department of Informatics and TelecommunicationsΠληροφορική
Supervisors info:
Μανόλης Κουμπαράκης, Καθηγητής, Τμήμα Πληροφορικής και Τηλεπικοινωνιών, Εθνικό και Καποδιστριακό Πανεπιστήμιο Αθηνών
Original Title:
Machine Learning Snowfall Retrieval Algorithms for Satellite Precipitation Estimates
Translated title:
Machine Learning Snowfall Retrieval Algorithms for Satellite Precipitation Estimates
Summary:
Remote sensing of snowfall has been proved to be a significant challenge since the start of the satellite era. Several techniques have been applied to satellite data, in order to estimate the fraction of frozen precipitation that reaches the surface. This thesis aims at investigating the efficacy of different Machine Learning (ML), and especially Deep Learning (DL) algorithms, in estimating the precipitation phase of NASA's Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM-IMERG). To achieve that, a training phase with hourly high-resolution numerical model outputs and in-situ observational data is chosen for the period of late-2020 and 2021. Results show that ML and DL models can estimate precipitation phase with relatively high accuracy, when compared to traditional methods, based on several case studies. The findings suggest that ML models offer a promising approach for advancing the nowcasting of snowfall and building a long-term archive dataset of IMERG-based snowfall, utilizing conventional near real-time data.
Main subject category:
Technology - Computer science
Keywords:
Machine Learning, Deep Learning, Snowfall, Satellite Precipitation, Precipitation Phase
