[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128513-en":3,"doc-seo-128513-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128513,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine Learning Snowfall Retrieval Algorithms for Satellite Precipitation Estimates - BSc Thesis","Remote sensing of snowfall is a significant challenge in the satellite era. This BSc thesis investigates how Machine Learning, especially Deep Learning, can estimate the precipitation phase—i.e., the fraction of frozen precipitation reaching the surface—using NASA’s Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (GPM-IMERG). Training uses hourly high-resolution numerical model outputs together with in-situ observations for late-2020 and 2021. Results from multiple case studies show relatively high accuracy compared with traditional approaches, supporting ML-based nowcasting and the creation of a long-term IMERG snowfall archive dataset from near real-time data.","NATIONAL AND KAPODISTRIAN UNIVERSITY OF ATHENS  \nSCHOOL OF SCIENCES  \nDEPARTMENT OF INFORMATICS AND TELECOMMUNICATIONS  \nBSc THESIS  \nMachine Learning Snowfall Retrieval Algorithms for Satellite Precipitation Estimates  \nIoannis Th. Dravilas  \nSupervisors: Manolis Koubarakis, Professor  \nStavros Dafis, Research Associate, IERSD/NOA  \nGeorgios Kyros, Research Associate, IERSD/NOA Konstantinos Lagouvardos, Research Director, IERSD/NOA  \nATHENS  \nJULY 2023  \nΕΘΝΙΚΟ ΚΑΙ ΚΑΠΟΔΙΣΤΡΙΑΚΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΑΘΗΝΩΝ  \nΣΧΟΛΗ ΘΕΤΙΚΩΝ ΕΠΙΣΤΗΜΩΝ  \nΤΜΗΜΑ ΠΛΗΡΟΦΟΡΙΚΗΣ ΚΑΙ ΤΗΛΕΠΙΚΟΙΝΩΝΙΩΝ  \nΠΤΥΧΙΑΚΗ ΕΡΓΑΣΙΑ  \nΑλγόριθμοι Μηχανικής Μάθησης για την ΑνίχνευσηΧιονόπτωσης σε Δορυφορικές Εκτιμήσεις Υετού  \nΙωάννης Θ. Δραβίλας  \nΕπιβλέποντες: Μανόλης Κουμπαράκης, ΚαθηγητήςΣταύρος Ντάφης, Επιστημονικός Συνεργάτης, ΙΕΠΒΑ/ΕΑΑΓεώργιος Κύρος, Επιστημονικός Συνεργάτης, ΙΕΠΒΑ/ΕΑΑΚωνσταντίνος Λαγουβάρδος, Διευθυντής Ερευνών, ΙΕΠΒΑ/ΕΑΑ  \nΑΘΗΝΑ  \nΙΟΥΛΙΟΣ 2023  \nBSc THESIS  \nMachine Learning Snowfall Retrieval Algorithms for Satellite Precipitation Estimates  \nIoannis Th. Dravilas  \nS. N.: 1115201900053  \nSUPERVISORS: Manolis Koubarakis, Professor  \nStavros Dafis, Research Associate, IERSD/NOA  \nGeorgios Kyros, Research Associate, IERSD/NOA  \nKonstantinos Lagouvardos, Research Director, IERSD/NOA  \nΠΤΥΧΙΑΚΗ ΕΡΓΑΣΙΑ  \nΑλγόριθμοι Μηχανικής Μάθησης για την Ανίχνευση Χιονόπτωσης σε Δορυφορικές  \nΕκτιμήσεις Υετού  \nΙωάννης Θ. ΔραβίλαςΑ . Μ .: 1115201900053  \nΕΠΙΒΛΕΠΟΝΤΕΣ: Μανόλης Κουμπαράκης, Καθηγητής  \nΣταύρος Ντάφης, Επιστημονικός Συνεργάτης, ΙΕΠΒΑ/ΕΑΑΓεώργιος Κύρος, Επιστημονικός Συνεργάτης, ΙΕΠΒΑ/ΕΑΑΚωνσταντίνος Λαγουβάρδος, Διευθυντής Ερευνών, ΙΕΠΒΑ/ΕΑΑ  \nABSTRACT  \nRemote 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 along-term archive dataset of IMERG-based snowfall, utilizing conventional near real-time data.  \nSUBJECT AREA: Machine Learning  \nKEYWORDS: Machine Learning, Deep Learning, Snowfall, Satellite Precipitation, Precipitation Phase  \nΠΕΡΙΛΗΨΗ  \nΗ αναγνώριση της χιονόπτωσης με τηλεπισκόπηση έχει αποδειχθεί μια δύσκολη πρό -κληση ήδη από τα πρώτα στάδια χρήσης δορυφόρων στην ανθρώπινη ιστορία . Στο παρελ -θόν έχουν εφαρμοστεί ποικίλες τεχνικές σε δορυφορικά δεδομένα, με σκοπό την εκτίμησητου ποσοστού των στέρεων κατακρημνισμάτων που φτάνουν στην επιφάνεια του εδάφους .Η παρούσα εργασία στοχεύει στη διερεύνηση της αποτελεσματικότητας ποικίλων αλγόριθ -μων Μηχανικής Μάθησης, καθώς και Νευρωνικών Δικτύων, για την εκτίμηση της φάσηςτου υετού στα δεδομένα του αλγόριθμου Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM-IMERG) της NASA. Προς αυτήν την κατεύθυνση, επι -λέχθηκε μια φάση εκπαίδευσης κατά την οποία χρησιμοποιούνται τα αποτελέσματα ενόςαριθμητικού μοντέλου πρόγνωσης καιρού σε ωριαίο χρονικό βήμα, μαζί με επιτόπιες πα -ρατηρήσεις εδάφους από μετεωρολογικούς σταθμούς για τις τελευταίες εβδομάδες του 2020 και το 2021 . Τα αποτελέσματα δείχνουν πως τα μοντέλα Μηχανικής Μάθησης, καιειδικότερα τα Νευρωνικά Δίκτυα, μπορούν να εκτιμήσουν τη φάση του υετού με σχετικάυψηλή ακρίβεια, σε σύγκριση με αντίστοιχες παραδοσιακές μεθόδο","cbCaipctl69h2dx9","https://ap.wps.com/l/cbCaipctl69h2dx9","pdf",6212145,1,49,"English","en",105,"# INTRODUCTION\n## Precipitation\n## Precipitation Phase\n## Machine Learning\n## Machine Learning Models Used in Meteorology\n## Linear Regression\n## Logistic Regression\n## Naive Bayes\n## Decision Trees\n## Random Forest\n## Gradient Boosted Trees\n## XGBoost\n# BACKGROUND AND RELATED WORK\n## Conventional Methods","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"The thesis addresses the difficulty of detecting snowfall using remote sensing from satellite data and estimating the precipitation phase at the surface.\"},{\"question\":\"Which data and period are used for training?\",\"answer\":\"Training uses hourly high-resolution numerical model outputs and in-situ observations for late 2020 and 2021.\"},{\"question\":\"How do the results compare with traditional methods?\",\"answer\":\"Across several case studies, Machine Learning and Deep Learning models estimate precipitation phase with relatively high accuracy compared to traditional approaches.\"}]","Machine Learning Snowfall Retrieval Algorithms for Satellite Precipitation Estimates - 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