{"id":960,"date":"2023-07-02T23:41:55","date_gmt":"2023-07-02T22:41:55","guid":{"rendered":"https:\/\/research.reading.ac.uk\/s2s-summit2023\/?page_id=960"},"modified":"2023-07-02T23:41:55","modified_gmt":"2023-07-02T22:41:55","slug":"abstract132","status":"publish","type":"page","link":"https:\/\/research.reading.ac.uk\/s2s-summit2023\/programme\/abstract132\/","title":{"rendered":"Abstract 132"},"content":{"rendered":"

[vc_row][vc_column][vc_column_text]Abstract ID:<\/strong> 132<\/p>\n

Improving Sub-seasonal to Seasonal Model Performance in The Tropics Using a Machine Learning\u00a0Approach<\/h2>\n

Lead Author:<\/strong> Nurdeka Hidayanto
\nIndonesian Agency for Meteorological, Climatological and Geophysics, Indonesia
\n<\/span><\/p>\n

Keywords:<\/strong> S2S, Precipitation, Machine Learning, Tropics, GBM<\/p>\n

Abstract:<\/strong> Subseasonal-to-seasonal (S2S) prediction play an important role as a bridge between conventional short-range weather forecasts and long-range seasonal forecasts. The S2S prediction has a time range which corresponds to beyond 2 weeks but less than a season, and practically is needed by specific sectors such as agriculture, water resources, and weather early warning for tropical cyclone. However, the studies investigating the performance of S2S prediction in Indonesia are still rare in Indonesia. . This study aims to investigate the performance of S2S model in forecasting rainfall in Indonesia and improve its performance by post-processing using Machine Learning (ML) techniques. Ten ML methods were utilized to improve the performance S2S model in forecasting rainfall in Indonesia. The S2S output data from the European Centre for Medium Range Weather Forecasts (ECMWF) from 1998 to 2017 were used along with 280 points observation data from meteorological stations and rain gauge in Indonesia. The results indicate that in general the ML model has the ability to improve the performance of the S2S model by more than 70% compared to the only S2S models as they were. The Gradient Boosting Machine (GBM) model got the best performance compared to the others.<\/p>\n

Co-authors:
\n<\/strong>Agita Vivi Wijayanti (Indonesian Agency for Meteorological, Climatological and Geophysics)
\nDonaldi Sukma Permana (Indonesian Agency for Meteorological, Climatological and Geophysics)
\nMuhammad Rifki Taufik (Indonesian Agency for Meteorological, Climatological and Geophysics)
\nKharisma Aprilina (Indonesian Agency for Meteorological, Climatological and Geophysics)
\nUmmu Ma’rufah (Indonesian Agency for Meteorological, Climatological and Geophysics)
\nJose Rizal (Indonesian Agency for Meteorological, Climatological and Geophysics)[\/vc_column_text][vc_separator][\/vc_column][\/vc_row][vc_row][vc_column width=”1\/6″][vc_single_image image=”344″ img_size=”full” onclick=”custom_link” link=”http:\/\/s2sprediction.net\/”][\/vc_column][vc_column width=”1\/6″][vc_single_image image=”345″ img_size=”full” onclick=”custom_link” link=”https:\/\/public.wmo.int\/en”][\/vc_column][vc_column width=”1\/6″][vc_single_image image=”346″ img_size=”full” onclick=”custom_link” link=”https:\/\/community.wmo.int\/activity-areas\/wwrp”][\/vc_column][vc_column width=”1\/6″][vc_single_image image=”347″ img_size=”full” onclick=”custom_link” link=”https:\/\/www.wcrp-climate.org\/”][\/vc_column][vc_column width=”1\/6″][vc_single_image image=”348″ img_size=”full”][\/vc_column][vc_column width=”1\/6″][vc_single_image image=”349″ img_size=”full” onclick=”custom_link” link=”https:\/\/www.reading.ac.uk\/”][\/vc_column][\/vc_row]<\/p>\n","protected":false},"excerpt":{"rendered":"

[vc_row][vc_column][vc_column_text]Abstract ID: 132 Improving Sub-seasonal to Seasonal Model Performance in The Tropics Using a Machine Learning\u00a0Approach Lead Author: Nurdeka Hidayanto Indonesian Agency for Meteorological, Climatological and Geophysics, Indonesia Keywords: S2S,…Read More ><\/a><\/p>\n","protected":false},"author":145,"featured_media":0,"parent":528,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"__cvm_playback_settings":[],"__cvm_video_id":"","footnotes":""},"coauthors":[13],"class_list":["post-960","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"\nAbstract 132 - WWRP\/WCRP S2S Summit 2023<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/research.reading.ac.uk\/s2s-summit2023\/programme\/abstract132\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Abstract 132 - 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