{"id":704,"date":"2023-07-02T18:00:05","date_gmt":"2023-07-02T17:00:05","guid":{"rendered":"https:\/\/research.reading.ac.uk\/s2s-summit2023\/?page_id=704"},"modified":"2023-07-02T18:00:05","modified_gmt":"2023-07-02T17:00:05","slug":"abstract016","status":"publish","type":"page","link":"https:\/\/research.reading.ac.uk\/s2s-summit2023\/programme\/abstract016\/","title":{"rendered":"Abstract 016"},"content":{"rendered":"

[vc_row][vc_column][vc_column_text]Abstract ID:<\/strong> 016<\/p>\n

The value of machine learning to improve seasonal forecasting in mid-latitudes: The example of surface air temperature in central Japan<\/h2>\n

Lead Author:<\/strong> Pascal Oettli
\nCenter for Environmental Remote Sensing (CEReS), Chiba University, Japan
\n<\/span><\/p>\n

Keywords:<\/strong> Seasonal prediction, Hybrid prediction, Machine learning, Statistical modeling, Information flow<\/p>\n

Abstract:<\/strong> Due to the ocean memory effect, sea-surface temperature anomalies are considered as the main source of seasonal predictability for precipitation and surface air temperature anomalies, at different time lag. In this way, conditions in the equatorial Pacific are known to influence the seasonal air temperature anomalies in numerous regions of the world. Thus, predicting sea-surface temperature conditions a few months ahead helps to estimate the sign of the surface air temperature anomalies.
\nIn the central region of Japan around Tokyo (called the Kanto region), considered as the centre of Japan’s politics and economy, a strong link exists between summer and winter temperatures, and the electric power demand. Knowing the sign and the intensity of surface air temperature anomalies in this region is crucial for power demand forecasting a few months ahead, for good planning of the fuel management and logistics.
\nOver the years, the SINTEX-F2 (APL\u2013VAiG\u2013JAMSTEC) seasonal prediction system proved its ability to accurately predict sea-surface temperature anomalies few months in advance. Nevertheless, skills drastically drops when it comes to predict surface air temperature anomalies in the mid-latitudes, particularly because the teleconnection patterns are not captured well by the dynamical system.
\nTaking the Kanto region as a case study, we propose a new type of hybrid prediction system of the surface air temperature which combines dynamical and statistical approaches. In this hybrid system, the statistical component is aimed to restore the teleconnections between sea-surface and surface air temperature anomalies, particularly in mid-latitudes. This component consists of a set of nine (9) different machine learning algorithms, including kernel, tree-based and boosting methods. The dynamical component provides the predictors (i.e., sea-surface temperature anomalies) of the surface air temperature anomalies.
\nResults show that at 2-month lead the hybrid model outperforms both the persistence and the SINTEX-F2 prediction of surface air temperature anomalies in the Kanto region. This is also true when prediction skill is assessed for each calendar month separately. Despite the model\u2019s strong performance, there are also some limitations, such as the limited sample size, making more difficult to calibrate the statistical model and to reliably evaluate its skill.<\/p>\n

Co-authors:
\n<\/strong>Masami Nonaka (Application Laboratory Research Institute for Value-Added-Information Generation (APL VAiG), Japan Agency for Marine-Earth Science and Technology (JAMSTEC))
\nIngo Richter (Application Laboratory Research Institute for Value-Added-Information Generation (APL VAiG), Japan Agency for Marine-Earth Science and Technology (JAMSTEC))
\nHiroyuki Koshiba (JERA Co., Inc.)
\nYosuke Tokiya (JERA Co., Inc.)
\nItsumi Hoshino (JERA Co., Inc.)
\nSwadhin K. Behera (Application Laboratory Research Institute for Value-Added-Information Generation (APL VAiG), Japan Agency for Marine-Earth Science and Technology (JAMSTEC))[\/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: 016 The value of machine learning to improve seasonal forecasting in mid-latitudes: The example of surface air temperature in central Japan Lead Author: Pascal Oettli Center for Environmental…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-704","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"\nAbstract 016 - 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\/abstract016\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Abstract 016 - 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