{"id":6438,"date":"2026-04-01T08:47:10","date_gmt":"2026-04-01T08:47:10","guid":{"rendered":"https:\/\/www.proefschriftmaken.nl\/portfolio\/dian-nur-ratri\/"},"modified":"2026-04-01T08:47:17","modified_gmt":"2026-04-01T08:47:17","slug":"dian-nur-ratri","status":"publish","type":"us_portfolio","link":"https:\/\/www.proefschriftmaken.nl\/en\/portfolio\/dian-nur-ratri\/","title":{"rendered":"Dian Nur Ratri"},"content":{"rendered":"","protected":false},"excerpt":{"rendered":"","protected":false},"author":8,"featured_media":6441,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"us_portfolio_category":[45],"class_list":["post-6438","us_portfolio","type-us_portfolio","status-publish","has-post-thumbnail","hentry","us_portfolio_category-new-template"],"acf":{"naam_van_het_proefschift":"Improving Seasonal Precipitation and Streamflow Forecasts for Java, Indonesia","samenvatting":"Er is geen Nederlandse samenvatting beschikbaar. De Engelse samenvatting vind je <a href=\"https:\/\/www.proefschriftmaken.nl\/en\/portfolio\/dian-nur-ratri\/\">hier<\/a>.","summary":"This thesis focuses on improving seasonal rainfall forecasts through post-processing\ntechniques, with a particular emphasis on Java, Indonesia. The primary objective of this\nresearch is to develop and evaluate bias correction methods for seasonal precipitation\nforecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF)\nSeasonal Forecasting System, Version 5 (SEASS). By improving forecast skills for critical\nagricultural months, this study aims to provide insights and tools that support better\ndecision-making and planning.\nChapter one provides the background and introduction, focusing on the importance\nof improving precipitation model forecasts with post-processing techniques and the sig-\nnificance of seasonal forecasting. This chapter lays the groundwork for the rest of the\nthesis.\nChapter two attempts to correct the biases in seasonal precipitation forecasts from\nthe ECMWF\u2019s SEASS system for Java, Indonesia, using empirical quantile mapping\n(EQM). The study demonstrates that bias correction enhances forecast accuracy, particu-\nlarly during critical agricultural months (July-September), and could support agricultural\nplanning.\nChapter three continues with the post-processing of seasonal forecasts, comparing a\nmore advanced statistical method with the traditional EQM approach. It also investigates\nthe impact of climate factors such as El Ni\u02dcno-Southern Oscillation (ENSO), Indian Dipole\nMode (IOD), Madden-Julian Oscillation (MJO), regional Sea Surface Temperature (SST),\nand geographical features on forecast accuracy, evaluating forecasts from 1981 to 2010,\nfocusing on July to October.\nChapter four emphasizes the importance of seasonal forecasts for hydrological mod-\nels, particularly in predicting streamflow. It evaluates the calibration of streamflow fore-\ncasts with lead times up to four months, using EQM-corrected rainfall data as the pri-\nmary input. Various metrics, including Continuous Ranked Probability Score Skill Score\n(CRPSS), Brier Skill Score (BSS), Mean Absolute Error (MAE), Root Mean Square Error\n(RMSE), and Relative Operating Characteristic Score (ROCS), are used for verification.\nThis chapter marks a pioneering effort in integrating hydrological models with seasonal\nrainfall forecasts in Indonesia.\nChapter five serves as a comprehensive overview of the primary findings and discus-\nsions, exploring how the EQM bias correction method can improve the seasonal rainfall\nforecasts of the ECMWF model for Java and the potential forecast skill improvements\nwhen incorporating multiple predictors in the statistical postprocessing of SEASS rainfall\nforecasts. This chapter also evaluates the significance of these bias correction methods\non seasonal rainfall and streamflow forecasts. Additionally, it outlines future research\ndirections to enhance seasonal forecasting in Indonesia.\n\ni\n\nContents\n\nContents iii\n\nList of Figures v\nList of Tables viii\n\nChapter 1: General Intoduction 1\n1.1 The importance of weather and climate models\u2019 performance for long-range\nforecasting ..... ............................... 3\n1.2 Improving seasonal forecasts through post-processing ............ 4\n1.3 Seasonal forecasting in Indonesia: precipitation and streamflow ...... 6\n1.4 Objective and research questions ....................... 7\n1.5 Study area .................. .................. 8\n1.6 Outline ................ ..................... 2: A Comparative Verification of Raw and Bias-Corrected ECMWF\nSeasonal Ensemble Precipitation Reforecasts in Java (Indonesia) 13\n2.1 Introduction ........................ ........... 15\n2.2 Empirical quantile mapping .. ........................ 17\n2.3 Verification methods ............ .................. 18\n2.4 Data .. ................................ ..... 21\n2.5 Result .............. ........................ 22\n2.6 Discussion and conclusions ................... ........ 3: Calibration of ECMWF Seasonal Ensemble Precipitation\nReforecasts in Java (Indonesia) Using Bias-Corrected Precipitation\nand Climate Indices 35\n3.1 Introduction ........................ ........... 37\n3.2 Data .. ................................ ..... 39\n3.3 Methods .......................... ........... 41\n3.4 Results ................................. ..... 44\n3.5 Discussion and conclusions ................... ........ 4: A Calibration of ECMWF SEASS Based Streamflow Forecast\nin Seasonal Hydrological Forecasting for Citarum River Basin, West\nJava, Indonesia 53\n4.1 Introduction ........................ ........... 55\n4.2 Models and data ..................... ........... 57\n4.3 Verification methods ............ .................. 59\n4.4 Results and discussion ........................ ..... 61\n4.5 Conclusion and future works .... ..................... 5: General Discussion 71\n5.1 Effectiveness of EQM in bias correction for ECMWF SEASS\u2019 skill .... 73\n5.2 Impact of incorporating multiple predictors in statistical post-processing\non SEASS precipitation forecast skill .................... . 74\n\niii\n\n5.3 Significance of EQM bias correction for streamflow forecasts ........ 75\n5.4 Future perspectives and recommendations for further research ....... 76","auteur":"Dian Nur Ratri","auteur_slug":"dian-nur-ratri","publicatiedatum":"16 april 2025","taal":"EN","url_flipbook":"https:\/\/ebook.proefschriftmaken.nl\/ebook\/diannurratri?iframe=true","url_download_pdf":"","url_epub":"","ordernummer":"FTP-202604010845","isbn":"978-94-6510-569-7","doi_nummer":"","naam_universiteit":"Wageningen University","afbeeldingen":6442,"naam_student:":"","binnenwerk":"","universiteit":"Wageningen University","cover":"","afwerking":"","cover_afwerking":"","design":""},"_links":{"self":[{"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/us_portfolio\/6438","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/us_portfolio"}],"about":[{"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/types\/us_portfolio"}],"author":[{"embeddable":true,"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/users\/8"}],"replies":[{"embeddable":true,"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/comments?post=6438"}],"version-history":[{"count":1,"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/us_portfolio\/6438\/revisions"}],"predecessor-version":[{"id":6439,"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/us_portfolio\/6438\/revisions\/6439"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/media\/6441"}],"wp:attachment":[{"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/media?parent=6438"}],"wp:term":[{"taxonomy":"us_portfolio_category","embeddable":true,"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/us_portfolio_category?post=6438"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}