Dian Nur Ratri

This thesis focuses on improving seasonal rainfall forecasts through post-processing
techniques, with a particular emphasis on Java, Indonesia. The primary objective of this
research is to develop and evaluate bias correction methods for seasonal precipitation
forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF)
Seasonal Forecasting System, Version 5 (SEASS). By improving forecast skills for critical
agricultural months, this study aims to provide insights and tools that support better
decision-making and planning.
Chapter one provides the background and introduction, focusing on the importance
of improving precipitation model forecasts with post-processing techniques and the sig-
nificance of seasonal forecasting. This chapter lays the groundwork for the rest of the
thesis.
Chapter two attempts to correct the biases in seasonal precipitation forecasts from
the ECMWF’s SEASS system for Java, Indonesia, using empirical quantile mapping
(EQM). The study demonstrates that bias correction enhances forecast accuracy, particu-
larly during critical agricultural months (July-September), and could support agricultural
planning.
Chapter three continues with the post-processing of seasonal forecasts, comparing a
more advanced statistical method with the traditional EQM approach. It also investigates
the impact of climate factors such as El Ni˜no-Southern Oscillation (ENSO), Indian Dipole
Mode (IOD), Madden-Julian Oscillation (MJO), regional Sea Surface Temperature (SST),
and geographical features on forecast accuracy, evaluating forecasts from 1981 to 2010,
focusing on July to October.
Chapter four emphasizes the importance of seasonal forecasts for hydrological mod-
els, particularly in predicting streamflow. It evaluates the calibration of streamflow fore-
casts with lead times up to four months, using EQM-corrected rainfall data as the pri-
mary input. Various metrics, including Continuous Ranked Probability Score Skill Score
(CRPSS), Brier Skill Score (BSS), Mean Absolute Error (MAE), Root Mean Square Error
(RMSE), and Relative Operating Characteristic Score (ROCS), are used for verification.
This chapter marks a pioneering effort in integrating hydrological models with seasonal
rainfall forecasts in Indonesia.
Chapter five serves as a comprehensive overview of the primary findings and discus-
sions, exploring how the EQM bias correction method can improve the seasonal rainfall
forecasts of the ECMWF model for Java and the potential forecast skill improvements
when incorporating multiple predictors in the statistical postprocessing of SEASS rainfall
forecasts. This chapter also evaluates the significance of these bias correction methods
on seasonal rainfall and streamflow forecasts. Additionally, it outlines future research
directions to enhance seasonal forecasting in Indonesia.

i

Contents

Contents iii

List of Figures v
List of Tables viii

Chapter 1: General Intoduction 1
1.1 The importance of weather and climate models’ performance for long-range
forecasting ..... ............................... 3
1.2 Improving seasonal forecasts through post-processing ............ 4
1.3 Seasonal forecasting in Indonesia: precipitation and streamflow ...... 6
1.4 Objective and research questions ....................... 7
1.5 Study area .................. .................. 8
1.6 Outline ................ ..................... 2: A Comparative Verification of Raw and Bias-Corrected ECMWF
Seasonal Ensemble Precipitation Reforecasts in Java (Indonesia) 13
2.1 Introduction ........................ ........... 15
2.2 Empirical quantile mapping .. ........................ 17
2.3 Verification methods ............ .................. 18
2.4 Data .. ................................ ..... 21
2.5 Result .............. ........................ 22
2.6 Discussion and conclusions ................... ........ 3: Calibration of ECMWF Seasonal Ensemble Precipitation
Reforecasts in Java (Indonesia) Using Bias-Corrected Precipitation
and Climate Indices 35
3.1 Introduction ........................ ........... 37
3.2 Data .. ................................ ..... 39
3.3 Methods .......................... ........... 41
3.4 Results ................................. ..... 44
3.5 Discussion and conclusions ................... ........ 4: A Calibration of ECMWF SEASS Based Streamflow Forecast
in Seasonal Hydrological Forecasting for Citarum River Basin, West
Java, Indonesia 53
4.1 Introduction ........................ ........... 55
4.2 Models and data ..................... ........... 57
4.3 Verification methods ............ .................. 59
4.4 Results and discussion ........................ ..... 61
4.5 Conclusion and future works .... ..................... 5: General Discussion 71
5.1 Effectiveness of EQM in bias correction for ECMWF SEASS’ skill .... 73
5.2 Impact of incorporating multiple predictors in statistical post-processing
on SEASS precipitation forecast skill .................... . 74

iii

5.3 Significance of EQM bias correction for streamflow forecasts ........ 75
5.4 Future perspectives and recommendations for further research ....... 76