PREDICTION OF AUSTRALIAN SUMMER MONSOON (ASM) MONSOON AND THE BEGINNING OF RAINFALL (BRS) IN JAVA ISLAND BY USING CLIMATE FORECAST SYSTEM (CFS) VERSION 2 OUTPUT

Monsoon is an annual cycle that distinguishes the condition of dry and wet atmosphere. In Indonesia, there are two kind of global monsoon such as Asia Summer Monsoon (ASM) and Australia Summer Monsoon (AuSM) which is causing wet and dry season. Predicting the beginning of wet seasons and onset m...

Full description

Saved in:
Bibliographic Details
Main Author: Rafi Al Hariri Nst, Muhammad
Format: Final Project
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/69907
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:69907
spelling id-itb.:699072022-12-19T10:45:23ZPREDICTION OF AUSTRALIAN SUMMER MONSOON (ASM) MONSOON AND THE BEGINNING OF RAINFALL (BRS) IN JAVA ISLAND BY USING CLIMATE FORECAST SYSTEM (CFS) VERSION 2 OUTPUT Rafi Al Hariri Nst, Muhammad Indonesia Final Project Early monsoon season, summer monsoon Australia, CFSv2, Correction, Raw, accuracy, downscaling INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/69907 Monsoon is an annual cycle that distinguishes the condition of dry and wet atmosphere. In Indonesia, there are two kind of global monsoon such as Asia Summer Monsoon (ASM) and Australia Summer Monsoon (AuSM) which is causing wet and dry season. Predicting the beginning of wet seasons and onset monsun monsoon is important due to its affect to lots of aspect of life such as Agriculture. Java Island, as an area which is consistently influenced by monsoon, has been being the economic center of Indonesia. These are the reasons why predicting onset monsun monsoon and the beginning of rain seasons is important in Java island. As a seasonal forecast model, Climate Forecast System (CFS) has been coupled between its atmosphere, ocean, and contingental distribution. However, CFS’s output relatifly coarse need to be downscaled, to improve its output and reduce its bias. By using Spatial Disaggregation Bias-Correction, the bias of rainfall has been reduced proved by its positive Brier Skill Score (BSS) relative with CFS raw data. Based on the beginning of rain season definition, the results indicate, there is slightly difference between the CFS-corr and CFS-raw outputs. As it is shown by its Brier Score values, which is indicating that CFS-corr and CFS-raw have lowaccuracy for predicting the beginning of rain seasons. However, for defining the onset of monsoon, CFS has a fairly good accuracy based on its BS value that is close to 0. Thus, by evaluating the prediction of the onset monsoon and the beginning of rain season relative with the TRMM data output, CFS can be used operationally to predict the delay of onset monsoonand the beginning of rainfall with BMKG old criteria. text
institution Institut Teknologi Bandung
building Institut Teknologi Bandung Library
continent Asia
country Indonesia
Indonesia
content_provider Institut Teknologi Bandung
collection Digital ITB
language Indonesia
description Monsoon is an annual cycle that distinguishes the condition of dry and wet atmosphere. In Indonesia, there are two kind of global monsoon such as Asia Summer Monsoon (ASM) and Australia Summer Monsoon (AuSM) which is causing wet and dry season. Predicting the beginning of wet seasons and onset monsun monsoon is important due to its affect to lots of aspect of life such as Agriculture. Java Island, as an area which is consistently influenced by monsoon, has been being the economic center of Indonesia. These are the reasons why predicting onset monsun monsoon and the beginning of rain seasons is important in Java island. As a seasonal forecast model, Climate Forecast System (CFS) has been coupled between its atmosphere, ocean, and contingental distribution. However, CFS’s output relatifly coarse need to be downscaled, to improve its output and reduce its bias. By using Spatial Disaggregation Bias-Correction, the bias of rainfall has been reduced proved by its positive Brier Skill Score (BSS) relative with CFS raw data. Based on the beginning of rain season definition, the results indicate, there is slightly difference between the CFS-corr and CFS-raw outputs. As it is shown by its Brier Score values, which is indicating that CFS-corr and CFS-raw have lowaccuracy for predicting the beginning of rain seasons. However, for defining the onset of monsoon, CFS has a fairly good accuracy based on its BS value that is close to 0. Thus, by evaluating the prediction of the onset monsoon and the beginning of rain season relative with the TRMM data output, CFS can be used operationally to predict the delay of onset monsoonand the beginning of rainfall with BMKG old criteria.
format Final Project
author Rafi Al Hariri Nst, Muhammad
spellingShingle Rafi Al Hariri Nst, Muhammad
PREDICTION OF AUSTRALIAN SUMMER MONSOON (ASM) MONSOON AND THE BEGINNING OF RAINFALL (BRS) IN JAVA ISLAND BY USING CLIMATE FORECAST SYSTEM (CFS) VERSION 2 OUTPUT
author_facet Rafi Al Hariri Nst, Muhammad
author_sort Rafi Al Hariri Nst, Muhammad
title PREDICTION OF AUSTRALIAN SUMMER MONSOON (ASM) MONSOON AND THE BEGINNING OF RAINFALL (BRS) IN JAVA ISLAND BY USING CLIMATE FORECAST SYSTEM (CFS) VERSION 2 OUTPUT
title_short PREDICTION OF AUSTRALIAN SUMMER MONSOON (ASM) MONSOON AND THE BEGINNING OF RAINFALL (BRS) IN JAVA ISLAND BY USING CLIMATE FORECAST SYSTEM (CFS) VERSION 2 OUTPUT
title_full PREDICTION OF AUSTRALIAN SUMMER MONSOON (ASM) MONSOON AND THE BEGINNING OF RAINFALL (BRS) IN JAVA ISLAND BY USING CLIMATE FORECAST SYSTEM (CFS) VERSION 2 OUTPUT
title_fullStr PREDICTION OF AUSTRALIAN SUMMER MONSOON (ASM) MONSOON AND THE BEGINNING OF RAINFALL (BRS) IN JAVA ISLAND BY USING CLIMATE FORECAST SYSTEM (CFS) VERSION 2 OUTPUT
title_full_unstemmed PREDICTION OF AUSTRALIAN SUMMER MONSOON (ASM) MONSOON AND THE BEGINNING OF RAINFALL (BRS) IN JAVA ISLAND BY USING CLIMATE FORECAST SYSTEM (CFS) VERSION 2 OUTPUT
title_sort prediction of australian summer monsoon (asm) monsoon and the beginning of rainfall (brs) in java island by using climate forecast system (cfs) version 2 output
url https://digilib.itb.ac.id/gdl/view/69907
_version_ 1822006158456520704