Analysis of rainfall patterns in Kalimantan using fast fourier transform (FFT) and empirical orthogonal function (EOF)

Nurdiati, Sri; Khatizah, Elis; Najib, Mohamad Khoirun; Hidayah, Rand

doi:10.1088/1742-6596/1796/1/012053

Cited by 8 publications

(7 citation statements)

References 4 publications

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“…This is likely due to the complexity of the dynamics of the atmosphere over the region, which is located over the equator. In general, the precipitation in East Kalimantan has a typical monsoonal-type pattern [6]. This monsoonal type is characterized by the driest months from June to August and the wettest months from December to February [6].…”

Section: Introductionmentioning

confidence: 99%

“…In general, the precipitation in East Kalimantan has a typical monsoonal-type pattern [6]. This monsoonal type is characterized by the driest months from June to August and the wettest months from December to February [6]. In addition, the heavy rainfall over the central Maritime Continent is also often associated with an organized convective system associated with Boreal Summer Intraseasonal Oscillation (BSISO) [7,8].…”

Section: Introductionmentioning

confidence: 99%

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Moisture Origin and Transport for Extreme Precipitation over Indonesia's New Capital City, Nusantara in August 2021

Purwaningsih

Lubis

Hermawan

et al. 2022

Preprint

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Nusantara, the Indonesia's New Capital City, experienced a rare extreme rainfall event on 27 to 28 August 2021. This unusual heavy rainfall occurred during the dry season and caused severe flooding and landslides. To better understand the underlying mechanisms for such extreme precipitation events, we investigated the moisture sources and transport processes using the Lagrangian model HYSPLIT. Our findings revealed that moisture was mostly transported to Nusantara along three major routes, namely from Borneo Island (BRN, 53.73%), the Banda Sea, and its Surroundings (BSS, 32.03%), and Sulawesi Island (SUL, 9.05%). Overall, BRN and SUL acted as the main sources of terrestrial moisture, while the BSS was the main oceanic moisture origin having a lower contribution than that of its terrestrial counterpart. The terrestrial moisture transport from BRN was mainly driven by the large-scale high vortex flow, while the moisture transport from the SUL was driven by the circulations induced by boreal summer intraseasonal oscillation (BSISO) and low-frequency variability associated with La Niña. The near-surface oceanic moisture transport from BSS is mainly associated with prevailing winds due to the Australian Monsoon system. These insights into moisture sources and pathways can potentially improve skill in predictions of summer precipitation extremes in Indonesia's New Capital City, Nusantara, and benefit natural resource managers in the region.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Moisture Origin and Transport for Extreme Precipitation over Indonesia's New Capital City, Nusantara in August 2021

Purwaningsih

Lubis

Hermawan

et al. 2022

Preprint

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“…Time series. Time series data X(t) is presented as a model consisting of the following functions [6,7]:…”

Section: Methodsmentioning

confidence: 99%

“…In his research, the FFT method has been used to analyze the normality of rainfall data in the West Sumatra region [6], while other used the FFT method to analyze the periodicity of the monthly rainfall cycle in the southern area of the Jatiluhur reservoir, Subang Regency, West Java [1]. Meanwhile, the FFT method to analyze rainfall patterns in the island of Kalimantan [7].…”

Section: E Correlation Of Daily Rainfallmentioning

confidence: 99%

Correlation of BMKG with TRMM for daily and monthly rainfall series in Banten region

Zakaria

2022

JESR

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Daily and monthly rainfall data series are necessary data for planning purposes in Civil Engineering and other fields. Incomplete rainfall data often occurs, so that rainfall data must be estimated based on rainfall data from several other nearby locations. The addition of rainfall data can lead to inaccurate planning. Rainfall data used for planning in the civil engineering sector is usually taken from the BMKG station. This data is taken from the rainfall station above the ground. Besides, that can also produce rainfall data from TRMM. Can take rainfall data from TRMM at all locations according to a coordinate of location. This rainfall data denotes an average rainfall taken from the satellite approximately 250 meters above the ground surface. An equation will be obtained by comparing the daily and monthly rainfall data from the two data sources. Based on TRMM rainfall, we can use the equation to estimate ground rainfall in a location. In this study, daily rainfall, monthly rainfall, the spectrum of daily and monthly rainfall data from BMKG are compared with rainfall and the spectrum of daily and monthly rainfall data from TRMM. The analysis results show that the monthly rainfall data from TRMM and BMKG correlate better than daily rainfall data.

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“…The rainfall patterns in Kalimatan are classified as equatorial and monsoonal. Equatorial rainfall patterns are found in majority parts of East, West, and North Kalimantan, according to fast Fourier transform and empirical orthogonal function analysis [13]. In the meantime, the majority of South and Central Kalimantan experiences ❒ ISSN: 2252-8938 monsoonal rainfall patterns.…”

mentioning

confidence: 99%

Sensitivity and feature importance of climate factors and evaluation of different machine learning models for predicting fire hotspots in Kalimantan, Indonesia

Nurdiati,

Nugrahani,

Bukhari

et al. 2024

IJ-AI

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<p>Hotspots as indicators of forest fires capable of quickly monitoring large areas are often predicted using various machine learning methods. However, there is still little research that analyzes the sensitivity and feature importance of each predictor that forms a machine learning prediction model. This study evaluates and compares several machine learning methods to predict hotspots in Kalimantan. Using the most accurate machine learning model, each climate factor used as a predictor is analyzed for its sensitivity and feature importance. Some of the machine learning methods used include random forest, gradient boosting, Bayesian regression, and artificial neural networks. Meanwhile, several measures of sensitivity and feature importance used are variance-based, density-based, and distribution-based sensitivity indices, as well as permutation and Shapley feature importance. Evaluation of the ML model concluded that the Bayesian linear regression model outperformed other ML models, based on RMSE and explained variance score. Meanwhile, tree-based models, such as random forest and gradient boosting, are indicative of overfit. Based on the results of sensitivity analysis and feature importance, the number of dry days is the most important feature for the Bayesian linear regression model in predicting the number of hotspots in Kalimantan.</p>

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Analysis of rainfall patterns in Kalimantan using fast fourier transform (FFT) and empirical orthogonal function (EOF)

Cited by 8 publications

References 4 publications

Moisture Origin and Transport for Extreme Precipitation over Indonesia's New Capital City, Nusantara in August 2021

Moisture Origin and Transport for Extreme Precipitation over Indonesia's New Capital City, Nusantara in August 2021

Correlation of BMKG with TRMM for daily and monthly rainfall series in Banten region

Sensitivity and feature importance of climate factors and evaluation of different machine learning models for predicting fire hotspots in Kalimantan, Indonesia

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