Correcting Radar Rainfall Estimates Based on Ground Elevation Function

Hambali, Roby; Legono, Djoko; Jayadi, Rachmad

doi:10.22146/jcef.49395

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…The obs rain gauges are verified and calibrated regularly every year. Obs rain gauges validate remote sensing-based rainfall measurements such as radar and weather satellites [6]. Table 1 shows the location of the obs rain gauges and the coordinate grid based on the focus of the study area.…”

Section: Methodsmentioning

confidence: 99%

Design of drought early warning system based on standardized precipitation index prediction using hybrid ARIMA-MLP in Banten province

Soekirno,

Ananda,

Wicaksana

et al. 2024

IJ-AI

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Drought Early Warning System (DEWS) is an effort to disseminate early warning information based on climate and hydrology aspects. The DEWS design uses ARIMA, MLP, and hybrid ARIMA-MLP models to predict drought based on SPI for 1, 3, and 6 months. Predictions were made using ERA5 monthly rainfall data from 1981-2022 corrected based on observation data on 9 grids of observation rain gauges in Banten Province. The design of the ARIMA model is determined by selecting the combination of p and q parameters with the lowest AIC value, while the MLP architecture is determined by referring to the study literature and by trial and error testing. ARIMA models and hybrid models are not able to follow actual data fluctuations and have high error values in both SPI1, SPI3, and SPI6, so they are not recommended in this study. The MLP model has the best prediction ability, namely in SPI6 prediction with NSE value reaching >0.5 and RMSE value.

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Section: Methodsmentioning

confidence: 99%

Design of drought early warning system based on standardized precipitation index prediction using hybrid ARIMA-MLP in Banten province

Soekirno,

Ananda,

Wicaksana

et al. 2024

IJ-AI

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“…[0, ∞) 0 (Hambali et al, 2019;Baig et al, 2022) Relative bias (RB) RB = Gupta et al, 1999;Farzana et al, 2019) where X observed rainfall; Y SPPs; and n amount of data. contributing factor to the use of SPPs in hydrological analyses.…”

Section: Data Descriptionmentioning

confidence: 99%

Analysis of Extreme Rainfall in the Mt. Merapi Area

Anita Yuliana,

Joko Sujono,

Karlina

2023

jcef

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The slopes of Mount Merapi (Mt. Merapi) are an area prone to hydrological disasters due to elevation and orography. Hydrological disasters that have the potential to occur include floods, erosion, landslides, and drought which are closely related to extreme rainfall. Spatial and temporal variability of rainfall in mountainous areas requires rainfall data that can represent rainfall events. Therefore, this research aims to obtain the reliability of satellite rainfall data in the extreme rainfall indices. The CHIRPS, GPM-IMERG FINAL (IMERG-F) and GPM-IMERG LATE (IMERG-L) will be used in the reliability analysis of satellite-based rainfall compared to observed rainfall station. To validate satellite rain data, statistical criteria are utilized with parameters such as Correlation Coefficient (R), Root Mean Squared Error (RMSE), and Relative Bias (RB). Satellite-based rainfall estimates have a weak to moderate correlation (0.19 – 0.55), the RMSE value is relatively good (12.18 – 31.35 mm) and the observed bias tends to underestimate the estimated values. The capabilities of the IMERG-F, IMERG-L and CHIRPS satellites as alternative rainfall data in the Mt. Merapi area are quite good where IMERG-L has the best performance in capturing rainfall above 50 mm (R50mm), Consecutive Dry Days (CDD) indices, max 1–day and 5-day precipitation (Rx1day and Rx5day). The potential for extreme rainfall that is most prone to trigger lava floods occurs in the western region of Mt. Merapi at Ngandong Station (Sta. Ngandong). In this region, there is a high occurrence of extreme rainfall events. For instance, there were 501 instances of R50mm with an intensity of 77 mm day-1, Total Precipitation (PRCPTOT) reaches 3385 mm, Rx5day reaches 393 mm, and Consecutive Wet Days (CWD) lasts for 30 days. The results of this analysis can assist in climate understanding and modeling of extreme rainfall relevant to the region and support water resource management and disaster risk mitigation.

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“…This radar rainfall station was still under study. Several studies have used radar rainfall data, such as in [2][3][4][5]. However, the researchers and the other users have not utilized the 3D features of the recorded radar rainfall data extensively.…”

Section: Introductionmentioning

confidence: 99%

Estimating Additional Flood Warning Time Using Raindrops Intensity Data Captured by X-Band Mp Radar

Rahardjo¹,

Syahmi²,

Legono³

2021

GEOMATE

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The current monitoring system of flood warnings on Mt. Merapi slope, Yogyakarta, Indonesia relies on rainfall gages and visual observation. Most flood events occurred with a fast-moving front resulting in a very short available warning time. An X-Band Multi-Parameter Radar installed at the southwest slope of Mt. Merapi measures raindrop intensity high above the ground and provides rainfall information earlier. This paper presents the effort to estimate the additional warning time available by extracting binary radar data, transforming it into 3D grid data, and visualize it into meaningful charts. This study compared the IDW and NN interpolation methods and verified the grid resolution and the limiting distance, r, of the IDW method for obtaining optimal performance. This study also conducted a qualitative review on the sequences of horizontal contour images and the comparison of horizontal and vertical grid resolutions. The estimation of time lag of peak raindrop intensity of different elevations was evaluated qualitatively by overlaying time-series data at four elevations and quantitatively by conducting a cross-correlation analysis of pairs of time series data of +3000 m and +1300 m elevations with horizontal offset for considering wind drift. The result shows that the optimal value of r is 400 m, and the optimal grid resolution is 100x100x100 m. It found that the approximate additional warning time was 6 minutes. The conclusion is that the developed interpolation method is reliable for raindrop analysis. The cross-correlation analysis gives a conservative estimate of the additional warning time.

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Correcting Radar Rainfall Estimates Based on Ground Elevation Function

Cited by 5 publications

References 21 publications

Design of drought early warning system based on standardized precipitation index prediction using hybrid ARIMA-MLP in Banten province

Design of drought early warning system based on standardized precipitation index prediction using hybrid ARIMA-MLP in Banten province

Analysis of Extreme Rainfall in the Mt. Merapi Area

Estimating Additional Flood Warning Time Using Raindrops Intensity Data Captured by X-Band Mp Radar

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