Assessment of Satellite Precipitation Data Sets for High Variability and Rapid Evolution of Typhoon Precipitation Events in the Philippines

Aryastana, Putu; Liu, Chian‐Yi; Jou, Ben Jong‐Dao; Cayanan, Esperanza O.; Punay, Jason Pajimola; Chen, Ying‐Nong

doi:10.1029/2022ea002382

Cited by 9 publications

(9 citation statements)

References 107 publications

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“…Similar results for FAR, MAR, POD, and BIAS can be found in Supporting Information (Figure S7). BIAS shows that CMFD overestimates at light intensity while underestimates at high intensity, which is consistent with previous studies (Aryastana et al., 2022; Tang et al., 2021; Tian et al., 2018). And the detection capability of calibrated‐CMFD is also improved especially for the large precipitation magnitude.…”

Section: Discussionsupporting

confidence: 92%

Performance Evaluation and Improvement of CMFD's Precipitation Products Over Shanghai City, China

Lei

Zhou

Zhuang

et al. 2023

Earth and Space Science

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Precipitation products with high resolution for urban areas remain an important and challenging issue. The main purposes of this study are to evaluate the accuracy of the China Meteorological Forcing Data Set (CMFD) in the Shanghai megacity and to improve the accuracy of the CMFD precipitation. With the rain gauge observations for the 2013–2018 period, we evaluate the error and detection ability of CMFD at the sub‐daily, daily, and monthly scales, and examine the spatial variation of CMFD performance over the region. The Geographic Differential Analysis (GDA) method is used to calibrate the CMFD precipitation data set. The results show that the performance of CMFD precipitation varies from sub‐daily to monthly time scales with better accuracy at the monthly scale in the study region. The CMFD can capture the spatial variability over the region with less error in the north and outliers in the center. The GDA‐based calibration framework can significantly improve the capability of CMFD products, especially at the sub‐daily and daily scales. The calibrated CMFD can provide reliable precipitation estimates and capture more extreme precipitation events. The improved CMFD products can provide a key basis for applications related to hydrological and meteorological hazard monitoring and assessment in the Shanghai area. The overall framework is scalable and could be readily generalized to other regions, and the calibrated product can be used in the research of urban natural disaster risks and other studies.

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Section: Discussionsupporting

confidence: 92%

Performance Evaluation and Improvement of CMFD's Precipitation Products Over Shanghai City, China

Lei

Zhou

Zhuang

et al. 2023

Earth and Space Science

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“…From the AUC analysis, it can be determined that IMERG is the best rain product among GSMaP and PERSIANN that can be used especially in Badung Regency. The best performance of the IMERG product in predicting rainfall events preceding landslide occurrences may be attributed to its high temporal resolution, which enables it to more effectively capture regional variations in sub-daily precipitation [17], [35], [36]. The current findings suggest that the IMERG dataset holds a high potential for establishing rainfall thresholds.…”

Section: Threshold Performance Analysismentioning

confidence: 70%

Performance of high-resolution satellite rainfall datasets in developing rainfall-duration threshold for landslide incidents over Badung Regency

Aryastana,

Dewi,

Wahyuni

2024

IOP Conf. Ser.: Earth Environ. Sci.

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Satellite-based rainfall datasets provide high-resolution worldwide rainfall information, which has potential used in identifying rainfall conditions that trigger landslides. Landslides can be forecasted by rainfall thresholds which is used as an early warning system. The threshold model used needs to be validated to know the accuracy in forecasting landslide occurrences provoked by rainfall events. The objective of the current study is to evaluate the ability of three high-resolution satellite-based rainfall datasets (IMERG, GSMaP, and PERSIANN) to develop a rainfall thresholds model for landslide occurrences in Badung Regency. The recent study used cumulative rainfall events (1, 3, 5, 7, 10, 15, 21, and 30 days) leading up to the incidents of landslides. The determination of rainfall threshold values used the statistical distribution namely: first (Q1), second (Q2), and third quartile (Q3). Validation of rainfall threshold results was conducted utilizing receiver operating characteristic (ROC) curves and the area under curve (AUC). The analysis results show that the first quartile (Q1) exhibited the finest accuracy and gives a good estimation of landslide occurrence. Moreover, among all cumulative rainfall events, the 15-day cumulative rainfall demonstrates the highest AUC value (> 0.75), implying a greater likelihood of triggering landslide events over Badung Regency.

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“…In addition, previous researchers also observed that the use of hourly rainfall data has better capabilities compared to daily rainfall data, which causes a decrease in general predictability [25]. However, the assessment of the PERSIANN dataset revealed its inferiority when compared to IMERG and GSMaP in identifying intense rainfall [40].…”

Section: Discussionmentioning

confidence: 95%

A study of rainfall thresholds for landslides in Badung Regency using satellite-derived rainfall grid datasets

Aryastana,

Dewi,

Wahyuni

2024

IJAAS

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Integrating field rainfall data with satellite data improves data accuracy and overcomes rainfall data limitations for rain thresholds. Integration can involve field rainfall data, satellite rainfall data, or a different satellite dataset. Merging these rainfall data sources provides more spatial coverage of satellite data. To determine how well rainfall thresholds predict rainfall-triggered landslides, the threshold model must be validated. This study will evaluate satellite rainfall data before and after integration in developing a rainfall threshold model for landslide prediction in Badung Regency. To do so, the study used a cumulative rainfall threshold over 3, 7, 15, and 30 days and two rainfall satellite products (integrated merged multi-satellite retrievals (IMERG) and precipitation estimation from remotely sensed information using artificial neural networks (PERSIANN)). Median, first, and third quartiles were used to set thresholds. The area under the curve (AUC) was calculated to validate rainfall threshold outcomes using receiver operating characteristic (ROC) curves. Analysis showed that integrating satellite rainfall data into the rainfall threshold model for landslide prediction yields better results than other methods. An AUC value of 0.903 (90.3%) for the 30-day cumulative rainfall thresholds supports this claim. This model could be a good input for a landslide early warning system in Badung Regency.

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Assessment of Satellite Precipitation Data Sets for High Variability and Rapid Evolution of Typhoon Precipitation Events in the Philippines

Cited by 9 publications

References 107 publications

Performance Evaluation and Improvement of CMFD's Precipitation Products Over Shanghai City, China

Performance Evaluation and Improvement of CMFD's Precipitation Products Over Shanghai City, China

Performance of high-resolution satellite rainfall datasets in developing rainfall-duration threshold for landslide incidents over Badung Regency

A study of rainfall thresholds for landslides in Badung Regency using satellite-derived rainfall grid datasets

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