Comparison and Correlation Among Measurement Results of Observatory, Hellman, and Tipping Bucket Sensors

Maftukhah, Tatik; Wijonarko, Sensus; Rustandi, Dadang

doi:10.14203/instrumentasi.v40i1.52

Cited by 5 publications

(5 citation statements)

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“…However, on hourly time scales (Figure 3), AWS shows a better correlation than MRG. Although the performance evaluation of MRG and AWS operated by BMKG showed very good accuracy [45,95], it is possible for these rain gauges to have errors in operation. AWS uses the automatic tipping-bucket rain gauge.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of GPM IMERG Performance Using Gauge Data over Indonesian Maritime Continent at Different Time Scales

et al. 2022

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Accurate precipitation observations are crucial for water resources management and as inputs for a gamut of hydrometeorological applications. Precipitation data from Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) have recently been widely used to complement traditional rain gauge systems. However, the satellite precipitation data needs to be validated before being widely used in the applications and this is still missing over the Indonesian maritime continent (IMC). We conducted a validation of the IMERG product version 6 for this region. The evaluation was carried out using gauge data in the period from 2016 to 2020 for three types of IMERG: Early (E), Late (L), and Final (F) from annual, monthly, daily and hourly data. In general, the annual and monthly data from IMERG showed a good correlation with the rain gauge, with the mean correlation coefficient (CC) approximately 0.54–0.78 and 0.62–0.79, respectively. About 80% of stations in the IMC area showed a very good correlation between gauge data and IMERG-F estimates (CC = 0.7–0.9). For the daily assessment, the CC value was in the range of 0.39 to 0.44 and about 40% of stations had a correlation of 0.5–0.7. IMERG had a fairly good ability to detect daily rain in which the average probability of detection (POD) for all stations was above 0.8. However, the false alarm ratio (FAR) value is quite high (<0.5). For hourly data, IMERG’s performance was still poor with CC around 0.03–0.28. For all assessments, IMERG generally overestimated rainfall in comparison with rain gauge. The accuracy of the three types of IMERG in IMC was also influenced by season and topography. The highest and lowest CC values were observed for June–July–August and December–January–February, respectively. However, categorical statistics (POD, FAR and critical success index) did not show any clear seasonal variation. The CC value decreased with higher altitude, but with slight difference for each IMERG type. For all assessments conducted, IMERG-F generally showed the best rainfall observations in IMC, but with slightly difference from IMERG-E and IMERG-L. Thus, IMERG-E and IMERG-L data that had a faster latency than IMERG-F show potential to be used in rainfall observations in IMC.

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Section: Discussionmentioning

confidence: 99%

Evaluation of GPM IMERG Performance Using Gauge Data over Indonesian Maritime Continent at Different Time Scales

et al. 2022

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“…In this case, the IUT is a rain gauge especially a tipping bucket sensor. The sensor will not be discussed anymore, but can be obtained for example from , Maftukhah et al (2016Maftukhah et al ( , 2019, Prakosa et al (2016Prakosa et al ( , 2018, Wijonarko ( , 2018, Qiyaman et al (2018aQiyaman et al ( , 2018b).…”

Section: Basic Theorymentioning

confidence: 99%

A Data Processing Program for Web Based Rain Gauge Calibrators

Maftukhah

Wijonarko²

2019

instrum.

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“…Globally, all types of rain gauges can be divided into catching and non-catching instruments (Lanza & Stagi, 2002;Lanza, 2005;Lanza et al, 2007;Lanza & Stagi, 2008;Lanza & Stagi, 2009;Lanza, Vuerich, & Gnecco, 2010;Lanza, Stagnaro, & Cauteruccio, 2018). Although there are some kinds of catching rain gauges such as double decker tipping bucket , the most favorite types are observatory, Hellmann, and tipping bucket (Maftukhah, Wijonarko, & Rustandi, 2016). Even, remote-sensing measurements (satellite, radar, etc) can not substitute ground-based observations (Plummer, Allsopp, & Lopez, 2003).…”

Section: Basic Theorymentioning

confidence: 99%

Web Based Rain Gauge Calibrator

Wijonarko

Maftukhah²,

Rustandi³

et al. 2019

instrum.

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Rain gauges in Indonesia need recalibration at least twice a year, viz. at the end of dry season where the rain gauges will soon work harder and at the end of rainy season where usually there are many garbage on each rain gauge orifice. Based on the direct surveys in the fields and discussions at some rain gauge calibration laboratories in Indonesian western, central, and eastern parts, the calibration officers in the visited offices generally used manual rain gauge calibrators to recalibrate rain gauges on their fields. Therefore, the dependency of calibration results to the human beings is very high. Meanwhile to avoid data gathering discontinuation, rain gauges should be recalibrated in situ. To cope with this problem and other issues related to rain gauge calibrators, a research to develop a web based rain gauge calibrator was carried out. The research result using research and development method showed that this calibrator can work well as an in situ and ex situ rain gauge calibrator.

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Comparison and Correlation Among Measurement Results of Observatory, Hellman, and Tipping Bucket Sensors

Cited by 5 publications

References 0 publications

Evaluation of GPM IMERG Performance Using Gauge Data over Indonesian Maritime Continent at Different Time Scales

Evaluation of GPM IMERG Performance Using Gauge Data over Indonesian Maritime Continent at Different Time Scales

A Data Processing Program for Web Based Rain Gauge Calibrators

Web Based Rain Gauge Calibrator

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