Estimation of Thunderstorm Activity in Tawau, Sabah Using GPS Data

Suparta, Wayan; Zulkeple, Siti Katrina; Putro, Wahyu Sasongko

doi:10.1166/asl.2017.8391

Cited by 7 publications

(5 citation statements)

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“…As previous studies revealed, a precipitation event highly correlates with the intensity of regional PWV variation [36][37][38]. Singh et al [39] analyzed monthly and seasonal PWV variations over the Arabian Sea and the Bay of Bengal and found a close relationship between PWV variation and monsoon; then Singh et al [40] claimed that PWV can be used as an indicator in monsoon forecasting.…”

Section: Introductionmentioning

confidence: 99%

Development of an Improved Model for Prediction of Short-Term Heavy Precipitation Based on GNSS-Derived PWV

Wang

et al. 2020

Remote Sensing

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Nowadays, the Global Navigation Satellite Systems (GNSS) have become an effective atmospheric observing technique to remotely sense precipitable water vapor (PWV) mainly due to their high spatiotemporal resolutions. In this study, from an investigation for the relationship between GNSS-derived PWV (GNSS-PWV) and heavy precipitation, it was found that from several hours before heavy precipitation, PWV was probably to start with a noticeable increase followed by a steep drop. Based on this finding, a new model including five predictors for heavy precipitation prediction is proposed. Compared with the existing 3-factor model that uses three predictors derived from the ascending trend of PWV time series (i.e., PWV value, PWV increment and rate of the PWV increment), the new model also includes two new predictors derived from the descending trend: PWV decrement and rate of PWV decrement. The use of the two new predictors for reducing the number of misdiagnosis predictions is proposed for the first time. The optimal set of monthly thresholds for the new five-predictor model in each summer month were determined based on hourly GNSS-PWV time series and precipitation records at three co-located GNSS/weather stations during the 8-year period 2010–2017 in the Hong Kong region. The new model was tested using hourly GNSS-PWV and precipitation records obtained at the above three co-located stations during the summer months in 2018 and 2019. Results showed that 189 of the 198 heavy precipitation events were correctly predicted with a lead time of 5.15 h, and the probability of detection reached 95.5%. Compared with the 3-factor method, the new model reduced the FAR score by 32.9%. The improvements made by the new model have great significance for early detection and predictions of heavy precipitation in near real-time.

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Section: Introductionmentioning

confidence: 99%

Development of an Improved Model for Prediction of Short-Term Heavy Precipitation Based on GNSS-Derived PWV

Wang

et al. 2020

Remote Sensing

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“…where, the parameter yi, xi, n stand for estimation result, true value, and total number of data, respectively. In another study, MAE is used to calculate absolute error from carbon capture model [18] and thunderstorm activity [19] between estimation true value and estimation result. Furthermore, we also calculate the percentage between real output and estimation result, we use VAF by (2) [20], [21].…”

Section: Discussionmentioning

confidence: 99%

The preliminary study of carbon x-change rakyat using blockchain application

Putro,

Rahmi,

Asditama

et al. 2024

IJECE

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Today’s air pollution is detrimental to the environment, particularly in Indonesia. Carbon dioxide (CO2) and nitrogen oxide (NOx) are present in the atmosphere due to air pollution. Many individuals employ reforestation to lessen the influence of CO2 and NOx gases on the atmosphere. However, in the digitalized era, lowering carbon emissions may also be accomplished through a carbon credit exchange. Thus, in this study we investigate the performance of the carbon x-change rakyat (CXR) based on blockchain platform utilizing the stress test approach. We provided four scenarios with 10,000 to 100,000 transactions evaluated on the CXR blockchain system i.e., transfer, insert, remove, and update. The outcome demonstrates CXR’s effectiveness with 100% success and 0% failure rate based on testing and statistical computations calculation. The mean absolute error (MAE), variance accounted for (VAF), and percent error (PE) are obtained with values ranging from 0.38% to 4.67%. In this study, the transaction per-second (TPS) is used to calculate include error request (IER) and exclude error request (EER) values around 312 to 746 milliseconds (ms). In addition, the TPS of CXR based on blockchain platform is a capability to create and trace database carbon certificate ownership (nonfinancial activity). It means CXR based on the blockchain platform has a fast response to process carbon certificate ownership for transactions across local and international countries in the world.

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“…Following up, Yao et al [15] introduced a new algorithm to improve the success rate of rainfall prediction reported by Benevides et al [14], using data from the sub-tropical stations (of China). GPS-PWV values are also widely used in many remote sensing applications like the analysis of severe weather conditions [16] such as storms, thunderstorms [17], flash-floods [18], heavy rainfall events monitoring [13], rainfall forecasting [7,14,15,19,20], cloud microphysics and dynamics studies [21,22].…”

Section: Introductionmentioning

confidence: 99%

GPS-PWV Based Improved Long-Term Rainfall Prediction Algorithm for Tropical Regions

2019

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Global positioning system (GPS) satellite delay is extensively used in deriving the precipitable water vapor (PWV) with high spatio–temporal resolution. One of the recent applications of GPS derived PWV values are to predict rainfall events. In the literature, there are rainfall prediction algorithms based on GPS-PWV values. Most of the algorithms are developed using data from temperate and sub-tropical regions. Mostly these algorithms use maximum PWV rate, maximum PWV variation and monthly PWV values as a criterion to predict the rain events. This paper examines these algorithms using data from the tropical stations and proposes the use of maximum PWV value for better prediction. When maximum PWV value and maximum rate of increment criteria are implemented on the data from the tropical stations, the false alarm ( F A ) rate is reduced by almost 17% as compared to the results from the literature. There is a significant reduction in F A rates while maintaining the true detection ( T D ) rates as high as that of the literature. A study done on the varying historical length of data and lead time values shows that almost 80% of the rainfall can be predicted with a false alarm of 26 . 4 % for a historical data length of 2 hours and a lead time of 45 min to 1 hour.

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Estimation of Thunderstorm Activity in Tawau, Sabah Using GPS Data

Cited by 7 publications

References 0 publications

Development of an Improved Model for Prediction of Short-Term Heavy Precipitation Based on GNSS-Derived PWV

Development of an Improved Model for Prediction of Short-Term Heavy Precipitation Based on GNSS-Derived PWV

The preliminary study of carbon x-change rakyat using blockchain application

GPS-PWV Based Improved Long-Term Rainfall Prediction Algorithm for Tropical Regions

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