Estimation of Rainfall Rate Cumulative Distribution in Indonesia Using Global Satellite Mapping of Precipitation Data

Oktaviani, Rini; Marzuki, Marzuki

doi:10.18502/keg.v1i2.4450

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…The weather condition in Indonesia varies in time scales (Ramadhan et al, 2022b), such as seasonal variations (Marzuki et al 2016b;Oktaviani, 2019;Ramadhan et al, 2022a), intra-seasonal (Marzuki, et al, 2016a), and diurnal variations (Marzuki et al, 2016b;Marzuki et al, 2021Marzuki et al, , 2022. Although this data can cover seasonal and intra-seasonal variations, this variation is not discussed in this study because the data used is only one year.…”

Section: 𝜏 =mentioning

confidence: 98%

Influence of Weather Conditions on COVID-19 Case in Several Regions of Indonesia

Deswanti

Marzuki

Muharsyah

et al. 2022

jppipa, pendidikan ipa, fisika, biologi, kimia

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This study investigates the relationship between COVID-19 and weather conditions in six regions in Indonesia, namely South Jakarta, East Jakarta, North Jakarta, Jember Regency, Semarang City, and West Pasaman Regency. The study uses the daily number of positive cases of COVID-19 and weather data. Weather parameters used in this study are the average, maximum, and minimum temperature, rainfall, and relative humidity. Weather data were obtained from surface observation of Agency for Meteorology, Climatology and Geophysics (BMKG) and reanalysis product (ERA-5) from European Centre for Medium-Range Weather Forecasts (ECMWF). The effect of weather conditions on the number of COVID-19 cases was evaluated using the Spearman and Kendall correlation. It was found that the number of positive cases of COVID-19 had a relationship with weather parameters. A negative correlation was found between the temperature and the number of positive COVID-19 cases. Thus, the number of positive COVID-19 cases increases when the temperature decreases. On the other hand, rainfall and relative humidity positively correlate to the number of positive cases of COVID-19, which means that when the rainfall and relative humidity increase, the number of positive cases of COVID-19 increases. The relationship between weather conditions and the number of COVID-19 cases is more apparent in areas with a higher population density, such as Jakarta. The results of this study can be valuable information in handling COVID-19 cases.

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Section: 𝜏 =mentioning

confidence: 98%

Influence of Weather Conditions on COVID-19 Case in Several Regions of Indonesia

Deswanti

Marzuki

Muharsyah

et al. 2022

jppipa, pendidikan ipa, fisika, biologi, kimia

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“…Recently, a combination of TRMM 3B42 and 3B43 had been tested to model one-minute rainfall in Kototabang, West Sumatra, Indonesia [11]. Data with very high spatial resolution (0.1) such as Global Satellite Mapping of Precipitation (GSMaP) and Global Precipitation Measurement (GPM) were used and the results were validated with rain gauge data at Kototabang [12,13]. Similar to previous studies based on TRMM data, modified ITU-R P.837-6 using GSMaP and GPM obtained a lower one-minute rainfall rate at Kototabang than the default ITU-R P.837-6.…”

Section: Introductionmentioning

confidence: 99%

International Telecommunication Union-Radiocommunication Sector P.837-6 and P.837-7 performance to estimate Indonesian rainfall

et al. 2020

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This work evaluated the performance of International Telecommunication Union-Radiocommunication Sector (ITU-R) P.837-6 and P.837-7 models (Annex 1) to estimate one-minute rainfall rates in Indonesia. In addition to the default ITU-R P.837-6, the input of ITU-R P.837-6 is also modified using data which has better spatial resolution, i.e. a combination of Tropical Rainfall Measuring Mission (TRMM) 3A25 and 3B43 (ITU-R+3A25+3B43), 3B42 and 3B43 (ITU-R+3B42+3B43), Global Satellite Mapping of Precipitation (ITU-R+GSMaP), and Global Precipitation Measurement (ITU-R+GPM). Among the five test sites, the default ITU-R P.837-6 and ITU-R+3A25+3B43 could predict one-minute rainfall rates at two locations accurately. The ITU-R P.837-7 exhibited a marginally better performance for sites that had a high percentage of very heavy rain, particularly at large (1%) and small (0.001%) percentages of time exceeded. The spatial distribution of rainfall rate produced by ITU-R P.837-7 and ITU-R+3A25+3B43 was closer to the pattern demonstrated by recent satellite precipitation measurements.

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“…Everywhere in the world, the rate of rainfall varies greatly from place to place. The measurement and accuracy of empirical data enhance the development of rain attenuation for a specific location, which is also reliant on the understanding and accuracy of the rainfall rate [2,[12][13][14][15][16][17]. Due to the inherent enormous fluctuation of rainfall at a particular point, the cumulative distribution of the measured rain rate depends on the rain gauge's sampling time; and rain gauges with short integration time are recommended for radio-climatic observations because they account for these fluctuations on the communication links [18].…”

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confidence: 99%

Derivation of Regression Coefficients and Conversion Factors for 1-Minute Rain Rate Statistics in A Tropical Environment

Igwe¹

2023

JAITA

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An important characteristic of rainfall levels at a particular place is the statistical distribution of rainfall rate. In this paper, 1-minute, 5-minute and 30-minute integration time rainfall data were obtained from three different weather stations in Physics Department, Federal University of Technology, Minna, North Central Nigeria. The aim is to derive regression coefficients and conversion factors for predicting rainfall rate of 1-minute integration time from rainfall rates of other integration times. This was achieved by employing Segal, Flavin and Watson rain rate models. The results obtained revealed that there is a power law relationship between 1-minuterain rate (R1) and the equiprobable 5-minute rain rate (R5); and between 1-minute rain rate (R1) and the equiprobable 30-minute rain rate (R30). Also,it was observed that the values of regression coefficients a and b, and conversion factors Ce and CR derived in Minna were different from values derived at other locations in and outside Nigeria when comparisons were made. These discrepancies are attributed to the differences in climatic conditions between the regions among other factors. Therefore, these findings have further corroborated claims by earlier researchers that different regression coefficients and conversion factors are needed for different locations for 1-minute rain rate conversion.

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Estimation of Rainfall Rate Cumulative Distribution in Indonesia Using Global Satellite Mapping of Precipitation Data

Abstract: .

Cited by 5 publications

References 4 publications

Influence of Weather Conditions on COVID-19 Case in Several Regions of Indonesia

Influence of Weather Conditions on COVID-19 Case in Several Regions of Indonesia

International Telecommunication Union-Radiocommunication Sector P.837-6 and P.837-7 performance to estimate Indonesian rainfall

Derivation of Regression Coefficients and Conversion Factors for 1-Minute Rain Rate Statistics in A Tropical Environment

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