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A Comparison of Rainfall Estimation Using Himawari-8 Satellite Data in Different Indonesian Topographies

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A Comparison of Rainfall Estimation Using Himawari-8 Satellite Data in Different Indonesian Topographies International Journal of Remote Sensing and Earth Science Vol. 17 No. 2 December 2020: 189-200 A COMPARISON OF RAINFALL ESTIMATION USING HIMAWARI-8 SATELLITE DATA IN DIFFERENT INDONESIAN TOPOGRAPHIES Nadine Ayasha1* 1Indonesian Agency of Meteorology Climatology and Geophysics *e-mail: [email protected] Received: 08 October 2020; Revised: 27 November 2020; Approved: 21 December 2020 Abstract. The Himawari-8 satellite can be used to derive precipitation data for rainfall estimation. This study aims to test several methods for such estimation employing the Himawari-8 satellite. The methods are compared in three regions with different topographies, namely Bukittinggi, Pontianak and Ambon. The rainfall estimation methods that are tested are auto estimator, IMSRA, non-linear relation and non- linear inversion approaches. Based on the determination of the statistical verification (RMSE, standard deviation and correlation coefficient) of the amount of rainfall, the best method in Bukittinggi and Pontianak was shown to be IMSRA, while for the Ambon region was the non-linear relations. The best methods from each research area were mapped using the Google Maps Application Programming Interface (API). Keywords: Rainfall Estimation, Himawari-8 Satellite, Google Maps API 1 INTRODUCTION systems has contributed greatly to their Remote sensing is the science of use in weather forecasts (Tan, 2014). In obtaining information about the Earth's fact, observations using weather satellites surface without having direct contact can provide hourly weather information with it. This is done by sensing and over a fairly wide area coverage. Such recording the reflected energy and environmental and weather satellite data processing, analysing and applying the can be obtained real time, but their use information (NRC, 2003). Nowadays, remains very limited in the wider remote sensing for the observation of the community (Suwarsono et al., 2009). atmosphere is very important. This is However, the most recent weather because not all areas on the earth's satellite and its data can be used by the surface can be covered by in situ wider community, namely the Japanese observations (Alfuadi, 2016). In addition, Himawari-8 satellite, which is a new remote sensing has contributed greatly to generation of meteorological satellites various other fields and led to the with sophisticated optical sensors development of various sensors (Lillesand (Bessho et al., 2016). In addition, et al., 2015). Remote sensing sensors precipitation data can be derived from the themselves are divided into two types, Himawari-8 satellite data which can be namely remote sensing with active useful in mitigating hydrometeorological instruments sensors as found in weather disasters (Alfuadi, 2016). radar and passive instrument sensors Several studies using the Himawari- found in weather satellites (Stull, 2015). 8 satellite have also been conducted to Weather satellites can be used to estimate rainfall in various areas. For identify cloud patterns and structures example, Rani, et al. (2016) used the auto related to dynamic weather conditions. estimator method in Pangkalpinang The development of weather satellite Meteorological Station; the INSAT 189 http://dx.doi.org/10.30536/j.ijreses.2020.v17.a3441 @National Institute of Aeronautics and Space of Indonesia (LAPAN) Nadine Ayasha Multispectral Rainfall Algorithm (IMSRA) method was employed by Alfuadi (2016) in Palangkaraya; and the non-linear relation method with non-linear inversion were used by Alfuadi and Wandala (2016) in Muara Teweh and Palangkaraya. However, some of these studies only focus on one region topography. In fact, the latitude, slope and altitude in some areas will affect weather activities, such as cloud formation and rain, due to the Figure 2-1: Map of the research area uneven solar irradiation on the earth's The data used were from Himawari- surface (Sucahyono and Ribudiyanto, 8 channel IR-1, obtained from 2009). Therefore, it is necessary to ftp://202.90.199.115. IR-1 was used conduct research into and test the because IR-1 data can be converted into methods used for different topography precipitation data (Alfuadi, 2016). The and weather systems. This study verification of the rainfall estimation was proposes to test the methods that are made using rainfall data from the AWS generally used in various topographies in center BMKG. Rainfall date cases were Indonesia (highlands, lowlands and employed in the research are shown in islands). Apart from testing the methods, Table 2-1: the research will display rainfall estimation results into Google Maps Table 2-1: Rainfall date cases (Source - BMKG, 2017 and 2018) Application Programming Interface (API) Bukittinggi platform. 15 April 2017 Heavy rainfall (60.8 The Google Maps API platform mm/day) allows users to integrate Google Maps into 25 June 2017 Moderate rainfall their respective websites by adding their (36.9 mm/day) own data points (Davis, 2006). The 28 June 2017 Moderate rainfall (46.5 mm/day) advantage of the API is that it is possible Pontianak to modify maps according to user needs 10 Sept 2017 Heavy rainfall (70.8 and integrate them with the data to be mm/day) used (Akanbi and Agunbiade, 2013). 11 Nov 2017 Very Heavy rainfall Therefore, this study will utilise the (187.4 mm/day) 13 Oct 2018 Heavy rainfall (76.4 Google Maps API to add data points in the mm/day) form of rainfall estimation results (light, Ambon moderate, heavy and very heavy rain) into 28 May 2018 Very heavy rainfall a map which interesting and easy to (100.2 mm/day) understand by users 29 June 2018 Very heavy rainfall (111 mm/day) 30 June 2018 Heavy rainfall (75 2 MATERIALS AND METHODOLOGY mm/day) 2.1 Location and Data The locations of this research were The tools used in the research were in Bukittinggi (GAW Bukit Kototabang), Python 3.7 to extract the value of the Pontianak (Supadio Meteorological cloud top temperature (T) from the IR-1 Station) and Ambon (Pattimura data. These data are in netCDF (.nc) Meteorological Station) (as shown in red format, so Python 3.7 extracted the on the map in Figure 2-1). temperature in this format. JavaScript 190 International Journal of Remote Sensing and Earth Science Vol. 17 No. 2 December 2020 A Comparison of Rainfall… was also used to prepare the sripct or the and T is the cloud top temperature in mapping of rainfall estimation, and the Kelvin. Google Maps API platform was used for mapping the best method of rainfall 2.2.2 Statistical Verification Method estimation result. After obtaining the results of the rainfall estimation described above, we 2.2 Methods verified the results using RMSE, Two methods were employed, correlation coefficient and standard rainfall estimation equation and deviation and incorporated them into a statistical verification of the rainfall Taylor diagram. We also calculated the estimation method bias of the rainfall estimation. 2.2.1 Rainfall Estimation Method a. RMSE a. Auto Estimator Based on Wang and Lu (2018), the Based on Vicente et al (1998), the RMSE equation is : equation for auto estimator is : 2 ∑푁 (푥i−푦푖) (2-5) RMSE = √ 푛=1 R = 1.1183 .1011 푒푥푝(−3.6382 .10−2 .푇1.2) (2-1) 푁 where R is the rainfall estimation where N is the amount of data, xi is the (mm/hour) and T is the cloud top rainfall estimation (mm) and yi is the real temperature in Kelvin. observation of rainfall (mm). b. Correlation Coefficient b. IMSRA In line with Saefuddin et al (2009), The equation used in the research of the correlation coefficient equation is : Gairola et al. (2011) was : 훴(푥푖−푥̅)(푦푖−푦̅) (2-6) R=8.613098 ∗ 푒푥푝 (−(T−197.97)/15.7061 ) (2-2) rxy = √∑(푥푖−푥̅ )2√∑(푦푖−푦̅ )2 where R is the rainfall estimation where xi is the rainfall estimation (mm) (mm/hour) and T is the cloud top and yi is the real observation of rainfall (mm). temperature in Kelvin. c. Bias c. Non-Linear Relation Bias is used to establish whether The equation used in the research of the value of the data is underestimated or Suwarsono et al. (2019) was : overestimated. The equation of bias based R = 2 .1025 .T −10.256 (2-3) on Santos et al (2011) is : 1 Bias = ∑푛 (푥푖 − 푦푖) (2-7) Where R is rainfall estimation (mm/hour) 푁 푖=1 and T is the cloud top temperature in Kelvin. where xi is the rainfall estimation (mm) and yi is the real observation of rainfall d. Non-Linear Inversion (mm). Based on Octari et al (2015), the equation for non-linear inversion is : d. Standard Deviation Based on Supranto (2008), the R = 1.380462 .10−7 푒3789.518 /T (2-4) equation of standard deviation is: Where R is rainfall estimation (mm/hour) International Journal of Remote Sensing and Earth Science Vol. 17 No. 2 December 2020 191 Nadine Ayasha 2 ∑푁 (푥i−µ) (2-8) was the case on 25 and 28 June 2017. σ = √ 푛=1 푁 Furthermore, based on the rainfall bias graph (Figure 3-2), on 15 April 2017 there 2 where (xi-µ) is the deviation from was an increased in rainfall bias the observation to the true mean. (overestimation) in the auto estimator and non-linear relation methods. This is 3 RESULTS AND DISCUSSION because of a time lag between the 3.1 Rainfall Estimation Results detected cloud top temperature and the Beside the statistical verification real rainfall. On 25 June 2017, there was results, the determination of the best a drastic reduction (underestimation) at method was also based on the Taylor 12.00 UTC. This was due to 22.9 mm/hr diagram results (the red point closest to of heavy rainfall resulting from cloud top the observation point being the best temperature -6.4350C. In other words, method).
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