Rationalization of Rainfall Station Density in the Jatiroto Sub-Watershed Using Ground and Satellite Rainfall Data (original) (raw)

The ratio of BMKG and TRMM Rainfall Data in West Java Province by Using Statistical Parameter and Correlation Analysis

Journal of Engineering and Scientific Research, 2021

Weather observations can be done in two ways, namely weather observations based on weather stations and based on remote sensing such as satellites. One of these weather study data is rainfall measured from the BMKG rain observation post and TRMM satellite observations. To see the pattern of the distribution of rain that has occurred, the two weather observations can be connected as a reference for the distribution of rain. The purpose of this study was to analyze the correlation value of rainfall data between BMKG and TRMM by looking at the comparison graph and analyzing the comparison of statistical parameters. This research was conducted using daily rainfall data from 1998-2018 at four rain stations in West Java Province and taking descriptive decisions in the form of pictures and graphs in the form of daily, monthly and annual data. Based on the analysis results, the largest correlation value is in the annual cumulative with a value of 0,88-0,94, the smaller the number of days, t...

Development of an Equation to Estimate the Monthly Rainfall: A Case Study for Catarman, Northern Samar, Philippines

International Journal of Trend in Scientific Research and Development, 2020

This study aimed to derived an equation to estimate the monthly rainfall for Catarman, Northern Samar.The observed monthly rainfall data for Catarman N. Samar, Catbalogan Samar, Legazpi City and Masbate were obtained from the Philippine Atmospheric Geographical Astronomical Services Administration (PAGASA). The monthly rainfall records of the three (3) neighboring stations (Catbalogan, Legazpi, Masbate) were used to identify which of the existing rainfall prediction methods, namely, Normal Ratio Method, Distance Power Method and Multi Linear Regression Method is the basis in the development of a new equation. The accuracy by which the existing methods predict the observed monthly rainfall in Catarman was evaluated using T-test for correlated samples and the Pearson’s Correlation Coefficient. Since none of the methods produced estimates nearest to the observed monthly rainfall in Catarman, an equation has been derived:

Assessment of the correlation between TMPA satellite-based and rain gauge rainfall

2015

Accurate rainfall data at high spatial and temporal resolution is necessary for many hydrological and water management application, and especially in data scarce river watershed. Satellite-based rainfall estimation can be used as an alternative source of rainfall information, but need area-specific calibration and validation. The main study focused was to assess the correlation between TMPA satellite-based rainfall and raingauge rainfall for Peninsular Malaysia, for a period of five years (July 2009 - June 2014). The specific objectives include; (i) to establish the satellite-based rainfall of study area, and (ii) to analyse the correlation between satellite-based rainfall and raingauge rainfall. In this study, Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) version 7 satellite-based rainfall data obtained from public domain, and monthly rainfall from Jabatan Pengairan dan Saliran, Malaysia (JPS) rain-gauge stations were used. Meanwhile, Geographica...

Validation of Three Daily Satellite Rainfall Products in a Humid Tropic Watershed, Brantas, Indonesia: Implications to Land Characteristics and Hydrological Modelling

Hydrology

A total of three different satellite products, CHIRPS, GPM, and PERSIANN, with different spatial resolutions, were examined for their ability to estimate rainfall data at a pixel level, using 30-year-long observations from six locations. Quantitative and qualitative accuracy indicators, as well as R2 and NSE from hydrological estimates, were used as the performance measures. The results show that all of the satellite estimates are unsatisfactory, giving the NRMSE ranging from 6 to 30% at a daily level, with CC only 0.21–0.36. Limited number of gauges, coarse spatial data resolution, and physical terrain complexity were found to be linked with low accuracy. Accuracy was slightly better in dry seasons or low rain rate classes. The errors increased exponentially with the increase in rain rates. CHIPRS and PERSIANN tend to slightly underestimate at lower rain rates, but do show a consistently better performance, with an NRMSE of 6–12%. CHRIPS and PERSIANN also exhibit better estimates o...