TEMPLATE DESIGN © s.com 1.Rainfall data in 7 (seven) observation stations in NTB collected from Kediri climatologycal station. Data range from 2001 to Hourly rainfall estimated data by TRMM satellite (3B42) which had temporal resolution 3 hourly and saptial resolutin 0,25° x 0,25°. DETERMINING A METHOD TO ESTIMATE DATA TRMM SATELLITE IN NUSA TENGGARA BARAT Abstract Contact information Nusa Tenggara Barat (NTB) had a land area about ,07 Km 2 but it’s only had 57 stations of rainfall observation. In this case, it doesn’t enough to represent the rainfall observation in NTB considering of its topography variation. Nowadays, there is an instrument to observing a rainfall by remote sensing technique by TRMM (Tropical Rainfall Measurement Mission) satellite which is can observe a rainfall in wide area that doesn’t had a station of rainfall observation. A data of TRMM is in a grid form so it couldn’t indicate the value of a rainfall directly at the point of area. Because of that, the data should be estimated before used. The purpose of this research is to determining the best method to estimate data of TRMM. There are three methods to estimating the data, Inverse Distance Weighted (IDW), multiple regressions, and Principal Component Analisys (PCA). Data of TRMM using 3B42 data which had spatial resolution for 0,25 0 x 0,25 0 from 2001 – Station of rainfall observation in 7 points in NTB used to be compared with the estimated data to be verified. The methods used to verify are correlation and Root Mean Square Error (RMSE). The result showed that the best method to estimate TRMM data is multiple regressions method, which had a correlation value 0,70 and RMSE value 39,7. The best estimated area by using the best estimated method is Sumbawa station of rainfall observation which had a correlation value 0,81 and RMSE value 29,1. Keywords: IDW, multiple regressions, PCA, TRMM Materials Jambi Climatologycal Station Indonesian Agency for Meteorolgy, Climatology and Geophisics Jalan Lintas Jambi Muara Bulian KM.18 Kec. Jaluko Kab. Muaro Jambi T: (0741) / E: Location Interest Research located in NTB because of data availability and also NTB had various topographic condition. NTB divide to Lombok island and Sumbawa island terdiri dari Pulau Lombok dan Pulau Sumbawa, which had a geographic posititon between 115° 46' - 119° 5‘ E and 8° 10' - 9° 5‘ S. NTB had a terrain area about ,07 Km 2. NTB consist of 8 regency and 2 city. The capital city is in Mataram. Selong (East Lombok regency) is the higest city with elevation 148 m above sea level. Raba (Bima regency) is the lowest city with elevation 13 m abve sea level. Methods The tabulation of rainfall estimated data of TRMM satellite did by Ms. Excell software and to estimate the TRMM data using three methods Inverse Distance Weighted (IDW), multiple regressions and Principal component analisys (PCA). The tabulation for IDW using Ms. Excell and for multiple regressions and PCA using Mintitab 14 software. The verification method is correllation coefficient (r) dan Root Mean Square Error (RMSE) wich tabulated by Ms. Excell. Conclusion The verification results showed that from each method can be concluded the best method to estimate TRMM data ini Nusa Tenggara Barat is multiple regressions method wich had a correllation value 0.70 and RMSE value 39,7. Results Correllation value from multiple regressions method to estimate the TRMM data is the higest, PCA the second and IDW the third. RMSE value from IDW method is the higest, PCA the second and multiple regressions is the last Ageraging the value from each verification methods, the best method to estimate the TRMM data is multiple regressions method, PCA the second and IDW the third. Figure 8. Spatial correllation Figure 10. Rainfall average by TRMM Figure 9. Rainfall average by observation Figure 3. TRMM grid data in NTB Figure 4. TRMM sensors No.Pos Hujan Koordinat LintangBujur 1Sekotong Praya- Aikmual Ampenan Selaparang Kopang Mujur Stamet Sumbawa Stamet Bima Figure 1. NTB province Figure 6. RMSE value Figure 5. r value Figure 7. Average r and RMSE value Figure 2. Rainfall observation station