PENGOLAHAN GEOLOKASI PRODUK DATA GAS RUMAH KACA (GRK) DARI SATELIT SUOMI NPP ATMS DAN CRIS DENGAN METODE INTERPOLASI RADIAL BASIS FUNCTION
Abstract
Geolocation processing to produce spatial greenhouse gases data products consisting of CH4, CO2 and N20 gases has been carried out systematically. The greenhouse gases data are derived from Enviomental Data Record (EDR) Suomi NPP Satellite CrIS and ATMS Sensor products. During this process, there is an obstacle while performing the information data of greenhouse gases concentrations, due to the result of systematic processing files from EDR are still in netcdf format, so that it could not be distributed to users as they expected. The unique of  unlimited netcdf format is that, it displays only numeric values with irregular resolution, unregistered and incompatible with commonly processing data software. This research aims to produce geolocation processing module in order to provide information of greenhouse gases data spatially by using coordinate pixel registration method into image data, convert Digital Number (DN) value with scale corresponding to Indonesian region and interpolation value between pixels with Radial Basis Function (RBF) method using linear function. The result from the geolocation processing module of greenhouse gases data product are concentration information from some altitude level. The product is in geotiff format with 50 km spasial resolusion.
Â
Abstrak
Pengolahan geolokasi untuk menghasilkan produk data gas rumah kaca (GRK) spasial yang terdiri dari gas CH4,CO2 dan N20 telah dilakukan secara sistematis. Data gas rumah kaca tersebut dihasilkan dari produk Enviomental Data Record (EDR) Satelit Suomi NPP Sensor CrIS dan ATMS. Hingga saat ini terdapat permasalahan dalam penyajian data informasi konsentrasi gas rumah kaca, yaitu file hasil pengolahan sistematis masih dalam format netcdf sehingga belum dapat didistribusikan untuk melayani kebutuhan pengguna. Format netcdf terbatas hanya menampilkan nilai berupa angka, resolusi yang tidak seragam, belum teregistrasi dan tidak compatible dengan aplikasi pengolahan data yang umumnya digunakan. Penelitian ini bertujuan untuk menghasilkan modul pengolahan geolokasi  yang dapat menyajikan informasi data gas rumah kaca secara spasial. Metode yang digunakan dalam penelitian ini adalah registrasi piksel koordinat ke dalam data citra, konversi nilai Digital Number (DN). Interpolasi nilai antar piksel menggunakan metode Radial Basis Function (RBF) dengan fungsi linier. Hasil dari penelitian ini adalah modul pengolahan geolokasi produk data yang dapat menyajikan informasi konsentrasi gas rumah kaca pada beberapa level ketinggian. Produk yang dihasilkan dalam format geotiff dengan resolusi spasial 50 km.
Keywords
Full Text:
PDFReferences
Aiger, D., Kokiopoulou, E., & Rivlin, E., (2013). Random Grids : Fast Approximate Nearest Neighbors and Range Searching for Image Search. In ICCV Converence. https://doi.org/10.1109/ICCV.2013.431
Beirle, S., Uhl, S., Pu¯, J., & Wagner, T., (2010). Retrieval of tropospheric column densities of NO 2 from combined SCIAMACHY nadir/limb measurements. Atmos. Meas. Tech, 3, 283–299. Retrieved from www.atmos-meas-tech.net/3/283/2010/
Boersma, K. F., Eskes, H. J., & Brinksma, E. J., (2004). Error analysis for tropospheric NO 2 retrieval from space. Journal of Geophysical Research, 109(D4), D04311. https://doi.org/10.1029/2003JD003962
Chahine, M. T., Pagano, T. S., Aumann, H. H., Atlas, R., Barnet, C., Blaisdell, J., & Zhou, L., (2006). Improving weather forecasting and providing new data on greenhouse gases. Bulletin of the American Meteorological Society, 87(7), 911–926. https://doi.org/10.1175/BAMS-87-7-911
Chung, E. S., & Soden, B. J., (2010). Investigating the influence of carbon dioxide and the stratosphere on the long-term tropospheric temperature monitoring from HIRS. Journal of Applied Meteorology and Climatology, 49(9), 1927–1937. https://doi.org/10.1175/2010JAMC2486.1
Funke, B., López-Puertas, M., Bermejo-Pantaleón, D., von Clarmann, T., Stiller, G. P., Höpfner, M., & Kaufmann, M., (2007). Analysis of nonlocal thermodynamic equilibrium CO 4.7 μm fundamental, isotopic, and hot band emissions measured by the Michelson Interferometer for Passive Atmospheric Sounding on Envisat. Journal of Geophysical Research Atmospheres, 112(11), 1–11. https://doi.org/10.1029/2006JD007933
Han, Y., Revercomb, H., Cromp, M., Gu, D., Johnson, D., Mooney, D., & Zavyalov, V., (2013). Suomi NPP CrIS measurements, sensor data record algorithm, calibration and validation activities, and record data quality. Journal of Geophysical Research Atmospheres, 118(22), 12734–12748. https://doi.org/10.1002/2013JD020344
Hengl, T., Heuvelink, G.B.M., Tadić P.MP., & Pebesma, E.J.,(2012). Spatio-temporal Prediction of Daily Temperatures Using Time-series of MODIS LST Images. Vol 107: p265-277. https://doi.org/10.1007/s00704-011-0464-2
Indradjad, A., & Salyasari, N., (2016). Pengolahan Data Atmosfer Level SDR dari Sensor ATMS danCrIS Pada Satelit S-NPP. In Prosiding SNSA 2016 (pp. 20–26).
Indradjad, A., & Salyasari, N., (2017). Sistem Informasi Gas Rumah Kaca dari Data ATMS dan CrIS S-NPP Berbasis Google Earth. In Prosiding SNSA 2017.
Livesey, N.J., Filipiak, M.J., Froidevaux, L., Read, W.G., Lambert, A., Santee, M.L., & Webster, C.R., (2008). Validation of Aura Microwave Limb Sounder O 3 and CO observations in the upper troposphere and lower stratosphere. Journal of Geophysical Research, 113(D15), D15S02. https://doi.org/10.1029/2007JD008805
Montzka, S.A., Dlugokencky, E. J., & Butler,J.H., (2011). Non-CO2 greenhouse gases and climate change. Nature, 476(7358), 43–50. https://doi.org/10.1038/nature10322
NOAA, NESDIS, OSPO & ESPC., (2015). The NOAA Unique CrIS/ATMS Product System External Users Manual Version 4.1: NOAA NESDIS CENTER for SATELLITE APPLICATIONS and RESEARCHNOAA. (2017). NUCAPS Sounding Products: SNPP Global Gridded 0.5 deg lat x 2 deg lon Images, diunduh 18 Mei 2017 dari http://www.ospo.noaa.gov/Products/atmosphere/soundings/nucaps/NUCAPS_gridded.html
Prata, A.J., & Bernardo, C., (2007). Retrieval of volcanic SO2 column abundance from Atmospheric Infrared Sounder data. Journal of Geophysical Research Atmospheres, 112(20), 1–17. https://doi.org/10.1029/2006JD007955
Strow, L.L., & Hannon, S.E., (2008). A 4-year zonal climatology of lower tropospheric CO2 derived from ocean-only Atmospheric Infrared Sounder observations. Journal of Geophysical Research Atmospheres, 113(18), 1–20. https://doi.org/10.1029/2007JD009713
Thies, B., & Bendix, J., (2011). Satellite based remote sensing of weather and climate: Recent achievements and future perspectives. Meteorological Applications, 18(3), 262–295. https://doi.org/10.1002/met.288
Uhlir, K., & Skala, V., (2005). Reconstruction of damaged images using radial basis functions. Proc.13th European Signal Processing Conference (EUSIPCO), Antalya, Turkey, pp. 160–163
Weng, F., Zou, X., Wang, X., Yang, S., & Goldberg, M. D., (2012). Introduction to Suomi national polar-orbiting partnership advanced technology microwave sounder for numerical weather prediction and tropical cyclone applications. Journal of Geophysical Research Atmospheres, 117(19), 1–14. https://doi.org/10.1029/2012JD018144
Wright, G.B. (2003). Radial Basis Function Interpolation: Numerical and Analytical Developments. (Doctor of Philosophy Department of Applied Mathematics Thesis), University of Colorado, Colorado.
Yoshida, Y., Ota, Y., Eguchi, N., Kikuchi, N., Nobuta, K., Tran, H., &Yokota, T., (2011). Retrieval algorithm for CO 2 and CH 4 column abundances from short-wavelength infrared spectral observations by the Greenhouse gases observing satellite. Atmospheric Measurement Techniques, 4(4), 717–734. https://doi.org/10.5194/amt-4-717-2011
Refbacks
- There are currently no refbacks.