Improvement of Google Earth Engine-Based Multi-satellite Rainfall Estimation using Rain Gauge Data in South Sulawesi
Abstract
Precipitation, particularly rainfall, is vital in understanding weather and climate. In Indonesia, the uneven distribution of in situ rainfall observations poses a challenge to accurately measuring surface rainfall. Remote sensing systems and cloud computing technologies, such as Google Earth Engine (GEE), offer potential solutions. This study evaluates the spatial distribution and performance of four multi-satellite rainfall estimates available in GEE, namely CHIRPS, GSMAP, GPM-IMERG, and PERSIANN-CDR, before and after calibration using BMKG rain gauge data in South Sulawesi during the 2018–2023 period. The original multi-satellite data showed significant differences from observations, with O_CHPS demonstrating the best spatial similarity. Calibration using the Geographical Differential Analysis (GDA) method successfully improved accuracy, reduced bias, lowered RMSE, and increased RSQ across annual to daily scales. After calibration, C_PRSN provided the best spatial distribution and performance. Sensitivity to elevation and rainfall intensity was also enhanced, with improved detection indicators such as POD and CSI, particularly for light to moderate rainfall events. This study underscores the importance of calibration in improving the accuracy of satellite-based rainfall products, supporting hydrometeorological research and disaster management efforts.
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References
[1] C. D. Ahrens and R. Henson, Meteorology Today: An Introduction to Weather, Climate, and Environment, 12th ed. Boston: Cengage Learning, 2019.
[2] H. Chen, X. Yin, X. Huang, et al., Climatic characteristics and main weather patterns of extreme precipitation in the middle Yangtze River valley, Journal of Water and Climate Change, vol. 15, no. 1, pp. 192–208, Jan. 2024, doi: 10.2166/wcc.2023.545.
[3] I. Mutaqin Ali, L. P. Yudho, D. Sianturi, Strategi Pertahanan Laut dalam Pengamanan Alur Laut Kepulauan Indonesia untuk Mewujudkan Keamanan Maritim dan Mempertahankan Kedaulatan Indonesia, Jurnal Education and Development, vol. 10, no. 2, pp. 319–324, May 2022, Accessed: Dec. 24, 2024. [Online]. Available: https://journal.ipts.ac.id/index.php/ED/article/view/3742.
[4] Q. Zeng, Y. Wang, L. Chen, et al., Inter-Comparison and Evaluation of Remote Sensing Precipitation Products over China from 2005 to 2013, Remote Sens (Basel), vol. 10, no. 2, p. 168, Jan. 2018, doi: 10.3390/rs10020168.
[5] X. Pan, H. Wu, S. Chen, et al., Evaluation and Applicability Analysis of GPM Satellite Precipitation over Mainland China, Remote Sens., vol. 15, no. 11, Jun. 2023, doi: 10.3390/rs15112866.
[6] P. Rincón-Avalos, A. Khouakhi, O. Mendoza-Cano, et al., Evaluation of satellite precipitation products over Mexico using Google Earth Engine, J. Hydroinform., vol. 24, no. 4, pp. 711–729, Jul. 2022, doi: 10.2166/hydro.2022.122.
[7] S. Dubey, H. Gupta, M. K. Goyal, et al., Evaluation of precipitation datasets available on Google earth engine over India, Int. J. Climatol., vol. 41, no. 10, pp. 4844–4863, Aug. 2021, doi: 10.1002/joc.7102.
[8] A. H. Pattiraja, Analisis Hubungan Data Hujan Satelit PERSIANN-CDR & TRMM 3B42 dengan Data Hujan BMKG pada Stasiun Hujan di Wilayah Sumba Barat, Jurnal Kacapuri: Jurnal Keilmuan Teknik Sipil, vol. 6, no. 1, p. 128, Jul. 2023, doi: 10.31602/jk.v6i1.11692.
[9] Z. Shen and H. Wu, A comparative analysis of merging strategies for satellite precipitation estimates and ground observations over Chinese mainland, J. Atmos. Sol-terr. Phy., vol. 246, May 2023, doi: 10.1016/j.jastp.2023.106072.
[10] C. Mo, M. Zhang, Y. Ruan, et al., Accuracy analysis of IMERG satellite rainfall data and its application in long-term runoff simulation, Water (Switz.), vol. 12, no. 8, Aug. 2020, doi: 10.3390/W12082177.
[11] N. Nurhamidah, R. Andari, A. Junaidi, et al., Evaluation of the Compatibility of TRMM Satellite Data with Precipitation Observation Data, Int. J. Inf. Visual., vol. 7, no. 2, p. 287, May 2023, doi: 10.30630/joiv.7.2.1578.
[12] I. A. Khalid and S. Sitanggang, Machine Learning-Based Spatial Downscaling on Precipitation Satellite Data in Riau Province, Indonesia, TURCOMAT, vol. 13, no. 02, pp. 7–16, 2022, doi: 10.17762/turcomat.v13i2.12114.
[13] C. Chen, Q. Chen, B. Qin, et al., Comparison of Different Methods for Spatial Downscaling of GPM IMERG V06B Satellite Precipitation Product Over a Typical Arid to Semi-Arid Area, Front. Earth Sci., vol. 8, Nov. 2020, doi: 10.3389/feart.2020.536337.
[14] C. Chen, S. Zhao, Z. Duan, et al., An Improved Spatial Downscaling Procedure for TRMM 3B43 Precipitation Product Using Geographically Weighted Regression, in Ieee J-stars., Institute of Electrical and Electronics Engineers, Sep. 2015, pp. 4592–4604. doi: 10.1109/JSTARS.2015.2441734.
[15] H. Zhu, H. Liu, Q. Zhou, et al., Towards an Accurate and Reliable Downscaling Scheme for High-Spatial-Resolution Precipitation Data, Remote Sens., vol. 15, no. 10, May 2023, doi: 10.3390/rs15102640.
[16] C. Funk, P. Petersen, M. Landsfeld, et al., The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes, Sci. Data, vol. 2, no. 1, p. 150066, Dec. 2015, doi: 10.1038/sdata.2015.66.
[17] G. J. Huffman, E. F. Stocker, D. T. Bolvin, et al., GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06 (GPM_3IMERGHH), Goddard Earth Sciences Data and Information Services Center (GES DISC).
[18] T. Kubota, K. Aonashi, T. Ushio, et al., Global Satellite Mapping of Precipitation (GSMaP) Products in the GPM Era, in Satellite Precipitation Measurement, vol. 1, V. Levizzani, C. Kidd, D. B. Kirschbaum, et al., Eds., Cham: Springer, 2020, pp. 355–373. doi: 10.1007/978-3-030-24568-9_20.
[19] H. Ashouri, K. Hsu, S. Sorooshian, et al., PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies, Bull Am Meteorol Soc, vol. 96, no. 1, pp. 69–83, Jan. 2015, doi: 10.1175/BAMS-D-13-00068.1.
[20] BMKG, Data penakar hujan (in situ), Unpublished data, 2024. Badan Meteorologi, Klimatologi, dan Geofisika.
[21] Badan Informasi Geospasial, Batas Wilayah Administrasi, 2023, BIG. Accessed: Jan. 02, 2025. [Online]. Available: https://geoservices.big.go.id/portal/apps/webappviewer/index.html?id=cb58db080712468cb4bfd408dbde3d70.
[22] United States Geological Survey, USGS EROS Archive - Digital Elevation - Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global, 2014, USGS. doi: 10.5066/F7PR7TFT.
[23] B. Latos, T. Lefort, M. Flatau, et al., Equatorial waves triggering extreme rainfall and floods in Southwest Sulawesi, Indonesia, Mon Weather Rev, vol. 149, no. 5, pp. 1381–1401, May 2021, doi: 10.1175/MWR-D-20-0262.1.
[2] H. Chen, X. Yin, X. Huang, et al., Climatic characteristics and main weather patterns of extreme precipitation in the middle Yangtze River valley, Journal of Water and Climate Change, vol. 15, no. 1, pp. 192–208, Jan. 2024, doi: 10.2166/wcc.2023.545.
[3] I. Mutaqin Ali, L. P. Yudho, D. Sianturi, Strategi Pertahanan Laut dalam Pengamanan Alur Laut Kepulauan Indonesia untuk Mewujudkan Keamanan Maritim dan Mempertahankan Kedaulatan Indonesia, Jurnal Education and Development, vol. 10, no. 2, pp. 319–324, May 2022, Accessed: Dec. 24, 2024. [Online]. Available: https://journal.ipts.ac.id/index.php/ED/article/view/3742.
[4] Q. Zeng, Y. Wang, L. Chen, et al., Inter-Comparison and Evaluation of Remote Sensing Precipitation Products over China from 2005 to 2013, Remote Sens (Basel), vol. 10, no. 2, p. 168, Jan. 2018, doi: 10.3390/rs10020168.
[5] X. Pan, H. Wu, S. Chen, et al., Evaluation and Applicability Analysis of GPM Satellite Precipitation over Mainland China, Remote Sens., vol. 15, no. 11, Jun. 2023, doi: 10.3390/rs15112866.
[6] P. Rincón-Avalos, A. Khouakhi, O. Mendoza-Cano, et al., Evaluation of satellite precipitation products over Mexico using Google Earth Engine, J. Hydroinform., vol. 24, no. 4, pp. 711–729, Jul. 2022, doi: 10.2166/hydro.2022.122.
[7] S. Dubey, H. Gupta, M. K. Goyal, et al., Evaluation of precipitation datasets available on Google earth engine over India, Int. J. Climatol., vol. 41, no. 10, pp. 4844–4863, Aug. 2021, doi: 10.1002/joc.7102.
[8] A. H. Pattiraja, Analisis Hubungan Data Hujan Satelit PERSIANN-CDR & TRMM 3B42 dengan Data Hujan BMKG pada Stasiun Hujan di Wilayah Sumba Barat, Jurnal Kacapuri: Jurnal Keilmuan Teknik Sipil, vol. 6, no. 1, p. 128, Jul. 2023, doi: 10.31602/jk.v6i1.11692.
[9] Z. Shen and H. Wu, A comparative analysis of merging strategies for satellite precipitation estimates and ground observations over Chinese mainland, J. Atmos. Sol-terr. Phy., vol. 246, May 2023, doi: 10.1016/j.jastp.2023.106072.
[10] C. Mo, M. Zhang, Y. Ruan, et al., Accuracy analysis of IMERG satellite rainfall data and its application in long-term runoff simulation, Water (Switz.), vol. 12, no. 8, Aug. 2020, doi: 10.3390/W12082177.
[11] N. Nurhamidah, R. Andari, A. Junaidi, et al., Evaluation of the Compatibility of TRMM Satellite Data with Precipitation Observation Data, Int. J. Inf. Visual., vol. 7, no. 2, p. 287, May 2023, doi: 10.30630/joiv.7.2.1578.
[12] I. A. Khalid and S. Sitanggang, Machine Learning-Based Spatial Downscaling on Precipitation Satellite Data in Riau Province, Indonesia, TURCOMAT, vol. 13, no. 02, pp. 7–16, 2022, doi: 10.17762/turcomat.v13i2.12114.
[13] C. Chen, Q. Chen, B. Qin, et al., Comparison of Different Methods for Spatial Downscaling of GPM IMERG V06B Satellite Precipitation Product Over a Typical Arid to Semi-Arid Area, Front. Earth Sci., vol. 8, Nov. 2020, doi: 10.3389/feart.2020.536337.
[14] C. Chen, S. Zhao, Z. Duan, et al., An Improved Spatial Downscaling Procedure for TRMM 3B43 Precipitation Product Using Geographically Weighted Regression, in Ieee J-stars., Institute of Electrical and Electronics Engineers, Sep. 2015, pp. 4592–4604. doi: 10.1109/JSTARS.2015.2441734.
[15] H. Zhu, H. Liu, Q. Zhou, et al., Towards an Accurate and Reliable Downscaling Scheme for High-Spatial-Resolution Precipitation Data, Remote Sens., vol. 15, no. 10, May 2023, doi: 10.3390/rs15102640.
[16] C. Funk, P. Petersen, M. Landsfeld, et al., The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes, Sci. Data, vol. 2, no. 1, p. 150066, Dec. 2015, doi: 10.1038/sdata.2015.66.
[17] G. J. Huffman, E. F. Stocker, D. T. Bolvin, et al., GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06 (GPM_3IMERGHH), Goddard Earth Sciences Data and Information Services Center (GES DISC).
[18] T. Kubota, K. Aonashi, T. Ushio, et al., Global Satellite Mapping of Precipitation (GSMaP) Products in the GPM Era, in Satellite Precipitation Measurement, vol. 1, V. Levizzani, C. Kidd, D. B. Kirschbaum, et al., Eds., Cham: Springer, 2020, pp. 355–373. doi: 10.1007/978-3-030-24568-9_20.
[19] H. Ashouri, K. Hsu, S. Sorooshian, et al., PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies, Bull Am Meteorol Soc, vol. 96, no. 1, pp. 69–83, Jan. 2015, doi: 10.1175/BAMS-D-13-00068.1.
[20] BMKG, Data penakar hujan (in situ), Unpublished data, 2024. Badan Meteorologi, Klimatologi, dan Geofisika.
[21] Badan Informasi Geospasial, Batas Wilayah Administrasi, 2023, BIG. Accessed: Jan. 02, 2025. [Online]. Available: https://geoservices.big.go.id/portal/apps/webappviewer/index.html?id=cb58db080712468cb4bfd408dbde3d70.
[22] United States Geological Survey, USGS EROS Archive - Digital Elevation - Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global, 2014, USGS. doi: 10.5066/F7PR7TFT.
[23] B. Latos, T. Lefort, M. Flatau, et al., Equatorial waves triggering extreme rainfall and floods in Southwest Sulawesi, Indonesia, Mon Weather Rev, vol. 149, no. 5, pp. 1381–1401, May 2021, doi: 10.1175/MWR-D-20-0262.1.
Published
2025-01-10
How to Cite
ISMAIL, Prayoga et al.
Improvement of Google Earth Engine-Based Multi-satellite Rainfall Estimation using Rain Gauge Data in South Sulawesi.
BULETIN FISIKA, [S.l.], v. 26, n. 1, p. 19 – 30, jan. 2025.
ISSN 2580-9733.
Available at: <https://ojs.unud.ac.id/index.php/buletinfisika/article/view/121525>. Date accessed: 21 jan. 2025.
Section
Articles
