Articles | Volume 29, issue 3
https://doi.org/10.5194/hess-29-799-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-799-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Feb 2025
Research article |
| 13 Feb 2025
| 13 Feb 2025
Refining remote sensing precipitation datasets in the South Pacific with an adaptive multi-method calibration approach
Óscar Mirones
Santander Meteorology Group, Instituto de Física de Cantabria IFCA, CSIC-UC, 39005 Santander, Spain
Dept. Matemática Aplicada y Ciencias de la Computación (MACC), Universidad de Cantabria, 39005 Santander, Spain
Data Science and Climate Group, Universidad de Cantabria, Unidad Asociada al CSIC, Santander, Spain
Sixto Herrera
Dept. Matemática Aplicada y Ciencias de la Computación (MACC), Universidad de Cantabria, 39005 Santander, Spain
Maialen Iturbide
Santander Meteorology Group, Instituto de Física de Cantabria IFCA, CSIC-UC, 39005 Santander, Spain
Jorge Baño Medina
Santander Meteorology Group, Instituto de Física de Cantabria IFCA, CSIC-UC, 39005 Santander, Spain
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California, La Jolla, San Diego, California, USA
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Cited articles
Aghakouchak, A., Nasrollahi, N., and Habib, E.: Accounting for Uncertainties of the TRMM Satellite Estimates, Remote Sens.-Basel, 1, 606–619, https://doi.org/10.3390/rs1030606, 2009. a, b, c
Almazroui, M.: Calibration of TRMM rainfall climatology over Saudi Arabia during 1998–2009, Atmos. Res., 99, 400–414, https://doi.org/10.1016/j.atmosres.2010.11.006, 2011. a, b
Arshad, M., Ma, X., Yin, J., Ullah, W., Ali, G., Ullah, S., Liu, M., Shahzaman, M., and Ullah, I.: Evaluation of GPM-IMERG and TRMM-3B42 precipitation products over Pakistan, Atmos. Res., 249, 105341, https://doi.org/10.1016/j.atmosres.2020.105341, 2021. a
As-syakur, A. R., Osawa, T., Miura, F., Nuarsa, I. W., Ekayanti, N. W., Dharma, I. G. B. S., Adnyana, I. W. S., Arthana, I. W., and Tanaka, T.: Maritime Continent rainfall variability during the TRMM era: The role of monsoon, topography and El Niño Modoki, Dynam. Atmos. Oceans, 75, 58–77, https://doi.org/10.1016/j.dynatmoce.2016.05.004, 2016. a
Australian Bureau of Meteorology and CSIRO: Climate change in the Pacific: scientific assessment and new research. Volume 1: Regional Overview, Pacific Climate Change Science Program, Aspendale, Victoria, https://www. pacificclimatechangescience.org/publications/reports/report-climate-change-in-the-pacific-scientific-assessment-and-new-research (last access: June 2024), 2011. a
Baltaci, H., Gokturk, O. M., Kındap, T., Unal, A., and Karaca, M.: Atmospheric circulation types in Marmara Region (NW Turkey) and their influence on precipitation, Int. J. Climatol., 35, 1810–1820, https://doi.org/10.1002/joc.4122, 2015. a
Bedia, J., Baño-Medina, J., Legasa, M. N., Iturbide, M., Manzanas, R., Herrera, S., Casanueva, A., San-Martín, D., Cofiño, A. S., and Gutiérrez, J. M.: Statistical downscaling with the
downscaleR package (v3.1.0): contribution to the VALUE intercomparison experiment, Geosci. Model Dev., 13, 1711–1735, https://doi.org/10.5194/gmd-13-1711-2020, 2020. a
Casanueva, A., Bedia, J., Herrera, S., Fernández, J., and Gutiérrez, J. M.: Direct and component-wise bias correction of multi-variate climate indices: the percentile adjustment function diagnostic tool, Climatic Change, 147, 411–425, https://doi.org/10.1007/s10584-018-2167-5, 2018. a
Efron, B. and Gong, G.: A leisurely look at the bootstrap, the jackknife, and cross-validation, Am. Stat., 37, 36–48, 1983. a
Giarno, G., Hadi, M. P., Suprayogi, S., and Murti, S. H.: Distribution of Accuracy of TRMM Daily Rainfall in Makassar Strait, Forum Geografi, 32, 38–52, https://journals.ums.ac.id/fg/article/download/5774/4074 (last access: 12 February 2025), 2018. a
Greene, J. S., Klatt, M., Morrissey, M., and Postawko, S.: The Comprehensive Pacific Rainfall Database, J. Atmos. Ocean. Tech., 25, 71–82, https://doi.org/10.1175/2007JTECHA904.1, 2008. a
Gutiérrez, J. M., Maraun, D., Widmann, M., Huth, R., Hertig, E., Benestad, R., Roessler, O., Wibig, J., Wilcke, R., Kotlarski, S., San Martín, D., Herrera, S., Bedia, J., Casanueva, A., Manzanas, R., Iturbide, M., Vrac, M., Dubrovsky, M., Ribalaygua, J., Pórtoles, J., Räty, O., Räisänen, J., Hingray, B., Raynaud, D., Casado, M. J., Ramos, P., Zerenner, T., Turco, M., Bosshard, T., Štěpánek, P., Bartholy, J., Pongracz, R., Keller, D. E., Fischer, A. M., Cardoso, R. M., Soares, P. M. M., Czernecki, B., and Pagé, C.: An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross-validation experiment, Int. J. Climatol., 39, 3750–3785, 2019. a
Gutjahr, O. and Heinemann, G.: Comparing precipitation bias correction methods for high-resolution regional climate simulations using COSMO-CLM: Effects on extreme values and climate change signal, Theor. Appl. Climatol., 114, 511–529, https://doi.org/10.1007/s00704-013-0834-z, 2013. a
Hay, L. E., McCabe Jr., G. J., Wolock, D. M., and Ayers, M. A.: Simulation of precipitation by weather type analysis, Water Resour. Res., 27, 493–501, https://doi.org/10.1029/90WR02650, 1991. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b, c, d
Huffman, G., Bolvin, D., Nelkin, E., and Adler, R.: TRMM (TMPA) Precipitation L3 1 day 0.25 degree × 0.25 degree V7, edited by: Savtchenko, A., Goddard Earth Sciences Data and Information Services Center (GES DISC), https://disc.gsfc.nasa.gov/datasets/TRMM_3B42_Daily_7/summary (last access: December 2024), https://doi.org/10.5067/TRMM/TMPA/DAY/7, 2016. a, b
Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K.-L., Joyce, R. J., Kidd, C., Nelkin, E. J., Sorooshian, S., Stocker, E. F., Tan, J., Wolff, D. B., and Xie, P.: Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (GPM) Mission (IMERG), in: Satellite Precipitation Measurement, edited by: Levizzani, V., Kidd, C., Kirschbaum, D. B., Kummerow, C. D., Nakamura, K., and Turk, F. J., vol. 67, pp. 343–353, Springer International Publishing, https://doi.org/10.1007/978-3-030-24568-9_19, series Title: Advances in Global Change Research, 2020. a
Huth, R., Beck, C., Philipp, A., Demuzere, M., Ustrnul, Z., Cahynová, M., Kyselý, J., and Tveito, O. E.: Classifications of atmospheric circulation patterns: recent advances and applications, Ann. N. Y. Acad. Sci., 1146, 105–152, https://doi.org/10.1196/annals.1446.019, 2008. a
Islam, M. N., Das, S., and Uyeda, H.: Calibration of TRMM Derived Rainfall Over Nepal During 1998–2007, Open Atmospheric Science Journal, 4, 12–23, https://doi.org/10.2174/1874282301004010012, 2010. a, b
Iturbide, M., Bedia, J., Herrera, S., Baño-Medina, J., Fernández, J., Frías, M., Manzanas, R., San-Martín, D., Cimadevilla, E., Cofiño, A., and Gutiérrez, J.: The R-based climate4R open framework for reproducible climate data access and post-processing, Environ. Modell. Softw., 111, 42–54, https://doi.org/10.1016/j.envsoft.2018.09.009, 2019. a
Jury, M. W., Herrera, S., Gutiérrez, J. M., and Barriopedro, D.: Blocking representation in the ERA-Interim driven EURO-CORDEX RCMs, Clim. Dynam., 52, 3291–3306, https://doi.org/10.1007/s00382-018-4335-8, 2019. a
Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J., and Neumann, C. J.: The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying Tropical Cyclone Data, B. Am. Meteorol. Soc., 91, 363–376, https://doi.org/10.1175/2009BAMS2755.1, 2010. a
Kotlarski, S., Szabó, P., Herrera, S., Räty, O., Keuler, K., Soares, P. M., Cardoso, R. M., Bosshard, T., Pagé, C., Boberg, F., Gutiérrez, J. M., Isotta, F. A., Jaczewski, A., Kreienkamp, F., Liniger, M. A., Lussana, C., and Pianko-Kluczyńska, K.: Observational uncertainty and regional climate model evaluation: A pan-European perspective, Int. J. Climatol., 39, 3730–3749, https://doi.org/10.1002/joc.5249, 2019. a
Manzanas, R. and Gutiérrez, J. M.: Process-conditioned bias correction for seasonal forecasting: a case-study with ENSO in Peru, Clim. Dynam., 52, 1673–1683, https://doi.org/10.1007/s00382-018-4226-z, 2019. a
Maraun, D., Widmann, M., Gutiérrez, J. M., Kotlarski, S., Chandler, R. E., Hertig, E., Wibig, J., Huth, R., and Wilcke, R. A.: VALUE: A framework to validate downscaling approaches for climate change studies, Earths Future, 3, 1–14, https://doi.org/10.1002/2014EF000259, 2015. a, b
Maraun, D., Shepherd, T. G., Widmann, M., Zappa, G., Walton, D., Gutiérrez, J. M., Hagemann, S., Richter, I., Soares, P. M. M., Hall, A., and Mearns, L. O.: Towards process-informed bias correction of climate change simulations, Nat. Clim. Change, 7, 764–773, https://doi.org/10.1038/nclimate3418, 2017. a
Mirones, O., Bedia, J., Fernandez-Granja, J., Herrera, S., Van Vloten, S., Pozo, A., Cagigal, L., and Méndez, F.: Weather-type-conditioned calibration of Tropical Rainfall Measuring Mission precipitation over the South Pacific Convergence Zone, Int. J. Climatol., 43, 1193–1210, https://doi.org/10.1002/joc.7905, 2023a. a, b, c, d, e, f, g, h, i, j
Mirones, O., Bedia, J., Herrera, S., Iturbide, M., and Baño-Medina, J.: Refining Remote Sensing precipitation Datasets in the South Pacific: An Adaptive Multi-Method Approach for Calibrating the TRMM Product, Github [data set], https://github.com/SantanderMetGroup/notebooks/tree/2023_TRMM_adaptiveCal/2023_adaptiveCalibration (last access: 30 November 2024), 2023b. a
National Research Council: Assessment of the Benefits of Extending the Tropical Rainfall Measuring Mission: A Perspective from the Research and Operations Communities: Interim Report, The National Academies Press, Washington, DC, https://doi.org/10.17226/11195, 2006. a
Piani, C., Haerter, J. O., and Coppola, E.: Statistical bias correction for daily precipitation in regional climate models over Europe, Theor. Appl. Climatol., 99, 187–192, https://doi.org/10.1007/s00704-009-0134-9, 2010. a
Pike, M. and Lintner, B. R.: Application of Clustering Algorithms to TRMM Precipitation over the Tropical and South Pacific Ocean, J. Climate, 33, 5767–5785, https://doi.org/10.1175/JCLI-D-19-0537.1, 2020. a
Reiter, P., Gutjahr, O., Schefczyk, L., Heinemann, G., and Casper, M.: Does applying quantile mapping to subsamples improve the bias correction of daily precipitation?: DOES QUANTILE MAPPING BENEFIT FROM SUBSAMPLING?, Int. J. Climatol., 38, 1623–1633, https://doi.org/10.1002/joc.5283, 2018. a
Riediger, U. and Gratzki, A.: Future weather types and their influence on mean and extreme climate indices for precipitation and temperature in Central Europe, Meteorol. Z., 23, 231–252, https://doi.org/10.1127/0941-2948/2014/0519, 2014. a
Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., Rudolf, B., and Ziese, M.: GPCC Full Data Reanalysis Version 6.0 at 0.5deg: Monthly Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historic Data, Global Precipitation Climatology Centre (GPCC), https://doi.org/10.5676/DWD_GPCC/FD_M_V7_050, 2011. a
Sekaranom, A. B. and Masunaga, H.: Origins of Heavy Precipitation Biases in the TRMM PR and TMI Products Assessed with CloudSat and Reanalysis Data, J. Appl. Meteorol. Clim., 58, 37–54, https://doi.org/10.1175/JAMC-D-18-0011.1, 2019. a
Simpson, J., Kummerow, C., Tao, W., and Adler, R. F.: On the Tropical Rainfall Measuring Mission (TRMM), Meteorol. Atmos. Phys., 60, 19–36, 1996. a
Stehlik, J. and Bardossy, A.: Multivariate stochastic downscaling model for generating daily precipitation series based on atmospheric circulation, J. Hydrol., 256, 120–141, https://doi.org/10.1016/S0022-1694(01)00529-7, 2002. a
Taylor, R. C.: An atlas of Pacific islands rainfall, Tech. rep., Hawaii Inst of Geophysics Honolulu, 1973. a
Themeßl, M. J., Gobiet, A., and Leuprecht, A.: Empirical-statistical downscaling and error correction of daily precipitation from regional climate models, Int. J. Climatol., 31, 1530–1544, https://doi.org/10.1002/joc.2168, 2011. a
Trigo, R. M. and DaCamara, C. C.: Circulation weather types and their influence on the precipitation regime in Portugal, Int. J. Climatol., 20, 1559–1581, https://doi.org/10.1002/1097-0088(20001115)20:13<1559::AID-JOC555>3.0.CO;2-5, 2000. a
Vincent, E. M., Lengaigne, M., Menkes, C. E., Jourdain, N. C., Marchesiello, P., and Madec, G.: Interannual variability of the South Pacific Convergence Zone and implications for tropical cyclone genesis, Clim. Dynam., 36, 1881–1896, https://doi.org/10.1007/s00382-009-0716-3, 2011. a
Vrac, M. and Naveau, P.: Stochastic downscaling of precipitation: From dry events to heavy rainfalls, Water Resour. Res., 43, W07402, https://doi.org/10.1029/2006WR005308, 2007. a
Vuillaume, J.-F. and Herath, S.: Improving global rainfall forecasting with a weather type approach in Japan, Hydrolog. Sci. J., 62, 167–181, https://doi.org/10.1080/02626667.2016.1183165, 2017. a
Waliser, D. E. and Gautier, C.: A Satellite-derived Climatology of the ITCZ, J. Climate, 6, 2162–2174, https://doi.org/10.1175/1520-0442(1993)006<2162:ASDCOT>2.0.CO;2, 1993. a
Wetterhall, F., Halldin, S., and Xu, C.-Y.: Seasonality properties of four statistical-downscaling methods in central Sweden, Theor. Appl. Climatol., 87, 123–137, https://doi.org/10.1007/s00704-005-0223-3, 2007. a
Wetterhall, F., Pappenberger, F., He, Y., Freer, J., and Cloke, H. L.: Conditioning model output statistics of regional climate model precipitation on circulation patterns, Nonlin. Processes Geophys., 19, 623–633, https://doi.org/10.5194/npg-19-623-2012, 2012. a
Zhou, Z., Lu, D., Yong, B., Shen, Z., Wu, H., and Yu, L.: Evaluation of GPM-IMERG Precipitation Product at Multiple Spatial and Sub-Daily Temporal Scales over Mainland China, Remote Sens.-Basel, 15, 1237, https://doi.org/10.3390/rs15051237, 2023. a
Short summary
We devised an adaptive method for calibrating remote sensing precipitation in the South Pacific. By classifying data into weather types and applying varied techniques, we achieve improved calibration. Results showed enhanced accuracy in mean and extreme precipitation indices across locations. The method offers customization options and effectively addresses intense rainfall events. Its versatility allows for application in diverse scenarios, supporting a better understanding of climate impacts.
We devised an adaptive method for calibrating remote sensing precipitation in the South Pacific....
