• Acharya, S. C., R. Nathan, Q. J. Wang, C.-H. Su, and N. Eizenberg, 2019: An evaluation of daily precipitation from atmospheric reanalyses over Australia. Hydrol. Earth Syst. Sci., 23, 33873403, https://doi.org/10.5194/hess-23-3387-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Adam, J. C., E. A. Clark, D. P. Lettenmaier, and E. F. Wood, 2006: Correction of global precipitation products for orographic effects. J. Climate, 19, 1538, https://doi.org/10.1175/JCLI3604.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • AghaKouchak, A., A. Farahmand, F. S. Melton, J. Teixeira, M. C. Anderson, B. D. Wardlow, and C. R. Hain, 2015: Remote sensing of drought: Progress, challenges and opportunities. Rev. Geophys., 53, 452480, https://doi.org/10.1002/2014RG000456.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anders, A. M., and S. W. Nesbitt, 2015: Altitudinal precipitation gradients in the tropics from Tropical Rainfall Measuring Mission (TRMM) precipitation radar. J. Hydrometeor., 16, 441448, https://doi.org/10.1175/JHM-D-14-0178.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Asong, Z. E., S. Razavi, H. S. Wheater, and J. S. Wong, 2017: Evaluation of Integrated Multisatellite Retrievals for GPM (IMERG) over southern Canada against ground precipitation observations: A preliminary assessment. J. Hydrometeor., 18, 10331050, https://doi.org/10.1175/JHM-D-16-0187.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beck, H. E., E. F. Wood, M. Pan, C. K. Fisher, D. G. Miralles, A. I. van Dijk, T. R. McVicar, and R. F. Adler, 2019a: MSWEP V2 global 3-hourly 0.1° precipitation: Methodology and quantitative assessment. Bull. Amer. Meteor. Soc., 100, 473500, https://doi.org/10.1175/BAMS-D-17-0138.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beck, H. E., and Coauthors, 2019b: Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS. Hydrol. Earth Syst. Sci., 23, 207224, https://doi.org/10.5194/hess-23-207-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Becker, A., P. Finger, A. Meyer-Christoffer, B. Rudolf, K. Schamm, U. Schneider, and M. Ziese, 2013: A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901–present. Earth Syst. Sci. Data, 5, 7199, https://doi.org/10.5194/essd-5-71-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bookhagen, B., and M. R. Strecker, 2008: Orographic barriers, high-resolution TRMM rainfall, and relief variations along the eastern Andes. Geophys. Res. Lett., 35, L06403, https://doi.org/10.1029/2007GL032011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Builes-Jaramillo, A., and G. Poveda, 2018: Conjoint analysis of surface and atmospheric water balances in the Andes-Amazon system. Water Resour. Res., 54, 34723489, https://doi.org/10.1029/2017WR021338.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Buytaert, W., R. Celleri, P. Willems, B. D. Bièvre, and G. Wyseure, 2006: Spatial and temporal rainfall variability in mountainous areas: A case study from the south Ecuadorian Andes. J. Hydrol., 329, 413421, https://doi.org/10.1016/j.jhydrol.2006.02.031.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cao, Q., T. H. Painter, W. R. Currier, J. D. Lundquist, and D. P. Lettenmaier, 2018: Estimation of precipitation over the OLYMPEX domain during winter 2015/16. J. Hydrometeor., 19, 143160, https://doi.org/10.1175/JHM-D-17-0076.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carr, N., and Coauthors, 2015: The influence of surface and precipitation characteristics on TRMM Microwave Imager rainfall retrieval uncertainty. J. Hydrometeor., 16, 15961614, https://doi.org/10.1175/JHM-D-14-0194.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chavez, S. P., and K. Takahashi, 2017: Orographic rainfall hot spots in the Andes-Amazon transition according to the TRMM Precipitation Radar and in situ data. J. Geophys. Res. Atmos., 122, 58705882, https://doi.org/10.1002/2016JD026282.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chiaravalloti, F., L. Brocca, A. Procopio, C. Massari, and S. Gabriele, 2018: Assessment of GPM and SM2RAIN-ASCAT rainfall products over complex terrain in southern Italy. Atmos. Res., 206, 6474, https://doi.org/10.1016/j.atmosres.2018.02.019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Derin, Y., and Coauthors, 2019: Evaluation of GPM-era global satellite precipitation products over multiple complex terrain regions. Remote Sens., 11, 2936, https://doi.org/10.3390/rs11242936.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dinku, T., S. Chidzambwa, P. Ceccato, S. J. Connor, and C. F. Ropelewski, 2008: Validation of high-resolution satellite rainfall products over complex terrain. Int. J. Remote Sens., 29, 40974110, https://doi.org/10.1080/01431160701772526.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Espinoza, J. C., S. Chavez, J. Ronchail, C. Junquas, K. Takahashi, and W. Lavado, 2015: Rainfall hotspots over the southern tropical Andes: Spatial distribution, rainfall intensity, and relations with large-scale atmospheric circulation. Water Resour. Res., 51, 34593475, https://doi.org/10.1002/2014WR016273.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Funk, C., and Coauthors, 2015: The climate hazards infrared precipitation with stations–A new environmental record for monitoring extremes. Sci. Data, 2, 150066, https://doi.org/10.1038/sdata.2015.66.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gopalan, K., N.-Y. Wang, R. Ferraro, and C. Liu, 2010: Status of the TRMM 2A12 land precipitation algorithm. J. Atmos. Oceanic Technol., 27, 13431354, https://doi.org/10.1175/2010JTECHA1454.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hou, A. Y., and Coauthors, 2014: The Global Precipitation Measurement mission. Bull. Amer. Meteor. Soc., 95, 701722, https://doi.org/10.1175/BAMS-D-13-00164.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houze, R. A., 2012: Orographic effects on precipitating clouds. Rev. Geophys., 50, RG1001, https://doi.org/10.1029/2011RG000365.

  • Huffman, G. J., and Coauthors, 1997: The Global Precipitation Climatology Project (GPCP) combined precipitation dataset. Bull. Amer. Meteor. Soc., 78, 520, https://doi.org/10.1175/1520-0477(1997)078<0005:TGPCPG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., and Coauthors, 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8, 3855, https://doi.org/10.1175/JHM560.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., D. T. Bolvin, and E. J. Nelkin, 2017: Integrated Multi-satellitE Retrievals for GPM (IMERG) technical documentation. NASA Tech. Doc., 48 pp., https://pmm.nasa.gov/sites/default/files/document_files/IMERG_doc.pdf.

  • Huffman, G. J., and Coauthors, 2018: NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG): Algorithm Theoretical Basis Doc., version 5.2, 29 pp., https://pmm.nasa.gov/sites/default/files/document_files/IMERG_ATBD_V5.2.pdf.

  • Huffman, G. J., D. T. Bolvin, E. J. Nelkin, E. F. Stocker, and J. Tan, 2019: V06 IMERG release notes. NASA Tech. Doc., 6 pp., https://pmm.nasa.gov/sites/default/files/document_files/IMERG_V06_release_notes_190503.pdf.

  • Joyce, R. J., and P. Xie, 2011: Kalman filter–based CMORPH. J. Hydrometeor., 12, 15471563, https://doi.org/10.1175/JHM-D-11-022.1.

  • Kidd, C., A. Becker, G. J. Huffman, C. L. Muller, P. Joe, G. Skofronick-Jackson, and D. B. Kirschbaum, 2017: So, how much of the Earth’s surface is covered by rain gauges? Bull. Amer. Meteor. Soc., 98, 6978, https://doi.org/10.1175/BAMS-D-14-00283.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kirschbaum, D. B., and Coauthors, 2017: NASA’s remotely sensed precipitation: A reservoir for applications users. Bull. Amer. Meteor. Soc., 98, 11691184, https://doi.org/10.1175/BAMS-D-15-00296.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kirstetter, P.-E., Y. Hong, J. J. Gourley, M. Schwaller, W. Petersen, and Q. Cao, 2015: Impact of sub-pixel rainfall variability on spaceborne precipitation estimation: Evaluating the TRMM 2A25 product. Quart. J. Roy. Meteor. Soc., 141, 953966, https://doi.org/10.1002/qj.2416.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Libertino, A., A. Sharma, V. Lakshmi, and P. Claps, 2016: A global assessment of the timing of extreme rainfall from TRMM and GPM for improving hydrologic design. Environ. Res. Lett., 11, 054003, https://doi.org/10.1088/1748-9326/11/5/054003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, D., and B. Yong, 2018: Evaluation and hydrological utility of the latest GPM IMERG V5 and GSMaP V7 precipitation products over the Tibetan Plateau. Remote Sens., 10, 2022, https://doi.org/10.3390/rs10122022.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Manz, B., S. Páez-Bimos, N. Horna, W. Buytaert, B. Ochoa-Tocachi, W. Lavado-Casimiro, and B. Willems, 2017: Comparative ground validation of IMERG and TMPA at variable spatio-temporal scales in the tropical Andes. J. Hydrometeor., 18, 24692489, https://doi.org/10.1175/JHM-D-16-0277.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mayor, Y. G., I. Tereshchenko, M. Fonseca-Hernández, D. A. Pantoja, and J. M. Montes, 2017: Evaluation of error in IMERG precipitation estimates under different topographic conditions and temporal scales over Mexico. Remote Sens., 9, 503, https://doi.org/10.3390/rs9050503.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mohr, K. I., D. Slayback, and K. Yager, 2014: Characteristics of precipitation features and annual rainfall during the TRMM era in the central Andes. J. Climate, 27, 39824001, https://doi.org/10.1175/JCLI-D-13-00592.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrissey, M. L., J. A. Maliekal, J. S. Greene, and J. Wang, 1995: The uncertainty of simple spatial averages using rain gauge networks. Water Resour. Res., 31, 20112017, https://doi.org/10.1029/95WR01232.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • O, S., U. Foelsche, G. Kirchengast, J. Fuchsberger, J. Tan, and W. A. Petersen, 2017: Evaluation of GPM IMERG Early, Late, and Final rainfall estimates using WegenerNet gauge data in southeastern Austria. Hydrol. Earth Syst. Sci., 21, 65596572, https://doi.org/10.5194/hess-21-6559-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ochoa-Tocachi, B. F., and Coauthors, 2018: High-resolution hydrometeorological data from a network of headwater catchments in the tropical Andes. Sci. Data, 5, 180080, https://doi.org/10.1038/sdata.2018.80.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ossa-Moreno, J., G. Keir, N. McIntyre, M. Cameletti, and D. Rivera, 2019: Comparison of approaches to interpolating climate observations in steep terrains with low-density gauging networks. Hydrol. Earth Syst. Sci., 23, 47634781, https://doi.org/10.5194/hess-23-4763-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Padrón, R. S., B. P. Wilcox, P. Crespo, and R. Célleri, 2015: Rainfall in the Andean Páramo: New insights from high-resolution monitoring in southern Ecuador. J. Hydrometeor., 16, 985996, https://doi.org/10.1175/JHM-D-14-0135.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prakash, S., A. K. Mitra, A. AghaKouchak, Z. Liu, H. Norouzi, and D. Pai, 2018: A preliminary assessment of GPM-based multi-satellite precipitation estimates over a monsoon dominated region. J. Hydrol., 556, 865876, https://doi.org/10.1016/j.jhydrol.2016.01.029.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ramsauer, T., T. Weiß, and P. Marzahn, 2018: Comparison of the GPM IMERG Final precipitation product to RADOLAN weather radar data over the topographically and climatically diverse Germany. Remote Sens., 10, 2029, https://doi.org/10.3390/rs10122029.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sahlu, D., E. I. Nikolopoulos, S. A. Moges, E. N. Anagnostou, and D. Hailu, 2016: First evaluation of the Day-1 IMERG over the Upper Blue Nile basin. J. Hydrometeor., 17, 28752882, https://doi.org/10.1175/JHM-D-15-0230.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sharifi, E., R. Steinacker, and B. Saghafian, 2016: Assessment of GPM-IMERG and other precipitation products against gauge data under different topographic and climatic conditions in Iran: Preliminary results. Remote Sens., 8, 135, https://doi.org/10.3390/rs8020135.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shen, Y., A. Xiong, Y. Wang, and P. Xie, 2010: Performance of high-resolution satellite precipitation products over China.. J. Geophys. Res., 115, D02114, https://doi.org/10.1029/2009JD012097.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shige, S., S. Kida, H. Ashiwake, T. Kubota, and K. Aonashi, 2013: Improvement of TMI rain retrievals in mountainous areas. J. Appl. Meteor. Climatol., 52, 242254, https://doi.org/10.1175/JAMC-D-12-074.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, Q., C. Miao, Q. Duan, H. Ashouri, S. Sorooshian, and K.-L. Hsu, 2018: A review of global precipitation data sets: Data sources, estimation, and intercomparisons. Rev. Geophys., 56, 79107, https://doi.org/10.1002/2017RG000574.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tan, J., W. A. Petersen, and A. Tokay, 2016: A novel approach to identify sources of errors in IMERG for GPM ground validation. J. Hydrometeor., 17, 24772491, https://doi.org/10.1175/JHM-D-16-0079.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tan, J., W. A. Petersen, P.-E. Kirstetter, and Y. Tian, 2017: Performance of IMERG as a function of spatiotemporal scale. J. Hydrometeor., 18, 307319, https://doi.org/10.1175/JHM-D-16-0174.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tan, J., W. A. Petersen, G. Kirchengast, D. C. Goodrich, and D. B. Wolff, 2018: Evaluation of Global Precipitation Measurement rainfall estimates against three dense gauge networks. J. Hydrometeor., 19, 517532, https://doi.org/10.1175/JHM-D-17-0174.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tang, G., Y. Ma, D. Long, L. Zhong, and Y. Hong, 2016: Evaluation of GPM Day-1 IMERG and TMPA version-7 legacy products over mainland China at multiple spatiotemporal scales. J. Hydrol., 533, 152167, https://doi.org/10.1016/j.jhydrol.2015.12.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tang, G., A. Behrangi, D. Long, C. Li, and Y. Hong, 2018: Accounting for spatiotemporal errors of gauges: A critical step to evaluate gridded precipitation products. J. Hydrol., 559, 294306, https://doi.org/10.1016/j.jhydrol.2018.02.057.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tang, L., Y. Tian, F. Yan, and E. Habib, 2015: An improved procedure for the validation of satellite-based precipitation estimates. Atmos. Res., 163, 6173, https://doi.org/10.1016/j.atmosres.2014.12.016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tian, F., S. Hou, L. Yang, H. Hu, and A. Hou, 2018: How does the evaluation of the GPM IMERG rainfall product depend on gauge density and rainfall intensity? J. Hydrometeor., 19, 339349, https://doi.org/10.1175/JHM-D-17-0161.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Villarini, G., and W. F. Krajewski, 2007: Evaluation of the research version TMPA three-hourly 0.25° × 0.25° rainfall estimates over Oklahoma. Geophys. Res. Lett., 34, L05402, https://doi.org/10.1029/2006GL029147.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Villarini, G., P. V. Mandapaka, W. F. Krajewski, and R. J. Moore, 2008: Rainfall and sampling uncertainties: A rain gauge perspective. J. Geophys. Res., 113, D11102, https://doi.org/10.1029/2007JD009214.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Viviroli, D., H. H. Dürr, B. Messerli, M. Meybeck, and R. Weingartner, 2007: Mountains of the world, water towers for humanity: Typology, mapping, and global significance. Water Resour. Res., 43, W07447, https://doi.org/10.1029/2006WR005653.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wasko, C., R. M. Parinussa, and A. Sharma, 2016: A quasi-global assessment of changes in remotely sensed rainfall extremes with temperature. Geophys. Res. Lett., 43, 12 65912 668, https://doi.org/10.1002/2016GL071354.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, R., F. Tian, L. Yang, H. Hu, H. Lu, and A. Hou, 2017: Ground validation of GPM IMERG and TRMM 3B42V7 rainfall products over southern Tibetan Plateau based on a high-density rain gauge network. J. Geophys. Res. Atmos., 122, 910924, https://doi.org/10.1002/2016JD025418.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zambrano-Bigiarini, M., A. Nauditt, C. Birkel, K. Verbist, and L. Ribbe, 2017: Temporal and spatial evaluation of satellite-based rainfall estimates across the complex topographical and climatic gradients of Chile. Hydrol. Earth Syst. Sci., 21, 12951320, https://doi.org/10.5194/hess-21-1295-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zulkafli, Z., W. Buytaert, C. Onof, B. Manz, E. Tarnavsky, W. Lavado, and J.-L. Guyot, 2014: A comparative performance analysis of TRMM 3B42 (TMPA) versions 6 and 7 for hydrological applications over Andean–Amazon river basins. J. Hydrometeor., 15, 581592, https://doi.org/10.1175/JHM-D-13-094.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Evaluation of IMERG V05B 30-Min Rainfall Estimates over the High-Elevation Tropical Andes Mountains

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  • 1 Centre for Water in the Minerals Industry, Sustainable Minerals Institute, The University of Queensland, Brisbane, Queensland, Australia
  • 2 School of Civil Engineering, The University of Queensland, Brisbane, Queensland, Australia
  • 3 Water Research Centre, School of Civil and Environmental Engineering, University of New South Wales, Kensington, New South Wales, Australia
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Abstract

Satellite-based estimates of rainfall are frequently used to complement scarce networks of gauges. Understanding uncertainties is an important step, but it is often hindered by a lack of validation data or misrepresented by spatial-scale-related uncertainties, which are especially important in spatially variable regions such as mountains. This study evaluates the Integrated Multisatellite Retrievals for GPM (IMERG) V05B 30-min estimates for all three runs (Early, Late, Final) over the high tropical Andes. A unique dataset containing 15 rain gauges located within one IMERG grid at elevations ranging from 3800 to 4600 m provides a first evaluation opportunity in this topographical context. The evaluation was based on categorical, statistical, and graphical methods. Error dependencies on precipitation characteristics and data source of the IMERG estimate were investigated. We show that IMERG severely underdetects precipitation events, thus underestimating precipitation depths. Poor detection is partially attributable to the low-intensity nature of precipitation over the region. However, tracing the error to the data source highlights limitations in passive microwave retrievals over the full range of intensities. No IMERG run has best overall performance, emphasizing that run suitability is application specific. The impact of gauge density on performance metrics was also evaluated and showed that subdaily IMERG accuracy is overestimated by sparse networks. A minimum of six gauges was required at the 30-min increment so that performance metrics are within 0.1 points of their true scores. We provide the first comprehensive assessment of 30-min IMERG in a mountainous setting, highlighting the importance of high-density networks for accurate subdaily evaluations.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-20-0114.s1.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Nevenka Bulovic, n.bulovic@uq.edu.au

Abstract

Satellite-based estimates of rainfall are frequently used to complement scarce networks of gauges. Understanding uncertainties is an important step, but it is often hindered by a lack of validation data or misrepresented by spatial-scale-related uncertainties, which are especially important in spatially variable regions such as mountains. This study evaluates the Integrated Multisatellite Retrievals for GPM (IMERG) V05B 30-min estimates for all three runs (Early, Late, Final) over the high tropical Andes. A unique dataset containing 15 rain gauges located within one IMERG grid at elevations ranging from 3800 to 4600 m provides a first evaluation opportunity in this topographical context. The evaluation was based on categorical, statistical, and graphical methods. Error dependencies on precipitation characteristics and data source of the IMERG estimate were investigated. We show that IMERG severely underdetects precipitation events, thus underestimating precipitation depths. Poor detection is partially attributable to the low-intensity nature of precipitation over the region. However, tracing the error to the data source highlights limitations in passive microwave retrievals over the full range of intensities. No IMERG run has best overall performance, emphasizing that run suitability is application specific. The impact of gauge density on performance metrics was also evaluated and showed that subdaily IMERG accuracy is overestimated by sparse networks. A minimum of six gauges was required at the 30-min increment so that performance metrics are within 0.1 points of their true scores. We provide the first comprehensive assessment of 30-min IMERG in a mountainous setting, highlighting the importance of high-density networks for accurate subdaily evaluations.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-20-0114.s1.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Nevenka Bulovic, n.bulovic@uq.edu.au
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