• Aadhar, S., and V. Mishra, 2017: High-resolution near real-time drought monitoring in South Asia. Sci. Data, 4, 170145, https://doi.org/10.1038/sdata.2017.145.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • AghaKouchak, A., and A. Mehran, 2013: Extended contingency table: Performance metrics for satellite observations and climate model simulations. Water Resour. Res., 49, 71447149, https://doi.org/10.1002/wrcr.20498.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ashouri, H., K. L. Hsu, S. Sorooshian, D. K. Braithwaite, K. R. Knapp, L. D. Cecil, B. R. Nelson, and O. P. Prat, 2015: PERSIANN-CDR: Daily precipitation climate data record from multisatellite observations for hydrological and climate studies. Bull. Amer. Meteor. Soc., 96, 6983, https://doi.org/10.1175/BAMS-D-13-00068.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barrett, E. C., and D. W. Martin, 1981: Use of Satellite Data in Rainfall Monitoring. Academic Press, 34 pp.

  • Basistha, A., D. S. Arya, and N. K. Goel, 2008: Spatial distribution of rainfall in Indian Himalayas–A case study of Uttarakhand region. Water Resour. Manage., 22, 13251346, https://doi.org/10.1007/s11269-007-9228-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beck, H. E., T. R. McVicar, A. I. van Dijk, J. Schellekens, R. A. de Jeu, and L. A. Bruijnzeel, 2011: Global evaluation of four AVHRR–NDVI data sets: Intercomparison and assessment against Landsat imagery. Remote Sens. Environ., 115, 25472563, https://doi.org/10.1016/j.rse.2011.05.012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beria, H., T. Nanda, D. S. Bisht, and C. Chatterjee, 2017: Does the GPM mission improve the systematic error component in satellite rainfall estimates over TRMM? An evaluation at a pan-India scale. Hydrol. Earth Syst. Sci., 21, 61176134, https://doi.org/10.5194/hess-21-6117-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bharti, V., and C. Singh, 2015: Evaluation of error in TRMM 3B42V7 precipitation estimates over the Himalayan region. J. Geophys. Res. Atmos., 120, 12 45812 473, https://doi.org/10.1002/2015JD023779.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bharti, V., C. Singh, J. Ettema, and T. A. R. Turkington, 2016: Spatiotemporal characteristics of extreme rainfall events over the Northwest Himalaya using satellite data. Int. J. Climatol., 36, 39493962, https://doi.org/10.1002/joc.4605.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, C., Q. Chen, Z. Duan, J. Zhang, K. Mo, Z. Li, and G. Tang, 2018: Multiscale comparative evaluation of the GPM IMERG v5 and TRMM 3B42 v7 precipitation products from 2015 to 2017 over a climate transition area of China. Remote Sens., 10, 944, https://doi.org/10.3390/rs10060944.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chevuturi, A., A. P. Dimri, S. Das, A. Kumar, and D. Niyogi, 2015: Numerical simulation of an intense precipitation event over Rudraprayag in the central Himalayas during 13–14 September 2012. J. Earth Syst. Sci., 124, 15451561, https://doi.org/10.1007/s12040-015-0622-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dahiya, G., P. Jena, S. Garg, and S. Azad, 2020: Inter-comparison of high-resolution satellite estimates for cloudburst events in the Northwest Himalaya. Himalayan Weather and Climate and Their Impact on the Environment, A. P. Dimri et al., Eds., Springer, 3–17.

    • Crossref
    • Export Citation
  • Das, S., R. Ashrit, and M. W. Moncrieff, 2006: Simulation of a Himalayan cloudburst event. J. Earth Syst. Sci., 115, 299313, https://doi.org/10.1007/BF02702044.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Derin, Y., and K. K. Yilmaz, 2014: Evaluation of multiple satellite-based precipitation products over complex topography. J. Hydrometeor., 15, 14981516, https://doi.org/10.1175/JHM-D-13-0191.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dimri, A. P., and S. K. Dash, 2012: Wintertime climatic trends in the western Himalayas. Climatic Change, 111, 775800, https://doi.org/10.1007/s10584-011-0201-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dimri, A. P., A. Chevuturi, D. Niyogi, R. J. Thayyen, K. Ray, S. N. Tripathi, A. K. Pandey, and U. C. Mohanty, 2017: Cloudbursts in Indian Himalayas: A review. Earth-Sci. Rev., 168, 123, https://doi.org/10.1016/j.earscirev.2017.03.006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dinku, T., P. Ceccato, E. Grover-Kopec, M. Lemma, S. J. Connor, and C. F. Ropelewski, 2007: Validation of satellite rainfall products over East Africa’s complex topography. Int. J. Remote Sens., 28, 15031526, https://doi.org/10.1080/01431160600954688.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dinku, T., F. Ruiz, S. J. Connor, and P. Ceccato, 2010: Validation and intercomparison of satellite rainfall estimates over Colombia. J. Appl. Meteor. Climatol., 49, 10041014, https://doi.org/10.1175/2009JAMC2260.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dinku, T., K. Hailemariam, R. Maidment, E. Tarnavsky, and S. Connor, 2014: Combined use of satellite estimates and rain gauge observations to generate high-quality historical rainfall time series over Ethiopia. Int. J. Climatol., 34, 24892504, https://doi.org/10.1002/joc.3855.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feidas, H., 2010: Validation of satellite rainfall products over Greece. Theor. Appl. Climatol., 99, 193216, https://doi.org/10.1007/s00704-009-0135-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ferraro, R. R., 1997: Special sensor microwave imager derived global rainfall estimates for climatological applications. J. Geophys. Res., 102, 16 71516 735, https://doi.org/10.1029/97JD01210.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ferraro, R. R., F. Weng, N. C. Grody, and L. Zhao, 2000: Precipitation characteristics over land from the NOAA-15 AMSU sensor. Geophys. Res. Lett., 27, 26692672, https://doi.org/10.1029/2000GL011665.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Funk, C., and et al. , 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
  • Goswami, B. N., V. Venugopal, D. Sengupta, M. S. Madhusoodanan, and P. K. Xavier, 2006: Increasing trend of extreme rain events over India in a warming environment. Science, 314, 14421445, https://doi.org/10.1126/science.1132027.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goswami, P., and K. V. Ramesh, 2008: Extreme rainfall events: Vulnerability analysis for disaster management and observation system design. Curr. Sci., 98, 10371044.

    • Search Google Scholar
    • Export Citation
  • Guhathakurta, P., O. P. Sreejith, and P. A. Menon, 2011: Impact of climate change on extreme rainfall events and flood risk in India. J. Earth Syst. Sci., 120, 359373, https://doi.org/10.1007/s12040-011-0082-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Herrera, S., J. M. Gutiérrez, R. Ancell, M. R. Pons, M. D. Frías, and J. Fernández, 2012: Development and analysis of a 50-year high-resolution daily gridded precipitation dataset over Spain (Spain02). Int. J. Climatol., 32, 7485, https://doi.org/10.1002/joc.2256.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hessels, T. M., 2015: Comparison and validation of several open access remotely sensed rainfall products for the Nile Basin. M.S. thesis, Dept. of Water Management, Delft University of Technology, 233 pp., http://resolver.tudelft.nl/uuid:3566f883-16fd-4465-be43-6b2037baa6ff.

  • Hofstra, N., M. New, and C. McSweeney, 2010: The influence of interpolation and station network density on the distributions and trends of climate variables in gridded daily data. Climate Dyn., 35, 841858, https://doi.org/10.1007/s00382-009-0698-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, Y., K. Hsu, S. Sorooshian, and X. Gao, 2004: Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system. J. Appl. Meteor., 43, 18341853, https://doi.org/10.1175/JAM2173.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, Y., D. Gochis, J. T. Cheng, K. L. Hsu, and S. Sorooshian, 2007: Evaluation of PERSIANN-CCS rainfall measurement using the NAME event rain gauge network. J. Hydrometeor., 8, 469482, https://doi.org/10.1175/JHM574.1.

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

  • Hsu, K., X. Gao, S. Sorooshian, and H. V. Gupta, 1997: Precipitation estimation from remotely sensed information using artificial neural networks. J. Appl. Meteor., 36, 11761190, https://doi.org/10.1175/1520-0450(1997)036<1176:PEFRSI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., and D. T. Bolvin, 2014: TRMM and other data precipitation data set documentation. NASA TRMM Doc., 42 pp., http://precip.gsfc.nasa.gov/pub/trmmdocs/3B42_3B43_doc.pdf.

  • Huffman, G. J., and et al. , 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., R. F. Adler, D. T. Bolvin, and E. J. Nelkin, 2010: The TRMM multi-satellite precipitation analysis (TMPA). Satellite Rainfall Applications for Surface Hydrology, Springer, 3–22.

    • Crossref
    • Export Citation
  • Huffman, G. J., D. T. Bolvin, D. Braithwaite, K. Hsu, R. Joyce, and P. Xie, 2014: NASA Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM (IMERG). Algorithm Theoretical Basis Doc., version 4.4, 26 pp., https://pps.gsfc.nasa.gov/Documents/IMERG_ATBD_V4.pdf.

  • Joyce, R. J., J. E. Janowiak, P. A. Arkin, and P. Xie, 2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeor., 5, 487503, https://doi.org/10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Katsanos, D., A. Retalis, and S. Michaelides, 2016: Validation of a high-resolution precipitation database (CHIRPS) over Cyprus for a 30-year period. Atmos. Res., 169, 459464, https://doi.org/10.1016/j.atmosres.2015.05.015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumar, B., K. C. Patra, and V. Lakshmi, 2016: Daily rainfall statistics of TRMM and CMORPH: A case for trans-boundary Gandak River basin. J. Earth Syst. Sci., 125, 919934, https://doi.org/10.1007/s12040-016-0710-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumar, P., and A. K. Varma, 2017: Assimilation of INSAT-3D hydro-estimator method retrieved rainfall for short-range weather prediction. Quart. J. Roy. Meteor. Soc., 143, 384394, https://doi.org/10.1002/qj.2929.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kummerow, C., and et al. , 2001: The evolution of the Goddard Profiling Algorithm (GPROF) for rainfall estimation from passive microwave sensors. J. Appl. Meteor., 40, 18011820, https://doi.org/10.1175/1520-0450(2001)040<1801:TEOTGP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mei, Y., E. N. Anagnostou, E. I. Nikolopoulos, and M. Borga, 2014: Error analysis of satellite precipitation products in mountainous basins. J. Hydrometeor., 15, 17781793, https://doi.org/10.1175/JHM-D-13-0194.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mishra, A. K., 2013: Effect of rain gauge density over the accuracy of rainfall: A case study over Bangalore, India. SpringerPlus, 2, 311, https://doi.org/10.1186/2193-1801-2-311.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mitra, A. K., I. M. Momin, E. N. Rajagopal, S. Basu, M. N. Rajeevan, and T. N. Krishnamurti, 2013: Gridded daily Indian monsoon rainfall for 14 seasons: Merged TRMM and IMD gauge analyzed values. J. Earth Syst. Sci., 122, 11731182, https://doi.org/10.1007/s12040-013-0338-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mitra, A. K., N. Kaushik, A. K. Singh, S. Parihar, and S. C. Bhan, 2018: Evaluation of INSAT-3D satellite derived precipitation estimates for heavy rainfall events and its validation with gridded GPM (IMERG) rainfall dataset over the Indian region. Remote Sens. Appl. Soc. Environ., 9, 9199, https://doi.org/10.1016/j.rsase.2017.12.006.

    • Search Google Scholar
    • Export Citation
  • Mondal, A., V. Lakshmi, and H. Hashemi, 2018: Intercomparison of trend analysis of multisatellite monthly precipitation products and gauge measurements for river basins of India. J. Hydrol., 565, 779790, https://doi.org/10.1016/j.jhydrol.2018.08.083.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nair, S., G. Srinivasan, and R. Nemani, 2009: Evaluation of multi-satellite TRMM derived rainfall estimates over a western state of India. J. Meteor. Soc. Japan, 87, 927939, https://doi.org/10.2151/jmsj.87.927.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nandargi, S., and O. N. Dhar, 2012: Extreme rainstorm events over the northwest Himalayas during 1875–2010. J. Hydrometeor., 13, 13831388, https://doi.org/10.1175/JHM-D-12-08.1.

    • Search Google Scholar
    • Export Citation
  • Orlanski, I., 1975: A rational subdivision of scales for atmospheric processes. Bull. Amer. Meteor. Soc., 56, 527530, https://doi.org/10.1175/1520-0477-56.5.527.

    • Search Google Scholar
    • Export Citation
  • Pai, D. S., L. Sridhar, M. Rajeevan, O. P. Sreejith, N. S. Satbhai, and B. Mukhopadhyay, 2014: Development of a new high spatial resolution (0.25° × 0.25°) long period (1901–2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region. Mausam, 65, 118.

    • Search Google Scholar
    • Export Citation
  • Parida, B. R., S. N. Behera, O. Bakimchandra, A. C. Pandey, and N. Singh, 2017: Evaluation of satellite-derived rainfall estimates for an extreme rainfall event over Uttarakhand, Western Himalayas. Hydrology, 4, 22, https://doi.org/10.3390/hydrology4020022.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prakash, S., V. Sathiyamoorthy, C. Mahesh, and R. M. Gairola, 2014: An evaluation of high-resolution multisatellite rainfall products over the Indian monsoon region. Int. J. Remote Sens., 35, 30183035, https://doi.org/10.1080/01431161.2014.894661.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prakash, S., A. K. Mitra, I. M. Momin, D. S. Pai, E. N. Rajagopal, and S. Basu, 2015: Comparison of TMPA-3B42 versions 6 and 7 precipitation products with gauge-based data over India for the southwest monsoon period. J. Hydrometeor., 16, 346362, https://doi.org/10.1175/JHM-D-14-0024.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prakash, S., A. K. Mitra, A. AghaKouchak, Z. Liu, H. Norouzi, and D. S. 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
  • Qin, Y., Z. Chen, Y. Shen, S. Zhang, and R. Shi, 2014: Evaluation of satellite rainfall estimates over the Chinese Mainland. Remote Sens., 6, 11 64911 672, https://doi.org/10.3390/rs61111649.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rahman, S. H., D. Sengupta, and M. Ravichandran, 2009: Variability of Indian summer monsoon rainfall in daily data from gauge and satellite. J. Geophys. Res., 114, D17113, https://doi.org/10.1029/2008JD011694.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rajeevan, M., J. Bhate, and A. K. Jaswal, 2008: Analysis of variability and trends of extreme rainfall events over India using 104 years of gridded daily rainfall data. Geophys. Res. Lett., 35, L18707, https://doi.org/10.1029/2008GL035143.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rao, V. K., A. K. Mitra, K. K. Singh, G. Bharathi, R. R. Kumar, K. Ray, and S. Ramakrishna, 2020: Evaluation of INSAT-3D derived TPW with AIRS retrievals and GNSS observations over the Indian region. Int. J. Remote Sens., 41, 11391169, https://doi.org/10.1080/01431161.2019.1657604.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roy Bhowmik, S. K., and A. K. Das, 2007: Rainfall analysis for Indian monsoon region using the merged rain gauge observations and satellite estimates: Evaluation of monsoon rainfall features. J. Earth Syst. Sci., 116, 187198, https://doi.org/10.1007/s12040-007-0019-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sadeghi, M., A. Akbari Asanjan, M. Faridzad, V. Afzali Gorooh, P. Nguyen, K. Hsu, S. Sorooshian, and D. Braithwaite, 2019: Evaluation of PERSIANN-CDR constructed using GPCP V2.2 and V2.3 and a comparison with TRMM 3B42 V7 and CPC unified gauge-based analysis in global scale. Remote Sens., 11, 2755, https://doi.org/10.3390/rs11232755.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scofield, R. A., and R. J. Kuligowski, 2003: Status and outlook of operational satellite precipitation algorithms for extreme-precipitation events. Wea. Forecasting, 18, 10371051, https://doi.org/10.1175/1520-0434(2003)018<1037:SAOOOS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sen Roy, S., and R. C. Balling Jr., 2004: Trends in extreme daily precipitation indices in India. Int. J. Climatol., 24, 457466, https://doi.org/10.1002/joc.995.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shepard, D., 1968: A two-dimensional interpolation function for irregularly spaced data. Proc. 1968 23rd ACM National Conf., New York, NY, ACM, 517–524, https://doi.org/10.1145/800186.810616.

    • Crossref
    • Export Citation
  • Singh, A. K., V. Singh, K. K. Singh, J. N. Tripathi, A. Kumar, A. K. Soni, M. Sateesh, and C. Khadke, 2018: A case study: Heavy rainfall event comparison between daily satellite rainfall estimation products with IMD gridded rainfall over peninsular India during 2015 winter monsoon. J. Indian Soc. Remote Sens., 46, 927935, https://doi.org/10.1007/s12524-018-0751-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Singh, R. B., and S. Mal, 2014: Trends and variability of monsoon and other rainfall seasons in Western Himalaya, India. Atmos. Sci. Lett., 15, 218226, https://doi.org/10.1002/asl2.494.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Toté, C., D. Patricio, H. Boogaard, R. Van der Wijngaart, E. Tarnavsky, and C. Funk, 2015: Evaluation of satellite rainfall estimates for drought and flood monitoring in Mozambique. Remote Sens., 7, 17581776, https://doi.org/10.3390/rs70201758.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. International Geophysics Series, Vol. 100, Academic Press, 648 pp.

All Time Past Year Past 30 Days
Abstract Views 214 214 35
Full Text Views 30 30 7
PDF Downloads 36 36 12

Performance Analysis of IMD High-Resolution Gridded Rainfall (0.25° × 0.25°) and Satellite Estimates for Detecting Cloudburst Events over the Northwest Himalayas

View More View Less
  • 1 School of Basic Sciences, Indian Institute of Technology Mandi, Himachal Pradesh, India
© Get Permissions
Restricted access

Abstract

The presence of a sparse rain gauge network in complex terrain like the Himalayas has encouraged the present study for the concerned evaluation of Indian Meteorological Department (IMD) ground-based gridded rainfall data for highly prevalent events like cloudbursts over the northwest Himalayas (NWH). To facilitate the abovementioned task, we intend to evaluate the performance of these observations at 0.25° × 0.25° (latitude–longitude) resolution against a predefined threshold (i.e., 99.99th percentile), thereby initially comprehending the success of IMD data in capturing the cloudburst events reported in media during 2014–16. Further, seven high-resolution satellite products, namely, CMORPH V0.x, PERSIANN-CDR, TMPA 3B42RT V7, IMERG V06B, INSAT-3D multispectral rainfall (IMR), CHIRPS V.2, and PERSIANN-CCS are evaluated against the IMD dataset. The following are our main results. 1) Six out of 18 cloudburst events are detected using IMD gridded data. 2) The contingency statistics at the 99.99th percentile reveal that the probability of detection (POD) of TMPA varies from 19.4% to 53.9% over the geographical stretch of NWH, followed by PERSIANN-CDR (18.6%–48.4%) and IMERG (4.9%–17.8%). 3) A new metric proposed as improved POD (IPOD) has been developed in this work, which takes into account the temporal lag that exists between observed and satellite estimates during an event period. Results show that for an event analysis IPOD provides a better comparison. The IPOD for TMPA is 32.8%–74.4%, followed by PERSIANN-CDR (34.4%–69.11%) and IMERG (15.3%–39.0%). 4) The conclusion stands as precipitation estimates obtained from CHIRPS are most suitable for monitoring cloudburst events over NWH with IPOD of 60.5%–78.6%.

Corresponding author: Sarita Azad, sarita@iitmandi.ac.in

Abstract

The presence of a sparse rain gauge network in complex terrain like the Himalayas has encouraged the present study for the concerned evaluation of Indian Meteorological Department (IMD) ground-based gridded rainfall data for highly prevalent events like cloudbursts over the northwest Himalayas (NWH). To facilitate the abovementioned task, we intend to evaluate the performance of these observations at 0.25° × 0.25° (latitude–longitude) resolution against a predefined threshold (i.e., 99.99th percentile), thereby initially comprehending the success of IMD data in capturing the cloudburst events reported in media during 2014–16. Further, seven high-resolution satellite products, namely, CMORPH V0.x, PERSIANN-CDR, TMPA 3B42RT V7, IMERG V06B, INSAT-3D multispectral rainfall (IMR), CHIRPS V.2, and PERSIANN-CCS are evaluated against the IMD dataset. The following are our main results. 1) Six out of 18 cloudburst events are detected using IMD gridded data. 2) The contingency statistics at the 99.99th percentile reveal that the probability of detection (POD) of TMPA varies from 19.4% to 53.9% over the geographical stretch of NWH, followed by PERSIANN-CDR (18.6%–48.4%) and IMERG (4.9%–17.8%). 3) A new metric proposed as improved POD (IPOD) has been developed in this work, which takes into account the temporal lag that exists between observed and satellite estimates during an event period. Results show that for an event analysis IPOD provides a better comparison. The IPOD for TMPA is 32.8%–74.4%, followed by PERSIANN-CDR (34.4%–69.11%) and IMERG (15.3%–39.0%). 4) The conclusion stands as precipitation estimates obtained from CHIRPS are most suitable for monitoring cloudburst events over NWH with IPOD of 60.5%–78.6%.

Corresponding author: Sarita Azad, sarita@iitmandi.ac.in
Save