Abstract
Selection of a best suited satellite-based gridded rainfall product (SGRP) is challenging due to their significant variations at spatial and temporal scale. The present study comprehensively evaluated the SGRPs (Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), Tropical Rainfall Measuring Mission (TRMM), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and SM2RAIN algorithm-based product) at daily scale for the period 1998–2013, with reference to gauge-based gridded rainfall product generated by Indian Meteorological Department (IMD) over the Mahanadi river basin in eastern India. Spatio-temporal evaluation of SGRPs was carried out intra-seasonally using both descriptive (event detection) and value-based approaches (coherence in measurement values). Studies that require accurate representation of rainfall event occurrence (descriptive parameter approach) results indicated that TRMM, CHIRPS, SM2RAIN, and CHIRPS gridded datasets performed better during winter, pre-monsoon, south-west monsoon, and north-east monsoon, respectively. On the contrary, studies that require precise measurements of rainfall values on a daily scale (value-based parameter approach) indicated that CHIRPS (during winter and pre-monsoon) and SM2RAIN (during south-west monsoon and north-east monsoon) may be used. Further, best performing rainfall products at seasonal scale were identified through ensembling approach. CHIRPS datasets prominently identified rainfall events across the study region during the winter, pre-monsoon, and the north-east monsoon periods. However, during the southwest monsoon period, SM2RAIN gridded dataset best represented the rainfall pattern of the study region. An ensembled gridded precipitation product is also generated incorporating the grid-level performance of various SRGPs.
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Data availability
Data analyzed in this study were a combination of (a) reanalysis of existing datasets (TRMM, CHIRPS,SM2RAIN, and PERSIANN), which are openly available at locations cited in the reference section, and (b) IMD datasets, which is subject to confidentiality agreements, where supporting data can only be made available to bona fide researchers subject to a non-disclosure agreement.
Code availability
The code for analyzing and processing the datasets may be provided upon reasonable request and discretion of authors.
References
Adler RF, George JH, Alfred C, Ralph F, Ping-Ping X, John J, Bruno R (2003) The Version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). J Hydrometeorol 4:1147–1167. https://doi.org/10.1175/1525-7541(2003)0042.0.CO;2
Alexander LV, Zhang X, Peterson TC, Caesar J, Gleason B, Klein AMGT, Haylock M (2006) Global observed changes in daily climate extremes of temperature and precipitation. J Geophys Res Atmos 111(D5). https://doi.org/10.1029/2005JD006290
Ashouri, Hamed, Kuo-Lin H, Soroosh S, Dan KB, Kenneth RK, Dewayne CL, Brian RN, Olivier PP (2015) PERSIANN-CDR: daily precipitation climate data record from multisatellite observations for hydrological and climate studies. Bull Am Meteorol Soc 96:69–83. https://doi.org/10.1175/BAMS-D-13-00068.1
Brocca L, Luca C, Christian M, Tommaso M, Sebastian H, Stefan H, Richard K, Wouter D, Wolfgang W, Vincenzo L (2014) Soil as a natural rain gauge: estimating global rainfall from satellite soil moisture data. J Geophys Res Atmos 119:5128–5141. https://doi.org/10.1002/2014JD021489
Chen M, Pingping X, John EJ, Phillip AA (2002) Global land precipitation: a 50-yr monthly analysis based on gauge observations. J Hydrometeorol 3:249–266
Ciabatta L, Massari C, Brocca L, Gruber A, Reimer C, Hahn S, Paulik C, Dorigo W, Kidd R, Wagner W (2018) SM2RAIN-CCI: a new global long-term rainfall data set derived from ESA CCI soil moisture. Earth Syst Sci Data 10:267–280. https://doi.org/10.5194/essd-10-267-2018
Cifelli R, Chandrasekar V, Lim S, Kennedy PC, Wang Y, Rutledge SA (2011) A new dual-polarization radar rainfall algorithm: application in Colorado precipitation events. J Atmos Ocean Technol 28:352–364. https://doi.org/10.1175/2010JTECHA1488.1
Cloke HL, Pappenberger F (2009) Ensemble flood forecasting: a review. J Hydrol 375:613–626. https://doi.org/10.1016/j.jhydrol.2009.06.005
Ebert EE, Janowiak JE, Kidd C (2007) Comparison of near-real-time precipitation estimates from satellite observations and numerical models. Bull Am Meteorol Soc 88:47–64. https://doi.org/10.1175/BAMS-88-1-47
Efstratiadis A, Koutsoyiannis D (2010) One decade of multi-objective calibration approaches in hydrological modelling: a review. Hydrol Sci J 55:58–78. https://doi.org/10.1080/02626660903526292
Funk C, Pete P, Martin L, Diego P, James V, Shraddhanand S, Gregory H (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
Golian S, Moazami S, Kirstetter PE et al (2015) Evaluating the performance of merged multi-satellite precipitation products over a complex terrain. Water Resour Manag 29:4885–4901. https://doi.org/10.1007/s11269-015-1096-6
Gupta V, Manoj KJ, Pushpendra KS, Vishal S (2019) An assessment of global satellite-based precipitation datasets in capturing precipitation extremes: a comparison with observed precipitation dataset in India. Int J Climatol. https://doi.org/10.1002/joc.6419
Huffman GJ, David TB, Eric JN, David BW, Robert FA, Guojun G, Yang H, Kenneth PB, Erich FS (2007) The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8:38–55. https://doi.org/10.1175/JHM560.1
Javanmard S, Akiyo Y, Masato N, Bodaghjamali J, Kawamoto H (2010) Comparing high-resolution gridded precipitation data with satellite rainfall estimates of TRMM3B42 over Iran. Adv Geosci 25:119–125. https://doi.org/10.5194/adgeo-25-119-2010
Joyce Robert J, John EJ, Phillip AA, Pingping X (2004) CMORPH: a method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J Hydrometeorol 5:17
Kidd BCP, Turk J, Huffman GJ, Joyce R, Hsu KL, Braithwaite D (2011) Intercomparison of high-resolution precipitation products over northwest Europe. J Hydrometeorol 13:67–83. https://doi.org/10.1175/JHM-D-11-042.1
Kidd C, Huffman G (2011) Global precipitation measurement. Meteorol Appl 18:334–353. https://doi.org/10.1002/met.284
Kidd C, Becker A, Huffman GJ, Muller CL, Joe P, Skofronick-Jackson G, Kirschbaum DB (2017) So, how much of the Earth’s surface is covered by rain gauges? Bull Am Meteorol Soc 98:69–78. https://doi.org/10.1175/bams-d-14-00283.1
Kneis D, Chatterjee C, Singh R (2014) Evaluation of TRMM rainfall estimates over a large Indian river basin (Mahanadi). Hydrol Earth Syst Sci Discuss 11:1169–1201. https://doi.org/10.5194/hessd-11-1169-2014
Kucera PA, Elizabeth EE, Joseph TF, Vincenzo L, Dalia K, Francisco JT, Alexander L, Borsche M (2013) Precipitation from space: advancing earth system science. Bull Am Meteorol Soc 94:365–375. https://doi.org/10.1175/BAMS-D-11-00171.1
Levizzani V, Kidd C, Aonashi K, Bennartz R, Ferraro RR, Huffman GJ, Roca R, Turk FJ, Wang NY (2018) The activities of the international precipitation working group. Q J R Meteorol Soc 144(S1):3–15. https://doi.org/10.1002/qj.3214
Liu YY, Parinussa RM, Dorigo WA, de Jeu RAM, Wagner W, van Dijk AIJM, McCabe MF, Evans JP (2011) Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrol Earth Syst Sci 15:425–436. https://doi.org/10.5194/hess-15-425-2011
Michaelides S, Levizzani V, Anagnostou E, Bauer P, Kasparis T, Lane JE (2009) Precipitation: measurement, remote sensing, climatology and modeling. Atmos Res 94:512–533. https://doi.org/10.1016/j.atmosres.2009.08.017
Nicholson SE, Some B, McCollum J, Nelkin E, Klotter D, Berte Y, Diallo BM (2003) Validation of TRMM and other rainfall estimates with a high-density gauge dataset for West Africa. Part II: validation of TRMM rainfall products. J Appl Meteorol 42:1355–1368. https://doi.org/10.1175/1520-0450(2003)042%3c1355:VOTAOR%3e2.0.CO;2
Pai DS, Rajeevan M, Sreejith OP, Mukhopadhyay B, Satbha NS (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(1):1–8. https://doi.org/10.54302/mausam.v65i1.851
Prakash S (2019) Performance assessment of CHIRPS, MSWEP, SM2RAIN-CCI, and TMPA precipitation products across India. J Hydrol 571:50–59. https://doi.org/10.1016/j.jhydrol.2019.01.036
Shah HL, Mishra V (2015) Uncertainty and bias in satellite-based precipitation estimates over Indian subcontinental basins: implications for real-time streamflow simulation and flood prediction. J Hydrometeorol 17:615–636. https://doi.org/10.1175/JHM-D-15-0115.1
Shepard D (1968) A two-dimensional interpolation function for irregularly-spaced data. Proceedings of the 1968 ACM National Conference, New York, 27–29 August 1968, pp 517–524. https://doi.org/10.1145/800186.810616
Shreyashi SM, Abhisek S, Akhilesh K (2021) Catchment specific evaluation of Aphrodite’s and TRMM derived gridded precipitation data products for predicting runoff in a semi gauged watershed of Tropical India. Geocarto Int 36(11):1292–1308
Strangeways I (2010) A history of rain gauges. Weather 65:133–138. https://doi.org/10.1002/wea.548
Sun Q, Chiyuan M, Qingyun D, Hamed A, Soroosh S, Kuo-Lin H (2018) A review of global precipitation data sets: data sources, estimation, and intercomparisons. Rev Geophys 56:79–107. https://doi.org/10.1002/2017RG000574
Tapiador FJ, Navarro A, Levizzani V, García-Ortega E, Huffman GJ, Kidd C, Kucera PA (2017) Global precipitation measurements for validating climate models. Atmos Res 197:1–20. https://doi.org/10.1016/j.atmosres.2017.06.021
Tian Y, Peters-Lidard CD (2007) Systematic anomalies over inland water bodies in satellite-based precipitation estimates. Geophys Res Lett 34. https://doi.org/10.1029/2007GL030787
Vose RS, Schmoyer RL, Steurer PM, Peterson TC, Heim R, Karl TR, Eischeid JK. (1992) The Global historical climatology network: long-term monthly temperature, precipitation, sea level pressure, and station pressure data. Oak Ridge National Lab., TN (United States). Carbon Dioxide Information Analysis Center. https://doi.org/10.2172/10178730
Yang Y, Lou Y (2014) Evaluating the performance of remote sensing precipitation products CMORPH, PERSIANN, and TMPA, in the arid region of northwest China. Theoret Appl Climatol 118:429–445. https://doi.org/10.1007/s00704-013-1072-0
Zambrano F, Brian W, Tsegaye T, Lillo-Saavedra M, Octavio L (2017) Evaluating satellite-derived long-term historical precipitation datasets for drought monitoring in Chile. Atmos Res 186:26–42. https://doi.org/10.1016/j.atmosres.2016.11.006
Acknowledgements
The authors are grateful to Dr. Raj Kumar, Director, NRSC, for the support and suggestions during the execution of the study. The authors are very much thankful to various agencies for providing the rainfall gridded products in the open-source domain necessary for this study. Authors sincerely appreciate the anonymous reviewers, Editor, and Associate Editor for the critical review that has significantly improved the quality of manuscript.
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NRSR: methodology, software, validation, formal analysis, investigation, resources, data curation, writing—original draft, writing—review and editing, visualization; VMC: conceptualization, methodology, investigation, resources, writing—review and editing, supervision, project administration; VVR: resources, supervision, editing, project administration; CSJ: resources, supervision, editing, project administration.
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NR, S., Chowdary, V.M., Vala, V.R. et al. Spatio-temporal evaluation of event detection and measurement coherence among satellite rainfall products for ensembled dataset generation. Theor Appl Climatol 148, 1477–1497 (2022). https://doi.org/10.1007/s00704-022-04002-x
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DOI: https://doi.org/10.1007/s00704-022-04002-x