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Satellite precipitation product: Applicability and accuracy evaluation in diverse region

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Abstract

Satellite precipitation products, e.g., Tropical Rainfall Measuring Mission version-07 (hereafter TRMM) and its successor Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (hereafter IMERG) are being used at a global scale for rainfall estimation. Recently, SM2RAIN-ASCAT (hereafter SM2RAIN) is a novel addition to satellite-based precipitation products which gives the rainfall estimates from the knowledge of soil moisture state and is based on ‘bottom to top’ approach. A comparative assessment of any newly developed product or a new version of the product is quite vital for algorithm developers and users. Hence, this research work was carried out to evaluate the accuracy and applicability of SM2RAIN, in comparison to in-situ data, TRMM, and IMERG in diverse regions of Pakistan. The comparative analysis was performed on a temporal scale (daily and monthly) and seasonal scale (spring, autumn, summer, and winter) using five performance metrics namely, root mean square error (RMSE), correlation coefficient (CC), false alarm ratio (FAR), the probability of detection (POD), and critical success index (CSI). The results showed that: (1) SM2RAIN is a better rainfall estimation product in the dry region (having avg. CC>0.35), however, less effective in hilly and mountainous terrain having high rainfall intensity; (2) SM2RAIN provides more satisfactory estimates in winter and autumn seasons, while relative poor in the summer season; (3) SM2RAIN performs better in terms of rainfall detection with an average POD of 0.61; (4) the overall performance of SM2RAIN is very convincing and it was concluded that SM2RAIN can be a feasible satellite product for most of the areas of Pakistan. It is noteworthy here to mention that this could be the preliminary assessment of SM2RAIN in diverse climatic zones of Pakistan.

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References

  1. 1

    Zhang Y, Sun A, Sun H, et al. Error adjustment of TMPA satellite precipitation estimates and assessment of their hydrological utility in the middle and upper Yangtze River basin, china. Atmos Res, 2019, 216: 52–64

  2. 2

    Villarini G, Mandapaka P V, Krajewski W F, et al. Rainfall and sampling uncertainties: A rain gauge perspective. J Geophys Res, 2008, 113: D11102

  3. 3

    Kidd C, Becker A, Huffman G J, et al. So, how much of the Earth’s surface is covered by rain gauges? Bull Amer Meteorol Soc, 2017, 98: 69–78

  4. 4

    Jameson A R, Kostinski A B. Spurious power-law relations among rainfall and radar parameters. Q J R Meteorol Soc, 2002, 128: 2045–2058

  5. 5

    Huffman G J, Bolvin D T, Nelkin E J, et al. The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol, 2007, 8: 38–55

  6. 6

    Heinemann T, Latanzio A, Roveda F. The Eumetsat multi-sensor precipitation estimate (MPE). In: Second International Precipitation Working group (IPWG) Meeting. 2002. 23–27

  7. 7

    Heinemann T, Kerényi J. The EUMETSAT multi sensor precipitation estimate (MPE): Concept and validation. In: EUMETSAT Users Conf. Weimar, 2003

  8. 8

    Hsu K, Gao X, Sorooshian S, et al. Precipitation estimation from remotely sensed information using artificial neural networks. J Appl Meteorol, 1997, 36: 1176–1190

  9. 9

    Joyce R J, Janowiak J E, Arkin P A, et al. CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J Hydrometeorol, 2004, 5: 487–503

  10. 10

    Joyce R J, Xie P. Kalman filter-based CMORPH. J Hydrometeorol, 2011, 12: 1547–1563

  11. 11

    Huffman G J, Bolvin D T, Braithwaite D, et al. NASA global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG). Algorithm theoretical basis document, version 06, 2015, 4: 30

  12. 12

    Huffman G J, Bolvin D T, Nelkin E J. Day 1 IMERG Final Run Release Notes. Greenbelt: NASA/GSFC, 2015

  13. 13

    Palmer T N, Brankovic C, Molteni F, et al. The European Centre for Medium-range Weather Forecasts (ECMWF) program on extended-range prediction. Bull Amer Meteorol Soc, 1990, 71: 1317–1330

  14. 14

    Dee D P, Uppala S M, Simmons A J, et al. The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Q J R Meteorol Soc, 2011, 137: 553–597

  15. 15

    Funk C, Peterson P, Landsfeld M, et al. The climate hazards infrared precipitation with stations—A new environmental record for monitoring extremes. Sci Data, 2015, 2: 150066

  16. 16

    Conti F L, Hsu K L, Noto L V, et al. Evaluation and comparison of satellite precipitation estimates with reference to a local area in the Mediterranean Sea. Atmos Res, 2014, 138: 189–204

  17. 17

    Mei Y, Anagnostou E N, Nikolopoulos E I, et al. Error analysis of satellite precipitation products in mountainous basins. J Hydrometeorol, 2014, 15: 1778–1793

  18. 18

    Skok G, Žagar N, Honzak L, et al. Precipitation intercomparison of a set of satellite- and raingauge-derived datasets, ERA Interim reanalysis, and a single WRF regional climate simulation over Europe and the North Atlantic. Theor Appl Climatol, 2016, 123: 217–232

  19. 19

    Stampoulis D, Anagnostou E N, Nikolopoulos E I. Assessment of high-resolution satellite-based rainfall estimates over the Mediterranean during heavy precipitation events. J Hydrometeorol, 2013, 14: 1500–1514

  20. 20

    Anagnostou E N, Maggioni V, Nikolopoulos E I, et al. Benchmarking high-resolution global satellite rainfall products to radar and rain-gauge rainfall estimates. IEEE Trans Geosci Remote Sens, 2009, 48: 1667–1683

  21. 21

    Gourley J J, Hong Y, Flamig Z L, et al. Hydrologic evaluation of rainfall estimates from radar, satellite, gauge, and combinations on Ft. Cobb basin, Oklahoma. J Hydrometeorol, 2011, 12: 973–988

  22. 22

    Maggioni V, Vergara H J, Anagnostou E N, et al. Investigating the applicability of error correction ensembles of satellite rainfall products in river flow simulations. J Hydrometeorol, 2013, 14: 1194–1211

  23. 23

    Qiao L, Hong Y, Chen S, et al. Performance assessment of the successive Version 6 and Version 7 TMPA products over the climate-transitional zone in the southern Great Plains, USA. J Hydrol, 2014, 513: 446–456

  24. 24

    Brown J E M. An analysis of the performance of hybrid infrared and microwave satellite precipitation algorithms over India and adjacent regions. Remote Sens Environ, 2006, 101: 63–81

  25. 25

    Derin Y, Yilmaz K K. Evaluation of multiple satellite-based precipitation products over complex topography. J Hydrometeorol, 2014, 15: 1498–1516

  26. 26

    Gao Y C, Liu M. Evaluation of high-resolution satellite precipitation products using rain gauge observations over the Tibetan Plateau. Hydrol Earth Syst Sci, 2012, 9: 9503–9532

  27. 27

    Guo H, Chen S, Bao A, et al. Inter-comparison of high-resolution satellite precipitation products over Central Asia. Remote Sens, 2015, 7: 7181–7211

  28. 28

    Anjum M N, Ding Y, Shangguan D, et al. Performance evaluation of latest integrated multi-satellite retrievals for Global Precipitation Measurement (IMERG) over the northern highlands of Pakistan. Atmos Res, 2018, 205: 134–146

  29. 29

    Anjum M N, Ding Y, Shangguan D, et al. Evaluation of high-resolution satellite-based real-time and post-real-time precipitation estimates during 2010 extreme flood event in Swat River Basin, Hindukush region. Adv Meteorol, 2016, 2016: 1–8

  30. 30

    Cheema M J M, Bastiaanssen W G M. Local calibration of remotely sensed rainfall from the TRMM satellite for different periods and spatial scales in the Indus Basin. Int J Remote Sens, 2012, 33: 2603–2627

  31. 31

    Hussain M S, Lee S. Long-term variability and changes of the precipitation regime in Pakistan. Asia-Pac J Atmos Sci, 2014, 50: 271–282

  32. 32

    Hussain Y, Satgé F, Hussain M B, et al. Performance of CMORPH, TMPA, and PERSIANN rainfall datasets over plain, mountainous, and glacial regions of Pakistan. Theor Appl Climatol, 2018, 131: 1119–1132

  33. 33

    Iqbal M F, Athar H. Validation of satellite based precipitation over diverse topography of Pakistan. Atmos Res, 2018, 201: 247–260

  34. 34

    Khan S I, Hong Y, Gourley J J, et al. Evaluation of three high-resolution satellite precipitation estimates: Potential for monsoon monitoring over Pakistan. Adv Space Res, 2014, 54: 670–684

  35. 35

    Rahman K, Shang S, Shahid M, et al. Developing an ensemble precipitation algorithm from satellite products and its topographical and seasonal evaluations over Pakistan. Remote Sens, 2018, 10: 1835

  36. 36

    Rehman A, Chishtie F, Qazi W, et al. Evaluation of three-hourly TMPA rainfall products using telemetric rain gauge observations at Lai Nullah basin in Islamabad, Pakistan. Remote Sens, 2018, 10: 2040

  37. 37

    Muhammad W, Yang H, Lei H, et al. Improving the regional applicability of satellite precipitation products by ensemble algorithm. Remote Sens, 2018, 10: 577

  38. 38

    Iqbal M F, Athar H. Variability, trends, and teleconnections of observed precipitation over Pakistan. Theor Appl Climatol, 2018, 134: 613–632

  39. 39

    Almazroui M. Calibration of TRMM rainfall climatology over Saudi Arabia during 1998–2009. Atmos Res, 2011, 99: 400–414

  40. 40

    Islam M N, Das S, Uyeda H. Calibration of TRMM derived rainfall over Nepal during 1998–2007. Open Atmos Sci J, 2010, 4: 12–23

  41. 41

    Wang W, Lu H, Zhao T, et al. Evaluation and comparison of daily rainfall from latest GPM and TRMM products over the Mekong River Basin. IEEE J Sel Top Appl Earth Observations Remote Sens, 2017, 10: 2540–2549

  42. 42

    Brocca L, Moramarco T, Melone F, et al. A new method for rainfall estimation through soil moisture observations. Geophys Res Lett, 2013, 40: 853–858

  43. 43

    Brocca L, Massari C, Ciabatta L, et al. Rainfall estimation from in situ soil moisture observations at several sites in Europe: An evaluation of the SM2RAIN algorithm. J Hydrol Hydromech, 2015, 63: 201–209

  44. 44

    Ciabatta L, Brocca L, Massari C, et al. Integration of satellite soil moisture and rainfall observations over the Italian territory. J Hydrometeorol, 2015, 16: 1341–1355

  45. 45

    Ciabatta L, Brocca L, Massari C, et al. Rainfall-runoff modelling by using SM2RAIN-derived and state-of-the-art satellite rainfall products over Italy. Int J Appl Earth Observation Geoinf, 2016, 48: 163–173

  46. 46

    Chiaravalloti F, Brocca L, Procopio A, et al. Assessment of GPM and SM2RAIN-ASCAT rainfall products over complex terrain in southern Italy. Atmos Res, 2018, 206: 64–74

  47. 47

    Paredes-Trejo F, Barbosa H, Rossato Spatafora L. Assessment of SM2RAIN-derived and state-of-the-art satellite rainfall products over northeastern Brazil. Remote Sens, 2018, 10: 1093

  48. 48

    Hirpa F A, Gebremichael M, Hopson T. Evaluation of high-resolution satellite precipitation products over very complex terrain in Ethiopia. J Appl Meteorol Climatol, 2010, 49: 1044–1051

  49. 49

    Thiemig V, Rojas R, Zambrano-Bigiarini M, et al. Validation of satellite-based precipitation products over sparsely gauged African river basins. J Hydrometeorol, 2012, 13: 1760–1783

  50. 50

    Tan M, Ibrahim A, Duan Z, et al. Evaluation of six high-resolution satellite and ground-based precipitation products over Malaysia. Remote Sens, 2015, 7: 1504–1528

  51. 51

    Chen Y, Ebert E E, Walsh K J E, et al. Evaluation of TMPA 3B42 daily precipitation estimates of tropical cyclone rainfall over Australia. J Geophys Res-Atmos, 2013, 118: 11,966-11,978

  52. 52

    Ullah W, Wang G, Ali G, et al. Comparing multiple precipitation products against in-situ observations over different climate regions of Pakistan. Remote Sens, 2019, 11: 628

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Author information

Correspondence to Si Chen.

Additional information

This work was supported by the Project of Hubei Key Laboratory of Regional Development and Environmental Response (Hubei University) (Grant No. 2018A003). The authors would also like to thank the developers of the GPM and TRMM products and specifically Brocca, L., et al. for providing the SM2RAIN data.

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Cite this article

Muhammad, E., Muhammad, W., Ahmad, I. et al. Satellite precipitation product: Applicability and accuracy evaluation in diverse region. Sci. China Technol. Sci. (2020) doi:10.1007/s11431-019-1457-3

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Keywords

  • SM2RAIN
  • satellite product
  • TRMM
  • IMERG
  • Pakistan