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Theoretical and Applied Climatology

, Volume 130, Issue 1–2, pp 249–260 | Cite as

Trends of precipitation extreme indices over a subtropical semi-arid area using PERSIANN-CDR

  • Pari-Sima Katiraie-BoroujerdyEmail author
  • Hamed Ashouri
  • Kuo-lin Hsu
  • Soroosh Sorooshian
Original Paper

Abstract

In this study, satellite-based daily precipitation estimation data from precipitation estimation from remotely sensed information using artificial neural networks (PERSIANN)-climate data record (CDR) are being evaluated in Iran. This dataset (0.25°, daily), which covers over three decades of continuous observation beginning in 1983, is evaluated using rain-gauge data for the period of 1998–2007. In addition to categorical statistics and mean annual amount and number of rainy days, ten standard extreme indices were calculated to observe the behavior of daily extremes. The results show that PERSIANN-CDR exhibits reasonable performance associated with the probability of detection and false-alarm ratio, but it overestimates precipitation in the area. Although PERSIANN-CDR mostly underestimates extreme indices, it shows relatively high correlations (between 0.6316–0.7797) for intensity indices. PERSIANN-CDR data are also used to calculate the trend in annual amounts of precipitation, the number of rainy days, and precipitation extremes over Iran covering the period of 1983–2012. Our analysis shows that, although annual precipitation decreased in the western and eastern regions of Iran, the annual number of rainy days increased in the northern and northwestern areas. Statistically significant negative trends are identified in the 90th percentile daily precipitation, as well as the mean daily precipitation from wet days in the northern part of the study area. The positive trends of the maximum annual number of consecutive dry days in the eastern regions indicate that the dry periods became longer in these arid areas.

Keywords

Daily Precipitation Extreme Index Zagros Mountain Satellite Product Geostationary Earth Orbit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. AghaKouchak A, Behrangi A, Sorooshian S, Hsu KL, Amitai E (2011) Evaluation of satellite-retrieved extreme precipitation rates across the central United States. J Geophys Res 116:D02115. doi: 10.1029/2010JD014741 CrossRefGoogle Scholar
  2. Alexander LV, Arblaster JM (2009) Assessing trends in observed and modeled climate extremes over Australia in relation to future projections. Int J Climatol 29(3):417–435CrossRefGoogle Scholar
  3. Alexander LV et al (2006) Global observed changes in daily climate extremes of temperature and precipitation. J Geophys Res 111, D05109. doi: 10.1029/2005JD006290 Google Scholar
  4. Ashouri H, Hsu KL, Sorooshian S, Braithwaite DK, Knapp KR, Cecil LD, Nelson BR, Prat OP (2015a) PERSIANN-CDR: daily precipitation climate data record from multi-satellite observations for hydrological and climate studies. Bull Am Meteorol Soc 96:69–83. doi: 10.1175/BAMS-D-13-00068.1 CrossRefGoogle Scholar
  5. Ashouri H, Sorooshian S, Hsu K, Bosilovich MG, Lee J, Wehner MF (2015b) Evaluation of NASA’s MERRA precipitation product in reproducing the observed trend and distribution of extreme precipitation events in the United States. J Hydrometeor 17:693–711. doi: 10.1175/JHM-D-15-0097.1 CrossRefGoogle Scholar
  6. Ashouri H, Nguyen P, Thorstensen A, Hsu KL, Sorooshian S, Braithwaite D (2016) Assessing the efficacy of high-resolution satellite-based PERSIANN-CDR precipitation product in streamflow simulation. J Hydrmeteor (under rev.)Google Scholar
  7. Behrangi A, Khakbaz B, Jaw TC, AghaKouchak A, Hsu KL (2011) Hydrologic evaluation of satellite precipitation products over a mid-size basin. J Hydrol 397:225–237, doi: 10.1016/j.jhydrol.2010.11.043
  8. 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. doi: 10.1175/BAMS-88-1-47 CrossRefGoogle Scholar
  9. Frich P, Alexander LV, Della-Marta P, Gleason B, Haylock M, Klein Tank AMG, Peterson TC (2002) Observed coherent changes in climatic extremes during the second half of the twentieth century. Clim Res 19:193–212. doi: 10.3354/cr019193 CrossRefGoogle Scholar
  10. Groisman PY et al (1999) Changes in the probability of heavy precipitation: Important indicators of climatic change. Clim Change 42:243–283. doi: 10.1023/A:1005432803188 CrossRefGoogle Scholar
  11. Hsu KL, Gao X, Sorooshian S, Gupta HV (1997) Precipitation estimation from remotely sensed information using artificial neural networks. J Appl Meteor Climatol 36(9):1176–1190. doi: 10.1175/1520-0450(1997)036<1176:PEFRSI>2.0.CO;2 CrossRefGoogle Scholar
  12. Karl TR, Knight RW (1998) Secular trends of precipitation amount, frequency, and intensity in the United States. Bull Am Meteorol Soc 79:231–241. doi: 10.1175/1520-0477(1998)079<0231:STOPAF>2.0.CO;2 CrossRefGoogle Scholar
  13. Karl TR, Nicholls N, Ghazi A (1999) CLIVAR/GCOS/WMO workshop on indices and indicators for climate extremes: workshop summary. Clim Change 42:3–7. doi: 10.1023/A:1005491526870 CrossRefGoogle Scholar
  14. Karl TR, Knight RW, Plummer N (1995) Trends in high-frequency climate variability in the twentieth century. Nature 377:217–220. doi: 10.1038/377217a0 CrossRefGoogle Scholar
  15. Katiraie-Boroujerdy PS, Nasrollahi N, Hsu KL, Sorooshian S (2013) Evaluation of satellite-based precipitation estimation over Iran. J Arid Environ 97:205–219. doi: 10.1016/j.jaridenv.2013.05.013 CrossRefGoogle Scholar
  16. Kidd C, Bauer P, Turk J, Huffman G, Joyce R, Hsu KL, Braithwaite DK (2012) Inter-comparison of high-resolution precipitation products over northwest Europe. J Hydrometeor 13:67–83. doi: 10.1175/JHM-D-11-042.1 CrossRefGoogle Scholar
  17. Kidd C, Huffman G (2011) Global precipitation measurement. Met Apps 18:334–353. doi: 10.1002/met.284 CrossRefGoogle Scholar
  18. Klein Tank AMG et al (2006) Changes in daily temperature and precipitation extremes in central and south Asia. J Geophys Res 111:D16105. doi: 10.1029/2005JD006316 CrossRefGoogle Scholar
  19. Knapp KR, Ansari S, Bain CL, Bourassa MA, Dickinson MJ, Funk C, Helms CN, Hennon CC, Holmes CD, Huffman GJ, Kossin JP, Lee H-T, Loew A, Magnusdottir G (2011) Globally gridded satellite observations for climate studies. Bull Am Meteorol Soc 92:893–907. doi: 10.1175/2011BAMS3039.1 CrossRefGoogle Scholar
  20. Lanzante JR (1996) Resistant, robust and non-parametric techniques for the analysis of climate data: theory and examples, including applications to historical radiosonde station data. Int J Climatol 16:1197–1226. doi: 10.1002/(SICI)1097-0088(199611)16:11<1197::AID-JOC89>3.0.CO;2-L CrossRefGoogle Scholar
  21. Miao C, Ashouri H, Hsu K L, Sorooshian S, Duan Q (2015) Evaluation of the PERSIANN-CDR rainfall estimates in capturing the behavior of extreme precipitation events over China, J. Hydrometeor. 16(1), doi: org/ 10.1175/JHM-D-14-0174.1
  22. Mildrexler D, Zhao M, Running SW (2011) Satellite finds highest land skin temperatures on earth. Bull Am Meteorol Soc 92:850–860. doi: 10.1175/2011BAMS3067.1 CrossRefGoogle Scholar
  23. Moazami S, Golian S, Kavianpour MR, Hong Y (2013) Comparison of PERSIANN and V7 TRMM Multi-satellite precipitation analysis (TMPA) products with rain gauge data over Iran. Int J Remote Sensing 34(22):8156–8171. doi: 10.1080/01431161.2013.833360 CrossRefGoogle Scholar
  24. Nguyen P, Sellars S, Thorstensen A, Tao Y, Ashouri H, Braithwaite D, Hsu K, Sorooshian S (2014) Satellites track precipitation of Super Typhoon Haiyan. Eos Trans Amer Geophys Union 95:133–135. doi: 10.1002/2014EO160002 CrossRefGoogle Scholar
  25. Peterson TC et al (2001) Report on the activities of the Working Group on Climate Change Detection and Related Rapporteurs 1998–2001. WMO, Rep. WCDMP-47, WMO-TD 1071, Geneve, Switzerland, p 143Google Scholar
  26. Sneyers R (1990) On the statistical analysis of series of observations, WMO Technical Note, No 143. World Meteorology Organization, Geneva, p 192Google Scholar
  27. Sorooshian S, Hsu KL, Gao X, Gupta HV, Imam B, Braithwaite DK (2000) Evaluation of PERSIANN system satellite–based estimates of tropical rainfall. Bull Am Meteorol Soc 81:2035–2046. doi: 10.1175/1520-0477(2000)081<2035:EOPSSE>2.3.CO;2 CrossRefGoogle Scholar
  28. Tan ML, Ibrahim AL, Duan Z, Cracknell AP, Chaplot V (2015) Evaluation of six high-resolution satellite and ground-based precipitation products over Malaysia. Remot Sens 7:1504–1528. doi: 10.3390/rs70201504
  29. Tian Y, Peters-Lidard CD (2010) A global map of uncertainties in satellite-based precipitation measurements. Geophys Res Lett 37:L24407. doi: 10.1029/2010GL046008 CrossRefGoogle Scholar
  30. Tian Y, Peters-Lidard CD, Eylander JB, Joyce RJ, Huffman GJ, Adler RF, Hsu KL, Turk FJ, Garcia M, Zeng J (2009) Component analysis of errors in satellite-based precipitation estimates. J Geophys Res 114:D24101, doi: 10.1029/2009JD011949
  31. Wilks D.S. (2006) Statistical methods in the atmospheric sciences, 2nd ed. Academic Press, 627ppGoogle Scholar
  32. Yang X, Yong B, Hong Y, Chen S, Zhang X (2016) Error analysis of multi-satellite precipitation estimates with an independent raingauge observation network over a medium-sized humid basin, Hydrological Sci J, doi:  10.1080/02626667.2015.1040020
  33. Zhai P, Zhang X, Wan H, Pan X (2005) Trends in total precipitation and frequency of daily precipitation extremes over China. J Climate 18(7):1096–1108CrossRefGoogle Scholar
  34. Zhu Q, Xuan W, Liu L, Xu YP (2016) Evaluation and hydrological application of precipitation estimates derived from PERSIANN-CDR, TRMM 3B42V7, and NCEP-CFSR over humid regions in China. Hydrol Process. doi: 10.1002/hyp.10846

Copyright information

© Springer-Verlag Wien 2016

Authors and Affiliations

  • Pari-Sima Katiraie-Boroujerdy
    • 1
    Email author
  • Hamed Ashouri
    • 2
  • Kuo-lin Hsu
    • 2
  • Soroosh Sorooshian
    • 2
  1. 1.Faculty of Marine Science and TechnologyTehran North Branch, Islamic Azad UniversityTehranIran
  2. 2.Department of Civil and Environmental EngineeringCenter for Hydrometeorology and Remote Sensing (CHRS), The Henry Samueli School of Engineering, University of CaliforniaIrvineUSA

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