CONSULTA DIAGNÒSTICA DE MEDICINA INTERNA
MEDICINA INTERNA
Satellite-based estimation of precipitation, specifically the TRMM-based TMPA 701
3B42 V7 analysis has allowed us to examine climatology and variations of recent intense 702
daily precipitation during 1998-2013. This study analyzes different parameters 703
representing mean and intense precipitation, their relationships and variability, 704
contributing to our understanding of the precipitation behavior at climate scale. The 705
relatively short 16-year data record used here limits our examination of extreme daily 706
precipitation, but allows for a careful study of intense daily precipitation, including its 707
climatology and variations that should be related to events such as floods and landslides. 708
This capability is due to the TMPA’s relatively high quality, i.e., mostly microwave- 709
based, high resolution (3-hr, 0.25° lat./lon.), continual calibration, continuity, and 710
uniformity over a near-global domain (50°N-50°S). 711
Climatologies of mean rainfall, conditional mean rainfall and fraction of rainfall days 712
are examined and compared, and begin to reveal areas and times where and when heavier 713
rainfall is more frequent. We tested several commonly used parameters to define intense 714
precipitation, such as: mean monthly 95th percentile of daily precipitation, mean and 715
fraction of precipitation that exceeds the 95th percentile, fraction of intense precipitation 716
above fixed daily thresholds of 25 and 50 mm day-1, and mean rainfall above same fixed 717
thresholds, to infer which parameter best characterizes the heavy rainfall regime. Spatial 718
patterns and features of various mean and intense precipitation parameters compare well 719
to each other, with some differences over land and ocean. The overall results show a very 720
strong relationship between mean and intense precipitation. Specifically, consistently 721
high significant correlations with a strong positive linear relationship are observed 722
between two interesting parameters: the monthly mean precipitation (mean precipitation 723
≥0 mm day-1, Rmean) and intense precipitation (mean precipitation ≥25 mm day-1, R25). 724
Among the various examined parameters, the one best characterizing the intense 725
rainfall is observed to be a ratio between intense precipitation and mean precipitation 726
(R25/Rmean). This ratio is considered to be a good measure of rainfall intensity. The results 727
show high significant correlations and a strong positive linear relationship between these 728
two parameters (R25 and Rmean), explaining the contribution of intense rainfall to total 729
rainfall. We consider this analysis to be a first attempt of this kind to determine the best 730
measure for intense rainfall. 731
From analysis of interannual variability of intense precipitation, we confirm a strong 732
positive linear relationship and consistently high correlations between mean and intense 733
precipitation anomalies, especially during El Niño and La Niña periods. The results also 734
confirm that El Niño and La Niña events with their effects equally contribute to the 735
observed connection between mean and intense precipitation, with no significant 736
difference between land and ocean. In general, most of the intense rainfall events are 737
observed over tropical ocean. However, the results show that intense rainfall events are 738
stronger and more frequent during La Niña periods over tropical land and during El Niño 739
periods over tropical ocean. Overall, the observed change in intense rainfall anomalies is 740
lower than the one observed in mean rainfall anomalies during El Niño/La Niña period, 741
and it decreases with a higher fixed threshold (a factor of 0.65-0.75 for R25 and 0.40-0.50 742
for R50). 743
Intense rainfall is the major factor of causing floods and landslides worldwide, 744
especially in recent years. Therefore, improved knowledge of the intense precipitation 745
regime and its strong connection to the mean precipitation on a global to regional scale 746
can offer more consistent current and near-future estimates of heavy rainfall, providing 747
help in preventing damages caused by extreme events (i.e., floods, flash floods, and 748
landslides). Future research should focus on the analysis of intense precipitation and 749
flood events, together with economic and social systems, including additional factors 750
such as population, critical infrastructures, and financial indices of cost that help estimate 751
the potential risks and impacts of extreme and heavy rainfall events. Providing accurate 752
estimates of future intense precipitation events due to climate change, global warming, 753
and/or natural climate variability (i.e., ENSO) is crucial. 754
For the future, the Global Precipitation Measurement (GPM) mission will 755
significantly contribute to an improved record of intense precipitation, extending to a 756
more global domain, and allowing for a longer, enhanced climate record to be used. This 757
should allow a better specification of the intense precipitation relationships in the coming 758
years. 759
Acknowledgments 760
This research was supported by the NASA NEWS Program and the NASA PMM 761
Program. TRMM 3B42 V7 product data were provided by the NASA Precipitation 762
Processing System (PPS) at ftp://trmmopen.pps.eosdis.nasa.gov/trmmdata/. 763
Niño3.4 Index data were provided by the CPC NCEP NOAA at 764
http://www.esrl.noaa.gov/psd/data/climateindices/list/. 765
The authors thank anonymous reviewers for their helpful comments and suggestions. 766
References 767
1. Adler, R. F., and Coauthors, 2003: The Version-2 Global Precipitation 768
Climatology Project (GPCP) monthly precipitation analysis (1979–present). J. 769
Hydrometeor., 4, 1147–1167, doi:10.1175/1525-
770
7541(2003)004<1147:TVGPCP>2.0.CO;2. 771
2. Adler, R. F., G. Gu, J.-J. Wang, G. J. Huffman, S. Curtis, and D. Bolvin, 2008: 772
Relationships between global precipitation and surface temperature on interannual 773
and longer timescales (1979–2006). J. Geophys. Res., 113, D22104, 774
doi:10.1029/2008JD010536. 775
3. Adler, R. F., J.-J. Wang, G. Gu, and G. J. Huffman, 2009: A ten-year tropical 776
rainfall climatology based on a composite of TRMM products. J. Meteor. Soc. 777
Japan, 87A, 281–293, doi:10.2151/jmsj.87A.281.
778
4. Allan, R. P., and B. J. Soden, 2008: Atmospheric Warming and the Amplification 779
of Precipitation Extremes. Science, 321, 1481-1484, 780
doi:10.1126/science.1160787. 781
5. Arkin, P. A., J. Turk, and E. Ebert, 2008: Pilot Evaluation of High Resolution 782
Precipitation Products (PEHRPP): A contribution to GPM planning. 783
6. Benestad, R. E., D. Nychka, and L. O. Mearns, 2012: Spatially and temporally 784
consistent prediction of heavy precipitation from mean values. Nat. Clim. Change, 785
2, 544-547, doi:10.1038/NCLIMATE1497. 786
7. Benestad, R. E., 2013: Association between trends in daily rainfall percentiles and
787
the global mean temperature. J. Geophys. Res. Atmos., 118, 10802–10810, 788
doi:10.1002/jgrd.50814. 789
8. Cavalcanti, I. F. A., 2012: Large scale and synoptic features associated with 790
extreme precipitation over South America: A review and case studies for the first 791
decade of the 21st century. Atmos. Res., 118, 27-40, 792
doi:10.1016/j.atmosres.2012.06.012. 793
9. Curtis, S., A. Salahuddin, R. F. Adler, G. J. Huffman, G. Gu, and Y. Hong, 2007: 794
Precipitation Extremes Estimated by GPCP and TRMM: ENSO Relationships. J. 795
Hydrometeor., 8, 678–689, doi:10.1175/JHM601.1.
796
10. Dai, A., I. Y. Fung, and A. D. Del Genio, 1997: Surface observed global land 797
precipitation variations during 1900–88. J. Climate, 10, 2943–2962, 798
doi:10.1175/1520-0442(1997)010<2943:SOGLPV>2.0.CO;2. 799
11. Dai, A., 2011: Drought under global warming: A review. Wiley Interdisciplinary 800
Reviews: Climate Change, 2, 45-65, doi:10.1002/wcc.81.
801
12. Easterling, D. R., J. L. Evans, P. Y. Groisman, T. R. Karl, K. E. Kunkel, and P. 802
Ambenje, 2000: Observed variability and trends in extreme climate events: A 803
brief review. Bull. Amer. Meteor. Soc., 81, 417–425, doi:10.1175/1520- 804
0477(2000)081<0417:OVATIE>2.3.CO;2. 805
13. Ebert, E. E., J. Janowiak, and C. Kidd, 2007: Comparison of near-real time 806
precipitation estimates from satellite observations and numerical models. Bull. 807
Amer. Meteor. Soc., 88, 47–64, doi:10.1175/BAMS-88-1-47.
808
14. Grimm, A. M., and R. G. Tedeschi, 2009: ENSO and extreme rainfall events in 809
South America. J. Climate, 22, 1589-1609, doi:10.1175/2008JCLI2429.1. 810
15. Grimm, A. M., 2011: Interannual climate variability in South America: impacts 811
on seasonal precipitation, extreme events, and possible effects of climate change. 812
Stoch. Environ. Res. Risk. Assess., 25, 537-554, doi:10.1007/s00477-010-0420-1.
813
16. Grimm, A. M. and J. P. J. Saboia, 2015: Interdecadal Variability of the South 814
American Precipitation in the Monsoon Season. J. Climate, 28, 755-775, 815
doi:10.1175/JCLI-D-14-00046.1. 816
17. Grimm, A. M., N. C. Laureanti, R. B. Rodakoviski, and C. B. Gama, 2016: 817
Interdecadal variability and extreme precipitation events in South America during 818
the monsoon season. Climate Research, 68, 2-3, doi: 10.3354/cr01375. 819
18. Gu, G., R. F. Adler, G. J. Huffman, and S. Curtis, 2007: Tropical Rainfall 820
Variability on Interannual-to-Interdecadal and Longer Time Scales Derived from 821
the GPCP Monthly Product. J. Climate, 20, 4033–4046, doi:10.1175/JCLI4227.1 822
19. Gustaffson, M., D. Rayner, and D. Chen, 2010: Extreme rainfall events in 823
southern Sweden: where does the moisture come from? Tellus, 62A, 605-616, 824
doi:10.1111/j.1600-0870.2010.00456.x. 825
20. Held, I. M., and B. J. Soden, 2006: Robust responses of the hydrological cycle to 826
global warming. J. Climate, 19, 5686–5699, doi:10.1175/JCLI3990.1. 827
21. Huffman, G. J., R. F. Adler, M. Morrissey, D. Bolvin, S. Curtis, R. Joyce, B. 828
McGavock, and J. Susskind, 2001: Global precipitation at one-degree daily 829
resolution from multi-satellite observations. J. Hydrometeor., 2, 36-50, 830
doi:10.1175/1525-7541(2001)002<0036:GPAODD>2.0.CO;2. 831
22. Huffman, G. J., and Coauthors, 2007: The TRMM Multisatellite Precipitation 832
Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation 833
Estimates at Fine Scales. J. Hydrometeor., 8, 38–55, doi:10.1175/JHM560.1. 834
23. Huffman, G. J., R. F. Adler, D. T. Bolvin, and G. Gu, 2009: Improving the global 835
precipitation record: GPCP Version 2.1. Geophys. Res. Lett., 36, L17808, 836
doi:10.1029/2009GL040000. 837
24. Huffman, G. J., R. F. Adler, D. T. Bolvin, and E. J. Nelkin, 2010: The TRMM 838
Multi-satellite Precipitation Analysis (TMPA). Chapter 1 in Satellite Rainfall 839
Applications for Surface Hydrology, F. Hossain and M. Gebremichael, Eds.,
840
Springer Verlag, ISBN: 978-90-481-2914-0, 3-22. [Available online at 841
ftp://precip.gsfc.nasa.gov/betsy/huffman/papers/TMPA_hydro_rev.pdf.] 842
25. Huffman, G. J., and D. T. Bolvin, 2012: TRMM and Other Data Precipitation 843
Data Set Documentation. Technical product document, 1-37. [Available online at 844
ftp://trmmopen.gsfc.nasa.gov/pub/merged/V7Documents/3B4XRT_doc_V7.pdf.] 845
26. IPCC, 2007. Climate Change 2007: The Physical Science Basis, S. Solomon, D. 846
Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor, H. L. Miller, 847
Eds., Contribution of Working Group I to the Fourth Assessment Report of the 848
Intergovernmental Panel on Climate Change, Cambridge University Press, 849
Cambridge, United Kingdom/New York, NY, USA, 996 pp. 850
27. Karl, T. R., N. Nicholls, and A. Ghazi, 1999: CLIVAR/GCOS/WMO workshop 851
on indices and indicators for climate extremes: Workshop summary. Climatic 852
Change, 42, 3-7, doi:10.1023/A:1005491526870.
853
28. Kharin, V., F. W. Zwiers, X. Zhang, and G. C. Hergerl, 2007: Changes in 854
temperature and precipitation extremes in IPCC ensemble of coupled model 855
simulations. J. Climate, 20, 1419–1444, doi:10.1175/JCLI4066.1. 856
29. Kirschbaum, D., R. Adler, D. Adler, C. Peters-Lidard, and G. Huffman, 2012: 857
Global Distribution of Extreme Precipitation and High-Impact Landslides in 2010 858
Relative to Previous Years. J. Hydrometeor., 13, 1536–1551, doi:10.1175/JHM- 859
D-12-02.1. 860
30. Lau, K.-M., and H.-T. Wu, 2011: Climatology and changes in tropical oceanic 861
rainfall characteristics inferred from Tropical Rainfall Measuring Mission 862
(TRMM) data (1998-2009). J. Geophys. Res., 116, D17111, 863
doi:10.1029/2011JD015827. 864
31. Lau, W. K.-M., H.-T. Wu, and K.-M. Kim, 2013: A canonical response of 865
precipitation characteristics to global warming from CMIP5 models. Geophys. 866
Res. Lett., 40, 3163–3169, doi:10.1002/grl.50420.
867
32. Liu, C. and R. P. Allan, 2012: Multi-satellite observed responses of precipitation 868
and its extremes to interannual climate variability. J. Geophys. Res., 117, D03101, 869
doi:10.1029/2011JD016568. 870
33. Min, S-K., X. Zhang, F. W. Zwiers, and G. C. Hegerl, 2011: Human contribution 871
to more-intense precipitation extremes. Nature, 470, 378-381, 872
doi:10.1038/nature09763. 873
34. O’Gorman, P.A., 2012: Sensitivity of tropical precipitation extremes to climate 874
change. Nature Geoscience, 5, 697-700, doi:10.1038/ngeo1568. 875
35. Peterson, T.C., C. Folland, G. Gruza, W. Hogg, A. Mokssit, and N. Plummer, 876
2001: Report on the Activities of the Working Group on Climate Change 877
Detection and Related Rapporteurs 1998-2001. World Meteorological 878
Organization, Rep. WCDMP-47, WMO-TD 1071, Geneva, Switzerland, 143 pp.
879
36. Sapiano, M. R. P., and P. A. Arkin, 2009: An inter-comparison and validation of 880
high resolution satellite precipitation estimates with three-hourly gauge data. J. 881
Hydrometeor., 10(1), 149-166, doi:10.1175/2008JHM1052.1.
882
37. Scheel, M. L. M., M. Rohrer, C. Huggel, D. S. Villar, E. Silvestre, and G. J. 883
Huffman, 2011: Evaluation of TRMM Multi-satellite Precipitation Analysis 884
(TMPA) performance in the Central Andes region and its dependency on spatial 885
and temporal resolution. Hydrol. Earth Syst. Sci., 15, 2649-2663, 886
doi:10.5194/hess-15-2649-2011. 887
38. Schneider, U., A. Becker, A. Meyer-Christoffer, M. Ziese and B. Rudolf, 2011: 888
Global Precipitation Analysis Products of the GPCC. Global Precipitation 889
Climatology Centre (GPCC), DWD, Internet Publikation, 13 pp. 890
39. Shiu, C.-J., S. C. Liu, C. Fu, A. Dai, and Y. Sun, 2012: How much do 891
precipitation extremes change in a warming climate? Geophys. Res. Lett., 39, 892
L17707, doi:10.1029/2012GL052762. 893
40. Su, F., Y. Hong, and D. P. Lettenmaier, 2008: Evaluation of TRMM Multisatellite 894
Precipitation Analysis (TMPA) and its utility in hydrologic prediction in the La 895
Plata basin. J. Hydrometeor., 9, 622–640, doi:10.1175/2007JHM944.1. 896
41. Tian, Y., and Coauthors, 2009: Component analysis of errors in satellite-based 897
precipitation estimates. J. Geophys. Res., 114, D24101, 898
doi:10.1029/2009JD011949. 899
42. Trenberth, K. E., A. Dai, R. M. Rasmussen, and D. B. Parsons, 2003: The 900
changing character of precipitation. Bull. Amer. Meteor. Soc., 84, 1205–1217, 901
doi:10.1175/BAMS-84-9-1205. 902
43. Wang, J.-J., R. F. Adler, G. J. Huffman, and D. Bolvin, 2014: An updated TRMM 903
composite climatology of tropical rainfall and its validation. J. Climate, 27, 273- 904
284, doi:10.1175/JCLI-D-13-00331.1. 905
44. Wu H., R. F. Adler, Y. Hong, Y. Tian, and F. Policelli, 2012: Evaluation of 906
Global Flood Detection Using Satellite-Based Rainfall and a Hydrologic Model. 907
J. Hydrometeor., 13, 1268–1284, doi:10.1175/JHM-D-11-087.1.
Figure Captions 909
Figure 1 (a) Annual, (b) January, and (c) July mean precipitation climatology (Rmean, mm 910
day-1), derived from the TMPA 3B42 Version 7 data during 1998-2013. 911
Figure 2 (a) Annual, (b) January, and (c) July mean climatological fraction of days with 912
precipitation >0 mm day-1 (F0=Rmean/Rcond), derived from the TMPA 3B42 Version 7 data 913
during 1998-2013. 914
Figure 3 (a) Annual, (b) January, and (c) July mean climatological conditional 915
precipitation rate (Rcond, mm day-1), derived from the TMPA 3B42 Version 7 data during 916
1998-2013. 917
Figure 4 (a) Annual, (b) January, and (c) July mean 95th percentile daily precipitation 918
value (R95p, mm day-1), derived from the TMPA 3B42 Version 7 data during 1998-2013. 919
Figure 5 (a) Annual, (b) January, and (c) July mean precipitation ≥95th percentile daily 920
precipitation (R95, mm day-1), derived from the TMPA 3B42 Version 7 data during 1998- 921
2013. 922
Figure 6 Annual, January, and July mean precipitation (a)-(c) ≥25 mm day-1 threshold 923
(R25; mm day-1) and (d)-(f) ≥50 mm day-1 threshold (R50; mm day-1), derived from the 924
TMPA 3B42 Version 7 data during 1998-2013. 925
Figure 7 (a) Annual, (b) January, and (c) July mean climatological fraction of daily 926
precipitation ≥25 mm day-1 (F25=R25/Rmean), derived from the TMPA 3B42 Version 7 927
data during 1998-2013. 928
Figure 8 Scatter plots of monthly climatology of mean precipitation (Rmean, mm day-1) 929
and intense precipitation (a)-(b) ≥25 mm day-1 (R
25, mm day-1) and (c)-(d) ≥50 mm day-1 930
(R50, mm day-1) over the TRMM domain (50°N-50°S), separately over land and ocean, 931
derived from the TMPA 3B42 Version 7 data during 1998-2013. The color bar indicates 932
the number of grid points with the attributed values in a logarithmic scale. Median (black 933
dashed line) is calculated for a distribution of all 12 months. Correlation coefficient and 934
RMS difference between intense rainfall (R25 and R50) and Rmean are displayed. 935
Correlation values are significant at the 95% confidence level. 936
Figure 9 Mean precipitation anomalies (mm day-1) for 4 seasons (DJF, MAM, JJA, SON) 937
for (a)-(d) El Niño and (e)-(h) La Niña period, derived from the TMPA 3B42 Version 7 938
data during 1998-2013. 939
Figure 10 Scatter plots of mean precipitation (Rmean, mm day-1) and intense precipitation 940
≥25 mm day-1 (R25, mm day-1) anomalies for El Niño, La Niña and neutral period, over 941
the tropical domain (23.5°N-23.5°S), over (a)-(c) land and (d)-(f) ocean, derived from the 942
TMPA 3B42 Version 7 data during 1998-2013. The color bar indicates the number of 943
grid points with the attributed values in a logarithmic scale. Correlation coefficient 944
between R25 and Rmean is displayed. Correlation values are significant at the 95% 945
confidence level. 946
Figure 11 (a) Time series of mean precipitation (Rmean, mm day-1; blue line) and intense 947
precipitation ≥25 mm day-1 (R25, mm day-1; black line) and≥50 mm day-1 (R50, mm day-1; 948
red line) anomalies, and (b) scatter plot of Rmean, R25, and R50, and Niño3.4 index 949
anomalies, averaged over a 5°x5° box over the West Pacific Ocean (6°S-1°S, 177°E- 950
182°E), derived from the TMPA 3B42 Version 7 precipitation data during 1998-2013. 951
Correlation coefficient and RMS difference between intense rainfall (R25 and R50) and 952
Rmean are displayed in (a), and correlation coefficient between Rmean, R25, and R50 with 953
respect to the Niño3.4 index in (b). Correlation values are significant at the 95% 954
confidence level. 955
Figure 12 Scatter plots of mean precipitation (Rmean; mm day-1) and intense precipitation 956
≥25 mm day-1 (R25, mm day-1; filled circles and black line) and ≥50 mm day-1 (R50, mm 957
day-1; empty circles and red line) anomalies, with corresponding linear fit for El Niño 958
(red), La Niña (blue) and neutral period (black), averaged over a 5°x5° box over: (a) the 959
West Pacific Ocean, (b) Philippines, (c) La Plata basin, and (d) Amazonia, derived from 960
the TMPA 3B42 Version 7 data during 1998-2013. 5°x5° boxes are chosen denoting two 961
largest maximums and minimums observed in global anomalies. Correlation coefficient 962
and RMS difference between intense rainfall (R25 and R50) and Rmean are displayed. 963
Correlation values are significant at the 95% confidence level. 964
Table Captions 965
Table 1 Definitions and units for monthly parameters of mean and intense precipitation. 966
Tables 967
Table 1 Definitions and units for monthly parameters of mean and intense precipitation. 968
Definition Parameter Unit
Daily precipitation R mm day-1