• 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, doi:10.1175/BAMS-D-13-00068.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bacmeister, J., M. Wehner, R. Neale, A. Gettelman, C. Hannay, P. Lauritzen, J. Caron, and J. Truesdale, 2014: Exploratory high-resolution climate simulations using the Community Atmosphere Model (CAM). J. Climate, 27, 30733099, doi:10.1175/JCLI-D-13-00387.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Covey, C., P. J. Gleckler, C. Doutriaux, D. N. Williams, A. Dai, J. Fasullo, K. E. Trenberth, and A. Berg, 2016: Metrics for the diurnal cycle of precipitation: Toward routine benchmarks for climate models. J. Climate, 29, 44614471, doi:10.1175/JCLI-D-15-0664.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, A., 2001: Global precipitation and thunderstorm frequencies. Part I: Seasonal and interannual variations. J. Climate, 14, 10921111, doi:10.1175/1520-0442(2001)014<1092:GPATFP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, A., 2006: Precipitation characteristics in eighteen coupled climate models. J. Climate, 19, 46054630, doi:10.1175/JCLI3884.1.

  • Dai, A., and K. E. Trenberth, 2004: The diurnal cycle and its depiction in the Community Climate System Model. J. Climate, 17, 930951, doi:10.1175/1520-0442(2004)017<0930:TDCAID>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, A., and T. Zhao, 2017: Uncertainties in historical changes and future projections of drought. Part I: Estimates of historical drought changes. Climatic Change, doi:10.1007/s10584-016-1705-2, in press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, A., F. Giorgi, and K. E. Trenberth, 1999: Observed and model simulated precipitation diurnal cycles over the contiguous United States. J. Geophys. Res., 104, 63776402, doi:10.1029/98JD02720.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, A., X. Lin, and K.-L. Hsu, 2007: The frequency, intensity, and diurnal cycle of precipitation in surface and satellite observations over low- and mid-latitudes. Climate Dyn., 29, 727744, doi:10.1007/s00382-007-0260-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deni, S. M., A. A. Jemain, and K. Ibrahim, 2010: The best probability models for dry and wet spells in peninsular Malaysia during monsoon seasons. Int. J. Climatol., 30, 11941205, doi:10.1002/joc.1972.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Foufoula-Georgiou, E., and D. Lettenmaier, 1987: A Markov renewal model for rainfall occurrences. Water Resour. Res., 23, 875884, doi:10.1029/WR023i005p00875.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gehne, M., T. M. Hamill, G. N. Kiladis, and K. E. Trenberth, 2016: Comparison of global precipitation estimates across a range of temporal and spatial scales. J. Climate, 29, 77737795, doi:10.1175/JCLI-D-15-0618.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gutowski, W. J., Jr., S. G. Decker, R. A. Donavon, Z. Pan, R. W. Arritt, and E. S. Takle, 2003: Temporal–spatial scales of observed and simulated precipitation in central U.S. climate. J. Climate, 16, 38413847, doi:10.1175/1520-0442(2003)016<3841:TSOOAS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Herold, N., L. V. Alexander, M. G. Donat, S. Contractor, and A. Becker, 2016: How much does it rain over land? Geophys. Res. Lett., 43, 341348, doi:10.1002/2015GL066615.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, doi:10.1175/1520-0450(1997)036<1176:PEFRSI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., and Coauthors, 2007: The TRMM Multi-satellite Precipitation Analysis: Quasi-global, multi-year, combined-sensor precipitation estimates at fine scale. J. Hydrometeor., 8, 3855, doi:10.1175/JHM560.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., R. F. Adler, D. T. Bolvin, and G. Gu, 2009: Improving the global precipitation record: GPCP version 2.1. Geophys. Res. Lett., 36, L17808, doi:10.1029/2009GL040000.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Joyce, R. J., and P. Xie, 2011: Kalman filter–based CMORPH. J. Hydrometeor., 12, 15471563, doi:10.1175/JHM-D-11-022.1.

  • 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, doi:10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kay, J. E., and Coauthors, 2015: The Community Earth System Model (CESM) Large Ensemble Project: A community resource for studying climate change in the presence of climate variability. Bull. Amer. Meteor. Soc., 96, 13331349, doi:10.1175/BAMS-D-13-00255.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kidd, C., T. Matsui, J. Chern, K. Mohr, C. Kummerow, and D. Randel, 2016: Global precipitation estimates from cross-track passive microwave observations using a physically based retrieval scheme. J. Hydrometeor., 17, 383400, doi:10.1175/JHM-D-15-0051.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ma, H.-Y., S. Xie, J. S. Boyle, S. A. Klein, and Y. Zhang, 2013: Metrics and diagnostics for precipitation-related processes in climate model short-range hindcasts. J. Climate, 26, 15161534, doi:10.1175/JCLI-D-12-00235.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maggioni, V., P. C. Meyers, and M. D. Robinson, 2016: A review of merged high-resolution satellite precipitation product accuracy during the Tropical Rainfall Measuring Mission (TRMM) era. J. Hydrometeor., 17, 11011117, doi:10.1175/JHM-D-15-0190.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mindling, G. W., 1918: Hourly duration of precipitation at Philadelphia. Mon. Wea. Rev., 46, 517520, doi:10.1175/1520-0493(1918)46<517:HDOPAP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prat, O. P., and B. R. Nelson, 2015: Evaluation of precipitation estimates over CONUS derived from satellite, radar, and rain gauge data sets at daily to annual scales (2002–2012). Hydrol. Earth Syst. Sci., 19, 20372056, doi:10.5194/hess-19-2037-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schleiss, M., and J. A. Smith, 2016: Two simple metrics for quantifying rainfall intermittency: The burstiness and memory of interamount times. J. Hydrometeor., 17, 421436, doi:10.1175/JHM-D-15-0078.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schneider, U., A. Becker, P. Finger, A. Meyer-Christoffer, M. Ziese, and B. Rudolf, 2014: GPCC’s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theor. Appl. Climatol., 115, 1540, doi:10.1007/s00704-013-0860-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stephens, G., and Coauthors, 2010: Dreary state of precipitation in global models. J. Geophys. Res., 115, D24211, doi:10.1029/2010JD014532.

    • Search Google Scholar
    • Export Citation
  • Sun, Y., S. Solomon, A. Dai, and R. W. Portmann, 2006: How often does it rain? J. Climate, 19, 916934, doi:10.1175/JCLI3672.1.

  • Toreti, A., and Coauthors, 2013: Projections of global changes in precipitation extremes from Coupled Model Intercomparison Project phase 5 models. Geophys. Res. Lett., 40, 48874892, doi:10.1002/grl.50940.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., 1998: Atmospheric moisture residence times and cycling: Implications for rainfall rates with climate change. Climatic Change, 39, 667694, doi:10.1023/A:1005319109110.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., 2011: Changes in precipitation with climate change. Climate Res., 47, 123138, doi:10.3354/cr00953.

  • Trenberth, K. E., A. Dai, R. M. Rasmussen, and D. B. Parsons, 2003: The changing character of precipitation. Bull. Amer. Meteor. Soc., 84, 12051217, doi:10.1175/BAMS-84-9-1205.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., A. Dai, G. van der Schrier, P. D. Jones, J. Barichivich, K. R. Briffa, and J. Sheffield, 2014: Global warming and changes in drought. Nat. Climate Change, 4, 1722, doi:10.1038/nclimate2067.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., Y. Zhang, and J. T. Fasullo, 2015: Relationships among top-of-atmosphere radiation and atmospheric state variables in observations and CESM. J. Geophys. Res. Atmos., 120, 10 07410 090, doi:10.1002/2015JD023381.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wolff, D. B., and B. L. Fisher, 2008: Comparisons of instantaneous TRMM ground validation and satellite rain-rate estimates at different spatial scales. J. Appl. Meteor. Climatol., 47, 22152237, doi:10.1175/2008JAMC1875.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, P., R. Joyce, S. Wu, S.-H. Yoo, Y. Yaroah, F. Sun, and R. Lin, 2017: Reprocessed, bias-corrected CMORPH global high-resolution precipitation estimates from 1998. J. Hydrometeor., doi:10.1175/JHM-D-16-0168.1, in press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, T., R. Yu, H. Chen, A. Dai, and Y. Pan, 2008: Summer precipitation frequency, intensity, and diurnal cycle over China: A comparison of satellite data with rain gauge observations. J. Climate, 21, 39974010, doi:10.1175/2008JCLI2028.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zolina, O., 2014: Multidecadal trends in the duration of wet spells and associated intensity of precipitation as revealed by very dense observational network. Environ. Res. Lett., 9, 025003, doi:10.1088/1748-9326/9/2/025003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zolina, O., C. Simmer, S. K. Gulev, and S. Kollet, 2010: Changing structure of European precipitation: Longer wet periods leading to more abundant rainfalls. Geophys. Res. Lett., 37, L06704, doi:10.1029/2010GL042468.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zolina, O., C. Simmer, K. Belyaev, S. K. Gulev, and P. Koltermann, 2013: Changes in the duration of European wet and dry spells during the last 60 years. J. Climate, 26, 20222047, doi:10.1175/JCLI-D-11-00498.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1027 485 22
PDF Downloads 986 580 38

Intermittency in Precipitation: Duration, Frequency, Intensity, and Amounts Using Hourly Data

View More View Less
  • 1 National Center for Atmospheric Research, Boulder, Colorado
  • | 2 CIRES, University of Colorado, Boulder, Boulder, Colorado
Restricted access

Abstract

Intermittency is a core characteristic of precipitation, not well described by data and very poorly modeled. Detailed analyses are made of near-global gridded (about 1°) hourly or 3-hourly precipitation rates from two updated observational datasets [3-hourly TRMM 3B42, version 7, and hourly CMORPH, version 1.0, bias corrected (CRT)] and from special runs of CESM from January 1998 to December 2013 to obtain hourly values. The analyses explore the intermittency of precipitation: the frequency, intensity, duration, and amounts. A comparison is made for all products using several metrics with a focus on the duration of events, and a new metric is proposed based on the ratio of the frequency of precipitation at certain rates (0.2–2 mm h−1) for hourly versus 3-hourly versus daily data. For all seasons and rain rates, TRMM values are similar in pattern to CMORPH, but durations are about 80%–85%. It is mainly over land in the monsoons that CMORPH exceeds TRMM rain durations. Observed duration of precipitation events in CMORPH over oceans are 12–15 h in the tropics and subtropics, much less than the ~20 h for CESM. Hence, the observational results differ somewhat but both are considerably different from the model, which has too much precipitation overall, and it precipitates far too often at low rates and not enough for intense rates, with the divide about 1–2 mm h−1. There is a need to properly represent precipitation phenomena and processes either explicitly or implicitly (parameterized).

Denotes content that is immediately available upon publication as open access.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Kevin E. Trenberth, trenbert@ucar.edu

Abstract

Intermittency is a core characteristic of precipitation, not well described by data and very poorly modeled. Detailed analyses are made of near-global gridded (about 1°) hourly or 3-hourly precipitation rates from two updated observational datasets [3-hourly TRMM 3B42, version 7, and hourly CMORPH, version 1.0, bias corrected (CRT)] and from special runs of CESM from January 1998 to December 2013 to obtain hourly values. The analyses explore the intermittency of precipitation: the frequency, intensity, duration, and amounts. A comparison is made for all products using several metrics with a focus on the duration of events, and a new metric is proposed based on the ratio of the frequency of precipitation at certain rates (0.2–2 mm h−1) for hourly versus 3-hourly versus daily data. For all seasons and rain rates, TRMM values are similar in pattern to CMORPH, but durations are about 80%–85%. It is mainly over land in the monsoons that CMORPH exceeds TRMM rain durations. Observed duration of precipitation events in CMORPH over oceans are 12–15 h in the tropics and subtropics, much less than the ~20 h for CESM. Hence, the observational results differ somewhat but both are considerably different from the model, which has too much precipitation overall, and it precipitates far too often at low rates and not enough for intense rates, with the divide about 1–2 mm h−1. There is a need to properly represent precipitation phenomena and processes either explicitly or implicitly (parameterized).

Denotes content that is immediately available upon publication as open access.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Kevin E. Trenberth, trenbert@ucar.edu
Save