• Catto, J. L., C. Jakob, G. Berry, and N. Nicholls, 2012: Relating global precipitation to atmospheric fronts. Geophys. Res. Lett., 39, L10805, https://doi.org/10.1029/2012GL051736.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dias, J., S. N. Tulich, and G. N. Kiladis, 2012: An object-based approach to assessing the organization of tropical convection. J. Atmos. Sci., 69, 24882504, https://doi.org/10.1175/JAS-D-11-0293.1.

    • 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, https://doi.org/10.1175/JCLI-D-15-0618.1.

    • 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, https://doi.org/10.1029/2009GL040000.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, https://doi.org/10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kursinski, A. L., and S. L. Mullen, 2008: Spatiotemporal variability of hourly precipitation over the eastern contiguous United States from Stage IV multisensor analyses. J. Hydrometeor., 9, 321, https://doi.org/10.1175/2007JHM856.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lau, K.-H., and N.-C. Lau, 1990: Observed structure and propagation characteristics of tropical summertime synoptic scale disturbances. Mon. Wea. Rev., 118, 18881913, https://doi.org/10.1175/1520-0493(1990)118<1888:OSAPCO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lau, N.-C., and M. W. Crane, 1995: A satellite view of the synoptic-scale organization of cloud properties in midlatitude and tropical circulation systems. Mon. Wea. Rev., 123, 19842006, https://doi.org/10.1175/1520-0493(1995)123<1984:ASVOTS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lau, N.-C., and M. W. Crane, 1997: Comparing satellite and surface observations of cloud patterns in synoptic-scale circulation systems. Mon. Wea. Rev., 125, 31723189, https://doi.org/10.1175/1520-0493(1997)125<3172:CSASOO>2.0.CO;2.

    • 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, https://doi.org/10.1175/JHM-D-15-0190.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Paulat, M., C. Frei, M. Hagen, and H. Wernli, 2008: A gridded dataset of hourly precipitation in Germany: Its construction, climatology and application. Meteor. Z., 17, 719732, https://doi.org/10.1127/0941-2948/2008/0332.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Romilly, T. G., and M. Gebremichael, 2011: Evaluation of satellite rainfall estimates over Ethiopian river basins. Hydrol. Earth Syst. Sci., 15, 15051514, https://doi.org/10.5194/hess-15-1505-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smalley, M., T. L’Ecuyer, M. Lebsock, and J. Haynes, 2014: A comparison of precipitation occurrence from NCEP Stage IV QPE product and the CloudSat Cloud Profiling Radar. J. Hydrometeor., 15, 444458, https://doi.org/10.1175/JHM-D-13-048.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., 1991: Storm tracks in the Southern Hemisphere. J. Atmos. Sci., 48, 21592178, https://doi.org/10.1175/1520-0469(1991)048<2159:STITSH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., and Y. Zhang, 2018: How often does it really rain? Bull. Amer. Meteor. Soc., 99, 289298, https://doi.org/10.1175/BAMS-D-17-0107.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., Y. Zhang, and M. Gehne, 2017: Intermittency in precipitation: Duration, frequency, intensity, and amounts using hourly data. J. Hydrometeor., 18, 13931412, https://doi.org/10.1175/JHM-D-16-0263.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • White, R. H., D. S. Battisti, and G. Skok, 2017: Tracking precipitation events in time and space in gridded observational data. Geophys. Res. Lett., 44, 86378646, https://doi.org/10.1002/2017GL074011.

    • 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. J. Hydrometeor., 18, 16171641, https://doi.org/10.1175/JHM-D-16-0168.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 20 20 3
PDF Downloads 17 17 4

Near-Global Covariability of Hourly Precipitation in Space and Time

View More View Less
  • 1 National Center for Atmospheric Research, Boulder, Colorado
© Get Permissions
Restricted access

Abstract

A detailed analysis of hourly precipitation from 60°N to 60°S for the covariability is performed at 0.25° resolution using the new CMORPH dataset. For all points, correlations are computed with surrounding points both concurrently and for various leads and lags up to a day. Results are more coherent over the oceans than land; the contours of constant correlation tend to be elliptical, oriented northeast–southwest in the northern extratropics and southeast–northwest in the southern extratropics. An ellipse is fitted to the correlation pattern, and major and minor axis vectors and eccentricity are mapped. Based upon both the isotropic correlations and ellipse, points are allocated to one of 20 clusters, and 16 are documented. Over the main extratropical ocean storm tracks, correlations exceed 0.8 for points 50 km distant and fall to about 0.3 at about 5° radius. In the tropics values drop to 0.65 within 50 km and 0.2 at 5° radius. Over land, values are lower in summer and drop to 0.1 at 5° radius. Decorrelation e-folding distances range from less than 50 km over land to 200 km over extratropical ocean storm tracks. The movement of precipitation is compared with mean atmospheric winds. The lead–lag relationships indicate movement of systems but reveal the relatively short lifetimes of precipitation, of less than 12 h, even taking movement into account. The orientation of the ellipse reflects the structures of rain phenomena (fronts, etc.) rather than movement. These statistics demonstrate that daily averages fail to capture the essential character of precipitation.

ORCID: 0000-0002-1445-1000.

© 2018 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: Kevin E. Trenberth, trenbert@ucar.edu

Abstract

A detailed analysis of hourly precipitation from 60°N to 60°S for the covariability is performed at 0.25° resolution using the new CMORPH dataset. For all points, correlations are computed with surrounding points both concurrently and for various leads and lags up to a day. Results are more coherent over the oceans than land; the contours of constant correlation tend to be elliptical, oriented northeast–southwest in the northern extratropics and southeast–northwest in the southern extratropics. An ellipse is fitted to the correlation pattern, and major and minor axis vectors and eccentricity are mapped. Based upon both the isotropic correlations and ellipse, points are allocated to one of 20 clusters, and 16 are documented. Over the main extratropical ocean storm tracks, correlations exceed 0.8 for points 50 km distant and fall to about 0.3 at about 5° radius. In the tropics values drop to 0.65 within 50 km and 0.2 at 5° radius. Over land, values are lower in summer and drop to 0.1 at 5° radius. Decorrelation e-folding distances range from less than 50 km over land to 200 km over extratropical ocean storm tracks. The movement of precipitation is compared with mean atmospheric winds. The lead–lag relationships indicate movement of systems but reveal the relatively short lifetimes of precipitation, of less than 12 h, even taking movement into account. The orientation of the ellipse reflects the structures of rain phenomena (fronts, etc.) rather than movement. These statistics demonstrate that daily averages fail to capture the essential character of precipitation.

ORCID: 0000-0002-1445-1000.

© 2018 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: Kevin E. Trenberth, trenbert@ucar.edu
Save