• Anderberg, M. R., 1973: Cluster Analysis for Applications. Elsevier, 359 pp.

  • Arthur, D., and S. Vassilvitskii, 2007: k-means++: the advantages of careful seeding. Proc. 18th Annual ACM–SIAM Symp. Discrete Algorithms, New Orleans, LA, ACM–SIAM, 1027–1035.

  • Caliński, T., and J. Harabasz, 1974: A dendrite method for cluster analysis. Commun. Stat. Theory Methods, 3 (1), 127, https://doi.org/10.1080/03610927408827101.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cui, W., X. Dong, B. Xi, Z. Feng, and J. Fan, 2020: Can the GPM IMERG final product accurately represent MCSs’ precipitation characteristics over the central and eastern United States? J. Hydrometeor., 21, 3957, https://doi.org/10.1175/JHM-D-19-0123.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davies, D. L., and D. W. Bouldin, 1979: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell., PAMI-1, 224227, https://doi.org/10.1109/TPAMI.1979.4766909.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Giles, J. A., R. C. Ruscica, and C. G. Menéndez, 2020: The diurnal cycle of precipitation over South America represented by five gridded datasets. Int. J. Climatol., 40, 668686, https://doi.org/10.1002/joc.6229.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., and et al. , 2019a: NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG). Algorithm Theoretical Basis Doc., version 06, 38 pp., https://gpm.nasa.gov/sites/default/files/document_files/IMERG_ATBD_V06_0.pdf.

  • Huffman, G. J., E. F. Stocker, D. T. Bolvin, E. J. Nelkin, and J. Tan, 2019b: GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree × 0.1 degree V06. Goddard Earth Sciences Data and Information Services Center, accessed 1 June 2020, https://doi.org/10.5067/GPM/IMERG/3B-HH/06.

    • Crossref
    • Export Citation
  • Jakob, C., and G. Tselioudis, 2003: Objective identification of cloud regimes in the tropical western Pacific. Geophys. Res. Lett., 30, 2082, https://doi.org/10.1029/2003GL018367.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Janowiak, J. E., V. E. Kousky, and R. J. Joyce, 2005: Diurnal cycle of precipitation determined from the CMORPH high spatial and temporal resolution global precipitation analyses. J. Geophys. Res., 110, D23105, https://doi.org/10.1029/2005JD006156.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jin, D., L. Oreopoulos, D. Lee, N. Cho, and J. Tan, 2018: Contrasting the co-variability of daytime cloud and precipitation over tropical land and ocean. Atmos. Chem. Phys., 18, 30653082, https://doi.org/10.5194/acp-18-3065-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jin, D., L. Oreopoulos, D. Lee, J. Tan, and K. Kim, 2020: Large-scale characteristics of tropical convective systems through the prism of cloud regime. J. Geophys. Res. Atmos., 125, e2019JD021157, https://doi.org/10.1029/2019JD031157.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ketchen, D. J., and C. L. Shook, 1996: The application of cluster analysis in strategic management research: An analysis and critique. Strategic Manage. J., 17, 441458, https://doi.org/10.1002/(SICI)1097-0266(199606)17:6<441::AID-SMJ819>3.0.CO;2-G.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kikuchi, K., and B. Wang, 2008: Diurnal precipitation regimes in the global tropics. J. Climate, 21, 26802696, https://doi.org/10.1175/2007JCLI2051.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • King, M. D., and et al. , 2003: Cloud and aerosol properties, precipitable water, and profiles of temperature and water vapor from MODIS. IEEE Trans. Geosci. Remote Sens., 41, 442458, https://doi.org/10.1109/TGRS.2002.808226.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kummerow, C., and et al. , 2000: The status of the Tropical Rainfall Measuring Mission (TRMM) after two years in orbit. J. Appl. Meteor., 39, 19651982, https://doi.org/10.1175/1520-0450(2001)040<1965:TSOTTR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kummerow, C., D. L. Randel, M. Kulie, N.-Y. Wang, R. Ferraro, S. Joseph Munchak, and V. Petkovic, 2015: The evolution of the Goddard profiling algorithm to a fully parametric scheme. J. Atmos. Oceanic Technol., 32, 22652280, https://doi.org/10.1175/JTECH-D-15-0039.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, D., L. Oreopoulos, G. J. Huffman, W. B. Rossow, and I.-S. Kang, 2013: The precipitation characteristics of ISCCP tropical weather states. J. Climate, 26, 772788, https://doi.org/10.1175/JCLI-D-11-00718.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luo, Z. J., R. C. Anderson, W. B. Rossow, and H. Takahashi, 2017: Tropical cloud and precipitation regimes as seen from near-simultaneous TRMM, CloudSat, and CALIPSO observations and comparison with ISCCP. J. Geophys. Res. Atmos., 122, 59886003, https://doi.org/10.1002/2017JD026569.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • MacQueen, J., 1967: Some methods for classification and analysis of multivariate observations. Statistics, Vol. 1, Proceedings of the Berkeley Symposium on Mathematical Statistics and Probability, University of California Press, 281–297.

  • Maranan, M., A. H. Fink, P. Knippertz, L. K. Amekudzi, W. A. Atiah, and M. Stengel, 2020: A process-based validation of GPM IMERG and its sources using a mesoscale rain gauge network in the West African forest zone. J. Hydrometeor., 21, 729749, https://doi.org/10.1175/JHM-D-19-0257.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mason, S., C. Jakob, A. Protat, and J. Delanoë, 2014: Characterizing observed midtopped cloud regimes associated with Southern Ocean shortwave radiation biases. J. Climate, 27, 61896203, https://doi.org/10.1175/JCLI-D-14-00139.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oreopoulos, L., and W. B. Rossow, 2011: The cloud radiative effects of International Satellite Cloud Climatology Project weather states. J. Geophys. Res., 116, D12202, https://doi.org/10.1029/2010JD015472.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oreopoulos, L., N. Cho, D. Lee, S. Kato, and G. J. Huffman, 2014: An examination of the nature of global MODIS cloud regimes. J. Geophys. Res. Atmos., 119, 83628383, https://doi.org/10.1002/2013JD021409.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oreopoulos, L., N. Cho, D. Lee, and S. Kato, 2016: Radiative effects of global MODIS cloud regimes. J. Geophys. Res. Atmos., 121, 22992317, https://doi.org/10.1002/2015JD024502.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pike, M., and B. R. Lintner, 2020: Application of clustering algorithms to TRMM precipitation over the tropical and South Pacific Ocean. J. Climate, 33, 57675785, https://doi.org/10.1175/JCLI-D-19-0537.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pincus, R., S. Platnick, S. A. Ackerman, R. S. Hemler, and R. J. P. Hofmann, 2012: Reconciling simulated and observed views of clouds: MODIS, ISCCP, and the limits of instrument simulators. J. Climate, 25, 46994720, https://doi.org/10.1175/JCLI-D-11-00267.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Platnick, S., M. D. King, S. A. Ackerman, W. P. Menzel, B. A. Baum, J. C. Riedi, and R. A. Frey, 2003: The MODIS cloud products: Algorithms and examples from Terra. IEEE Trans. Geosci. Remote Sens., 41, 459473, https://doi.org/10.1109/TGRS.2002.808301.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Platnick, S., and et al. , 2017a: The MODIS cloud optical and microphysical products: Collection 6 updates and examples from Terra and Aqua. IEEE Trans. Geosci. Remote Sens., 55, 502525, https://doi.org/10.1109/TGRS.2016.2610522.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Platnick, S., M. D. King, and P. A. Hubanks, 2017b: MODIS Atmosphere L3 Daily Product (C6.1). NASA MODIS Adaptive Processing System, Goddard Space Flight Center, accessed 1 June 2020, https://doi.org/10.5067/MODIS/MOD08_D3.061.

    • Crossref
    • Export Citation
  • Platnick, S., and et al. , 2018: MODIS cloud optical properties: User guide for the collection 6/6.1 Level-2 MOD06/MYD06 product and associated Level-3 datasets, version 1.1. NASA Doc., 150 pp., https://atmosphere-imager.gsfc.nasa.gov/sites/default/files/ModAtmo/MODISCloudOpticalPropertyUserGuideFinal_v1.1_1.pdf.

  • Robertson, A. W., N. Vigaud, J. Yuan, and M. K. Tippett, 2020: Toward identifying subseasonal forecasts of opportunity using North American weather regimes. Mon. Wea. Rev., 148, 18611875, https://doi.org/10.1175/MWR-D-19-0285.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rossow, W. B., and R. A. Schiffer, 1999: Advances in understanding clouds from ISCCP. Bull. Amer. Meteor. Soc., 80, 22612287, https://doi.org/10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rossow, W. B., G. Tselioudis, A. Polak, and C. Jakob, 2005: Tropical climate described as a distribution of weather states indicated by distinct mesoscale cloud property mixtures. Geophys. Res. Lett., 32, L21812, https://doi.org/10.1029/2005GL024584.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rossow, W. B., A. Mekonnen, C. Pearl, and W. Goncalves, 2013: Tropical precipitation extremes. J. Climate, 26, 14571466, https://doi.org/10.1175/JCLI-D-11-00725.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., and et al. , 2002: The CloudSat mission and the A-train: A new dimension of space-based observations of clouds and precipitation. Bull. Amer. Meteor. Soc., 83, 17711790, https://doi.org/10.1175/BAMS-83-12-1771.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., and et al. , 2008: CloudSat mission: Performance and early science after the first year of operation. J. Geophys. Res., 113, D00A18, https://doi.org/10.1029/2008JD009982.

    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., M. A. Smalley, and M. D. Lebsock, 2019: The cloudy nature of tropical rains. J. Geophys. Res. Atmos., 124, 171188, https://doi.org/10.1029/2018JD029394.

    • Search Google Scholar
    • Export Citation
  • Tan, J., and L. Oreopoulos, 2019: Subgrid precipitation properties of mesoscale atmospheric systems represented by MODIS cloud regimes. J. Climate, 32, 17971812, https://doi.org/10.1175/JCLI-D-18-0570.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tan, J., C. Jakob, W. B. Rossow, and G. Tselioudis, 2015: Increases in tropical rainfall driven by changes in frequency of organized deep convection. Nature, 519, 451454, https://doi.org/10.1038/nature14339.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tan, J., G. J. Huffman, D. T. Bolvin, and E. J. Nelkin, 2019a: IMERG V06: Changes to the morphing algorithm. J. Atmos. Oceanic Technol., 36, 24712482, https://doi.org/10.1175/JTECH-D-19-0114.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tan, J., G. J. Huffman, D. T. Bolvin, and E. J. Nelkin, 2019b: Diurnal cycle of IMERG V06 precipitation. Geophys. Res. Lett., 46, 13 58413 592, https://doi.org/10.1029/2019GL085395.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tselioudis, G., W. Rossow, Y. Zhang, and D. Konsta, 2013: Global weather states and their properties from passive and active satellite cloud retrievals. J. Climate, 26, 77347746, https://doi.org/10.1175/JCLI-D-13-00024.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vernekar, A. D., B. P. Kirtman, and M. J. Fennessy, 2003: Low-level jets and their effects on the South American summer climate as simulated by the NCEP Eta Model. J. Climate, 16, 297311, https://doi.org/10.1175/1520-0442(2003)016<0297:LLJATE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Winker, D. M., M. A. Vaughan, A. Omar, Y. Hu, K. A. Powell, Z. Liu, W. H. Hunt, and S. A. Young, 2009: Overview of the CALIPSO mission and CALIOP data processing algorithms. J. Atmos. Oceanic Technol., 26, 23102323, https://doi.org/10.1175/2009JTECHA1281.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, S., and E. A. Smith, 2006: Mechanisms for diurnal variability of global tropical rainfall observed from TRMM. J. Climate, 19, 51905226, https://doi.org/10.1175/JCLI3883.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • You, Y., V. Petkovic, J. Tan, R. Kroodsma, W. Berg, C. Kidd, and C. Peters-Lidard, 2020: Evaluation of V05 precipitation estimates from GPM constellation radiometers using KuPR as the reference. J. Hydrometeor., 21, 705728, https://doi.org/10.1175/JHM-D-19-0144.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, Z., and S. Platnick, 2011: An assessment of differences between cloud effective particle radius retrievals for marine water clouds from three MODIS spectral bands. J. Geophys. Res., 116, D20215, https://doi.org/10.1029/2011JD016216.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, J., J. Kug, J. Park, and S. An, 2020: Diversity of North Pacific meridional mode and its distinct impacts on El Niño–Southern Oscillation. Geophys. Res. Lett., 47, e2020GL088993, https://doi.org/10.1029/2020GL088993.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 119 119 25
Full Text Views 61 61 16
PDF Downloads 75 75 26

Cloud–Precipitation Hybrid Regimes and Their Projection onto IMERG Precipitation Data

View More View Less
  • 1 a Universities Space Research Association, Columbia, Maryland
  • | 2 b NASA Goddard Space Flight Center, Greenbelt, Maryland
  • | 3 c Morgan State University, Baltimore, Maryland
© Get Permissions Rent on DeepDyve
Restricted access

Abstract

To better understand cloud–precipitation relationships, we extend the concept of cloud regimes developed from two-dimensional joint histograms of cloud optical thickness and cloud-top pressure from MODIS to include precipitation information. Taking advantage of the high-resolution IMERG precipitation dataset, we derive cloud–precipitation “hybrid” regimes by implementing a k-means clustering algorithm with advanced initialization and objective measures to determine the optimal number of clusters. By expressing the variability of precipitation rates within 1° grid cells as histograms and varying the relative weight of cloud and precipitation information in the clustering algorithm, we obtain several editions of hybrid cloud–precipitation regimes (CPRs) and examine their characteristics. In the deep tropics, when precipitation is weighted weakly, the cloud part centroids of the hybrid regimes resemble their counterparts of cloud-only regimes, but combined clustering tightens the cloud–precipitation relationship by decreasing each regime’s precipitation variability. As precipitation weight progressively increases, the shape of the cloud part centroids becomes blunter, while the precipitation part sharpens. When cloud and precipitation are weighted equally, the CPRs representing high clouds with intermediate to heavy precipitation exhibit distinct enough features in the precipitation parts of the centroids to allow us to project them onto the 30-min IMERG domain. Such a projection overcomes the temporal sparseness of MODIS cloud observations associated with substantial rainfall, suggesting great application potential for convection-focused studies for which characterization of the diurnal cycle is essential.

Significance Statement

Clouds and precipitation are related in close but complex ways. In this work we attempt to provide a classification of daytime cloud–precipitation co-occurrence and covariability, with emphasis on tropical regions. We achieve such a classification using a k-means clustering algorithm applied to cloud fraction and precipitation intensity histograms, which yields “hybrid” clusters, that is, groups whose members have similar cloud and precipitation properties. These hybrid clusters reveal more detailed features of coincident daytime cloud and precipitation systems than do clusters in which clouds and precipitation are treated separately. Moreover, the realization that precipitation features associated with high and optically thick clouds have very distinct patterns enables hybrid cluster prediction solely on the basis of precipitation information. This has the important implication that rarer cloud observations can be extended to the more frequent (including nighttime) precipitation domain.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JAMC-D-20-0253.s1.

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

Corresponding author: Daeho Jin, daeho.jin@nasa.gov

Abstract

To better understand cloud–precipitation relationships, we extend the concept of cloud regimes developed from two-dimensional joint histograms of cloud optical thickness and cloud-top pressure from MODIS to include precipitation information. Taking advantage of the high-resolution IMERG precipitation dataset, we derive cloud–precipitation “hybrid” regimes by implementing a k-means clustering algorithm with advanced initialization and objective measures to determine the optimal number of clusters. By expressing the variability of precipitation rates within 1° grid cells as histograms and varying the relative weight of cloud and precipitation information in the clustering algorithm, we obtain several editions of hybrid cloud–precipitation regimes (CPRs) and examine their characteristics. In the deep tropics, when precipitation is weighted weakly, the cloud part centroids of the hybrid regimes resemble their counterparts of cloud-only regimes, but combined clustering tightens the cloud–precipitation relationship by decreasing each regime’s precipitation variability. As precipitation weight progressively increases, the shape of the cloud part centroids becomes blunter, while the precipitation part sharpens. When cloud and precipitation are weighted equally, the CPRs representing high clouds with intermediate to heavy precipitation exhibit distinct enough features in the precipitation parts of the centroids to allow us to project them onto the 30-min IMERG domain. Such a projection overcomes the temporal sparseness of MODIS cloud observations associated with substantial rainfall, suggesting great application potential for convection-focused studies for which characterization of the diurnal cycle is essential.

Significance Statement

Clouds and precipitation are related in close but complex ways. In this work we attempt to provide a classification of daytime cloud–precipitation co-occurrence and covariability, with emphasis on tropical regions. We achieve such a classification using a k-means clustering algorithm applied to cloud fraction and precipitation intensity histograms, which yields “hybrid” clusters, that is, groups whose members have similar cloud and precipitation properties. These hybrid clusters reveal more detailed features of coincident daytime cloud and precipitation systems than do clusters in which clouds and precipitation are treated separately. Moreover, the realization that precipitation features associated with high and optically thick clouds have very distinct patterns enables hybrid cluster prediction solely on the basis of precipitation information. This has the important implication that rarer cloud observations can be extended to the more frequent (including nighttime) precipitation domain.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JAMC-D-20-0253.s1.

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

Corresponding author: Daeho Jin, daeho.jin@nasa.gov

Supplementary Materials

    • Supplemental Materials (PDF 10.7 MB)
Save