气候变化领域集成服务门户(CCPortal): Application of clustering algorithms to TRMM precipitation over the tropical and South Pacific Ocean

CCPortal

DOI	10.1175/JCLI-D-19-0537.1
	Application of clustering algorithms to TRMM precipitation over the tropical and South Pacific Ocean
	Pike M.; Lintner B.R.
发表日期	2020
ISSN	0894-8755
起始页码	5767
结束页码	5785
卷号	33 期号:13
英文摘要	Understanding multiscale rainfall variability in the South Pacific convergence zone (SPCZ), a southeastward-oriented band of precipitating deep convection in the South Pacific, is critical for both the human and natural systems dependent on its rainfall, and for interpreting similar off-equatorial diagonal convection zones around the globe. A k-means clustering method is applied to daily austral summer (December–February) Tropical Rainfall Measuring Mission (TRMM) satellite rainfall to extract representative spatial patterns of rainfall over the SPCZ region for the period 1998–2013. For a k 5 4 clustering, pairs of clusters differ predominantly via spatial translation of the SPCZ diagonal, reflecting either warm or cool phases of El Niño–Southern Oscillation (ENSO). Within each of these ENSO phase pairs, one cluster exhibits intense precipitation along the SPCZ while the other features weakened rainfall. Cluster temporal behavior is analyzed to investigate higher-frequency forcings (e.g., the Madden–Julian oscillation and synoptic-scale disturbances) that trigger deep convection where SSTs are sufficiently warm. Pressure-level winds and specific humidity from the Climate Forecast System Reanalysis are composited with respect to daily cluster assignment to investigate differences between active and quiescent SPCZ conditions to reveal the conditions supporting enhanced or suppressed SPCZ precipitation, such as low-level poleward moisture transport from the equator. Empirical orthogonal functions (EOFs) of TRMM precipitation are computed to relate the ‘‘modal view’’ of SPCZ variability associated with the EOFs to the ‘‘state view’’ associated with the clusters. Finally, the cluster number is increased to illustrate the change in TRMM rainfall patterns as additional degrees of freedom are permitted. © 2020 American Meteorological Society.
英文关键词	Atmospheric pressure; Climatology; Degrees of freedom (mechanics); Nanofluidics; Natural convection; Oceanography; Orthogonal functions; Rain; Rain gages; Tropics; Empirical Orthogonal Function; Intense precipitation; K-means clustering method; Poleward moisture transport; South pacific convergence zones; South pacific oceans; Southern oscillation; Tropical rainfall measuring mission satellites; K-means clustering; air-sea interaction; algorithm; atmospheric forcing; convection; El Nino-Southern Oscillation; moisture transfer; precipitation assessment; spatial data; TRMM; Pacific Ocean; Pacific Ocean (South); Pacific Ocean (Tropical)
语种	英语
来源期刊	Journal of Climate
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/171262
作者单位	Department of Environmental Sciences, Rutgers, The State University of New Jersey, New Brunswick, NJ, United States; Department of Environmental Sciences, Rutgers, The State University of New Jersey, and Rutgers Institute of Earth, Ocean, and Atmospheric Sciences, New Brunswick, NJ, United States
推荐引用方式 GB/T 7714	Pike M.,Lintner B.R.. Application of clustering algorithms to TRMM precipitation over the tropical and South Pacific Ocean[J],2020,33(13).
APA	Pike M.,&Lintner B.R..(2020).Application of clustering algorithms to TRMM precipitation over the tropical and South Pacific Ocean.Journal of Climate,33(13).
MLA	Pike M.,et al."Application of clustering algorithms to TRMM precipitation over the tropical and South Pacific Ocean".Journal of Climate 33.13(2020).