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  • Climate classification/regimes x
  • Air–Sea Interactions from the Diurnal to the Intraseasonal during the PISTON, MISOBOB, and CAMP2Ex Observational Campaigns in the Tropics x
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Kyle Chudler, Weixin Xu, and Steven A. Rutledge

this feature classification dataset with the gridded TRMM 2A25 dataset (also included in TRMM UW), the frequency of occurrence for the different feature categories were tabulated according to both space and time. The BSISO drives changes in wind regimes between inactive and active phases around Luzon. The Cross-Calibrated Multi-Platform (CCMP; Atlas et al. 2011 ) wind retrieval product was used to obtain historical gridded wind data over the study region. CCMP utilizes a variational analysis

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Kenneth G. Hughes, James N. Moum, and Emily L. Shroyer

temperature measurements below the surface from ships ( Fairall et al. 1996 ). The daily changes also add up over longer time scales. Development of the widely used K -profile parameterization (KPP) mixing scheme was motivated in part by a need to ensure physically reasonable mixing on diurnal time scales given its influence over climate time scales ( Large et al. 1994 ). A necessary step in predicting or parameterizing the evolution of the near-surface temperature field is an observational depiction of

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Emily M. Riley Dellaripa, Eric D. Maloney, Benjamin A. Toms, Stephen M. Saleeby, and Susan C. van den Heever

. Other studies have focused on the changes in amplitude and phase of the DCP over the MC through different MJO large-scale conditions. Using TRMM observations, Peatman et al. (2014) found that the DCP amplitude over MC land peaks just prior to the arrival of large-scale active MJO convection. Birch et al. (2016) reached a similar conclusion with TRMM observations and regional climate model simulations, but focused on the DCP over Sumatra during different large-scale MJO conditions. Cloud

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