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  • Seasonal effects x
  • Understanding Diurnal Variability of Precipitation through Observations and Models (UDVPOM) x
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Song Yang and Eric A. Smith

global precipitation characteristics through analysis of the seasonal variability of convective and stratiform rainfall based on the use of long-term records of TRMM rainfall (8 yr over the 1998–2005 period) obtained from PR and TMI measurements—over the tropical global province. Overall, the principal scientific objectives of this study are 1) to understand the climatological variability of convective and stratiform precipitation on a seasonal basis, classified by continental and oceanic

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R. E. Carbone and J. D. Tuttle

lee of mountain ranges with an equatorward source of potentially warm, moist air ( Laing and Fritsch 1997 ). The resulting episodes of rainfall produce a sizeable fraction of seasonal precipitation in most of these regions. The ensemble of climate system model projections ( Solomon et al. 2007 , p. 859) highlights nearly all of these regions as having uncertain precipitation trends in the twenty-first century. This uncertainty is partly the consequence of inadequate convective representations in

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Alex C. Ruane and John O. Roads

1. Introduction Nearly all atmospheric activity is originally derived from external solar forcing. On time scales of a year and less, this forcing arrives in the form of a strong daily signal resulting from the rotation of the earth and a seasonal signal due to the earth’s orbit and inclination. The diurnal and annual cycles of solar insolation are therefore fundamental to the earth’s water cycle, but do not necessarily drive an equivalent response. Energy from these solar forcings interacts

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Song Yang, Kwo-Sen Kuo, and Eric A. Smith

properties so that the mechanisms of the diurnal cycle can be better explained physically. Modelers would then have a better quantitative basis from which to refine model processes that control the diurnal variability of precipitation. Yang and Smith (2008) describe the seasonal climatology of the diurnal cycle at global scales, its seasonal variability, and the contrasting behaviors of convective and stratiform components, and demonstrate that the secondary diurnal mode is largely modulated by the

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T. N. Krishnamurti, C. Gnanaseelan, A. K. Mishra, and A. Chakraborty

simplified by assuming that the normalized cloud updraft mass flux is a linear function of height, and the effects of cloud condensate loading and moisture content in the buoyancy calculations are ignored. Quasi equilibrium is achieved through relaxation of the sounding toward the equilibrium state in a prescribed time instead of simultaneously letting all cloud ensembles adjust the environment to a state of equilibrium. This original implementation of RAS1 is used in the model version using the GSFC

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Tianjun Zhou, Rucong Yu, Haoming Chen, Aiguo Dai, and Yang Pan

Trenberth 2004 ; Liang et al. 2004 ; Dai 2006 ; Demott et al. 2007 ; Lee et al. 2007 ). The diurnal cycle of precipitation, which comes largely from its frequency variations ( Dai et al. 1999 , 2007 ), has large spatial and seasonal variations. The dominant feature of the oceanic diurnal cycle is a rainfall maximum in early morning (0400–0600 LST), whereas warm-season precipitation peaks in late afternoon (1500–1900 LST) over most (but not all) land areas ( Dai 2001b ; Dai et al. 2007 ). This land

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Donald Wylie

. Jin , Y. , W. B. Rossow , and D. P. Wylie , 1996 : Comparison of the climatologies of high-level clouds from HIRS and ISCCP. J. Climate , 9 , 2850 – 2879 . Kondragunta , C. R. , 1996 : Seasonal and annual variability of the diurnal cycle of clouds. J. Geophys. Res. , 101 , D16 . 21377 – 21390 . Kondragunta , C. R. , and A. Gruber , 1994 : Diurnal variation of the ISCCP cloudiness. Geophys. Res. Lett. , 21 , 18 . 2015 – 2018 . Pavolonis , M. J. , A. K. Heidinger

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Munehisa K. Yamamoto, Fumie A. Furuzawa, Atsushi Higuchi, and Kenji Nakamura

observation period has ameliorated sampling biases ( Hirose and Nakamura 2005 ; Hirose et al. 2008 ). Hirose et al. (2008) showed that the minimum resolutions, with hourly rain samples from multiple precipitation events for 8 yr, are 0.2° and 0.5° in the yearly mean and the seasonal mean, respectively. The TRMM simultaneously observes precipitation–cloud systems with the PR and with microwave and infrared radiometers. These three sensors have different sensitivities for cloud and precipitation systems

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R. Cifelli, S. W. Nesbitt, S. A. Rutledge, W. A. Petersen, and S. Yuter

product has a gridded resolution of 4.2 km before August 2001 and 4.5 km afterward. All data acquired 1998–2004 (July–September) within 5° × 5° boxes centered on the nominal location of the field programs were used to determine the same precipitation characteristics as the RHB over the EPIC and TEPPS domains. The TRMM PR data were composited into 6-h time bins to reduce noise in the resulting parameter statistics. Effects of area averaging of TRMM data are discussed in Bowman et al. (2005) . In

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