Search Results

You are looking at 1 - 10 of 34 items for :

  • Boundary currents x
  • DYNAMO/CINDY/AMIE/LASP: Processes, Dynamics, and Prediction of MJO Initiation x
  • Refine by Access: All Content x
Clear All
Yue Ying and Fuqing Zhang

other modeling systems that account for additional error sources (e.g., model dynamics, physics parameterizations, and low-boundary-condition forcings). The predictability estimates from the MJO active phase event in this study may also differ from those estimated for other events. The current study only simulates a 15-day period within an MJO active phase, which is not long enough to estimate the predictability of MJO itself. In previous MJO predictability studies using global model simulations

Full access
Eric D. Skyllingstad and Simon P. de Szoeke

, convectively forced cold pools appear as cool and slightly drier air masses beneath convective systems, with wind gusts enhancing surface fluxes as they spread laterally. They have been observed in association with convection at many scales ranging from precipitating trade wind cumulus ( Zuidema et al. 2012 ) to mesoscale convective complexes ( Young et al. 1995 ). In midlatitudes, cold pool boundaries are known to trigger new convection and are often implicated in the development of severe storms

Full access
James N. Moum, Simon P. de Szoeke, William D. Smyth, James B. Edson, H. Langley DeWitt, Aurélie J. Moulin, Elizabeth J. Thompson, Christopher J. Zappa, Steven A. Rutledge, Richard H. Johnson, and Christopher W. Fairall

soundings (eight per day) of velocity, temperature, relative humidity, and pressure altitude; complete atmospheric surface turbulent flux measurements for comparison to bulk formulas using standard meteorological observations; boundary layer velocity profile measurements using W-band Doppler radar and high-resolution Doppler lidar; continuous C-band Doppler radar scans measuring radial velocity and radar reflectivity; particle size distributions and chemical composition of aerosols; upper-ocean current

Full access
Hyodae Seo, Aneesh C. Subramanian, Arthur J. Miller, and Nicholas R. Cavanaugh

( Seo et al. 2007b ; ). SCOAR currently couples one of two weather models, the Weather Research and Forecasting (WRF; Skamarock et al. 2008 ) Model or the Regional Spectral Model ( Juang and Kanamitsu 1994 ), to the Regional Ocean Modeling System (ROMS; Haidvogel et al. 2000 ; Shchepetkin and McWilliams 2005 ). This study uses the WRF-ROMS version of SCOAR, taking advantage of WRF’s latest physics options. The interacting boundary layer between WRF and ROMS is based

Full access
Douglas C. Stolz, Steven A. Rutledge, Weixin Xu, and Jeffrey R. Pierce

Arabian Sea, Bay of Bengal, and the tropical Indian Ocean ( Satheesh et al. 1999 ; Ramanathan et al. 2001 ). Hence, we represent CCN for our study by the boundary layer average for 1000–850 hPa. Background thermodynamics and low-level winds (1000–850-hPa average) derived from the Modern-Era Retrospective Analysis for Research and Applications (MERRA; ) are analyzed at 0000, 0600, 1200, and 1800 UTC and subsequently averaged over each full day in the current study

Full access
Simon P. de Szoeke, James B. Edson, June R. Marion, Christopher W. Fairall, and Ludovic Bariteau

1987 ) or boundary layer frictional wave-CISK (e.g., Wang and Rui 1990 ; Salby et al. 1994 )] or by a quasi-equilibrium between circulation and radiative-convective equilibrium ( Neelin et al. 1987 ; Emanuel 1987 ; Neelin and Zeng 2000 ). Other models with nonlinear interaction of smaller-scale waves (e.g., through triggering convection) predict organization of synoptic-scale convection into a large MJO envelope ( Majda and Stechmann 2009 ; Yang and Ingersoll 2013 ). The waves predicted by

Full access
Jianhao Zhang, Paquita Zuidema, David D. Turner, and Maria P. Cadeddu

retrieval. This is not typically done, but the constraint improves the subsequent profile retrieval by reducing compensating variability at different altitudes and improves the representation of the boundary layer moisture in particular. A flowchart ( Fig. 2 ) facilitates understanding of the approach described further below. Fig . 2. A flowchart indicating the column-integrated retrieval (red frames and arrows) and profile retrieval (green frames and arrows). The postcampaign calibration and quality

Full access
David M. Zermeño-Díaz, Chidong Zhang, Pavlos Kollias, and Heike Kalesse

UTC (2100 and 0900 local time). Their averages were used to represent daily means. Soundings with melting-level heights outside the typical range of 3.5–5.5 km ( Geerts and Dawei 2004 ) were considered unreliable and excluded from our study. Sounding estimates of boundary layer heights from four methods (C. Sivaraman et al. 2012, meeting presentation) were averaged to provide a daily mean best estimate. There are six MMCR profiles per minute in the ARSCL data over different vertical ranges with a

Full access
Hungjui Yu, Paul E. Ciesielski, Junhong Wang, Hung-Chi Kuo, Holger Vömel, and Ruud Dirksen

Global Climate Observing System (GCOS) program initiated a GCOS Reference Upper-Air Network (GRUAN) consisting of 18 sites worldwide ( Seidel et al. 2009 ). Vaisala RS92s are launched regularly at 13 of the current GRUAN candidate stations. GRUAN developed algorithms to correct systematic errors in RS92 data and to derive an uncertainty estimate for each data point and each parameter ( Immler and Sommer 2011 ; Dirksen et al. 2014 ). All current GRUAN algorithms are well documented in peer

Full access
Brandon W. Kerns and Shuyi S. Chen

when the data are updated with new swaths. Also, moderate changes in midlevel moisture with the boundary layer and upper levels remaining moist are not easily distinguished in terms of TPW. For these reasons, the evolution of TPW is most useful at determining strong gradients between moist (>~55 mm) and dry air (<~45 mm) masses, where there are large gradients of moisture through a deep layer of the atmosphere. Fortunately, these sharp moisture gradients are common ( Zhang et al. 2003 ). 3

Full access