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Xu Lu and Xuguang Wang

originally designed under a clear-sky assumption where the free atmosphere has little diffusion. Therefore, K m is always set to zero at the PBL top and the K m above the PBL is always following the clear-sky profiles in the HWRF PBL scheme. But this assumption is not suitable for the deep convection, such as the eyewall or spiral rainbands, where in-cloud turbulence creates large mixing above the PBL. Zhu et al. (2018) proposed a modified turbulent mixing parameterization scheme that replaces the

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Peter Black, Lee Harrison, Mark Beaubien, Robert Bluth, Roy Woods, Andrew Penny, Robert W. Smith, and James D. Doyle

near the wingtips of the aircraft allow signals to be received during aircraft turns when the large wing would result in shadowing of the two belly antennas and receivers. The HDSS also carries two cameras to record dropsonde ejection: one aft of the drop tubes facing forward and one forward of the drop tubes facing aft. These cameras also were used to document cloud structures ahead of and behind the aircraft ( Fig. 16 ). The mission monitor display for the WB-57 was used to maintain situational

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William A. Komaromi and James D. Doyle

hypothesis, referred to as active outflow , is that a source of upper-level forcing that acts to accelerate or otherwise enhance the TC outflow can ultimately drive changes in the strength or structure of the vortex below (e.g., Sadler 1976 ; Holland and Merrill 1984 ; Nong and Emanuel 2003 ). Here, we do not seek to determine whether outflow is more likely to be passive or active. Instead, we explore the hypothesis that active outflow may contribute to TC intensification under the right set of

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James D. Doyle, Jonathan R. Moskaitis, Joel W. Feldmeier, Ronald J. Ferek, Mark Beaubien, Michael M. Bell, Daniel L. Cecil, Robert L. Creasey, Patrick Duran, Russell L. Elsberry, William A. Komaromi, John Molinari, David R. Ryglicki, Daniel P. Stern, Christopher S. Velden, Xuguang Wang, Todd Allen, Bradford S. Barrett, Peter G. Black, Jason P. Dunion, Kerry A. Emanuel, Patrick A. Harr, Lee Harrison, Eric A. Hendricks, Derrick Herndon, William Q. Jeffries, Sharanya J. Majumdar, James A. Moore, Zhaoxia Pu, Robert F. Rogers, Elizabeth R. Sanabia, Gregory J. Tripoli, and Da-Lin Zhang

layer, all of which impact TC development and intensification to varying degrees. Several specialized TC field campaigns over the past 15 years have focused on various aspects of these processes, including the Coupled Boundary Layers Air–Sea Transfer (CBLAST; Black et al. 2007 ) experiment, the Tropical Cloud Systems and Processes (TCSP; Halverson et al. 2007 ) experiment, the NASA African Monsoon Multidisciplinary Analysis (NASA-AMMA or NAMMA; Zipser et al. 2009 ), The Observing System Research

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Shixuan Zhang, Zhaoxia Pu, and Christopher Velden

sources for use in operational data assimilation. Unfortunately, limitations of current data assimilation methodologies prevent most satellite radiances in the TC inner core and near environmental regions from being assimilated. This occurs because of cloud and precipitation contamination, although all-sky data assimilation has become an active research area in recent years ( Zhu et al. 2016 ). Fortunately, satellite-derived products, especially atmospheric motion vectors (AMVs; Velden et al. 1997

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T. Connor Nelson, Lee Harrison, and Kristen L. Corbosiero

in the TCI dataset, as well as to examine the updrafts and downdrafts observed by the XDDs. Hock and Franklin (1999) used RD-93 dropsondes to derive vertical velocity from GPS fall speeds and a single drag force estimate presumed to be representative for all individual sondes. This method is now routine, but more recent studies use a hydrostatic pressure-derived fall speed rather than the GPS fall speed (e.g., Wang et al. 2015 ). Sonde-derived vertical velocities have been used to examine the

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Daniel J. Cecil and Sayak K. Biswas

; Rosenkranz and Staelin 1972 ). Since the increase in foam is correlated with surface wind speed ( Ross and Cardone 1974 ; Webster et al. 1976 ; Swift et al. 1984 ; Tanner et al. 1987 ), emissivity increases with surface wind speed. The sensitivity to wind speed is greatest at hurricane force (>33 m s −1 ) and is therefore particularly useful for measuring the strongest winds. The four C-band channels also have varying sensitivity to rain, so rain rate and wind speed can be retrieved simultaneously

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Shixuan Zhang and Zhaoxia Pu

-Depression Investigation of Cloud-systems in the Tropics (PREDICT; Montgomery et al. 2012 ), have been conducted to obtain observations within various environments during the TC life cycle. These data have proven to be useful for understanding TC intensity changes. For instance, observational studies and model–observation comparisons based on these field campaigns (e.g., Black et al. 2002 ; Rogers et al. 2003 ; Houze et al. 2006 ; Jaimes et al. 2015 ) further proved the importance of vertical wind shear and sea

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Yi Dai, Sharanya J. Majumdar, and David S. Nolan

1. Introduction The tropical cyclone (TC)–environmental flow interaction (TCEFI) plays an important role in TC structure and intensity change. Although some indices and methods exist to represent the TCEFI, they have mainly been developed in an axisymmetric framework. However, the azimuthally asymmetric interaction also needs to be considered, because the environmental flow is often highly asymmetric relative to the TC, thus creating an asymmetric forcing on the TC. Environmental features such

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Jie Feng and Xuguang Wang

1. Introduction In recent decades, research has shown that the tropical cyclone (TC) outflow layer is critically related to the TC structure evolution and intensity change rather than just a mechanism to export TC energy at the upper troposphere. The outflow layer relative to the low- and midtroposphere has weaker inertial stability and thus is more susceptible to the environmental forcing ( Holland and Merrill 1984 ; Rappin et al. 2011 ). For example, the outflow can interact with a

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