Level of neutral buoyancy, deep convective outflow, and convective core: New perspectives based on 5 years of CloudSat data

Takahashi, H.; Luo, Zhengzhao Johnny; Stephens, Graeme L.

doi:10.1002/2016jd025969

Cited by 38 publications

(66 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Quantitatively identifying the altitudes where boundary layer mass is transported to has been challenging. Retrievals of the height of mass transport have been determined from aircraft measurements (e.g., Pickering et al, 1996), satellite measurements (Takahashi & Luo, 2012;Takahashi et al, 2017), multi-Doppler observations (e.g., Mullendore et al, 2013), and modeling studies (e.g., Barth et al, 2007;Bigelbach et al, 2014). While invaluable, aircraft measurements are limited temporally, cannot continuously sample every region of storm outflow, and generally are only available from field campaigns.…”

Section: Introductionmentioning

confidence: 99%

Retrievals of Convective Detrainment Heights Using Ground‐Based Radar Observations

2020

View full text Add to dashboard Cite

To better constrain model simulations, more observations of convective detrainment heights are needed. For the first time, ground‐based S band radar observations are utilized to create a comprehensive view of irreversible convective transport over a 7‐year period for the months of May and July across the United States. The radar observations are coupled with a volumetric radar echo classification scheme and a methodology that uses the convective anvil as proxy for convective detrainment to determine the level of maximum detrainment (LMD) for deep moist convection. The LMD height retrievals are subset by month (i.e., May and July), by morphology (i.e., mesoscale convective system, MCS, and quasi‐isolated strong convection, QISC), and region (i.e., northcentral, southcentral, northeast, and southeast). Overall, 135,890 deep convective storms were successfully sampled and had a mean LMD height of 8.6 km or tropopause‐relative mean LMD height of −4.3 km; however, LMD heights were found to extend up to 2 km above the tropopause. May storms had higher mean tropopause‐relative LMD heights, but July storms contained the highest overall LMD heights that more commonly extended above the tropopause. QISC had higher mean tropopause‐relative LMD heights and more commonly had LMD heights above the tropopause while only a few MCSs had LMD heights above the tropopause. The regional analysis showed that northern regions have higher mean LMD heights due to large amounts of diurnally driven convection being sampled in the southern regions. By using the anvil top, the highest possible convective detrainment heights extended up to 6 km above the tropopause.

show abstract

Section: Introductionmentioning

confidence: 99%

Retrievals of Convective Detrainment Heights Using Ground‐Based Radar Observations

2020

View full text Add to dashboard Cite

show abstract

“…In particular, in the weak-friction limit (Figure 7) the parcel rises far above the LNB, and it actually attains its maximum vertical velocity at the LNB. Such behavior is arguably unrealistic, since the LNB is typically regarded as an estimate of the cloud top, and since maximum vertical velocities are typically attained below the LNB or cloud top (for example, Takahashi and Luo, 2012;Takahashi et al, 2017). These discrepancies could be regarded as due to the absence of interactions between the parcel and its environment in the traditional (weak-friction) parcel model.…”

Section: Summary and Comparisonsmentioning

confidence: 99%

Weak‐ and strong‐friction limits of parcel models: Comparisons and stochastic convective initiation time

Hernández-Dueñas

Smith

Stechmann

2019

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

For moist convection, models of individual parcel dynamics are valuable for their simple formulation and predictions of cloud properties. Here, two limiting idealized cases of parcel theory are investigated: the weak‐ and strong‐friction limits. The weak‐friction limit is a traditional limit with no momentum drag and the dynamics are a Hamiltonian system for the parcel's height and vertical velocity. A strong‐friction limit is derived and studied here and its limiting form involves a balance between frictional drag and buoyancy, which provides a differential equation for parcel height as a function of time. In the two limiting regimes, analytical formulas are presented and compared for quantities such as maximum vertical velocity and cloud‐top height. For example, in the strong‐friction limit, the cloud‐top height coincides with the level of neutral buoyancy (LNB), whereas in the weak‐friction limit the cloud‐top height is far above the LNB. This comparison suggests that the strong‐friction limit may provide more realistic predictions of some averaged cloud properties. In general, since frictional effects and individual parcel properties can vary even within a single cloud, the predictions of the weak‐ and strong‐friction limits can be viewed as upper and lower bounds for the behavior of more realistic finite‐friction scenarios. Finally, in a stochastic version of the parcel model, in the strong‐friction limit, analytical formulas are derived for convective initiation time. Applications to convective parametrizations are discussed; for example, the formulas for convective initiation time could be applied as stochastic convective triggers.

show abstract

“…As a proxy for convective activity, the anvil productivity of deep convection was analyzed over the tropical ocean by combining CloudSat and CALIPSO products (Deng et al, ). Luo et al () and Takahashi et al () aimed at retrieving information about inner deep convective cloud dynamics by combining CloudSat, CALIPSO, and ECMWF reanalysis of the atmospheric state as well as other A‐Train satellite products. These research studies successfully illustrate the characteristics of deep convective clouds by defining distinctively intense or organized cloud systems.…”

Section: Introductionmentioning

confidence: 99%

Characteristics of Ice Clouds Over Mountain Regions Detected by CALIPSO and CloudSat Satellite Observations

et al. 2019

View full text Add to dashboard Cite

This study examines the characteristics of orographic ice clouds in steep mountain regions using 3 years of CloudSat and CALIPSO satellite products. A combination of radar and lidar cloud fraction data is used to identify ice cloud systems. Additionally, the retrieved ice water content (IWC) and ice number concentration (NI) are used to analyze the dominant ice cloud microphysics in convective‐ and cirrus‐type clouds. The analysis shows that temporally averaged values of the IWC and NI are larger in mountain regions than in land and ocean regions. For convective clouds over mountains, both the IWC and NI have larger values at atmospheric temperatures warmer than 250 K, suggesting a dominant role for the freezing of supercooled liquid water. For cirrus clouds over mountains, however, only the NI has larger values at atmospheric temperatures colder than 240 K, indicating the importance of homogeneous ice nucleation. These characteristics of ice‐phase clouds in terms of the IWC and NI in mountain regions are distinct, with a horizontal scale smaller than 300 km. This study suggests that it is useful to categorize ice clouds in mountain regions in addition to ocean and land regions to evaluate the microphysical properties (mass and number) of such ice clouds in atmospheric models.

show abstract

Level of neutral buoyancy, deep convective outflow, and convective core: New perspectives based on 5 years of CloudSat data

Cited by 38 publications

References 40 publications

Retrievals of Convective Detrainment Heights Using Ground‐Based Radar Observations

Retrievals of Convective Detrainment Heights Using Ground‐Based Radar Observations

Weak‐ and strong‐friction limits of parcel models: Comparisons and stochastic convective initiation time

Characteristics of Ice Clouds Over Mountain Regions Detected by CALIPSO and CloudSat Satellite Observations

Contact Info

Product

Resources

About