Characteristics of Water Vapor Turbulence Profiles in Convective Boundary Layers During the Dry and Wet Seasons Over Darwin

Osman, M.; Turner, David D.; Heus, Thijs; Newsom, Rob

doi:10.1029/2017jd028060

Cited by 6 publications

(8 citation statements)

References 80 publications

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“…The correlation coefficient between

\overset{q_{I}^{' 2}}{true}

and

g_{I}^{2}

is 0.70 for the observation data in this study, which is in good agreement with previous observational studies at the SGP (Turner, Wulfmeyer, et al, , where r = 0.67) and the ARM Darwin (Osman et al, , where r = 0.83) sites. The correlation between these two variables in the LES output is markedly smaller ( r = 0.16), which may be related to the much smaller water vapor gradient values in the LES output relative to the observational data (see Figure a).…”

Section: Resultssupporting

confidence: 93%

“…This requires that the turbulent nature of the CBL does not change over this sampling period. We have used the same approach outlined in Turner, Wulfmeyer, et al () and Osman et al (), wherein we searched for periods of 2 h in length where the CBL was quasi‐stationary (i.e., z i was constant throughout the period, there were no apparent synoptic boundaries passing over the site, etc.) for our analysis.…”

Section: Discussionmentioning

confidence: 99%

“…However, a thorough validation of these relationships using observational data is missing. Turner, Wulfmeyer, et al () and Osman et al () used multiyear remote sensing data to look at water vapor variance in the IL and CBL, but the insight was limited because these data sets did not include wind shear and temperature gradients. Wulfmeyer et al () discussed how a new generation of water vapor Raman lidar, Doppler lidar, and wind profiler instruments, if all collocated at one site, could be used to evaluate these similarity relationships in the IL.…”

Section: Introductionmentioning

confidence: 99%

“…Both observations (e.g., Berg et al, ; Couvreux et al, ; Mahrt, ) and numerical simulations (e.g., Deardorff, ; Mellado et al, ) have offered significant insights into the moisture distribution and transport around the IL. For example, it is known that dry air from the free atmosphere can penetrate deep into the CBL, resulting in a negative skewness or third‐order moment of the water vapor mixing ratio near the top of the CBL, whereas the penetration of convective eddies above the CBL top results in a positive skewness or third‐order moment (e.g., Couvreux et al, ; Mellado et al, ; Osman et al, ; Turner, Wulfmeyer, et al, ; Wulfmeyer et al, ). This deep dry entrainment can also affect the water vapor budget and variance at the surface (e.g., Santanello et al, ; Simmer et al, ).…”

Section: Introductionmentioning

confidence: 99%

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Validating the Water Vapor Variance Similarity Relationship in the Interfacial Layer Using Observations and Large‐Eddy Simulations

et al. 2019

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In previous work, the similarity relationship for the water vapor variance in the interfacial layer (IL) at the top of the convective boundary layer (CBL) was proposed to be proportional to the convective velocity scale and the gradients of the water vapor mixing ratio and the Brunt‐Vaisala frequency in the entrainment zone. In the presence of wind shear in the IL, the similarity relationship was hypothesized to also include a dependence on the gradient Richardson number. Simultaneous measurements of the surface buoyancy flux, wind‐shear profiles from a radar wind profiler, water vapor mixing ratio and temperature measurements and their gradients from a Raman lidar provide a unique opportunity to thoroughly examine the function used in defining the variance and validate it. These observations were made over the Atmospheric Radiation Measurement Southern Great Plains site. We identified 19 cases from 2016 during which the CBL was quasi‐stationary and well mixed for at least 2 hr in the afternoon. Furthermore, we simulated the CBL using a large‐eddy simulation (LES) model for these cases and derived the water vapor variance and other profiles to test the similarity function. Utilizing this unique combination of observations and LES, we demonstrate that the water vapor variance in the IL has little‐to‐no dependence on wind shear. Furthermore, we demonstrate that the predicted variance using the original similarity function matches the observed and LES‐modeled variance very well, with linear correlations between the two variances of 0.82 and 0.95, respectively.

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“…The correlation coefficient between

\overset{q_{I}^{' 2}}{true}

and

g_{I}^{2}

Section: Resultssupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Validating the Water Vapor Variance Similarity Relationship in the Interfacial Layer Using Observations and Large‐Eddy Simulations

et al. 2019

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“…Lidar, aircraft and tethered balloon measurements often show skewed and multimodal temperature and humidity PDFs (Wood and Field 2000;Wulfmeyer et al 2010;Turner et al 2014;Behrendt et al 2015;Osman et al 2018;Price 2006).…”

Section: Bimodal Cloud Scheme 1) Entrainment Zone Definitionmentioning

confidence: 99%

A Bimodal Diagnostic Cloud Fraction Parameterization. Part I: Motivating Analysis and Scheme Description

Weverberg

Morcrette

Boutle

et al. 2021

Monthly Weather Review

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Cloud fraction parameterizations are beneficial to regional, convection-permitting numerical weather prediction. For its operational regional mid-latitude forecasts, the UK Met Office uses a diagnostic cloud fraction scheme which relies on a unimodal, symmetric subgrid saturation-departure distribution. This scheme has been shown before to underestimate cloud cover and hence an empirically-based bias correction is used operationally to improve performance. This first of a series of two papers proposes a new diagnostic cloud scheme as a more physically-based alternative to the operational bias correction. The new cloud scheme identifies entrainment zones associated with strong temperature inversions. For model grid boxes located in this entrainment zone, co-located moist and dry Gaussian modes are used to represent the subgrid conditions. The mean and width of the Gaussian modes, inferred from the turbulent characteristics, are then used to diagnose cloud water content and cloud fraction. It is shown that the new scheme diagnoses enhanced cloud cover for a given grid-box mean humidity, similar to the current operational approach. It does so, however, in a physically meaningful way. Using observed aircraft data and ground-based retrievals over the Southern Great Plains in the US, it is shown that the new scheme improves the relation between cloud fraction, relative humidity and liquid water content. An emergent property of the scheme is its ability to infer skewed and bimodal distributions from the large-scale state that qualitatively compare well against observations. A detailed evaluation and resolution sensitivity study will follow in part II.

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A New Research Approach for Observing and Characterizing Land–Atmosphere Feedback

Wulfmeyer

Turner

Baker

et al. 2018

Bulletin of the American Meteorological Society

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38Forecast errors with respect to wind, temperature, moisture, clouds, and precipitation largely 39 correspond to the limited capability of current earth system models to capture and simulate 40 land-atmosphere feedback. To facilitate its realistic simulation in next generation models, an 41 improved process understanding of the related complex interactions is essential. To this end, 42 accurate 3D observations of key variables in the land-atmosphere (L-A) system with high 43 vertical and temporal resolution from the surface to the free troposphere are indispensable. 44Recently, we developed a synergy of innovative ground-based, scanning active remote sens-45 ing systems for 2D to 3D measurements of wind, temperature, and water vapor from the sur-46 face to the lower troposphere that is able to provide comprehensive data sets for characteriz-47 Motivation 71The land-atmosphere (L-A) system includes the soil, the land cover such as vegetation, and 72 the overlying atmosphere. The interaction of variables, e.g. related to the water and energy 73 budgets, results in characteristic natural variabilities and regimes as well as their changes due 74 to anthropogenic influences. The planetary boundary layer (PBL) is part of the L-A system 75 and represents the interface between the land surface and the free troposphere. Through an 76 exchange of momentum, energy and water, the dynamics, the thermodynamic structure, and 77 the evolution of the PBL affect the formation of shallow and deep clouds, convection initia-78 tion, and thus precipitation (Sherwood et al. 2010, Behrendt et al. 2011, Santanello et al. 79 2011, van den Hurk et al. 2011. One of the most complex feedback 80 loops is between soil moisture and precipitation (Seneviratne et al. 2010, Guillod et al. 2015, 81 Santanello et al. 2017). Precipitation can be influenced directly by the surface fluxes (Ek and 82Holtslag 2004), and indirectly via PBL dynamics and mesoscale circulations (Taylor et al. 83 2012). 84The PBL state and its evolution are strongly influenced by non-linear feedbacks in the L-A 85 system. These are due to two-way interactions between radiation, soil, vegetation, and atmos-86 pheric variables, which result in the diurnal cycles of surface fluxes. The feedbacks are rele-87 vant from local to global scales (Mahmood et al. 2013, Stéfanon et al. 2014, and their 88 strength varies both regionally and seasonally in dependence of soil moisture, advection, and 89 climate regimes. In locations where these feedbacks play an important role, it is likely that 90 they will become even more important due to anthropogenic climate change (Dirmeyer et al. 91 2012). Thus, to improve our understanding of the state and the evolution of the L-A system as 92 well as the dynamics and thermodynamics of the PBL, it is critical that feedbacks and fluxes 93 between the different components, including entrainment at the top of the PBL, are well char-94 4 acterized and appropriately represented in weather, climate, and earth system models (e.g., 95 Se...

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Characteristics of Water Vapor Turbulence Profiles in Convective Boundary Layers During the Dry and Wet Seasons Over Darwin

Cited by 6 publications

References 80 publications

Validating the Water Vapor Variance Similarity Relationship in the Interfacial Layer Using Observations and Large‐Eddy Simulations

Validating the Water Vapor Variance Similarity Relationship in the Interfacial Layer Using Observations and Large‐Eddy Simulations

A Bimodal Diagnostic Cloud Fraction Parameterization. Part I: Motivating Analysis and Scheme Description

A New Research Approach for Observing and Characterizing Land–Atmosphere Feedback

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