The spatial scale dependence of water vapor variability inferred from observations from a very tall tower

Pressel, Kyle G.; Collins, William D.; Desai, Ankur R.

doi:10.1002/2013jd021141

Cited by 8 publications

(10 citation statements)

References 51 publications

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“…Wood and Field (2011) observed that the cloud-chord-length distributions derived from the MODIS imager are essentially indistinguishable between the across-track and along-track directions, implying that the cloud-chord-length distributions are horizontally isotropic. Pressel and Collins (2012) found that temperature and specific humidity soundings from the Atmospheric Infrared Sounder (AIRS) instrument largely exhibit horizontal isotropy. Therefore, the twodimensional curtain sampling of CloudSat can be viewed as representative of the three-dimensional atmosphere.…”

Section: Datamentioning

confidence: 99%

“…The scale dependence of atmospheric fields and their variability are routinely explored using observations, numerical simulations, and theoretical models (e.g., Lovejoy 1982;Nastrom and Gage 1985;Nastrom et al 1986;Cahalan et al 1994;Tjemkes and Visser 1994;Pierrehumbert 1996;Cho et al 1999;Wood and Taylor 2001;Lindborg 2007;Kahn and Teixeira 2009;Bacmeister and Stephens 2011;Kahn et al 2011;Pressel and Collins 2012;Fischer et al 2013;Pressel et al 2014;Barker et al 2017). Wood and Field (2011) show that the probability distribution function (PDF) of horizontal cloud sizes from the Moderate Resolution Imaging Spectroradiometer (MODIS) follows a power law characterized by an exponent of approximately 25/3.…”

Section: Introductionmentioning

confidence: 99%

“…This work describes the scaling of CloudSat profile chord length by quantifying each CloudSat cloud-type distribution and, independently, cloud and clear-sky distributions. While numerous investigations have focused on the horizontal scaling of cloud imagery (e.g., Wood and Field 2011), or the horizontal scaling of thermodynamic profiles (e.g., Kahn and Teixeira 2009;Pressel and Collins 2012;Fischer et al 2012), we attempt to separately derive the horizontal and vertical scaling exponents of cloud-chord-length PDFs. To date, very little attention has been paid to the scaling behavior in the vertical dimension, especially for clouds; some exceptions are noted for aerosols and wind shear.…”

Section: Introductionmentioning

confidence: 99%

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Horizontal and Vertical Scaling of Cloud Geometry Inferred from CloudSat Data

Guillaume

Kahn

Yue

et al. 2018

Journal of the Atmospheric Sciences

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A method is described to characterize the scale dependence of cloud chord length using cloud-type classification reported with the 94-GHz CloudSat radar. The cloud length along the CloudSat track is quantified using horizontal and vertical structures of cloud classification separately for each cloud type and for all clouds independent of cloud type. While the individual cloud types do not follow a clear power-law behavior as a function of horizontal or vertical scale, a robust power-law scaling of cloud chord length is observed when cloud type is not considered. The exponent of horizontal length is approximated by β ≈ 1.66 ± 0.00 across two orders of magnitude (~10–1000 km). The exponent of vertical thickness is approximated by β ≈ 2.23 ± 0.03 in excess of one order of magnitude (~1–14 km). These exponents are in agreement with previous studies using numerical models, satellites, dropsondes, and in situ aircraft observations. These differences in horizontal and vertical cloud scaling are consistent with scaling of temperature and horizontal wind in the horizontal dimension and with scaling of buoyancy flux in the vertical dimension. The observed scale dependence should serve as a guide to test and evaluate scale-cognizant climate and weather numerical prediction models.

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Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Horizontal and Vertical Scaling of Cloud Geometry Inferred from CloudSat Data

Guillaume

Kahn

Yue

et al. 2018

Journal of the Atmospheric Sciences

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“…Using aircraft-based lidar observations, Fischer et al (2013) showed that values of 𝛽 for height-resolved q are near −5/3 in air masses with deep convection, while values of 𝛽 are in excess of −2 in nonconvective air masses. Over land using surface-based tower observations, Pressel et al (2014) showed that q have values of 𝛽 near −5/3 in the convective mixed boundary layer and values of 𝛽 in excess of −2 in the nocturnal boundary layer. Similar results were obtained over the SEP Ocean using VOCALS-REx in situ aircraft observations (K11).…”

Section: Relationship Between Column Water Vapor and 𝛼 Smentioning

confidence: 99%

“…Power law exponents for a variety of quantities can vary strongly with altitude, latitude, cloud regime, and diurnal and seasonal cycles (e.g., KT09; Nastrom & Gage, 1985; Pressel et al., 2014) and can be anisotropic between the horizontal and vertical directions (Lovejoy et al., 2010). The spectral breaks that are observed around 400–800 km typically emerge from climatological averages.…”

Section: Introductionmentioning

confidence: 99%

Two Decades of Temperature and Specific Humidity Variance Scaling With the Atmospheric Infrared Sounder

Kahn,

Fetzer,

Teixeira

et al. 2023

JGR Atmospheres

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A two‐decade climatology of height‐resolved horizontal variance scaling exponents (𝛼) for temperature (T) and specific humidity (q) is described using Aqua Atmospheric Infrared Sounder (AIRS) sounding profiles. The AIRS Team Version 6 (V6), Version 7 (V7), and Community Long‐term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) retrieval algorithms are compared to European Centre for Medium‐Range Weather Forecasts Reanalysis v5 (ERA5) and Modern‐Era Retrospective analysis for Research and Applications, Version 2 (MERRA‐2) reanalyses. Large‐scale T exponents derived between 600 and 1,200 km (𝛼L) show close agreement between V6, V7, and CLIMCAPS algorithms. However, small‐scale q exponents derived between 150 and 400 km (𝛼S) are in poor agreement, including an unrealistically steep 𝛼S in the planetary boundary layer (PBL) in the V7 and CLIMCAPS algorithms that is caused by a combination of algorithm damping and overconstraint by the first guess fields. ERA5 and MERRA‐2 reanalyses have large values of 𝛼L and 𝛼S for both T and q that indicate reduced small‐scale variability in the reanalysis fields. Differences in 𝛼S between free‐running MERRA‐2 AMIP and MERRA‐2 are negligible, implying that suppressed small‐scale variability in reanalyses is imposed by the background model and not caused by the data assimilation process. AIRS has positively skewed T distributions in the tropical‐free troposphere that is consistent with positively buoyant air parcels in convection, and negative skewness in the PBL that is related to the existence of cold pools, behavior that is mostly absent in ERA5 and MERRA‐2. AIRS provides a global view of scale‐dependent variance and skewness that is useful for subgrid parameterization development and validation of weather and climate prediction models.

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Airborne Measurements of Scale‐Dependent Latent Heat Flux Impacted by Water Vapor and Vertical Velocity Over Heterogeneous Land Surfaces During the CHEESEHEAD19 Campaign

Lin,

Wang,

Chu

et al. 2024

JGR Atmospheres

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The water vapor transport associated with latent heat flux (LE) in the planetary boundary layer (PBL) is critical for the atmospheric hydrological cycle, radiation balance, and cloud formation. The spatiotemporal variability of LE and water vapor mixing ratio (rv) are poorly understood due to the scale‐dependent and nonlinear atmospheric transport responses to land surface heterogeneity. Here, airborne in situ measurements with the wavelet technique are utilized to investigate scale‐dependent relationships among LE, vertical velocity (w) variance (), and rv variance () over a heterogeneous surface during the Chequamegon Heterogeneous Ecosystem Energy‐balance Study Enabled by a High‐density Extensive Array of Detectors 2019 (CHEESEHEAD19) field campaign. Our findings reveal distinct scale distributions of LE, , and at 100 m height, with a majority scale range of 120 m–4 km in LE, 32 m–2 km in , and 200 m–8 km in . The scales are classified into three scale ranges, the turbulent scale (8–200 m), large‐eddy scale (200 m–2 km), and mesoscale (2–8 km) to evaluate scale‐resolved LE contributed by and . The large‐eddy scale in PBL contributes over 70% of the monthly mean total LE with equal parts (50%) of contributions from and . The monthly temporal variations mainly come from the first two major contributing classified scales in LE, , and . These results confirm the dominant role of the large‐eddy scale in the PBL in the vertical moisture transport from the surface to the PBL, while the mesoscale is shown to contribute an additional ∼20%. This analysis complements published scale‐dependent LE variations, which lack detailed scale‐dependent vertical velocity and moisture information.

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The spatial scale dependence of water vapor variability inferred from observations from a very tall tower

Cited by 8 publications

References 51 publications

Horizontal and Vertical Scaling of Cloud Geometry Inferred from CloudSat Data

Horizontal and Vertical Scaling of Cloud Geometry Inferred from CloudSat Data

Two Decades of Temperature and Specific Humidity Variance Scaling With the Atmospheric Infrared Sounder

Airborne Measurements of Scale‐Dependent Latent Heat Flux Impacted by Water Vapor and Vertical Velocity Over Heterogeneous Land Surfaces During the CHEESEHEAD19 Campaign

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