• Ackerman, T. P., and G. M. Stokes, 2003: The Atmospheric Radiation Measurement Program. Phys. Today, 56, 3844.

  • Ahlgrimm, M., and R. Forbes, 2012: The impact of low clouds on surface shortwave radiation in the ECMWF model. Mon. Wea. Rev., 140, 37833794.

    • Search Google Scholar
    • Export Citation
  • Berg, L. K., and E. I. Kassianov, 2008: Temporal variability of fair-weather cumulus statistics at the ACRF SGP site. J. Climate, 21, 33443358.

    • Search Google Scholar
    • Export Citation
  • Berg, L. K., E. I. Kassianov, C. N. Long, and D. L. Mills Jr., 2011: Surface summertime radiative forcing by shallow cumuli at the Atmospheric Radiation Measurement Southern Great Plains site. J. Geophys. Res., 116, D01202, doi:10.1029/2010JD014593.

    • Search Google Scholar
    • Export Citation
  • Betts, A. K., M. Köhler, and Y. Zhang, 2009: Comparison of river basin hydrometeorology in ERA-Interim and ERA-40 reanalyses with observations. J. Geophys. Res., 114, D02101, doi:10.1029/2008JD010761.

    • Search Google Scholar
    • Export Citation
  • Boers, R., E. W. Eloranta, and R. L. Coulter, 1984: Lidar observations of mixed layer dynamics: Tests of parameterized entrainment models of mixed layer growth rate. J. Climate Appl. Meteor., 23, 247266.

    • Search Google Scholar
    • Export Citation
  • Bretherton, C. S., J. R. McCaa, and H. Grenier, 2004: A new parameterization for shallow cumulus convection and its application to marine shallow subtropical cloud-topped boundary layers. Part I: Description and 1D results. Mon. Wea. Rev., 132, 864882.

    • Search Google Scholar
    • Export Citation
  • Brock, F. V., K. C. Crawford, R. L. Elliott, G. W. Cuperus, S. J. Stadler, H. L. Johnson, and M. D. Eilts, 1995: The Oklahoma Mesonet: A technical overview. J. Atmos. Oceanic Technol., 12, 519.

    • Search Google Scholar
    • Export Citation
  • Brown, A. R., and Coauthors, 2002: Large-eddy simulation of the diurnal cycle of shallow cumulus convection over land. Quart. J. Roy. Meteor. Soc., 128, 10751093.

    • Search Google Scholar
    • Export Citation
  • Cheinet, S., 2004: A multiple mass flux parameterization for the surface-generated convection. Part II: Cloudy cores. J. Atmos. Sci., 61, 10931113.

    • Search Google Scholar
    • Export Citation
  • Chen, F., and R. Avissar, 1994: Impact of land-surface moisture variability on local shallow convective cumulus and precipitation in large-scale models. J. Appl. Meteor., 33, 13821401.

    • Search Google Scholar
    • Export Citation
  • Chiu, J. C., C.-H. Huang, A. Marshak, I. Slutsker, D. M. Giles, B. N. Holben, Y. Knyazikhin, and W. J. Wiscombe, 2010: Cloud optical depth retrievals from the Aerosol Robotic Network (AERONET) cloud mode observations. J. Geophys. Res., 115, D14202, doi:10.1029/2009JD013121.

    • Search Google Scholar
    • Export Citation
  • Clothiaux, E. E., T. P. Ackerman, G. G. Mace, K. P. Moran, R. T. Marchand, M. Miller, and B. E. Martner, 2000: Objective determination of cloud heights and radar reflectivities using a combination of active remote sensors at the ARM CART sites. J. Appl. Meteor., 39, 645665.

    • Search Google Scholar
    • Export Citation
  • Clothiaux, E. E., and Coauthors, 2001: The ARM Millimeter Wave Cloud Radars (MMCRs) and the Active Remote Sensing of Clouds (ARSCL) Value Added Product (VAP). U.S. Department of Energy Tech. Memo. ARM VAP-002.1, 56 pp.

  • Cohn, S. A., and W. M. Angevine, 2000: Boundary layer height and entrainment zone thickness measured by lidars and wind-profiling radars. J. Appl. Meteor., 39, 12331247.

    • Search Google Scholar
    • Export Citation
  • Coulman, C. E., and J. Warner, 1977: Temperature and humidity structure of the sub-cloud layer over land. Bound.-Layer Meteor., 11, 467484.

    • Search Google Scholar
    • Export Citation
  • Crum, T. D., R. B. Stull, and E. W. Eloranta, 1987: Coincident lidar and aircraft observations of entrainment into thermals and mixed layers. J. Climate Appl. Meteor., 26, 774788.

    • Search Google Scholar
    • Export Citation
  • Cuijpers, J. W. M., and P. G. Duynkerke, 1993: Large eddy simulation of trade wind cumulus clouds. J. Atmos. Sci., 50, 38943908.

  • Davidson, B., 1968: The Barbados oceanographic and meteorological experiment. Bull. Amer. Meteor. Soc., 49, 928934.

  • Dong, X., P. Minnis, and B. Xi, 2005: A climatology of midlatitude continental clouds from the ARM SGP Central Facility. Part I: Low-level cloud macrophysical, microphysical, and radiative properties. J. Climate, 18, 13911410.

    • Search Google Scholar
    • Export Citation
  • Ek, M., and L. Mahrt, 1994: Daytime evolution of relative humidity at the boundary layer top. Mon. Wea. Rev., 122, 27092720.

  • Ek, M., and A. Holtslag, 2004: Influence of soil moisture on boundary layer cloud development. J. Hydrometeor., 5, 8699.

  • Findell, K. L., and E. A. B. Eltahir, 2003: Atmospheric controls on soil moisture–boundary layer interactions. Part I: Framework development. J. Hydrometeor., 4, 552569.

    • Search Google Scholar
    • Export Citation
  • Hägeli, P., D. G. Steyn, and K. B. Strawbridge, 2000: Spatial and temporal variability of mixed-layer depth and entrainment zone thickness. Bound.-Layer Meteor., 97, 4771.

    • Search Google Scholar
    • Export Citation
  • Kain, J. S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170181.

  • Kim, S.-W., S.-U. Park, and C.-H. Moeng, 2003: Entrainment processes in the convective boundary layer with varying wind shear. Bound.-Layer Meteor., 108, 221245.

    • Search Google Scholar
    • Export Citation
  • Kuettner, J., and J. Holland, 1969: The BOMEX Project. Bull. Amer. Meteor. Soc., 50, 394402.

  • Lenaerts, J., C. van Heerwaarden, and J. Vilà-Guerau de Arellano, 2009: Shallow convection over land: A mesoscale modelling study based on idealized WRF experiments. J. Wea. Climate West. Mediterr., 6, 5166.

    • Search Google Scholar
    • Export Citation
  • Lenderink, G., and Coauthors, 2004: The diurnal cycle of shallow cumulus clouds over land: A single-column model intercomparison study. Quart. J. Roy. Meteor. Soc., 130, 33393364, doi:10.1256/qj.03.122.

    • Search Google Scholar
    • Export Citation
  • Long, C. N., and T. P. Ackerman, 2000: Identification of clear skies from broadband pyranometer measurements and calculation of downwelling shortwave cloud effects. J. Geophys. Res., 105 (D12), 15 60915 626.

    • Search Google Scholar
    • Export Citation
  • Long, C. N., and Y. Shi, 2008: An automated quality assessment and control algorithm for surface radiation measurements. Open Atmos. Sci. J., 2, 2337.

    • Search Google Scholar
    • Export Citation
  • Min, Q., E. Joseph, and M. Duan, 2004: Retrievals of thin cloud optical depth from a multifilter rotating shadowband radiometer. J. Geophys. Res., 109, D02201, doi:10.1029/2003JD003964.

    • Search Google Scholar
    • Export Citation
  • Neggers, R. A. J., A. P. Siebesma, and H. J. J. Jonker, 2002: A multiparcel model for shallow cumulus convection. J. Atmos. Sci., 59, 16551668.

    • Search Google Scholar
    • Export Citation
  • Paluch, I. R., 1979: The entrainment of air in Colorado cumuli. J. Atmos. Sci., 36, 24672478.

  • Pergaud, J., V. Masson, S. Malardel, and F. Couvreux, 2009: A parameterization of dry thermals and shallow cumuli for mesoscale numerical weather prediction. Bound.-Layer Meteor., 132, 83106, doi:10.1007/s10546-009-9388-0.

    • Search Google Scholar
    • Export Citation
  • Rabin, R. M., and D. W. Martin, 1996: Satellite observations of shallow cumulus coverage over the central United States: An exploration of land use impact on cloud cover. J. Geophys. Res., 101 (D3), 71497155.

    • Search Google Scholar
    • Export Citation
  • Rauber, R. M., and Coauthors, 2007: Rain in shallow cumulus over the ocean: The RICO campaign. Bull. Amer. Meteor. Soc., 88, 19121928.

    • Search Google Scholar
    • Export Citation
  • Schneider, J. M., D. K. Fisher, R. L. Elliott, G. O. Brown, and C. P. Bahrmann, 2003: Spatiotemporal variations in soil water: First results from the ARM SGP CART network. J. Hydrometeor., 4, 106120.

    • Search Google Scholar
    • Export Citation
  • Schrieber, K., R. Stull, and Q. Zhang, 1996: Distributions of surface-layer buoyancy versus lifting condensation level over a heterogeneous land surface. J. Atmos. Sci., 53, 10861107.

    • Search Google Scholar
    • Export Citation
  • Siebesma, A. P., and J. W. M. Cuijpers, 1995: Evaluation of parametric assumptions for shallow cumulus convection. J. Atmos. Sci., 52, 650666.

    • Search Google Scholar
    • Export Citation
  • Siebesma, A. P., and Coauthors, 2003: A large eddy simulation intercomparison study of shallow cumulus convection. J. Atmos. Sci., 60, 12011219.

    • Search Google Scholar
    • Export Citation
  • Soares, P. M. M., P. M. A. Miranda, A. P. Siebesma, and J. Teixeira, 2004: An eddy-diffusivity/mass-flux parametrization for dry and shallow cumulus convection. Quart. J. Roy. Meteor. Soc., 130, 33653383.

    • Search Google Scholar
    • Export Citation
  • Stevens, B., 2007: On the growth of layers of non-precipitating cumulus convection. J. Atmos. Sci., 64, 29162931.

  • Strokes, G. M., and S. E. Schwatz, 1994: The Atmospheric Radiation Measurement (ARM) program: Programmatic background and design of the cloud and radiation test bed. Bull. Amer. Meteor. Soc., 75, 12011221.

    • Search Google Scholar
    • Export Citation
  • Stull, R., 1985: A fair-weather cumulus cloud classification scheme for mixed-layer studies. J. Climate Appl. Meteor., 24, 4956.

  • Susĕlj, K., J. Teixeira, and G. Matheou, 2012: Eddy diffusivity/mass flux and shallow cumulus boundary layer: An updraft PDF multiple mass flux scheme. J. Atmos. Sci., 69, 15131533.

    • Search Google Scholar
    • Export Citation
  • Turner, D. D., S. A. Clough, J. Liljegren, E. Clothiaux, K. Cady-Pereira, and K. Gaustad, 2007a: Retrieving liquid water path and precipitable water vapor from Atmospheric Radiation Measurement (ARM) microwave radiometers. IEEE Trans. Geosci. Remote Sens., 45, 36803690, doi:10.1109/TGRS.2007.903703.

    • Search Google Scholar
    • Export Citation
  • Turner, D. D., and Coauthors, 2007b: Thin liquid water clouds: Their importance and our challenge. Bull. Amer. Meteor. Soc., 88, 177190.

    • Search Google Scholar
    • Export Citation
  • Vilà-Guerau de Arellano, J., 2007: Role of nocturnal turbulence and advection in the formation of shallow cumulus over land. Quart. J. Roy. Meteor. Soc., 133, 16151627, doi:10.1002/qj.138.

    • Search Google Scholar
    • Export Citation
  • Wesely, M., D. Cook, and R. Coulter, 1995: Surface heat flux data from energy balance Bowen ratio systems. Preprints, Ninth Symp. on Meteorological Observations and Instrumentation, Charlotte, NC, Amer. Meteor. Soc., 486–489.

  • Wetzel, P. J., S. Argentini, and A. Boone, 1996: Role of land surface in controlling daytime cloud amount: Two case studies in the GCIP-SW area. J. Geophys. Res., 101 (D3), 73597370.

    • Search Google Scholar
    • Export Citation
  • Wilde, N. P., R. B. Stull, and E. W. Eloranta, 1985: The LCL zone and cumulus onset. J. Climate Appl. Meteor., 24, 640657.

  • Wu, C., B. Stevens, and A. Arakawa, 2009: What controls the transition from shallow to deep convection? J. Atmos. Sci., 66, 17931806.

    • Search Google Scholar
    • Export Citation
  • Xie, S., R. T. Cederwall, and M. Zhang, 2004: Developing long-term single-column model/cloud system–resolving model forcing data using numerical weather prediction products constrained by surface and top of the atmosphere observations. J. Geophys. Res., 109, D01104, doi:10.1029/2003JD004045.

    • Search Google Scholar
    • Export Citation
  • Xie, S., and Coauthors, 2010: ARM climate modeling best estimate data. Bull. Amer. Meteor. Soc., 91, 1320.

  • Yi, C., K. J. Davis, and B. W. Berger, 2001: Long-term observations of the dynamics of the continental planetary boundary layer. J. Atmos. Sci., 58, 12881299.

    • Search Google Scholar
    • Export Citation
  • Zhang, M. H., and J. L. Lin, 1997: Constrained variational analysis of sounding data based on column-integrated budgets of mass, heat, moisture, and momentum: Approach and application to ARM measurements. J. Atmos. Sci., 54, 15031524.

    • Search Google Scholar
    • Export Citation
  • Zhang, Y., and S. A. Klein, 2010: Mechanisms affecting the transition from shallow to deep convection over land: Inferences from observations of the diurnal cycle collected at the ARM Southern Great Plains site. J. Atmos. Sci., 67, 29432959.

    • Search Google Scholar
    • Export Citation
  • Zhao, C., and Coauthors, 2012: Toward understanding of differences in current cloud retrievals of arm ground-based measurements. J. Geophys. Res., 117, D10206, doi:10.1029/2011JD016792.

    • Search Google Scholar
    • Export Citation
  • Zhu, P., and B. Albrecht, 2002: A theoretical and observational analysis on the formation of fair-weather cumuli. J. Atmos. Sci., 59, 19832005.

    • Search Google Scholar
    • Export Citation
  • Zhu, P., and B. Albrecht, 2003: Large eddy simulations of continental shallow cumulus convection. J. Geophys. Res., 108, 4453, doi:10.1029/2002JD003119.

    • Search Google Scholar
    • Export Citation
  • Zhu, P., and C. S. Bretherton, 2004: A simulation study of shallow moist convection and its impact on the atmospheric boundary layer. Mon. Wea. Rev., 132, 23912409.

    • Search Google Scholar
    • Export Citation
  • View in gallery
    Fig. 1.

    (top) Conceptual diagram of forced vs active shallow cumulus clouds. (bottom) Diurnal-cycle composite of cloud fraction as a function of height above ground for thin- and thick-ShCu days based on ARM ARSCL 10s data.

  • View in gallery
    Fig. 2.

    Scatterplot of the difference between observed cloud top and LFC (cloud top minus LFC) vs cloud depth around 1130 LST. Open circles (solid dots) denote individual clouds observed on thick (thin)-cloud days from 1100 to 1200 LST.

  • View in gallery
    Fig. 3.

    Diurnal-cycle composite of cloud macrophysical properties on thick (black)- and thin (gray)-ShCu days. (a) Vertically projected cloud fraction (PCF, solid) and the PCF from clouds with depth greater or less than 300 m (PCF300+ or PCF300−, respectively; dashed). (b) Cloud top and cloud base. (c) Cloud depth and cloud chord length (CCL).

  • View in gallery
    Fig. 4.

    Distribution of cloud depth and cloud chord length of individual clouds. Color shading shows the joint PDF. Univariate PDF of cloud chord length is denoted by dashed black line with unit of percentage (y axis divided by 100). Univariate PDF of cloud depth is denoted by solid black line with unit of percentage (x axis divided by 100).

  • View in gallery
    Fig. 5.

    Diurnal-cycle composite of LWP based on CMBE hourly mean MWRRET data on thin (gray)- and thick (black)-cloud days. The width of the shading on either side of the mean value denotes one standard error of the mean across all the sample days in each regime.

  • View in gallery
    Fig. 6.

    Diurnal-cycle composite of cloud’s radiative impact at the surface based on CMBE QCRAD data and cloud grid data at central facility. (a) Surface radiative flux difference (thin days minus thick days; positive denotes downward into surface) in total radiation (solid), net shortwave (dotted), and net longwave (dashed). (b) Surface radiative flux difference in downward shortwave (gray dotted), upward shortwave (black dotted), downward longwave (gray dashed), and upward longwave (black dashed). (c) Shortwave cloud radiative forcing at the surface, which is clear-sky shortwave downward radiative flux minus the actual whole-sky shortwave downward radiative flux at the surface. The length of the vertical line on either side of the mean value denotes one standard error of the mean across all the sample days in each regime.

  • View in gallery
    Fig. 7.

    (top) Vertical profile of RH at (a) 2330 LST of previous day, (b) 0530, (c) 1130, and (d) 1730 LST on thin (red)- and thick (blue)-ShCu days based on LSSONDE data. The width of the color shading on either side of the mean value denotes one standard error of the mean across all of the sample days in each regime. (bottom) Diurnal-cycle composites of RH (%) on (e) thin- and (f) thick-ShCu days based on Raman lidar measurements.

  • View in gallery
    Fig. 8.

    Composite soundings for potential temperature and mixing ratio on thin (dashed)- and thick (solid)-ShCu days based on LSSONDE data.

  • View in gallery
    Fig. 9.

    (top) Composite mean mixed-layer depth, cloud base, cloud top, inversion (Inv.) base height, and LFC at 1130 (crosses) and 1730 LST (circles). (middle) Composite mean environmental atmospheric stability in cloud layer, the layer above cloud top (400 m), and the first inversion layer at 1130 (crosses) and 1730 LST (circles). (bottom) Composite mean atmospheric stability at 1.5–2.5 (squares) and 2.5–3.5 km (triangles) at 0530, 1130, and 1730 LST. Thin- and thick-ShCu days are in gray and black, respectively. The extent of the vertical bar on either side of the mean value denotes one standard error of the mean across all of the sample days in each regime. These calculations are based on LSSONDE and ARSCL data.

  • View in gallery
    Fig. 10.

    Diurnal-cycle composites of (a) surface sensible and (b) latent heat fluxes, (c) evaporative fraction, and (d) soil moisture content on thin (gray)- and thick (black)-ShCu days at central facility. The heat fluxes and evaporative fraction are based on CMBE BAEBBR data and soil moisture content is based on SWATS data at 5 cm beneath the surface. The width of the shading or the length of the vertical line on either side of the mean value denotes one standard error of the mean across all the sample days in each regime.

  • View in gallery
    Fig. 11.

    Diurnal-cycle composites of (a) temperature and (b) its std dev, (c) MR and (d) its std dev, and (e) wind speed and (f) its mesoscale variability on thin (gray)- and thick (black)-cloud days. The mean and std dev are calculated based on SMOS data at central facility and four Oklahoma MESONET stations within 50 km. The length of the vertical line on either side of the mean value denotes one standard error of the mean across all the sample days in each regime.

  • View in gallery
    Fig. 12.

    Diurnal-cycle composites of horizontal wind fields (a),(b) U and (c),(d) V based on 915-MHz wind profiler data at SGP CF, (e),(f) vertical motion (omega; hPa h−1), and (g),(h) horizontal moisture advection (g kg−1 day−1) based on SGP continuous forcing data on (left) thin- and (right) thick-ShCu days. Stippled regions indicate where the difference between thin and thick days is of statistical significance at the 95% confidence level.

  • View in gallery
    Fig. 13.

    Absolute t values from Student’s t tests for the differences between composite means of thin- and thick-ShCu days. Unless explicitly stated, variables at 1130 LST are compared. “EM MR HAdv” refers to horizontal advection of water vapor mixing ratio in the early morning, between midnight and 6 a.m. The horizontal solid line denotes a confidence level of 95%. Negative (positive) t values are solid (stippled) and denote smaller (larger) composite means on thick-ShCu days relative to the mean on thin- ShCu days.

  • View in gallery
    Fig. 14.

    Scatterplots of hourly mean surface RH from SMOS data and cloud base from ARSCL data. Black line is the regression between the two. Red line is the average calculated LCL based on surface RH, temperature, and pressure from SMOS data. Color scale shows the corresponding hourly mean cloud depth.

  • View in gallery
    Fig. 15.

    Mixing diagram (Paluch diagram) of total water mixing ratio and liquid water potential temperature for the composite of sounding data (gray circles) at 1130 LST for (left) thin and (right) thick fair-weather ShCu days. Zero-buoyancy lines (saturation curves) are denoted by solid (dotted) lines, which are calculated based on sounding data at levels of composite observed cloud base (black), cloud top (blue), and inversion base (red) (see Fig. 9). The black dot labeled “BL” denotes BL air. The environmental air at the level of cloud base, inversion base, or cloud top are denoted by black dot labeled “CB,” red dot labeled “IN,” or blue dot labeled “CT,” respectively. The gray triangle denotes air with a θl that is 0.5 K higher (lower) than the inversion air at the inversion base. The gray long-dashed lines denote two hypothetical zero-buoyancy lines if inversion level θl were 0.5 K higher or lower than the actual θl. The stippled area denotes the possible values that a BL air parcel may have if BL inhomogeneity is considered at 1130 LST. This area encompasses one standard deviation of BL inhomogeneity about the mean value (Fig. 11).

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 651 253 36
PDF Downloads 454 197 10

Factors Controlling the Vertical Extent of Fair-Weather Shallow Cumulus Clouds over Land: Investigation of Diurnal-Cycle Observations Collected at the ARM Southern Great Plains Site

Yunyan ZhangLawrence Livermore National Laboratory, Livermore, California

Search for other papers by Yunyan Zhang in
Current site
Google Scholar
PubMed
Close
and
Stephen A. KleinLawrence Livermore National Laboratory, Livermore, California

Search for other papers by Stephen A. Klein in
Current site
Google Scholar
PubMed
Close
Full access

Abstract

Summertime observations for 13 yr at the Atmospheric Radiation Measurement Southern Great Plains site are used to study fair-weather shallow cumuli (ShCu). To roughly separate forced from active ShCu, days are categorized into “thin-” or “thick-” ShCu days according to whether the daytime-average cloud depth exceeds 300 m. By comparing diurnal-cycle composites of these two regimes, the authors document differences in cloud properties and their radiative impacts. The differences in environmental conditions provide clues as to what controls ShCu vertical extent.

Higher boundary layer (BL) relative humidity (RH) is found on thick-cloud days, associated with large-scale moisture advection before sunrise. This higher BL RH not only contributes to a lower cloud base but also to the penetrating ability of an air parcel to reach higher levels, and thus leads to larger cloud vertical extent.

Although not as significant as BL RH, ShCu vertical extent also varies with thermal stability and surface fluxes. Enhanced stability above cloud on thin-cloud days may limit cloud vertical extent. A larger sensible heat flux on thin-cloud days encourages greater entrainment of dry air into the BL, whereas a larger latent heat flux on thick-cloud days helps sustain higher afternoon BL RH. These heat flux differences help maintain the BL RH differences that appear to control cloud vertical extent.

This study provides observational evidence that forced clouds are related to BL large-eddy overshoots limited by a stronger inversion whereas higher moisture and a weaker stability above favor active cumuli with greater vertical extent.

Corresponding author address: Yunyan Zhang, Atmospheric, Earth & Energy Division, Lawrence Livermore National Laboratory, Mail Code L-103, P.O. Box 808, Livermore, CA 94551. E-mail: zhang25@llnl.gov

Abstract

Summertime observations for 13 yr at the Atmospheric Radiation Measurement Southern Great Plains site are used to study fair-weather shallow cumuli (ShCu). To roughly separate forced from active ShCu, days are categorized into “thin-” or “thick-” ShCu days according to whether the daytime-average cloud depth exceeds 300 m. By comparing diurnal-cycle composites of these two regimes, the authors document differences in cloud properties and their radiative impacts. The differences in environmental conditions provide clues as to what controls ShCu vertical extent.

Higher boundary layer (BL) relative humidity (RH) is found on thick-cloud days, associated with large-scale moisture advection before sunrise. This higher BL RH not only contributes to a lower cloud base but also to the penetrating ability of an air parcel to reach higher levels, and thus leads to larger cloud vertical extent.

Although not as significant as BL RH, ShCu vertical extent also varies with thermal stability and surface fluxes. Enhanced stability above cloud on thin-cloud days may limit cloud vertical extent. A larger sensible heat flux on thin-cloud days encourages greater entrainment of dry air into the BL, whereas a larger latent heat flux on thick-cloud days helps sustain higher afternoon BL RH. These heat flux differences help maintain the BL RH differences that appear to control cloud vertical extent.

This study provides observational evidence that forced clouds are related to BL large-eddy overshoots limited by a stronger inversion whereas higher moisture and a weaker stability above favor active cumuli with greater vertical extent.

Corresponding author address: Yunyan Zhang, Atmospheric, Earth & Energy Division, Lawrence Livermore National Laboratory, Mail Code L-103, P.O. Box 808, Livermore, CA 94551. E-mail: zhang25@llnl.gov

1. Introduction

Fair-weather nonprecipitating shallow cumuli (ShCu) are often observed during summertime over land. They are small in size, optically thin, and short lived but still cause a significant net radiative cooling at the surface (Turner et al. 2007b; Berg and Kassianov 2008; Berg et al. 2011). Fair-weather shallow cumulus clouds interact with boundary layer (BL) turbulence and alter the transport of mass, momentum, moisture, and air pollutants from the boundary layer to the free troposphere. Continental fair-weather ShCu are tightly coupled with the underlying land surface through a strong diurnal cycle of surface fluxes. These clouds root from the most vigorously convective thermals (Boers et al. 1984). The variability among thermals results in an entrainment zone at the top of convective BL and analogously there is a zone of thermal saturation levels. As the two zones overlap, cumulus clouds form (Wilde et al. 1985) typically 1–2 h before the mean convective mixed-layer height reaches the lifting condensation level (LCL) of surface air (Boers et al. 1984).

There is extensive literature on oceanic trade cumulus, which is characterized by a well-mixed subcloud layer, a transitional cloud base layer, a conditionally unstable cloud layer, and a capping inversion layer (Siebesma and Cuijpers 1995; Siebesma et al. 2003; Stevens 2007). Compared with its oceanic counterpart, continental cumuli have received less attention. Brown et al. (2002) performed a large-eddy simulation and found that cloud layer structures of shallow cumulus over land are similar to oceanic trade cumulus based on a 1-day observation at the Southern Great Plains (SGP) site operated by the Atmospheric Radiation Measurement (ARM) program (Strokes and Schwatz 1994; Ackerman and Stokes 2003). Results on the same case from single-column models, with parameterizations identical to global climate models, are diversified owing to generally poor turbulence parameterizations and coupling between turbulence and convective parameterizations (Lenderink et al. 2004; Zhu and Bretherton 2004; Lenaerts et al. 2009). Many new parameterizations for turbulence and convection (Bretherton et al. 2004; Cheinet 2004; Kain 2004; Neggers et al. 2002; Pergaud et al. 2009; Soares et al. 2004; Susĕlj et al. 2012) have been developed with a focus on the simulation of oceanic trade cumulus in comparison with observations, particularly for the Barbados Oceanographic and Meteorological Experiment (BOMEX) and Rain in Cumulus over the Ocean (RICO) field campaigns (Davidson 1968; Kuettner and Holland 1969; Rauber et al. 2007). A greater focus is needed on continental shallow convection (Ahlgrimm and Forbes 2012); however, an immediate question arises: do we have a robust test bed based on observational statistics?

Stull (1985) defined “forced” and “active” fair-weather shallow cumulus. Forced cumulus clouds result from thermals that overshoot the LCL but never reach the level of free convection (LFC). Given their negative buoyancy, forced cumulus are usually flat with the horizontal extent sometimes comparable with the mixed-layer depth. In contrast, active cumuli may possess positive buoyancy, penetrate the entrainment zone, reach their LFC, and are further capped by the upper inversion, if present. Crum et al. (1987) suggested observations should aim to address both forced and active ShCu on their different dynamics and vertical extent.

How high may thermals reach—the LCL or even the LFC? Previous studies suggest this is determined by thermals’ rooting surface air properties, environmental conditions, and entrainment rate. Specific ideas previously proposed include the following:

The long-term and extensive summertime diurnal-cycle observations at the ARM SGP site provide an excellent dataset to test these theories regarding the influence of external factors in determining the regime of convection (Berg and Kassianov 2008; Zhang and Klein 2010). On some days one observes only forced ShCu whereas on other days active ShCu are present accompanied with forced ones. These clouds are usually locally generated, tightly coupled with surface and BL conditions, and thus are suitable for the testing of convection and turbulence parameterizations of large-scale and mesoscale models.

As shown in the conceptual cloud structures in Fig. 1 (top), we assume forced clouds are thin while active clouds are thicker. In the following, we will make categorizations based on the observed cloud vertical extent or cloud physical geometrical depth and use “thin” and “thick” clouds to roughly represent forced and active ShCu (this notion is tested below). Motivated by a belief that it will be important for models to correctly simulate the preponderance of either forced or active clouds on a given day, we concentrate on better defining the characteristics of thin- and thick-cloud days. In particular, we aim to establish a robust test bed for future modeling studies by answering the following questions:

  1. How different are cloud properties and their radiative impacts between days of thin and thick clouds?

  2. How do environmental conditions differ between days of thin and thick clouds and do the differences provide any clues as to what controls ShCu vertical extent?

Fig. 1.
Fig. 1.

(top) Conceptual diagram of forced vs active shallow cumulus clouds. (bottom) Diurnal-cycle composite of cloud fraction as a function of height above ground for thin- and thick-ShCu days based on ARM ARSCL 10s data.

Citation: Journal of the Atmospheric Sciences 70, 4; 10.1175/JAS-D-12-0131.1

The remaining parts of the paper are as follows: the observations and classification of thin-and thick-ShCu days are presented in section 2, the comparison of cloud macrophysics and radiative properties between the two categories is shown in section 3, the difference between environmental factors is shown in section 4, a discussion of the possible physics relating the difference in environmental parameters to cloud vertical extent is presented in section 5, and conclusions are drawn in section 6.

2. Data and methodology

a. ARM observations

The observations are from the ARM SGP central facility (CF) or in the region within a 50-km radius of the CF and processed to hourly means to suit our analysis. For data details and archive location, if not listed below, please refer to section 2 in Zhang and Klein (2010). We use the following:

b. Warm-season thin and thick shallow cumulus

We improve and expand on the approach in Berg and Kassianov (2008) to select fair-weather ShCu days in May–August in the years 1997–2009. We identify 119 fair-weather ShCu days satisfying the following criteria:

  • The ABRFC precipitation rate is 0 mm day−1 at all hours of the day (from local midnight to midnight) in the region within 50 km of CF.

  • There is at least 3 h of daytime observations of clouds by ARSCL with clouds developing after sunrise and showing a diurnal variation of cloud fraction and cloud boundaries. Cloud tops are under 4 km and cloud bases gradually rise with time over the day. Finally, both the Millimeter Wavelength Cloud Radar (MMCR) and ceilometer [or MicroPause lidar (MPL)] are operating.

    • These criteria assure that clouds develop locally and are tied to BL processes. A rising cloud base is consistent with an entraining BL driven by surface fluxes, which gradually lowers the value of RH at the surface and thus increases the LCL of surface air (Betts et al. 2009). With both the MMCR and MPL or ceilometer operating, we reduce the uncertainties in estimating cloud boundaries when cloud is observed by only one instrument. Above 4 km, there is usually no cloud or cloud fraction less than 5% except that on a few days when there is some high cirrus above 10 km.

  • Clouds are constrained to be ShCu by manually screening out other cloud types (such as stratus) using the total sky imager (TSI). When the TSI is not available, whole-sky imager observations are used instead.

  • Fair-weather conditions are ensured using satellite images from P. Minnis’s group at the NASA Langley Center (http://www-angler.larc.nasa.gov/). Images are examined manually to ensure that the cloud field develops homogeneously in the region around CF and is not affected by other large-scale weather phenomena such as fronts, dryline, or mesoscale convective systems.

After selecting these days, we further classify them as either thin or thick according to whether the daily-average cloud depth is less or greater than 300 m. There are 43 thin- and 76 thick-ShCu days.

Why is a value of 300 m chosen? Furthermore, is this threshold consistent with the definition of forced and active ShCu in Stull (1985)? Figure 2 shows the difference between observed cloud top and LFC plotted as a function of cloud depth using the clouds observed within half an hour of the 1130 LST sounding for our selected days. LFC is calculated based on sounding data by adiabatically lifting the BL air parcel with the maximum equivalent potential temperature. Individual clouds are identified using periods of time when 10s ARSCL data continuously register cloudy conditions. For each individual cloud, cloud top is the maximum height the upper boundary reaches and cloud base is the time average of lower boundary height. Cloud depth is the maximum vertical extent of an individual cloud. In Fig. 2, the overwhelming majority of clouds with depth greater than 300 m have a cloud-top height exceeding the LFC and vice versa for clouds less than 300 m deep. (Note that because there is a spectrum of clouds, not every individual cloud on thick days is greater than 300 m deep.) Therefore, we believe that the 300-m threshold for daily averaged cloud depth should roughly separate days with only forced ShCu from days with active ShCu.

Fig. 2.
Fig. 2.

Scatterplot of the difference between observed cloud top and LFC (cloud top minus LFC) vs cloud depth around 1130 LST. Open circles (solid dots) denote individual clouds observed on thick (thin)-cloud days from 1100 to 1200 LST.

Citation: Journal of the Atmospheric Sciences 70, 4; 10.1175/JAS-D-12-0131.1

3. Cloud properties

a. Cloud fraction

We first focus on cloud fraction based on 10s ARSCL data without distinguishing each individual cloud. The bottom panels of Fig. 1 show the diurnal-cycle composite of thin- and thick-ShCu cloud fraction, created by first offsetting their vertical coordinates to have cloud base equal to the composite mean, and then averaging all the offset profiles for a given time of day into a single composite profile. Doing this preserves the diurnal evolution signal better and is less subject to the impact of day-to-day variation. As a result of our selection criteria, the cloud base gradually rises during the day, and cloud top and cloud fraction both show diurnal evolution. There are some common behaviors: cloud fraction maximizes close to cloud base and then decreases with height, and cloud fraction increases in the morning, peaks in the early afternoon, and decreases in the late afternoon. There are also differences: on thick-cloud days, cloud tends to be deeper with both lower cloud base and higher cloud top, and cloud tends to initiate and peak earlier. This is consistent with the vertically projected cloud fraction (PCF) based on 10s ARSCL data shown in Fig. 3a—for instance, cloud fraction is higher than 5% after 0900 LST compared to 1100 LST on thin-cloud days; the PCF reaches its maximum of 33% at 1330 LST on thick-cloud days, about 1 h ahead of the diurnal maximum of 24% on thin-cloud days. The major contribution to the total PCF on thick-cloud days is from cloud profiles with cloud depth greater than 300 m, and this contribution roughly decreases from 90% at 0930 LST to 47% at 1730 LST. While on thin-cloud days, the major contribution to the total PCF is from cloud profiles with cloud depth less than 300 m, with this contribution increasing from 80% at 1030 LST to 93% at 1730 LST.

Fig. 3.
Fig. 3.

Diurnal-cycle composite of cloud macrophysical properties on thick (black)- and thin (gray)-ShCu days. (a) Vertically projected cloud fraction (PCF, solid) and the PCF from clouds with depth greater or less than 300 m (PCF300+ or PCF300−, respectively; dashed). (b) Cloud top and cloud base. (c) Cloud depth and cloud chord length (CCL).

Citation: Journal of the Atmospheric Sciences 70, 4; 10.1175/JAS-D-12-0131.1

b. Individual cloud statistics

Besides cloud fraction, it is of great interest to explore the statistics of individual clouds. Figure 3b shows the diurnal-cycle composite of mean and maximum values of cloud boundaries in a given hour. On days with thin clouds, both cloud top and cloud base increase with time until 1630 LST with a slight decrease after that; while on the days with thick clouds, the cloud top grows to its diurnal maximum around 1530 LST and decreases afterward whereas the cloud base reaches its maximum at the same time and then levels out. Figure 3c shows cloud depth and CCL, which roughly represents the horizontal extent of individual clouds. As ARSCL data are measured from vertical pointing radars and lidars, the cloud chord length is calculated from the duration of time a cloud is overhead of the radar and the wind speed at cloud-base level measured by the 915-MHz wind profiler. The average cloud depth is about 850 m at 0830 LST, and then decreases to 450 m over the course of the day for thick clouds; however, the maximum cloud depth slightly increases from 1000 LST to noon and then decreases. The change in average cloud depth is small for thin clouds with a daytime-average cloud depth of about 200 m, while the maximum cloud depth peaks around 470 m at 1330 LST.

Figure 4 shows the probability density function (PDF) of cloud occurrence as a function of cloud depth and cloud chord length and the joint PDF conditioned on both variables. On thin-cloud days, the PDF of cloud depth peaks at 33% between 100 and 200 m while it peaks at about 8%–10% between 200 and 800 m on thick-cloud days. The shape of the PDF of cloud chord length is very similar for thin- and thick-ShCu days, with a peak at about 9% between 200 and 400 m on thick-cloud days while 12% on thin-cloud days, then decreasing with increasing chord length.

Fig. 4.
Fig. 4.

Distribution of cloud depth and cloud chord length of individual clouds. Color shading shows the joint PDF. Univariate PDF of cloud chord length is denoted by dashed black line with unit of percentage (y axis divided by 100). Univariate PDF of cloud depth is denoted by solid black line with unit of percentage (x axis divided by 100).

Citation: Journal of the Atmospheric Sciences 70, 4; 10.1175/JAS-D-12-0131.1

Table 1 lists statistics about daytime averages and the probability of observing an individual cloud with certain properties. The average cloud depth on thick-cloud days is about 720 m, triple that of thin days. On thin-ShCu days, most clouds are thinner than 300 m and it is difficult to find a cloud thicker than 600 m. On thick-cloud days, most clouds are thicker than 300 m and half are greater than 600 m. The tail of the chord-length PDF for thick-cloud days (Fig. 4) is wider and there are fewer clouds with small chord length, so the average chord length is about 990 m on thick days—210 m larger than the one on thin days. With aspect ratio defined as cloud depth divided by cloud chord length, the average cloud aspect ratio is 2 on thick days—4 times bigger than that on thin days (Table 1). It is rare to have clouds with aspect ratio greater than 1 on thin-cloud days; however, on thick-cloud days, more than 40% of the clouds have aspect ratios exceeding 1.

Table 1.

Statistics of individual clouds on thin- and thick-cloud days. The average value is the daytime observed average. The percentage is the probability to find cloud with certain properties among all the valid cloud observations on thin- or thick-cloud days.

Table 1.

c. Liquid water path

Figure 5 shows the diurnal-cycle composite of column-integrated LWP based on CMBE MWRRET hourly mean data. The LWP is larger on thick-cloud days with a diurnal maximum at 1330 LST of 30 g m−2—double the value on thin-cloud days. Note that these are averaged values of clear and cloudy times; the average in-cloud values of LWP are larger (e.g., 55 and 35 g m−2 for diurnal maxima on thick- and thin-cloud days). Since the uncertainty in an individual MWR LWP measurement may be up to 20 g m−2, these LWP retrievals for ShCu come with a significant degree of uncertainty (Dong et al. 2005; Turner et al. 2007b). It is worthwhile to examine the fine-resolution in-cloud values of LWP. Based on 10s ARSCL and 30s MWRRET data we find that for cloud depths up to 800 m, the in-cloud LWP increases almost linearly with cloud depth consistent with an average in-cloud liquid water content (LWC) of approximately 0.1 g m−3. The CRED dataset also shows the retrieved in-cloud LWC to range from 0.03 to 0.15 g m−3, usually increasing with height with no significant difference between thin- and thick-cloud days for the same height above cloud base. Since the LWC retrieval algorithms are usually based on radar reflectivity constrained by MWR-measured LWP, it would be best to have another independent measurement such as cloud optical thickness. Potential candidates under development might be data from the Aerosol Robotic Network (AERONET; Chiu et al. 2010) or Multifilter Rotating Shadowband Radiometers (MFRSR; Min et al. 2004).

Fig. 5.
Fig. 5.

Diurnal-cycle composite of LWP based on CMBE hourly mean MWRRET data on thin (gray)- and thick (black)-cloud days. The width of the shading on either side of the mean value denotes one standard error of the mean across all the sample days in each regime.

Citation: Journal of the Atmospheric Sciences 70, 4; 10.1175/JAS-D-12-0131.1

d. Surface radiation

Since we found differences in the cloud macrophysics between thin- and thick-ShCu days, differences in radiative impact are also expected. This is shown in Figs. 6a and 6b, which plot the difference in surface radiative fluxes (thin minus thick) at the SGP CF. On thin-cloud days, the net radiative flux shows larger downward component into the surface from 1200 to 1300 LST, but the daytime-average net radiative flux shows 10 W m−2 more upward radiation from surface to atmosphere. This results from 2 W m−2 more net shortwave being absorbed but 12 W m−2 more net longwave escaping on thin-cloud days relative to thick-cloud days. Decomposing further, upward longwave radiation changes dominate the net longwave for thin-cloud days. There is 5 W m−2 less on thin days in downward longwave radiation until 1430 LST. There is a significant larger amount of downward shortwave radiation on thin days, with diurnal peak of 40 W m−2 at 1230 LST; however, on daytime average, this is greatly compensated by the larger amount of upward short- and longwave radiation in total. The larger upward longwave radiation from surface is related to the higher surface temperature on thin-cloud days (shown in Fig. 11).

Fig. 6.
Fig. 6.

Diurnal-cycle composite of cloud’s radiative impact at the surface based on CMBE QCRAD data and cloud grid data at central facility. (a) Surface radiative flux difference (thin days minus thick days; positive denotes downward into surface) in total radiation (solid), net shortwave (dotted), and net longwave (dashed). (b) Surface radiative flux difference in downward shortwave (gray dotted), upward shortwave (black dotted), downward longwave (gray dashed), and upward longwave (black dashed). (c) Shortwave cloud radiative forcing at the surface, which is clear-sky shortwave downward radiative flux minus the actual whole-sky shortwave downward radiative flux at the surface. The length of the vertical line on either side of the mean value denotes one standard error of the mean across all the sample days in each regime.

Citation: Journal of the Atmospheric Sciences 70, 4; 10.1175/JAS-D-12-0131.1

The longwave radiation has a relatively smaller diurnal amplitude. The diurnal change in the shortwave radiative fluxes is modulated not only by the solar insolation but also by the diurnal cycle of cloud fraction and cloud optical thickness. Figure 6c shows the surface shortwave cloud radiative forcing, which is the difference between the clear-sky downward shortwave radiative flux and the actual whole-sky downward shortwave radiative flux based on surface cloud grid data. Significant difference between thin- and thick-cloud days in cloud radiative forcing is found between 0900 and 1300 LST, with a peak at 1230 LST (e.g., 115 W m−2 on thick days and 70 W m−2 on thin-cloud days).

4. Environmental conditions

a. Relative humidity

Figure 7 shows RH measured by balloon sounding at times near midnight, sunrise, noon, and sunset. Of the total ShCu days, we have valid soundings for 75%, 74%, 62%, and 81% of the time, respectively. Clearly there is a larger RH in the lowest 2 km at all times, with at least 10% greater RH on thick-cloud days. Above 2 km, there is no statistically significant difference. This phenomenon is also confirmed by the moisture measurements from the Raman lidar shown in Fig. 7. RH is higher from midnight to dawn as well as during the daytime on thick-cloud days. The composite RH values are in good agreement between these two independent measurements. However, if we collocate sounding data and the observed average cloud boundaries from ARSCL within half an hour of sounding times (not shown), the maximum RH in the subcloud layer (i.e., the RH close to cloud-base level) is almost the same between thin- and thick-cloud days and does not change much, with 90% at 1130 and 88% at 1730 LST.

Fig. 7.
Fig. 7.

(top) Vertical profile of RH at (a) 2330 LST of previous day, (b) 0530, (c) 1130, and (d) 1730 LST on thin (red)- and thick (blue)-ShCu days based on LSSONDE data. The width of the color shading on either side of the mean value denotes one standard error of the mean across all of the sample days in each regime. (bottom) Diurnal-cycle composites of RH (%) on (e) thin- and (f) thick-ShCu days based on Raman lidar measurements.

Citation: Journal of the Atmospheric Sciences 70, 4; 10.1175/JAS-D-12-0131.1

Since RH combines the effect of both moisture and temperature, we may wonder which variable dominates the higher RH signal on thick-cloud days? Figure 8 shows the composite profiles for both potential temperature and water vapor mixing ratio (MR) below 4 km. Although not shown, above 4 km, the profiles are almost the same on both thin and thick days. From midnight to dawn, it is cooler and moister under 2 km on thick-cloud days so both temperature and moisture contribute to the higher BL RH. However, just before noon, the BL temperature difference becomes negligible and the mixing ratio is about 2–2.5 g kg−1 larger on thick-cloud days than thin ones. As mixed layer develops rapidly from late morning to afternoon, it becomes warmer under 2 km again on thin-cloud days, which results in a larger decrease of BL RH from 1130 to 1730 LST (8% compared with 4% on thick-cloud days at 1 km).

Fig. 8.
Fig. 8.

Composite soundings for potential temperature and mixing ratio on thin (dashed)- and thick (solid)-ShCu days based on LSSONDE data.

Citation: Journal of the Atmospheric Sciences 70, 4; 10.1175/JAS-D-12-0131.1

b. Atmospheric stability

Figure 9 (top) compares composite mean mixed-layer top, cloud base, cloud top, inversion base, and LFC. Cloud base and cloud top are based on ARSCL data within half an hour of the sounding time and are defined as described in section 2b. Mixed-layer top, inversion base, and LFC are calculated based on sounding data. Temperature lapse rate at each height is calculated as the regression slope of temperature versus height in 100-m intervals. Inversions are defined as layers when temperature increases with height. On some days, there exist several inversion layers aloft; here we present the properties of the lowest inversion. The average cloud base is generally a few hundred meters higher than the average mixed-layer depth, which suggests the air parcels contributing to cloud formation are the large-eddy overshoots with most positive buoyancy. This is consistent with Coulman and Warner (1977). At both times considered, the thin clouds’ boundaries are between the mixed-layer top and inversion base. This is also true for thick clouds at 1730 LST. But at 1130 LST, thick clouds’ average top is well above inversion base, suggesting penetration of thermals through the temperature inversion. Note that at 1130 LST, thick clouds’ average top is higher than the average LFC, which is consistent with Fig. 2.

Fig. 9.
Fig. 9.

(top) Composite mean mixed-layer depth, cloud base, cloud top, inversion (Inv.) base height, and LFC at 1130 (crosses) and 1730 LST (circles). (middle) Composite mean environmental atmospheric stability in cloud layer, the layer above cloud top (400 m), and the first inversion layer at 1130 (crosses) and 1730 LST (circles). (bottom) Composite mean atmospheric stability at 1.5–2.5 (squares) and 2.5–3.5 km (triangles) at 0530, 1130, and 1730 LST. Thin- and thick-ShCu days are in gray and black, respectively. The extent of the vertical bar on either side of the mean value denotes one standard error of the mean across all of the sample days in each regime. These calculations are based on LSSONDE and ARSCL data.

Citation: Journal of the Atmospheric Sciences 70, 4; 10.1175/JAS-D-12-0131.1

BL RH is larger on thick-cloud days, but free-tropospheric RH differences seem minor. Is temperature stratification important? The atmospheric stability υ/dz is calculated as the regression slope of virtual potential temperature versus height in the layers of subcloud, cloud, inversion, and a layer of 400 m above cloud. In subcloud layers, it is usually well mixed with neutral stability (not shown). Figure 9 shows that in the cloud layer, the stability ranges from 3 to 6 K km−1. In the layer of 400 m above clouds, the stability is significantly larger on thin-cloud days (e.g., 2 K km−1 higher at 1130 LST and 2.5 K km−1 higher at 1730 LST). The stability strength in inversion layer ranges from 12 to 16 K km−1.

Since we use hourly average cloud top around sounding times as a reference to calculate the stability, the values of stability above cloud top might not be accurate. We further examine the stability at different fixed height levels. We choose 1.5–2.5 km to coarsely represent the average cloud layer, as the average cloud base and top are in this layer, and use 2.5–3.5 km to represent the average layer above cloud top. Such choices also facilitate a check on 0530 LST when there is no cloud yet. Figure 9 shows that at the level of 2.5–3.5 km, stability does not increase much with time; however, at all times the stability is higher on thin-cloud days than on thick ones, although the difference is not statistically significant at 0530 LST (Fig. 13). At the level of 1.5–2.5 km, the stability is lower on thin days at 0530 and 1730 LST, because there is often a deeper residual layer in the early morning and the mixed layer in the late afternoon that can penetrate this level.

c. Early-morning conditions

Previous studies suggest that a stronger near-surface stable layer may retard the growth of convective BL and a previous-day residual layer may enhance convective BL growth by enhancing entrainment due to the lack of temperature stratification (Wetzel et al. 1996; Hägeli et al. 2000). Is there any difference in the early-morning surface stable layer and residual layer between thin- and thick-ShCu days? Based on soundings at 0530 LST, υ/dz in the lowest 500 m is 13.0 ± 0.5 K km−1on thin days versus 12.9 ± 0.4 K km−1 on thick days and is 3.0 ± 0.3 K km−1 (thin) versus 2.8 ± 0.2 K km−1 (thick) for the layer between 500 and 1500 m above ground. This suggests that the early-morning temperature stratification below 1500 m does not play a role in determining the vertical extent of ShCu.

Findell and Eltahir (2003) defined two atmospheric measures—the convective triggering potential (CTP) and low-level humidity index (HI)—whose early-morning values suggest the later development of convective cloud and precipitation over warm land surfaces. The value of the CTP is determined by integrating the area between the environmental temperature profile and a moist adiabat drawn upward from the observed temperature 100 mb above the surface to a point 300 mb above the surface (Findell and Eltahir 2003). HI is the sum of the dewpoint depressions at levels of 0.5 and 1.5 km above ground. Do these measures also differ between thin- and thick-ShCu days? The composite CTP is −78 ± 7 J kg−1 on thick days while it is −127 ± 10 J kg−1 on thin-cloud days. The composite HI is 7.8 ± 0.4 K (thick) versus 13.0 ± 0.5 K (thin). These values are consistent with the suggested criteria in Findell and Eltahir (2003) that with negative CTP and HI below 15 K, nonprecipitating shallow clouds will form very likely over wet soils.

d. Surface turbulent fluxes

Surface turbulent fluxes are very important for driving convective BL growth and supply moisture through evaporation or evapotranspiration. Figure 10 shows the diurnal-cycle composites of sensible and latent heat fluxes; evaporative fraction, which is defined as latent/(sensible + latent); and volumetric soil moisture content measured at SGP CF. Surface turbulent heat fluxes are in phase with solar radiation, with latent (sensible) heat flux peaking at 370 (180) W m−2 on thick-cloud days compared with 320 (240) W m−2 on thin-cloud days. The evaporative fraction is larger on thick-ShCu days. In particular, the average of the composite mean evaporative fraction in the afternoon is around 0.7 compared with 0.6 on thin days, which are comparable with other values observed over land (Schrieber et al. 1996). However, there is no significant difference in soil moisture content. Day-to-day variations sometimes show sensible heat flux exceeding latent heat flux, especially with low soil moisture content. This occurs 29% of the time on thin-ShCu days but only 14% of the time on thick-cloud days.

Fig. 10.
Fig. 10.

Diurnal-cycle composites of (a) surface sensible and (b) latent heat fluxes, (c) evaporative fraction, and (d) soil moisture content on thin (gray)- and thick (black)-ShCu days at central facility. The heat fluxes and evaporative fraction are based on CMBE BAEBBR data and soil moisture content is based on SWATS data at 5 cm beneath the surface. The width of the shading or the length of the vertical line on either side of the mean value denotes one standard error of the mean across all the sample days in each regime.

Citation: Journal of the Atmospheric Sciences 70, 4; 10.1175/JAS-D-12-0131.1

e. Surface meteorology and inhomogeneity

As hinted by the difference in the surface fluxes, it is worthwhile to examine the surface meteorological conditions. Surface heterogeneity is also of interest as previous studies argue its relevance to cloud fraction (Chen and Avissar 1994). Figure 11 shows the diurnal cycle of average meteorological conditions and their spatial standard deviation (std dev) based on SMOS and OKM data within 50 km of the central facility. Here we use the standard deviation across five locations to represent spatial heterogeneity. Surface air temperature is higher with a maximum difference of 2 K around 1500 LST on thin-cloud days. The near-surface water vapor mixing ratio peaks around 0800 LST, decreases through sunset, and increases again during late evening. On thin-cloud days it is drier by about 1.5 g kg−1. This drying effect results in a lower value of mean moist static energy. The variability across the five stations is larger in temperature fields from late morning to early afternoon on thick-cloud days; the variability on thin days is greater before sunrise and after sunset. A greater variability in the moisture field is found during afternoon and evening hours on thin-cloud days, which might be attributed to more rapid growth of mixed layer and stronger entrainment of dry air into the BL. There is no significant difference in mean wind speed or mesoscale wind variability.

Fig. 11.
Fig. 11.

Diurnal-cycle composites of (a) temperature and (b) its std dev, (c) MR and (d) its std dev, and (e) wind speed and (f) its mesoscale variability on thin (gray)- and thick (black)-cloud days. The mean and std dev are calculated based on SMOS data at central facility and four Oklahoma MESONET stations within 50 km. The length of the vertical line on either side of the mean value denotes one standard error of the mean across all the sample days in each regime.

Citation: Journal of the Atmospheric Sciences 70, 4; 10.1175/JAS-D-12-0131.1

After examining the inhomogeneity in surface meteorology, it would be natural to ask, “What about heterogeneity in surface fluxes and soil moisture?” We find larger sensible heat flux spatial variability and smaller soil moisture variability on thick-cloud days, but find no significant difference in the spatial variability of latent heat flux. This result is tentative, however, since the data are only available from facilities in the region north of SGP CF. Additionally, we note the absence of any significant correlation between surface inhomogeneities and cloud fraction.

f. Large-scale winds, subsidence, and advection

Figures 12a–d show the diurnal cycle of wind fields from the wind profiler. At levels above 2 km, northeasterlies prevail on thick-cloud days while northwesterly flow dominates on thin-cloud days. At levels below 2 km, southwesterly flow prevails in the morning and shifts to southeasterly in the afternoon. The southerly component is much stronger on thick-cloud days: in the morning, the depth of southerly flow extends past 2.5 km for thick-cloud days and only to about 1.7 km on thin-cloud days. The wind profiler measurement indicates larger wind speed between 0.3 and 1.5 km on thick-cloud days, especially from midnight to dawn.

Fig. 12.
Fig. 12.

Diurnal-cycle composites of horizontal wind fields (a),(b) U and (c),(d) V based on 915-MHz wind profiler data at SGP CF, (e),(f) vertical motion (omega; hPa h−1), and (g),(h) horizontal moisture advection (g kg−1 day−1) based on SGP continuous forcing data on (left) thin- and (right) thick-ShCu days. Stippled regions indicate where the difference between thin and thick days is of statistical significance at the 95% confidence level.

Citation: Journal of the Atmospheric Sciences 70, 4; 10.1175/JAS-D-12-0131.1

Figures 12e–h show the horizontal moisture advection calculated from wind and moisture gradient based on variational analysis from Rapid Update Cycle model after applying constraints of the observations at SGP (Zhang and Lin 1997; Xie et al. 2004). Below 850 hPa there is significantly greater moistening in the lower atmosphere in the early-morning hours on thick-cloud days, while on thin days there is low-level drying. The differences contribute to the moister conditions in the earlier morning sounding found on thick-cloud days. As shown, there is a stronger southerly component at the same level on thick-cloud days. In addition, there is more frequent westerly component on thin-cloud days, which occurs on 76% of thin days as opposed to 48% of thick-cloud days. Around sunrise time, there is weak upward motion, possibly facilitating transport of the moisture from lower to upper levels on thick-cloud days. Large-scale subsidence prevails and gradually gets stronger from morning to afternoon, especially above 700 hPa in the afternoon hours on thick-cloud days. There is no significant difference in subsidence under 700 hPa between thin- and thick-cloud days during daytime.

g. What environment factors are the most different?

After examining many parameters that may contribute to the difference between thin- and thick-ShCu days, we may wonder what effects dominate. Figure 13 illustrates t values for a set of environmental parameters, with the solid line indicating the 95% confidence level. Unless otherwise noted, values are for 1130 LST (chosen because this is the sounding time nearest to ShCu onset). Some variables are also tested at early-morning times: 0530 LST or averages between midnight and 0600 LST (called “early-morning average”).

Fig. 13.
Fig. 13.

Absolute t values from Student’s t tests for the differences between composite means of thin- and thick-ShCu days. Unless explicitly stated, variables at 1130 LST are compared. “EM MR HAdv” refers to horizontal advection of water vapor mixing ratio in the early morning, between midnight and 6 a.m. The horizontal solid line denotes a confidence level of 95%. Negative (positive) t values are solid (stippled) and denote smaller (larger) composite means on thick-ShCu days relative to the mean on thin- ShCu days.

Citation: Journal of the Atmospheric Sciences 70, 4; 10.1175/JAS-D-12-0131.1

Figure 13 reinforces our earlier analysis on higher BL RH on thick-cloud days. The RH below 1.5 km at 0530 LST is much larger on thick-cloud days, whose significance level is well above 95%. This results from higher horizontal moisture advection into SGP region in the early morning. This high moisture signal on thick-cloud days is well preserved at 1130 LST, as the subcloud-layer RH is also significantly higher.

Another signal that stands out is the atmospheric stability, as υ/dz between 2.5 and 3.5 km exceeds the 95% significance level at 1130 LST. This suggests that the persistence of less-stable conditions on thick- cloud days might allow deeper clouds to develop. However, such difference is not so significant at 0530 LST.

We also notice a larger sensible heat flux on thin days at 1130 LST that is significant above the 95% level and an evaporative fraction that is smaller on thin days. Differences in latent heat flux and soil moisture at 1130 LST are not significant.

Regarding wind shear and large-scale subsidence, there is no significant difference at cloud levels (e.g., 1.5–2.5 km) at 1130 LST. We also examined the boundary inhomogeneity difference in surface temperature, mixing ratio, and wind speed. There is a higher inhomogeneity of surface temperature at 1130 LST on thick-cloud days, for which we do not have an immediate explanation.

We also calculate the average buoyancy for the cloud layer based on 1130 LST sounding data and ARSCL data within half an hour of the sounding time. The values are 0.42 ± 0.15 K on thick-cloud days and −0.34 ± 0.13 K on thin-cloud days. Such difference is of 95% significance and will be explained in the following section.

5. Discussion

The analysis above shows that on days with different cloud vertical extent (which we interpret as forced versus active ShCu), BL RH is most different, though there also appears to be marginally significant difference in atmospheric stability above cloud and surface fluxes. Here we offer potential physical explanations as to why cumulus cloud thickness may be sensitive to these environmental parameters.

a. On cloud base

How does RH underneath clouds affect cloud base? Figure 14 shows the correlation coefficient between hourly mean observed cloud base and surface RH is −0.87 as cloud bases lower with larger RH values. Such a high correlation between hourly mean quantities also suggests the observed ShCu is closely related to local conditions at SGP as they are “locally generated” from convection that originates at the surface and also short lived. The observed cloud base is generally higher than the LCL calculated from surface RH, temperature, and pressure, suggesting air parcels rising from surface experience some mixing with their environment. However, some data points coincide with calculated the LCL, suggesting that some of the air reaching cloud base may preserve an undiluted convective core to form clouds, which is consistent with Berg and Kassianov (2008) and Crum et al. (1987).

Fig. 14.
Fig. 14.

Scatterplots of hourly mean surface RH from SMOS data and cloud base from ARSCL data. Black line is the regression between the two. Red line is the average calculated LCL based on surface RH, temperature, and pressure from SMOS data. Color scale shows the corresponding hourly mean cloud depth.

Citation: Journal of the Atmospheric Sciences 70, 4; 10.1175/JAS-D-12-0131.1

Thus, it is plausible to hypothesize that the higher RH present on thick days is responsible for the lower cloud base. However, regarding the cloud vertical extent, cloud base is not the whole story; for instance, among all ShCu days the correlation between cloud depth and cloud base is about −0.36 while the correlation between cloud depth and cloud top is about 0.53. We also need to know what controls cloud top.

b. On cloud top

To further explore the effect of BL RH and atmospheric stability, we use a Paluch diagram (Fig. 15) for conserved thermodynamic variables (Paluch 1979; Wu et al. 2009; Zhang and Klein 2010). In this diagram, an air parcel will preserve its thermodynamic properties (total water mixing ratio qt and liquid water potential temperature θl) if it is lifted adiabatically without mixing with its environment. We use this diagram to analyze the ability of an air parcel to penetrate the inversion.

Fig. 15.
Fig. 15.

Mixing diagram (Paluch diagram) of total water mixing ratio and liquid water potential temperature for the composite of sounding data (gray circles) at 1130 LST for (left) thin and (right) thick fair-weather ShCu days. Zero-buoyancy lines (saturation curves) are denoted by solid (dotted) lines, which are calculated based on sounding data at levels of composite observed cloud base (black), cloud top (blue), and inversion base (red) (see Fig. 9). The black dot labeled “BL” denotes BL air. The environmental air at the level of cloud base, inversion base, or cloud top are denoted by black dot labeled “CB,” red dot labeled “IN,” or blue dot labeled “CT,” respectively. The gray triangle denotes air with a θl that is 0.5 K higher (lower) than the inversion air at the inversion base. The gray long-dashed lines denote two hypothetical zero-buoyancy lines if inversion level θl were 0.5 K higher or lower than the actual θl. The stippled area denotes the possible values that a BL air parcel may have if BL inhomogeneity is considered at 1130 LST. This area encompasses one standard deviation of BL inhomogeneity about the mean value (Fig. 11).

Citation: Journal of the Atmospheric Sciences 70, 4; 10.1175/JAS-D-12-0131.1

To examine the penetrating ability of the BL air parcel, one examines if the air parcel’s buoyancy is greater than zero at the levels of observed cloud base, cloud top, and inversion layer based on composite sounding data for thin- and thick-cloud days. At cloud base, even though the BL air parcel is not above zero buoyancy on both days, it is much closer on thick-cloud days (e.g., Δθυ is −1.0 K as compared with −1.7 K on thin-cloud days). On thick days, the observed inversion base is lower than cloud top while on thin-cloud days it is not.

As evident in Fig. 15, when air is saturated the zero-buoyancy line becomes more parallel to isolines of total moisture because of the latent heat release from condensation. Although on both thin and thick days ShCu may all originate as a forced cloud, when reaching deeper levels, air parcel with higher moisture content becomes less susceptible to be negatively buoyant. This is because condensation starts at a lower cloud base level and there is a larger accumulated latent heat release by the time an air parcel reaches the inversion base on thick-cloud days. Thus, a higher moisture in the BL air leads to a stronger penetrating ability of BL air parcels.

If further considering the boundary inhomogeneity at 1130 LST (Fig. 11), which is shown as stippling surrounding the BL point in Fig. 15, there is even a larger possibility of BL air to penetrate inversion base and then reach cloud top on thick-cloud days.

What about the effect of stability above cloud? It is hard to track the cloud growth and its interaction with environmental stability dynamically unless large-eddy simulation tools are used. However, we may design a thought experiment to artificially change the temperature at inversion base, to roughly represent a change in the stability and qualitatively display the effect of stability. We chose 0.5 K based on inversion strength and the difference in υ/dz between 2.5 and 3.5 km in Fig. 9. Figure 15 shows that if the inversion were 0.5 K cooler on thin-cloud days (see light gray dashed lines) then the moistest and warmest BL air parcels would reach neutral buoyancy in the inversion layer. Likewise, if the inversion layer were 0.5 K warmer on thick-cloud days, then all BL air parcel would be negatively buoyant in the inversion layer. From this, we conclude that the differences in stability above cloud are large enough to affect cloud vertical extent.

A further question arises: how significant is the effect of the inversion temperature change as compared with the influence from higher BL moisture? On thin-cloud days, Δθυ between BL air parcel and its environment at inversion increases from −1.3 to −0.8 K if the inversion temperature lowers 0.5 K. However, if thin days had the BL moisture of thick days, the 2 g kg−1 increase in BL moisture would, all other things the same, raise Δθυ from −1.3 to +0.6 K. This suggests the effect of the BL moisture difference is more dominant for cloud vertical extent than the difference in inversion-layer stability.

The mixing line diagram helps us explain the role of both BL RH and stability using the composite soundings of thin- and thick-cloud days. While there is some diversity in the buoyancy profiles calculated from individual soundings, the buoyancy profile calculated from the composite sounding reasonably well matches the buoyancy profiles in the great majority of individual soundings on thin- or thick-cloud days. This is further confirmed by the significant difference between thin- and thick-cloud days in the cloud-layer buoyancy based on individual soundings (Fig. 13). We are also aware that our analysis assumes that BL air properties are preserved undiluted, are subject to adiabatic reversible processes, and describe the most vigorous thermal; however, in reality, thermals consist of a spectrum of entraining plumes and processes are nonadiabatic and much more complicated than our idealization.

c. On BL moisture supply

What is the role of the difference in surface fluxes? The difference in surface fluxes and evaporative fraction between thin- and thick-cloud days only becomes significant after 1030 LST (Fig. 9). It is unlikely that differences in surface fluxes contribute to the large difference in BL RH that already exists since early morning. However, a larger sensible heat flux on thin days contributes to a higher mixed layer and more vigorous mixing of dry air into the BL. This entrainment of dry air contributes to lower BL moisture in the afternoons of thin-cloud days—a tendency reinforced by the smaller latent heat fluxes on thin-cloud days. The difference and change in BL RH supports these arguments. For instance, the average RH on thick-cloud days is 6% higher at 1130 LST and becomes 10% higher at 1730 LST than that on thin-cloud days. And on thick-cloud days, the decrease of RH from 1130 to 1730 LST is also smaller: 4% compared with 8% on thin-cloud days. In this way, we are inclined to think that the surface flux differences reinforce the primary role of early-morning RH in determining cloud vertical extent.

6. Summary

Observations in May–August for 13 yr at the ARM Southern Great Plains site have been used to categorize the diurnal cycle for thin and thick nonprecipitating ShCu to roughly represent “forced” versus “active” shallow cumulus regimes. We compare cloud macrophysics, radiative impact, and environmental parameters between these two regimes to document the characteristics of ShCu and investigate the factors that control the cloud vertical extent.

The average depth of individual clouds on thin-cloud days is about 220 m and more than 75% of cloud depths are less than 300 m. The cloud depth on thick-cloud days is triple that of thin-cloud days and half of the clouds are thicker than 600 m. The difference in the distribution of cloud chord length is small; however, the average is 200 m larger on thick-cloud days owing to less frequent small clouds and occasional cases of very large width. The average cloud aspect ratio (depth/length) is about 0.5 on thin-cloud days and 2 on thick-cloud days. Cloud base on thick-cloud days falls between 1 and 2 km most of the time while that of thin clouds is generally between 2 and 3 km; cloud tops are mostly between 2 and 3 km for both regimes. There is much less shortwave radiation reaching the surface on the days with thick clouds owing to higher projected cloud fraction and reflectance; however, this is compensated by more upward shortwave and longwave (because of warmer surface temperature) radiation, and thus there is 10 W m−2 more loss of energy at the surface on thin-cloud days.

When comparing environmental conditions between the two ShCu regimes at 1130 LST, higher RH is found in the BL on thick-cloud days while there is no significant difference in RH at the cloud layer or above cloud; a weaker atmospheric stability is found above cloud top on thick-cloud days but there is no significant difference in the cloud or subcloud layer. In the predawn hours, horizontal moisture advection into the SGP region is positive on thick-cloud days at levels below 850 hPa related to stronger wind from the south. Larger evaporative fraction is also found on thick-cloud days during late morning and afternoon, which helps maintain higher BL RH later in the day. Variance of cloud depth results from both cloud base and cloud top on thick-cloud days. Cloud base is found to be highly correlated with surface RH, which reinforces the idea that ShCu are locally generated and closely tied to local meteorological conditions. Through conservative-variable diagram analysis, we investigate the penetrating ability of BL air and conclude that higher BL RH and also weaker stability above make an air parcel less susceptible to become negatively buoyant because of larger accumulated condensational heat release on thick-cloud days.

This study is among the first few to systematically investigate forced and active clouds based on long-term composite observations and further confirms that forced clouds are often related to BL large-eddy overshooting and are limited by a stronger inversion layer while higher moisture content in BL and a weaker stable layer above favor active clouds with significant vertical extent.

Through this study and Zhang and Klein (2010), we have established an observational test bed of different convection regimes over SGP, from forced and active nonprecipitating fair-weather ShCu to ShCu that transitions to late-afternoon deep convection. In the future we intend to use these results to test large-scale parameterizations using single-column models. Large-eddy simulations will also be performed to test the relative importance of different environmental conditions such as RH in and above BL, υ/dz above cloud, and surface fluxes in controlling the different ShCu types and affecting the transition from shallow to deep convection over land. New instruments and measurements at ARM sites, such as retrievals of vertical velocity and cloud properties, will be a great help for such studies in the near future.

Acknowledgments

The authors thank Pavlos Kollias and Arunchandra Chandra for interacting with us on the vertical velocity of shallow cumuli over land through which we were led to ask what controls the thickness of shallow cumuli. The authors sincerely thank Larry Berg for providing shallow cumulus day index, Christine J. Chiu for AERONET cloud optical depth, Qilong Min for MFRSR cloud optical depth, and Shaocheng Xie, Renata McCoy, and Chuanfeng Zhao for discussions on CMBE and CRED data. The authors thank Peter Caldwell for comments on the manuscript. Data from the U.S. Department of Energy as part of the Atmospheric Radiation Measurement (ARM) Climate Research Facility Southern Great Plains site were used. The Oklahoma Mesonet data were used. This work was supported primarily by the Department of Energy’s Atmospheric System Research, an Office of Science, Office of Biological, and Environmental Research program. Lawrence Livermore National Laboratory is operated for the DOE by Lawrence Livermore National Security, LLC under Contract DE-AC52-07NA27344.

REFERENCES

  • Ackerman, T. P., and G. M. Stokes, 2003: The Atmospheric Radiation Measurement Program. Phys. Today, 56, 3844.

  • Ahlgrimm, M., and R. Forbes, 2012: The impact of low clouds on surface shortwave radiation in the ECMWF model. Mon. Wea. Rev., 140, 37833794.

    • Search Google Scholar
    • Export Citation
  • Berg, L. K., and E. I. Kassianov, 2008: Temporal variability of fair-weather cumulus statistics at the ACRF SGP site. J. Climate, 21, 33443358.

    • Search Google Scholar
    • Export Citation
  • Berg, L. K., E. I. Kassianov, C. N. Long, and D. L. Mills Jr., 2011: Surface summertime radiative forcing by shallow cumuli at the Atmospheric Radiation Measurement Southern Great Plains site. J. Geophys. Res., 116, D01202, doi:10.1029/2010JD014593.

    • Search Google Scholar
    • Export Citation
  • Betts, A. K., M. Köhler, and Y. Zhang, 2009: Comparison of river basin hydrometeorology in ERA-Interim and ERA-40 reanalyses with observations. J. Geophys. Res., 114, D02101, doi:10.1029/2008JD010761.

    • Search Google Scholar
    • Export Citation
  • Boers, R., E. W. Eloranta, and R. L. Coulter, 1984: Lidar observations of mixed layer dynamics: Tests of parameterized entrainment models of mixed layer growth rate. J. Climate Appl. Meteor., 23, 247266.

    • Search Google Scholar
    • Export Citation
  • Bretherton, C. S., J. R. McCaa, and H. Grenier, 2004: A new parameterization for shallow cumulus convection and its application to marine shallow subtropical cloud-topped boundary layers. Part I: Description and 1D results. Mon. Wea. Rev., 132, 864882.

    • Search Google Scholar
    • Export Citation
  • Brock, F. V., K. C. Crawford, R. L. Elliott, G. W. Cuperus, S. J. Stadler, H. L. Johnson, and M. D. Eilts, 1995: The Oklahoma Mesonet: A technical overview. J. Atmos. Oceanic Technol., 12, 519.

    • Search Google Scholar
    • Export Citation
  • Brown, A. R., and Coauthors, 2002: Large-eddy simulation of the diurnal cycle of shallow cumulus convection over land. Quart. J. Roy. Meteor. Soc., 128, 10751093.

    • Search Google Scholar
    • Export Citation
  • Cheinet, S., 2004: A multiple mass flux parameterization for the surface-generated convection. Part II: Cloudy cores. J. Atmos. Sci., 61, 10931113.

    • Search Google Scholar
    • Export Citation
  • Chen, F., and R. Avissar, 1994: Impact of land-surface moisture variability on local shallow convective cumulus and precipitation in large-scale models. J. Appl. Meteor., 33, 13821401.

    • Search Google Scholar
    • Export Citation
  • Chiu, J. C., C.-H. Huang, A. Marshak, I. Slutsker, D. M. Giles, B. N. Holben, Y. Knyazikhin, and W. J. Wiscombe, 2010: Cloud optical depth retrievals from the Aerosol Robotic Network (AERONET) cloud mode observations. J. Geophys. Res., 115, D14202, doi:10.1029/2009JD013121.

    • Search Google Scholar
    • Export Citation
  • Clothiaux, E. E., T. P. Ackerman, G. G. Mace, K. P. Moran, R. T. Marchand, M. Miller, and B. E. Martner, 2000: Objective determination of cloud heights and radar reflectivities using a combination of active remote sensors at the ARM CART sites. J. Appl. Meteor., 39, 645665.

    • Search Google Scholar
    • Export Citation
  • Clothiaux, E. E., and Coauthors, 2001: The ARM Millimeter Wave Cloud Radars (MMCRs) and the Active Remote Sensing of Clouds (ARSCL) Value Added Product (VAP). U.S. Department of Energy Tech. Memo. ARM VAP-002.1, 56 pp.

  • Cohn, S. A., and W. M. Angevine, 2000: Boundary layer height and entrainment zone thickness measured by lidars and wind-profiling radars. J. Appl. Meteor., 39, 12331247.

    • Search Google Scholar
    • Export Citation
  • Coulman, C. E., and J. Warner, 1977: Temperature and humidity structure of the sub-cloud layer over land. Bound.-Layer Meteor., 11, 467484.

    • Search Google Scholar
    • Export Citation
  • Crum, T. D., R. B. Stull, and E. W. Eloranta, 1987: Coincident lidar and aircraft observations of entrainment into thermals and mixed layers. J. Climate Appl. Meteor., 26, 774788.

    • Search Google Scholar
    • Export Citation
  • Cuijpers, J. W. M., and P. G. Duynkerke, 1993: Large eddy simulation of trade wind cumulus clouds. J. Atmos. Sci., 50, 38943908.

  • Davidson, B., 1968: The Barbados oceanographic and meteorological experiment. Bull. Amer. Meteor. Soc., 49, 928934.

  • Dong, X., P. Minnis, and B. Xi, 2005: A climatology of midlatitude continental clouds from the ARM SGP Central Facility. Part I: Low-level cloud macrophysical, microphysical, and radiative properties. J. Climate, 18, 13911410.

    • Search Google Scholar
    • Export Citation
  • Ek, M., and L. Mahrt, 1994: Daytime evolution of relative humidity at the boundary layer top. Mon. Wea. Rev., 122, 27092720.

  • Ek, M., and A. Holtslag, 2004: Influence of soil moisture on boundary layer cloud development. J. Hydrometeor., 5, 8699.

  • Findell, K. L., and E. A. B. Eltahir, 2003: Atmospheric controls on soil moisture–boundary layer interactions. Part I: Framework development. J. Hydrometeor., 4, 552569.

    • Search Google Scholar
    • Export Citation
  • Hägeli, P., D. G. Steyn, and K. B. Strawbridge, 2000: Spatial and temporal variability of mixed-layer depth and entrainment zone thickness. Bound.-Layer Meteor., 97, 4771.

    • Search Google Scholar
    • Export Citation
  • Kain, J. S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170181.

  • Kim, S.-W., S.-U. Park, and C.-H. Moeng, 2003: Entrainment processes in the convective boundary layer with varying wind shear. Bound.-Layer Meteor., 108, 221245.

    • Search Google Scholar
    • Export Citation
  • Kuettner, J., and J. Holland, 1969: The BOMEX Project. Bull. Amer. Meteor. Soc., 50, 394402.

  • Lenaerts, J., C. van Heerwaarden, and J. Vilà-Guerau de Arellano, 2009: Shallow convection over land: A mesoscale modelling study based on idealized WRF experiments. J. Wea. Climate West. Mediterr., 6, 5166.

    • Search Google Scholar
    • Export Citation
  • Lenderink, G., and Coauthors, 2004: The diurnal cycle of shallow cumulus clouds over land: A single-column model intercomparison study. Quart. J. Roy. Meteor. Soc., 130, 33393364, doi:10.1256/qj.03.122.

    • Search Google Scholar
    • Export Citation
  • Long, C. N., and T. P. Ackerman, 2000: Identification of clear skies from broadband pyranometer measurements and calculation of downwelling shortwave cloud effects. J. Geophys. Res., 105 (D12), 15 60915 626.

    • Search Google Scholar
    • Export Citation
  • Long, C. N., and Y. Shi, 2008: An automated quality assessment and control algorithm for surface radiation measurements. Open Atmos. Sci. J., 2, 2337.

    • Search Google Scholar
    • Export Citation
  • Min, Q., E. Joseph, and M. Duan, 2004: Retrievals of thin cloud optical depth from a multifilter rotating shadowband radiometer. J. Geophys. Res., 109, D02201, doi:10.1029/2003JD003964.

    • Search Google Scholar
    • Export Citation
  • Neggers, R. A. J., A. P. Siebesma, and H. J. J. Jonker, 2002: A multiparcel model for shallow cumulus convection. J. Atmos. Sci., 59, 16551668.

    • Search Google Scholar
    • Export Citation
  • Paluch, I. R., 1979: The entrainment of air in Colorado cumuli. J. Atmos. Sci., 36, 24672478.

  • Pergaud, J., V. Masson, S. Malardel, and F. Couvreux, 2009: A parameterization of dry thermals and shallow cumuli for mesoscale numerical weather prediction. Bound.-Layer Meteor., 132, 83106, doi:10.1007/s10546-009-9388-0.

    • Search Google Scholar
    • Export Citation
  • Rabin, R. M., and D. W. Martin, 1996: Satellite observations of shallow cumulus coverage over the central United States: An exploration of land use impact on cloud cover. J. Geophys. Res., 101 (D3), 71497155.

    • Search Google Scholar
    • Export Citation
  • Rauber, R. M., and Coauthors, 2007: Rain in shallow cumulus over the ocean: The RICO campaign. Bull. Amer. Meteor. Soc., 88, 19121928.

    • Search Google Scholar
    • Export Citation
  • Schneider, J. M., D. K. Fisher, R. L. Elliott, G. O. Brown, and C. P. Bahrmann, 2003: Spatiotemporal variations in soil water: First results from the ARM SGP CART network. J. Hydrometeor., 4, 106120.

    • Search Google Scholar
    • Export Citation
  • Schrieber, K., R. Stull, and Q. Zhang, 1996: Distributions of surface-layer buoyancy versus lifting condensation level over a heterogeneous land surface. J. Atmos. Sci., 53, 10861107.

    • Search Google Scholar
    • Export Citation
  • Siebesma, A. P., and J. W. M. Cuijpers, 1995: Evaluation of parametric assumptions for shallow cumulus convection. J. Atmos. Sci., 52, 650666.

    • Search Google Scholar
    • Export Citation
  • Siebesma, A. P., and Coauthors, 2003: A large eddy simulation intercomparison study of shallow cumulus convection. J. Atmos. Sci., 60, 12011219.

    • Search Google Scholar
    • Export Citation
  • Soares, P. M. M., P. M. A. Miranda, A. P. Siebesma, and J. Teixeira, 2004: An eddy-diffusivity/mass-flux parametrization for dry and shallow cumulus convection. Quart. J. Roy. Meteor. Soc., 130, 33653383.

    • Search Google Scholar
    • Export Citation
  • Stevens, B., 2007: On the growth of layers of non-precipitating cumulus convection. J. Atmos. Sci., 64, 29162931.

  • Strokes, G. M., and S. E. Schwatz, 1994: The Atmospheric Radiation Measurement (ARM) program: Programmatic background and design of the cloud and radiation test bed. Bull. Amer. Meteor. Soc., 75, 12011221.

    • Search Google Scholar
    • Export Citation
  • Stull, R., 1985: A fair-weather cumulus cloud classification scheme for mixed-layer studies. J. Climate Appl. Meteor., 24, 4956.

  • Susĕlj, K., J. Teixeira, and G. Matheou, 2012: Eddy diffusivity/mass flux and shallow cumulus boundary layer: An updraft PDF multiple mass flux scheme. J. Atmos. Sci., 69, 15131533.

    • Search Google Scholar
    • Export Citation
  • Turner, D. D., S. A. Clough, J. Liljegren, E. Clothiaux, K. Cady-Pereira, and K. Gaustad, 2007a: Retrieving liquid water path and precipitable water vapor from Atmospheric Radiation Measurement (ARM) microwave radiometers. IEEE Trans. Geosci. Remote Sens., 45, 36803690, doi:10.1109/TGRS.2007.903703.

    • Search Google Scholar
    • Export Citation
  • Turner, D. D., and Coauthors, 2007b: Thin liquid water clouds: Their importance and our challenge. Bull. Amer. Meteor. Soc., 88, 177190.

    • Search Google Scholar
    • Export Citation
  • Vilà-Guerau de Arellano, J., 2007: Role of nocturnal turbulence and advection in the formation of shallow cumulus over land. Quart. J. Roy. Meteor. Soc., 133, 16151627, doi:10.1002/qj.138.

    • Search Google Scholar
    • Export Citation
  • Wesely, M., D. Cook, and R. Coulter, 1995: Surface heat flux data from energy balance Bowen ratio systems. Preprints, Ninth Symp. on Meteorological Observations and Instrumentation, Charlotte, NC, Amer. Meteor. Soc., 486–489.

  • Wetzel, P. J., S. Argentini, and A. Boone, 1996: Role of land surface in controlling daytime cloud amount: Two case studies in the GCIP-SW area. J. Geophys. Res., 101 (D3), 73597370.

    • Search Google Scholar
    • Export Citation
  • Wilde, N. P., R. B. Stull, and E. W. Eloranta, 1985: The LCL zone and cumulus onset. J. Climate Appl. Meteor., 24, 640657.

  • Wu, C., B. Stevens, and A. Arakawa, 2009: What controls the transition from shallow to deep convection? J. Atmos. Sci., 66, 17931806.

    • Search Google Scholar
    • Export Citation
  • Xie, S., R. T. Cederwall, and M. Zhang, 2004: Developing long-term single-column model/cloud system–resolving model forcing data using numerical weather prediction products constrained by surface and top of the atmosphere observations. J. Geophys. Res., 109, D01104, doi:10.1029/2003JD004045.

    • Search Google Scholar
    • Export Citation
  • Xie, S., and Coauthors, 2010: ARM climate modeling best estimate data. Bull. Amer. Meteor. Soc., 91, 1320.

  • Yi, C., K. J. Davis, and B. W. Berger, 2001: Long-term observations of the dynamics of the continental planetary boundary layer. J. Atmos. Sci., 58, 12881299.

    • Search Google Scholar
    • Export Citation
  • Zhang, M. H., and J. L. Lin, 1997: Constrained variational analysis of sounding data based on column-integrated budgets of mass, heat, moisture, and momentum: Approach and application to ARM measurements. J. Atmos. Sci., 54, 15031524.

    • Search Google Scholar
    • Export Citation
  • Zhang, Y., and S. A. Klein, 2010: Mechanisms affecting the transition from shallow to deep convection over land: Inferences from observations of the diurnal cycle collected at the ARM Southern Great Plains site. J. Atmos. Sci., 67, 29432959.

    • Search Google Scholar
    • Export Citation
  • Zhao, C., and Coauthors, 2012: Toward understanding of differences in current cloud retrievals of arm ground-based measurements. J. Geophys. Res., 117, D10206, doi:10.1029/2011JD016792.

    • Search Google Scholar
    • Export Citation
  • Zhu, P., and B. Albrecht, 2002: A theoretical and observational analysis on the formation of fair-weather cumuli. J. Atmos. Sci., 59, 19832005.

    • Search Google Scholar
    • Export Citation
  • Zhu, P., and B. Albrecht, 2003: Large eddy simulations of continental shallow cumulus convection. J. Geophys. Res., 108, 4453, doi:10.1029/2002JD003119.

    • Search Google Scholar
    • Export Citation
  • Zhu, P., and C. S. Bretherton, 2004: A simulation study of shallow moist convection and its impact on the atmospheric boundary layer. Mon. Wea. Rev., 132, 23912409.

    • Search Google Scholar
    • Export Citation
Save