Skip to main content
Log in

Evolution of Stratocumulus Over Land: Comparison of Ground and Aircraft Observations with Numerical Weather Prediction Simulations

  • Article
  • Published:
Boundary-Layer Meteorology Aims and scope Submit manuscript


Forecasting of low cloud continues to challenge numerical weather prediction. With this in mind, surface and airborne observations were made over East Anglia, UK, during March 2011 to investigate stratus and stratocumulus advecting from the sea over land. Four surface sites were deployed at various distances inland aligned approximately along the flow. In situ data include cloud-droplet measurements from an aircraft operating off the coast and a tethered balloon 100 km inland. Comparisons of thermodynamic and cloud properties are made with Met Office operational model simulations at horizontal resolutions of 4 and 1.5 km. The clouds contained droplet concentrations up to 600 cm\(^{-3}\) within polluted outflow off Europe. These measurements were compared to three different model schemes for predicting droplet concentration: two of them perform well at low to moderate concentrations but asymptote to 375 cm\(^{-3}\). Microwave radiometers at the ground sites retrieved liquid water paths that reduced with distance inland and were generally below 200 g m\(^{-2}\). The modelled water path performs well upstream but more erratically far inland. Comparisons of thermodynamic profiles are made within both Lagrangian and Eulerian frameworks and show the model predicted changes in equivalent potential temperature generally within 1 K, with occasional errors of 2 K or more. The modelled cloud-top temperatures were in good agreement with the observations down to \(-\)\(^{\circ }\)C, but the magnitude of the temperature inversion, although good at times, was too small by on average 1.6 K. The different simulations produced different cloud-top water contents due to a combination of resolution and scientific upgrades to the model, but they generally underestimate the amount of cloud water. Major changes, such as the mesoscale temporary cloud breaks on 2 March 2011 and the complete clearance on 4 March, were seemingly predicted by the model for the correct reasons.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others


  • Abel SJ, Shipway BJ (2007) A comparison of cloud resolving model simulations of trade wind cumulus with aircraft observations taken during RICO. Q J R Meteorol Soc 133:781–794

    Article  Google Scholar 

  • Abel SJ, Walters DN, Allen G (2010) Evaluation of stratocumulus cloud prediction in the Met Office forecast model during VOCALS-REx. Atmos Chem Phys 10:10541–10559

    Article  Google Scholar 

  • Ackerman A, Toon OW, Taylor JP, Johnson DW, Hobbs PV, Ferek RJ (2000) Effects of aerosols on cloud albedo: evaluation of Twomey’s parameterization of cloud susceptibility using measurements of ship tracks. J Atmos Sci 57:2684–2695

    Article  Google Scholar 

  • Bellouin N, Rae J, Jones A, Johnson C, Haywood J, Boucher O (2011) Aerosol forcing in the Climate Model Intercomparison Project (CMIP5) simulations by HadGEM2-ES and the role of ammonium nitrate. J Geophys Res 116:D20206

    Article  Google Scholar 

  • Boutle IA, Eyre JEJ, Lock AP (2014) Seamless stratocumulus simulation across the Turbulent Grey Zone. Mon Weather Rev 142:1655–1668

    Article  Google Scholar 

  • Boutle IA, Abel SJ (2012) Microphysical controls on the stratocumulus topped boundary-layer structure during VOCALS-REx. Atmos Chem Phys 12:2849–2863

    Article  Google Scholar 

  • Clark PA, Harcourt SA, Macpherson B, Mathison CT, Cusack S, Naylor M (2008) Prediction of visibility and aerosol within the operational Met Office Unified Model I: model formulation and variational assimilation. Q J R Meteorol Soc 134:1801–1816

    Article  Google Scholar 

  • Driedonks AGM, Duynkerke PG (1989) Current problems in the stratocumulus-topped atmospheric boundary layer. Boundary-Layer Meteorol 46:275–303

    Article  Google Scholar 

  • Gordon CT, Rosati A, Gudgel R (2000) Tropical sensitivity of a coupled model to specified ISCCP low clouds. J Clim 13:2239–2260

    Article  Google Scholar 

  • Hahn CJ, Warren SG (2007) A gridded climatology of clouds over land (1971–96) and ocean (1954–97) from surface observations worldwide. NDP-026E, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge, TN, 71 pp

  • Hannay C, Williamson DL, Hack JJ, Kiehl JT, Olson JG, Klein SA, Bretherton CS, Köhler M (2009) Evaluation of forecasted southeast pacific stratocumulus in the NCAR, GFDL and ECMWF models. J Clim 22:2871–2889

    Article  Google Scholar 

  • Haywood J, Bush M, Abel S, Claxton B, Coe H, Crosier J, Harrison M, Macpherson B, Naylor M, Osborne S (2008) Prediction of visibility and aerosol within the operational Met Office Unified Model II: validation of model performance using observational data. Q J R Meteorol Soc 134:1817–1832

    Article  Google Scholar 

  • Jones A, Roberts DL, Slingo A (1994) A climate model study of indirect radiative forcing by anthropogenic sulphate aerosols. Nature 370:450–453

    Article  Google Scholar 

  • Korolev AV, Strapp JW, Isaac GA (1998) The Nevzorov airborne hot-wire LWC—TWC probe: principle of operation and performance characteristics. J Atmos Oceanic Technol 15:1495–1510

  • Kristiansen J, Bjorge D, Edwards JM, Rooney GG (2012) Soil field model interoperability: challenges and impact on screen temperature forecast skill during the Nordic winter. J Hydrometeorol 13:1215–1232

    Article  Google Scholar 

  • Lance S, Brock CA, Rogers D, Gordon JA (2010) Water droplet calibration of the Cloud Droplet Probe (CDP) and in-flight performance in liquid, ice and mixed-phase clouds during ARCPAC. Atmos Meas Tech 3:1683–1706

    Article  Google Scholar 

  • Lapworth AJ, Mason PJ (1988) The New Cardington Balloon-Borne turbulence probe system. J Atmos Oceanic Technol 5:699–714

    Article  Google Scholar 

  • Marenco F, Johnson B, Turnbull K, Newman S, Haywood J, Webster H, Ricketts H (2011) Airborne lidar observations of the 2010 Eyjafjallajökull volcanic ash plume. J Geophys Res 116:D00U05

    Google Scholar 

  • Mittermaier M (2012) A critical assessment of surface cloud observations and their use for verifying cloud forecasts. Q J R Meteorol Soc 138:1794–1807

    Article  Google Scholar 

  • Morcrette CJ, O’Connor EJ, Petch JC (2012) Evaluation of two cloud parametrization schemes using ARM and Cloud-Net observations. Q J R Meteorol Soc 138:964–979

    Article  Google Scholar 

  • Price J (2003) Operation of a radiometrics WVR-1100 passive microwave radiometer at the Met Office Research Unit, Cardington. Cardington Technical Note No. 61, 18 pp

  • Price J (2011) Radiation Fog. Part I: observations of stability and drop size distributions. Boundary-Layer Meteorol 139:167–191

    Article  Google Scholar 

  • Price JD, Vosper S, Brown A, Ross A, Clark P, Davies F, Horlacher V, Claxton B, McGregor J, Hoare J, Jemmett-Smith B, Sheridan P (2011) COLPEX: field and numerical studies over a region of small hills. Bull Am Meteorol Soc 92:1636–1650

    Article  Google Scholar 

  • Randall DA, Wood RA, Bony S, Colman R, Fichefet T, Fyfe J, Kattsov V, Pitman A, Shukla J, Srinivasan J, Stouffer RJ, Sumi A, Taylor KE (2007) Climate models and their evaluation. In: Solomon S, Qin D, Manning M, Chen Z, Marquis MC, Averyt KB, Tignor M, Miller HL (eds) Climate change 2007: the physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, New York, NY, 996 pp

  • Roberts NM, Cole SJ, Forbes RM, Moore RJ, Boswell D (2009) Use of high-resolution NWP rainfall and river flow forecasts for advance warning of the Carlisle flood, north-west England. Met Apps 16:23–34

    Article  Google Scholar 

  • Tang YM, Capon R, Forbes R, Clark P (2009) Fog prediction using a very high resolution numerical weather prediction model forced with a single profile. Met Apps 16:129–141

    Article  Google Scholar 

  • Walters DN, Best MJ, Bushell AC, Copsey D, Edwards JM, Falloon PD, Harris CM, Lock AP, Manners JC, Morcrette CJ, Roberts MJ, Stratton RA, Webster S, Wilkinson JM, Willett MR, Boutle IA, Earnshaw PD, Hill PG, MacLachlan C, Martin GM, Moufouma-Okia W, Palmer MD, Petch JC, Rooney GG, Scaife AA, Williams KD (2011) The Met Office Unified Model Global Atmosphere 3.0/3.1 and JULES Global Land 3.0/3.1 configurations. Geosci Model Dev 4:919–941

    Article  Google Scholar 

  • Wilkinson JM, Porson ANF, Bornemann FJ, Weeks M, Field PR, Lock AP (2013) Improved microphysical parametrization of drizzle and fog for operational forecasting using the Met Office Unified Model. Q J R Meteorol Soc 139:488–500

    Article  Google Scholar 

  • Wilson DR, Bushell AC, Kerr-Munslow AM, Price JD, Morcrette CJ (2008) PC2: a prognostic cloud fraction and condensation scheme I: scheme description. Q J R Meteorol Soc 134:2093–2107

    Article  Google Scholar 

  • Wood R (2005) Drizzle in stratiform boundary layer clouds. Part I: vertical and horizontal structure. J Atmos Sci 6:3011–3033. doi:10.1175/JAS3529.1

    Article  Google Scholar 

  • Wood R (2012) REVIEW: stratocumulus clouds. Mon Weather Rev 140:2373–2423

    Article  Google Scholar 

  • Zingerle C, Nurmi P (2008) Monitoring and verifying cloud forecasts originating from operational numerical models. Met Apps 15:325–330

    Article  Google Scholar 

Download references


Thanks go to: University of Leeds (Barbara Brooks) for deploying the microwave radiometer at Weybourne; University of East Anglia (Brian Bandy) for allowing us to use the Weybourne Atmospheric Observatory; Chilbolton Facility for Atmospheric and Radio Research (funded by the Science and Technology Facilities Council) for provision of their remote sensing data. FAAM is jointly funded by the Natural Environment Research Council and the Met Office.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Simon R. Osborne.


Appendix 1: Ground Instrumentation

The \(30\,\hbox {m}^{3}\) helium-filled tethered balloon payload used during COALESC comprised a light-weight turbulence probe and a DMT CDP. The CDP is essentially the same as the aircraft unit but modified to operate at lower air speeds (Price 2011). The turbulence probe is a miniaturized version of that described in Lapworth and Mason (1988). The turbulence probe can provide three-component vector winds up to 20 Hz and this can be combined with its fast-response platinum-resistance thermometer (PRT) to compute heat flux. We use the mean wind speed from the turbulence probe to calculate the CDP sample volume. In addition to the PRT, the turbulence probe also houses a slow-response thermistor and a Vaisala HMP44 humicap, both mounted in an aspirated housing. The CDP and turbulence probe were mounted 3 m apart on the vertical tether beneath the balloon (and 60 m beneath the balloon itself), such that small errors in flow velocity across the CDP sample area are possible. But as the CDP data are averaged over at least 60 s, then flow velocity and time synchronization (\(\approx \)1 s) errors are negligible.

Continuous column-integrated vapour and liquid water monitoring with passive microwave radiometers was established at Weybourne, Denver Sluice in addition to the permanent deployment at Chilbolton. The measured brightness temperatures (at approximately 23 and 32 GHz) are first converted into optical depths and then a regression method is used to convert the optical depths into LWP values. This regression, detailed in Price (2003), is based on LWP values from integrated radiosonde ascents that assume adiabatic ascent. The same retrieval algorithm has been applied to all three radiometer datasets. During the experiments Vaisala RS92 radiosondes were released from Weybourne, Denver and Cardington at 60–90 min intervals. Radiosondes transmit data on pressure, temperature, relative humidity and winds at 0.5 Hz with a 5–6 m height resolution.

There were cloud-base ceilometers installed at Cardington, Denver (both Vaisala LD40) and Chilbolton (Vaisala CT75K). Shortwave (Kipp & Zonen CM22 pyranometers, 0.2–2.8 \(\upmu \)m) and longwave (Kipp & Zonen CG4 pyrgeometer, 4.5–40 \(\upmu \)m) broad-band radiometers are part of the permanent instrumental fits at Cardington and Chilbolton. They measure hemispherical irradiance with a nominal 3 \(\%\) accuracy. At Cardington both downwelling and upwelling irradiances are measured; at Chilbolton only shortwave downwelling and longwave upwelling irradiances are measured. The Chilbolton 35 GHz cloud radar, whereby typical liquid cloud droplets act as Rayleigh scatterers, provides a measure of cloud-top height.

For aerosol measurements on the ground at Cardington, a Grimm 1.109 aerosol monitor sampling at 3.5 m height was operated. This measures across the diameter range 0.25–30 \(\upmu \)m and so covers the accumulation and coarse mode. Its minimum size is similar to that of the aircraft PCASP and so the total concentrations from the two instruments can be compared.

Appendix 2: Aircraft Instrumentation

A wing-mounted CDP was used to count cloud droplets and determine their size for nominal diameters of 2–50 \(\upmu \)m over 30 size channels. The CDP measures the forward scatter (4–12 \(^{\circ }\)) from droplets interacting with 658 nm light from a diode laser. Channel 1 is ignored in the CDP data analysis, such that the minimum size detection was 3.5 \(\upmu \)m diameter. With a fixed laser cross-sectional area (\(0.24\,\hbox {mm}^{2}\)), knowledge of the sample volume is directly related to the air flow velocity.

Liquid water content can be measured by integrating the CDP size distribution but also by using the Nevzorov hot-wire devices. The efficiency of the Nevzorov is reduced in drizzling clouds where drop sizes are in excess of 50 \(\upmu \)m because the drops are too large to be collected and hence completely evaporated by the hot wire. A baseline drift of the instrument also needs to be removed during cloud-free periods due to the effects of pressure, temperature and true air speed. Water vapour mixing ratio on the aircraft on 1, 2 and 16 March was measured with a SpectraSensors Water Vapor Sensing System (WVSS-II) using a technique of absorption spectroscopy. The manufacturer quotes an accuracy of 5 \(\%\). Rosemount/Goodrich 102 total air temperature sensors were used as a measure of air temperature and the true air speed and wind components were derived from a five-port turbulence probe mounted on the nose of the aircraft.

An elastic backscattering lidar with daytime capability, suitable for aerosol and cloud observations, was fitted to the aircraft. It operates at 355 nm, has depolarization, and is mounted for nadir view (Marenco et al. 2011). Clouds are detected using a system of thresholds for the uncalibrated range-corrected signal \(P\), capable to highlight large spikes due to the beam hitting an optically dense target. A cloud top is detected if: \(P(R)> 4000; P(R)> 3P (R\ - 200)\); and also if no other cloud top has been found between (\(R\ - 500\)) and \(R\), where \(R\) is the range from the lidar to the observed layer (in metres). Once a cloud is detected, its top range \(R_{c}\) is set as the first point starting at \(R\) and moving inward to (\(R\ - 200\)), where \(P(R_{c}) < 1.5P (R\ - 200)\). Clouds identified within 500 m of the terrain are automatically discarded as surface return, although this limit was a little too high for some of the very low cloud in Fig. 3.

A DMT passive cavity aerosol spectrometer probe (PCASP) was used to count and size dehydrated aerosol particles out of cloud for diameters 0.2–3 \(\upmu \)m i.e. covers the accumulation mode. The total concentration of the PCASP can be used as a measure of particulate pollution. Because the PCASP covers the accumulation mode that contains the majority of cloud condensation nuclei at supersaturations typically found in stratocumulus, the PCASP concentration measured below cloud can be related to the cloud-droplet concentration (Jones et al. 1994).

Appendix 3: Droplet Generation Schemes

The MURK visibility scheme (Clark et al. 2008) is used as a basis of a formulation to diagnose \(N_\mathrm{d}\); this was implemented in model version PS26, allowing better sensitivity to air mass. This converts aerosol mass (\(m_\mathrm{aer}\)), a forecast product in itself, into aerosol number (\(N_\mathrm{aer}\)) using the method shown in Haywood et al. (2008),

$$\begin{aligned} N_\mathrm{aer} = n_{0}\left( \frac{m_\mathrm{aer}}{m_{0}}\right) ^\rho \end{aligned}$$

where \(n_{0}\), \(m_{0}\) and \(\rho \) are tuneable parameters: \(\rho \) was fixed at 0.5; for PS26–30, \(n_{0} = 2\times 10^{9}\) and \(m_{0} = 1.8956\times 10^{-8}\); for PS31 and later versions \(n_{0} = 5\times 10^{8}\) and \(m_{0} = 1.458\times 10^{-8}\). \(N_\mathrm{aer}\) is then converted into \(N_\mathrm{d}\) using the Jones et al. (1994) curve (which asymptotes to \(375\,\hbox {cm}^{-3}\)) that has been used in the climate version of the MetUM,

$$\begin{aligned} N_\mathrm{d} = 3.75\times 10^{8}\left[ 1.0-\mathrm{exp}(-2.5\times 10^{-9}N_\mathrm{aer})\right] . \end{aligned}$$

In the analysis presented here we have used the PS26 and PS31 formulation of Eqs. 1 and 2 to predict \(N_\mathrm{d}\). We also included a third test method that uses Eq. 2 but an alternative to Eq. 1 called the CLASSIC aerosol scheme used for climate modelling (Bellouin et al. 2011). The CLASSIC scheme assumes a log-normal aerosol size distribution with fixed mean radius (0.11 \(\upmu \)m) and standard deviation (1.35) for a particular aerosol species (in this case the Haywood et al. (2008) VISURB aerosol) so that only \(N_\mathrm{aer}\) increases with increasing \(m_\mathrm{aer}\). This is in contrast to Eq. 1 where the mean radius implicitly increases with \(m_\mathrm{aer}\) (as well as number) because \(\rho \) is fixed. The value of \(\rho \) is that prescribed by Clark et al. (2008) on the basis of observations in Haywood et al. (2008).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Osborne, S.R., Abel, S.J., Boutle, I.A. et al. Evolution of Stratocumulus Over Land: Comparison of Ground and Aircraft Observations with Numerical Weather Prediction Simulations. Boundary-Layer Meteorol 153, 165–193 (2014).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: