Skip to main content
Log in

An Optimal Inverse Method Using Doppler Lidar Measurements to Estimate the Surface Sensible Heat Flux

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

Abstract

Inverse methods are widely used in various fields of atmospheric science. However, such methods are not commonly used within the boundary-layer community, where robust observations of surface fluxes are a particular concern. We present a new technique for deriving surface sensible heat fluxes from boundary-layer turbulence observations using an inverse method. Doppler lidar observations of vertical velocity variance are combined with two well-known mixed-layer scaling forward models for a convective boundary layer (CBL). The inverse method is validated using large-eddy simulations of a CBL with increasing wind speed. The majority of the estimated heat fluxes agree within error with the proscribed heat flux, across all wind speeds tested. The method is then applied to Doppler lidar data from the Chilbolton Observatory, UK. Heat fluxes are compared with those from a mast-mounted sonic anemometer. Errors in estimated heat fluxes are on average 18 %, an improvement on previous techniques. However, a significant negative bias is observed (on average \(-63\,\%\)) that is more pronounced in the morning. Results are improved for the fully-developed CBL later in the day, which suggests that the bias is largely related to the choice of forward model, which is kept deliberately simple for this study. Overall, the inverse method provided reasonable flux estimates for the simple case of a CBL. Results shown here demonstrate that this method has promise in utilizing ground-based remote sensing to derive surface fluxes. Extension of the method is relatively straight-forward, and could include more complex forward models, or other measurements.

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

Access this article

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

Similar content being viewed by others

References

  • Angevine WM, Doviak RJ, Sorbjan Z (1994) Remote sensing of vertical velocity variance and surface heat flux in a convective boundary layer. J Appl Meteorol 33:977–983

    Article  Google Scholar 

  • Bannister R (2003) The method of least squares to invert an orbit problem. Am J Phys 71:1268–1275

    Article  Google Scholar 

  • Barlow JF, Dunbar TM, Neimitz EG, Wood CR, Gallagher MW, Davies F, O’Connor E, Harrison RM (2011) Boundary layer dynamics over London, UK as observed using Doppler lidar during Repartee-II. Atmos Chem Phys 11:2111–2125

    Article  Google Scholar 

  • Beare RJ (2008) The role of shear in the morning transition boundary layer. Boundary-Layer Meteorol 129:395–410

    Article  Google Scholar 

  • Bouniol D, Illingworth A, Hogan R (2004) Deriving turbulent kinetic energy dissipation rate within clouds using ground based radar. In: Proceedings of ERAD, pp 281–285

  • Chai T, Lin CL (2004) Retrieval of microscale flow structures from high-resolution Doppler lidar data using an adjoint model. J Atmos Sci 61:1500–1520

    Article  Google Scholar 

  • Cleugh HA, Grimmond CSB (2001) Modelling regional scale surface energy exchanges and CBL growth in a heterogeneous, urban–rural landscape. Boundary-Layer Meteorol 98:1–31

    Article  Google Scholar 

  • Davis JC, Collier CG, Davies F, Bozier KE (2008) Spatial variations of sensible heat flux over an urban area measured using Doppler lidar. Meteorol Appl 15:367–380

    Article  Google Scholar 

  • Deardorff JW (1970) Convective velocity and temperature scales for the unstable planetary boundary layer and for Rayleigh convection. J Atmos Sci 27:1211–1213

    Article  Google Scholar 

  • Drennan WD, Zhang JA, French JR, McCormick C, Black PG (2007) Turbulent fluxes in the hurricane boundary layer. Part II: latent heat flux. J Atmos Sci 64:1103–1115

    Article  Google Scholar 

  • Engelbart DAM, Kallistratova M, Kouznetsov R (2007) Determination of the turbulent fluxes of heat and momentum in the ABL by ground-based remote-sensing techniques (a review). Meteorol Z 16:326–335

    Article  Google Scholar 

  • Gal-Chen T, Xu M (1992) Estimations of atmospheric boundary-layer fluxes and other turbulence parameters from Doppler lidar data. J Geophys Res 97:409–423

    Article  Google Scholar 

  • Hogan R (2007) A variational scheme for retrieving rainfall rate and hail reflectivity fraction from polarization radar. J Appl Meteorol 46:1544–1564

    Google Scholar 

  • Hogan RJ, Grant ALM, Illingworth AJ, Pearson GN, O’Connor EJ (2008) Vertical velocity variance and skewness in clear and cloud-topped boundary layers as revealed by Doppler lidar. Q J R Meteorol Soc 135:635–643

    Article  Google Scholar 

  • Kaimal JC, Wyngaard JC, Haugen DA, Cote OR, Izumi Y, Caughey SJ, Readings CJ (1976) Turbulence structure in the convective boundary layer. J Atmos Sci 33:2152–2169

    Article  Google Scholar 

  • Lenschow DH, Wulfmeyer V (2000) Measuring second through fourth order moments in noisy data. J Atmos Ocean Technol 17:1330–1347

    Article  Google Scholar 

  • Lenschow DH, Wyngaard JC, Pennell WT (1980) Mean-field and second-moment budgets in a Baroclinic, convective boundary layer. J Atmos Sci 37:1313–1326

    Article  Google Scholar 

  • Lenschow DH, Mann J, Kristensen L (1994) How long is long enough when measuring fluxes and other turbulence statistics? J Atmos Ocean Technol 11:661–673

    Article  Google Scholar 

  • Lenschow DH, Lothon M, Mayor SD, Sullivan PP, Canut G (2012) A comparison of higher-order vertical velocity moments in the convective boundary layer from lidar with in situ measurements and LES. Boundary-Layer Meteorol 143:107–123

    Article  Google Scholar 

  • Lorenc AC (1986) Analysis methods for numerical weather prediction. Q J R Meteorol Soc 112:1177–1194

    Article  Google Scholar 

  • Newsom RK, Banta RM (2004) Assimilating coherent Doppler lidar measurements into a model of the atmospheric boundary layer. Part I: algorithm development and sensitivity to measurement error. J Atmos Ocean Technol 21:1328–1345

    Article  Google Scholar 

  • O’Connor EJ, Illingworth AJ, Brooks IM, Westbrook CD, Hogan RJ, Davies F, Brooks BJ (2010) A method for estimating the turbulent kinetic energy dissipation rate from a vertically-pointing Doppler lidar and independent evaluation from balloon-borne in-situ measurements. J Atmos Ocean Technol 27:1652–1664

    Article  Google Scholar 

  • Pearson G, Davies F, Collier C (2009) An analysis of the UFAM pulsed Doppler lidar for observing the boundary layer. J Atmos Ocean Technol 26:240–250

    Article  Google Scholar 

  • Rodgers CD (2000) Inverse methods for atmospheric sounding. World Scientific Publishing, London, pp i–xvi

  • Roth M (2000) Review of atmospheric turbulence over cities. Q J R Meteorol Soc 26:941–990

    Article  Google Scholar 

  • Rudd AC, Robins AG, Lepley JJ, Belcher SE (2011) An inverse method for determining source characteristics for emergency response applications. Boundary-Layer Meteorol 144:1–20

    Article  Google Scholar 

  • Rye BJ, Hardesty RM (1993) Discrete spectral peak estimation in incoherent backscatter heterodyne lidar. II: correlogram accumulation. IEEE Trans Geosci Remote Sens 31:28–35

    Article  Google Scholar 

  • Shutts GJ, Gray MEB (1994) A numerical modelling study of the geostrophic adjustment process following deep convection. Q J R Meteorol Soc 120:1145–1178

    Google Scholar 

  • Sorbjan Z (1988) Local similarity in the convective boundary layer. Boundary-Layer Meteorol 45:237–250

    Article  Google Scholar 

  • Sorbjan Z (1990) Similarity scales and universal profiles of statistical moments in the convective boundary layer. J Appl Meteorol 29:762–775

    Article  Google Scholar 

  • Sorbjan Z (1991) Evaluation of local similarity functions in the convective boundary layer. J Appl Meteorol 30:1565–1583

    Article  Google Scholar 

  • Sullivan PP, Patton EG (2011) The effect of mesh resolution on convective boundary layer statistics and structures generated by large-eddy simulation. J Atmos Sci 68:8995–9005

    Article  Google Scholar 

  • Willmott CJ, Ackleson SG, Davis RE, Feddema JJ, Klink KM, Legates DR, O’Donnell J, Rowe CM (1985) Statistics for the evaluation and comparison of models. J Geophys Res 90:2395–2415

    Article  Google Scholar 

  • Young GS (1988) Turbulence structure of the convective boundary layer. Part I: variability of normalized turbulence statistics. J Atmos Sci 45:719–726

    Article  Google Scholar 

Download references

Acknowledgments

The authors wish to thank Alan Grant for running the LEM to provide CBL simulations, and guidance in interpretation of the results. T. Dunbar was funded through a Natural Environment Research Council Grant Reference Number NE/F00706X/1.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to J. F. Barlow.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Dunbar, T.M., Barlow, J.F. & Belcher, S.E. An Optimal Inverse Method Using Doppler Lidar Measurements to Estimate the Surface Sensible Heat Flux. Boundary-Layer Meteorol 150, 49–67 (2014). https://doi.org/10.1007/s10546-013-9858-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10546-013-9858-2

Keywords

Navigation