Advertisement

Atmospheric and Oceanic Optics

, Volume 30, Issue 5, pp 462–474 | Cite as

Remote sounding and mesoscale synoptic models in studying the urban boundary layer

  • V. P. YushkovEmail author
Remote Sensing of Atmosphere, Hydrosphere, and Underlying Surface
  • 17 Downloads

Abstract

We analyze how remote sounding instruments can help to improve our understanding of the atmospheric boundary layer and how regional synoptic models can be used as a hindcasting tool in studying and perfecting boundary layer models. A method is suggested for estimating the quality of boundary layer reproduction in these models using remote sensing data.

Keywords

remote sounding mesoscale model boundary layer megalopolis urban anomaly hindcasting 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    A. H. Murphy, “Skill scores based on the mean square error and their relationships to the correlation coefficient,” Mon. Weather. Rev. 116 (12), 2417–2424 (1988).ADSCrossRefGoogle Scholar
  2. 2.
    D. R. Stauffer and N. L. Seaman, “Use of four-dimensional data assimilation in a limited-area mesoscale model. Part I: Experiments with synoptic-scale data,” Mon. Weather. Rev. 118 (6), 1250–1277 (1990).ADSCrossRefGoogle Scholar
  3. 3.
    T. R. Oke, Boundary layer climates (Routledge, 2002).Google Scholar
  4. 4.
    F. Chen, H. Kusaka, M. Tewari, J. W. Bao, and H. Hirakuchi, “Utilizing the coupled WRF/LSM/Urban modeling system with detailed urban classification to simulate the urban heat island phenomena over the Greater Houston area,” in Fifth Sympos. Urban Environ. 2004, pp. 9–11.Google Scholar
  5. 5.
    Y. Chen, W. M. Jiang, N. Zhang, X. F. He, and R. W. Zhou, “Numerical simulation of the anthropogenic heat effect on urban boundary layer structure,” Theor. Appl. Climatol. 97 (1–2), 123–134 (2009).ADSCrossRefGoogle Scholar
  6. 6.
    S. H. Lee, S. W. Kim, W. M. Angevine, L. Bianco, S. A. McKeen, C. J. Senff, M. Trainer, S. C. Tucker, and R. J. Zamora, “Evaluation of urban surface parameterizations in the WRF model using measurements during the Texas Air Quality Study 2006 field campaign,” Atmos. Chem. Phys. 11 (5), 2127–2143 (2011).ADSCrossRefGoogle Scholar
  7. 7.
    K. Trusilova, S. Schubert, H. Wouters, B. Fruh, S. Grossman-Clarke, M. Demuzere, and P. Becker, “The urban land use in the COSMO-CLM model: A comparison of three parameterizations for Berlin,” Meteorol. Z. 25 (2), 231–244 (2016).CrossRefGoogle Scholar
  8. 8.
    W. Tao, J. Liu, G. A. Ban-Weiss, D. A. Hauglustaine, L. Zhang, Q. Zhang, and S. Tao, “Effects of urban land expansion on the regional meteorology and air quality of Eastern China,” Atmos. Chem. Phys. 15, 8597–8614 (2015).ADSCrossRefGoogle Scholar
  9. 9.
    E. N. Kadygrov, I. N. Kuznetsova, and G. S. Golitsyn, “Heat island in the boundary air layer over large cities: New results from remote data,” Dokl. Akad. Nauk 385 (4), 541–548 (2002).Google Scholar
  10. 10.
    http://attex.netGoogle Scholar
  11. 11.
    R. D. Kuznetsov, “LATAN-3 sodar for investigation of the atmospheric boundary layer,” Atmos. Ocean. Opt. 20 (8), 684–687 (2007).Google Scholar
  12. 12.
    M. A. Kallistratova, I. V. Petenko, and E. A. Shurygin, “Sodar researches of the wind velocity field in the lower atmosphere,” Izv. Akad. Nauk SSSR, Fiz. Atmos. Okeana 23 (5), 451–461 (1987).Google Scholar
  13. 13.
    I. N. Kuznetsova, E. N. Kadygrov, E. A. Miller, and M. I. Nakhaev, “Characteristics of lowest 600 m atmospheric layer temperature on the basis of MTP-5 profiler data,” Opt. Atmos. Okeana 25 (10), 877–883 (2012).Google Scholar
  14. 14.
    N. L. Byzova, V. N. Ivanov, and M. K. Matskevich, “Measurement of the vortex components in the lower 300-m air layer,” Izv. Akad. Nauk, Fiz. Atmos. Okeana 32 (3), 323–328 (1996).Google Scholar
  15. 15.
    T. N. Palmer, “Predicting uncertainty in forecasts of weather and climate,” Rep. Prog. Phys. 63 (2), 71–116 (2000).ADSCrossRefGoogle Scholar
  16. 16.
    V.P. Yushkov, “A probabilistic description of atmospheric turbulence,” Moscow Univ. Phys. Bull. 68 (4), 330–337 (2013).ADSCrossRefGoogle Scholar
  17. 17.
    M. A. Kallistratova and R. D. Kouznetsov, “Low-level jets in the Moscow region in summer and winter observed with a sodar network,” Bound.-Lay. Meteorol. 143 (1), 159–175 (2012).ADSCrossRefGoogle Scholar
  18. 18.
    V. P. Yushkov, R. D. Kuznetsov, and M. A. Kallistratova, “Mean wind speed profiles in the ai basin of Moscow,” Rus. Meterol. Hydrol. 33 (10), 624–631 (2008).CrossRefGoogle Scholar
  19. 19.
    V. P. Yushkov, “Synoptic fluctuations of wind speed in the atmospheric boundary layer,” Rus. Meterol. Hydrol. 37 (4), 226–234 (2012).CrossRefGoogle Scholar
  20. 20.
    V. P. Yushkov, “Estimation of spatial inhomogeneities of thermal stratification in the boundary layer of the Moscow megalopolis from remote sensing,” Atmos. Ocean. Opt. 29 (1), 56–66 (2016).CrossRefGoogle Scholar
  21. 21.
    V. P. Yushkov, “What can be measured by the temperature profiler?,” Rus. Meterol. Hydrol. 39 (12), 838–846 (2014).CrossRefGoogle Scholar
  22. 22.
    V. P. Yushkov, M. A. Kallistratova, R. D. Kuznetsov, G. A. Kurbatov, and V. F. Kramar, “Experience in measuring the wind-velocity profile in an urban environment with a Doppler sodar,” Izv., Atmos. Ocean. Phys. 43 (2), 168–180 (2007).CrossRefGoogle Scholar
  23. 23.
    G. I. Gorchakov, E. N. Kadygrov, V. E. Kunitsyn, V. I. Zakharov, E. G. Semutnikova, A. V. Karpov, G. A. Kurbatov, and S. I. Sitanskii, “Moscow heat island in blocking anticyclone in summer,” Dokl. Akad. Nauk 456 (5), 591–595 (2014).Google Scholar
  24. 24.
    A. V. Troitskii, “Remote determination of atmospheric temperature from spectral radiometric measurements in the ?=5-mm line,” Radiophys. Quantum Electron. 29 (8), 670–678 (1986).ADSCrossRefGoogle Scholar
  25. 25.
    A. S. Vyazankin, E. N. Kadygrov, N. F. Mazurin, A. V. Troitskii, and G. N. Shur, “Comparison between data of microwave radiometer and high-altitude meteorological mast during measurements of the temperature profile and structure of its inhomogeneities,” Meteorol. Gidrol. No. 3, 34–44 (2001).Google Scholar
  26. 26.
    S. Crewell and U. Lohnert, “Accuracy of boundary layer temperature profiles,” IEEE Trans. Geosci. Remote Sens. 45 (7), 2195–2201 (2007).ADSCrossRefGoogle Scholar
  27. 27.
    M. D. Tsyrulnikov, “Stochastic modelling of model errors: A simulation study,” Q. J. R. Meteorol. Soc. 131 (613), 3345–3371 (2005).ADSCrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2017

Authors and Affiliations

  1. 1.Moscow State UniversityMoscowRussia

Personalised recommendations