Skip to main content
SpringerLink
Log in

Sensitivity of surface roughness parameters on the simulation of boundary layer winds over a complex terrain site Kaiga in western India

  • Original Paper
  • Published:
Meteorology and Atmospheric Physics Aims and scope Submit manuscript

Abstract

Kaiga, in the Western Ghats region, has a hilly topography and dense forest canopy. In this study, the sensitivity of surface roughness length (\(z_{\text{o}}\)) on the boundary layer winds over Kaiga is investigated using the Weather Research and Forecasting (WRF) model for summer and winter seasons. Observational analysis shows high frequency of calm winds due to the flow blockage by the surrounding hills and surface drag from forest cover in the Kaiga valley. To realistically simulate the surface winds, surface roughness length is estimated using the sonic anemometer data and tower wind speed measurements employing the logarithmic profile relationship under neutral stability conditions for different months. The estimated \(z_{o}\) varies in the range of 0.96–1.84 m in different seasons. To study the effect of forest cover on the wind field, WRF simulations are conducted using various roughness factors within the range of default value for evergreen broadleaf forest in the MODIS land-cover data used in the model and the highest estimated value, i.e., \(z_{\text{o}}\) = 0.5 m, 0.75 m, 1.0 m, 1.25 m, and 1.50 m. Simulations show considerable overestimation of wind speed in control run (\(z_{\text{o}}\)\(z_{o}\) = 0.50 m) and experiments with increased roughness length reduced the bias in the surface wind speed, wind direction, and temperature. On average, the simulated winds are corrected by 2 m/s and 3 m/s for \(z_{\text{o}}\) = 1.0 m and \(z_{\text{o}}\) = 1.50 m, respectively. Increasing the surface roughness length also improved the prediction of the frequency of occurrence of calm winds to some extent. The assimilative capacity of the Kaiga valley atmosphere is evaluated by estimating the ventilation coefficient for the winter and summer seasons. It has been found that the model overestimated the surface winds and thus overpredicted the ventilation coefficient. By modifying the surface roughness length, the overestimation in the ventilation coefficient is corrected to an extent.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Data availability

FNL data were downloaded from https://rda.ucar.edu/datasets/ds083.2/. Apart from that, the datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  • Adiga BB, Hegde MN, Nayak PD, Sundaram M (1997) Meteorological summary report for Kaiga project site for the period from October 1993 to September 1996. BARC/1997/I/003, Bhabha Atomic Research Centre, Mumbai, India

  • Anthes RA (1984) Enhancement of convective precipitation by mesoscale variation in vegetative covering in semiarid regions. J Clim Appl Meteorol 23:541–554

    Article  Google Scholar 

  • Aravind A, Srinivas CV, Shrivastava R, Hegde MN, Seshadri H, Mohapatra DK (2022) Simulation of atmospheric flow field over the complex terrain of Kaiga using WRF: sensitivity to model resolution and PBL physics. Meteorol Atmos Phys 134:13. https://doi.org/10.1007/s00703-021-00848-4

    Article  Google Scholar 

  • Arya SPS (1981) Parameterizing the height of the stable atmospheric boundary layer. J Appl Meteorol 20:1192–1202

    Article  Google Scholar 

  • Arya SP (2001) Introduction to micrometeorology (2nd edition). Academic Press, p 420

    Google Scholar 

  • Beyrich F (1997) Mixing height estimation from sodar data—a critical discussion. Atmos Environ 31(23):3941–3953. https://doi.org/10.1016/S1352-2310(97)00231-8

    Article  Google Scholar 

  • Beyrich F, Richter SH, Weisensee U, Kohsiek W, Lohse H, de Bruin HAR, Foken Th, Göckede M, Berger F, Vogt R, Batchvarova E (2002) Experimental determination of turbulent fluxes over the heterogeneous LITFASS area: Selected results from the LITFASS-98 experiment. Theor Appl Climatol 73:19–34. https://doi.org/10.1007/s00704-002-0691-7

    Article  Google Scholar 

  • Brutsaert W (1982) Evaporation into the atmosphere, theory, history, and applications. D. Reidel, Boston, p 299

    Book  Google Scholar 

  • Businger JA, Wyngaard JC, Izumi Y, Bradley EF (1971) Flux profile relationships in the atmospheric surface layer. J Atmos Sci 28:181–189

    Article  Google Scholar 

  • Campbell PC, Bash JO, Spero TL (2019) Updates to the Noah land surface model in WRF-CMAQ to improve simulated meteorology, air quality, and deposition. J Adv Model Earth Syst 11:231–256. https://doi.org/10.1029/2018MS001422

    Article  Google Scholar 

  • Charney JG (1975) Dynamics of deserts and drought in the Sahel. Q J R Meteorol Soc 101:193–202

    Article  Google Scholar 

  • Charnock H (1955) Wind stress on a water surface. Q J R Meteorol Soc 81(350):639–640. https://doi.org/10.1002/qj.49708135027

    Article  Google Scholar 

  • Chen F, Dudhia J (2001) Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon Weather Rev 129:569–585

    Article  Google Scholar 

  • de Bruin HAR, Moore CJ (1985) Zero-plane displacement and aerodynamic roughness length for tall vegetation, derived from a simple mass conservation hypothesis. Bound-Layer Meteorol 31:39–49

    Article  Google Scholar 

  • Dickinson RE, Kenney PJ (1986) Biosphere-atmosphere transfer scheme (BATS) for the NCAR community climate model. National Centre for Atmospheric Research, Boulder, CO, Tech Note/TN-275+STR

  • Dorman JL, Sellers PJ (1989) A global climatology of albedo, roughness length and stomatal resistance for atmospheric general circulation models as represented by the Simple Biosphere Model (SiB). J Appl Meteorol 28:833–855

    Article  Google Scholar 

  • Dudhia J (1989) Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J Atmos Sci 46:3077–3107

    Article  Google Scholar 

  • Dyer AJ, Hicks BB (1970) Flux-gradient relationships in the constant flux layer. Q J R Meteorol Soc 96:715–721

    Article  Google Scholar 

  • Ek MB, Mitchell KE, Lin Y, Rogers E, Grunmann P, Koren V, Gayno G, Tarpley JD (2003) Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J GeophysRes Atmos 108(D22):8851. https://doi.org/10.1029/2002JD003296

    Article  Google Scholar 

  • Entekhabi D, Asrar GR, Betts AK, Beven KJ, Bras RL, Duffy CJ, Dunne T, Koster RD, Lettenmaier DP, McLaughlin DB, Shuttleworth WJ, van Genuchten MT, Wei M-Y, Wood EF (1999) An agenda for land surface hydrology research and a call for the second international hydrological decade. Bull Am Meteorol Soc 80:2043–2058

    Article  Google Scholar 

  • Gallagher MW, Nemitz E, Dorsey JR, Fowler D, Sutton MA, Flynn M, Duyzer J (2002) Measurements and parameterizations of small aerosol deposition velocities to grassland, arable crops, and forest: influence of surface roughness length on deposition. J Geophys Res Atmos 107(D12):4154

    Article  Google Scholar 

  • Garratt JR (1978) Flux profile relations above tall vegetation. Q J R Meteorol Soc 104:199–211

    Article  Google Scholar 

  • Garratt JR (1982) Surface fluxes and the nocturnal boundary layer height. J Appl Meteorol 21:725–729

    Article  Google Scholar 

  • Garratt JR (1992) The atmospheric boundary layer. University Press, Cambridge

    Google Scholar 

  • Grell GA, Dudhia J, Stauffer DR (1995) A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5). NCAR Tech. Note NCAR/TN-398+STR, p 122

  • Grimmond CSB, Oke TR (1999) Aerodynamic properties of urban areas derived from analysis of surface form. J Appl Meteorol 38(9):1262–1292

    Article  Google Scholar 

  • Han C, Ma Y, Su Z, Chen X, Zhang L, Li M, Sun F (2015) Estimates of effective aerodynamic roughness length over mountainous areas of the Tibetan Plateau. Quart J Royal Meteorol Soc 141:1457–1465

    Article  Google Scholar 

  • Hariprasad KBRR, Srinivas CV, Bagavath Singh A, Vijaya Bhaskara Rao S, Baskaran R, Venkatraman B (2014) Numerical simulation and inter-comparison of boundary layer structure with different PBL schemes in WRF using experimental observations at a tropical site. Atmos Res 145:27–44

    Article  Google Scholar 

  • Hegde MN, Vishnu MS, Ravi PM, Nayak PD, Hegde AG (2011) Studies on the ground level wind distribution at Kaiga site using ultrasonic anemometer. Radiat Prot Environ 34(1):60–62

    Google Scholar 

  • Hong S-Y, Lim J-OJ (2006) The WRF single-moment microphysics scheme (WSM6). J Korean Meteorol Soc 42:129–151

    Google Scholar 

  • Hong S, Lakshmi V, Small EE, Chen F, Tewari M, Manning KW (2009) Effects of vegetation and soil moisture on the simulated land surface processes from the coupled WRF/Noah model. J Geophys Res. https://doi.org/10.1029/2008JD011249

    Article  Google Scholar 

  • Jacobson MZ (2005) Fundamentals of atmospheric modeling. University Press, Cambridge

    Book  Google Scholar 

  • Jacquemin B, Noilhan J (1990) Sensitivity study and validation of a land surface parameterization using the HAPEX-MOBILHY data set. Bound-Layer Meteorol 52:93–134

    Article  Google Scholar 

  • Jancewicz K, Szymanowski M (2017) The relevance of surface roughness data qualities in diagnostic modeling of wind velocity in complex terrain: a case study from the Śnieżnik Massif (SW Poland). Pure Appl Geophys. https://doi.org/10.1007/s00024-016-1297-9

    Article  Google Scholar 

  • Jimenez PA, Dudhia J, Gonzalez-Rouco JF, Navarro J, Montavez JP, Garcia-Bustamante E (2012) A revised scheme for the WRF surface layer formulation. Mon Weather Rev 140:898–918. https://doi.org/10.1175/MWR-D-11-00056.1

    Article  Google Scholar 

  • Kaimal JC, Finnigan JJ (1994) Atmospheric boundary layer flows: Their structure and measurement. Oxford University Press, p 289

    Book  Google Scholar 

  • Kaimal JC, Abshire NL, Chadwick RB, Decker MT, Hooke WH, Kroepfli RA, Neff WD, Pasqualucci F, Hildebrand PH (1982) Estimating the depth of the daytime convective boundary layer. J Appl Meteorol 21:1123–1129

    Article  Google Scholar 

  • Kain JS (2004) The Kain-Fritsch convective parameterization: an update. J Appl Meteorol 43(1):170–181. https://doi.org/10.1175/1520-0450(2004)043%3c0170:TKCPAU%3e2.0.CO;2

    Article  Google Scholar 

  • Kanawade VP, Srivastava AK, Ram K, Asmi E, Vakkari V, Soni VK, Varaprasad V, Sarangi C (2019) What caused severe air pollution episode of November 2016 in New Delhi? Atmos Environ. https://doi.org/10.1016/j.atmosenv.2019.117125

    Article  Google Scholar 

  • Kim EJ, Hong SY (2010) Impact of air-sea interaction on East Asian summer monsoon climate in WRF. J Geophys Res 115:D19118. https://doi.org/10.1029/2009JD013253

    Article  Google Scholar 

  • Krayenhoff ES, Voogt JA (2010) Impacts of urban albedo increase on local air temperature at daily through annual time scales: Model results and synthesis of previous work. J Appl Meteorol Climatol 49:1634–1648

    Article  Google Scholar 

  • Kustas WP, Choudhury BJ, Moran MS, Reginato RJ, Jackson RD, Gay LW, Weaver HL (1989) Determination of sensible heat flux over sparse canopy using thermal infrared data. Agric for Meteorol 44:97–216

    Article  Google Scholar 

  • Leese J, Jackson T, Pitman A, Dirmeyer P (2001) GEWEX/BAHC international workshop on soil moisture monitoring, analysis, and prediction for hydro-meteorological and hydro-climatological applications. Bull Am Meteorol Soc 82:1423–1430

    Article  Google Scholar 

  • Lettau H (1969) Note on aerodynamic roughness-parameter estimation on the basis of roughness element description. J Appl Meteorol 8:828–832

    Article  Google Scholar 

  • Lo AK (1976) An analytical-empirical method for determining the aerodynamic roughness length and zero-plane displacement. Bound-Layer Meteorol 12:141–151

    Article  Google Scholar 

  • Lu L, Liu S, Xu Z, Yang K, Cai X, Jia L, Wang J (2009) The characteristics and parameterization of aerodynamic roughness length over heterogeneous surfaces. Adv Atmos Sci 26:180–190. https://doi.org/10.1007/s00376-009-0180-3

    Article  Google Scholar 

  • Manju N, Balakrishnan R, Mani N (2002) Assimilative capacity and pollutant dispersion studies for the industrial zone of Manali. Atmos Environ 36:3461–3471

    Article  Google Scholar 

  • Menut L, Pérez C, Haustein K, Bessagnet B, Prigent C, Alfaro S (2013) Impact of surface roughness and soil texture on mineral dust emission fluxes modeling. J Geophys Res Atmos 118:6505–6520. https://doi.org/10.1002/jgrd.50313

    Article  Google Scholar 

  • Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the long wave. J Geophys Res 102(D14):16663–16682

    Article  Google Scholar 

  • Murty BP, Tangirala RS (1990) An assessment of the assimilative capacity of the atmosphere at Delhi. Atmos Environ Part A Gen Top 24:845–848. https://doi.org/10.1016/0960-1686(90)90285-U

    Article  Google Scholar 

  • Nakanishi M, Niino H (2004) An improved Mellor-Yamada level-3 model with condensation physics: Its design and verification. Bound-Layer Meteorol 112:1–31

    Article  Google Scholar 

  • Nelli NR, Temimi M, Fonseca RM, Weston MJ, Thota MS, Valappil VK, Branch O, Wulfmeyer V, Wehbe Y, Al Hosary T, Shalaby A, Al Shamsi N, Al Naqbi H (2020) Impact of roughness length on WRF simulated Land-Atmosphere interactions over a hyper-arid region. J Earth Space Sci. https://doi.org/10.1029/2020EA001165

    Article  Google Scholar 

  • Panofsky H, Dutton J (1984) Atmospheric turbulence. Wiley, New York, p 397

    Google Scholar 

  • Pena DA, Gryning S-E, Mann J (2010) On the length-scale of the wind profile. Q J R Meteorol Soc 136(653):2119–2131. https://doi.org/10.1002/qj.714

    Article  Google Scholar 

  • Rama Krishna TVBPS, Reddy MK, Reddy RC, Singh RN (2004) Assimilative capacity and dispersion of pollutants due to industrial sources in Visakhapatnam bowl area. Atmos Environ 38:6775–6787

    Article  Google Scholar 

  • Rao KG (1996) Roughness length and drag coefficient at two MONTBLEX-90 tower stations. Proc Indian Acad Sci Earth Planet Sci 105(3):273–287. https://doi.org/10.1007/BF02841883

    Article  Google Scholar 

  • Rao KG (2008a) PRWONAM—an innovative approach to accurate mesoscale weather prediction for southern peninsula monogram. Indian Space Research Organisation, Bangalore, p 76

    Google Scholar 

  • Rao KG (2008b) PRWONAM for mesoscale research in India and predictions over Shar–Kalpakkam–Bangalore region. In: Manikiam B, Murthy TGK (eds) Technology development for atmospheric research and applications, Indian Space Research Organisation, New BEL Road, Bangalore 560012. ISRO Publication, pp 387–462

    Google Scholar 

  • Rao KG, Reddy NN (2018) Surface layer structure for ten categories of land surfaces of the Indian region with instrumented mini boundary layer mast network (MBLM-Net) establishment during PRWONAM. J Atmos Solar Terr Phys 173:66–95. https://doi.org/10.1016/j.jastp.2018.03.014

    Article  Google Scholar 

  • Rao KG, Ramakrishna G, Reddy NN (2011) Impact of meso-net observations on short-term prediction of intense weather systems during PRWONAM: Part I—on wind variations. J Atmos Sol Terr Phys 73:965–985. https://doi.org/10.1016/j.jastp.2010.08.019

    Article  Google Scholar 

  • Rao KG, Muhsin M, Reddy NN, Rao TN, Kumar M, Ananth AG, Ghosh A, Dutta G, Reddy KK, Emperumal K, Ramgopal K, Murali S, Singh VC, Kundu SS, Bopanna MB (2012) Characterization of surface layer at 14 locations differing in land surface patterns with measurements from instrumented Mini Boundary Layer Mast Network (MBLM-NET) establishment during PRWONAM. Indian Space Research Organisation, Bangalore, Scientific Report, ISRO-sr:02 p 128

  • Reddy NN, Rao KG (2016) Roughness lengths at four stations within the micrometeorological network over the Indian monsoon region. Bound-Layer Meteorology 158(1):151–164. https://doi.org/10.1007/s10546-015-0080-2

    Article  Google Scholar 

  • Reijmer CH, Van Meijgaard E, Van Den Broeke MR (2004) Numerical studies with a regional atmospheric climate model based on changes in the roughness length for momentum and heat over Antarctica. Bound-Layer Meteorol 111(2):313–337. https://doi.org/10.1023/b:boun.0000016470.23403.ca

    Article  Google Scholar 

  • Richter H, Western A, Chiew F (2004) The effect of soil and vegetation parameters in the ECMWF land surface scheme. J Hydrometeorol 5:1131–1146

    Article  Google Scholar 

  • Shin HH, Hong SY (2011) Intercomparison of planetary boundary-layer parameterizations in the WRF model for a single day from CASES-99. Bound-Layer Meteorol 139:261–281

    Article  Google Scholar 

  • Shukla J, Mintz Y (1982) Influence of land-surface evapotranspiration on the Earth’s climate. Science 215(4539):498–1501. https://doi.org/10.1126/science.215.4539.1498

    Article  Google Scholar 

  • Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Duda MG, Huang X-Y, Wang W, Powers JG (2008) A description of the advanced research WRF version 3. NCAR Technical Note NCAR/TN-475+STR. pp 113. Available online at, Access date is 29 July 2013 www.mmm.ucar.edu/wrf/users/docs/arw_v3_bw.pdf

  • Srinivas CV, Nagaraju C, Venkatesan R, Rao KG, Venkatraman B (2011) Impact of satellite derived vegetation and field soil observations on mesoscale atmospheric model simulations over tropical Indian region during PRWONAM. Asian J Geoinform 11(2):1–23

    Google Scholar 

  • Steeneveld GJ, Mauritsen T, DeBruijn EIF, De Arellano JVG, Svensson G, Holtslag AAM (2008) Evaluation of limited-area models for the representation of the diurnal cycle and contrasting nights in CASES-99. J Appl Meteorol Climatol 47:869–887

    Article  Google Scholar 

  • Stull RB (1988) An introduction to boundary layer meteorology. Kluwer Academic Publishers, Dordrecht, p 666

    Book  Google Scholar 

  • Sud YC, Fennessy MJ (1982) A Study of the influence of surface albedo on July circulation in semi-arid regions using the GLAS GCM. J Climatol 2:105–125

    Article  Google Scholar 

  • Sud YC, Smith WE (1985) The influence of surface roughness of deserts on the July circulation—a numerical study. Bound-Layer Meteorol 33:15–49

    Article  Google Scholar 

  • Sud YC, Shukla J, Mintz Y (1988) Influence of land surface roughness on atmospheric circulation and precipitation: a sensitivity study with a general circulation model. J Appl Meteorol 27:1036–1054

    Article  Google Scholar 

  • Tandon A, Yadav S, Attri AK (2010) Coupling between meteorological factors and ambient aerosol load. Atmos Environ 44:1237–1243

    Article  Google Scholar 

  • Varquez ACG, Nakayoshi M, Kanda M (2015) The effects of highly detailed urban roughness parameters on a sea-breeze numerical simulation. Bound-Layer Meteorol 154(3):449–469. https://doi.org/10.1007/s10546-014-9985-4

    Article  Google Scholar 

  • Vautard R, Cattiaux J, Yiou P, Thepaut JN, Ciais P (2010) Northern Hemisphere atmospheric stilling partly attributed to an increase in surface roughness. Nat Geosci 3:756–761. https://doi.org/10.1038/ngeo979

    Article  Google Scholar 

  • Vittal Murty KPR, Viswanadham DV, Sadhuram Y (1980) Mixing heights and ventilation coefficient for urban centres in India. Bound-Layer Meteorol 19:441–451. https://doi.org/10.1007/BF00122344

    Article  Google Scholar 

  • Wagner P, Schaefer K (2017) Influence of mixing layer height on air pollutant concentrations in an urban street canyon. Urban Clim 22:64–79

    Article  Google Scholar 

  • Wallace JM, Hobbs PV (2006) Atmospheric science: an introductory survey, 2nd edn. Academic Press, New York

    Google Scholar 

  • Wever N (2012) Quantifying trends in surface roughness and the effect on surface wind speed observations. J Geophys Res 117:D11104. https://doi.org/10.1029/2011JD017118

    Article  Google Scholar 

  • Zhu X, Tang G, Guo J, Hu B, Song T, Wang L, Xin J, Gao W, Münkel C, Schäfer K, Li X, Wang Y (2018) Mixing layer height on the North China Plain and meteorological evidence of serious air pollution in southern Hebei. Atmos Chem Phys 18:4897–4910. https://doi.org/10.5194/acp-18-4897-2018

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Homi Bhabha National Institute, Training School Complex, Anushaktinagar, 400094, Mumbai, India

    Arun Aravind & C. V. Srinivas

  2. Environmental Assessment Division, Indira Gandhi Centre for Atomic Research, Kalpakkam, 603102, India

    C. V. Srinivas

  3. Environmental Survey Laboratory, Kaiga, Karnataka, 581400, Karwar, India

    M. N. Hegde

  4. Safety Research Institute, Atomic Energy Regulatory Board, Kalpakkam, 603102, India

    Arun Aravind, H. Seshadri & D. K. Mohapatra

Authors
  1. Arun Aravind
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. V. Srinivas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. N. Hegde
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. H. Seshadri
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. D. K. Mohapatra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arun Aravind.

Additional information

Responsible Editor: Silvia Trini Castelli.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aravind, A., Srinivas, C.V., Hegde, M.N. et al. Sensitivity of surface roughness parameters on the simulation of boundary layer winds over a complex terrain site Kaiga in western India. Meteorol Atmos Phys 134, 71 (2022). https://doi.org/10.1007/s00703-022-00912-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00703-022-00912-7

Search

Navigation