Atmospheric models are known to underestimate land surface temperature and, by association, 2 m air temperature over dry arid regions during the day due to the treatment of the thermal roughness length also known as roughness length of heat. The thermal roughness length can be controlled by the Zilitinkevich parameter, known as Czil, which is a tunable parameter within the models. Three different scenarios with the WRF model are run to test the impact of the Czil parameter on the simulations using two land surface models: the Noah and Noah-MP models. In this study, a modified version of the Noah-MP model is tested, in which the Czil parameter, and, therefore, the thermal roughness length varies depending on the land cover and vegetation height. The model domain is over the United Arab Emirates (UAE) where the major land cover type is desert. The following configurations are tested: the Noah model with Czil = 0.1, Noah model with Czil = 0.5 and the Noah-MP model with Czil = 0.5 over desert. Results of 2 m air temperature are verified against three stations in the UAE. Mean gross error of the diurnal 2 m temperature was reduced by up to 1.48 and 1.54 °C in the 24 and 48 h forecasts, respectively. This reduced the cold bias in the model. This improvement in air temperature showed to improve the diurnal cycle of relative humidity at the three monitoring stations as well as the duration of the sea breeze in some cases.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Abbs, D. J., & Physick, W. (1992). Sea-breeze observations and modelling: A review. Australian Meteorological Magazine, 41, 7–19.
Ajjaji, R., Al-katheri, A. A., & Khaled, A. L. (2008). Evaluation of United Arab Emirates WRF two-way nested model on a set of thick coastal fog situations. United Nations: United Nations Development Programme, Air Force and Air Defense Meteorological Department.
Bang, C.-H., Lee, J.-W., & Hong, S.-Y. (2008). Predictability experiments of fog and visibility in local airports over Korea using the WRF model. Journal of Korean Society for Atmospheric Environment, 24(E2), 92–101.
Bartok, J., Bott, A., & Gera, M. (2012). Fog prediction for road traffic safety in a coastal desert region. Boundary Layer Meteorology, 145(3), 485–506.
Bartoková, I., Bott, A., Bartok, J., & Gera, M. (2015). Fog prediction for road traffic safety in a coastal desert region: Improvement of nowcasting skills by the machine-learning approach. Boundary Layer Meteorology, 157(3), 501–516.
Bastidas, L. A., Hogue, T. S., Sorooshian, S., Gupta, H. V., & Shuttleworth, W. J. (2006). Parameter sensitivity analysis for different complexity land surface models using multicriteria methods. Journal of Geophysical Research Atmospheres, 111, D20.
Braud, I., Noilhan, J., Bessemoulin, P., Mascart, P., Haverkamp, R., & Vauclin, M. (1993). Bare-ground surface heat and water exchanges under dry conditions: Observations and parameterization. Boundary Layer Meteorology, 66(1), 173–200.
Carslaw, D. C., & Ropkins, K. (2012). Openair—an R package for air quality data analysis. Environmental Modelling and Software, 27–28, 52–61. https://doi.org/10.1016/j.envsoft.2011.09.008.
Chaouch, N., Temimi, M., Weston, M., & Ghedira, H. (2017). Sensitivity of the meteorological model WRF-ARW to planetary boundary layer schemes during fog conditions in a coastal arid region. Atmospheric Research, 187, 106–127.
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. Monthly Weather Review, 129(4), 569–585.
Chen, F., Janjić, Z., & Mitchell, K. (1997). Impact of atmospheric surface-layer parameterizations in the new land-surface Scheme of the NCEP mesoscale eta model. Boundary Layer Meteorology, 85, 391–421.
Chen, Y., Yang, K., He, J., Qin, J., Shi, J., Du, J., et al. (2011). Improving land surface temperature modeling for dry land of China. Journal of Geophysical Research Atmospheres, 116, D20.
Chen, Y., Yang, K., Zhou, D., Qin, J., & Guo, X. (2010). Improving the Noah land surface model in arid regions with an appropriate parameterization of the thermal roughness length. Journal of Hydrometeorology, 11(4), 995–1006.
Chen, F., & Zhang, Y. (2009). On the coupling strength between the land surface and the atmosphere: From viewpoint of surface exchange coefficients. Geophysical Research Letters, 36, 10.
De Villiers, M. P., & Van Heerden, J. (2007). Fog at Abu Dhabi international airport. Weather, 62(8), 209–214.
Ek, M. B., Mitchell, K. E., Lin, Y., Rogers, E., Grunmann, P., Koren, V., et al. (2003). Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. Journal of Geophysical Research Atmospheres, 108, D22.
Fares, A., Temimi, M., Morgan, K., & Kelleners, T. J. (2013). In-situ and remote soil moisture sensing technologies for vadose zone hydrology. Vadose Zone Journal, 12(2), vzj2013.03.0058. https://doi.org/10.2136/vzj2013.03.0058.
Gultepe, I., Müller, M. D., & Boybeyi, Z. (2006). A new visibility parameterization for warm-fog applications in numerical weather prediction models. Journal of Applied Meteorology and Climatology, 45(11), 1469–1480. https://doi.org/10.1175/jam2423.1.
Gultepe, I., Pearson, G., Milbrandt, J. A., Hansen, B., Platnick, S., Taylor, P., et al. (2009). The fog remote sensing and modeling field project. Bulletin of the American Meteorological Society, 90(3), 341–360. https://doi.org/10.1175/2008bams2354.1.
Gultepe, I., Tardif, R., Michaelides, S. C., Cermak, J., Bott, A., Bendix, J., et al. (2007). Fog research: A review of past achievements and future perspectives. Pure and Applied Geophysics, 164(6), 1121–1159. https://doi.org/10.1007/s00024-007-0211-x.
Harris, I., Jones, P. D., Osborn, T. J., & Lister, D. H. (2014). Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 Dataset. International Journal of Climatology, 34(3), 623–642. https://doi.org/10.1002/joc.3711.
Hogue, T. S., Bastidas, L. A., Gupta, H. V., & Sorooshian, S. (2006). Evaluating model performance and parameter behavior for varying levels of land surface model complexity. Water Resources Research, 42, 8.
Hogue, T. S., Bastidas, L., Gupta, H., Sorooshian, S., Mitchell, K., & Emmerich, W. (2005). Evaluation and transferability of the Noah land surface model in semiarid environments. Journal of Hydrometeorology, 6(1), 68–84.
Hong, S.-Y., Dudhia, J., & Chen, S.-H. (2004). A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Monthly Weather Review, 132(1), 103–120.
Huang, X., Wang, W., & Powers, J. (2008). A description of the advanced research WRF version 3. NCAR technical note, NCAR, BoulderSpeer M, Wiles P, Pepler A (2009).
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., & Collins, W. D. (2008). Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res, 113, D13.
Kain, J. S. (2004). The Kain–Fritsch convective parameterization: An update. Journal of Applied Meteorology, 43(1), 170–181.
Kain, J. S., & Fritsch, J. M. (1990). A one-dimensional entraining/detraining plume model and its application in convective parameterization. Journal of the Atmospheric Sciences, 47(23), 2784–2802.
LeMone, M. A., Tewari, M., Chen, F., Alfieri, J. G., & Niyogi, D. (2008). Evaluation of the Noah land surface model using data from a fair-weather IHOP_2002 day with heterogeneous surface fluxes. Monthly Weather Review, 136(12), 4915–4941.
Liang, X., Wood, E. F., Lettenmaier, D. P., Lohmann, D., Boone, A., Chang, S., et al. (1998). The Project for Intercomparison of Land-surface Parameterization Schemes (PILPS) phase 2 (c) Red-Arkansas River basin experiment: 2. Spatial and temporal analysis of energy fluxes. Global and Planetary Change, 19(1), 137–159.
Mitchell, K., Ek, M., Wong, V., Lohmann, D., Koren, V., Schaake, J., et al. (2005) ‘Noah Land Surface Model (LSM) User’s Guide’ NCAR Research Application Laboratory (RAL). pp. 1–26.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., & Clough, S. A. (1997). Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. Journal of Geophysical Research Atmospheres, 102(D14), 16663–16682.
Niu, G. Y., Yang, Z. L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., et al. (2011). The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements. Journal of Geophysical Research Atmospheres, 116(12), 1–19.
Norouzi, H., Temimi, M., Prigent, C., Turk, J., Khanbilvardi, R., Tian, Y., et al. (2015). Assessment of the consistency among global microwave land surface emissivity products. Atmospheric Measurement Techniques, 8(3), 1197–1205. https://doi.org/10.5194/amt-8-1197-2015.
Pitman, A. J. (2003). The evolution of, and revolution in, land surface schemes designed for climate models. International Journal of Climatology, 23(5), 479–510.
Pitman, A. J., Henderson-Sellers, A., Desborough, C. E., Yang, Z.-L., Abramopoulos, F., Boone, A., et al. (1999). Key results and implications from phase 1 (c) of the project for intercomparison of land-surface parametrization schemes. Climate Dynamics, 15(9), 673–684.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G., et al. (2008). A description of the advanced research WRF version 3. Boulder: NCAR.
Sukoriansky, S., Galperin, B., & Perov, V. (2005). Application of a new spectral theory of stably stratified turbulence to the atmospheric boundary layer over sea ice. Boundary Layer Meteorology, 117(2), 231–257.
Tewari, M., Chen, F., Wang, W., Dudhia, J., LeMone, M. A., Mitchell, K., et al. (2004). Implementation and verification of the unified NOAH land surface model in the WRF model. 20th conference on weather analysis and forecasting/16th conference on numerical weather prediction.
Timbal, B., & Henderson-Sellers, A. (1998). Intercomparisons of land-surface parameterizations coupled to a limited area forecast model. Global and Planetary Change, 19(1), 247–260.
Wang, W., Bruyère, C., Duda, M., Dudhia, J., Gill, D., Lin, H. C., et al. (2009). Advanced research WRF (ARW) version 3 modeling users guide, mesoscale & microscale meteorology division. National Center for Atmospheric Research (NCAR), USA. http://www.mmm.ucar.edu.ar/wrf/users/docs/arw_v3.pdf.
Wei, H., Xia, Y., Mitchell, K. E., & Ek, M. B. (2013). Improvement of the Noah land surface model for warm season processes: Evaluation of water and energy flux simulation. Hydrological Processes, 27(2), 297–303.
Wood, E. F., Lettenmaier, D. P., Liang, X., Lohmann, D., Boone, A., Chang, S., et al. (1998). The project for intercomparison of land-surface parameterization schemes (PILPS) Phase 2 (c) Red–Arkansas River basin experiment: 1. Experiment description and summary intercomparisons. Global and Planetary Change, 19(1), 115–135.
Yan, B., & Weng, F. (2011). Effects of microwave desert surface emissivity on AMSU-A data assimilation. IEEE Transactions on Geoscience and Remote Sensing, 49(4), 1263–1276. https://doi.org/10.1109/TGRS.2010.2091508.
Yang, K., Koike, T., Ishikawa, H., Kim, J., Li, X., Liu, H., et al. (2008). Turbulent flux transfer over bare-soil surfaces: Characteristics and parameterization. Journal of Applied Meteorology and Climatology, 47(1), 276–290.
Zheng, W., Wei, H., Wang, Z., Zeng, X., Meng, J., Ek, M., et al. (2012). Journal of Geophysical Research Atmospheres, 117, D6.
Zilitinkevich, S. S. (1995). Air Pollution III—volume I Air pollution theory and simulation: Pollution dispersion aspects of coherent structure of convective flows. Boston: Computational Mechanics Publ.
About this article
Cite this article
Weston, M., Chaouch, N., Valappil, V. et al. Assessment of the Sensitivity to the Thermal Roughness Length in Noah and Noah-MP Land Surface Model Using WRF in an Arid Region. Pure Appl. Geophys. 176, 2121–2137 (2019). https://doi.org/10.1007/s00024-018-1901-2
- roughness length
- 2 m temperature