Pure and Applied Geophysics

, Volume 176, Issue 4, pp 1807–1826 | Cite as

Evaluation of WRF-simulated multilevel soil moisture, 2-m air temperature, and 2-m relative humidity against in situ observations in India

  • B. Kantha RaoEmail author
  • V. Rakesh


The ability of the Weather Research and Forecasting (WRF) model to simulate multilevel soil moisture (SM), 2-m air temperature (T2m), and 2-m relative humidity (RH2m) was evaluated for five different locations in India. WRF model simulations were carried out for 30 cases during different seasons with two different land surface schemes, viz. Noah and Rapid Update Cycle (RUC). The simulations were compared with in situ observations taken routinely at 30-min time intervals at the five selected locations. Statistical evaluation showed that, although the model could simulate SM reasonably well [with the majority of cases falling in the < 25% relative error (RE) category] at different depths for Delhi (DLH) and Gulbarga (GLB), the model errors were high (with most cases falling in the > 50% RE category) for Almora (ALR), Hyderabad (HYD), and Cochin (CHN). In case of T2m, model errors were high (RE > 15%) over hilly terrain, e.g., at ALR, while errors were relatively lower (RE < 10%) for plane areas such as HYD, GLB, DLH, and CHN. In general, the diurnal variation showed that the model underestimated (overestimated) afternoon temperatures during nonrainy (rainy) days. RH2m was also well simulated by the model at the locations HYD, GLB, and CHN, although it underestimated RH2m during morning hours at the locations ALR and DLH. Overall, the comparison showed that the WRF model could reproduce the near-surface temperature and humidity for plane areas such as HYD, GLB, and CHN reasonably well, but has limitations for complex terrains, e.g., at ALR, and highly polluted cities such as DLH.


Soil moisture 2-m air temperature 2-m relative humidity WRF Land surface models 



The authors are grateful to the CSIR for making available the meteorological observation data. The authors acknowledge the National Center for Atmospheric Research (NCAR) for its support for the WRF modeling system ( and the National Centers for Environmental Prediction (NCEP) for making available the analysis data ( The CSIR 4PI high-performance computing (HPC) facility used for computing is gratefully acknowledged. The authors also acknowledge support and encouragement from the Head CSIR 4PI.

Supplementary material

24_2018_2022_MOESM1_ESM.docx (3.2 mb)
Supplementary material 1 (DOCX 3323 kb)


  1. Attada, R., Kumar, P., & Dasari, H. R. (2018). Assessment of land surface models in a high-resolution atmospheric model during Indian summer monsoon. Pure and Applied Geophysics, 17, 1–26.Google Scholar
  2. Banks, R. F., et al. (2016). Sensitivity of boundary-layer variables to PBL schemes in the WRF model based on surface meteorological observations, lidar, and radiosondes during the HygrA-CD campaign. Atmospheric Research, 176, 185–201.CrossRefGoogle Scholar
  3. Beljaars, A. C. M., et al. (1996). The anomalous rainfall over the United States during July 1993: Sensitivity to land surface parameterization and soil moisture anomalies. Monthly Weather Review, 124(3), 362–383.CrossRefGoogle Scholar
  4. Benjamin, S. G., et al. (2004). Mesoscale weather prediction with the RUC hybrid isentropic–terrain-following coordinate model. Monthly Weather Review, 132(2), 473–494.CrossRefGoogle Scholar
  5. Bhimala, K. R., & Goswami, P. (2015). A comparison of ASCAT soil moisture data with in situ observations over the Indian region: A multiscale analysis. IEEE Transactions on Geoscience and Remote Sensing, 53(10), 5425–5434.CrossRefGoogle Scholar
  6. Chang, H. I., et al. (2009). The role of land surface processes on the mesoscale simulation of the July 26, 2005 heavy rain event over Mumbai, India. Global and Planetary Change, 67(1–2), 87–103.CrossRefGoogle Scholar
  7. Chaurasia, S., Tung, D. T., Thapliyal, P. K., & Joshi, P. C. (2011). Assessment of the AMSR-E soil moisture product over India. International Journal of Remote Sensing, 32(23), 7955–7970.CrossRefGoogle Scholar
  8. Chawla, I., et al. (2018). Assessment of the weather research and forecasting (WRF) model for simulation of extreme rainfall events in the upper Ganga Basin. Hydrology and Earth System Sciences, 22(2), 1095.CrossRefGoogle Scholar
  9. 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.CrossRefGoogle Scholar
  10. Chen, F., et al. (1996). Modeling of land surface evaporation by four schemes and comparison with FIFE observations. Journal of Geophysical Research: Atmospheres, 101(D3), 7251–7268.CrossRefGoogle Scholar
  11. Chen, F., et al. (1997). Impact of atmospheric surface-layer parameterizations in the new land-surface scheme of the NCEP mesoscale Eta model. Boundary-Layer Meteorology, 85(3), 391–421.CrossRefGoogle Scholar
  12. Collow, T. W., Robock, A., & Wu, W. (2014). Influences of soil moisture and vegetation on convective precipitation forecasts over the United States Great Plains. Journal of Geophysical Research: Atmospheres, 119(15), 9338–9358.Google Scholar
  13. Das, S., et al. (2008). Skills of different mesoscale models over Indian region during monsoon season: Forecast errors. Journal of Earth System Science, 117(5), 603–620.CrossRefGoogle Scholar
  14. Dash, S. K., Sahu, D. K., & Sahu, S. C. (2013). Impact of AWS observations in WRF-3DVAR data assimilation system: a case study on abnormal warming condition in Odisha. Natural Hazards, 65(1), 767–798.CrossRefGoogle Scholar
  15. Douville, H. (2010). Relative contribution of soil moisture and snow mass to seasonal climate predictability: A pilot study. Climate Dynamics, 34(6), 797–818.CrossRefGoogle Scholar
  16. Dumedah, G., Walker, J. P., & Merlin, O. (2015). Root-zone soil moisture estimation from assimilation of downscaled soil moisture and ocean salinity data. Advances in Water Resources, 84, 14–22.CrossRefGoogle Scholar
  17. Durre, I., Wallace, J. M., & Lettenmaier, D. P. (2000). Dependence of extreme daily maximum temperatures on antecedent soil moisture in the contiguous United States during summer. Journal of Climate, 13(14), 2641–2651.CrossRefGoogle Scholar
  18. Ek, M. B., 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).Google Scholar
  19. Fennessy, M. J., & Shukla, J. (1999). Impact of initial soil wetness on seasonal atmospheric prediction. Journal of Climate, 12(11), 3167–3180.CrossRefGoogle Scholar
  20. Fischer, E. M., Seneviratne, S. I., Lüthi, D., & Schär, C. (2007). Contribution of land–atmosphere coupling to recent European summer heat waves. Geophysical Research Letters, 34(6). Google Scholar
  21. Goswami, P., et al. (2012). Real-time quantitative rainfall forecasts at hobli-level over Karnataka: evaluation for the winter monsoon 2010. Current Science, 1426–1433.Google Scholar
  22. Goswami, P., & Rakesh, V. (2016). An assessment of optimality of observations in high-resolution weather forecasting. Pure and Applied Geophysics, 173(4), 1359–1377.CrossRefGoogle Scholar
  23. Guo, Z., et al. (2006). GLACE: the global land–atmosphere coupling experiment. Part II: analysis. Journal of Hydrometeorology, 7(4), 611–625.CrossRefGoogle Scholar
  24. Haiyun, B., et al. (2016). Comparison of soil moisture in GLDAS model simulations and in situ observations over the Tibetan Plateau. Journal of Geophysical Research: Atmospheres, 121(6), 2658–2678.Google Scholar
  25. Hanna, S. R., & Yang, R. (2001). Evaluations of mesoscale models’ simulations of near-surface winds, temperature gradients, and mixing depths. Journal of Applied Meteorology, 40(6), 1095–1104.CrossRefGoogle Scholar
  26. Kantharao, B., & Rakesh, V. (2018). Observational evidence for the relationship between spring soil moisture and June rainfall over the Indian region. Theoretical and Applied Climatology, 132(3–4), 835–849.CrossRefGoogle Scholar
  27. Kar, S. C., Mali, P., & Routray, A. (2014). Impact of land surface processes on the South Asian monsoon simulations using WRF modeling system. Pure and Applied Geophysics, 171(9), 2461–2484.CrossRefGoogle Scholar
  28. Koren, V., et al. (1999). A parameterization of snowpack and frozen ground intended for NCEP weather and climate models. Journal of Geophysical Research: Atmospheres, 104(D16), 19569–19585.CrossRefGoogle Scholar
  29. Koster, R. D., et al. (2003). Observational evidence that soil moisture variations affect precipitation. Geophysical Research Letters, 30(5).Google Scholar
  30. Koster, R. D., et al. (2004). Regions of strong coupling between soil moisture and precipitation. Science, 305(5687), 1138–1140.CrossRefGoogle Scholar
  31. Koster, R. D., et al. (2006). GLACE: the global land–atmosphere coupling experiment. Part I: overview. Journal of Hydrometeorology, 7(4), 590–610.CrossRefGoogle Scholar
  32. Kumar, P., et al. (2011). Impact of additional surface observation network on short range weather forecast during summer monsoon 2008 over Indian subcontinent. Journal of Earth System Science, 120(1), 53–64.CrossRefGoogle Scholar
  33. Lee, C. B., et al. (2016). Performance evaluation of four different land surface models in WRF. Asian Journal of Atmospheric Environment, 10(1), 42–50.CrossRefGoogle Scholar
  34. Mahrt, L., & Ek, M. (1984). The influence of atmospheric stability on potential evaporation. Journal of Climate and Applied Meteorology, 23(2), 222–234.CrossRefGoogle Scholar
  35. Mahrt, L., & Pan, H. (1984). A two-layer model of soil hydrology. Boundary-Layer Meteorology, 29(1), 1–20.CrossRefGoogle Scholar
  36. Miao, J. F., et al. (2008). Evaluation of MM5 mesoscale model at local scale for air quality applications over the Swedish west coast: Influence of PBL and LSM parameterizations. Meteorology and Atmospheric Physics, 99(1–2), 77–103.CrossRefGoogle Scholar
  37. Moradkhani, H., Hsu, K. L., Gupta, H., & Sorooshian, S. (2005). Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter. Water Resources Research, 41(5).Google Scholar
  38. Murphy, A. H. (1991). Forecast verification: Its complexity and dimensionality. Monthly Weather Review, 119, 1590–1601.CrossRefGoogle Scholar
  39. Murphy, A. H. (1993). What is a good forecast? An essay on the nature of goodness in weather forecasting. Weather and Forecasting, 8, 281–293.CrossRefGoogle Scholar
  40. Pal, J. S., & Eltahir, E. A. (2001). Pathways relating soil moisture conditions to future summer rainfall within a model of the land–atmosphere system. Journal of Climate, 14(6), 1227–1242.CrossRefGoogle Scholar
  41. Pal, P., Rakesh, V., Singh, R., & Joshi, P. C. (2007). Impact of satellite derived land surface parameters in regional climate simulations over India. Hydrology review (Jalvigyan Sameeksha), 22, 133–156.Google Scholar
  42. Pan, H. L., & Mahrt, L. (1987). Interaction between soil hydrology and boundary-layer development. Boundary-Layer Meteorology, 38(1–2), 185–202.CrossRefGoogle Scholar
  43. Rakesh, V., and Goswami, P. (2011). Impact of background error statistics on forecasting of tropical cyclones over the north Indian Ocean. Journal of Geophysical Research: Atmospheres, 116(D20).Google Scholar
  44. Rakesh, V., & Goswami, P. (2015). Impact of data assimilation on high-resolution rainfall forecasts: A spatial, seasonal and category analysis. Journal of Geophysical Research: Atmospheres, 120, 359–377. Scholar
  45. Rakesh, V., Goswami, P., & Prakash, V. S. (2016). Evaluation of high resolution rainfall forecasts over Karnataka for the 2011 southwest and northeast monsoon seasons. Meteorological Applications, 22(1), 37–47.CrossRefGoogle Scholar
  46. Ratnam, J. V., et al. (2017). Sensitivity of Indian summer monsoon simulation to physical parameterization schemes in the WRF model. Climate Research, 74(1), 43–66.CrossRefGoogle Scholar
  47. Schaake, J. C., et al. (1996). Simple water balance model for estimating runoff at different spatial and temporal scales. Journal of Geophysical Research: Atmospheres, 101(D3), 7461–7475.CrossRefGoogle Scholar
  48. Seneviratne, S. I., Lüthi, D., Litschi, M., & Schär, C. (2006). Land–atmosphere coupling and climate change in Europe. Nature, 443(7108), 205.CrossRefGoogle Scholar
  49. Skamarock, W.C., et al. (2008). A Description of the Advanced Research WRF Version 3, NCAR technical note, NCAR/TN-475 + STR.Google Scholar
  50. Smirnova, T. G., Brown, J. M., & Benjamin, S. G. (1997). Performance of different soil model configurations in simulating ground surface temperature and surface fluxes. Monthly Weather Review, 125(8), 1870–1884.CrossRefGoogle Scholar
  51. Smirnova, T. G., et al. (2000). Parameterization of cold-season processes in the MAPS land-surface scheme. Journal of Geophysical Research: Atmospheres, 105(D3), 4077–4086.CrossRefGoogle Scholar
  52. Smirnova, T. G., et al. (2016). Modifications to the rapid update cycle land surface model (RUC LSM) available in the weather research and forecasting (WRF) model. Monthly Weather Review, 144(5), 1851–1865.CrossRefGoogle Scholar
  53. Song, Z., et al. (2012). Effects of surface tillage regimes on soil moisture and temperature of spring corn farmland in Northeast China. Transactions of the Chinese Society of Agricultural Engineering, 28(16), 108–114.Google Scholar
  54. Srivastava, P. K., et al. (2015). Performance evaluation of WRF-Noah land surface model estimated soil moisture for hydrological application: Synergistic evaluation using SMOS retrieved soil moisture. Journal of Hydrology, 529, 200–212.CrossRefGoogle Scholar
  55. Tomasi, E. L., et al. (2017). Optimization of noah and noah_MP WRF land surface schemes in snow-melting conditions over complex terrain. Monthly Weather Review, 145, 4727–4745.CrossRefGoogle Scholar
  56. Unnikrishnan, C. K., et al. (2016). Impact of high resolution land surface initialization in Indian summer monsoon simulation using a regional climate model. Journal of Earth System Science, 125(4), 677–689.CrossRefGoogle Scholar
  57. Viterbo, P., & Betts, A. K. (1999). Impact on ECMWF forecasts of changes to the albedo of the boreal forests in the presence of snow. Journal of Geophysical Research: Atmospheres, 104(D22), 27803–27810.CrossRefGoogle Scholar
  58. Wilks, D. S. (2006). Statistical methods in the atmospheric sciences. International Geophysics Series (2nd ed., Vol. 91). San Diego: Academic.Google Scholar
  59. Yang, J., et al. (2015). Enhancing hydrologic modelling in the coupled weather research and forecasting–urban modelling system. Boundary-Layer Meteorology, 155(1), 87–109.CrossRefGoogle Scholar
  60. Zeng, X. M., et al. (2015). WRF-simulated sensitivity to land surface schemes in short and medium ranges for a high-temperature event in East China: A comparative study. Journal of Advances in Modeling Earth Systems, 7(3), 1305–1325.CrossRefGoogle Scholar
  61. Zhang, D. L., & Zheng, W. Z. (2004). Diurnal cycles of surface winds and temperatures as simulated by five boundary layer parameterizations. Journal of Applied Meteorology, 43(1), 157–169.CrossRefGoogle Scholar
  62. Zhang, S., Lövdahl, L., Grip, H., Tong, Y., Yang, X., & Wang, Q. (2009). Effects of mulching and catch cropping on soil temperature, soil moisture and wheat yield on the Loess Plateau of China. Soil and Tillage Research, 102(1), 78–86.CrossRefGoogle Scholar
  63. Zhang, H., Pu, Z., & Zhang, X. (2013). Examination of errors in near-surface temperature and wind from WRF numerical simulations in regions of complex terrain. Weather and Forecasting, 28(3), 893–914.CrossRefGoogle Scholar
  64. Zu-Heng, H., et al. (2014). Evaluation of the WRF model with different land surface schemes: A drought event simulation in Southwest China during 2009–10. Atmospheric and Oceanic Science Letters, 7(2), 168–173.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.CSIR Fourth Paradigm Institute (CSIR-4PI)BangaloreIndia

Personalised recommendations