Pure and Applied Geophysics

, Volume 176, Issue 1, pp 389–407 | Cite as

Performance Evaluation of High-Resolution Land Data Assimilation System (HRLDAS) Over Indian Region

  • H. P. Nayak
  • Palash Sinha
  • A. N. V. Satyanarayana
  • A. Bhattacharya
  • U. C. MohantyEmail author


The present study evaluates the skill of a High-Resolution Land Data Assimilation System (HRLDAS) in simulating soil moisture (SM), soil temperature (ST) and sensible heat flux (SHF) for the Indian region (5°–39°N; 60°–100°E). The HRLDAS framework uses uncoupled Noah Land Surface Model (LSM) that integrates near-surface atmospheric parameters and land surface parameters from observations and analysis for the period January 2001–October 2013 at 20 km spatial resolution. The HRLDAS takes about 1 year to reach its quasi-equilibrium state for clay soil. The HRLDAS simulated ST and SM reasonably agree with the in situ observations. The simulated ST shows a negative bias in the monsoon season over the Gujarat, Mandla, and Kharagpur. The SM is under-estimated and the under-estimation increases with soil depth at Kharagpur, India. The negative bias in TRMM precipitation forcing causes under-estimation of SM. The simulated SM shown higher saturation point than observations. The daytime SHF has positive bias during the pre-monsoon, monsoon seasons and agrees well with observations in the post-monsoon season at Ranchi, India. The Noah 1D sensitivity experiments revealed that there is a need to revisit soil field capacity and porosity parameter for improving the skill of the HRLDAS.


Land data assimilation soil moisture soil temperature sensible heat flux 



The Indian Space Research Organization (ISRO) and Ministry of Earth Sciences (MoES), Government of India is sincerely acknowledged for providing financial support to conduct this research. Department of Science and Technology (DST) and MoES, Govt. of India is acknowledged for providing financial support in establishment and maintenance of the micro-meteorological tower facility. Global Modeling and Assimilation Office (GMAO) and Goddard Earth Science Data and Information Service Center (GES DISC) is sincerely acknowledged for providing Modern Era Retrospective-Analysis for Research and Applications (MERRA) and Global Land Data Assimilation (GLDAS) data, respectively. National Centers for Environmental Prediction (NCEP) is acknowledged for providing NCEP final analysis. National Aeronautics Space Administration is acknowledged for 3 hourly satellite rainfall estimates. Meteorological and Oceanographic Satellite Data Archival Centre (MOSDAC) is acknowledged for providing in situ observations. The authors are very much thankful to Dr. Manoj Kumar for providing Ranchi flux observation. The first author is indebted to IIT Kharagpur for providing facilities to conduct his PhD work. The first author is very much grateful to late Dr. M. Mandal for his guidance and support at the initial stage of this research. The authors are thankful to the anonymous reviewers for their valuable suggestions and comments.

Supplementary material

24_2018_1946_MOESM1_ESM.docx (572 kb)
Supplementary material 1 (DOCX 571 kb)


  1. Asharfa, S., Dobler, A., & Ahrens, B. (2012). Soil moisture-precipitation feedback processes in the Indian summer monsoon season. Journal of Hydrometeorology, 13, 1461–1474.CrossRefGoogle Scholar
  2. Avissar, R., & Pielke, R. A. (1989). A parameterization of heterogeneous land surfaces for atmospheric numerical models and its impact on regional meteorology. Monthly Weather Review, 117, 2113–2136.CrossRefGoogle Scholar
  3. Baldocchi, D. D., et al. (2001). FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bulletin of the American Meteorological Society, 82, 2415–2434.CrossRefGoogle Scholar
  4. Betts, A. K., & Ball, J. H. (1998). FIFE surface climate and site-average dataset 1987–89. Journal of Atmospheric Science, 55, 1091–1108.CrossRefGoogle Scholar
  5. Bhattacharya, A., & Mandal, M. (2015). Evaluation of Noah land–surface models in predicting soil temperature and moisture at two tropical sites in India. Meteorological Applications, 22, 505–512.CrossRefGoogle Scholar
  6. Case, J. L., et al. (2008). Impacts of high-resolution land surface initialization on regional sensible weather forecasts from the WRF model. Journal of Hydrometeorology, 9, 1249–1266.CrossRefGoogle Scholar
  7. Chen, F., Janjic, Z., & Mitchell, K. (1997). Impact of atmospheric surface layer parameterization in the new land-surface scheme of the NCEP Mesoscale Eta numerical model. Boundary-Layer Meteorology, 185, 391–421.CrossRefGoogle Scholar
  8. Chen, F., & Mitchell, K. (1999). Using GEWEX/ISLSCP forcing data to simulate global soil moisture fields and hydrological cycle for 1987–1988. Journal of the Meteorological Society of Japan, 77, 1–16.CrossRefGoogle Scholar
  9. Chen, F., et al. (1996). Modeling of land–surface evaporation by four schemes and comparison with FIFE observations. Journal of Geophysical Research, 101, 7251–7268.CrossRefGoogle Scholar
  10. Chen, F., et al. (2007). Description and evaluation of the characteristics of the NCAR high-resolution land data assimilation system. Journal of Applied Meteorology and Climatology, 46, 649–713.Google Scholar
  11. Cosgrove, B. A., et al. (2003a). Land surface model spin-up behavior in the North American land data assimilation system NLDAS. Journal of Geophysical Research, 108(D22), 8845. Scholar
  12. Cosgrove, B. A., et al. (2003b). Real-time and retrospective forcing in the North American Land Data Assimilation System NLDAS) project. Journal of Geophysical Research, 108D(22), 8842. Scholar
  13. Dai, Y., et al. (2003). The Common Land Model CLM. Bulletin of the American Meteorological Society, 84, 1013–1023.CrossRefGoogle Scholar
  14. 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, 108(D22), 8851. Scholar
  15. Godfrey, C. M., & Stensrud, D. J. (2008). Soil temperature and moisture errors in operational Eta Model analysis. Journal of Hydrometeorology, 9, 367–387.CrossRefGoogle Scholar
  16. Goncalves, G. G. L., et al. (2006). Impact of different initial soil moisture fields on Eta model weather forecasts for South America. Journal of Geophysical Research, 111, D17102. Scholar
  17. Henderson-Sellers, A., Pitman, J. A., Love, K. P., Irannejad, P., & Chen, H. T. (1995). The project for intercomparison of land surface parameterization schemes PILPS. Phases 2 and 3. Bulletin of the American Meteorological Society, 764, 489–503.CrossRefGoogle Scholar
  18. Henderson-Sellers, A., Yang, Z. L., & Dickinson, R. E. (1993). The project for intercomparison of land-surface parameterization schemes. Bulletin of the American Meteorological Society, 74, 1335–1349.CrossRefGoogle Scholar
  19. Hirish, L. A., et al. (2014). Impact of land surface initialization approach on sub seasonal forecast skill: A regional analysis in the southern hemisphere. Journal of Hydrometeorology, 15, 300–319.CrossRefGoogle Scholar
  20. Holt, T. R., et al. (2006). Effect of land–atmosphere interactions on the IHOP 24–25 May 2002 convection case. Monthly Weather Review, 134, 113–133.CrossRefGoogle Scholar
  21. Koren, V., Schaake, J., Mitchell, K., Duan, Q. Y., & Chen, F. (1999). A parameterization of snowpack and frozen ground intended for NCEP weather and climate models. Journal of Geophysical Research, 104, 19569–19585.CrossRefGoogle Scholar
  22. Koster, R. D., & Milly, C. P. (1997). The interplay between transpiration and runoff formulations in land surface schemes used with atmospheric models. Journal of Climate, 10, 1578–1591.CrossRefGoogle Scholar
  23. Koster, R. D., & Suarez, M. J. (1996). Energy and water balance calculations in the Mosaic LSM. NASA Tech Memo 104606, 9, 76.Google Scholar
  24. Koster, R. D., & Suarez, M. J. (2001). Soil moisture memory in climate models. Journal of Hydrometeorology, 2, 558–570.CrossRefGoogle Scholar
  25. Koster, R. D., & Suarez, M. J. (2003). Impact of land surface initialization on seasonal precipitation and temperature prediction. Journal of Hydrometeorology, 4, 408–423.CrossRefGoogle Scholar
  26. Koster, R. D., Suarez, M. J., Ducharne, A., Stiglitz, M., & Kumar, P. (2000). A catchment-based approach to modeling land surface processes in a GCM. Part 1: Model structure. Journal of Geophysical Research, 105(D20), 24809–24822.CrossRefGoogle Scholar
  27. Koster, R. D., et al. (2004). Realistic initialization of land surface states: Impacts on sub-seasonal forecast skill. Journal of Hydrometeorology, 5, 1049–1063.CrossRefGoogle Scholar
  28. Koster, R. D., et al. (2010). The contribution of land surface initialization to subseasonal forecast skill: First results from the GLACE-2 project. Geophysical Research Letters, 37, L02402. Scholar
  29. Koster, R. D., et al. (2011). The second phase of the global land-atmosphere coupling experiment: Soil moisture contributions to subseasonal forecast skill. Journal of Hydrometeorology, 12, 805–822. Scholar
  30. Li, Y., Zhao, M., Motesharrei, S., Mu, Q., Kalnay, E., & Li, S. (2015). Local cooling and warming effects of forest based on satellite data. Nature Communications, 6, 6603. Scholar
  31. Liang, X., Lettenmaier, D. P., Wood, E. F., & Burges, S. J. (1994). A simple hydrologically based model of land surface water and energy fluxes for GSMs. Journal of Geophysical Research, 99(D7), 14415–14428.CrossRefGoogle Scholar
  32. Lim, Y., et al. (2012). A land data assimilation system using the MODIS-derived land data and its application to numerical weather prediction in East Asia. Asia-Pacific Journal of Atmospheric Sciences, 481, 83–95.CrossRefGoogle Scholar
  33. Liu, Z., Ostrenga, D., Teng, W., & Kempler, S. (2012). Tropical rainfall measuring mission (TRMM) precipitation data and services for research and applications. Bulletin of the American Meteorological Society, 93, 1317–1325.CrossRefGoogle Scholar
  34. Mahrt, L., & Ek, K. (1984). The influence of atmospheric stability on potential evaporation. Journal of Applied Meteorology and Climatology, 23, 222–234.CrossRefGoogle Scholar
  35. Mahrt, L., & Pan, H. L. (1984). A two-layer model of soil hydrology. Boundary-Layer Meteorology, 29, 1–20.CrossRefGoogle Scholar
  36. Miller, J., Barlage, M., Zeng, X., Wei, H., Mitchell, K., & Tarpley, D. (2006). Sensitivity of the NCEP/Noah land surface model to the MODIS green vegetation fraction dataset. Geophysical Research Letters, 33, L13404.CrossRefGoogle Scholar
  37. Mitchell, K. E., et al. (2004). The multi-institution North American land data assimilation system (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system. Journal of Geophysical Research, 109, D07S90. Scholar
  38. Nayak, S., & Mandal, M. (2012). Impact of land use and land cover change on temperature trends over Western India. Current Science, 102(8), 1166–1173.Google Scholar
  39. Osuri, K. K., Nadimpalli, R., Mohanty, U. C., Chen, F., Rajeevan, M., & Niyogi, D. (2017). Improved prediction of severe thunderstorms over the Indian Monsoon region using high-resolution soil moisture and temperature initialization. Scientific Reports Nature, 7, 41377. Scholar
  40. Pan, H. L., & Mahrt, L. (1987). Interaction between soil hydrology and boundary-layer development. Boundary-Layer Meteorology, 38, 185–202.CrossRefGoogle Scholar
  41. Parthasarathy, B., Munot, A. A., & Kothawale, D. R. (1995) Monthly and seasonal rainfall series for all-India homogeneous regions and meteoro-logical subdivisions: 1871–1994, Res. Rep. RR-065. Indian Inst. of Trop. Meteorol., Pune, p. 113.Google Scholar
  42. Rajesh, P. V., Pattnaik, S., Rai, D., Osuri, K. K., Mohanty, U. C., & Tripathy, S. (2016). Role of land state in a high resolution mesoscale model for simulating the Uttarakhand heavy rainfall event over India. Journal of Earth System Science, 125(3), 475–498.CrossRefGoogle Scholar
  43. Rienecker, M., et al. (2011). MERRA: NASA’s modern-era retrospective analysis for research and applications. Journal of Climate, 24(14), 3624–3648.CrossRefGoogle Scholar
  44. Robock, A., Schlosser, A., Vinnikov, K., Speranskaya, N., & Entin, J. (1998). Evaluation of AMIP soil moisture simulations. Global and Planetary Change, 19, 181–208.CrossRefGoogle Scholar
  45. Robock, A., et al. (2000). The global soil moisture data bank. Bulletin of the American Meteorological Society, 81, 1281–1299.CrossRefGoogle Scholar
  46. Rodell, M., Houser, P. R., Berg, A. A., & Famiglietti, J. S. (2005). Evaluation of 10 methods for initializing a land surface model. Journal of Hydrometeorology, 6, 146–155.CrossRefGoogle Scholar
  47. Rodell, M., et al. (2004). The global land data assimilation system. Bulletin of the American Meteorological Society, 85, 381–394.CrossRefGoogle Scholar
  48. Saha, S. K., Dirmeyer, P. A., & Chase, T. N. (2016). Investigating the impact of land-use land-cover change on Indian summer monsoon daily rainfall and temperature during 1951–2005 using a regional climate model. Hydrology and Earth System Sciences, 20, 1765–1784.CrossRefGoogle Scholar
  49. Trier, S. B., Chen, F., & Manning, K. W. (2004). A study of convection initiation in a mesoscale model using high-resolution land surface initial conditions. Monthly Weather Review, 132, 2954–2976.CrossRefGoogle Scholar
  50. Unnikrishnan, C. K., Rajeevan, M., Rao, S. V. B., & Kumar, M. (2013). Development of a high resolution land surface dataset for the South Asian monsoon region. Current Science, 1059, 1235–1246.Google Scholar
  51. Wang, F., Wang, L., Koike, T., Zhou, H., Yang, K., Wang, A., & Li, W. (2011). Evaluation and application of a fine-resolution global dataset in a semiarid mesoscale river basin with a distributed biosphere hydrological model. Journal of Geophysical Research, 116, D21108.Google Scholar
  52. Wolters, D., Heerwaarden, C. V., Arellano, J. V. D., Cappelaere, B., & Ramier, D. (2010). Effects of soil moisture gradients on the path and the intensity of a West African squall line. Quarterly Journal of the Royal Meteorological Society, 136, 2162–2217.CrossRefGoogle Scholar
  53. Yang, Z. L., Dickinson, R. E., Henderson-Sellers, A., & Pitman, A. J. (1995). Preliminary study of spin-up processes in land surface models with the first stage data of Project for Inter-comparison of Land Surface Parameterization Schemes Phase 1a). Journal of Geophysical Research, 100(D8), 16553–16578.CrossRefGoogle Scholar
  54. Zaitchik, B. F., Rodell, M., & Olivera, F. (2010). Evaluation of the global land data assimilation system using global river discharge data and a source-to-sink routing scheme. Water Resource Research, 46(W06507), 1–17.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • H. P. Nayak
    • 1
    • 2
  • Palash Sinha
    • 2
  • A. N. V. Satyanarayana
    • 1
  • A. Bhattacharya
    • 1
  • U. C. Mohanty
    • 2
    Email author
  1. 1.Centre for Oceans, Rivers, Atmosphere and Land SciencesIndian Institute of Technology KharagpurKharagpurIndia
  2. 2.School of Earth Ocean and Climate Science, 309 Basic Science BuildingIndian Institute of Technology BhubaneswarBhubaneswarIndia

Personalised recommendations