High Resolution Land Surface Analysis over India using HRLDAS

1. Land surface analysis over India using High Resolution Land Data Assimilation System (HRLDAS) Centre for Oceans, Rivers, Atmosphere and Land Sciences (CORAL) Indian Institute of Technology Kharagpur, INDIA H P Nayak and M Mandal

2. Outline • Introduction • Objectives • Methodology and data used • Results and discussion • Summary and conclusions • Reference

3. Introduction • Land surface exchanges mass, energy and moisture with overlying atmosphere. • These exchanges are represented in weather and climate system model through coupled land surface models. • The inaccurate land surface initial condition provided to weather and climate system models leads to inaccurate predictions. • Therefore, reasonably accurate estimation of land surface parameter at high resolution is important for weather and climate prediction. • There is no regional analysis of land surface parameter over Indian region.

4. Objectives • Preparation of land surface analysis using High Resolution Land Data Assimilation System (HRLDAS) • Validation of prepared analysis with observation

5. Preparation of land surface analysis using High Resolution Land Data Assimilation System (HRLDAS)

6. Methodology and data used • High Resolution Land Data Assimilation System (HRLDAS) version 3.4.1 has been used to prepare land surface analysis for the period 2001-2013 over India region (60E-100E, 5N-40N). • The heart of the HRLDAS infrastructure is the Noah LSM (Chen and Dudhia 2001; Ek et al. 2003). • The Noah LSM is based on coupling of the diurnally dependent Penman potential evaporation approach of Mahrt and Ek (1984), the multilayer soil model of Mahrt and Pan (1984), and the primitive canopy model of Pan and Mahrt (1987). • It has been extended by Chen et al. (1996) to include the modestly complex canopy resistance approach of Noilhan and Planton (1989) and Jacquemin and Noilhan (1990) and by Koren et al. (1999) to include frozen ground physics

7. Methodology and data used Noah LSM Physics : Soil Prognostic Equation Soil moisture • - Richard’s Equation for soil water movement • - D (soil water diffusibility) , K (hydraulicconductivety) functions (soil texture, soilmoisture) • represents sources (rainfall) and sinks (evaporation) • Soil temperature - C (volumetric heat capacity), Kt (thermal conductivity)functions (soil texture, soil moisture) - Soil temperature information used to compute ground heat flux

8. Methodology and data used

9. Methodology and data used • The HRLDAS is integrated from January -2001 to October -2013. The forcing time step is one hour and integration time step is 15 min. • To investigate the spin-up time, HRLDAS is integrated recursively for three years with 2001 atmospheric forcing. • The regional analysis of land surface parameters at 20 Km special resolution and hourly temporal resolution is prepared for the period January 2001-October 2013 over Indian region (5N-40N, 60E-100E)

10. Spin-up • The Root mean-square difference (RMSD) between current year and successive year recursive run is computed for each soil category using following mathematical relation. • Where M is the particular month considered for spin-up study and n is the number of time step in the month M. Yk is the land surface analysis for kth recursive run

11. Soil moisture spin-up 25 cm 5 cm Time (month) 70 cm 150 cm Soil texture category

12. Soil temperature spin-up 25cm 5cm Time (month) 70cm 150cm Soil texture Category

13. Heat flux spin-up LHF SHF Time (month) Soil texture category

14. Validation of land surface analysis

15. Observational sites used for validation 50m micrometeorological tower at IIT Kharagpur - A National facility

16. Gujarat Godhra Mandla Kharagpur Diurnal variation of soil temperature over India

17. Error in soil temperature at 5 cm

18. Diurnal variation of soil temperature at Kharagpur with depth

19. Diurnal variation of sensible heat flux at Ranchi

20. 5cm 25cm 70cm 150cm Inter-seasonal variation of soil temperature at Kharagpur

21. Inter-seasonal variation of soil moisture at Kharagpur

22. Noah 1D simulation of soil moisture at Kharagpur

23. Inter-annual variation of soil temperature and soil moisture at central India (Mandla)

24. Summary and conclusions • The result shows the spin–up of HRLDAS is about 1.5 years. However, it takes more than two year to reach equilibrium state. • The result indicates that simulated soil temperature agrees reasonably well with observations. • The soil moisture at Kharagpur shows that the soil moisture is under-estimated in the prepared analysis. This is due to fact that the precipitation assimilated is taken from TRMM which under-estimates the precipitation. • Simulation of 1D Noah LSM at Kharagpur confirms that precipitation from micro-meteorological tower is significantly improves soil moisture simulation

25. Summary and conclusions • The analysis overestimates the day time sensible heat flux and underestimated the nocturnal sensible heat flux at Ranchi. The over estimation of day time sensible heat flux is maximum during pre-monsoon season and minimum during post monsoon season • The diurnal variation of soil temperature and sensible heat flux is well captured in the analysis. Inter-seasonal variation of soil temperature and soil moisture is also well represented in the analysis. • Analysis of inter-annual variability of soil temperature concluded that there is no increasing/ decreasing trend in soil temperature during the 2001-2012. Analysis of inter-annual variability of soil moisture indicates during winter, spring and summer month soil moisture shows increasing trend in recent years (2010-2012).

26. Acknowledgement • I would like to thank my research group and friends for their suggestions, guidance and well wishes. • I would like to acknowledge the Indian Space Research Organization (ISRO) providing financial support to carry out research and IIT Kharagpur for providing research facility. • The Global land data assimilation (GLDAS), Modern Era Retrospective-Analysis for Research and Applications (MERRA) and National centre for environment prediction (NCEP) is acknowledged for providing necessary datasets.

27. References Anthes R. A. (1984): Enhancement of convective precipitation by mesoscale variation in vegetation covering in semiarid region. Journal of applied meteorology and climatology, 23, 865-889 Avissiar R. and Liu Y. (1996): Three dimensional numerical study of shallow convective clouds and precipitation induced by land surface forcing. Journal of Geophysical Res., 101(D3), 7499-7518 Case and co-authors (2008): Impacts of High-Resolution Land Surface Initialization on Regional Sensible Weather Forecasts from the WRF Model.  J. Hydrometeor, 9, 1249–1266 Chen F. and co-authors, (1996): Modeling of land-surface evaporation by four schemes and comparison with FIFE observations. J. Geophys. Res., 101, 7251–7268 Chen F., Janjic Z., and Mitchell K., (1997): Impact of atmospheric surface layer parameterization in the new land-surface scheme of the NCEP Mesoscale Eta numerical model. Bound.-Layer Meteor., l85, 391-421 Chen, F and Coauthors, (2007): Description and evaluation of the characteristics of the NCAR high-resolution land data assimilation system. J Appi. Meteor. Climatol., 46, 649-713

28. !!Thank you!! Hara Prasad Nayak hpnayak@iitkgp.ac.in

29. USGS soil texture category

30. Data required for LDAS Forcing fields • Air temperature at 2m • Surface pressure • Specific humidity at 2m • Precipitation rate • Horizontal u-component of wind at10m • Downward shortwave radiation flux at the surface • Horizontal v-component of wind at10m • Downward longwave radiation flux at the surface • Source model terrain elevation Initialization fields • Soil moisture at four levels • Soil temperature at four levels • Skin temperature • Water equivalent of snow depth • Canopy water content Parametric fields • Green vegetation fraction • Soil category • Maximum green vegetation fraction • Minimum green vegetation fraction • Vegetation category • Deep soil temperature