1. Land surface analysis over India using High Resolution Land Data Assimilation System (HRLDAS)
H P Nayak and M Mandal
Centre for Oceans, Rivers, Atmosphere and Land Sciences (CORAL)
Indian Institute of Technology Kharagpur, INDIA
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2. Methodology and data used Results and discussion
Outline
Introduction
Objectives
Methodology and data used
Results and discussion
Summary and conclusions
Reference
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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.
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4. Objectives
Preparation of land surface analysis using High Resolution Land Data Assimilation System (HRLDAS)
Validation of prepared analysis with observation
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5. Preparation of land surface analysis using High Resolution Land Data Assimilation System (HRLDAS)
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6. Methodology and data used
High Resolution Land Data Assimilation System (HRLDAS) version has been used to prepare land surface analysis for the period 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
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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 (hydraulic conductivety) functions (soil texture, soil moisture)
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
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8. Methodology and data used
Sources
Land use
20 category 30s resolution MODIS land use
Green vegetation fraction
MODIS fpar resolution green vegetation fraction
Atmospheric forcing
MERRA hourly data at (1/2° ×2/3°) (Air temperature, specific humidity, Pressure, U wind, V wind)
Precipitation
TRMM hourly precipitation at 0.25°
Radiation
GLDAS 3 hourly solar radiation at 0.25°
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9. Methodology and data used
The HRLDAS is integrated from January to October 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)
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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
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11. Soil moisture spin-up 5 cm 70 cm 25 cm 150 cm Time (month) 1 Sand 3
Soil texture category
Time (month)
5 cm
70 cm
25 cm
150 cm 1
Sand 3
Sandy Loam 4
Silt Loam 6
Loam 7
Sandy Clay Loam 9
Clay Loam 12
Clay 16
Other (land-ice)
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12. Soil temperature spin-up
Soil texture Category
Time (month)
5cm
70cm
25cm
150cm 1
Sand 3
Sandy Loam 4
Silt Loam 6
Loam 7
Sandy Clay Loam 9
Clay Loam 12
Clay 16
Other (land-ice)
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13. Heat flux spin-up LHF SHF Time (month) 1 Sand 3 Sandy Loam 4 Silt Loam
Soil texture category
Time (month)
LHF
SHF 1
Sand 3
Sandy Loam 4
Silt Loam 6
Loam 7
Sandy Clay Loam 9
Clay Loam 12
Clay 16
Other (land-ice)
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14. Validation of land surface analysis
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15. Observational sites used for validation
50m micrometeorological tower at
IIT Kharagpur - A National facility
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16. Diurnal variation of soil temperature over India
Gujarat
Godhra
Mandla
Kharagpur
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17. Error in soil temperature at 5 cm
Station
Pre-monsoon
Monsoon
Post-monsoon
RSME
Correlation
Gujarat
0.54
0.99
1.46
0.98
2.84
Godhra
1.08
1.44
0.95
3.70
0.97
Mandla
2.48
1.16
0.93
Kharagpur
0.48
1.65
1.09
0.92
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18. Diurnal variation of soil temperature at Kharagpur with depth
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19. Diurnal variation of sensible heat flux at Ranchi
Seasons
RMSE
Correlation
Pre-monsoon
27.54
0.98
Monsoon
18.56
Post-monsoon
9.16
0.95
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20. Inter-seasonal variation of soil temperature at Kharagpur
5cm
150cm
70cm
25cm
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21. Inter-seasonal variation of soil moisture at Kharagpur
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22. Noah 1D simulation of soil moisture at Kharagpur
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23. Inter-annual variation of soil temperature and soil moisture at central India (Mandla)
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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
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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 Analysis of inter-annual variability of soil moisture indicates during winter, spring and summer month soil moisture shows increasing trend in recent years ( ).
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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.
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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,
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),
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,
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,
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28. !!Thank you!!
Hara Prasad Nayak
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29. USGS soil texture category1
Sand 3
Sandy Loam 4
Silt Loam 6
Loam 7
Sandy Clay Loam 9
Clay Loam 12
Clay 16
Other (land-ice)
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30. Data required for LDAS Forcing fields Parametric 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
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