Enhancing Noah Land Surface Model Prediction Skill over Indian Subcontinent by Assimilating SMOPS Blended Soil Moisture
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. The Land Parameter Retrieval Model (LPRM) AMSR-E Soil Moisture
2.3. SMOPS Blended Products
2.4. Global Data Assimilation System (GDAS)
2.5. Ecoclimap
2.6. ISMN Ground Data for Tibetan Plateau
2.7. Land Surface Model Framework
LSM Spin-up
2.8. Data Assimilation Framework
2.8.1. Ensemble Kalman Filter
2.8.2. Perturbation Attributes
2.9. Radiative Transfer Model
3. Study Setup
3.1. Study Overview
- (a)
- Data assimilation, where the Noah LSM was initialized with spinup runs(explained in Section 2.7), and then the initialized model was assimilated with SMOPS soil moisture by using NASA’s LIS framework for 0.25° × 0.25° spatial resolution with the procedure as shown in Figure 3. A similar procedure was used by Kumar et al. [43].
- (b)
- The monthly mean results for JJAS months from data assimilation were compared with the GLDAS soil moisture data and LPRM AMSR-E soil moisture. Two spatial domains within study region were studied using daily mean time series plots. Domain 1 was over central India, while domain 2 was over the Central Tibetan Plateau (CTP).In this stage, the results over the CTP were also compared with the ground soil moisture observations from ISMN data.
- (c)
- The land surface variables from the data assimilation were utilized to simulate the TOA brightness temperature at 10.7 GHz and at an incidence angle of 55°, which were compared with the actual AMSR-E Tb.
3.2. Comparing Simulated Soil Moisture with LPRM AMSR-E soil Moisture
3.3. Statistical comparison between Assimilated, GLDAS and LPRM Soil Moisture
3.4. Brightness Temperature Simulation Experiment
4. Results
4.1. Evaluation of Simulated Soil Moisture
4.1.1. Time series plot for Domain 1 (Central India)
4.1.2. Time series plots for Domain 2 (CTP)
4.1.3. Error Estimation using Triple Collocation
4.2. Comparisonbetween CMEM simulated and LPRM AMSR-ETB
5. Discussions
6. Summary and Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Variable | Spatial Resolution | Temporal Resolution | Source |
---|---|---|---|
Forcing Data | |||
Near surface air temperature | 0.47° × 0.47° | 3 Hourly | GDAS |
Near surface specific humidity | |||
Total incident shortwave radiation | |||
Incident Longwave Radiation | |||
Eastward wind | |||
Northward wind | |||
Surface pressure | |||
Rainfall rate | |||
Convective rainfall rate | |||
LSM Parameters | |||
Landcover | 0.01 × 0.01 | - | AVHRR/UMD |
Soil Texture | 0.25 × 0.25 | - | FAO |
Soil Fraction (clay, sand, silt) | 0.25 × 0.25 | - | FAO |
Slope type | 0.01 × 0.01 | - | NCEP_LIS |
Elevation Data | - | - | SRTM |
Albedo | 0.01 × 0.01 | Monthly | NCEP_LIS |
Greenness fraction | 0.01 × 0.01 | - | NCEP_LIS |
RTM Parameters | |||
Soil fraction | 0.25 × 0.25 | - | Ecoclimap/FAO |
Geopotential | 0.25 × 0.25 | - | NCEP |
Vegetation fraction | 0.25 × 0.25 | - | Ecoclimap |
Vegetation Type | 0.25 × 0.25 | - | Ecoclimap |
Variable | Perturbation Type | Standard Deviation | Cross Correlation | ||||
---|---|---|---|---|---|---|---|
P | SW | LW | |||||
Forcing Perturbation | Precipitation (P) | Multiplicative | 0.5 (mm) | 1.0 | −0.8 | 0.5 | |
Downward Shortwave (SW) | Multiplicative | 0.3 (W·m−2) | −0.8 | 1.0 | −0.5 | ||
Downward Longwave (LW) | Additive | 50 (W·m−2) | 0.5 | −0.5 | 1.0 | ||
SM1 | SM2 | SM3 | SM4 | ||||
State Variable Perturbation | SM Layer 1 | Additive | 6.00 × 10−3 m3·m−3 | 1.0 | 0.6 | 0.4 | 0.2 |
SM Layer 2 | Additive | 1.10 × 10−4 m3·m−3 | 0.6 | 1.0 | 0.6 | 0.4 | |
SM Layer 3 | Additive | 6.00 × 10−5 m3·m−3 | 0.4 | 0.6 | 1.0 | 0.6 | |
SM Layer 4 | Additive | 4.00 × 10−5 m3·m−3 | 0.2 | 0.4 | 0.6 | 1.0 |
Module | Variable | Parameterization | |
---|---|---|---|
Scheme One | Scheme Two | ||
Soil | Soil dielectric constant | Wang | Wang |
Soil effective temperature | Choudhury | Choudhury | |
Smooth emissivity | Fresnel | Fresnel | |
Soil roughness | Choudhury | Wegmueller | |
Vegetation | Vegetation optical depth | Kirdyashev | Wigneron |
Atmosphere | Atmospheric optical depth | Pellarin | Pellarin |
Snow | Snow reflectivity | Pulliainen | Pulliainen |
LPRM AMSR-E | DA | GLDAS | ||
---|---|---|---|---|
Correlation(r) | 10 June | 1 | 0.9349 | 0.8789 |
11 June | 1 | 0.9277 | 0.9029 | |
10 July | 1 | 0.9444 | 0.8952 | |
11 July | 1 | 0.9773 | 0.9546 | |
10 August | 1 | 0.9786 | 0.9501 | |
11 August | 1 | 0.9819 | 0.9637 | |
10 September | 1 | 0.9874 | 0.9567 | |
11 September | 1 | 0.9837 | 0.9555 | |
RMSD | 10 June | 0 | 0.0462 | 0.0593 |
11 June | 0 | 0.0502 | 0.0528 | |
10 July | 0 | 0.0490 | 0.0641 | |
11 July | 0 | 0.0321 | 0.0415 | |
10 August | 0 | 0.0320 | 0.0453 | |
11 August | 0 | 0.0298 | 0.0384 | |
10 September | 0 | 0.0248 | 0.0422 | |
11 September | 0 | 0.0279 | 0.0415 | |
Standard Deviation | 10 June | 0.1221 | 0.1300 | 0.1185 |
11 June | 0.1219 | 0.1344 | 0.1162 | |
10 July | 0.1426 | 0.1489 | 0.1360 | |
11 July | 0.1391 | 0.1487 | 0.1343 | |
10 August | 0.1451 | 0.1534 | 0.1360 | |
11 August | 0.1434 | 0.1531 | 0.1417 | |
10 September | 0.1435 | 0.1516 | 0.1430 | |
11 September | 0.1398 | 0.1496 | 0.1382 |
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Nair, A.S.; Indu, J. Enhancing Noah Land Surface Model Prediction Skill over Indian Subcontinent by Assimilating SMOPS Blended Soil Moisture. Remote Sens. 2016, 8, 976. https://doi.org/10.3390/rs8120976
Nair AS, Indu J. Enhancing Noah Land Surface Model Prediction Skill over Indian Subcontinent by Assimilating SMOPS Blended Soil Moisture. Remote Sensing. 2016; 8(12):976. https://doi.org/10.3390/rs8120976
Chicago/Turabian StyleNair, Akhilesh S., and J. Indu. 2016. "Enhancing Noah Land Surface Model Prediction Skill over Indian Subcontinent by Assimilating SMOPS Blended Soil Moisture" Remote Sensing 8, no. 12: 976. https://doi.org/10.3390/rs8120976