DEnKF–Variational Hybrid Snow Cover Fraction Data Assimilation for Improving Snow Simulations with the Common Land Model
Abstract
:1. Introduction
2. Methods
2.1. Common Land Model
2.2. Variational Method
2.3. Deterministic Ensemble Kalman Filter
2.4. Coupled Method
2.5. Error Evaluation Methods
3. Experimental Design and Data Sets
3.1. Experimental Sites
Site | Lon | Lat | Elevation (m) | Mean SD (cm) | Mean SWE (mm) | Pairs | Num | σobs (%) |
---|---|---|---|---|---|---|---|---|
Aletai | 88.083 | 47.733 | 737 | 32.71 | 88.53 | 86 | 253 | 27.75 |
Buerjin | 86.867 | 47.400 | 456 | 15.41 | 26.84 | 80 | 125 | 27.78 |
Fuyun | 89.517 | 46.983 | 826 | 15.99 | 69.77 | 91 | 206 | 22.85 |
Jimunai | 85.867 | 47.433 | 984 | 22.80 | 36.68 | 83 | 248 | 28.56 |
Qinghe | 90.383 | 46.667 | 1 200 | 14.35 | 61.37 | 96 | 175 | 29.70 |
3.2. MODIS Snow Cover Fraction
3.3. AMSR-E Snow Water Equivalent
3.4. Experimental Design
Experiments | Description |
---|---|
Simulation | No snow DA |
1DVar | Snow DA using 1DVar with static covariance from NMC method |
DEnKF | Snow DA using DEnKF with ensemble size of 25 |
EnVar-Beta 0.5 | Snow DA using a hybrid method like Zhang, Zhang and Poterjoy [36] with ensemble size of 25, weighting coefficient of 0.5 |
DEnVar-Beta 0.5 | Snow DA using the proposed hybrid method with ensemble size of 25, weighting coefficient of 0.5 in Equation (11) |
DEnVar-Beta 0.8 | Sensitivity DEnVar with ensemble size of 25, weighting coefficient of 0.8 in Equation (11) |
DEnVar-Beta 1.0 | Sensitivity DEnVar with ensemble size of 25, weighting coefficient of 1.0 in Equation (11) |
DEnVar-Beta 1.0–2 | Same as DEnVar-Beta1.0 with MODIS SCF observation error of 10% |
4. Results
4.1. Snow Data Assimilation Using Different Methods
4.1.1. Comparison with In situ SD Observations
Index | Experiment | Aletai | Buerjin | Fuyun | Jimunai | Qinghe |
---|---|---|---|---|---|---|
Bias (m) | Simulation | −0.1759 | −0.0529 | −0.0398 | −0.1887 | −0.0811 |
1DVar | −0.1221 | −0.0385 | −0.0141 | −0.1670 | −0.0630 | |
DEnKF | −0.0071 | −0.0301 | 0.0714 | −0.1311 | −0.0218 | |
EnVar-Beta 0.5 | −0.0453 | −0.0295 | 0.0459 | −0.1549 | −0.0369 | |
DEnVar-Beta 0.5 | 0.0185 | −0.0290 | 0.0897 | −0.1011 | 0.0028 | |
RMSE (m) | Simulation | 0.2011 | 0.0689 | 0.0577 | 0.2070 | 0.0927 |
1DVar | 0.1476 | 0.0618 | 0.0336 | 0.1860 | 0.0759 | |
DEnKF | 0.0645 | 0.0569 | 0.0878 | 0.1456 | 0.0453 | |
EnVar-Beta 0.5 | 0.0851 | 0.0563 | 0.0637 | 0.1696 | 0.0543 | |
DEnVar-Beta 0.5 | 0.0678 | 0.0550 | 0.1058 | 0.1192 | 0.0391 | |
Correlation | Simulation | 0.8391 | 0.8582 | 0.9617 | 0.5808 | 0.8560 |
1DVar | 0.8966 | 0.8599 | 0.9782 | 0.6281 | 0.8809 | |
DEnKF | 0.9450 | 0.8820 | 0.9259 | 0.8162 | 0.9304 | |
EnVar-Beta 0.5 | 0.9273 | 0.8828 | 0.9422 | 0.7807 | 0.9169 | |
DEnVar-Beta 0.5 | 0.9434 | 0.8885 | 0.9146 | 0.8183 | 0.9463 |
4.1.2. Comparison with AMSR-E SWE Observations
Index | Experiment | Aletai | Buerjin | Fuyun | Jimunai | Qinghe |
---|---|---|---|---|---|---|
Bias (mm) | Simulation | −60.6890 | −10.7972 | −51.1207 | −30.8255 | −51.9142 |
1DVar | −54.1836 | −6.5794 | −49.6650 | −26.8484 | −50.5498 | |
DEnKF | −36.1520 | −9.1861 | −38.9930 | −21.8406 | −46.2824 | |
EnVar-Beta 0.5 | −43.9760 | −8.9642 | −43.0146 | −26.2549 | −48.6462 | |
DEnVar-Beta 0.5 | −30.7429 | −8.9699 | −36.0278 | −16.3484 | −42.4852 | |
Simulation | 72.1651 | 19.5319 | 59.9968 | 34.2670 | 60.4474 | |
RMSE (mm) | 1DVar | 66.0648 | 17.2661 | 58.9658 | 30.9795 | 59.0669 |
DEnKF | 49.4169 | 18.9730 | 49.2967 | 27.0125 | 54.2969 | |
EnVar-Beta 0.5 | 57.0112 | 18.9613 | 52.9896 | 30.3120 | 56.7938 | |
DEnVar-Beta 0.5 | 44.7758 | 18.9999 | 46.4075 | 23.9441 | 50.1661 | |
Correlation | Simulation | 0.5940 | 0.4602 | 0.5499 | 0.1439 | 0.3393 |
1DVar | 0.6361 | 0.5819 | 0.5339 | 0.2447 | 0.3715 | |
DEnKF | 0.7420 | 0.4782 | 0.6021 | 0.3305 | 0.5072 | |
EnVar-Beta 0.5 | 0.6809 | 0.4769 | 0.5712 | 0.2767 | 0.4538 | |
DEnVar-Beta 0.5 | 0.7657 | 0.4740 | 0.6339 | 0.3495 | 0.5936 |
4.2. Sensitivity to Weighting Coefficients
4.3. Sensitivity to Observation Error
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Xu, J.; Shu, H.; Dong, L. DEnKF–Variational Hybrid Snow Cover Fraction Data Assimilation for Improving Snow Simulations with the Common Land Model. Remote Sens. 2014, 6, 10612-10635. https://doi.org/10.3390/rs61110612
Xu J, Shu H, Dong L. DEnKF–Variational Hybrid Snow Cover Fraction Data Assimilation for Improving Snow Simulations with the Common Land Model. Remote Sensing. 2014; 6(11):10612-10635. https://doi.org/10.3390/rs61110612
Chicago/Turabian StyleXu, Jianhui, Hong Shu, and Lin Dong. 2014. "DEnKF–Variational Hybrid Snow Cover Fraction Data Assimilation for Improving Snow Simulations with the Common Land Model" Remote Sensing 6, no. 11: 10612-10635. https://doi.org/10.3390/rs61110612