Parameter Estimation Based on a Local Ensemble Transform Kalman Filter Applied to El Niño–Southern Oscillation Ensemble Prediction
Abstract
:1. Introduction
2. Materials and Methods
2.1. Zebiak–Cane Model
2.2. LETKF-Based Parameter Estimation
2.3. Covariance Inflation Scheme
2.4. Model Parameters
2.5. Experimental Designs
2.5.1. Experimental Designs in OSSE Framework
2.5.2. Experimental Designs in the Real World
3. Results
3.1. Results of the OSSE Framework
3.2. Results in Real-World Experiments
3.3. Validation of Optimized Parameters
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Appendix B
References
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Schemes | FI | CCI | EPES | RTPS | RTPP | New |
---|---|---|---|---|---|---|
Convergence times | 296 | 323 | 27 | 312 | 365 | 288 |
Estimated values | 0.7631 | 0.7793 | 0.8006 | 0.7318 | 0.7249 | 0.7492 |
Relative errors(%) | 1.75 | 3.91 | 6.75 | 2.42 | 3.35 | 0.11 |
Pars. | Physical Meanings | Units | Truths | Initial Guesses |
---|---|---|---|---|
gam1 | Strength of mean upwelling advection term | - | 0.75 | 0.6 |
gam2 | Strength of anomalous upwelling advection term | - | 0.75 | 0.6 |
tda1 | Amplitude of subsurface temperature anomaly +h perturbations | °C | 28 | 22.4 |
tda2 | Amplitude of subsurface temperature anomaly for −h perturbations | °C | −40 | −48 |
tdb1 | Affect the nonlinearity of subsurface temperature anomaly for +h perturbations | m−1 | 1.25 | 1.0 |
tdb2 | Affect the nonlinearity of subsurface temperature anomaly for −h perturbations | m−1 | 3.0 | 2.4 |
Experiments | Assimilated | Estimated | (a,b) |
---|---|---|---|
onlySE | SSTA | SSTA | (-,-) |
SPE | SSTA | gam2\SSTA | (0.15,0.08) |
MPE | SSTA | gam1\gam2\tda1\tda2\ tdb1\tdb2\SSTA | (0.31,0.016)\(0.30,0.015)\(4.12,1.69)\ (6.58,3.44)\(0.09,0.05)\(0.45,0.20) |
Experiments | Assimilated | Estimated | (a,b) |
---|---|---|---|
OnlySE | SSTA | SSTA | (-,-) |
MPE | SSTA | gam1\gam2\tda1\ tda2\tdb1\tdb2\SSTA | (0.02,0.01)\(0.02,0.01)\ (0.80,0.38)\(1.14,0.54)\ (0.04,0.02)\(0.08,0.04) |
Relative Errors | gam1 | gam2 | tda1 | tda2 | tdb1 | tdb2 |
---|---|---|---|---|---|---|
Initial | −20% | −20% | 20% | 20% | 20% | 20% |
Estimated | 0.03% | −6% | −2.7% | 10.1% | 0.05% | −6.2% |
State Variables | SSTA | ZWA | ULDA | TeA | ||||
---|---|---|---|---|---|---|---|---|
Exps. | onlySE | MPE | onlySE | MPE | onlySE | MPE | onlySE | MPE |
Correlation coefficients | 0.9256 | 0.9594 | 0.8248 | 0.8356 | 0.7172 | 0.8205 | 0.7605 | 0.7893 |
RMSEs | 0.3071 | 0.2818 | 0.2318 | 0.2271 | 6.6496 | 6.5208 | 0.9197 | 0.9092 |
Experiments | Parameter Values (Truncated to Two Decimal Places) |
---|---|
Optimized Pars | gam1 = 0.73, gam2 = 0.70, tda1 = 26.24, tda2 = −37.48, tdb1 = 1.17, tdb2 = 2.81 |
Default Pars | gam1 = 0.75, gam2 = 0.75, tda1 = 28.00, tda2 = −40.00, tdb1 = 1.25, tdb2 = 3.00 |
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Gao, Y.; Tang, Y.; Song, X.; Shen, Z. Parameter Estimation Based on a Local Ensemble Transform Kalman Filter Applied to El Niño–Southern Oscillation Ensemble Prediction. Remote Sens. 2021, 13, 3923. https://doi.org/10.3390/rs13193923
Gao Y, Tang Y, Song X, Shen Z. Parameter Estimation Based on a Local Ensemble Transform Kalman Filter Applied to El Niño–Southern Oscillation Ensemble Prediction. Remote Sensing. 2021; 13(19):3923. https://doi.org/10.3390/rs13193923
Chicago/Turabian StyleGao, Yanqiu, Youmin Tang, Xunshu Song, and Zheqi Shen. 2021. "Parameter Estimation Based on a Local Ensemble Transform Kalman Filter Applied to El Niño–Southern Oscillation Ensemble Prediction" Remote Sensing 13, no. 19: 3923. https://doi.org/10.3390/rs13193923