Assimilation of Polarimetric Radar Data in Simulation of a Supercell Storm with a Variational Approach and the WRF Model
Abstract
:1. Introduction
2. Polarimetric Radar Observation Operator
2.1. Microphysics Models and Parametrization
2.2. Parameterized PRD Operators
3. The 3DVAR DA System
4. Experimental Design
5. Results of 3DVAR Analysis
5.1. The Root Mean Square Error Analysis
5.2. Evaluation of PRD Assimilation
5.3. Evaluation of Hydrometeor Analysis
5.4. Evaluation of Forecast
6. Summary and Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Abbreviation/Acronym | Explanation |
---|---|
PRD | Polarimetric radar data |
NWP | Numerical weather prediction |
DA | Data assimilation |
TL | Tangent linear model |
AD | Adjoint model |
3DVAR | Three-dimensional variational system/method |
4DVAR | Four-dimensional variational system/method |
EnKF | Ensemble Kalman filter |
EnSRF | Ensemble square-root Kalman filter |
Vr | Radial velocity |
ZH | Horizontal reflectivity |
ZDR | Differential reflectivity |
ϕHV | Differential phase |
KDP | Specific differential phase |
ρhv | Cross-correlation coefficient |
QPE | Quantitative precipitation estimation |
HC | Hydrometeor classification |
MP | Microphysical parameterization scheme of model |
SM | Single-moment scheme |
DM | Double-moment scheme |
WRF | Weather research and forecasting model |
WRF-ARW | Advanced research weather research and forecasting model |
MCS(s) | Mesoscale convective system(s) |
OSSE(s) | Observing system simulation experiment(s) |
DSD | Drop size distribution |
PSD | Particle size distribution |
Mixing ratio | |
Number concentration | |
Water content | |
Air density | |
Water density | |
Mass-weighted diameter | |
Percentage of melting | |
APRS | Advanced Regional Prediction System |
CAPS | Center for Analysis and Prediction of Storms |
NSSL | National Severe Storms Laboratory |
J08 | Represents the article of Jung et al., (2008) |
J10 | Represents the article of Jung et al., (2010) |
Z21 | Represents the article of Zhang et al., (2021) |
Background vector in the cost function | |
Analysis vector in the cost function | |
Observation vector in the cost function | |
Background error covariance matrix of model | |
Observation error covariance matrix of model | |
Forward operator | |
Constraints in the cost function | |
RUC | Rapid Update Cycle |
AGL | Above ground level |
Horizontal wind in u-direction | |
Horizontal wind in v-direction | |
Vertical velocity | |
Perturbation potential temperature | |
Mixing ratio of cloud water | |
Mixing ratio of cloud ice | |
Mixing ratio of rain water | |
Mixing ratio of snow | |
Mixing ratio of hail | |
Mixing ratio of graupel | |
Number concentration of cloud water | |
Number concentration of cloud ice | |
Number concentration of rain water | |
Number concentration of snow | |
Number concentration of hail | |
Number concentration of graupel | |
VCP | Volume coverage pattern |
WSR-88D | Weather surveillance radar—1988 Doppler |
RMSE(s) | Root mean square error(s) |
ExpVrZh | Experiment on assimilation of Vr and ZH |
ExpVrZhZdr | Experiment on assimilation of Vr, ZH, and ZDR |
ExpVrZhKdp | Experiment on assimilation of Vr, ZH, and KDP |
ExpVrZhRhv | Experiment on assimilation of Vr, ZH, and ρhv |
ExpVrZhPol | Experiment on assimilation of Vr, ZH, ZDR, KDP, and ρhv |
CPA | Convective precipitation area |
SPA | Stratiform precipitation area |
LWC | Liquid water content |
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α | R_ZH | R_ZDR | R_KDP | R_ρhv |
---|---|---|---|---|
1E0 | 0.699045165073124 | 0.483074981957829 | 1.02528819132801 | 0.490697018550403 |
1E-1 | 0.954886643047696 | 0.884597784411837 | 0.995278460334951 | 0.931147252389009 |
1E-2 | 0.995227804958050 | 0.987614013476037 | 0.999521333803539 | 0.992331436494260 |
1E-3 | 0.999519983964757 | 0.998752196825316 | 0.999952065828401 | 0.999224559666156 |
1E-4 | 0.999951970230481 | 0.999875126965556 | 0.999995205904266 | 0.999922370046164 |
1E-5 | 0.999995196913338 | 0.999987513140014 | 0.999999520577862 | 0.999992241080020 |
1E-6 | 0.999999520221590 | 0.999998772473737 | 0.999999952019359 | 0.999999175462863 |
1E-7 | 0.999999959777296 | 1.00000006087191 | 0.999999997600995 | 0.999999828029847 |
1E-8 | 1.00000001030094 | 1.00000292308563 | 1.00000002346292 | 1.00001425319475 |
1E-9 | 0.999999336652349 | 1.00002350529665 | 1.00000031440953 | 0.999973038437884 |
1E-10 | 1.00001449374567 | 1.00003817816193 | 0.999999667861502 | 0.999114397669863 |
1E-11 | 1.00019974710850 | 1.00046831421264 | 1.00001583156215 | 0.999457853977071 |
1E-12 | 1.00036815925652 | 0.995141863117389 | 1.00004815896344 | 0.996023290904985 |
1E-13 | 1.01047288813790 | 1.08940999759569 | 1.00214944004741 | 0.686912614417231 |
1E-14 | 1.01047288813790 | 1.38688726631185 | 1.01831314069334 | 3.43456307208615 |
Experiments | Observations | Description |
---|---|---|
ExpVrZh | Vr+ZH | Vr and ZH assimilated |
ExpVrZhZdr | Vr+ZH+ZDR | As ExpVrZh with additional ZDR assimilated |
ExpVrZhKdp | Vr+ZH+KDP | As ExpVrZh with additional KDP assimilated |
ExpVrZhRhv | Vr+ZH+ρhv | As ExpVrZh with additional ρhv assimilated |
ExpVrZhPol | Vr+ZH+ZDR+KDP+ρhv | As ExpVrZh with all PRD assimilated |
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Du, M.; Gao, J.; Zhang, G.; Wang, Y.; Heiselman, P.L.; Cui, C. Assimilation of Polarimetric Radar Data in Simulation of a Supercell Storm with a Variational Approach and the WRF Model. Remote Sens. 2021, 13, 3060. https://doi.org/10.3390/rs13163060
Du M, Gao J, Zhang G, Wang Y, Heiselman PL, Cui C. Assimilation of Polarimetric Radar Data in Simulation of a Supercell Storm with a Variational Approach and the WRF Model. Remote Sensing. 2021; 13(16):3060. https://doi.org/10.3390/rs13163060
Chicago/Turabian StyleDu, Muyun, Jidong Gao, Guifu Zhang, Yunheng Wang, Pamela L. Heiselman, and Chunguang Cui. 2021. "Assimilation of Polarimetric Radar Data in Simulation of a Supercell Storm with a Variational Approach and the WRF Model" Remote Sensing 13, no. 16: 3060. https://doi.org/10.3390/rs13163060