PowerPoint TemplateRadar Data Assimilation
5. Summary and Conclusions
INTRODUCTION
Heavy rainfall is one of the major severe weathers over East Asia producing devastating flash flood, and consequently causing fatalities and property damage. Heavy rainfall is usually resulted from individual mesoscale storms or mesoscale convective systems (MCSs) embedded in synoptic-scale disturbances (Kim and Lee, 2006).
In order to understand the evolution and development mechanisms of mesoscale convective storms responsible for heavy rainfall and better predict heavy rainfall events, high-resolution observations and data assimilation techniques are important components.
Radar data assimilation is a key scientific issue in numerical weather prediction of convective systems for very short-range forecasting (Wilson et al., 1998). In recent years considerable progress has been made in the assimilation of radar observations into convective-scale numerical models for heavy rainfall prediction, and the assimilation of radar rainfall estimates.
5th ICMCS
Dong-Kyou Lee
OBJECTIVES
The objective of this study is to investigate very short-range forecasting of the WRF model through the 3DVAR data assimilation of Dual-Doppler radar data (radial velocity and reflectivity) for a heavy rainfall case accompanying mesoscale convective systems (MCSs) over the Korean Peninsula.
The WRF 3DVAR system is modified by tuning scale lengths and observation error statistics. The increment analysis update (IAU) and the rapid update cycle (RUC) are performed using radar data in the modified 3DVAR system.
With the characteristics of radar data for the period of MCSs development, sensitivity tests of assimilation window and updated frequency are investigated.
5th ICMCS
Dong-Kyou Lee
12UTC 24 July 2003
00UTC 25 July 2003
5th ICMCS
Dong-Kyou Lee
22
23
00
LFC : 764 m AGL
CAPE rapidly increased for 6 hours from 18 UTC 24 to 00 UTC 25 July. Mesoscale convective systems with 3 convective cells developed in this unstable environment.
Gwangju 2418 UTC
Gwangju 2500 UTC
Detection range
Format
TIME-LAGGED AUTOCORRELATION OF REFLECTIVITY AND RADIAL VELOCITY
42 sequential data points of reflectivity and radial velocity for 6 hours were obtained.
21 UTC 24 – 01 UTC 25 July 2003 (6min interval)
5.5 km
5.0 km
4.5 km
3.0 km
5th ICMCS
Dong-Kyou Lee
RESULT OF AUTOCORRELATION
RESULT OF AUTOCORRELATION
Radial velocity (Vr) had a longer autocorrelation time scale than that of reflectivity.
The autocorrelations dropped to zero before 30 minutes.
Update frequency smaller than 60 minutes (2 x τ ) might be used for radar data assimilation, especially for RUC and IAU.
τ
Ref
Vr
Minute
Correlation
DATA ASSIMILATION SYSTEM
The scale lengths of background error (currently about 110 km for wind and 40 km for mixing ratio) were estimated for high resolution radar observations.
The average minimization ratio (about 25 %) of the cost function allowed further minimization to be possible by adjusting the observation errors.
A radar data assimilation system for Increment Analysis Update (IAU) and Rapid Update Cycle (RUC) with 3DVAR was developed.
Tuning of 3DVAR (Version 2.1) and a radar data assimilation system
5th ICMCS
Dong-Kyou Lee
TUNING OF BACKGROUND SCALE-LENGTH
The O-B (observation – background values) of radar data was calculated from two MCS cases and one frontal case. The O-B correlation decreased in short distance. It means that the radar observation can detect short-term scale phenomena. The locality of the radar reflectivity was higher than that of radical velocity
O-B Statistics
*
We used 30 analysis cases for evaluating the O-B statistics. The mean correlation of observation minus background values between observation points as a function of their distance is computed to determine the background scale-lengths, assuming that observation deviations from the background can be used to estimate observation and background error characteristics.
O-B corr , . . . , (=rainwater+snowwater) .
5th ICMCS
Dong-Kyou Lee
SCALE LENGTHS
9 km
4 km
radial velocity
- The locality of the radar observation can be reflected by tuning the scale length of the background error against a recursive filter.
- 9 km and 4 km of scale lengths are proper for radial velocity and reflectivity, respectively.
We estimated proper scale-lengths by comparing with recursive filters with various scale-lengths.
3DVAR background error scale length . scale length correlation background error recursive filter . Radial velocity reflectivity 6 .
5th ICMCS
Dong-Kyou Lee
The expectation value of the minimized cost function is given by a half of the effective number of observations (Desroziers and Ivanov, 2001).
Further minimization of the cost function can be made by scaling a cost function term to satisfy the expectation value of the minimized cost function.
The adjustment of scaling parameters are done by iteration.
ERROR TUNING IN 3DVAR
As stated eariler, the average minimization ratio of about 25 %, suggests that the further minimization of cost function is possible. Scaling of the minimized cost function is a good method for our purpose.
5th ICMCS
Dong-Kyou Lee
To adjust the real cost function to the expectation value, scaling parameters (or error factors) will be applied to each cost function term and to each observation type, and determined iteratively.
where i means iteration number. The estimation of and (Desroziers and Ivanov, 2002).
3DVAR ERROR TUNING
5th ICMCS
Dong-Kyou Lee
The numbers of effective radar radial velocity and reflectivity observations are 16,820 and 26,977, respectively, and the tuning parameters of the observation error converge rapidly.
3DVAR ERROR FACTORS
16820
1.00
0.72
0.69
0.68
Reflectivity
26977
1.00
1.95
1.95
1.95
Jb
1.00
1.04
1.11
1.13
For the 1st iteration, the scaling parameters almost converge already. From above, the current error for radial velocity is overestimated and that for reflectivity is under estimated.
16820, 26977, tuning parameters of observation .
5th ICMCS
Dong-Kyou Lee
IMPACT OF TUNING ON MINIMIZATION
The decrease in the cost function of the un-tuned 3DVAR and the tuned 3DVAR is 25 % and 64 %, respectively.
In particular, the ratio of the minimized cost function (J) and the number of used observation (p) is 1.38 and 0.53 for the un-tuned 3DVAR and the tuned 3DVAR, respectively.
J/E(J)=J/(p/2)=1 for ideal. Therefore, J/p=0.5 for perfect case.
3DVAR ,
cost function 3DVAR cost function 4 costfunction 25% , tuned 3DVAR cost function 4 64% .
0.5 untuned 1/38, tuned 0.53 tuned 3DVAR .
5th ICMCS
Dong-Kyou Lee
Three initialization experiments are performed.
- CON : IAU with un-tuned 3DVAR increments
- RUC : RUC with tuned 3DVAR results
- IAU : IAU with tuned 3DVAR increments
5th ICMCS
Dong-Kyou Lee
INITIALIZATION EXPERIMENTS
for a 3-h assimilation window
RUC
IAU
CON
Chart1
21
21
21
21
22
22
22
22
23
23
23
23
0
0
0
0
1
1
1
1
2
2
2
2
3
3
3
3
OBS
IAU
RUC
CON
OBS
CON
RUC
IAU
3 cell MCS 3 cell 3 .
5th ICMCS
Dong-Kyou Lee
RMSEs of radial velocity
The tuning of 3DVAR results in improvement in the forecast of wind field. The improvement is effective up to 4-hour forecast.
RMSE .
Chart1
2100
2100
2100
2200
2200
2200
2300
2300
2300
0000
0000
0000
0100
0100
0100
0200
0200
0200
0300
0300
0300
IAU
RUC
CON
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
RUC
IAU
UNTUN
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
RUC
IAU
UNTUN
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
RUC
IAU
UNTUN
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
RUC
IAU
UNTUN
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
RUC
IAU
UNTUN
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
RUC
IAU
UNTUN
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
RUC
IAU
UNTUN
5th ICMCS
Dong-Kyou Lee
AND UPDATE FREQUENCY
30-min update frequency had smaller RMSE during MCSs development.
The 3-h assimilation window and 30-min update frequency was relatively better.
Chart13
1800
1800
1800
1800
1.427969
1900
1900
1900
1900
4.08563
2000
2000
2000
2000
3.335464
2100
2100
2100
2100
3.435162
2200
2200
2200
2200
4.100366
2300
2300
2300
2300
4.77928
0000
0000
0000
0000
4.810415
0100
0100
0100
0100
4.767719
0200
0200
0200
0200
6.486776
0300
0300
0300
0300
5.963005
1H_12M
1H_30M
3H_12M
3H_30M
3H_60M
RMSE_6min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1H_12M
1H_30M
3H_12M
3H_30M
3H_60M
rainfall
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1H_12M
1H_30M
3H_12M
3H_30M
3H_60M
Sheet3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1H_12M
1H_30M
3H_12M
3H_30M
3H_60M
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1H_12M
1H_30M
3H_12M
3H_30M
3H_60M
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1H_12M
1H_30M
3H_12M
3H_30M
3H_60M
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1H_12M
1H_30M
3H_12M
3H_30M
3H_60M
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1H_12M
1H_30M
3H_12M
3H_30M
3H_60M
1H_12M
3H_12M
1H_30M
3H_30M
3H_60M
3
3.5
4
4.5
3
3.5
4
4.5
3
3.5
4
4.5
3
3.5
4
4.5
1800
1.341209
1.835924
1.705393
1.427969
1.341209
1.835924
1.705393
1.427969
1.341209
1.835924
1.705393
1.427969
1806
2.847588
2.677378
3.022991
3.19918
1812
1.581365
3.101967
3.003409
3.032275
2.181073
3.663795
3.455767
3.531865
1818
3.603515
3.59049
3.23136
3.130427
3.734185
3.609944
3.261206
3.333477
1824
3.99783
4.34459
3.969767
3.553552
1830
3.711865
2.255234
3.505801
3.419509
4.38934
2.554229
4.071292
4.00228
4.390629
2.555641
4.070939
4.00112
1836
3.028324
2.345896
3.176787
3.444347
1842
3.183408
3.131924
4.102511
4.005242
3.401996
3.190174
4.561576
4.66542
1848
2.764185
3.265153
3.328351
3.612121
3.324526
3.952096
4.194173
4.609572
1854
2.635893
2.969558
3.285243
3.474055
1900
2.595382
3.504832
3.562744
3.102586
2.681051
3.696568
3.854623
3.369062
3.135006
4.238346
4.547913
4.08563
1906
3.161174
3.781925
4.215824
3.876441
1912
3.717673
3.721021
3.794942
3.89993
3.456691
3.513101
3.708752
3.868884
1918
3.208254
3.41078
3.555125
3.651548
2.955084
3.210357
3.589488
3.89001
1924
4.20604
4.104815
4.015134
3.641748
1930
3.744289
3.446115
3.508419
3.10605
4.00432
3.777579
4.020165
3.676462
3.778004
3.598215
3.743179
3.335721
1936
2.584393
2.50021
2.738304
3.054055
1942
3.130652
3.407988
3.324719
3.489148
3.397531
3.746053
3.968539
4.102015
1948
3.746598
3.274606
3.324689
3.348071
3.865747
3.772049
4.118755
4.086964
1954
3.117124
2.748335
3.063351
2.90864
2000
1.486824
1.263522
1.319824
1.302774
2.758148
2.659812
2.854186
2.825539
1.486824
1.263522
1.319824
1.302774
2.958333
2.7234
2.846143
2.984263
3.350253
3.182744
3.323993
3.335464
2006
2.248125
1.845498
2.039495
2.573575
2.500112
2.437965
2.958466
3.037916
2.388105
2.314399
2.553897
2.755037
2012
2.251574
2.210732
2.221414
2.237418
2.621514
2.649494
3.082472
3.016791
3.209476
3.091038
3.184414
3.621237
3.04026
2.86933
2.994457
3.013422
2018
2.18098
2.727272
2.562622
2.718857
2.277586
2.911686
3.304967
3.48727
1.486824
1.263522
1.319824
1.302774
2.402103
2.919185
3.042415
3.258642
2024
2.143055
2.680057
2.404028
2.648917
2.423162
3.00113
3.092071
3.06816
2.37248
2.387074
2.375734
2.410887
2030
2.144136
2.085446
2.299408
2.445876
2.317961
2.314918
2.911859
3.014596
2.17468
2.761931
2.62121
2.791265
2.667777
2.471277
2.879502
2.908562
2.439645
2.276365
2.560871
2.675162
2036
2.303118
2.22034
2.716515
2.850534
2.500633
2.503813
3.158947
3.297791
2.388105
2.314399
2.553897
2.755037
2042
2.798946
2.408981
2.530942
2.74004
2.671494
2.489627
2.653493
3.082848
3.202662
2.805849
2.926997
3.221731
3.011992
2.829687
3.045986
3.304515
2048
2.541605
2.596031
2.823313
2.832809
2.394765
2.4458
2.885431
3.106889
2.992959
3.01376
3.326077
3.370042
2.889433
2.9082
3.397227
3.343348
2054
2.939053
2.944061
2.782703
2.876587
2.76398
2.830962
2.960022
3.088811
3.37304
3.393599
3.060578
3.395839
3.067026
3.076445
3.073395
3.408894
2100
2.67239
2.707965
2.687994
3.019277
2.570106
2.568862
2.673617
3.230417
3.209476
3.091038
3.184414
3.621237
2.99793
2.878335
3.153731
3.678135
2.93579
2.819996
2.923806
3.435162
2106
2.800967
2.831769
2.993128
3.131774
2.9282
2.954953
3.151979
3.244506
3.184238
3.049858
3.03608
3.275931
3.099207
2.893211
3.112509
3.297499
2112
2.563727
2.883751
3.004087
2.863994
2.647783
3.156663
3.141301
3.043725
3.053456
3.189323
3.09442
3.128973
2.919199
3.038838
3.315788
3.296445
2118
2.967845
2.823381
2.911066
3.239212
3.093928
2.790394
2.823847
3.302518
3.12986
2.994015
3.05645
3.63781
2.927912
2.743419
3.318934
3.967939
2124
3.680339
3.648748
4.055663
3.644491
3.549372
3.630739
3.99472
3.502183
3.673138
3.862719
4.177809
3.77748
3.356323
3.588666
4.128461
3.934072
2130
3.632275
3.672831
3.829216
3.65596
3.543758
3.513368
3.692088
3.478347
3.474453
3.495067
3.902411
3.800226
3.325749
3.335227
3.932917
3.746511
2136
4.338787
5.186537
4.726943
4.653371
4.205698
4.922193
4.486184
4.502975
3.82818
4.624612
4.450172
4.657727
3.730981
4.615809
4.425704
4.698925
2142
4.270233
5.062724
5.532811
5.612761
4.020857
4.646977
5.040266
5.204394
3.794317
4.368209
4.779404
5.033424
3.648618
4.210154
4.593261
5.03182
2148
4.551933
5.254364
5.511768
5.160812
4.250061
4.732336
4.808549
4.483398
3.994466
4.533979
4.721798
4.459045
3.919915
4.466692
4.649212
4.546192
2154
4.395269
4.933965
5.393456
5.293455
4.28869
4.761303
5.12443
4.695251
4.155283
4.604239
4.906845
4.78277
3.987583
4.417699
4.78609
4.696083
2200
4.719552
5.083608
5.252956
5.174197
4.561636
5.039652
5.216992
4.945353
4.493352
4.672527
4.838309
4.725149
4.305407
4.418964
4.603673
4.487216
4.655715
4.522441
4.422211
4.100366
2206
4.416069
4.885654
5.341109
5.35147
4.316828
4.861061
5.409974
5.273208
4.315433
4.656749
5.012397
5.109284
4.031844
4.359991
4.778615
4.781555
2212
5.080037
5.795928
6.250751
6.309883
5.091812
5.781659
6.254218
6.148784
4.817793
5.328129
5.578995
5.538383
4.393402
4.915097
5.276673
5.223845
2218
4.904186
5.772684
6.254134
6.045246
4.655449
5.443181
5.840953
5.519454
4.560141
5.081647
5.134069
4.786839
4.07161
4.624397
4.81357
4.56815
2224
4.773935
5.602454
5.543747
5.190465
4.531919
5.204876
5.282651
4.792473
4.498364
4.945441
4.563372
4.103109
4.016599
4.398837
4.091912
3.665457
2230
4.403315
5.069701
5.388528
5.421629
4.359008
4.738382
4.922692
4.926938
4.070207
4.420298
4.466856
4.384487
3.594111
3.749348
3.682787
3.809407
2236
4.192443
4.721208
5.091891
5.019968
4.036232
4.291369
4.449369
4.465131
3.656795
4.037831
4.141003
4.003722
3.425064
3.520279
3.350557
3.256029
2242
4.265125
4.492927
5.058319
4.886209
4.314188
4.284063
4.538824
4.340271
3.916616
4.063762
4.430856
4.241602
3.531001
3.387543
3.347217
3.292656
2248
3.996651
4.789303
5.476568
5.142491
3.80571
4.490555
4.776213
4.331944
3.790123
4.547092
4.817947
4.222908
3.094049
3.475313
3.484526
3.150058
2300
4.336019
4.873224
5.236415
5.169862
4.20116
4.457389
4.643151
4.432391
4.001482
4.484438
4.52063
4.162643
3.689964
3.575213
3.344409
3.273216
5.023282
4.836709
4.911994
4.77928
2306
4.445646
4.942298
5.457588
5.228808
4.310983
4.628111
4.716995
4.359743
4.182601
4.674048
4.83498
4.22991
3.876471
3.896022
3.573198
3.399429
2312
4.005957
4.790048
5.563811
5.48283
3.907766
4.541704
4.772459
4.502385
3.634478
4.482985
4.928132
4.581425
3.618745
3.981057
3.848836
3.56816
2318
4.30092
5.221419
5.664324
5.605064
4.188928
5.001722
5.15223
4.819812
3.970144
4.878933
5.085719
4.663987
3.947357
4.615583
4.454257
4.117265
2324
4.183986
5.002748
5.427048
5.276838
4.011134
4.815165
4.99438
4.567698
3.940339
4.710575
4.868295
4.40683
3.91674
4.291868
4.111635
3.658241
2330
4.409836
5.265852
5.559257
5.251802
3.93415
4.723186
4.904719
4.549134
3.931824
4.654599
4.812095
4.358489
3.98438
4.362595
4.186219
3.722014
2336
4.517663
5.257819
5.467336
5.136887
3.959475
4.666336
4.801938
4.511707
4.05863
4.680264
4.838253
4.415107
4.156682
4.437475
4.22744
3.835833
2342
4.627543
5.32301
5.532606
5.335158
3.906776
4.504978
4.679064
4.552167
4.203999
4.81003
5.060209
4.607605
4.149182
4.351624
4.135343
3.75413
2348
4.620561
5.513284
5.585062
5.337592
3.90576
4.718165
4.882832
4.64648
4.362745
5.11145
5.250809
4.805576
4.40157
4.862451
4.662914
4.071197
2354
5.14786
5.825598
5.870884
5.467721
4.323807
5.022197
5.089318
4.508381
4.939933
5.457284
5.502544
4.768025
4.798966
5.082047
4.842621
4.078969
0000
5.297458
5.888509
5.890599
5.398429
4.540407
5.192477
5.103449
4.381847
5.182856
5.593203
5.370369
4.608612
4.981695
5.267531
4.899266
4.112076
5.41952
5.803602
5.564015
4.810415
0006
5.726738
6.241102
6.153082
5.674496
4.882725
5.557929
5.417424
4.767593
5.480356
5.921405
5.690024
5.009767
5.290165
5.616949
5.153232
4.408599
0012
6.016973
6.167901
5.956717
5.43644
5.208637
5.53064
5.353596
4.616801
5.796957
5.938082
5.683948
4.93891
5.666845
5.652054
5.158216
4.438222
0018
6.617252
6.726891
5.984155
5.318679
5.832049
6.16773
5.395074
4.601536
6.316225
6.459581
5.621913
4.812195
6.113762
6.194383
5.210011
4.381161
0024
6.994937
6.896097
6.223208
5.57576
6.225965
6.262582
5.657533
4.98721
6.718406
6.563892
5.879804
5.155816
6.58186
6.331254
5.540891
4.779181
0030
6.382621
6.518071
5.630371
5.026872
5.471827
5.849201
5.118371
4.228923
6.027627
6.107022
5.289492
4.446954
5.793191
5.870384
5.016412
4.170815
0036
7.100546
6.95216
5.900046
5.549345
6.198276
6.315984
5.413622
4.851822
6.727929
6.574833
5.589438
5.048618
6.455659
6.317316
5.322807
4.777607
0042
6.720704
6.591173
5.558406
5.088206
5.95943
5.97602
5.088523
4.447039
6.457924
6.147185
5.207019
4.607263
6.113636
5.802948
4.904406
4.27347
0048
7.298712
7.141028
6.070445
5.281921
6.608564
6.529109
5.392832
4.621291
7.089211
6.660182
5.526326
4.765382
6.646992
6.279867
5.137891
4.398178
0054
7.559378
7.013659
6.015632
5.347198
6.908378
6.495965
5.397455
4.694019
7.394297
6.604387
5.49363
4.871246
6.967119
6.22111
5.141104
4.518304
0100
6.642883
6.711407
5.943275
5.370442
6.219767
6.225524
5.280631
4.50198
6.684696
6.303789
5.409729
4.737563
6.24381
5.978928
5.077234
4.396555
6.828626
6.669899
5.604791
4.767719
0106
7.211448
7.462458
6.608786
5.71846
6.924017
6.876088
5.903948
4.882213
7.246801
6.992183
6.065444
5.113634
6.657396
6.505639
5.67511
4.760009
0112
7.07476
7.206441
6.406788
5.469823
6.847847
6.564238
5.646988
4.660591
7.161333
6.769364
5.927611
4.96721
6.535436
6.281229
5.482202
4.560016
0118
7.644206
7.936807
7.017775
6.088724
7.389154
7.248421
6.186044
5.185188
7.642208
7.479945
6.466811
5.517503
7.073766
7.036728
6.075879
5.15219
0124
7.631681
7.402319
6.701519
5.863976
7.605321
6.810318
5.917688
5.059793
7.640342
6.992599
6.220218
5.357166
7.190756
6.525987
5.790885
5.017568
0130
7.200555
7.497622
6.723837
5.911055
7.213414
6.987041
5.985142
5.171563
7.21943
7.126161
6.265217
5.472466
6.76232
6.65048
5.814418
5.104249
0136
7.762582
7.683156
6.785182
5.740829
7.823478
7.195926
6.114069
4.95778
7.804163
7.324713
6.338247
5.276032
7.460064
6.953846
6.029137
4.995599
0142
7.761197
7.649533
6.759979
5.868282
7.885465
7.270123
6.176581
5.191883
7.798352
7.344617
6.37289
5.483657
7.526998
7.091363
6.127782
5.23781
0148
7.864573
7.554698
6.739915
5.665281
8.025375
7.260767
6.197711
4.883168
7.888626
7.271468
6.368222
5.269474
7.631721
7.143627
6.26244
5.037415
0154
7.654289
7.72098
6.842066
5.866494
7.818788
7.46022
6.382395
5.208185
7.712498
7.481908
6.519222
5.55758
7.47328
7.377907
6.468509
5.365899
0200
8.097509
8.335113
7.550732
6.664128
8.261789
8.149378
7.132442
5.900276
8.159636
8.075377
7.208795
6.294831
7.993912
8.102504
7.288122
6.150379
8.178285
8.42485
7.598645
6.486776
0206
7.677079
7.466342
6.544387
5.791417
7.912212
7.38175
6.167206
4.982852
7.715294
7.178379
6.175685
5.438188
7.669126
7.347823
6.324877
5.229136
0212
7.515734
7.344002
6.500149
5.612687
7.690058
7.232186
6.126245
4.832602
7.537522
7.050866
6.140096
5.279789
7.517383
7.234656
6.216295
4.985829
0218
8.211613
7.852682
7.027329
5.868657
8.350042
7.65814
6.576009
5.039583
8.200046
7.551043
6.657598
5.522355
8.214157
7.732926
6.693662
5.201613
0224
7.930243
7.963521
7.071711
5.999175
8.017234
7.682821
6.640797
5.224228
7.936209
7.646784
6.683585
5.637437
7.980727
7.777185
6.653749
5.284073
0230
7.491498
7.665956
7.109621
6.221785
7.507437
7.370476
6.736426
5.496782
7.438587
7.350636
6.743724
5.851804
7.53845
7.477573
6.757291
5.554904
0236
8.532478
8.987126
8.101374
7.199718
8.558505
8.517196
7.647822
6.583596
8.556492
8.60845
7.762775
6.910572
8.62294
8.692342
7.7101
6.703394
0242
8.347017
8.910352
8.591022
7.55228
8.693516
8.681023
8.309178
7.090387
8.418918
8.551315
8.27255
7.286709
8.804691
8.867911
8.317801
7.154119
0248
8.189051
8.52969
8.509913
7.746955
8.531096
8.331725
8.288587
7.438392
8.222679
8.13821
8.219609
7.561192
8.611701
8.498612
8.258654
7.436552
0254
7.934392
7.89614
7.410513
6.566131
8.369176
8.054478
7.446732
6.446723
8.00785
7.623882
7.205559
6.433327
8.456751
8.07268
7.417004
6.475897
0300
7.915103
7.506017
6.872594
5.86862
8.416709
7.602544
6.912789
5.887615
8.024784
7.337604
6.733651
5.754618
8.41087
7.655876
6.86693
5.822635
7.997647
7.585913
7.02316
5.963005
69
1800
1800
1800
1800
1806
1806
1806
1806
1812
1812
1812
1812
1818
1818
1818
1818
1824
1824
1824
1824
1830
1830
1830
1830
1836
1836
1836
1836
1842
1842
1842
1842
1848
1848
1848
1848
1854
1854
1854
1854
1900
1900
1900
1900
1906
1906
1906
1906
1912
1912
1912
1912
1918
1918
1918
1918
1924
1924
1924
1924
1930
1930
1930
1930
1936
1936
1936
1936
1942
1942
1942
1942
1948
1948
1948
1948
1954
1954
1954
1954
2000
2000
2000
2000
2006
2006
2006
2006
2012
2012
2012
2012
2018
2018
2018
2018
2024
2024
2024
2024
2030
2030
2030
2030
2036
2036
2036
2036
2042
2042
2042
2042
2048
2048
2048
2048
2054
2054
2054
2054
2100
2100
2100
2100
2106
2106
2106
2106
2112
2112
2112
2112
2118
2118
2118
2118
2124
2124
2124
2124
2130
2130
2130
2130
2136
2136
2136
2136
2142
2142
2142
2142
2148
2148
2148
2148
2154
2154
2154
2154
2200
2200
2200
2200
2206
2206
2206
2206
2212
2212
2212
2212
2218
2218
2218
2218
2224
2224
2224
2224
2230
2230
2230
2230
2236
2236
2236
2236
2242
2242
2242
2242
2248
2248
2248
2248
2300
2300
2300
2300
2306
2306
2306
2306
2312
2312
2312
2312
2318
2318
2318
2318
2324
2324
2324
2324
2330
2330
2330
2330
2336
2336
2336
2336
2342
2342
2342
2342
2348
2348
2348
2348
2354
2354
2354
2354
0000
0000
0000
0000
0006
0006
0006
0006
0012
0012
0012
0012
0018
0018
0018
0018
0024
0024
0024
0024
0030
0030
0030
0030
0036
0036
0036
0036
0042
0042
0042
0042
0048
0048
0048
0048
0054
0054
0054
0054
0100
0100
0100
0100
0106
0106
0106
0106
0112
0112
0112
0112
0118
0118
0118
0118
0124
0124
0124
0124
0130
0130
0130
0130
0136
0136
0136
0136
0142
0142
0142
0142
0148
0148
0148
0148
0154
0154
0154
0154
0200
0200
0200
0200
0206
0206
0206
0206
0212
0212
0212
0212
0218
0218
0218
0218
0224
0224
0224
0224
0230
0230
0230
0230
0236
0236
0236
0236
0242
0242
0242
0242
0248
0248
0248
0248
0254
0254
0254
0254
0300
0300
0300
0300
1H_12M
1H_30M
3H_12M
3H_30M
1.341209
1.341209
2.847588
1.581365
2.181073
3.603515
3.734185
3.99783
3.711865
4.38934
3.028324
3.183408
3.401996
2.764185
3.324526
2.635893
2.595382
2.681051
3.161174
3.717673
3.456691
3.208254
2.955084
4.20604
3.744289
4.00432
2.584393
3.130652
3.397531
3.746598
3.865747
3.117124
1.486824
1.486824
2.758148
2.958333
2.248125
2.388105
2.500112
2.251574
3.209476
2.621514
3.04026
2.18098
1.486824
2.277586
2.402103
2.143055
2.37248
2.423162
2.144136
2.17468
2.317961
2.667777
2.303118
2.388105
2.500633
2.798946
3.202662
2.671494
3.011992
2.541605
2.992959
2.394765
2.889433
2.939053
3.37304
2.76398
3.067026
2.67239
3.209476
2.570106
2.99793
2.800967
3.184238
2.9282
3.099207
2.563727
3.053456
2.647783
2.919199
2.967845
3.12986
3.093928
2.927912
3.680339
3.673138
3.549372
3.356323
3.632275
3.474453
3.543758
3.325749
4.338787
3.82818
4.205698
3.730981
4.270233
3.794317
4.020857
3.648618
4.551933
3.994466
4.250061
3.919915
4.395269
4.155283
4.28869
3.987583
4.719552
4.493352
4.561636
4.305407
4.416069
4.315433
4.316828
4.031844
5.080037
4.817793
5.091812
4.393402
4.904186
4.560141
4.655449
4.07161
4.773935
4.498364
4.531919
4.016599
4.403315
4.070207
4.359008
3.594111
4.192443
3.656795
4.036232
3.425064
4.265125
3.916616
4.314188
3.531001
3.996651
3.790123
3.80571
3.094049
4.336019
4.001482
4.20116
3.689964
4.445646
4.182601
4.310983
3.876471
4.005957
3.634478
3.907766
3.618745
4.30092
3.970144
4.188928
3.947357
4.183986
3.940339
4.011134
3.91674
4.409836
3.931824
3.93415
3.98438
4.517663
4.05863
3.959475
4.156682
4.627543
4.203999
3.906776
4.149182
4.620561
4.362745
3.90576
4.40157
5.14786
4.939933
4.323807
4.798966
5.297458
5.182856
4.540407
4.981695
5.726738
5.480356
4.882725
5.290165
6.016973
5.796957
5.208637
5.666845
6.617252
6.316225
5.832049
6.113762
6.994937
6.718406
6.225965
6.58186
6.382621
6.027627
5.471827
5.793191
7.100546
6.727929
6.198276
6.455659
6.720704
6.457924
5.95943
6.113636
7.298712
7.089211
6.608564
6.646992
7.559378
7.394297
6.908378
6.967119
6.642883
6.684696
6.219767
6.24381
7.211448
7.246801
6.924017
6.657396
7.07476
7.161333
6.847847
6.535436
7.644206
7.642208
7.389154
7.073766
7.631681
7.640342
7.605321
7.190756
7.200555
7.21943
7.213414
6.76232
7.762582
7.804163
7.823478
7.460064
7.761197
7.798352
7.885465
7.526998
7.864573
7.888626
8.025375
7.631721
7.654289
7.712498
7.818788
7.47328
8.097509
8.159636
8.261789
7.993912
7.677079
7.715294
7.912212
7.669126
7.515734
7.537522
7.690058
7.517383
8.211613
8.200046
8.350042
8.214157
7.930243
7.936209
8.017234
7.980727
7.491498
7.438587
7.507437
7.53845
8.532478
8.556492
8.558505
8.62294
8.347017
8.418918
8.693516
8.804691
8.189051
8.222679
8.531096
8.611701
7.934392
8.00785
8.369176
8.456751
7.915103
8.024784
8.416709
8.41087
1800
1800
1800
1800
1806
1806
1806
1806
1812
1812
1812
1812
1818
1818
1818
1818
1824
1824
1824
1824
1830
1830
1830
1830
1836
1836
1836
1836
1842
1842
1842
1842
1848
1848
1848
1848
1854
1854
1854
1854
1900
1900
1900
1900
1906
1906
1906
1906
1912
1912
1912
1912
1918
1918
1918
1918
1924
1924
1924
1924
1930
1930
1930
1930
1936
1936
1936
1936
1942
1942
1942
1942
1948
1948
1948
1948
1954
1954
1954
1954
2000
2000
2000
2000
2006
2006
2006
2006
2012
2012
2012
2012
2018
2018
2018
2018
2024
2024
2024
2024
2030
2030
2030
2030
2036
2036
2036
2036
2042
2042
2042
2042
2048
2048
2048
2048
2054
2054
2054
2054
2100
2100
2100
2100
2106
2106
2106
2106
2112
2112
2112
2112
2118
2118
2118
2118
2124
2124
2124
2124
2130
2130
2130
2130
2136
2136
2136
2136
2142
2142
2142
2142
2148
2148
2148
2148
2154
2154
2154
2154
2200
2200
2200
2200
2206
2206
2206
2206
2212
2212
2212
2212
2218
2218
2218
2218
2224
2224
2224
2224
2230
2230
2230
2230
2236
2236
2236
2236
2242
2242
2242
2242
2248
2248
2248
2248
2300
2300
2300
2300
2306
2306
2306
2306
2312
2312
2312
2312
2318
2318
2318
2318
2324
2324
2324
2324
2330
2330
2330
2330
2336
2336
2336
2336
2342
2342
2342
2342
2348
2348
2348
2348
2354
2354
2354
2354
0000
0000
0000
0000
0006
0006
0006
0006
0012
0012
0012
0012
0018
0018
0018
0018
0024
0024
0024
0024
0030
0030
0030
0030
0036
0036
0036
0036
0042
0042
0042
0042
0048
0048
0048
0048
0054
0054
0054
0054
0100
0100
0100
0100
0106
0106
0106
0106
0112
0112
0112
0112
0118
0118
0118
0118
0124
0124
0124
0124
0130
0130
0130
0130
0136
0136
0136
0136
0142
0142
0142
0142
0148
0148
0148
0148
0154
0154
0154
0154
0200
0200
0200
0200
0206
0206
0206
0206
0212
0212
0212
0212
0218
0218
0218
0218
0224
0224
0224
0224
0230
0230
0230
0230
0236
0236
0236
0236
0242
0242
0242
0242
0248
0248
0248
0248
0254
0254
0254
0254
0300
0300
0300
0300
1H_12M
1H_30M
3H_12M
3H_30M
1.835924
1.835924
2.677378
3.101967
3.663795
3.59049
3.609944
4.34459
2.255234
2.554229
2.345896
3.131924
3.190174
3.265153
3.952096
2.969558
3.504832
3.696568
3.781925
3.721021
3.513101
3.41078
3.210357
4.104815
3.446115
3.777579
2.50021
3.407988
3.746053
3.274606
3.772049
2.748335
1.263522
1.263522
2.659812
2.7234
1.845498
2.314399
2.437965
2.210732
3.091038
2.649494
2.86933
2.727272
1.263522
2.911686
2.919185
2.680057
2.387074
3.00113
2.085446
2.761931
2.314918
2.471277
2.22034
2.314399
2.503813
2.408981
2.805849
2.489627
2.829687
2.596031
3.01376
2.4458
2.9082
2.944061
3.393599
2.830962
3.076445
2.707965
3.091038
2.568862
2.878335
2.831769
3.049858
2.954953
2.893211
2.883751
3.189323
3.156663
3.038838
2.823381
2.994015
2.790394
2.743419
3.648748
3.862719
3.630739
3.588666
3.672831
3.495067
3.513368
3.335227
5.186537
4.624612
4.922193
4.615809
5.062724
4.368209
4.646977
4.210154
5.254364
4.533979
4.732336
4.466692
4.933965
4.604239
4.761303
4.417699
5.083608
4.672527
5.039652
4.418964
4.885654
4.656749
4.861061
4.359991
5.795928
5.328129
5.781659
4.915097
5.772684
5.081647
5.443181
4.624397
5.602454
4.945441
5.204876
4.398837
5.069701
4.420298
4.738382
3.749348
4.721208
4.037831
4.291369
3.520279
4.492927
4.063762
4.284063
3.387543
4.789303
4.547092
4.490555
3.475313
4.873224
4.484438
4.457389
3.575213
4.942298
4.674048
4.628111
3.896022
4.790048
4.482985
4.541704
3.981057
5.221419
4.878933
5.001722
4.615583
5.002748
4.710575
4.815165
4.291868
5.265852
4.654599
4.723186
4.362595
5.257819
4.680264
4.666336
4.437475
5.32301
4.81003
4.504978
4.351624
5.513284
5.11145
4.718165
4.862451
5.825598
5.457284
5.022197
5.082047
5.888509
5.593203
5.192477
5.267531
6.241102
5.921405
5.557929
5.616949
6.167901
5.938082
5.53064
5.652054
6.726891
6.459581
6.16773
6.194383
6.896097
6.563892
6.262582
6.331254
6.518071
6.107022
5.849201
5.870384
6.95216
6.574833
6.315984
6.317316
6.591173
6.147185
5.97602
5.802948
7.141028
6.660182
6.529109
6.279867
7.013659
6.604387
6.495965
6.22111
6.711407
6.303789
6.225524
5.978928
7.462458
6.992183
6.876088
6.505639
7.206441
6.769364
6.564238
6.281229
7.936807
7.479945
7.248421
7.036728
7.402319
6.992599
6.810318
6.525987
7.497622
7.126161
6.987041
6.65048
7.683156
7.324713
7.195926
6.953846
7.649533
7.344617
7.270123
7.091363
7.554698
7.271468
7.260767
7.143627
7.72098
7.481908
7.46022
7.377907
8.335113
8.075377
8.149378
8.102504
7.466342
7.178379
7.38175
7.347823
7.344002
7.050866
7.232186
7.234656
7.852682
7.551043
7.65814
7.732926
7.963521
7.646784
7.682821
7.777185
7.665956
7.350636
7.370476
7.477573
8.987126
8.60845
8.517196
8.692342
8.910352
8.551315
8.681023
8.867911
8.52969
8.13821
8.331725
8.498612
7.89614
7.623882
8.054478
8.07268
7.506017
7.337604
7.602544
7.655876
1800
1800
1800
1800
1806
1806
1806
1806
1812
1812
1812
1812
1818
1818
1818
1818
1824
1824
1824
1824
1830
1830
1830
1830
1836
1836
1836
1836
1842
1842
1842
1842
1848
1848
1848
1848
1854
1854
1854
1854
1900
1900
1900
1900
1906
1906
1906
1906
1912
1912
1912
1912
1918
1918
1918
1918
1924
1924
1924
1924
1930
1930
1930
1930
1936
1936
1936
1936
1942
1942
1942
1942
1948
1948
1948
1948
1954
1954
1954
1954
2000
2000
2000
2000
2006
2006
2006
2006
2012
2012
2012
2012
2018
2018
2018
2018
2024
2024
2024
2024
2030
2030
2030
2030
2036
2036
2036
2036
2042
2042
2042
2042
2048
2048
2048
2048
2054
2054
2054
2054
2100
2100
2100
2100
2106
2106
2106
2106
2112
2112
2112
2112
2118
2118
2118
2118
2124
2124
2124
2124
2130
2130
2130
2130
2136
2136
2136
2136
2142
2142
2142
2142
2148
2148
2148
2148
2154
2154
2154
2154
2200
2200
2200
2200
2206
2206
2206
2206
2212
2212
2212
2212
2218
2218
2218
2218
2224
2224
2224
2224
2230
2230
2230
2230
2236
2236
2236
2236
2242
2242
2242
2242
2248
2248
2248
2248
2300
2300
2300
2300
2306
2306
2306
2306
2312
2312
2312
2312
2318
2318
2318
2318
2324
2324
2324
2324
2330
2330
2330
2330
2336
2336
2336
2336
2342
2342
2342
2342
2348
2348
2348
2348
2354
2354
2354
2354
0000
0000
0000
0000
0006
0006
0006
0006
0012
0012
0012
0012
0018
0018
0018
0018
0024
0024
0024
0024
0030
0030
0030
0030
0036
0036
0036
0036
0042
0042
0042
0042
0048
0048
0048
0048
0054
0054
0054
0054
0100
0100
0100
0100
0106
0106
0106
0106
0112
0112
0112
0112
0118
0118
0118
0118
0124
0124
0124
0124
0130
0130
0130
0130
0136
0136
0136
0136
0142
0142
0142
0142
0148
0148
0148
0148
0154
0154
0154
0154
0200
0200
0200
0200
0206
0206
0206
0206
0212
0212
0212
0212
0218
0218
0218
0218
0224
0224
0224
0224
0230
0230
0230
0230
0236
0236
0236
0236
0242
0242
0242
0242
0248
0248
0248
0248
0254
0254
0254
0254
0300
0300
0300
0300
1H_12M
1H_30M
3H_12M
3H_30M
1.705393
1.705393
3.022991
3.003409
3.455767
3.23136
3.261206
3.969767
3.505801
4.071292
3.176787
4.102511
4.561576
3.328351
4.194173
3.285243
3.562744
3.854623
4.215824
3.794942
3.708752
3.555125
3.589488
4.015134
3.508419
4.020165
2.738304
3.324719
3.968539
3.324689
4.118755
3.063351
1.319824
1.319824
2.854186
2.846143
2.039495
2.553897
2.958466
2.221414
3.184414
3.082472
2.994457
2.562622
1.319824
3.304967
3.042415
2.404028
2.375734
3.092071
2.299408
2.62121
2.911859
2.879502
2.716515
2.553897
3.158947
2.530942
2.926997
2.653493
3.045986
2.823313
3.326077
2.885431
3.397227
2.782703
3.060578
2.960022
3.073395
2.687994
3.184414
2.673617
3.153731
2.993128
3.03608
3.151979
3.112509
3.004087
3.09442
3.141301
3.315788
2.911066
3.05645
2.823847
3.318934
4.055663
4.177809
3.99472
4.128461
3.829216
3.902411
3.692088
3.932917
4.726943
4.450172
4.486184
4.425704
5.532811
4.779404
5.040266
4.593261
5.511768
4.721798
4.808549
4.649212
5.393456
4.906845
5.12443
4.78609
5.252956
4.838309
5.216992
4.603673
5.341109
5.012397
5.409974
4.778615
6.250751
5.578995
6.254218
5.276673
6.254134
5.134069
5.840953
4.81357
5.543747
4.563372
5.282651
4.091912
5.388528
4.466856
4.922692
3.682787
5.091891
4.141003
4.449369
3.350557
5.058319
4.430856
4.538824
3.347217
5.476568
4.817947
4.776213
3.484526
5.236415
4.52063
4.643151
3.344409
5.457588
4.83498
4.716995
3.573198
5.563811
4.928132
4.772459
3.848836
5.664324
5.085719
5.15223
4.454257
5.427048
4.868295
4.99438
4.111635
5.559257
4.812095
4.904719
4.186219
5.467336
4.838253
4.801938
4.22744
5.532606
5.060209
4.679064
4.135343
5.585062
5.250809
4.882832
4.662914
5.870884
5.502544
5.089318
4.842621
5.890599
5.370369
5.103449
4.899266
6.153082
5.690024
5.417424
5.153232
5.956717
5.683948
5.353596
5.158216
5.984155
5.621913
5.395074
5.210011
6.223208
5.879804
5.657533
5.540891
5.630371
5.289492
5.118371
5.016412
5.900046
5.589438
5.413622
5.322807
5.558406
5.207019
5.088523
4.904406
6.070445
5.526326
5.392832
5.137891
6.015632
5.49363
5.397455
5.141104
5.943275
5.409729
5.280631
5.077234
6.608786
6.065444
5.903948
5.67511
6.406788
5.927611
5.646988
5.482202
7.017775
6.466811
6.186044
6.075879
6.701519
6.220218
5.917688
5.790885
6.723837
6.265217
5.985142
5.814418
6.785182
6.338247
6.114069
6.029137
6.759979
6.37289
6.176581
6.127782
6.739915
6.368222
6.197711
6.26244
6.842066
6.519222
6.382395
6.468509
7.550732
7.208795
7.132442
7.288122
6.544387
6.175685
6.167206
6.324877
6.500149
6.140096
6.126245
6.216295
7.027329
6.657598
6.576009
6.693662
7.071711
6.683585
6.640797
6.653749
7.109621
6.743724
6.736426
6.757291
8.101374
7.762775
7.647822
7.7101
8.591022
8.27255
8.309178
8.317801
8.509913
8.219609
8.288587
8.258654
7.410513
7.205559
7.446732
7.417004
6.872594
6.733651
6.912789
6.86693
1800
1800
1800
1800
1.427969
1806
1806
1806
1806
1812
1812
1812
1812
1818
1818
1818
1818
1824
1824
1824
1824
1830
1830
1830
1830
4.00112
1836
1836
1836
1836
1842
1842
1842
1842
1848
1848
1848
1848
1854
1854
1854
1854
1900
1900
1900
1900
4.08563
1906
1906
1906
1906
1912
1912
1912
1912
1918
1918
1918
1918
1924
1924
1924
1924
1930
1930
1930
1930
3.335721
1936
1936
1936
1936
1942
1942
1942
1942
1948
1948
1948
1948
1954
1954
1954
1954
2000
2000
2000
2000
3.335464
2006
2006
2006
2006
2012
2012
2012
2012
2018
2018
2018
2018
2024
2024
2024
2024
2030
2030
2030
2030
2.675162
2036
2036
2036
2036
2042
2042
2042
2042
2048
2048
2048
2048
2054
2054
2054
2054
2100
2100
2100
2100
3.435162
2106
2106
2106
2106
2112
2112
2112
2112
2118
2118
2118
2118
2124
2124
2124
2124
2130
2130
2130
2130
2136
2136
2136
2136
2142
2142
2142
2142
2148
2148
2148
2148
2154
2154
2154
2154
2200
2200
2200
2200
4.100366
2206
2206
2206
2206
2212
2212
2212
2212
2218
2218
2218
2218
2224
2224
2224
2224
2230
2230
2230
2230
2236
2236
2236
2236
2242
2242
2242
2242
2248
2248
2248
2248
2300
2300
2300
2300
4.77928
2306
2306
2306
2306
2312
2312
2312
2312
2318
2318
2318
2318
2324
2324
2324
2324
2330
2330
2330
2330
2336
2336
2336
2336
2342
2342
2342
2342
2348
2348
2348
2348
2354
2354
2354
2354
0000
0000
0000
0000
4.810415
0006
0006
0006
0006
0012
0012
0012
0012
0018
0018
0018
0018
0024
0024
0024
0024
0030
0030
0030
0030
0036
0036
0036
0036
0042
0042
0042
0042
0048
0048
0048
0048
0054
0054
0054
0054
0100
0100
0100
0100
4.767719
0106
0106
0106
0106
0112
0112
0112
0112
0118
0118
0118
0118
0124
0124
0124
0124
0130
0130
0130
0130
0136
0136
0136
0136
0142
0142
0142
0142
0148
0148
0148
0148
0154
0154
0154
0154
0200
0200
0200
0200
6.486776
0206
0206
0206
0206
0212
0212
0212
0212
0218
0218
0218
0218
0224
0224
0224
0224
0230
0230
0230
0230
0236
0236
0236
0236
0242
0242
0242
0242
0248
0248
0248
0248
0254
0254
0254
0254
0300
0300
0300
0300
5.963005
1H_12M
1H_30M
3H_12M
3H_30M
3H_60M
1.427969
1.427969
3.19918
3.032275
3.531865
3.130427
3.333477
3.553552
3.419509
4.00228
3.444347
4.005242
4.66542
3.612121
4.609572
3.474055
3.102586
3.369062
3.876441
3.89993
3.868884
3.651548
3.89001
3.641748
3.10605
3.676462
3.054055
3.489148
4.102015
3.348071
4.086964
2.90864
1.302774
1.302774
2.825539
2.984263
2.573575
2.755037
3.037916
2.237418
3.621237
3.016791
3.013422
2.718857
1.302774
3.48727
3.258642
2.648917
2.410887
3.06816
2.445876
2.791265
3.014596
2.908562
2.850534
2.755037
3.297791
2.74004
3.221731
3.082848
3.304515
2.832809
3.370042
3.106889
3.343348
2.876587
3.395839
3.088811
3.408894
3.019277
3.621237
3.230417
3.678135
3.131774
3.275931
3.244506
3.297499
2.863994
3.128973
3.043725
3.296445
3.239212
3.63781
3.302518
3.967939
3.644491
3.77748
3.502183
3.934072
3.65596
3.800226
3.478347
3.746511
4.653371
4.657727
4.502975
4.698925
5.612761
5.033424
5.204394
5.03182
5.160812
4.459045
4.483398
4.546192
5.293455
4.78277
4.695251
4.696083
5.174197
4.725149
4.945353
4.487216
5.35147
5.109284
5.273208
4.781555
6.309883
5.538383
6.148784
5.223845
6.045246
4.786839
5.519454
4.56815
5.190465
4.103109
4.792473
3.665457
5.421629
4.384487
4.926938
3.809407
5.019968
4.003722
4.465131
3.256029
4.886209
4.241602
4.340271
3.292656
5.142491
4.222908
4.331944
3.150058
5.169862
4.162643
4.432391
3.273216
5.228808
4.22991
4.359743
3.399429
5.48283
4.581425
4.502385
3.56816
5.605064
4.663987
4.819812
4.117265
5.276838
4.40683
4.567698
3.658241
5.251802
4.358489
4.549134
3.722014
5.136887
4.415107
4.511707
3.835833
5.335158
4.607605
4.552167
3.75413
5.337592
4.805576
4.64648
4.071197
5.467721
4.768025
4.508381
4.078969
5.398429
4.608612
4.381847
4.112076
5.674496
5.009767
4.767593
4.408599
5.43644
4.93891
4.616801
4.438222
5.318679
4.812195
4.601536
4.381161
5.57576
5.155816
4.98721
4.779181
5.026872
4.446954
4.228923
4.170815
5.549345
5.048618
4.851822
4.777607
5.088206
4.607263
4.447039
4.27347
5.281921
4.765382
4.621291
4.398178
5.347198
4.871246
4.694019
4.518304
5.370442
4.737563
4.50198
4.396555
5.71846
5.113634
4.882213
4.760009
5.469823
4.96721
4.660591
4.560016
6.088724
5.517503
5.185188
5.15219
5.863976
5.357166
5.059793
5.017568
5.911055
5.472466
5.171563
5.104249
5.740829
5.276032
4.95778
4.995599
5.868282
5.483657
5.191883
5.23781
5.665281
5.269474
4.883168
5.037415
5.866494
5.55758
5.208185
5.365899
6.664128
6.294831
5.900276
6.150379
5.791417
5.438188
4.982852
5.229136
5.612687
5.279789
4.832602
4.985829
5.868657
5.522355
5.039583
5.201613
5.999175
5.637437
5.224228
5.284073
6.221785
5.851804
5.496782
5.554904
7.199718
6.910572
6.583596
6.703394
7.55228
7.286709
7.090387
7.154119
7.746955
7.561192
7.438392
7.436552
6.566131
6.433327
6.446723
6.475897
5.86862
5.754618
5.887615
5.822635
3H_12M
1H_12M
1H_30M
3H_30M
obs
Chart2
Sheet2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1H_12M
1H_30M
3H_12M
3H_30M
Sheet3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1H_12M
1H_30M
3H_12M
3H_30M
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1H_12M
1H_30M
3H_12M
3H_30M
3H_60M
3H_12M
1H_12M
1H_30M
3H_30M
obs
SUMMARY AND CONCLUSION
In this study, radar data had much shorter scale-lengths in 3DVAR compared to the typical synoptic observations. The scale length of 9 km for radial velocity, and 4 km for reflectivity were used.
The error for radial velocity (2 m/s) was overestimated (70 % of the currently used error), and that for reflectivity (5 dBZ) was underestimated (190 % of the currently used error). Using the error factors led a rapid converge of radar data assimilation in one iteration.
In the radar data assimilation, the 3DVAR tuned by the error factors improved the maximum rainfall amount that was in better agreement with observation than that of the un-tuned 3DVAR. The RMS errors of radial velocity simulated by the tuned 3DVAR (RUC and IAU) were also smaller than that of the un-tuned 3DVAR.
The time-lagged autocorrelation of radar radial velocity and reflectivity during the period of the storm development was about 30 min and less than 30 min, respectively. In the IAU the 30-minute update cycles was better compared to other update cycles in this study.
In the 3DVAR assimilation using radar data, the assimilation window was more sensitive to the simulation of heavy rainfall than the update frequency.
5th ICMCS
Dong-Kyou Lee
Thank You !
5th ICMCS
Dong-Kyou Lee
5th ICMCS
Dong-Kyou Lee
5th ICMCS
Dong-Kyou Lee
465 data points
More data points had a shorter time period, and did not much change
the characteristics of the autocorrelation.
AUTOCORRELATION
Ref
Vr
GRID NUDGING AND IAU IN WRF
For the grid nudging, we have implemented time-variant nudging (=1) and target nudging (=3)
Surface (in planetary boundary layer) nudging (or IAU) is not yet implemented.
The nudging of U, V, and T is currently tested.
Nudging
IAU
As can be seen in the above equation, grid nudging calculates nudging term at each model integration steps, but IAU has a fixed forcing term over the assimilation period.
The analysis increment is given by 3DVAR. As nudging term various at each time steps, it may cause a high frequency numerical noise.
5th ICMCS
Dong-Kyou Lee
TIME-HEIGHT SECTION
Sang-Ju
0
90
22230123
1
2
3
4
5
6
7
8
9
1800190020002100220023000000010002000300
1
2
3
4
5
6
7
8
9
1800190020002100220023000000010002000300
1H_12M
3H_12M
1H_30M
3H_30M
1
2
3
4
5
6
7
8
9
1800190020002100220023000000010002000300
1
2
3
4
5
6
7
8
9
18002300
1
2
3
4
5
6
7
8
9
1800190020002100220023000000010002000300
1
2
3
4
5
6
7
8
9
1800190020002100220023000000010002000300
1
2
3
4
5
6
7
8
9
1800190020002100220023000000010002000300
Time (UTC)
1
2
3
4
5
6
7
8
9
1800190020002100220023000000010002000300
0
1
2
3
4
5
6
7
8
9
10
1234567
0
1
2
3
4
5
6
7
8
9
10
1234567
0
1
2
3
4
5
6
7
8
9
10
1234567
0
1
2
3
4
5
6
7
8
9
10
1234567
0
5
10
15
20
25
30
1234567
0
5
10
15
20
25
30
1234567
0
5
10
15
20
25
30
1234567
0
5
10
15
20
25
30
1234567