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Assimilation of GNSS data in KMA NWP models Eun-Hee Kim, Eunhee Lee, Yong Hee Lee, and Sangwon Joo IROWG-6, 21-27 September 2017, Colorado USA
Assimilation of GNSS data in KMA NWP models
Eun-Hee Kim, Eunhee Lee, Yong Hee Lee, and Sangwon Joo
IROWG-6, 21-27 September 2017, Colorado USA
Contents
§ Current status of GNSS data utilization at the KMA
§ GNSS-RO data assimilation in local model
- data coverage
- quality control
- preliminary results
§ Ground-based GNSS data assimilation in local model
- quality control
- impact test
§ Summary and Future Plans
Operational Models
Regional(RDAPS)• Resolution
12kmL70(UM)(0.11°x0.11° / top=80km)
• Target Length : 87hrs(6 hourly)
• Initialization : 4DVAR
Global(GDAPS)• Resolution
N768L70(UM)(~17km / top = 80km)
• Target Length288hrs (00/12UTC)87hrs (06/18UTC)
• Initialization : HybridEnsemble 4DVAR
Global EPS• Resolution
N400L70(UM)(~32km/ top = 80km)
• Target Length : 288hrs• Members : 49
Local(LDAPS) • Resolution
1.5~4km L70(UM)(1188´1148 / top=39km)
• Target Length : 36hrs• Initialization : 3DVAR
Local EPS• Resolution
3km L70(UM)(460´482 / top = 39km)
• Target Length : 72hrs• Members : 12
q KMA’s operational models are based on Unified Model.
• OPS : initial observation processing(QC, 1D-Var)
• VAR : 4D-Var/3D-Var data assimilation
Data spices Global Regional Local Element
GNSS-RO
COSMIC 1~6 O O
Planed(2018) bending angle
Metop-A/B O O
TanDem-XTerraSAR-XGrace-B
Planed(2017.10)
KOMPSAT-5 Planed
Ground based GNSS OPlaned
(2017.10) ZTD
Current GNSS Usage at the KMA
ü Challenge : global observation speices which covers local domain assimilated
q Purpose
• GNSS-RO : atmospheric upper layer → improved synoptic field
• Ground-based GNSS : moisture information in the lower level → improved precipitation
q Upgrade operational models(2017.10)
• additional GNSS-RO for global model and ground-based GNSS data in local model
Current GNSS-RO Usage at the KMAq Numerical Weather Prediction Model (The Met Office Unified Model, UM)
• Global/Regional model
q RO quantity assimilated
• Bending angle
• 1D operator
• Tangent point drift accounted for
q Observation error characteristics
• The bending angle errors are calculated
using a linear interpolation between these points.
• The background error covariances are calculated
using randomisation method.
q Impact
• Percentage contribution of total observation
• Evaluation period winter 2015 in global model
<Observation error rate(%)>
<FSOI>
Current GNSS-RO Usage at the KMAq Impact of KOMPSAT-5 RO data in Global model
• Experiment period : 2015.9.7.~9.14.
• KOMPSAT-5 RO data quality shows high accuracy similar to COSMIC data.
• The analysis field that assimilates KOMPSAT-5 RO data influences the temperature and
humidity field of the upper layer like other RO data.
• Analysis shows that RMSE of almost all fields is improved overall compared to CNTL.
CNTL KOMPSAT-5
Model KMA-UM (N512L70) KMA-UM (N512L70)
DA Hybrid Ens. 4DVAR (N216) Hybrid Ens. 4DVAR (N216)
Obs.Type 10 10+KOMPSAT-5 RO(142 points)
Cycle 6 hourly (early+late) 6 hourly (early+late)
<The analysis increment difference between CNTL and KOMPSAT-5>
<5 days average improvement rate against CNTL(%)>
from Eunhee Lee
GNSS-RO in local model
Data coverage in local modelq Average number of data(July 2016)
• ODB(in) : 3.5 trajectory
• OPS(used) : 2.3 trajectory
0
2
4
6
0 3 6 9 12 15 18 21
Num
ber
Time(UTC)
GNSS-ROdata(201607)inused
0.8
0.5
0.6
1.1
1.8
0.2
0.2
0.1
COSM1
COSM2
COSM6
Metop-A
Metop-B
TanDEM-X
TerraSAR-X
KOMPSAT-5
<Average number of in data(duplicate)>#5.3
1 July 2016 00UTC
<Average number of data per 3 hourly>
Data assimilation in LDAPS 3D-Var
Description that assigns PGE values
reject(Final PGE>0.5)
Missing data
Convergence test
Initial cost function test
Final cost function test
Some levels rejected on 1D-Var
Error in background data
Valid values < 10 in profile
accept(Final PGE=0.1) 1D-Var success
Experiment
Cold LDAPS (N768L70)Variable grid(Inner: 1.5km)
IC/DA 3DVAR (FGAT, IAU)
Obs. Type Sonde, Surface, Aircraft, Scatwind + RO
q Quality control
• Checks based on the size of the final probability gross error
q Result of QC
• No satellite bias correction
• The frequency distribution of O-B shows almost Gaussian, which can lead to assimilation
<Cold run><Quality control process>
Data assimilation in LDAPS 3D-Var
01 July 2016 00UTC
Metop-BMetop-A
Metop-B
Metop-A
q The analysis increment is large at around 8~16km.
Metop-B
Metop-A
IH : 11km~
IH : 8km~
Preliminary impact resultq Verification against ECMWF(July 2016)
• Difference of mean temperature at 100hPa
(Only 1.5km of fixed grid)
• Higher difference values(red in Fig.) are reduced
in EXPR
<CNTL-ECMWF> <EXPR-ECMWF>
Experiment
Model LDAPS (N768L70)Variable grid(Inner: 1.5km)
IC/DA 3DVAR (FGAT, IAU)
Obs. CNTL: 4(Sonde, Surface, Aircraft, Scatwind)EXPR: 4+ GNSS-RO
Cycle 3 hourly
Preliminary impact result
-3
-2
-1
0
1
2
3
6H 12H 18H 24H 30H 36H
improvem
entrate(%)
Fcsttime
GPH
300
250
200
150
100-3
-2
-1
0
1
2
3
6H 12H 18H 24H 30H 36H
improvem
entrate(%)
Fcsttime
Temp
300
250
200
150
100
-3
-2
-1
0
1
2
3
6H 12H 18H 24H 30H 36H
improvem
entrate(%)
Fcsttime
Wind
300
250
200
150
100-8
-3
2
7
6H 12H 18H 24H 30H 36H
improvem
entrate(%)
Fcsttime
RH
300
250
200
150
100
q Verification against Sonde
• RMSE improvement rate of upper layer(300-100hPa)
• Significant improvement at the upper temperature𝑅𝑀𝑆𝐸𝐼𝑅 =
𝑅𝑀𝑆𝐸()*+ − 𝑅𝑀𝑆𝐸-./0𝑅𝑀𝑆𝐸()*+
×100
Ground-based GNSSin local model
Current ground GNSS Usage at the KMAq Data preprocessing
• Only available for Global model
• Using the Bernese v5.0
• Assimilate Zenith Total Delay data
q Observation operator
• Refractivity exponential decay with height
𝑍𝑇𝐷 = 1078 9 𝑁;<=
;<>𝑑𝑧
𝑁 = AB*+CBD*E (T: temp, 𝑝G: water vapour press, a∙b: constant)
( ))exp()exp()exp(101
6iii bibibi
i
ii zczczc
cNyZenithDela ----=
+
-
ò + --= - 1 ))(exp(10 6 ib
ibi
z
z biii dzzzcNyZenithDela
( ))exp()exp()exp(101
6stationbb
ii czczcz
cNyZenithDela
ii----=
+
-
( ))exp()exp()exp(101
6iii biaibi
i
ii zczczc
cNyZenithDela ----=
+
-
E-GVAP (by GTS)
ground-based GNSS operation
#15 #40 #106
q Expansion of domestic sites
• 40 sites will be operationally used in October 2017 for local model
• 106 sites is ongoing for quality control
KASI : Korea Astronomy and Space Science Institute NGII : National Geographic Information InstituteNMSC : National Meteorological Satellite Center NMPT : National Maritime PNT Office
2015 2016 2017
Data quality
<O-B statistics by stations>
BHAO-KASI
CHJU-NGII
CNJU-NGII
DAEJ-KASI
JINJ-NGII
KANR-NGII
KWNJ-NGII
MKPO-KASI
MLYN-KASI
SBAO-KASI
SEOS-NGII
SKMA-KASI
SUWN-NGII
WNJU-NGII
WULJ-NGII
O-B(m)
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0.035
0.040
Stdev.
Bias
<Histogram by stations>
Org NewNewOrg
O-B O-B C-B
q ZTD data quality control and preprocessing
• Improvement of data quality by improving fixed sites(Courtesy of NMSC)
• In the New, the distribution of O-B have more of regularity
• ZTD calculation stability and improvement of O-B
New
Org fixed sites New fixed sitesDAEJ, SUWN,SHAO, LHAZ, IRKM, AIRA, CHAN, TSKB, USUD
DAEJ, SUWN,SHAO, LHAZ, IRKM, XIAN, URUM
DAEJ-KASI
SUWN-NGII
q Performance using ground GNSS data(Jan., July 2016)
• Lower geopotential, temperature, and wind field improvement than control experiment(OPER)
• Predictability of early quantitative precipitation
• Significant improvement in Summer relative humidity
Impact test
RH(‘16.7) 3% improvement
-0.10
-0.05
0.00
0.05
0.10
6 18 30 6 18 30 6 18 30 6 18 30 6 18 30 6 18 30
0.1mm 1mm 5mm 12.5mm 15mm 25mmETS(EX
PR-OPE
R)
SummerWinter
Initial time: 2016070600UTC(+00H)
OPER EXPR
Bias(July 2016)
-15 -10 -5 0 5 10 15
925
850
700
RMSEimprovementrate(%)
Height(hPa) 36H
30H24H18H12H6H
OPER not G-GNSS
EXPR 40 G-GNSS
July : 6~18H 7.2%January : 6~18H 3.8%
Equivalent Threat Score
Observation
Improvement study
<Observation error by station>
<Averaged RMSE by experiment>
OPER ZTD not assimilated
EXP1 ErrTZD=station (at least 20mm)
EXP2 ErrTZD=15mm
EXP3 ErrTZD=6mm (Met Office)
<Configure of sensitivity experiment>
(Desroziers et al., 2005)Observation error covariance estimation method using statistical values of observation increment and residual
q Optimization of ground GNSS data by improving observation error
• Observation error calculation for 15 domestic sites
• Observation error estimation and sensitivity experiment of GNSS
Summary and Plans
v Improvement of the GNSS-RO data§ Use more data with QC control § More impact study in local model
v Improvement and extension of the G-GNSS data§ Spatial thinning using observation error§ 15 minutes data in very-short range model§ E-GVAP+KMA data(by FTP) in global model
q Summary• GNSS-RO was used in local model as an upper layer data.
• RO affects the atmospheric upper layer and improves synoptic field. This was in comparison
with the ECMWF analysis field.
• The ground-based GNSS has improved the precipitation prediction performance by proving
lower moisture information.
• KMA will operationally used ground-based GNSS in local NWP system since October 2017.
q Plans
