ensemble data assimilation for upper...
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ENSEMBLE DATA ASSIMILATION FOR UPPER ATMOSPHERE SPECIFICATION AND FORECASTING
Tomoko Matsuo CU-CIRES/NOAA Space Weather Prediction Center
Data Assimilation for Space Weather, Santa Fe, NM, June 2016 1
References: Matsuo and Araujo-Pradere, RS, 2011; Lee et al., JGR, 2012; Matsuo et al., JGR, 2013; Lee et al., 2013; Matsuo, AGU monograph, 2014; Hsu et al., JGR, 2014; Chartier et al., JGR, 2015; Chen et al., JGR, 2016 Support: AFOSR grant FA9550-13-1-0058
What works and what doesn’t
Data Assimilation for Space Weather, Santa Fe, NM, June 2016 2
What works and what doesn’t
1 Strongly coupled thermosphere-ionosphere data assimilation approaches work better than weakly coupled approaches for both ionosphere and thermosphere specification and forecasting.
2 Large amount of indirect measurements (e.g. from GPS) are
more effective than small amount of direct measurements (e.g. from accelerometers) for global neutral mass density specification and forecasting.
3 Rapid forecast-assimilation cycling (e.g. ~10 minutes) helps
to reduce unrealistic model error growth due to unbalanced increment than slow cycling (e.g. ~1 hour).
4 State estimation works better for ionosphere and thermosphere
specification and forecasting than forcing estimation, as forcing parameter estimation is challenging if underlying dynamics that control forcing evolution are not included in forecast models.
Data Assimilation for Space Weather, Santa Fe, NM, June 2016 3
Forecast
Update (Assimilation)
WEAK COUPLING only through forecast cycles STRONG COUPLING through both assimilation/forecast steps
Data Assimilation for Space Weather, Santa Fe, NM, June 2016 4
Coupled thermosphere-ionosphere data assimilation
Data Assimilation Research Testbed [Anderson et al., 2001, 2003, 2009] Thermosphere-Ionosphere Electrodynamics GCM [Richmond et al.,1992]
Observations
Forecast
Assimilation
Ensemble square root filter with TIEGCM/DART
Ensemble Filter - DART
Model - TIEGCM
irregular and sparse
high-dimension dissipative forced dynamics
Data Assimilation for Space Weather, Santa Fe, NM, June 2016 5
Cycling
NCAR TIEGCM COSMIC or Ionosonde Electron Density
Ensemble Forecasting
Image Courtesy: UCAR & GFDL
Data Assimilation for Space Weather, Santa Fe, NM, June 2016 6
[Matsuo and Araujo-Pradere, submitted, 2011]
Electron density RMSE
[Matsuo and Araujo-Pradere, RS, 2011]
OSSEs – Global Ionosonde electron density
WEAK COUPLING
STRONG COUPLING
12 18 24 30 361
1.5
22.5
3
3.54
4.5
55.5
6
6.57
x 10−10
Hours since April 08 0000 UT
Mas
s de
nsity
(Kg/
m3 )
RMSE
Control Experiment1.update fe−
2.update fe− fO+
3.update fe− fO+ fT4.update fe− fO+ fU fV5.update fe− fO+ fO fO
26.update fe− fO+ fT fU fV fO fO
2
7.update fe− fT fU fV fO fO2
12 18 24 30 361
1.5
22.5
3
3.54
4.5
55.5
6
6.57
x 10−10
Hours since April 08 0000 UT
Mas
s de
nsity
(Kg/
m3 )
RMSE
Control Experiment1.update fe−
2.update fe− fO+
3.update fe− fO+ fT4.update fe− fO+ fU fV5.update fe− fO+ fO fO
26.update fe− fO+ fT fU fV fO fO
2
7.update fe− fT fU fV fO fO2
Strongly coupled ionosphere-thermosphere data assimilation yields better analysis than weakly coupled approaches
STRONGER COUPLING
[Hsu et al., JGR, 2015]
Global RMSE
12 18 24 30 361
1.5
22.5
3
3.54
4.5
55.5
6
6.57
x 10−10
Hours since April 08 0000 UT
Mas
s de
nsity
(Kg/
m3 )
RMSE
Control Experiment1.update fe−
2.update fe− fO+
3.update fe− fO+ fT4.update fe− fO+ fU fV5.update fe− fO+ fO fO
26.update fe− fO+ fT fU fV fO fO
2
7.update fe− fT fU fV fO fO2
Electron density RMSE (global)
Ensemble forecast initialized by COSMIC assimilation
Estimation of neutral composition is the key
initialization
Strongly coupled data assimilation can extend predictability of the ionosphere more than 24 hours
What works and what doesn’t
1 Strongly coupled thermosphere-ionosphere data assimilation approaches work better than weakly coupled approaches for both ionosphere and thermosphere specification and forecasting.
2 Large amount of indirect measurements (e.g. from GPS) are
more effective than small amount of direct measurements (e.g. from accelerometers) for global neutral mass density specification and forecasting.
3 Rapid forecast-assimilation cycling (e.g. ~10 minutes) helps
to reduce unrealistic model error growth due to unbalanced increment than slow cycling (e.g. ~1 hour).
4 State estimation works better for ionosphere and thermosphere
specification and forecasting than forcing estimation, as forcing parameter estimation is challenging if underlying dynamics that control forcing evolution are not included in forecast models.
Data Assimilation for Space Weather, Santa Fe, NM, June 2016 9
NCAR TIEGCM COSMIC Electron Density
CHAMP Mass Density
Image Courtesy: UCAR, GFDL & GFZ
Data Assimilation for Space Weather, Santa Fe, NM, June 2016 10
[kg/m3]
11 Data Assimilation for Space Weather, Santa Fe, NM, June 2016
WEAK COUPLING
Neutral mass density RMSE (over 320-450 km) OSSEs – CHAMP neutral mass density
12 18 24 30 361
1.5
22.5
3
3.54
4.5
55.5
6
6.57
x 10−10
Hours since April 08 0000 UT
Mas
s de
nsity
(Kg/
m3 )
RMSE
Control Experiment1.update fe−
2.update fe− fO+
3.update fe− fO+ fT4.update fe− fO+ fU fV5.update fe− fO+ fO fO
26.update fe− fO+ fT fU fV fO fO
2
7.update fe− fT fU fV fO fO2
12 18 24 30 361
1.5
22.5
3
3.54
4.5
55.5
6
6.57
x 10−10
Hours since April 08 0000 UT
Mas
s de
nsity
(Kg/
m3 )
RMSE
Control Experiment1.update fe−
2.update fe− fO+
3.update fe− fO+ fT4.update fe− fO+ fU fV5.update fe− fO+ fO fO
26.update fe− fO+ fT fU fV fO fO
2
7.update fe− fT fU fV fO fO2
12 18 24 30 361
1.5
22.5
3
3.54
4.5
55.5
6
6.57
x 10−10
Hours since April 08 0000 UT
Mas
s de
nsity
(Kg/
m3 )
RMSE
Control Experiment1.update fe−
2.update fe− fO+
3.update fe− fO+ fT4.update fe− fO+ fU fV5.update fe− fO+ fO fO
26.update fe− fO+ fT fU fV fO fO
2
7.update fe− fT fU fV fO fO2
[Matsuo et al., JGR, 2013]
Error reduction only occurs in vicinity of CHMAP orbit with limited global impact
Control
−180 −120 −60 0 60 120
−60
−30
0
30
60
x = [ fe− fO+ ]
−180 −120 −60 0 60 120
−60
−30
0
30
60
x = [ fe− fO+ fT ]
−180 −120 −60 0 60 120
−60
−30
0
30
60
x = [ fe− fO+ fU fV ]
Longitude
Latitude
−180 −120 −60 0 60 120
−60
−30
0
30
60
x = [ fe− fO+ fO fO2
]
−180 −120 −60 0 60 120
−60
−30
0
30
60
x = [ fe− fO+ fT fU fV fO fO2
]
−180 −120 −60 0 60 120
−60
−30
0
30
60
−150 −100 −50 0 50 100 150
−80
−60
−40
−20
0
20
40
60
80
1
2
3
4
5
6
7
8
9
10
11
x 10−10
Neutral mass density RMSE
[Kg/m3]
Data Assimilation for Space Weather, Santa Fe, NM, June 2016 12
OSSEs – COSMIC electron density
Global error reduction is achieved by assimilation of COSMIC data by coupled thermosphere-ionosphere data assimilation
Control
−180 −120 −60 0 60 120
−60
−30
0
30
60
x = [ fe− fO+ ]
−180 −120 −60 0 60 120
−60
−30
0
30
60
x = [ fe− fO+ fT ]
−180 −120 −60 0 60 120
−60
−30
0
30
60
x = [ fe− fO+ fU fV ]
Longitude
Latitude
−180 −120 −60 0 60 120
−60
−30
0
30
60
x = [ fe− fO+ fO fO2
]
−180 −120 −60 0 60 120
−60
−30
0
30
60
x = [ fe− fO+ fT fU fV fO fO2
]
−180 −120 −60 0 60 120
−60
−30
0
30
60
−150 −100 −50 0 50 100 150
−80
−60
−40
−20
0
20
40
60
80
1
2
3
4
5
6
7
8
9
10
11
x 10−10
Neutral mass density RMSE
[Kg/m3]
Data Assimilation for Space Weather, Santa Fe, NM, June 2016 13
OSSEs – COSMIC electron density
Global error reduction is achieved by assimilation of COSMIC data by coupled thermosphere-ionosphere data assimilation
0 6 12 18 24 30 36 42 48 54 60 66 721
1.52
2.53
3.54
4.55
5.56
6.57
7.58
x 10−10
Forecasting Hours
Mas
s de
nsity
(Kg/
m3 )
RMSE
Control Experimentupdate fe− fO+ fTupdate fe− fO+ fT fO fO2
update fe− fO+ fT fO fO2 fU fV
0 6 12 18 24 30 36 42 48 54 60 66 721
1.52
2.53
3.54
4.55
5.56
6.57
7.58
x 10−10
Forecasting Hours
Mas
s de
nsity
(Kg/
m3 )
RMSE
Control Experimentupdate fe− fO+ fTupdate fe− fO+ fT fO fO2
update fe− fO+ fT fO fO2 fU fV
Global RMSE
initialization
Impact of neutral temperature estimation lasts for 7 days
Ensemble forecast initialized by COSMIC assimilation
STRONGER COUPLING
Neutral mass density RMSE (global)
Strongly coupled data assimilation can extend predictability of the thermosphere more than 72 hours
NCAR TIEGCM COSMIC Electron Density Profile
CHAMP Mass Density
Image Courtesy: UCAR, GFDL & GFZ
Data Assimilation for Space Weather, Santa Fe, NM, June 2016 15
0.6 0.8 1 1.2 1.4 1.6x 10−12
−90
−60
−30
0
30
60
90
Latit
ude
[Kg/m3]
Control/MidnightAssimilation/MidnightControl/NoonAssimilation/Noon
Comparison to 2-day (30 orbits) of CHAMP density observations
Data Assimilation for Space Weather, Santa Fe, NM, June 2016 16 Mass density can be estimated from COSMIC electron density via coupled thermosphere-ionosphere data assimilation
RMS difference
What works and what doesn’t
1 Strongly coupled thermosphere-ionosphere data assimilation approaches work better than weakly coupled approaches for both ionosphere and thermosphere specification and forecasting.
2 Large amount of indirect measurements (e.g. from GPS) are
more effective than small amount of direct measurements (e.g. from accelerometers) for global neutral mass density specification and forecasting.
3 Rapid forecast-assimilation cycling (e.g. ~10 minutes) helps
to reduce unrealistic model error growth due to unbalanced increment than slow cycling (e.g. ~1 hour).
4 State estimation works better for ionosphere and thermosphere
specification and forecasting than forcing estimation, as forcing parameter estimation is challenging if underlying dynamics that control forcing evolution are not included in forecast models.
Data Assimilation for Space Weather, Santa Fe, NM, June 2016 17
NCAR TIEGCM GPS Total Electron Content
Image Courtesy: UCAR, GFDL & GFZ
Ensemble Forecasting
Data Assimilation for Space Weather, Santa Fe, NM, June 2016 18
TIEGCM
Prior
Posterior
[Chen et al., JGR, 2016]
19
Rapid cycling helps reduce unrealistic model error growth
[Chen et al., JGR, 2016]
Forecast
Assimilation
Cycling
60 minutes
30 minutes
10 minutes
What works and what doesn’t
1 Strongly coupled thermosphere-ionosphere data assimilation approaches work better than weakly coupled approaches for both ionosphere and thermosphere specification and forecasting.
2 Large amount of indirect measurements (e.g. from GPS) are
more effective than small amount of direct measurements (e.g. from accelerometers) for global neutral mass density specification and forecasting.
3 Rapid forecast-assimilation cycling (e.g. ~10 minutes) helps to
reduce unrealistic model error growth due to unbalanced increment than slow cycling (e.g. ~1 hour).
4 State estimation works better than forcing parameter estimation.
Forcing parameter estimation is challenging if underlying dynamics that control forcing evolution are not included in the forecast model.
Data Assimilation for Space Weather, Santa Fe, NM, June 2016 21
NCAR TIEGCM CHAMP Mass Density
Image Courtesy: UCAR, GFDL & GFZ
Data Assimilation for Space Weather, Santa Fe, NM, June 2016 22
23
6 12 18 24
20
40
60
80
100
Hours since March 28 0UT
[%]
Global RMSE (levels 19−24)
Prior MeanPosterior Mean
Data Assimilation for Space Weather, Santa Fe, NM, June 2016
[Matsuo et al., JGR, 2013]
OSSEs – CHAMP neutral mass density
Global error reduction achieved by forcing estimation Filter degeneracy issues. Parameter estimation works well when model errors originates only from parameter misspecification.
Neutral mass density RMSE (over 320-450 km)
What works and what doesn’t
1 Strongly coupled thermosphere-ionosphere data assimilation approaches work better than weakly coupled approaches for both ionosphere and thermosphere specification and forecasting.
2 Large amount of indirect measurements (e.g. from GPS) are
more effective than small amount of direct measurements (e.g. from accelerometers) for global neutral mass density specification and forecasting.
3 Rapid forecast-assimilation cycling (e.g. ~10 minutes) helps to
reduce unrealistic model error growth due to unbalanced increment than slow cycling (e.g. ~1 hour).
4 State estimation works better than forcing parameter estimation.
Forcing parameter estimation is challenging if underlying dynamics that control forcing evolution are not included in the forecast model.
Data Assimilation for Space Weather, Santa Fe, NM, June 2016 24