on instability in data assimilation · motivation and introduction data assimilation mathematical...
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On Instability in Data Assimilation
Roland Potthast1,2,3, Boris Marx3, Alexander Moodey2, Amos Lawless2, Peter Jan
van Leeuwen2
1German Meteorological Service (DWD),2University of Reading, United Kingdom
3University of Gottingen, Germany
Roland Potthast 1/43
Outline
Outline
1 Motivation and Introduction.
2 Variational Data AssimilationTikhonov and 3dVar
4dVar
Spectral Representation
3 Convergence Analysis for Cycled Assimilation 1: Range ArgumentsSetup: Constant System
Convergence for Data f ∈ R(H) / Divergence for f /∈ R(H)
4 Convergence Analysis 2: Regularization of AssimilationSetup: High-Frequency Damping Systems
Analysis Error Bounds
Roland Potthast 2/43
Outline
Outline
1 Motivation and Introduction.
2 Variational Data AssimilationTikhonov and 3dVar
4dVar
Spectral Representation
3 Convergence Analysis for Cycled Assimilation 1: Range ArgumentsSetup: Constant System
Convergence for Data f ∈ R(H) / Divergence for f /∈ R(H)
4 Convergence Analysis 2: Regularization of AssimilationSetup: High-Frequency Damping Systems
Analysis Error Bounds
Roland Potthast 2/43
Outline
Outline
1 Motivation and Introduction.
2 Variational Data AssimilationTikhonov and 3dVar
4dVar
Spectral Representation
3 Convergence Analysis for Cycled Assimilation 1: Range ArgumentsSetup: Constant System
Convergence for Data f ∈ R(H) / Divergence for f /∈ R(H)
4 Convergence Analysis 2: Regularization of AssimilationSetup: High-Frequency Damping Systems
Analysis Error Bounds
Roland Potthast 2/43
Outline
Outline
1 Motivation and Introduction.
2 Variational Data AssimilationTikhonov and 3dVar
4dVar
Spectral Representation
3 Convergence Analysis for Cycled Assimilation 1: Range ArgumentsSetup: Constant System
Convergence for Data f ∈ R(H) / Divergence for f /∈ R(H)
4 Convergence Analysis 2: Regularization of AssimilationSetup: High-Frequency Damping Systems
Analysis Error Bounds
Roland Potthast 2/43
Motivation and Introduction Data Assimilation
Motivation I: Numerical Weather Forecast ...
Warn and Protect
Plan Travel
Roland Potthast 3/43
Motivation and Introduction Data Assimilation
Motivation II: Numerical Weather Forecast ...
Logistics
Rivers and Environment
Air Control
Roland Potthast 4/43
Motivation and Introduction Data Assimilation
Data Assimilation at the Deutscher Wetterdienst
Roland Potthast 5/43
Motivation and Introduction Data Assimilation
Modelling of the Atmosphere: Geometry
GME/ICON
Resolution 30km
COSMO-EU
Resolution 7km
COSMO-DE
Resolution 2.8km
Roland Potthast 6/43
Motivation and Introduction Data Assimilation
Measurements for State Determination ...
Synop,
TEMP,
Radiosondes,
Buoys,
Airplanes
(AMDAR),
Radar,
Wind Profiler,
Scatterometer,
Radiances,
GPS/GNSS,
Ceilometer,
LidarRoland Potthast 7/43
Motivation and Introduction Data Assimilation
Mathematical Setup for Data Assimilation
State Space:X state space, containing all state
variables in one vector ϕϕ state of the atmosphere
tk time discretization point
ϕk state at time tkMk : X → X model operator at time tk ,
ϕk 7→ ϕk+1 = M(ϕk )
Observation SpaceYk observation space at time tkfk observation vector at time tkHk : X → Yk observation operatorx, y points in physical space
Roland Potthast 8/43
Motivation and Introduction Data Assimilation
Mathematical Setup for Data Assimilation
State Space:X state space, containing all state
variables in one vector ϕϕ state of the atmosphere
tk time discretization point
ϕk state at time tk
Mk : X → X model operator at time tk ,
ϕk 7→ ϕk+1 = M(ϕk )
Observation SpaceYk observation space at time tkfk observation vector at time tkHk : X → Yk observation operatorx, y points in physical space
Roland Potthast 8/43
Motivation and Introduction Data Assimilation
Mathematical Setup for Data Assimilation
State Space:X state space, containing all state
variables in one vector ϕϕ state of the atmosphere
tk time discretization point
ϕk state at time tkMk : X → X model operator at time tk ,
ϕk 7→ ϕk+1 = M(ϕk )
Observation SpaceYk observation space at time tkfk observation vector at time tkHk : X → Yk observation operatorx, y points in physical space
Roland Potthast 8/43
Motivation and Introduction Data Assimilation
Mathematical Setup for Data Assimilation
State Space:X state space, containing all state
variables in one vector ϕϕ state of the atmosphere
tk time discretization point
ϕk state at time tkMk : X → X model operator at time tk ,
ϕk 7→ ϕk+1 = M(ϕk )
Observation SpaceYk observation space at time tkfk observation vector at time tkHk : X → Yk observation operatorx, y points in physical space
Roland Potthast 8/43
Motivation and Introduction Data Assimilation
Data Assimilation Task
Definition (Data Assimilation Task)
Given measurements fk at tk for k = 1, 2, 3, ...determine the states ϕk from the equations
Hϕk = fk , k = 1, 2, 3, ... (1)
taking care of the model dynamics given by Mk .
Usually the measurement space is dynamic,
i.e. changing in every time-step.
In general H is a non-linear operator,
non-injective, ill-posed.
The value fk contains significant data error
with stochastic components and a dynamic
bias.
Roland Potthast 9/43
Motivation and Introduction Data Assimilation
Data Assimilation Task
Definition (Data Assimilation Task)
Given measurements fk at tk for k = 1, 2, 3, ...determine the states ϕk from the equations
Hϕk = fk , k = 1, 2, 3, ... (1)
taking care of the model dynamics given by Mk .
Usually the measurement space is dynamic,
i.e. changing in every time-step.
In general H is a non-linear operator,
non-injective, ill-posed.
The value fk contains significant data error
with stochastic components and a dynamic
bias.
Roland Potthast 9/43
Variational Data Assimilation Tikhonov and 3dVar
Outline
1 Motivation and Introduction.
2 Variational Data AssimilationTikhonov and 3dVar
4dVar
Spectral Representation
3 Convergence Analysis for Cycled Assimilation 1: Range ArgumentsSetup: Constant System
Convergence for Data f ∈ R(H) / Divergence for f /∈ R(H)
4 Convergence Analysis 2: Regularization of AssimilationSetup: High-Frequency Damping Systems
Analysis Error Bounds
Roland Potthast 10/43
Variational Data Assimilation Tikhonov and 3dVar
Tikhonov Data Assimilation
In every assimilation step k ∈ N we solve the variational minimization problem to find
the minimum of
J(ϕ) := α||ϕ− ϕ(b)k+1||
2+ ||fk+1 − Hϕ
(b)k+1||
2, ϕ ∈ X , (2)
where
ϕ(b)k+1 := Mϕ
(a)k , k = 0, 1, 2, ... (3)
For linear operators the minimum is given by the normal equations, which can be
reformulated into the update formula
ϕ(a)k+1 = ϕ
(b)k+1 + Rα
(fk+1 − Hϕ
(b)k+1
)(4)
with Rα = (αI + H∗H)−1H∗, or in terms of the analysis fields ϕ(a)k
ϕ(a)k+1 = Mϕ
(a)k + Rα
(fk+1 − HMϕ
(a)k
)(5)
for k = 0, 1, 2, ....Roland Potthast 11/43
Variational Data Assimilation Tikhonov and 3dVar
3dVAR - 1
1 Start with ϕ(a)0 and for k = 1, 2, 3, ... do:
2 Calculate first guess
ϕ(b)k = Mk−1ϕ
(a)k−1 (6)
3 Assimilate data fk at time tk calculating ϕ(a)k .
Roland Potthast 12/43
Variational Data Assimilation Tikhonov and 3dVar
3dVAR - 1
1 Start with ϕ(a)0 and for k = 1, 2, 3, ... do:
2 Calculate first guess
ϕ(b)k = Mk−1ϕ
(a)k−1 (6)
3 Assimilate data fk at time tk calculating ϕ(a)k .
Roland Potthast 12/43
Variational Data Assimilation Tikhonov and 3dVar
3dVAR - 1
1 Start with ϕ(a)0 and for k = 1, 2, 3, ... do:
2 Calculate first guess
ϕ(b)k = Mk−1ϕ
(a)k−1 (6)
3 Assimilate data fk at time tk calculating ϕ(a)k .
Roland Potthast 12/43
Variational Data Assimilation Tikhonov and 3dVar
3dVAR - 1
1 Start with ϕ(a)0 and for k = 1, 2, 3, ... do:
2 Calculate first guess
ϕ(b)k = Mk−1ϕ
(a)k−1 (6)
3 Assimilate data fk at time tk calculating ϕ(a)k .
Roland Potthast 12/43
Variational Data Assimilation Tikhonov and 3dVar
3dVAR - 2
Functional at time slice
J(ϕ) = ||ϕ− ϕ(b)||2B−1 + ||f − Hϕ||2R−1 (7)
Update Formula
ϕ(a)k = ϕ
(b)k + (B−1 + H∗R−1H)−1H∗R−1(fk − H(ϕ
(b)k ))
= ϕ(b)k + BH∗(R + HBH∗)−1︸ ︷︷ ︸
Kalman gain matrix
( fk − H(ϕ(b)k )︸ ︷︷ ︸
obs - first guess
). (8)
Roland Potthast 13/43
Variational Data Assimilation Tikhonov and 3dVar
3dVar = Tikhonov Regularization in a weighted space
We study a weighted scalar product⟨ϕ,ψ
⟩:=⟨ϕ, B−1ψ
⟩L2,⟨
f , g⟩
:=⟨
f ,R−1g⟩
L2(9)
with some self-adjoint invertible matrices B and R. The adjoint with respect to the
weighted scalar product is denoted by H′.
Then⟨f ,Hϕ
⟩=⟨
f ,R−1Hϕ⟩
L2=⟨
R−1f ,Hϕ⟩
L2=⟨
H∗R−1f , ϕ⟩
L2
=⟨
H∗R−1f , BB−1ϕ⟩
=⟨
BH∗R−1f , B−1ϕ⟩
L2=⟨
BH∗R−1f , ϕ⟩
=⟨
H′f , ϕ⟩.
This leads to
H′ = BH∗R−1 (10)
and thus
BH∗(R + HBH∗)−1 = BH∗R−1(I + HBH∗R−1)−1
= H′(I + HH′)−1 = (I + H′H)−1H′.
Roland Potthast 14/43
Variational Data Assimilation Tikhonov and 3dVar
3dVar = Tikhonov Regularization in a weighted space
We study a weighted scalar product⟨ϕ,ψ
⟩:=⟨ϕ, B−1ψ
⟩L2,⟨
f , g⟩
:=⟨
f ,R−1g⟩
L2(9)
with some self-adjoint invertible matrices B and R. The adjoint with respect to the
weighted scalar product is denoted by H′. Then⟨f ,Hϕ
⟩=⟨
f ,R−1Hϕ⟩
L2=⟨
R−1f ,Hϕ⟩
L2=⟨
H∗R−1f , ϕ⟩
L2
=⟨
H∗R−1f , BB−1ϕ⟩
=⟨
BH∗R−1f , B−1ϕ⟩
L2=⟨
BH∗R−1f , ϕ⟩
=⟨
H′f , ϕ⟩.
This leads to
H′ = BH∗R−1 (10)
and thus
BH∗(R + HBH∗)−1 = BH∗R−1(I + HBH∗R−1)−1
= H′(I + HH′)−1 = (I + H′H)−1H′.
Roland Potthast 14/43
Variational Data Assimilation Tikhonov and 3dVar
3dVar = Tikhonov Regularization in a weighted space
We study a weighted scalar product⟨ϕ,ψ
⟩:=⟨ϕ, B−1ψ
⟩L2,⟨
f , g⟩
:=⟨
f ,R−1g⟩
L2(9)
with some self-adjoint invertible matrices B and R. The adjoint with respect to the
weighted scalar product is denoted by H′. Then⟨f ,Hϕ
⟩=⟨
f ,R−1Hϕ⟩
L2=⟨
R−1f ,Hϕ⟩
L2=⟨
H∗R−1f , ϕ⟩
L2
=⟨
H∗R−1f , BB−1ϕ⟩
=⟨
BH∗R−1f , B−1ϕ⟩
L2=⟨
BH∗R−1f , ϕ⟩
=⟨
H′f , ϕ⟩.
This leads to
H′ = BH∗R−1 (10)
and thus
BH∗(R + HBH∗)−1 = BH∗R−1(I + HBH∗R−1)−1
= H′(I + HH′)−1 = (I + H′H)−1H′.
Roland Potthast 14/43
Variational Data Assimilation Tikhonov and 3dVar
3dVar = Tikhonov Regularization in a weighted space
We study a weighted scalar product⟨ϕ,ψ
⟩:=⟨ϕ, B−1ψ
⟩L2,⟨
f , g⟩
:=⟨
f ,R−1g⟩
L2(9)
with some self-adjoint invertible matrices B and R. The adjoint with respect to the
weighted scalar product is denoted by H′. Then⟨f ,Hϕ
⟩=⟨
f ,R−1Hϕ⟩
L2=⟨
R−1f ,Hϕ⟩
L2=⟨
H∗R−1f , ϕ⟩
L2
=⟨
H∗R−1f , BB−1ϕ⟩
=⟨
BH∗R−1f , B−1ϕ⟩
L2=⟨
BH∗R−1f , ϕ⟩
=⟨
H′f , ϕ⟩.
This leads to
H′ = BH∗R−1 (10)
and thus
BH∗(R + HBH∗)−1 = BH∗R−1(I + HBH∗R−1)−1
= H′(I + HH′)−1 = (I + H′H)−1H′.
Roland Potthast 14/43
Variational Data Assimilation 4dVar
Outline
1 Motivation and Introduction.
2 Variational Data AssimilationTikhonov and 3dVar
4dVar
Spectral Representation
3 Convergence Analysis for Cycled Assimilation 1: Range ArgumentsSetup: Constant System
Convergence for Data f ∈ R(H) / Divergence for f /∈ R(H)
4 Convergence Analysis 2: Regularization of AssimilationSetup: High-Frequency Damping Systems
Analysis Error Bounds
Roland Potthast 15/43
Variational Data Assimilation 4dVar
4dVar - 1 (functional)
Functional on time interval, minimize by a Gradient Method
J(ϕ) = ||ϕ− ϕ(b)||2B−1 +K∑
k=1
||fk − Hϕk ||2R−1 , ϕk = M[t0, tk ]ϕ
where M[s, t] denotes the model operator from time s to time t .
Roland Potthast 16/43
Variational Data Assimilation 4dVar
4dVar - 2 (cycling)
Functional
J(ϕ) = ||ϕ− ϕ(b)` ||
2
B−1 +K∑
k=1
||fk − Hϕ`+k ||2R−1 , ϕ`+k = M[t`, t`+k ]ϕ
for ` = 0, K , 2K , 3K , ...
Minimizer gives the analysis ϕ(a)` , next background is
ϕ(b)`+K = M[t`, t`+K ]ϕ
(a)` (11)
Roland Potthast 17/43
Variational Data Assimilation 4dVar
4dVar - 3 (with linear M rewrite as Tikhonov)
Assume that M is linear. Then by
H`ϕ :=
Hϕ
HM[t`, t`+1]ϕHM[t`, t`+2]ϕ
...
HM[t`, t`+K ]ϕ
f` :=
f`+1
f`+2...
f`+K
(12)
we can rewrite 4dVar as a three-dimensional assimilation algorithm solving
Hϕ = f`. (13)
The regularized solution provides ϕ(a)` , from which ϕ
(b)`+K is calculated by an application
of M[t`, t`+K ].
Roland Potthast 18/43
Variational Data Assimilation Spectral Representation
Outline
1 Motivation and Introduction.
2 Variational Data AssimilationTikhonov and 3dVar
4dVar
Spectral Representation
3 Convergence Analysis for Cycled Assimilation 1: Range ArgumentsSetup: Constant System
Convergence for Data f ∈ R(H) / Divergence for f /∈ R(H)
4 Convergence Analysis 2: Regularization of AssimilationSetup: High-Frequency Damping Systems
Analysis Error Bounds
Roland Potthast 19/43
Variational Data Assimilation Spectral Representation
Spectral Representation 1
Assume that H : X → Y is linear and compact. Then, there is a singular system{(ψn, gn, µn), n ∈ N} such that Hψn = µngn, H′gn = µnψn and
H′Hψn = µ2nψn, (14)
This yields
(αI + H′H)ψn = (α + µ2n)ψn, n ∈ N. (15)
With measurements
f =∞∑
n=1
fngn ∈ Y (16)
and denote the spectral coefficients of the Tikhonov solution ϕ by γn. 3dVar or Tikhonov
regularization, respectively, (αI + H′H)ϕ = H′f is equivalent to the spectral dampingscheme
γn =µn
α + µ2n
fn, n ∈ N. (17)
Roland Potthast 20/43
Variational Data Assimilation Spectral Representation
Spectral Representation 1
Assume that H : X → Y is linear and compact. Then, there is a singular system{(ψn, gn, µn), n ∈ N} such that Hψn = µngn, H′gn = µnψn and
H′Hψn = µ2nψn, (14)
This yields
(αI + H′H)ψn = (α + µ2n)ψn, n ∈ N. (15)
With measurements
f =∞∑
n=1
fngn ∈ Y (16)
and denote the spectral coefficients of the Tikhonov solution ϕ by γn. 3dVar or Tikhonov
regularization, respectively, (αI + H′H)ϕ = H′f is equivalent to the spectral dampingscheme
γn =µn
α + µ2n
fn, n ∈ N. (17)
Roland Potthast 20/43
Variational Data Assimilation Spectral Representation
Spectral Representation 1
Assume that H : X → Y is linear and compact. Then, there is a singular system{(ψn, gn, µn), n ∈ N} such that Hψn = µngn, H′gn = µnψn and
H′Hψn = µ2nψn, (14)
This yields
(αI + H′H)ψn = (α + µ2n)ψn, n ∈ N. (15)
With measurements
f =∞∑
n=1
fngn ∈ Y (16)
and denote the spectral coefficients of the Tikhonov solution ϕ by γn. 3dVar or Tikhonov
regularization, respectively, (αI + H′H)ϕ = H′f is equivalent to the spectral dampingscheme
γn =µn
α + µ2n
fn, n ∈ N. (17)
Roland Potthast 20/43
Variational Data Assimilation Spectral Representation
Spectral Representation 2
The true Inverse is
γ truen =
1
µnf truen . (18)
This inversion is unstable, if µn → 0, n→∞!
Tikhonov regularization is stable for α > 0
γn =µn
α + µ2n
fn, n ∈ N. (19)
Tikhonov shifts the eigenvalues of H′H by α.
Roland Potthast 21/43
Variational Data Assimilation Spectral Representation
Spectral Representation 2
The true Inverse is
γ truen =
1
µnf truen . (18)
This inversion is unstable, if µn → 0, n→∞!
Tikhonov regularization is stable for α > 0
γn =µn
α + µ2n
fn, n ∈ N. (19)
Tikhonov shifts the eigenvalues of H′H by α.
Roland Potthast 21/43
Variational Data Assimilation Spectral Representation
Spectral Representation 2
The true Inverse is
γ truen =
1
µnf truen . (18)
This inversion is unstable, if µn → 0, n→∞!
Tikhonov regularization is stable for α > 0
γn =µn
α + µ2n
fn, n ∈ N. (19)
Tikhonov shifts the eigenvalues of H′H by α.
Roland Potthast 21/43
Convergence Analysis for Cycled Assimilation 1: Range Arguments Setup: Constant System
Outline
1 Motivation and Introduction.
2 Variational Data AssimilationTikhonov and 3dVar
4dVar
Spectral Representation
3 Convergence Analysis for Cycled Assimilation 1: Range ArgumentsSetup: Constant System
Convergence for Data f ∈ R(H) / Divergence for f /∈ R(H)
4 Convergence Analysis 2: Regularization of AssimilationSetup: High-Frequency Damping Systems
Analysis Error Bounds
Roland Potthast 22/43
Convergence Analysis for Cycled Assimilation 1: Range Arguments Setup: Constant System
A system with constant dynamics
As a simple model system for study we use constant dynamics M = Identity , i.e
ϕ(b)k+1 = ϕ
(a)k , k = 1, 2, 3, ... (20)
for 3dVar. Also, we employ identical measurements fk ≡ f , k ∈ N.
Then, 3dVar is given by the iteration
ϕk = ϕk−1 + (αI + H′H)−1H′(f − Hϕk−1), k = 1, 2, 3, ... (21)
(This coincides with work of Engl on ’iterated Tikhonov regularization’!)
For the spectral coefficients γn,k of ϕk this leads to the iteration
γn,k = γn,k−1 +µn
α + µ2n
(fn,k − µnγn,k−1) (22)
Roland Potthast 23/43
Convergence Analysis for Cycled Assimilation 1: Range Arguments Setup: Constant System
A system with constant dynamics
As a simple model system for study we use constant dynamics M = Identity , i.e
ϕ(b)k+1 = ϕ
(a)k , k = 1, 2, 3, ... (20)
for 3dVar. Also, we employ identical measurements fk ≡ f , k ∈ N.
Then, 3dVar is given by the iteration
ϕk = ϕk−1 + (αI + H′H)−1H′(f − Hϕk−1), k = 1, 2, 3, ... (21)
(This coincides with work of Engl on ’iterated Tikhonov regularization’!)
For the spectral coefficients γn,k of ϕk this leads to the iteration
γn,k = γn,k−1 +µn
α + µ2n
(fn,k − µnγn,k−1) (22)
Roland Potthast 23/43
Convergence Analysis for Cycled Assimilation 1: Range Arguments Setup: Constant System
A system with constant dynamics
As a simple model system for study we use constant dynamics M = Identity , i.e
ϕ(b)k+1 = ϕ
(a)k , k = 1, 2, 3, ... (20)
for 3dVar. Also, we employ identical measurements fk ≡ f , k ∈ N.
Then, 3dVar is given by the iteration
ϕk = ϕk−1 + (αI + H′H)−1H′(f − Hϕk−1), k = 1, 2, 3, ... (21)
(This coincides with work of Engl on ’iterated Tikhonov regularization’!)
For the spectral coefficients γn,k of ϕk this leads to the iteration
γn,k = γn,k−1 +µn
α + µ2n
(fn,k − µnγn,k−1) (22)
Roland Potthast 23/43
Convergence Analysis for Cycled Assimilation 1: Range Arguments Setup: Constant System
Spectral Formula I
We employ
f = Hϕ(true) + δ, fn,k = µnγ(true)n + δn. (23)
and obtain
γn,k = γn,k−1 +µ2
n
α + µ2n
(γ(true)n − γn,k−1) +
µ2n
α + µ2n
δn
µn
Theorem (Spectral Formula I)
The 3dVar cycling for a constant dynamics with identical measurements
f = Hϕ(true) + δ lead to the spectral update formula
γn,k = (1− qn)γ(true)n + qnγn,k−1 +
(1− qn)
µnδn (24)
using
qn =α
α + µ2n
= 1− µ2n
α + µ2n
. (25)
Roland Potthast 24/43
Convergence Analysis for Cycled Assimilation 1: Range Arguments Setup: Constant System
Spectral Formula II
Theorem (Spectral Formula II)
The 3dVar cycling for a constant dynamics with identical measurements
f = Hϕ(true) + δ can be carried out explicitly. The development of its spectral
coefficients is given by
γn,k = (1− qkn )γ(true)
n + qknγn,0 +
(1− qkn )
µnδn (26)
using
qn =α
α + µ2n
= 1− µ2n
α + µ2n
. (27)
Proof. Induction over k. �
Roland Potthast 25/43
Convergence Analysis for Cycled Assimilation 1: Range Arguments Convergence for Data f ∈ R(H) / Divergence for f /∈ R(H)
Outline
1 Motivation and Introduction.
2 Variational Data AssimilationTikhonov and 3dVar
4dVar
Spectral Representation
3 Convergence Analysis for Cycled Assimilation 1: Range ArgumentsSetup: Constant System
Convergence for Data f ∈ R(H) / Divergence for f /∈ R(H)
4 Convergence Analysis 2: Regularization of AssimilationSetup: High-Frequency Damping Systems
Analysis Error Bounds
Roland Potthast 26/43
Convergence Analysis for Cycled Assimilation 1: Range Arguments Convergence for Data f ∈ R(H) / Divergence for f /∈ R(H)
Convergence for f ∈ R(H)
Theorem (Convergence for f ∈ R(H))
Cycled 3dVar for a constant dynamics and identical measurements f (true) + δ ∈ R(H)tends to the true solution ϕ(true) + σ with Hσ = δ for k →∞.
Proof. We study
γn,k = (1− qkn )γ(true)
n + qknγn,0 +
(1− qkn )
µnδn (28)
for k →∞.
Since 0 < qn < 1, we have
qkn → 0, k →∞, (1− qk
n )→ 1, k →∞. (29)
Since δ = Hσ the element σ with spectral coefficients δn/µn is in X and cycled 3dVar
converges towards ϕ(true) + σ. �
Roland Potthast 27/43
Convergence Analysis for Cycled Assimilation 1: Range Arguments Convergence for Data f ∈ R(H) / Divergence for f /∈ R(H)
Convergence for f ∈ R(H)
Theorem (Convergence for f ∈ R(H))
Cycled 3dVar for a constant dynamics and identical measurements f (true) + δ ∈ R(H)tends to the true solution ϕ(true) + σ with Hσ = δ for k →∞.
Proof. We study
γn,k = (1− qkn )γ(true)
n + qknγn,0 +
(1− qkn )
µnδn (28)
for k →∞.
Since 0 < qn < 1, we have
qkn → 0, k →∞, (1− qk
n )→ 1, k →∞. (29)
Since δ = Hσ the element σ with spectral coefficients δn/µn is in X and cycled 3dVar
converges towards ϕ(true) + σ. �
Roland Potthast 27/43
Convergence Analysis for Cycled Assimilation 1: Range Arguments Convergence for Data f ∈ R(H) / Divergence for f /∈ R(H)
Convergence for f ∈ R(H)
Theorem (Convergence for f ∈ R(H))
Cycled 3dVar for a constant dynamics and identical measurements f (true) + δ ∈ R(H)tends to the true solution ϕ(true) + σ with Hσ = δ for k →∞.
Proof. We study
γn,k = (1− qkn )γ(true)
n + qknγn,0 +
(1− qkn )
µnδn (28)
for k →∞.
Since 0 < qn < 1, we have
qkn → 0, k →∞, (1− qk
n )→ 1, k →∞. (29)
Since δ = Hσ the element σ with spectral coefficients δn/µn is in X and cycled 3dVar
converges towards ϕ(true) + σ. �
Roland Potthast 27/43
Convergence Analysis for Cycled Assimilation 1: Range Arguments Convergence for Data f ∈ R(H) / Divergence for f /∈ R(H)
Divergence for f 6∈ R(H)
Theorem (Divergence for f 6∈ R(H))
For a constant dynamics and identical measurements f (true) + δ 6∈ R(H) cycled 3dVardiverges for k →∞.
Proof. We study
γn,k = (1− qkn )γ(true)
n + qknγn,0 +
(1− qkn )
µnδn (30)
for k →∞. Let σk ∈ X denote the element with spectral coefficients
σn,k =(1− qk
n )
µnδn, k, n ∈ N.
which is well defined since for every fixed k ∈ N∣∣∣(1− qkn )
µn
∣∣∣ =∣∣∣(α + µ2
n)k − αk
(α + µ2n)kµn
∣∣∣ (31)
is bounded uniformly for n ∈ N.
Roland Potthast 28/43
Convergence Analysis for Cycled Assimilation 1: Range Arguments Convergence for Data f ∈ R(H) / Divergence for f /∈ R(H)
Divergence for f 6∈ R(H)
Theorem (Divergence for f 6∈ R(H))
For a constant dynamics and identical measurements f (true) + δ 6∈ R(H) cycled 3dVardiverges for k →∞.
Proof. We study
γn,k = (1− qkn )γ(true)
n + qknγn,0 +
(1− qkn )
µnδn (30)
for k →∞. Let σk ∈ X denote the element with spectral coefficients
σn,k =(1− qk
n )
µnδn, k, n ∈ N.
which is well defined since for every fixed k ∈ N∣∣∣(1− qkn )
µn
∣∣∣ =∣∣∣(α + µ2
n)k − αk
(α + µ2n)kµn
∣∣∣ (31)
is bounded uniformly for n ∈ N.Roland Potthast 28/43
Convergence Analysis for Cycled Assimilation 1: Range Arguments Convergence for Data f ∈ R(H) / Divergence for f /∈ R(H)
Divergence for f 6∈ R(H)
Since δ 6∈ R(H) we know that
SL :=L∑
n=1
∣∣∣ δn
µn
∣∣∣2 →∞, L→∞. (32)
Given C > 0 we can choose L such that SL > 2C. Then
||σk ||2 ≥L∑
n=1
∣∣∣(1− qkn )δn
µn
∣∣∣2 > C (33)
for k ∈ N sufficiently large, which proves
||σk || → ∞, k →∞ (34)
and the proof is complete. �
Roland Potthast 29/43
Convergence Analysis for Cycled Assimilation 1: Range Arguments Convergence for Data f ∈ R(H) / Divergence for f /∈ R(H)
Numerical Example: Dynamic Magnetic Tomography
100
101
102
103
2.16
2.18
2.2
2.22
2.24
2.26
2.28
2.3
2.32
2.34
2.36x 10
−3
time index k
erro
r
3dVar
Roland Potthast 30/43
Convergence Analysis 2: Regularization of Assimilation Setup: High-Frequency Damping Systems
Outline
1 Motivation and Introduction.
2 Variational Data AssimilationTikhonov and 3dVar
4dVar
Spectral Representation
3 Convergence Analysis for Cycled Assimilation 1: Range ArgumentsSetup: Constant System
Convergence for Data f ∈ R(H) / Divergence for f /∈ R(H)
4 Convergence Analysis 2: Regularization of AssimilationSetup: High-Frequency Damping Systems
Analysis Error Bounds
Roland Potthast 31/43
Convergence Analysis 2: Regularization of Assimilation Setup: High-Frequency Damping Systems
ϕ(a)k+1 − ϕ
(true)k+1 = M(ϕ
(a)k − ϕ
(true)k ) + RαHM
(ϕ(true)k − ϕ(a)
k
)+ Rαf
(δ)k+1
= (I − RαH)M(ϕ(a)k − ϕ
(true)k
)+ Rαf
(δ)k+1. (35)
We abbreviate the analysis error by
ek := ϕ(a)k − ϕ
(true)k , k = 1, 2, ... (36)
and define
Λ := (I − RαH)M (37)
to obtain the iteration formula
ek+1 = Λek + Rαf(δ)k+1, k = 0, 1, 2, ... (38)
Roland Potthast 32/43
Convergence Analysis 2: Regularization of Assimilation Setup: High-Frequency Damping Systems
System Evolution
Theorem
Assume that the error f(δ)k does not depend on k, i.e. that we feed some constant error
into the data assimilation scheme. Then, the error terms ek with initial error e0 described
by the update formula (38) evolve according to
ek = Λk e0 +( k−1∑`=0
Λ`)
e(δ) (39)
with e(δ) := Rαf (δ). If (I − Λ)−1 exists, it can be written as
ek = Λk e0 + (I − Λ)−1(I − Λk )e(δ) (40)
The update formula has been already known in the 1970s.
It was derived for a finite dimensional system with some well-posed observation
operator in a stochastic framework for the Kalman filter.
Roland Potthast 33/43
Convergence Analysis 2: Regularization of Assimilation Setup: High-Frequency Damping Systems
Frobenius Norm
Let {ψ` : ` ∈ N} be a complete orthonormal system in X . Then, any vector ϕ ∈ X
can be decomposed into its Fourier sum
ϕ =∞∑`=1
α`ψ` (41)
with α` := 〈ϕ,ψ`〉 for ` ∈ N. We apply this to Mϕ to derive
Mϕ =∞∑`=1
〈Mϕ,ψ`〉ψ` =∞∑`=1
∞∑j=1
αj〈Mϕj , ψ`〉ψ` =∞∑`=1
∞∑j=1
ψ`M`,jαj .
with the infinite matrix M`,j = 〈Mψj , ψ`〉. The Frobenius norm of M with respect to the
orthonormal system {ψ` : ` ∈ N} is defined as
||M||Fro :=∞∑`=1
∞∑j=1
|M`,j |2. (42)
Roland Potthast 34/43
Convergence Analysis 2: Regularization of Assimilation Setup: High-Frequency Damping Systems
Frobenius Systems: Damping of High Frequencies
Lemma
If M is self-adjoint with respect to the scalar product 〈·, ·〉, then its Frobenius norm (42)
is independent of the orthonormal system {ψ` : ` ∈ N}.
The identity operator I does not have a bounded Frobenius norm.
Systems with bounded Frobenius norm are damping on higher modes or
frequencies.
Roland Potthast 35/43
Convergence Analysis 2: Regularization of Assimilation Analysis Error Bounds
Outline
1 Motivation and Introduction.
2 Variational Data AssimilationTikhonov and 3dVar
4dVar
Spectral Representation
3 Convergence Analysis for Cycled Assimilation 1: Range ArgumentsSetup: Constant System
Convergence for Data f ∈ R(H) / Divergence for f /∈ R(H)
4 Convergence Analysis 2: Regularization of AssimilationSetup: High-Frequency Damping Systems
Analysis Error Bounds
Roland Potthast 36/43
Convergence Analysis 2: Regularization of Assimilation Analysis Error Bounds
Space Decomposition
Let the orthonormal system {ψ` : ` ∈ N} in X be given by the singular system of the
observation operator H : X → Y . In this case we define an orthogonaldecomposition of the space X by
X(n)1 := span{ψ1, ..., ψn}︸ ︷︷ ︸
lowermodes
, X(n)2 :=
highermodes︷ ︸︸ ︷span{ψn+1, ψn+2, ...} . (43)
Using the orthogonal projection operators P1 of X onto X1 and P2 of X onto X2, we
decompose M into
M1 := P1M, M2 := P2M. (44)
Using N = (I − RαH) and Λ = NM we obtain
Λ = N|X1 M1︸ ︷︷ ︸lower modes
+
higher modes︷ ︸︸ ︷N|X2 M2 . (45)
Roland Potthast 37/43
Convergence Analysis 2: Regularization of Assimilation Analysis Error Bounds
Norm estimates for N
The operator N maps Xj , j = 1, 2, into itself. This leads to the norm estimate
||Λϕ||2 = ||(N|X1 M1 + N|X2 M2)ϕ||2
= ||N|X1 M1ϕ||2 + ||N|X2 M2ϕ||2, (46)
leading to
||Λ||2 ≤ ||N|X1 ||2||M1||2 + ||N|X2 ||
2||M2||2. (47)
We derive estimates for all the above terms.
Roland Potthast 38/43
Convergence Analysis 2: Regularization of Assimilation Analysis Error Bounds
Norm Estimates 1
||Λ||2 ≤ ||N|X1 ||2||M1||2 + ||N|X2 ||
2||M2||2. (48)
Lemma
The norm of the operator N|X2 is given by
||N|X2 || = 1 (49)
for all n ∈ N and α > 0.
Lemma
Assume that M is self-adjoint with bounded Frobenius norm. Then, given ρ > 0 there is
n ∈ N such that for M2 = M(n)2 we have
||M2|| < ρ. (50)
Roland Potthast 39/43
Convergence Analysis 2: Regularization of Assimilation Analysis Error Bounds
Norm Estimates 2
||Λ||2 ≤ ||N|X1 ||2||M1||2 + ||N|X2 ||
2||M2||2. (51)
We need the freedom
||M1|| = c. (52)
Lemma
On X1 for N = I − RαH we have the norm estimate
||N|X1 || = sup`=1,..,n
| α
α + µ2n
| (53)
where µn are the singular values of the operator H ordered according to their size and
multiplicity. In particular, given ε > 0 and n ∈ N we can choose α > 0 sufficiently
small such that
||N|X1 || < ε. (54)
Roland Potthast 40/43
Convergence Analysis 2: Regularization of Assimilation Analysis Error Bounds
Stabilization of data assimilation
Recall
ek = Λk e0 + (I − Λ)−1(I − Λk )e(δ) (55)
Theorem
Assume that the system M is self-adjoint and its Frobenius norm is bounded and let αdenote the regularization parameter for a cycled data assimilation scheme. Then, for
α > 0 sufficiently small, we have ||Λ|| < 1. Assume that the observation error f (δ) is
bounded in norm by δ > 0. In this case, the analysis error is bounded over time with
lim supk→∞
||ek || ≤||Rα||δ
1− ||Λ||. (56)
Roland Potthast 41/43
Convergence Analysis 2: Regularization of Assimilation Analysis Error Bounds
Numerical Example
Roland Potthast 42/43
Summary Summary
Summary
Summary on DA Instability
3dVar (and 4dVar for linear systems) can be analysed as a cycled Tikhonovregularization.
We have shown that even for very stable dynamics where M = const cycled
Tikhonov with ill-posed observation operators diverges if we have data f 6∈ R(H).
The divergence carries over to 3dVar and 4dVar for simple systems.
For systems damping high frequencies we have shown that stability depends on
the choice of the regularization parameter α
For self-adjoint systems M with bounded Frobenius norm we can always achieve a
stable assimilation by choosing α appropriately, even if ||M||2 > 1.
Roland Potthast 43/43
Summary Summary
Summary
Summary on DA Instability
3dVar (and 4dVar for linear systems) can be analysed as a cycled Tikhonovregularization.
We have shown that even for very stable dynamics where M = const cycled
Tikhonov with ill-posed observation operators diverges if we have data f 6∈ R(H).
The divergence carries over to 3dVar and 4dVar for simple systems.
For systems damping high frequencies we have shown that stability depends on
the choice of the regularization parameter α
For self-adjoint systems M with bounded Frobenius norm we can always achieve a
stable assimilation by choosing α appropriately, even if ||M||2 > 1.
Roland Potthast 43/43