data assimilation - european space agency · 2013. 10. 23. · data assimilation alan o’neill...
TRANSCRIPT
Data Assimilation
Alan O’NeillData Assimilation Research Centre
University of Reading
Contents
• Motivation• Univariate (scalar) data assimilation• Multivariate (vector) data assimilation
– Optimal Interpolation (BLUE)– 3d-Variational Method– Kalman Filter– 4d-Variational Method
• Applications of data assimilation in earth system science
Motivation
What is data assimilation?
Data assimilation is the technique whereby observational data are combined with output from a numerical model to produce an optimal estimate of the evolving state of the system.
DARC
Why We Need Data Assimilation
• range of observations• range of techniques• different errors• data gaps• quantities not measured• quantities linked
DARC
Some Uses of Data Assimilation
• Operational weather and ocean forecasting
• Seasonal weather forecasting• Land-surface process• Global climate datasets• Planning satellite measurements• Evaluation of models and observations
DARC
Preliminary Concepts
What We Want To Know
csx
)()(
tt atmos. state vector
surface fluxes
model parameters
)),(),(()( csxX ttt =
What We Also Want To Know
Errors in models
Errors in observations
What observations to make
Numerical ModelDAS
DATA ASSIMILATION SYSTEM
O
Data Cache
A
A
B
F
model
observations
Error Statistics
The Data Assimilation Process
observations forecasts
estimates of state & parameters
compare reject adjust
errors in obs. & forecasts
X
t
observation
model trajectory
Data Assimilation:an analogy
Driving with your eyes closed: open eyes every 10 seconds and correct trajectory
DARC
Basic Concept of Data Assimilation
• Information is accumulated in time into the model state and propagated to all variables.
What are the benefits of data assimilation?
• Quality control• Combination of data• Errors in data and in model• Filling in data poor regions• Designing observational systems• Maintaining consistency• Estimating unobserved quantities
DARC
Methods of Data Assimilation
• Optimal interpolation (or approx. to it)
• 3D variational method (3DVar)
• 4D variational method (4DVar)
• Kalman filter (with approximations)
DARC
Types of Data Assimilation
• Sequential• Non-sequential• Intermittent• Continous
Sequential Intermittent Assimilation
analysis analysisanalysismodel model
obsobsobsobsobs obs
Sequential Continuous Assimilation
obs obs obs obs obs
Non-sequential Intermittent Assimilation
analysis + model analysis + model analysis + model
obs obs obs obs obs obs
Non-sequential Continuous Assimilation
analysis + model
obsobs
obsobsobs
obs
Statistical Approach to Data Assimilation
DARC
Data Assimilation Made Simple(scalar case)
Least Squares Method(Minimum Variance)
eduncorrelat
are tsmeasuremen twothe,0 )(
)(
0
21
22
22
21
21
21
22
11
>=<>=<
>=<
>=>=<<+=+=
εεσε
σε
εεεε
t
t
TT
TT
1
:unbiased be should analysis The
:nsobservatio theof
ncombinatiolinear a as Estimate
21
2211
t
=+⇒
>=<
+=
aa
TT
TaTaT
T
ta
a
Least Squares Method Continued
1 constraint thesubject to
)(
))()(()(
:error squaredmean its minimizingby Estimate
21
22
22
221
2221
22211
22
=+
+>=+=<
>−+−>=<−=<
11
aa
aaaa
TTaTTaTT
T
tttaa
a
σσεε
σ
Least Squares Method Continued
22
2
2
1 11
1
σσ
σ+
=
1
1a2
22
22
2 11
1
σσ
σ+
=
1
a
22
21
2
111σσσ
+=⇒a
The precision of the analysis is the sum of the precisions of the measurements. The analysis therefore has higher precision than any single measurement (if the statistics are correct).
Variational Approach
0for which of value theis
)()(21
)( 22
22
2
21
=∂∂
−+
−=
1
TJTT
TTTTTJ
a
σσ
)(TJ
T
Maximum Likelihood Estimate
• Obtain or assume probability distributions for the errors
• The best estimate of the state is chosen to have the greatest probability, or maximum likelihood
• If errors normally distributed,unbiased and uncorrelated, then states estimated by minimum variance and maximum likelihood are the same
Maximum Likelihood Approach (Baysian Derivation)
pdf.prior the2
)(exp )(
:is statisticserror Gaussian for truth theofon distributiy probabilit Then the
on).assimilati datain forecast background the(
,n observatioan madealready have weAssume
2
21
1
−−∝
1σTT
Tp
T
T
Maximum Likelihood Continued
).statisticsGaussian (for case ldimensiona-multifor holds eEquivalencfunction.cost theminimizing asanswer same get the We
truth. theestimate toln)(or pdfposterior theMaximizeT. oft independenfactor gnormalisin is )( since
2)(
exp 2
)(exp
)()()¦(
)¦(
:is n observatio given the pdfposterior for the formula sBayes'
2
21
21
22
22
2
22
2
Tp
TTTTTp
TpTTpTTp
T
−−
−−∝=
σσ
Simple Sequential Assimilation
22
1222
21
)1(
:is anceerror vari analysis theand ,)(
:bygiven is weight optimal The
".innovation" theis ) ( where) (
Let
ba
obb
boboba
ob
W
W
W
TTTTWTT
TTTT
σσ
σσσ
−=
+=
−−+=
==
−
Comments
• The analysis is obtained by adding first guess to the innovation.
• Optimal weight is background error variance multiplied by inverse of total variance.
• Precision of analysis is sum of precisions of background and observation.
• Error variance of analysis is error variance of background reduced by (1- optimal weight).
Simple Assimilation Cycle
• Observation used once and then discarded.
• Forecast phase to update and • Analysis phase to update and • Obtain background as
• Obtain variance of background as
bT 2bσ
aT 2aσ
)]([)( 1 iaib tTMtT =+
)()(ely alternativ )()( 21
221
2iaibibib tattt σσσσ == ++
Simple Kalman Filter
22,
221,
21,
,
,,11,
221
)(M)()(
:is covarianceerror backgroundForecast
M whereM
)()()(Then
)biased!not (model ,)]([)(
before. as step Analysis
Q
TM
TMTMTT
QtTMtT
iaibib
mia
mitiaitbib
mmitit
+>==<
∂∂=+=
+−=−=
>=<−=
++
++
+
σεσ
εε
εε
εε
Multivariate Data Assimilation
Multivariate Case
=
nx
xx
t 2
1
)(x vector state
=
my
y
y
t 2
1
)( vector nobservatio y
State Vectors
x
bxtx
ax
state vector (column matrix)
true state
background state
analysis, estimate of tx
Ingredients of Good Estimate of the State Vector (“analysis”• Start from a good “first guess” (forecast
from previous good analysis)• Allow for errors in observations and first
guess (give most weight to data you trust)
• Analysis should be smooth• Analysis should respect known physical
laws
Some Useful Matrix Properties
inversion) through conserved isproperty this(. unless 0,scalar the,
:matrix symmetrix for ssdefinitene Positive)()( : transposea of Inverse
)( :product a of Inverse
)( :product a of Transpose
T
T11T
111
TTT
0xxAxx
AAA
ABAB
ABAB
=>∀
=
=
=
−−
−−−
Observations
• Observations are gathered into an observation vector , called the observation vector.
• Usually fewer observations than variables in the model; they are irregularly spaced; and may be of a different kind to those in the model.
• Introduce an observation operator to map from model state space to observation space.
y
)(xx H→
Errors
Variance becomes Covariance Matrix
• Errors in xi are often correlated– spatial structure in flow– dynamical or chemical relationships
• Variance for scalar case becomes Covariance Matrix for vector case COV
• Diagonal elements are the variances of xi
• Off-diagonal elements are covariances between xi and xj
• Observation of xi affects estimate of xj
The Error Covariance Matrix
( )
><><><
><><><><><><
>==<
=
=
nnnn
n
n
n
n
eeeeee
eeeeeeeeeeee
eee
e
ee
.....................
...
...
...
.
.
.
21
22212
12111
T
21T
2
1
εε
εε
P
2iiiee σ>=<
Background Errors
• They are the estimation errors of the background state:
• average (bias)• covariance
tbb xx −=ε>< bε
>><−><−=< Tbb ))(( εεεεB
Observation Errors• They contain errors in the observation
process (instrumental error), errors in the design of , and “representativeness errors”, i.e. discretizaton errors that prevent from being a perfect representation of the true state.
H
tx
)( to xy H−=ε >< oε
>><−><−=< Toooo ))(( εεεεR
Control Variables
• We may not be able to solve the analysis problem for all components of the model state (e.g. cloud-related variables, or need to reduce resolution)
• The work space is then not the model space but the sub-space in which we correct , called control-variable space
bxxxx δ+= ba
Innovations and Residuals
• Key to data assimilation is the use of differences between observations and the state vector of the system
• We call the innovation
• We call the analysisresidual
)( bxy H−)( axy H−
Give important information
Analysis Errors
• They are the estimation errors of the analysis state that we want to minimize.
taa xx −=ε
Covariance matrix A
Using the Error Covariance Matrix
Recall that an error covariance matrix for the error in has the form:
T >=< εεC
If where is a matrix, then the error covariance for is given by:
Hxy = H
x
yTHCHC =y
C
BLUE Estimator• The BLUE estimator is given by:
• The analysis error covariance matrix is:
• Note that:
1TT
bba
)(
))((−+=
−+=
RHBHBHK
xyKxx H
BKHIA )( −=
1T11T11TT )()( −−−−− +=+ RHHRHBRHBHBH
Statistical Interpolation with Least Squares Estimation
• Called Best Linear Unbiased Estimator (BLUE).
• Simplified versions of this algorithm yield the most common algorithms used today in meteorology and oceanography.
Assumptions Used in BLUE• Linearized observation operator:
• and are positive definite.• Errors are unbiased:
• Errors are uncorrelated:
• Linear anlaysis: corrections to background depend linearly on (background – obs.).
• Optimal analysis: minimum variance estimate.
)()()( bb xxHxx −=− HHB R
0)( ttb >=−>=<−< xyxx H
0))()(( Tttb >=−−< xyxx H
Optimal Interpolation
))(( bba xyKxx H−+=
“analysis”
“background”(forecast)
observation
1RHBHBHK −+= )( TT
• linearity H H
• matrix inverse
• limited area
observation operator
∆
∆
∆
∆
∆
∆∆
∆
)( bxy H−=∆ at obs. point
bx
data void
END