a concept of environmental forecasting and variational organization of modeling technology vladimir...
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A Concept of Environmental Forecasting and Variational Organization
of Modeling Technology
Vladimir Penenko
Institute of Computational Mathematics and Mathematical Geophysics SD RAS
Challenges of environment forecasting:
•Predictability of climate-environment system?
• Stability of climatic system?
• sensitivity to perturbations of forcing
Features of environment forecasting: uncertainty • in the long-term behavior of the climatic system;• in the character of influence of man-made factors in the conditions of changing climate
Uncertainty
• Discrepancy between models and real phenomena
• insufficient accuracy of numerical schemes and algorithms
• lack and errors of input data
A CONCEPT OF ENVIRONMENTAL FORECASTING
• Basic idea:
we use inverse modeling technique
to assess risk and vulnerability of territory
(object) with respect to harmful impact
in addition to traditional forecasting the state functions variability by forward methods
The methodology is based on:•control theory,•sensitivity theory,•risk and vulnerability theory,
•variational principles in weak-constrained formulation,•combined use of models and observed data,• forward and inverse modeling procedures,• methodology for description of links between regional and global processes ( including climatic changes) by means of orthogonal decomposition of functional spaces for analysis of data bases and phase spaces of dynamical systems
Theoretical background
Basic elements for concept implementation:
models of processes data and models of measurements global and local adjoint problems constraints on parameters and state functions functionals: objective, quality, control, restrictions etc. sensitivity relations for target functionals and
constraints feedback equations for inverse problems
( ) ( ) 0L Gt
Y B Y f r
0 0 ,a a Y Y ;
( )tD state function ,
( )tDY parameter vector. G “space” operator of the model
Variational form
( , , ) ( ( ), ) 0tD
I L dDdt Y Y
*( )tD adjoint functions , ,r ξ ς are the terms describing
uncertainties and errors of thecorresponding objects.
Mathematical model of processes
Model of atmospheric dynamics
1u
u pu fv kw F
t x
v
1v
v pv f u F
t y
v
1w
w pw k u g F
t z
v
( ) 1p pp p p
v v
c cpp p c F f
t c c
v v
( ( 1 ) ) pdT T
v v
cT RT T F f
t c c
v v
10, (1 )a dp R T
t
v
Transport and transformationof humidity
v
vv l f q
qq S S F
t
v
1l
ll l lT l q
qq q v S F
t z
v
1f
ff f fT f q
qq q v S F
t z
v
c
cc c q
qq S F
t
v
ii i i i i ii
L div ( grad ) ((S ) f (x, t) r ) 0,t
u
Transport and transformation modelof gas pollutants and aerosols
Operators of transformation
i r
j
R Us (q )
g i i jiq 1 j 1
S ( ) P( ) ( ) k(q) s (q ) s (q )
0
0
1 1 1 1 11 1
2
1 1 1 21
1,
2
,
, 1,
M
M M
a i ik k km kk m
M
i ik k i i i ik
i i i
S K d
K d e
R Q i M
Variational form of model’s set:hydrodynamics+ chemistry+ hydrological cycle
t
n
i i iii 1 D
I , ((S ) f (x, t) r ) dDdt
Y
t
* * * *p T
D
pp div pdiv pp TT div dDdt u u u
* 0t
npu d dt
t
T
a a a
D
W dDdt
Variational form of convection-diffusion operators
t
iD
1div div
2 t t
u u
0
1grad grad
2t
D
dDdt dD
1
2t
n b b b
i
u R q d dtn
0b bR q boundary conditions on t
Model of observations
A set of measured data m , m on m
tD
[ ( )]m mH ,
[ ( )]mH models of observations.
the term describing uncertainty and errors adjoint function with respect to image of
observation mdDdt Radon’s or Dirac’s measure ) )m
t tD D
*5( , , ) [ ( )] 0
t
T
m m m m
D
I H W dDdt Y
Variational form
Goal functionals
( ) ( ) ( , ) , , 1,...,t
k k k k k
D
F t dDdt F k K x
kF are evaluated functions (differentiable in generilized sense, bounded,satisfying the Lipschitz's conditions), dDdtk are Radon’s or Dirac’s measures on tD , )(*
tk D .
Quality functionals for data assimilation
t
Tk m m m
D
H M H t dDdt x( ) ( ( )) ( ( )) ( , ) ,
mdDdt Radon’s or Dirac’s measures ) )mt tD D
Functionals for generalized description of information links in the system
Variational principle
* *
* *
( , , , , , , ) ( )
( , , ) ( , , )hkt
h hk k k
m DI I
Y r ξ ζ
Y
1 2 3 4 ( )0.5 ( ) ( ) ( ) ( )m h h h h
t t t
hT T T T
D D D R DW W W W r r ξ ξ ζ ζ
Augmented functional for computational technology
* *(, , ) / 0 ( , , , , , , , )h hk s s Y r ξ ζ
Algorithms for construction of numerical schemes
( , ) 0h
hktB G
Y f r
( ) ( , ) 0,h
T Tkt k k kB A
Y d
*5( ) ( ( ) ,
Thk k m
d H M
( ) 0k t t x
0 0 13 ( ,0), 0a kM t x
1 *2( , ) ( , ),kt M tr x x
14a kM Y Y
( , , )hk kI
YY
0( , ) ( , )hA G
Y Y
t i s t h e a p p r o x i m a t i o n o f t i m e d e r i v a t i v e sI n i t i a l g u e s s :
( 0 ) 0 ( 0 ) 0 ( 0 )0 , ,a a r Y Y
The universal algorithm of forward & inverse modeling
0( ) ( , Y) ( ,Y Y, )h hk k kI
0( ,Y Y, )Y
hk kI
The main sensitivity relations
Algorithm for calculation of sensitivity functions
Some elements of optimal forecasting and design
12 r x x*
k( ,t ) M ( ,t ),
13 0 0 xkM ( , ), t
1 14 4
hk kM M I ( ,Y , )
Y
Algorithms for uncertainty calculationbased on sensitivity analysis and
data assimilation:
in models of processes
in initial state
in model parameters and sources
1 5iM i,( , ) are weight matrices
15 1 x x* ( ,t ) M M ( ,t ),
in models of observations
Fundamental role of uncertainty functions
• integration of all technology components• bringing control into the system• regularization of inverse methods• targeting of adaptive monitoring • cost effective data assimilation
Optimal forecasting and design
Optimality is meant in the sense that estimations of the goal functionals do not depend on the variations :
• of the sought functions in the phase spaces of the dynamics of the physical system under study
• of the solutions of corresponding adjoint problems that generated by variational principles
• of the uncertainty functions of different kinds which explicitly included into the extended functionals
Construction of numerical approximations
• variational principle
• integral identity
• splitting and decomposition methods
• finite volumes method
• local adjoint problems
• analytical solutions
• integrating factors
1
* *1( ) ( )
jT
j js s mm m
s
H W H
Basic elements in frames of splitting and decomposition schemes:
p - number of stages
4DVar real time data assimilation algorithm
112. j j j j j
s s s s sf rt
1 *1 1,j j j
s s j jsr W t t t
1 111. , , 1, , 1j j
s jt t s p p
1
13. , , 1,
pj j
s js
t t j Jp
1
pjs
s
- operator of the model,
Scenario approach for environmental purposes
• Inclusion of climatic data via decomposition of phase spaces on set of orthogonal subspaces ranged with respect to scales of perturbations
• Construction of deterministic and deterministic-stochastic scenarios on the basis of orthogonal subspaces
• Models with leading phase spaces
1
11
1
2
3
4
5
9 18 0.57 0.16 0.055 0.014 0.0053 0.0012 0.00051 0.0001
1
1
1
2
2
3457
9 18 0.57 0.16 0.055 0.014 0.0053 0.0012 0.00051 0.0001
Yakutsk
1
1
1
2
2
3
3
45
7
9 18 0.57 0.16 0.055 0.014 0.0053 0.0012 0.00051 0.0001
Khanti-Mansiisk
1
11
2
2
3
3
4
66
9 18 0.57 0.16 0.055 0.014 0.0053 0.0012 0.00051 0.0001
Krasnoyarsk
1
1
2
23
3
4
5
67
9 18 0.57 0.16 0.055 0.014 0.0053 0.0012 0.00051 0.0001
Mondy
1
1
1
3
23
3
3
4
5
6
7
9 18 0.57 0.16 0.055 0.014 0.0053 0.0012 0.00051 0.0001
Tomsk
1
1
1
2 23
4 6
9 18 0.57 0.16 0.055 0.014 0.0053 0.0012 0.00051 0.0001
Tory
1
1
2
2
3
4
76
9 18 0.57 0.16 0.055 0.014 0.0053 0.0012 0.00051 0.0001
Ulan-Ude1
1
1
2
2
3
3
4
45
9 18 0.57 0.16 0.055 0.014 0.0053 0.0012 0.00051 0.0001
Ussuriisk
Ekaterinburg
“climatic” April
Long-term forecasting for Lake Baikal regionRisk function
0.1
0.1
0.1
0.2
0.2
0.2
0.2
0.2
0.3
0.3
0.3
0.3
0.3
0.4
0.4
0.4
0.5
0.5
0.5
0.6
0.6
0.6
0.7
0.7
0.70.
8
0.8
0.9
0.9
95 100 105 110 115
50
55
60
Baikalsk
Irkutsk
Ulan-Ude
Nijhne-Angarsk
Cheremkhovo
Bratsk
Angarsk
Surface layer, climatic October
Conclusion
• Algorithms for optimal environmental
forecasting and design are proposed
• The fundamental role of uncertainty is highlighted
number
eig
en
lavu
es
0 10 20 30
1
2
3
4
5
6
7
8
9
number
eig
en
lavu
es
10 20 30 40
1
2
3
4
5
6
7
8
9
10
11
12
number
eig
en
lavu
es
0 10 20 30 40 50
1
2
3
4
5
6
7
8
9
10
11
12
13
14
36 years
46 years
56 years
Separation of scales : climate/weather noise
Eigenvalues of Gram matrix as a measure of informativeness
of orthogonal subspaces