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Monitoring the assimilation system

Monitoring the Assimilation System

  • Effective monitoring of a such complex system with 108 degree of freedom and 107observations is a necessity. No just few indicators but a more complex set of measures to answer questions like

    • How much influent are the observations in the analysis?

    • How much influence is given to the a priori information?

    • How much does the estimate depend on one single influential obs?


Influence matrix introduction

Influence Matrix: Introduction

  • Unusual or influential data points are not necessarily bad observations but they may contain some of most interesting sample information

  • In Ordinary Least-Square the information is quantitatively available in the Influence Matrix

Tuckey 63, Hoaglin and Welsch 78, Velleman and Welsch 81


Influence matrix in ols

Influence Matrix in OLS

Y (mx1) observation vector

X (mxq) predictors matrix, full rank q

β (qx1) unknown parameters

 (mx1) error

m>q

  • OLS provide the solution

  • The fitted response is


Influence matrix properties

Self-Sensitivity regression analysis to provide protection against distortion by anomalous data

Cross-Sensitivity

Average Self-Sensitivity=q/m

S (mxm) symmetric, idempotent and positive definite matrix

Influence Matrix Properties

  • The diagonal element satisfy

  • It is seen


Influence matrix related findings

Influence Matrix Related Findings

  • CV score can be computed by relying on the all data estimate ŷ and Sii


Outline

  • Observation and background Influence

Outline

  • Findings related to data influence and information content

  • Toy model: 2 observations

  • Conclusion


Solution in the observation space

y deleted

Hxb

Hxb

Hxb

y

y

HK

I-HK

I-HK

I-HK

HK

HK

  • The analysis projected at the observation location

Solution in the Observation Space

K(qxp) gain matrix

H(pxq) Jacobian matrix

B(qxq)=Var(xb)

R(pxp)=Var(y)

The estimation ŷ is a weighted mean


Influence matrix

Influence Matrix deleted

Observation Influence is complementary to Background Influence


Influence matrix properties1

The diagonal element satisfy deleted

Influence Matrix Properties


Aircraft above 400 hpa u comp influence

Data Rich Region deleted

Data Poor Region

Aircraft above 400hPa U-Comp Influence


Synop dribu surface pressure influence

Dynamical active area deleted

Sii>1

due to

the numerical

approssimation

Synop&DRIBUSurface Pressure Influence


Ascat u comp influence

Mean deleted

ASCAT U-Comp Influence

STD


Toy model 2 observations

x deleted 1

y1

x2

y2

Find the expression for S as function of r and the expression of for α=0 and ~1

given the assumptions:

Toy Model: 2 Observations


Toy model 2 observations1

x deleted 1

y1

x2

y2

=0

Toy Model: 2 Observations

Sii

1

1/2

~1

1/3

r

0

1/2

1


Consideration 1

Where observations are dense deleted Sii tends to be small and the background sensitivities tend to be large and also the surrounding observations have large influence (off-diagonal term)

When observations are sparse Sii and the background sensitivity are determined by their relative accuracies (r) and the surrounding observations have small influence (off-diagonal term)

Consideration (1)


Toy model 2 observations2

x deleted 1

y1

x2

y2

=0

~1

Toy Model: 2 Observations


Consideration 2

When observation and background have similar accuracies (r), the estimate ŷ1 depends on y1 and x1 and an additional term due to the second observation. We see that if R is diagonal the observational contribution is devaluated with respect to the background because a group of correlated background values count more than the single observation (2-α2 → 2). Also by increasing background correlation, the nearby observation and background have a larger contribution

=0

~1

Consideration (2)


Global and partial influence

SYNOP the estimate

AIREP

SATOB

DRIBU

TEMP

PILOT

AMSUA

HIRS

SSMI

GOES

METEOSAT

QuikSCAT

Type

100% only Obs Influence

Global Observation Influence = GI =

0% only Model Influence

1 1000-850

2 850-700

3 700-500

4 500-400

5 400-300

6 300-200

7 200-100

8 100-70

9 70-50

10 50-30

11 30-0

Type

Area

Partial Observation Influence = PI =

Level

Level

Variable

Global and Partial Influence

u

v

T

q

ps

Tb

Variable


Dfs and oi

2012 ECMWF the estimate Operational

DFSand OI

GOI =18% GBI =82%


Dfs and oi 2012 operational versus next oper cycle

GOI =18% GBI =82% the estimate

DFS and OI: 2012 Operational versus Next Oper Cycle

versus

GOI =16% GBI =84%


Evolution of the B matrix: the estimate σbcomputed from EnDA

Xt+εStochastics

y+εo

SST+εSST

Xb+εb

y+εo

SST+εSST

AMSU-A ch 6


Evolution of the B matrix: the estimate σb from EnDA

N. Atlantic

ASCAT

N. Pacific


Evolution of the GOS: Interim the estimate Reanalysis Aircraft above 400 hPa

1999

2007


Evolution of the GOS: Interim Reanalysis the estimate

U-comp Aircraft, Radiosonde, Vertical Profiler, AMV

1999

2007


Evolution of the GOS: Interim the estimate Reanalysis AMSU-A

1999

2007


Conclusions

Data-sparse the estimate  Single observation

Sii=1

Model under-confident (1-Sii)

Data-dense

Sii=0

Model over-confident  tuning (1-Sii)

  • The Influence Matrix is well-known in multi-variate linear regression. It is used to identify influential data. Influence patterns are not part of the estimates of the model but rather are part of the conditions under which the model is estimated

  • Disproportionate influence can be due to:

    • incorrect data (quality control)

    • legitimately extreme observations occurrence

      • to which extent the estimate depends on these data

Conclusions


Conclusions1

Conclusions

  • Thinning is mainly performed to reduce the spatial correlation but also to reduce the analysis computational cost

    • Knowledge of the observations influence helps in selecting appropriate data density

  • Diagnose the impact of improved physics representation in the linearized forecast model in terms of observation influence


Data density radiosonde observation influence

U-Comp different observation system

Below 700 hPa

Data density: RadiosondeObservation Influence

Temperature

Below 700 hPa


Background and observation tuning in ecmwf 4d var

Model different observation system

Observations

Background and Observation Tuning in ECMWF 4D-Var


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