Slide 1© ECMWF Assimilation of ATOVS radiances at ECMWF: Bias correction and impact in NWP Enza Di...

Slide 1 © ECMWF

Assimilation of ATOVS radiances at ECMWF: Bias correction and impact in NWP

Enza Di Tomaso* and Niels BormannECMWF

*EUMETSAT fellow

Slide 2 © ECMWF



*EUMETSAT fellow

Slide 3 © ECMWF

Assimilated ATOVS radiances ●HIRS: channel 4-7, 11, 14, 15 over sea; 12 over sea + low orography only●AMSU-A: channels 5,6 over sea + low orography; 7-14 land+sea●AMSU-B/MHS: channel 5 over sea only; 3,4 sea+low orography

HIRS ( 3 used)

AMSU-A (5 used)

AMSU-B/MHS (3 used)

NOAA-15 no: unstable yes (not ch 6, 11, 14)

no: quality

NOAA-17 yes Instrument failed no (since Dec 09)

NOAA-18 no: unstable yes yes

NOAA-19 yes yes (not ch 8) yes (not ch 3)

AQUA n/a yes (not ch 5 & 7; 6 over sea only)

n/a

METOP-A yes yes(not ch 7)

yes

Slide 4 © ECMWF


HIRS ( 3 used)

AMSU-A (5 used)

AMSU-B/MHS (3 used)


no: quality





n/a


yes

Slide 5 © ECMWF


HIRS ( 3 used)

AMSU-A (5 used)

AMSU-B/MHS (3 used)


no: quality





n/a


yes

Part 1

Slide 6 © ECMWF


HIRS ( 3 used)

AMSU-A (5 used)

AMSU-B/MHS (3 used)


no: quality





n/a


yes

Part 1

Part 2

Slide 7 © ECMWF


HIRS ( 3 used)

AMSU-A (5 used)

AMSU-B/MHS (3 used)


no: quality





n/a


yes

Part 1

Part 2

Slide 8 © ECMWF


(Part 1) .


*EUMETSAT fellow

Slide 9 © ECMWF

Part 1: revision of AMSU-A bias correction

Bias correction of ch12 & ch14 (Part 1a)

AMSU/A(from http://disc.sci.gsfc.nasa.gov/AIRS/documentation/)

Slide 10 © ECMWF



Bias correction of ch5 to 8 (Part 1b, ongoing work)

AMSU/A(from http://disc.sci.gsfc.nasa.gov/AIRS/documentation/)

Slide 11 © ECMWF



Bias correction of ch5 to 8 (Part 1b, ongoing work)

Assimilation of surface-sensitive channels (future work)

(by Tom Greenwald)AMSU/A

(from http://disc.sci.gsfc.nasa.gov/AIRS/documentation/)

Slide 12 © ECMWF

Bias correction of ch 12 & 14: interaction between forecast model error and bias correction

T511 experiment (black) versus T255 experiment(red)

T1279 experiment (black) versus T255 experiment(red)

Issues with high spatial model resolution: radiosondes show resolution-dependent temperature biases in the stratosphere

Radiosonde T

RadiosondeT

N.Hemis

N.Hemis

Slide 13 © ECMWF

Experiment description●Revision of the bias correction of AMSU-A stratospheric channels peaking

where the forecast model error is particularly significant

– “noBC experiment”: no bias correction applied to AMSU-A ch12 and ch14

– “sBC experiment”: scan bias correction (polynomial in the scan angle and with no constant) applied to AMSU-A ch12 and ch14

– “N19 anchor experiment”: ● scan bias correction (with no constant) applied to AMSU-A ch12 and

ch14 on NOAA-19 ● scan bias and offset correction applied to AMSU-A ch12 and ch14 on

other satellites

Experiments were run over ‘summer’ (20 Jul – 31 Oct 2009) and ‘winter’ (6 Dec – 31 Mar 2010) at T511 resolution

Slide 14 © ECMWF







other satellites


Slide 15 © ECMWF

Departure statistics of the first guess and analysis

Radiosonde T

MetOp AMSU-A TB

N.Hemis

No bias correction of AMSU-A ch12 ad ch14 improves the fit to temperature observations

“noBC experiment” (black) versus control (red)“noBC experiment” BC (pink) versus control BC (green)

Slide 16 © ECMWF

Comparison with the SPARC climatology“noBC experiment” minus control control minus climate

Slide 17 © ECMWF

“noBC experiment” RMSE – control RMSE

Forecast impact“noBC experiment” versus control (verified against observations), summer

controlGOOD

“noBC experiment”

GOOD

The impact for the forecast of the 50hPa geopotential of the “noBC experiment” is positive in the extra-Tropics

Slide 18 © ECMWF


Forecast impact“noBC experiment” versus control (verified against observations), winter

controlGOOD


GOOD

The impact for the forecast of the 50hPa geopotential of the “noBC experiment” is positive in the extra-Tropics

Slide 19 © ECMWF


Forecast impact“noBC experiment” versus control (verified against own-analysis), summer

controlGOOD


GOOD

The impact for the forecast of the 500hPa geopotential of the “noBC experiment” is slightly negative in the Southern Hemisphere

Slide 20 © ECMWF


Forecast impact“noBC experiment” versus control (verified against own-analysis), winter

controlGOOD


GOOD

The impact for the forecast of the 500hPa geopotential of the “noBC experiment” is slightly negative in the Northern Hemisphere

Slide 21 © ECMWF







other satellites


Slide 22 © ECMWF


MetOp-A AMSU-A TB

NOAA-18 AMSU-A TB

S.Hemis

S.Hemis

The bias correction of AMSU-A ch12 (and ch14) onboard NOAA-18 is not adequately correcting the scan bias, as it tries to correct forinter-satellite biases

“sBC experiment” (black) versus “noBC experiment” (red)“sBC experiment” BC (pink) versus “noBC experiment” BC (green)

Slide 23 © ECMWF







other satellites


Slide 24 © ECMWF


Forecast impact“N19 anchor experiment” versus control (verified against own-analysis), summer

controlGOOD


GOOD

The impact for the forecast of the 500hPa geopotential of the “noBC experiment” is neutral also in the Southern Hemisphere

Slide 25 © ECMWF


Forecast impact“N19 anchor experiment” versus control (verified against own-analysis), winter

controlGOOD


GOOD

The impact for the forecast of the 500hPa geopotential of the “noBC experiment” is neutral also in the Northern Hemisphere

Slide 26 © ECMWF


MetOp-A AMSU-A TB

NOAA-18 AMSU-A TB

S.Hemis

S.Hemis

The bias correction of AMSU-A ch12 (and ch14) onboard NOAA-18 is now adequately correcting the scan bias

“N19 anchor experiment” (black) versus “noBC experiment” (red)“N19 anchor exp.” BC (pink) versus “noBC experiment” BC (green)

Slide 27 © ECMWF

Conclusions of part 1a

●We considered a revision of the bias correction of high stratospheric channels because of the interaction between the variational bias correction scheme (VarBC) and large forecast model biases in the upper atmosphere

– no bias correction of channels 12 and 14 has some negative forecast impact

– scan bias correction alone is affected by inter-satellite biases

– using one satellite as anchor for the others offers improvements to the previous solutions

Slide 28 © ECMWF

Bias correction of ch5 to 8: gamma-delta correction

●The observed bias is modelled with a constant fractional error in the optical depth (gamma) and a global constant (delta):

Bias = offset + bias due to errors in the channel transmittance

●Gamma coefficients are currently used in the radiative transfer up to NOAA-18 (not for NOAA-19 and MetOp-A), (work by P. Watts & A. McNally)

●Sources of error in the channel transmittance (not necessarily constant): – errors in the assumed gas concentration – errors in the absorption coefficient – inaccurate channel spectral response function

Slide 29 © ECMWF

Mean first guess departures with different gamma values control experiment (gamma = 1)

“gamma experiment” (gamma = 1.05)

Slide 30 © ECMWF

Conclusions of part 1b

●Values of gamma have been estimated for AMSU-A channels 5 to 8

●Experiments are running to show

– the impact of the updated gamma values for all AMSU-A

– whether gamma can correct air-mass dependent biases without the need of specific predictors in VarBC for channels 5 to 8

Slide 31 © ECMWF


(Part 2)


*EUMETSAT fellow

Thanks to Alan Geer for the IVER package

Slide 32 © ECMWF

Part 2: orbit constellation OSEs●characterise the benefit for NWP of having ATOVS data from three evenly-spaced

orbits versus data from a less optimal coverage●assess the benefit for NWP of assimilating ATOVS data from more than three satellites

MetOp-A

NOAA-18 NOAA-19 Aqua

NOAA-15

NOAA-16

NOAA-17

Satellite equatorial crossing times (local)

Ti

me

Slide 33 © ECMWF

Data coverage

“two-satellite experiment”* MetOp-A * NOAA-18

“NOAA-15 experiment”* MetOp-A * NOAA-18 * NOAA-15

“NOAA-19 experiment”* MetOp-A * NOAA-18 * NOAA-19

Sample coverage from a 6-hour period around 0Z

Slide 34 © ECMWF

Experiment description●“no-MW sounder experiment”: no AMSU-A and AMSU-B/MHS were assimilated

●“two-satellite experiment”: AMSU-A and AMSU-B/MHS on MetOp-A and NOAA-18 were assimilated

●“three-satellite experiments”:– “NOAA-15 experiment”: AMSU-A data were added from a third satellite NOAA-15– “NOAA-19 experiment”: AMSU-A data were added from a third satellite NOAA-19

●“all-satellite experiment”: all available ATOVS observations were assimilated

●The above set of experiments was run also in the case in which the advanced sounder instruments IASI and AIRS were denied

●Experiments were run over more than three months (14 April 2009 to 4 August 2009) at T255 resolution

Slide 35 © ECMWF


Both NOAA-15 and NOAA-19 bring some small improvement to the fit to temperature observations

Departure statistics for MetOp-A AMSU-A show some benefits from assimilating observations from NOAA-15 rather than NOAA-19

MetOp AMSU-A TB

Tropics

“three-satellite experiment” (black) versus “two-satellite experiment” (red)

“NOAA-15 experiment” (black) versus “NOAA-19 experiment” (red)

Radiosonde T

Slide 36 © ECMWF

Forecast impact

When averaged over the extra-Tropics the impact for the forecast of the geopotential of “NOAA-15 experiment” versus “NOAA-19 experiment” is neutral to slightly positive

“NOAA-15 exp” RMSE – “NOAA-19 exp” RMSE

“NOAA-19 experiment”

GOOD


GOOD

Slide 37 © ECMWF

Forecast impact

Both the assimilations of NOAA-15 and NOAA-19 data have a clearly positive forecast impact in the Southern Hemisphere compared to the use of two satellites only

Having ATOVS-like data from more than three satellites adds further benefit in terms of the forecast impact

“no-MW sounder experiment”

GOOD

“two-”, “three-”, “all-satellite

experiment” GOOD

“two-satellite” RMSE – “no-Mw sounder” RMSE “three-satellite” RMSE – “no-Mw sounder” RMSE “all-satellite” RMSE – “no-Mw sounder” RMSE

Slide 38 © ECMWF

Forecast impact

When IASI and AIRS are denied, the results show in general a stronger positive impact when additional ATOVS data are assimilated into the NWP system

“no-MW sounder experiment”

GOOD

“two-”, “three-”, “all-satellite

experiment” GOOD

“two-satellite” RMSE – “no-Mw sounder” RMSE “three-satellite” RMSE – “no-Mw sounder” RMSE “all-satellite” RMSE – “no-Mw sounder” RMSE

Slide 39 © ECMWF

Less thinning of data

●Comparing “three-satellite experiments” with a new “two-satellite experiment” where less data are removed

– less thinning of AMSU-A data

– additional field of view on each side of the scan

Slide 40 © ECMWF

Forecast impact“three-satellite experiment” versus “two-satellite experiment (less thinning)”

(verified against operational analysis)

“NOAA-15 exp” RMSE – “two-satellite (less thinning)” RMSE


GOOD

“two-satellite experiment

(less thinning)”GOOD

There is still some advantage in using three AMSU-A rather than two

Slide 41 © ECMWF

Conclusions of part 2

●ATOVS data in a more evenly-spaced orbit configuration give slightly better results in terms of forecast impact in the Southern Hemisphere than data from a less optimal coverage

●Both the assimilations of NOAA-15 and NOAA-19 observations have a positive forecast impact in the Southern Hemisphere in comparison to the use of just two satellites, and there is a clear advantage in assimilating all available ATOVS data

●The benefit of evenly-spaced orbits is expected to be stronger in limited area systems where the coverage plays a more crucial role

Slide 42 © ECMWF

Danke und Frohe Weihnachten!

Slide 43 © ECMWF

Additional slides:gamma-delta correction

Watts and McNally

Slide 44 © ECMWF

Modelling absorption coefficient errors

Slide 45 © ECMWF

Estimating gamma

Slide 46 © ECMWF

Additional slides: variational bias correction (VarBC)

Dick Dee and Niels Bormann

Slide 47 © ECMWF

Variational analysis and bias correction:A brief review of variational data assimilation

h(x)yRh(x)yx)(xBx)(xJ(x) 1Tb

1Tb Minimise

background constraint (Jb)observational constraint (Jo)

The input xb represents past information propagated by the forecast model(the model background)

The input [y – h(xb)] represents the new information entering the system(the background departures - sometimes called the innovation)

The function h(x) represents a model for simulating observations(the observation operator)

Minimising the cost function J(x) produces an adjustment to the model background based on all used observations

(the analysis)

Slide 48 © ECMWF

Variational analysis and bias correction:Error sources in the input data

h(x)yRh(x)yx)(xBx)(xJ(x) 1Tb

1Tb Minimise

background constraint (Jb)observational constraint (Jo)

Errors in the input [y – h(xb)] arise from: errors in the actual observations errors in the model background errors in the observation operator

There is no general method for separating these different error sources we only have data about differences there is no true reference in the real world

The analysis does not respond well to contradictory input information A lot of work is done to remove biases prior to assimilation:

ideally by removing the cause in practise by careful comparison against other data

Slide 49 © ECMWF

Scan bias and air-mass dependent bias for each sensor/channel were estimated off-line from background departures, and stored on files (Harris and Kelly 2001)

Past* scheme for radiance bias correction at ECMWF

errorn observatio random

positionscan latitude,

)(

)(

10

obs

i

N

i iair

scanscan

e

xpb

bb

obsairscan exbbxhy )()(

)()( xbbxhy airscanb

Error model for brightness temperature data:

where

Periodically estimate scan bias and predictor coefficients: typically 2 weeks of background departures 2-step regression procedure careful masking and data selection

Average the background departures:

*Replaced in operations September 2006 by VarBC (Variational Bias Correction)

Predictors, for instance: 1000-300 hPa thickness 200-50 hPa thickness surface skin temperature total precipitable water

Slide 50 © ECMWF

The need for an adaptive bias correction system

The observing system is increasingly complex and constantly changingIt is dominated by satellite radiance data:

biases are flow-dependent, and may change with time they are different for different sensors they are different for different channels

0

5

10

15

20

25

30

35

40

45

50

55

No. of sources

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Year

Number of satellite sources used at ECMWF

AEOLUSSMOSTRMMCHAMP/GRACECOSMICMETOPMTSAT radMTSAT windsJASONGOES radMETEOSAT radGMS windsGOES windsMETEOSAT windsAQUATERRAQSCATENVISATERSDMSPNOAA

How can we manage the bias corrections for all these different components?This requires a consistent approach and a flexible, automated system

Slide 51 © ECMWF

The bias in a given instrument/channel is described by (a few) bias parameters:typically, these are functions of air-mass and scan-position (the predictors)

These parameters can be estimated in a variational analysis along with the model state(Derber and Wu, 1998 at NCEP, USA)

Variational bias correction:The general idea

The standard variational analysis minimizes

Modify the observation operator to account for bias:

]βx[z TTT Include the bias parameters in the control vector:

Minimize instead (z)]h[yR(z)]h[yz)(zBz)(zJ(z) Tbz

Tb

~~ 11

h(x)][yRh(x)][yx)(xBx)(xJ(x) Tbx

Tb 11

),(~

)(~ xhzh

What is needed to implement this:

1. The modified operator and its TL + adjoint 2. A cycling scheme for updating the bias parameter estimates3. An effective preconditioner for the joint minimization

problem

),(~ xh

Slide 52 © ECMWF

Variational bias correction: The modified analysis problem

Jb: background constraint

Jo: observation constraint

h(x)yRh(x)yx)(xBx)(x(x) 1Tb

1Tb J

The original problem:

h(x)β)(x,byRh(x)β)(x,by

β)(βBβ)(βx)(xBx)(xβ)J(x,

o1T

o

b1β

Tbb

1x

Tb

Jb: background constraint for x J: background constraint for

Jo: bias-corrected observation constraint

The modified problem:

Parameter estimatefrom previous analysis

Slide 53 © ECMWF

Limitations of VarBC:Interaction with model bias

h(x)β)b(x,yRh(x)β)b(x,y

β)(βBβ)(βx)(xBx)(xβ)J(x,

1T

b1β

Tbb

1x

Tb

VarBC introduces some extra degrees of freedom in the analysis, to help improve the fit to the (bias-corrected) observations:

This may lead to undesired effects wheremodel bias is present, andfew observations are available, oronly observations with VarBC are present.VarBC will, over time, force agreement with the model background.

model

observations

VarBC may wrongly attribute model bias to the observations

This works well where the analysis is well-constrained by observations, and“anchoring” observations are available (e.g.,

radiosondes, GPSRO data).VarBC will correct any biased observations and produce a consistent consensus analysis.

model

abundant observations

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