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Slide 1© ECMWF Assimilation of ATOVS radiances at ECMWF: Bias correction and impact in NWP Enza Di Tomaso * and Niels Bormann ECMWF *EUMETSAT fellow.

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Presentation on theme: "Slide 1© ECMWF Assimilation of ATOVS radiances at ECMWF: Bias correction and impact in NWP Enza Di Tomaso * and Niels Bormann ECMWF *EUMETSAT fellow."— Presentation transcript:

1 Slide 1© ECMWF Assimilation of ATOVS radiances at ECMWF: Bias correction and impact in NWP Enza Di Tomaso * and Niels Bormann ECMWF *EUMETSAT fellow

2 Slide 2© ECMWF Assimilation of ATOVS radiances at ECMWF: Bias correction and impact in NWP Enza Di Tomaso * and Niels Bormann ECMWF *EUMETSAT fellow

3 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-15no: unstableyes (not ch 6, 11, 14) no: quality NOAA-17yesInstrument failedno (since Dec 09) NOAA-18no: unstableyes NOAA-19yesyes (not ch 8)yes (not ch 3) AQUAn/ayes (not ch 5 & 7; 6 over sea only) n/a METOP-Ayes (not ch 7) yes

4 Slide 4© 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-15no: unstableyes (not ch 6, 11, 14) no: quality NOAA-17yesInstrument failedno (since Dec 09) NOAA-18no: unstableyes NOAA-19yesyes (not ch 8)yes (not ch 3) AQUAn/ayes (not ch 5 & 7; 6 over sea only) n/a METOP-Ayes (not ch 7) yes

5 Slide 5© 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-15no: unstableyes (not ch 6, 11, 14) no: quality NOAA-17yesInstrument failedno (since Dec 09) NOAA-18no: unstableyes NOAA-19yesyes (not ch 8)yes (not ch 3) AQUAn/ayes (not ch 5 & 7; 6 over sea only) n/a METOP-Ayes (not ch 7) yes Part 1

6 Slide 6© 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-15no: unstableyes (not ch 6, 11, 14) no: quality NOAA-17yesInstrument failedno (since Dec 09) NOAA-18no: unstableyes NOAA-19yesyes (not ch 8)yes (not ch 3) AQUAn/ayes (not ch 5 & 7; 6 over sea only) n/a METOP-Ayes (not ch 7) yes Part 1 Part 2

7 Slide 7© 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-15no: unstableyes (not ch 6, 11, 14) no: quality NOAA-17yesInstrument failedno (since Dec 09) NOAA-18no: unstableyes NOAA-19yesyes (not ch 8)yes (not ch 3) AQUAn/ayes (not ch 5 & 7; 6 over sea only) n/a METOP-Ayes (not ch 7) yes Part 1 Part 2

8 Slide 8© ECMWF Assimilation of ATOVS radiances at ECMWF: Bias correction and impact in NWP (Part 1). Enza Di Tomaso * and Niels Bormann ECMWF *EUMETSAT fellow

9 Slide 9© ECMWF Part 1: revision of AMSU-A bias correction Bias correction of ch12 & ch14 (Part 1a) AMSU/A (from

10 Slide 10© ECMWF Part 1: revision of AMSU-A bias correction Bias correction of ch12 & ch14 (Part 1a) Bias correction of ch5 to 8 (Part 1b, ongoing work) AMSU/A (from

11 Slide 11© ECMWF Part 1: revision of AMSU-A bias correction Bias correction of ch12 & ch14 (Part 1a) Bias correction of ch5 to 8 (Part 1b, ongoing work) Assimilation of surface- sensitive channels (future work) (by Tom Greenwald) AMSU/A (from

12 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 Radiosonde T N.Hemis

13 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

14 Slide 14© 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

15 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)

16 Slide 16© ECMWF Comparison with the SPARC climatology “noBC experiment” minus control control minus climate

17 Slide 17© ECMWF “noBC experiment” RMSE – control RMSE Forecast impact “noBC experiment” versus control (verified against observations), summer control GOOD “noBC experiment” GOOD The impact for the forecast of the 50hPa geopotential of the “noBC experiment” is positive in the extra-Tropics

18 Slide 18© ECMWF “noBC experiment” RMSE – control RMSE Forecast impact “noBC experiment” versus control (verified against observations), winter control GOOD “noBC experiment” GOOD The impact for the forecast of the 50hPa geopotential of the “noBC experiment” is positive in the extra-Tropics

19 Slide 19© ECMWF “noBC experiment” RMSE – control RMSE Forecast impact “noBC experiment” versus control (verified against own-analysis), summer control GOOD “noBC experiment” GOOD The impact for the forecast of the 500hPa geopotential of the “noBC experiment” is slightly negative in the Southern Hemisphere

20 Slide 20© ECMWF “noBC experiment” RMSE – control RMSE Forecast impact “noBC experiment” versus control (verified against own-analysis), winter control GOOD “noBC experiment” GOOD The impact for the forecast of the 500hPa geopotential of the “noBC experiment” is slightly negative in the Northern Hemisphere

21 Slide 21© 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

22 Slide 22© ECMWF Departure statistics of the first guess and analysis MetOp-A AMSU-A TB NOAA-18 AMSU-A TB 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 for inter-satellite biases “sBC experiment” (black) versus “noBC experiment” (red) “sBC experiment” BC (pink) versus “noBC experiment” BC (green)

23 Slide 23© 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

24 Slide 24© ECMWF “noBC experiment” RMSE – control RMSE Forecast impact “N19 anchor experiment” versus control (verified against own-analysis), summer control GOOD “noBC experiment” GOOD The impact for the forecast of the 500hPa geopotential of the “noBC experiment” is neutral also in the Southern Hemisphere

25 Slide 25© ECMWF “noBC experiment” RMSE – control RMSE Forecast impact “N19 anchor experiment” versus control (verified against own-analysis), winter control GOOD “noBC experiment” GOOD The impact for the forecast of the 500hPa geopotential of the “noBC experiment” is neutral also in the Northern Hemisphere

26 Slide 26© ECMWF Departure statistics of the first guess and analysis MetOp-A AMSU-A TB NOAA-18 AMSU-A TB 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)

27 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

28 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

29 Slide 29© ECMWF Mean first guess departures with different gamma values control experiment (gamma = 1) “gamma experiment” (gamma = 1.05)

30 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

31 Slide 31© ECMWF Assimilation of ATOVS radiances at ECMWF: Bias correction and impact in NWP (Part 2) Enza Di Tomaso * and Niels Bormann ECMWF *EUMETSAT fellow Thanks to Alan Geer for the IVER package

32 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) TimeTime

33 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

34 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

35 Slide 35© ECMWF Departure statistics of the first guess and analysis 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

36 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 “NOAA-15 experiment” GOOD

37 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

38 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

39 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

40 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 “NOAA-15 experiment” GOOD “two-satellite experiment (less thinning)” GOOD There is still some advantage in using three AMSU-A rather than two

41 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

42 Slide 42© ECMWF Danke und Frohe Weihnachten!

43 Slide 43© ECMWF Additional slides: gamma-delta correction Watts and McNally

44 Slide 44© ECMWF Modelling absorption coefficient errors

45 Slide 45© ECMWF Estimating gamma

46 Slide 46© ECMWF Additional slides: variational bias correction (VarBC) Dick Dee and Niels Bormann

47 Slide 47© ECMWF Variational analysis and bias correction: A brief review of variational data assimilation Minimise background constraint (J b ) observational constraint (J o ) The input x b represents past information propagated by the forecast model (the model background) The input [y – h(x b )] 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)

48 Slide 48© ECMWF Variational analysis and bias correction: Error sources in the input data Minimise background constraint (J b ) observational constraint (J o ) Errors in the input [y – h(x b )] 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

49 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 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: hPa thickness hPa thickness surface skin temperature total precipitable water

50 Slide 50© ECMWF The need for an adaptive bias correction system The observing system is increasingly complex and constantly changing It 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 How can we manage the bias corrections for all these different components? This requires a consistent approach and a flexible, automated system

51 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: Include the bias parameters in the control vector: Minimize instead What is needed to implement this: 1.The modified operator and its TL + adjoint 2.A cycling scheme for updating the bias parameter estimates 3.An effective preconditioner for the joint minimization problem

52 Slide 52© ECMWF Variational bias correction: The modified analysis problem J b : background constraint J o : observation constraint The original problem: J b : background constraint for x J  : background constraint for  J o : bias-corrected observation constraint The modified problem: Parameter estimate from previous analysis

53 Slide 53© ECMWF Limitations of VarBC: Interaction with model bias 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 where model bias is present, and few observations are available, or only 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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