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Assimilation of ATOVS radiances at ECMWF: Bias correction and impact in NWP. Enza Di Tomaso * and Niels Bormann ECMWF *EUMETSAT fellow. 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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Assimilation of ATOVS radiances at ECMWF: Bias correction and impact in NWP Enza Di Tomaso* and Niels Bormann ECMWF *EUMETSAT fellow
Assimilation of ATOVS radiances at ECMWF: Bias correction and impact in NWP Enza Di Tomaso* and Niels Bormann ECMWF *EUMETSAT fellow
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+loworography
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+loworography
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+loworography Part 1
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+loworography Part 1 Part 2
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+loworography Part 1 Part 2
Assimilation of ATOVS radiances at ECMWF: Bias correction and impact in NWP(Part 1) . Enza Di Tomaso* and Niels Bormann ECMWF *EUMETSAT fellow
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/)
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 http://disc.sci.gsfc.nasa.gov/AIRS/documentation/)
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) AMSU/A (from http://disc.sci.gsfc.nasa.gov/AIRS/documentation/) (by Tom Greenwald)
Bias correction of ch 12 & 14: interaction between forecast model error and bias correction T511 experiment (black) versus T255 experiment(red) Radiosonde T N.Hemis Issues with high spatial model resolution: radiosondes show resolution-dependent temperature biases in the stratosphere T1279 experiment (black) versus T255 experiment(red) Radiosonde T N.Hemis
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
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
Departure statistics of the first guess and analysis “noBC experiment” (black) versus control (red) “noBC experiment” BC (pink) versus control BC (green) Radiosonde T N.Hemis No bias correction of AMSU-A ch12 ad ch14 improves the fit to temperature observations MetOp AMSU-A TB
Comparison with the SPARC climatology “noBC experiment” minus control control minus climate
Forecast impact “noBC experiment” versus control (verified against observations), summer The impact for the forecast of the 50hPa geopotential of the “noBC experiment” is positive in the extra-Tropics control GOOD “noBC experiment” GOOD “noBC experiment” RMSE – control RMSE
Forecast impact “noBC experiment” versus control (verified against observations), winter The impact for the forecast of the 50hPa geopotential of the “noBC experiment” is positive in the extra-Tropics control GOOD “noBC experiment” GOOD “noBC experiment” RMSE – control RMSE
Forecast impact “noBC experiment” versus control (verified against own-analysis), summer The impact for the forecast of the 500hPa geopotential of the “noBC experiment” is slightly negative in the Southern Hemisphere control GOOD “noBC experiment” GOOD “noBC experiment” RMSE – control RMSE
Forecast impact “noBC experiment” versus control (verified against own-analysis), winter The impact for the forecast of the 500hPa geopotential of the “noBC experiment” is slightly negative in the Northern Hemisphere control GOOD “noBC experiment” GOOD “noBC experiment” RMSE – control RMSE
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
Departure statistics of the first guess and analysis “sBC experiment” (black) versus “noBC experiment” (red) “sBC experiment” BC (pink) versus “noBC experiment” BC (green) MetOp-A 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 NOAA-18 AMSU-A TB S.Hemis
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
Forecast impact “N19 anchor experiment” versus control (verified against own-analysis), summer The impact for the forecast of the 500hPa geopotential of the “noBC experiment” is neutral also in the Southern Hemisphere control GOOD “noBC experiment” GOOD “noBC experiment” RMSE – control RMSE
Forecast impact “N19 anchor experiment” versus control (verified against own-analysis), winter The impact for the forecast of the 500hPa geopotential of the “noBC experiment” is neutral also in the Northern Hemisphere control GOOD “noBC experiment” GOOD “noBC experiment” RMSE – control RMSE
Departure statistics of the first guess and analysis “N19 anchor experiment” (black) versus “noBC experiment” (red) “N19 anchor exp.” BC (pink) versus “noBC experiment” BC (green) MetOp-A AMSU-A TB S.Hemis The bias correction of AMSU-A ch12 (and ch14) onboard NOAA-18 is now adequately correcting the scan bias NOAA-18 AMSU-A TB S.Hemis
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 hassome 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
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
Mean first guess departures with different gamma values control experiment (gamma = 1) “gamma experiment” (gamma = 1.05)
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
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
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 Satellite equatorial crossing times (local) MetOp-A NOAA-17 T i m e NOAA-16 NOAA-15 NOAA-18 NOAA-19 Aqua
Data coverage Sample coverage from a 6-hour period around 0Z “NOAA-15 experiment” *MetOp-A * NOAA-18 * NOAA-15 “two-satellite experiment” *MetOp-A * NOAA-18 “NOAA-19 experiment” *MetOp-A * NOAA-18 * NOAA-19
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
Departure statistics of the first guess and analysis “three-satellite experiment” (black) versus “two-satellite experiment” (red) Radiosonde T Tropics Both NOAA-15 and NOAA-19 bring some small improvement to the fit to temperature observations “NOAA-15 experiment” (black) versus “NOAA-19 experiment” (red) MetOp AMSU-A TB Departure statistics for MetOp-A AMSU-A show some benefits from assimilating observations from NOAA-15 rather than NOAA-19
Forecast impact “NOAA-19 experiment” GOOD “NOAA-15 experiment” GOOD 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
Forecast impact “no-MW sounder experiment” GOOD “two-”, “three-”, “all-satellite experiment” GOOD 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 “two-satellite” RMSE – “no-Mw sounder” RMSE “three-satellite” RMSE – “no-Mw sounder” RMSE “all-satellite” RMSE – “no-Mw sounder” RMSE
Forecast impact “no-MW sounder experiment” GOOD “two-”, “three-”, “all-satellite experiment” GOOD 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 “two-satellite” RMSE – “no-Mw sounder” RMSE “three-satellite” RMSE – “no-Mw sounder” RMSE “all-satellite” RMSE – “no-Mw sounder” RMSE
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
Forecast impact “three-satellite experiment” versus “two-satellite experiment (less thinning)” (verified against operational analysis) “NOAA-15 experiment” GOOD “two-satellite experiment (less thinning)” GOOD “NOAA-15 exp” RMSE – “two-satellite (less thinning)” RMSE There is still some advantage in using three AMSU-A rather than two
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
Additional slides:gamma-delta correction Watts and McNally
Additional slides:variational bias correction (VarBC) Dick Dee and Niels Bormann
Minimise background constraint(Jb) observational constraint (Jo) Variational analysis and bias correction:A brief review of variational data assimilation • The input xbrepresents 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)
Minimise background constraint(Jb) observational constraint (Jo) Variational analysis and bias correction:Error sources in the input data • 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
Error model for brightness temperature data: where Predictors, for instance: • 1000-300 hPa thickness • 200-50 hPa thickness • surface skin temperature • total precipitable water Average the background departures: Periodically estimate scan bias and predictor coefficients: • typically 2 weeks of background departures • 2-step regression procedure • careful masking and data selection Past* scheme for radiance bias correction at ECMWF Scan bias and air-mass dependent bias for each sensor/channelwere estimated off-line from background departures, and stored on files (Harris and Kelly 2001) *Replaced in operations September 2006 by VarBC (Variational Bias Correction)
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