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Infrared Sounding Data in the GMAO Data Assimilation System

Infrared Sounding Data in the GMAO Data Assimilation System. JCSDA Infrared Sounding Working Group (ISWG) 30 January 2009. Outline. GMAO atmospheric data assimilation system --- current & next generation Lessons learned from MERRA (reanalysis)

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Infrared Sounding Data in the GMAO Data Assimilation System

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  1. Infrared Sounding Data in the GMAO Data Assimilation System JCSDA Infrared Sounding Working Group (ISWG) 30 January 2009

  2. Outline • GMAO atmospheric data assimilation system --- current & next generation • Lessons learned from MERRA (reanalysis) • Observation sensitivity study --- introduction & application to AIRS • Plans Emily, H. C. Liu

  3. Next System (3D/4D VAR) AGCM Model Adjoint: FV core with simple physics Analysis: GSI-based 4D-VAR (Todling & Tremolet) Can switch to GSI-based analysis with IAU procedure (3D-VAR) Options for minimization algorithm (Conjugate Gradient, Quasi-Newton, Lanczos) Computation of time-dependent departures (OmF’s) Observation windowing flexibility Preliminary version of model-analysis interface Current System AGCM Finite-volume dynamic core Bacmiester moist physics Physics integrated under the Earth System Modeling Framwork (ESMF) Catchment land surface model Prescribed aerosols Interactive ozone Analysis Grid Point Statistical Interpolation (GSI) Apply Incremental Analysis Increments (IAU) to reduce shock of data insertion GMAO Atmospheric Data Assimilation System NASA GMAO

  4. Lessons Leaned from MERRA --- I • The current scheme for radiance bias correction in GSI is variational bias correction (VBC) as described in Derber and Wu (1998). The advantage of VBC is that the bias estimate is adaptive and consistent with all components in the analysis. However, VBC does not work well in regions where observations are sparse. It is prone to include systematic errors from the forecast model. • The analysis can be serious affected by the inclusion of systematic background errors into the bias estimation for observations. • Scan bias and airs-mass dependent bias for each sensor/channel are estimated. y=h(x)+bscan+bair(x)+einst bscan = bscan(scan position) bairs = βo+ ∑βiPi(x); i=1,2, …, N einst = random instrument error Emily, H. C. Liu

  5. The bias estimations for infrared surface channels are misled mostly by biases in the forecast model over land. • The scan angle bias correction is more vulnerable to biases in the forecast model. • The current fix for the problem ---> exclude the surface sensitivity infrared data over land from the analysis • The bias estimations for microwave surface channels do not significantly affected by systematic background error. The relative larger number of microwave surface observations maybe helpful in producing more objective estimations • Need to identify the sources of bias from land surface in GEOS-5 • May try to estimate the scan angle bias correction within the analysis. Emily, H. C. Liu

  6. Difference VBC ON VBCOFF daily coverage 6-hour coverage Lessons Learned from MERRA --- II • AMSU-A channel 14 peaks near stratopause where observations are sparse. The analysis may be benefit from turning VBC off for this channel. • MLS is a limb sounding instrument. It can provide detailed temperature structure from upper troposphere (316 hPa) up to the Mesosphere (0.001 hPa) with 3.5- 14 km vertical resolution. • To obtain statistically significant validation, one month of MLS data were collocated with GEOS-5 analyses. Approximately 95,000 collocations were found and used in the monthly statistics. • In between 10 hPa and 1 hPa, the agreement between MLS temperatures and GEOS-5 analyses is significantly improved for experiments with VBC turned off for AMSU-A channel 14 Emily, H. C. Liu

  7. VBC ON VBC OFF VBC OFF VBC ON More Evidence --- Validation with AIRS • AIRS channels 73-86 peaking in the stratosphere are not assimilated (passive). Therefore, OB-AN of brightness temperature for these passive AIRS channels can be used as a validation metric. • Biases are reduced significantly indicating the analyzed temperatures in the stratosphere agree better with observed AIRS radiances when VBC is off. Emily, H. C. Liu

  8. observations assimilated eb background forecast Error xb ea analysis forecast analysis adjoint model adjoint xv xa t-6h 00Z t+24h Adjoint Tools for Observation Impact Studies forecast error measure (dry energy, troposphere) analysis equation summed observation impact • The difference ea − eb = ∆e is due entirely to the assimilation of observations at 00Z. → measures the impact of the observations • ∆e < 0 indicates that the error of the forecast started from xa is less than that started from xb → the observations are beneficial • ∆e can be estimated as a sum of contributions from individual observations using information from the model adjoint, analysis adjoint, and the innovations • The impact of all or any arbitrary subset of observations (e.g. instrument type, channel, location) can be easily quantified by summing only the terms involving the desired elements of the innovation vector • Estimation of observation impact can be useful in improving data quality control and selection (e.g. data from hyperspectral instrument) Ron Gelaro & Yanqiu Zhu

  9. Total 24hr Forecast Error Reduction due to Observations NH Global SH Tropics (J/kg) (J/kg) July 2005 00UTC GEOS-5 Adjoint Data Assimilation System Ron Gelaro & Yanqiu Zhu

  10. Observations that improvedthe 24h forecast Observations that degraded the 24h forecast Observations that had small impact on 24h forecast Observation Impact on GEOS-5 24h Forecast for a Single Case (Reduction in energy-based global error measure for 00UTC 10 July 2005 ) Impact of 500mb Radiosonde Temps Impact of AIRS Ch.221 Radiances Error Reduction Error Increase Error Reduction Error Increase …the statistical aspects of data assimilation imply that there will be a mixture of positive and negative impacts, even for ‘good’ observations. Ron Gelaro & Yanqiu Zhu

  11. Control Control without AIRS moisture channels The Impact of AIRS by Channels Positive impact on forecast error reduction • The observation impact study indicates that the some of the AIRS moisture channels have negative impact on the forecast skills • The observation system experiments also indicate that the forecast skills are increased when moisture channels from AIRS were not included Negative impact on forecast reduction Ron Gelaro, Yanqiu Zhu, & Emily Liu

  12. H20 AIRS impact by channel degrade Localized examination of AIRS impacts July 2005 00UTC degrade AIRS impact map (All Channels) improve (20-50N, 0-80E) Ron Gelaro & Yanqiu Zhu

  13. Plans • Revisit channel selection and quality control for AIRS using GEOS-5 adjoint tool. • Refine bias correction for Infrared radiances • IASI assimilation + adjoint tool • Literature search --- looking for directions in using cloudy Infrared radiances in GEOS-5 • Assess the quality of AIRS cloud cleared radiances with GEOS-5 background --- experimental • Radiance monitoring Emily, H. C. Liu

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