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Software for Generating Simulated Observations for GMAO OSSE Prototype

This software generates a prototype set of simulated observations that are more realistic than the previous baseline observations used for NCEP/ECMWF OSSE. It addresses issues related to radiance assimilation, treatment of cloud effects, land and ice effects, precipitation effects, data thinning, and bias correction of radiance errors. The software is flexible, efficient, and provides extensive documentation.

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Software for Generating Simulated Observations for GMAO OSSE Prototype

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  1. Explanation of Software for Generating Simulated Observations for the GMAO OSSE Prototype By Ronald M. Errico (GMAO and GEST) Runhua Yang (GMAO and SSAI ) 27 August 2008

  2. Goal: Quickly generate a prototype baseline set of simulated observations that is significantly “more realistic” than the set of baseline observations used for the previous NCEP/ECMWF OSSE.

  3. Issues regarding radiance assimilation: • Treatment of the effects of clouds on IR • Treatment of the effects of land or ice on MW • Consideration of bias correction of radiances • Consideration of holes in cloud cover • Data thinning

  4. Treatment of cloud effects on IR radiances • For IR, clouds are radiatively black, except for very thin ones, • and therefore are radiatively equivalent to an elevated surface. • 2. Detected cloud-contaminated observations are discarded by GSI, • and therefore the presence of clouds mainly effects obs locations. • GSI treats all remaining observations as cloud free, and therefore • any undetected cloud effect is implicitly interpreted as part of • representativeness error. • The NR cloud distribution has not been verified with respect to its • effect on clouds as interpreted by GSI. We have therefore developed a flexible and tunable procedure that simply elevates the effective radiative surface to the height of any clouds present. Thanks to Joanna Joiner (NASA)

  5. Treatment of land, ice, and precip effects on MW radiances • Many MW channels are poorly modeled over ice and land, and • additionally, MW radiances are affected by precip. • The GSI does not currently attempt to assimilate such channels. We therefore treat MW analogously to IR, by elevating the surface over land or ice or in the presence of precip and thus causing the GSI QC to reject obs of channels thus affected.

  6. Data Thinning • The CRTM calculation, although fast, is still expensive. • 2. The GSI cannot consider radiances at all locations observed • and it therefore performs a thinning procedure. • The procedure incorporates “thinning boxes.” • For each box, only a single “best” observation location is retained. • If we knew a-priori which observations GSI would consider best, • and if GSI selection algorithm would never change, then we could • simply and only create radiances for those locations. We therefore also use thinning boxes, but in a way that retains more observations than GSI uses, so that GSI still conducts its own selection albeit considering fewer observations.

  7. Treatment of biases of radiance errors • Real observed radiances have many sources of significantly • biased error that must be removed before being assimilated. • Our simulation technique does not implicitly introduce • significant error biases. • 3. If we explicitly introduced the kind of identified bias that GSI • removes, we would be wasting our time, and if we introduced • an entirely different kind of bias that is either insignificant or • so far unknown to anyone, why or how would we do so? We conduct this OSSE GSI without radiance bias correction and leave consideration of biases to a separate study.

  8. Consideration of instrument plus representativeness error • Some representativeness error is already implicitly present • for a variety of reasons, but this should be statistically • smaller than GSI encounters in reality. • 2. Adequate validation with respect to a real assimilation • therefore requires that some additional error be added. Software is provided to add a random error to each observation, with the statistics to be tuned by the user as part of the validation process.

  9. Hopefully flexible and efficient software • All input and output observational data files in BUFR format • Currently uses single processor • Extensive internal and external documentation

  10. Three executables sim_obs_conv.x d_type input output sim_obs_rad.x d_type datetime cloud.rc input output add_error.x d_type datetime error.rc input output

  11. The file cloud.rc HIRS ncloud 3 irandom 1221 box_size 90 c_table high cld hcld 0.10 0.40 0.70 0.30 med cld mcld 0.10 0.40 0.70 0.60 low cld lcld 0.10 0.40 0.70 0.90 a b c sigma AMSU ncloud 4 irandom 1331 box_size 90 c_table land msk almk 0.10 0.10 0.10 0.70 ice msk ismk 0.10 0.10 0.10 0.70 c.precip conp .0002 .0002 .0002 0.50 s.precip rain .0002 .0002 .0002 0.70

  12. Table printed by sim_obs_rad SUMMARY TABLE: 81000 observation reports read for AIRS_ 35729 number of empty thinning boxes of all sub-types 0.4305 fraction of non-empty boxes 27013 number of observation reports written out 0.33349 fraction of reports written out vs. read in Fractions of simulated observation with surface set as: 0.4272 have surface as actual NR surface 0.2212 have surface set as 1.000 > sigma >= 0.800 0.0000 have surface set as 0.800 > sigma >= 0.600 0.1101 have surface set as 0.600 > sigma >= 0.400 0.2415 have surface set as 0.400 > sigma >= 0.200 0.0000 have surface set as 0.200 > sigma >= 0.000

  13. Validation Metrics • Numbers of observations QC-accepted for various types • RMS of y-H(x_b) for various types • Plots of spatial locations of accepted obs for rep. 6-hr periods • Geographical fields of monthly means and standard deviations • of analysis increments (x_a - x_b). • Impact metrics computed using the GSI adjoint. • OSEs for selected instrument sets

  14. Validation using the GSI adjoint Figure 5.1: Estimates of mean reductions of 1-day forecast error in the GMAO GEOS-5 DAS, measured in terms of the energy norm (units J/kg) for indicated sets of observations (from R. Gelaro and Y. Zhu, Tellus 2009).

  15. We give this preliminary software to others with the understanding that: • they agree to not submit any journal paper describing results from OSSE experiments using these observations or this software until either a paper reporting the tuned baseline results is submitted by the GMAO team or April 1, 2009, whichever date/event occurs earlier; • they agree to not present results produced using our untuned simulated data without clearly stating that the GMAO intended this early data for development purposes only; • they agree to inform us of any bugs clearly identified in our codes..

  16. Plans for improvements • More realistic treatment of rawindsondes, including identification of • significant levels and specification of drift locations • Specification of locations of cloud-track winds based on the presence • of trackable cloud features in the NR. • Inclusion of MSU data • Better treatment of surface emissivity for MW data. • Correct treatment of “surface” winds. • Correct height specification of profiler winds • Reconsider biases in radiance observations • Use of MPI with CRTM • Use of a different CRTM

  17. Happy Simulating !

  18. Distribution of cloud fraction f H L

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