Single-Column Models (SCMs) require observations to provide suitable initial and boundary conditions. To meet this need, the Atmospheric Radiation Measurement (ARM) program conducts Intensive Observing Periods (IOPs) to provide 3-hourly radiosondes and other observations. However, such high-frequency sonde launches can be expensive. Therefore, the ARM program can only support a couple IOPs each year with each one lasting 2-4 weeks. In order to reduce the need for high-frequency sonde launches and to potentially expand the periods available for SCM simulations, they have conducted a study using the Atmospheric Emitted Radiance Interferometer (AERI) and Geostationary Operational Environmental Satellite (GOES) temperature and moisture retrievals, instead of radiosondes, in the ARM variational analysis system (Zhang and Lin, 1997; Zhang et al., 2000) to derive the large-scale forcing terms for driving SCMs during the March 1999 IOP. They have compared the large-scale forcing and associated SCM simulations for various combinations of data sources used in the variational analysis

Cederwell, R. T.

Feltz, W. F.

Turner, D. D.

Xie, S. C.

Yio, J. J.

Zhang, M. H.

UNT Digital Library

Approved for public release; further dissemination unlimitedPreprintUCRL-JC-139675The AERI/GOES Retrievalsversus Radiosondes forDriving SCMsS.C. Xie, R.T. Cederwall, J.J. Yio, W.F. Feltz, D.D. Turnerand M.H. ZhangThis article was submitted to10th Atmospheric Radiation Measurement (ARM) Science TeamMeetingSan Antonio, TXMarch 13-17,2000July 13, 2000LawrenceLivermoreNationalLaboratoryU.S. Department of Energy DISCLAIMER  This document was prepared as an account of work sponsored by an agency of the United StatesGovernment.  Neither the United States Government nor the University of California nor any of theiremployees, makes any warranty, express or implied, or assumes any legal liability or responsibility forthe accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, orrepresents that its use would not infringe privately owned rights. Reference herein to any specificcommercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does notnecessarily constitute or imply its endorsement, recommendation, or favoring by the United StatesGovernment or the University of California.  The views and opinions of authors expressed herein do notnecessarily state or reflect those of the United States Government or the University of California, andshall not be used for advertising or product endorsement purposes.  This is a preprint of a paper intended for publication in a journal or proceedings. Since changes may bemade before publication, this preprint is made available with the understanding that it will not be citedor reproduced without the permission of the author.   This report has been reproduced directly from the best available copy.  Available to DOE and DOE contractors from the Office of Scientific and Technical Information P.O. Box 62, Oak Ridge, TN  37831 Prices available from (423) 576-8401 http://apollo.osti.gov/bridge/  Available to the public from the National Technical Information Service U.S. Department of Commerce 5285 Port Royal Rd., Springfield, VA  22161 http://www.ntis.gov/  OR  Lawrence Livermore National Laboratory Technical Information Department’s Digital Library http://www.llnl.gov/tid/Library.html  The AERI/GOES Retrievals versus Radiosondes for DrivingSCMsS. C. Xie, R. T. Cederwall, and J. J. YioLawrence Livermore National laboratoryLivermore, CaliforniaW. F. FeltzUniversity of Wisconsin-MadisonSpace Science and Engineering CenterMadison, WisconsinD. D. TurnerPacific Northwest National LaboratoryRichland, WashingtonM. H. ZhangState University of New York at Stony BrookStony Brook, New YorkIntroductionSingle-Column Models (SCMs) require observations to provide suitable initial andboundary conditions.  To meet this need, the Atmospheric Radiation Measurement(ARM) program conducts Intensive Observing Periods (IOPs) to provide 3-hourlyradiosondes and other observations. However, such high-frequency sonde launches canbe expensive.  Therefore, the ARM program can only support a couple IOPs each yearwith each one lasting 2 - 4 weeks.  In order to reduce the need for high-frequency sondelaunches and to potentially expand the periods available for SCM simulations, we haveconducted a study using the Atmospheric Emitted Radiance Interferometer (AERI) andGeostationary Operational Environmental Satellite (GOES) temperature and moistureretrievals, instead of radiosondes, in the ARM variational analysis system (Zhang andLin, 1997; Zhang et al., 2000) to derive the large-scale forcing terms for driving SCMsduring the March 1999 IOP.  We have compared the large-scale forcing and associatedSCM simulations for various combinations of data sources used in the variationalanalysis.AERI/GOES RetrievalsThe retrieved temperature and water vapor profiles are obtained from the combinationof the AERI and GOES retrievals.  There are five AERI instruments located near theSouthern Great Plain (SGP) Central Facility and the four SGP Boundary Facilities (seeFig. 1) to measure downwelling atmospheric radiance from 3.3 - 18.2 µm every eightminutes.  The temperature and water vapor profiles were retrieved from the radiancespectra through a physical retrieval algorithm proposed by Smith et al. (1999).  Due tothe strength of the IR signal at the surface from emission within the lower atmosphere,the weighting functions become quite broad at 2.5-3.0 km and thus the retrievals usingonly AERI data are limited to this altitude.   During precipitation events, profiles are notretrieved because a hatch is closed to protect the instrumentÕs foreoptics.  Above theupper planetary boundary layer (PBL), the temperature and water vapor profiles wereretrieved hourly from the GOES sounder brightness temperature data by using a physicalretrieval algorithm (Menzel et al. 1998, Ma et al. 1999).  In order for the GOES toretrieve temperature and water vapor profiles, the sky conditions must be clear or broken.The first guess used to produce the GOES physical retrieval of temperature and moistureis derived from the initial analysis of the National Weather Service ETA model.  SinceGOES does very little to influence the temperature profile over land and primarilymodifies the moisture structure, the AERI/GOES temperature profile above 700 mb isprimarily from the ETA model.  Detailed discussion of the data can be found in Turner etal. (2000), Feltz et al. (1998), and Smith et al. (1999).AERI/GOES retrieval strategies used in the variational analysisThe objective analysis method used in this study is the constrained variationalanalysis developed by Zhang and Lin (1997).  In the scheme, the atmospheric statevariables are forced to conserve the column-integrated mass, moisture, dry static energy,and momentum.  Table 1 lists the experiments conducted in this study.  Exp. S is thestandard run.  It uses temperature (T) and moisture (q) profiles from radiosondes andhorizontal wind fields (u, v) from radiosondes merged with wind profiler data.  TheRapid Update Cycle (RUC) model data is used as the background for the variationalanalysis.  Note that the moisture profile used in this study has been scaled to theMicrowave Radiometer (MWR) water vapor measurement in order to reduce the dry biasfound in regular sounding measurement.  Exp. A is the same as Exp. S except using theAERI/GOES retrieved temperature and moisture profiles.  Since we assume that there areno sounding data available in this case, the horizontal wind fields are only from windprofiler data in Exp. A.  Exp. B is the same as Exp. A except using the RUC horizontalwind data.  We conducted this experiment to study how the variational analysis issensitive to the wind fields.  As a reference, Exp. C shows results from the variationalanalysis using the RUC data only.  The constraints used in the variational analysis are thesame for all experiments.  These constraints include surface precipitation, latent andsensible heat fluxes, and net radiative fluxes at surface and top of atmosphere (TOA).Detailed description of these constraints can be found in Zhang and Lin (1997).  Thelocations for these data sources are shown in Fig. 1.Variational Analysis ResultsFig. 2 shows the missing retrievals and sounding data for the SGP central facility andthe four SGP boundary facilities.  The numbers in the color bar represent the number ofstations.  It is seen that the AERI/GOES retrievals are missing most of the stations duringprecipitation events.  Therefore, for the purpose of this paper, we ran the variationalanalysis only for data over the five non-precipitation days from 3/2/99 Ð 3/6/99.    Notethat the sounding data were missing from 3/11/99 Ð 3/12/99 because a large snow stormtemporarily halted the SCM IOP.(1) Atmospheric state variablesFigs. 3 and 4 show temperature and moisture differences from Exp. S for Exps. A, B,and C, respectively.  The temperature differences between radiosondes and retrievals(upper two panels) are large in the upper troposphere while the differences are relativelysmall (< 1 K) in the PBL.  Note that the temperature profile in the upper troposphere forExp. A is primarily from the ETA model, indicating that a more accurate model isrequired to provide a better estimate of the atmospheric state to the retrievals.  For themoisture field, the AREI/GOES retrievals are slightly more moist than the sondes (< 1g/kg) in the lower troposphere.  The figures also show differences in both the temperatureand moisture fields between the RUC and the sondes are relatively small compared to theretrievals.  This implies that the RUC model could provide a better first guess than theETA model for the retrievals.Figs. 5 and 6 show differences in the horizontal wind fields from Exp. S for Exps. A,B, and C, respectively.  Large biases from the sondes are shown in the upper tropospherefor both the wind profiler data (used in Exp. A) and the RUC model data (used in Exps. Band C).    Note that the soundings represent point measurements and the profilers measurewind over a 500 m thick layer.  Therefore, the two are not expected to completely agreewith each other.  However, the large discrepancies among different data sources couldcause problems in deriving the large-scale advective tendency and vertical motion.(2) Derived fieldsThe vertical velocity and the large-scale advective tendencies of temperature andmoisture shown in Figs. 7, 8, and 9 are derived from the objective analysis without usingany constraints.  Large differences are seen among these experiments in the figures.However, it is encouraging to see that these derived large-scale forcing terms agree wellwith those derived from sounding data when the constrained variational analysis is used(Figs. 10, 11, and 12) although differences are large in the original upper air data (e.g.wind fields) as discussed above.  Exps. A, B, and C successfully capture the main eventsshown in Exp. S.  This is consistent with Zhang et al. (2000), in which they found that thevariational constraining processing significantly reduced the sensitivity of the final dataproducts.   In the figures, the forcings in Exp. A are generally weaker than those in Exp.S.  A better agreement with Exp. S is obtained when the RUC wind fields replace thewind profiler data (Exp. B).  The forcing fields derived from Exp. B are closer to those inExp. C than those in Exp. A, indicating the importance of wind fields in the objectiveanalysis.SCM ResultsSensitivity of SCM simulations to the forcing data derived from the constrainedvariational analysis is investigated in the study.  The CCM3 SCM with a modifiedconvection triggering condition (Xie and Zhang, 2000) is used in the experiments.  Figs.12 and 13 display the temperature and moisture errors produced by the SCM using theforcing derived from Exps. S, A, B, and C.  The temperature and moisture profiles fromExp. S are used as the observed values.  Generally, errors produced by these experimentsare very similar.  All experiments produce cold biases in the temperature field and moistbiases in the moisture field.   The SCM is not sensitive to the small difference in theforcing data.  The principal behavior of the SCM can be captured using these forcingdata.SummaryIn this study, we conducted several experiments to evaluate the use of AERI/GOESretrievals instead of radiosondes in the variational analysis.  This study shows that theAERI/GOES retrievals can provide relatively accurate temperature and moisture profileswithin the boundary layer.  However, the temperatures above the PBL show large biases.A work to improve the upper level temperature retrievals by using the RUC temperatureprofile as the first guess in the GOES physical retrievals is ongoing.The large difference in winds between radiosondes and wind profiler data presents thelargest problem in using remotely sensed data when radiosondes are not available.  TheRUC model also shows large error in the wind fields.  These differences can significantlyaffect the derived large-scale forcing terms if no constraints are used in the objectiveanalysis.  However, it is encouraging to see that the constrained variational analysis canlargely desensitize the derived large-scale forcings to differences in the original upper airdata.  SCM tests also show that main behaviors of the SCM can be captured using thelarge-scale forcing derived from the variational analysis using the AERI/GOES retrievals,compared to the forcing derived from radiosondes.This work was performed under the auspices of the U.S. Dept. of Energy atUC/LLNL under contract no. W-7405-Eng-48.ReferencesFeltz, W. F., W. L. Smith, R. O. Knuteson, H. E. Revercomb, H. M. Woolf, and H. B.Howell, 1998: Meteorological applications of temperature and water vapor retrievalsfrom the ground-based atmospheric emitted radiance interferometer (AERI). J. Appl.Meteor., 37, 857-875.Ma, X. L., T. J. Schmit, and W. L. Smith, 1999: A nonlinear physical retrievalalgorithmÑits application to the GOES-8/9 sounder. J. Appl. Meteor., 38, 501-513.Menzel, W. P., F. C. Holt, T. J. Schmit, R. M. Aune, A. J. Schreiner, G. S. Wade, and D.G. Gray, 1998: Application of GOES-8/9 soundings to weather forecasting andnowcasting.  Bull. Amer. Meteor. Soc., 79, 2059-2077.Smith, W. L., W. F. Feltz, R. O. Knuteson, H. E. Revercomb, H. B. Howell, and H. M.Woolf, 1999: The retrieval of planetary boundary layer structure using ground-basedinfrared spectral radiance measurements.  J. Atmos. Oceanic Technol., 16, 323-333.Turner, D. D., W. F. Feltz, and R. A. Ferrare, 2000: Continuous water profiles fromoperational active and passive remote sensors. Bull. Amer. Meteor. Soc., 81, 1301-1317.Xie, S. C., and M. H. Zhang, 2000: Impact of the convection triggering function on theSingle-Column Model simulations.  J. Geophys. Res., 105, 14983-14996.Zhang, M. H., and J. L. Lin, 1997: Constrained variational analysis of sounding databases on column-integrated budgets of mass, heat, moisture, and momentum:Approach and application to ARM measurements. J. Atmos. Sci., 54, 1503-1524.Zhang, M. H., J. L. Lin, R. T. Cederwall, J. J. Yio, and S. C. Xie, 2000: Objectiveanalysis of ARM IOP data: method, features, and sensitivity. Submitted to Mon. Wea.Rev.CaptionsTable 1. Summary of experiments.Fig. 1 Locations: Radiosondes (×), AERI (_), NOAA wind profilers (∆), RUC modelgrid (+), and final analysis grid (*).Fig. 2 Missing retrieval and sounding data among the SGP Central Facility and the fourSGP Boundary Facilities.Fig. 3 Temperature differences from Exp. S for Exps. A, B, and C.Fig. 4 Moisture differences from Exp. S for Exps. A, B, and C.Fig. 5 Horizontal wind (u) differences from Exp. S for Exps. A, B, and C.Fig. 6 Horizontal wind (v) differences from Exp. S for Exps. A, B, and C.Fig. 7 Derived vertical velocity without using constraints in the objective analysis.Fig. 8 Derived large-scale temperature tendency without using constraints in the objectiveanalysisFig.9 Derived large-scale moisture tendency without using constraints in the objectiveanalysis.Fig. 10 Derived vertical velocity using the constrained variational analysis.Fig. 11 Derived large-scale temperature tendency using the constrained variational            analysis.Fig. 12 Derived large-scale moisture tendency using the constrained variational analysis.Fig. 13 Temperature errors produced by the SCM using the derived large-scale forcingfrom Exps. S, A, B, and C.Fig. 14 Moisture errors produced by the SCM using the derived large-scale forcing fromExps. S, A, B, and C.Table 1.  Summary of experimentsExperiments Brief DescriptionS Standard run. T and q are from radiosondes; u and v are fromradiosondes merged with wind profiler data; the RUC data areused as background.A T and q are from retrievals; u and v are from wind profilers; theRUC data are used as background.B Same as A except using the RUC horizontal winds.C T, q, u, and v are all from the RUC model.Fig. 1 Locations: Radiosondes (×), AERI (_), NOAA wind profilers (∆), RUC model    grid (+), and final analysis grid (*).Fig. 2 Missing retrieval and sounding data among the SGP Central Facility and the fourSGP Boundary Facilities.Fig. 3 Temperature differences from Exp. S for Exps. A, B, and C.Fig. 4 Moisture differences from Exp. S for Exps. A, B, and C.Fig. 5 Horizontal wind (u) differences from Exp. S for Exps. A, B, and C.Fig. 6 Horizontal wind (v) differences from Exp. S for Exps. A, B, and C.Fig. 7 Derived vertical velocity without using constraints in the objective analysis.Fig. 8 Derived large-scale temperature tendency without using constraints in the objectiveanalysisFig.9 Derived large-scale moisture tendency without using constraints in the objectiveanalysis.Fig. 10 Derived vertical velocity using the constrained variational analysis.Fig. 11 Derived large-scale temperature tendency using the constrained variational            analysis.Fig. 12 Derived large-scale moisture tendency using the constrained variational analysis.Fig. 13 Temperature errors produced by the SCM using the derived large-scale forcingfrom Exps. S, A, B, and C.Fig. 14 Moisture errors produced by the SCM using the derived large-scale forcing fromExps. S, A, B, and C.

The AERI/GOES Retrievals Versus Radiosondes for Driving SCMs

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The AERI/GOES Retrievals Versus Radiosondes for Driving SCMs

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