Table Of Content;
OBSERVING SYSTEM FORECAST EXPERIMENTS AT THE DAO
2.1
Robert Atlas*
Data Assimilation Office
NASA/3oddard Space Flight Center, Greenbelt, Maryland
1. INTRODUCTION Experiments performed in this manner provide a
quantitative assessment of the value of a selected type
Since the advent of meteorological satellites in the of data to the specific data assimilation system (DAS)
1960's, numerous experiments ilave been conducted in that was used. In addition, the OSE also provides use-
order to evaluate the impact of :hese and other data on ful information on the effectiveness of the DAS. This
atmospheric analysis and prediction. Such studies have information can be used to improve the utilization of
included both OSE'S and OS.%E's. The OSE's were this and other data in the DAS, aswell as to determine
conducted to evaluate the impact of specific observa- the value of the data.
tions or classes of observation:, on analyses and fore- The methodology currently used for OSSE's is
casts. Such experiments have been performed for se-
very similar to that described above for OSE's. How-
lected types of conventional da:a and for various satel- ever, this methodology has evolved considerably since
lite data sets as they became available. (See for exam-
these experiments were first carried out in the 1950's
ple the 1989 ECMWF/EUMETSAT workshop proceed-
and 60's (Arnold and Dey, 1986). The earliest simu-
ings on "The use of satellite data in operational numer- lation studies proceeded according to the following se-
ical weather prediction" and the references contained
quence of steps. First, an artificial history of the atmo-
therein.) The OSSE's were co_ducted to evaluate the sphere wascreated by numerical integration of a model.
potential for future observing ._ystems to improve Nu- Second, simulated "data" were created from the his-
merical Weather Prediction (NWP) and to plan for the tory by the addition of random variations to the history
Global Weather Experiment and more recently for EOS
values for temperature, wind, and pressure. Third, the
(Atlas et al., 1985a; Arnold and Dey, 1986; Hoffman numerical integration that created the history was re-
et al., 1990). In addition, O-<SE's have been run to peated, but with the meteorological variables in the
evaluate trade-offs in the desi_:n of observing systems model replaced by the simulated data at locations and
and observing networks (Atlas and Emmitt, 1991; Ro- times corresponding to the assumed pattern of obser-
haly and Krishnamurti, 1993), i_nd to test new method- vations.
ology for data assimilation (Atas and Bloom, 1989).
OSSE's of this type were conducted by Charney
et al. (1969), Halem and Jastrow (1970), Jastrow
and Halem (1970, 1973), Williamson and Kasahara
(1971), Kasahara (1972), Gordon et al. (1972), and
2. EXPERIMENTAL DESIGN others in preparation for the Global Weather Experi-
ment. These studies provided an analysis of the Global
Although there are many possibilities for how an Atmospheric Research Program (GARP) data require-
OSE may be conducted, the rrost typical procedure is ments, the "useful" range of predictability, and the
as follows: First a "Control" data assimilation cycle is need for reference level data. From the results, it
performed. This isfollowed by one or more experimen- was concluded that the assimilation of satellite-derived
tal assimilations in which a pa-ticular type of data (or temperature profiles meeting the GARP data specifica-
specific observations) are either withheld or added to tions would yield a substantial improvement to the ac-
the Control. Forecasts are then generated from both curacy of numerical weather forecasts. A later study by
the Control and experimental assimilations every few Cane et aL(1981) using a modifed OSSE procedure in-
days (to achieve relative indel:endence of the forecast dicated similar potential for satellite surface wind data.
sample). The analyses and forecasts from each as-
An examination of the underlying rationale for the
similation are then verified and compared in order to
early simulation studies (Jastrow and Halem, 1973),
determine the impact of each data type being evalu-
as well as a comparison of the results of the above
ated.
studies with the results of subsequent real data im-
*Corresponding author addT_s._: Dr. Robert Atlas, Code
910.3, Goddard Space Flight Center, Greenbelt, MD 20771;
e-mail: [email protected].
pact tests (e.g. Haleru et al., lC._82; Baker el al., 1984) 4. Forecasts produced from the Control and Experi-
indicated several important limitations. The most im- mental assimilations. As with the OSE's, forecasts
portant weakness stems from the fact that the same are generated every few days to develop an inde-
numerical model was used both to generate the sim- pendent sample. The analyses and forecasts are
ulated observations and to te_t the effectiveness of then verified against the nature run to obtain a
these observations. (This is referred to as the "iden- quantitative estimate of the impact of proposed
tical twin" problem.) Other weaknesses relate to the observing systems and the expected accuracies of
model-dependence of the studi{:s and the specification the analysis and forecast products that incorporate
of observational errors as random. These weaknesses the new data.
can lead to overly optimistic re:;ults and incorrect con-
clusions from an OSSE. An important component of the OSSE that im-
The current methodology used for OSSE's was de- proves the interpretation of results isvalidation against
signed to increase the realism and usefulness of such a corresponding OSE. In this regard, the accuracy of
experiments (Atlas et aL, 1985b). In essence, the anal- analyses and forecasts and the impact of already exist-
ysis/forecast simulation system consists of the follow- ing observing systems in simulation is compared with
ing elements : the corresponding accuracies and data impacts in the
real world. Ideally, both the simulated and real results
1. A long atmospheric model _ntegration using a very should be similar. Under these conditions, no calibra-
high resolution "state of the art" numerical model tion is necessary and the OSSE results may be inter-
to provide a complete record of the assumed preted directly. If this is not the case, then calibration
"true" state of the atmosphere (referred to as the of the OSSE results can be attempted by determining
"nature run" or "reference atmosphere"). For the the constant of proportionality between the OSE and
OSSE to be meaningful, it is essential that the OSSE impact as described by Hoffman et al. (1990).
nature run be realistic, i.e. possess a model cli-
matology, average storm tracks, etc. that agrees
with observations to within prespecified limits. 3. RESULTS AND CONCLUSIONS
2. Simulated conventional and space-based observa- A large number of observing system experiments
tions from the nature run. All of the observations (OSE's) have been conducted to assess the impact of
different types of atmospheric observations. In general,
should be simulated with observed (or expected)
coverages, resolution, and accuracy. In addition, these experiments have shown space-based observing
bias and horizontal and vetical correlations of er- systems to be very important components of the Global
Observing System. Nevertheless, conventional surface-
rors with each other and with the synoptic sit-
based observations are still of critical importance.
uation should be introduced appropriately. Two
approaches have been use:l for this purpose (At- Observing system simulation experiments
las et el., lg85b; Hoffman et eL, 1990). The (OSSE's) provide an effective means to evaluate the
simpler approach is to interpolate the nature run potential impact of a proposed observing system, as
values to the observation iocations and then add well as to determine tradeoffs in their design, and to
evaluate data assimilation methodology. Great care
appropriate errors. The more complicated (and
must be taken to ensure realism of the OSSE's, and
expensive) approach is to attempt to retrieve ob-
servations from the natur_ run in the same way in the interpretation of OSSE results. OSSE's have
as observations are retrieved from the real atmo- shown considerable potential for space-based lidar
wind profiles and for improved space-based surface
sphere.
winds, however, quantitative results from OSSE's may
3. Control and experimental data assimilation cycles. only be valid for instruments meeting the expected
These are identical to the assimilation cycles in an accuracies and coverages that were simulated, and for
OSE except that only simulated data are assimi- the data assikilation systems used in the particular
lated. In order to avoid the identical twin problem, OSSE.
a different model from that used to generate the At the conference, results will be presented from
nature run is used for assimilation and forecast- OSE's and OSSE's that have been conducted over the
ing. Typically this model has less accuracy and last several years at the DAO. These will include OSE's
resolution than the nature model. Ideally, the dif- to assess the impact of current satellite observations
ferences between the assimilation and nature mod- in the DAO's data assimilation system, and OSSE's
els should approximate th_ differences between a aimed at assessing the future impact of space-based
"state of the art" model a_d the real atmosphere. lidar winds and other advanced sounders, as well as
experiments to evaluate trade-,)ffs in the design of fu- Gordon, C.T., L. Umscheid, Jr. and K. Miyakoda,
ture observing systems 1972: Simulation experiments for determining
wind data requirements in the tropics. J. Atmos.
4. ACKNOWLEDGMENTS Sci., 29, 1064-1075.
The author gratefully acknowledges the support Haleru, M., E. Kalnay-Rivas, W. Baker, and R. Atlas,
of NASA HQ . Several of the OSSE's referred to in 1982: An assessment of the FGGE satellite observ-
this paper were performed in collaboration with G.D. ing systems during SOP-1. Bull. Amer. Meteor.
Emmitt and S. Wood. I would also like to thank S.C. 5oc., 63, 407-426.
Bloom, J.C. Jusem, E. Brin, J. Ardizzone, J.Terry, and
Hoffman, R.N., C.Grassotti, R. Isaacs, J. Louis, T.
D. Bungato for significant conlributions to this work.
Nehrkorn, and D. Norquist, 1990: Assessment of
the impact of simulated satellite lidar wind and
5. REFERENCES retrieved 183 GHz water vapor observations on a
global data assimilation system. Mon. Wea. Rev.,
Arnold, C.P.,Jr., and C.H. I)ey, 1986: Observing- 118, 2513-2542.
system simulation experiments: Past, present, and
future. Bull. Amer. Mete._r. Soc., 67, 687-695. Jastrow, R. and M. Halem, 1970: Simulation studies
related to GARP. Bull. Amer. Meteor. 5oc., 51,
Atlas, R., E. Kalnay and M. Halem, 1985a: The im- 490-513.
pact of satellite temperature sounding and wind
data on numerical weather prediction. Optical En- Johnson, D.S., 1994: Evolution of the U.S. meteo-
gineering, 24,341-346. rological satellite program. Bull. Amer. Meteor.
Soc., TS, 1705-1708.
Atlas, R., E. Kalnay, J. Susski_d, W. E. Baker and M.
Halem, 1985b: Simulatiol studies of the impact Kasahara, 1972: Simulation experiments for meteoro-
of future observing systems on weather prediction. logical observing systems for GARP. Bull. Amer.
Proc. Seventh Conf. on tlWP, 145-151. Meteor. 5oc., 53, 252-264.
Atlas, R., 1990: Simulation studies of the impact Kutzbach, G., 1979: The Thermal Theory of Cy-
of satellite temperature _,nd humidity retrievals. clones. American Meteorological Society, Boston,
GEWEX Workshop Report on Retrievals of Tem- Massachussets, 255 pp.
perature and Humidity R_,trievals from 5pace.
Lally, V.E., 1985: Upper air insitu observing systems.
Atlas, R. and G.D. Emmitt, 1991: Implications of Handbook of Applied Meteorology, John Wiley &
several orbit inclinations for the impact of LAWS Sons, New York, N.Y.
on global climate studies. Second Symposium on
Rohaly, G.D. and T.N. Krishnamurti, 1993: An ob-
Global Change Studies, New Orleans, 28-32.
serving system siumulation experiment for the
Baker,W.E., G.D. Emmitt, F. Robertson, R.M. At- Laser Atmospheric Wind Sounder (LAWS). J. Ap-
las, J.E. Molinari, D,A. Eiowdle, J. Paegle, R.M. plied Meteor., 32, 1453-1471.
Hardesty, R.T. Menzies,-'.N. Krishnamurti, R.A.
Wiltiamson, D. and A. Kasahara, 1971: Adaptation
Brown, M.J. Post, J.R. %nderson, A.C. Lorenc,
of meteorological variables forced by updating. J.
and J. McEIroy, 1995: Lidar measured winds from
Atmos. 5ci., 28, 1313-1324.
space: A key component Forweather and climate
prediction. Bull, Amer. Meteor. 5oc., T6(6),
869-888.
Cane, M.A., V.J. Cardone, M. Halem, and I. Hal-
berstam, 1981: On the sensitivity of numerical
weather prediction to remotely sensed marine sur-
face wind data: A simulation study. J. Geophys.
Res., 86, 8093-8106.
Charney, J., M. Halem, and R. Jastrow, 1969: Use
of incomplete historical cata to infer the present
state of the atmosphere J. Atmos. 5ci., 26,
1160-1163.
