Joint OSSE Progress
Simulation of observation and initial results.
Michiko Masutani
August 2010
http://www.emc.ncep.noaa.gov/research/JointOSSEs
1
What is OSSE
Observing System Simulation Experiments (OSSEs) are typically designed to use
data assimilation ideas to investigate the potential impacts of prospective observing
systems.
In an OSSE, simulated rather than real observations are the input to a data
assimilation system (DAS for short). Simulated observational values are drawn from
the Nature Run, proxi truth atmosphere for OSSE.
The Nature Run is a long, uninterrupted forecast by a NWP model whose statistical
behaviour matches that of the real atmosphere
Introductory presentation about data assimilation.
Jones, Andrew S. 2008:What is Data Assimilation? A Tutorial, Presentation at AMS data
assimilation forum. AMS annual meeting, New Orleans, LA, January 20-24,2008
http://www.emc.ncep.noaa.gov/research/JointOSSEs/references/Jones_Data_Assimilation_Ed_Forum.ppt
Data Denial Experiments
Real data
OSE
Observing System Experiment
– Typically aimed at assessing the
impact of a given existing data
type on a system
– Using existing observational data
and operational analyses, the
candidate data are either added
to withheld from the forecast
system, and the impact is
assessed
– Control run (all operationally used
observations)
– Perturbation run (control plus
candidate data)
– Compare!
Simulated data (OSSE)
Observing System Simulation
Experiment
• Typically aimed at assessing the
impact of a hypothetical data type
on a forecast system
• Simulated atmosphere (“Nature
Run”)
• Simulated reference observations
(corresponding to existing
observations)
• Simulate perturbation observations
(object of study)
• Verify simulated observation
• Simulate observational error
• Control run (all operationally used
observations)
• Perturbation run (control plus
candidate data)
• Compare!
Costly in terms of computing and
manpower
3
Current observing system
Nature Run
Simulation of
Existing data +
Proposed data
(DWL, CrIS, ATMS,
UAS, etc)
OSSE
DATA DUMP
DATA DUMP
Real
Radiance
data
GDAS
Adding Observational
error
OSSE
Quality Control
Quality Control
(Real conventional
data)
Verification of data
Simulated
Radiance data
(Simulated conventional
data)
OSSE DA
(Include new obs)
DA
NWP forecast
NWP forecast
NCEP system
OSSE
DAS,OSE,
OSSE Diagram
Diagram by
Lars Peter Riishojgaard JCSDA
Full OSSEs
Data impact on analysis and forecast will be evaluated.
A Full OSSE can provide detailed quantitative evaluations of
the configuration of observing systems.
A Full OSSE can use an existing operational system and
help the development of an operational system
.
Existing Data assimilation system and velification method
are used for Full OSSEs. This will help development of DAS
and verification tools.
Masutani, M., T. W. Schlatter, R. M. Errico, A. Stoffelen, E. Andersson, W. Lahoz, J. S.. Woollen, G. D.
Emmitt,L.-P. Riishøjgaard, S. J. Lord : Observing System Simulation Experiments. Chapter 24 of Data
Assimilation: Making sense of observations, published from Springer in 2010. The final draft of the
chapter which describe OSSE is posted at
http://www.emc.ncep.noaa.gov/research/JointOSSEs/references/Springer_Data_Assimilation_Ch24_OSSE_Draft.pdf
7
Advantages of Full OSSEs
While “Rapid Response OSSEs” or “QuickOSSEs” may
provide less expensive yet important and reliable insights to
potential data impacts from proposed new observing systems,
full OSSEs offer a more realistic representation of those data
impacts on analyses and forecasts.
A Full OSSE can use the full extent of an existing operational
forecast system and provide input to the preparation of that
system for the ingestion of the new data set(s) in an
operational setting (e.g. ADM).
8
Why an International Joint OSSE capability
•
Full OSSEs are expensive
– Nature Run, entire reference observing system, additional
observations must be simulated. Sharing one Nature Run
saves $$.
– Calibration experiments, perturbation experiments must be
assessed according to standard operational practices and
using operational metrics and tools
•
OSSE-based decisions have international stakeholders
– Decisions on major space systems have important scientific,
technical, financial and political ramifications
– Community ownership and oversight of OSSE capability is
important for maintaining credibility
•
Independent but related data assimilation systems allow us to test
robustness of answers
9
OSSE Calibration
● In order to conduct calibration all major existing observations must be simulated.
● The calibration includes adjusting observational error.
● If the difference can be explained, we will be able to interpret the OSSE results as to real
data impact.
● The results from calibration experiments provide guidelines for interpreting OSSE
results on data impact in the real world.
● Without calibration, quantitative evaluation data impact using OSSE could mislead the
meteorological community. In this OSSE, calibration is performed and presented.
Simulation of control data for calibration
● Simulation of control data require significant funding for specific OSSEs
● GMAO and NCEP are simulating control data
Experience from the past
In 1993, ECMWF produced Nature Run in T213.
Observations are simulated while the NR was produced
The data were distributed with significant cost
Problems
Radiance data was not simulated correctly and cannot be used.
SST was kept constant and that affect the OSSE results.
Took long time to find fund to conduct OSSEs
OSSE using ECMWF T213 Nature Run data
Stoffelen, A., G. J. Marseille, F. Bouttier, D. Vasiljevic, S. De Haan And C.
Cardinali 2006:ADM-Aeolus Doppler wind lidar Observing System Simulation
Experiment, Quar.J.Roy. Metorol. Soc. , 619, 1927-1948
Focused on NH due to the lack of radiance data and showed DWL impact over
ocean.
NOAA simulated observation including radiance data and conduct OSSEs to evaluation DWL.
Masutani, M., J. S. Woollen, S. J. Lord, G. D. Emmitt, T. J. Kleespies, S. A. Wood, S. Greco, H. Sun, J.
Terry, V. Kapoor, R. Treadon, and K. A. Campana, 2009. Observing System Simulation Experiments at the
National Centers for Environmental Prediction. J. Geophys. Res., 114, doi:10.1029/2009JD012528.
Results were mainly presented over NH, which has less affected by constant SST. (To be published from
JGR)
New Nature Run by ECMWF
Produced by Erik Andersson(ECMWF)
Based on discussion with
JCSDA, NCEP, GMAO, GLA, SIVO, SWA, NESDIS, ESRL, and ECMWF
Low Resolution Nature Run
Spectral resolution : T511 , Vertical levels: L91, 3 hourly dump
Initial conditions: 12Z May 1st, 2005 , Ends at: 0Z Jun 1,2006
Daily SST and ICE: provided by NCEP
Model: Version cy31r1
Two High Resolution Nature Runs
35 days long
Hurricane season: Starting at 12z September 27,2005,
Convective precipitation over US: starting at 12Z April 10, 2006
T799 resolution, 91 levels, one hourly dump
Get initial conditions from T511 NR
Note: This data must not be used for commercial purposes and re-distribution rights are not given. User
lists are maintained by Michiko Masutani and ECMWF.
Archive and Distribution
To be archived in the MARS system at ECMWF
To access T511 NR, set expver = etwu
Copies are available to designated users for research purposes & users
known to ECMWF
Saved at NCEP, ESRL, and NASA/GSFC
Complete data available from portal at NASA/NCCS portal
http://portal.nccs.nasa.gov/osse
Contacts: Michiko Masutani (michiko.masutani@noaa.gov), and
Password protected . Accounts arei arranged by Ellen Salmon (Ellen.M.Salmon@NASA.gov)
Gradsdods access is available for T511 NR. The data can be downloaded in grib1,
NetCDF, or binary. The data can be retrieved globally or for selected regions.
Provide IP number to: Arlindo da Silva (Arlindo.Dasilva@nasa.gov)
13
Supplemental low resolution regular lat lon data
1deg x 1deg for T511 NR
Pressure level data: 31 levels,
Potential temperature level data: 315,330,350,370,530K
Selected surface data for T511 NR:
Convective precip, Large scale precip,
MSLP,T2m,TD2m, U10,V10, HCC, LCC, MCC, TCC, Sfc Skin Temp
T511 verification data is posted from NCAR CISL Research Data
Archive. Data set ID ds621.0. Currently an NCAR account is required
for access.
(Also available from NCEP HPSS, ESRL, NCAR, NRL/MRY, Univ. of Utah,
JMA, Mississippi State Univ., JPL and Environment of Canada as well as
NASA/NCCS portal)
14
[Usage and credit]
This data must not be used for commercial purposes and re-distribution rights
are not given. ECMWF and Joint OSSEs must be given credit in any
publications in which this data is used. If you are interested in using the data
set it is necessary to send E-mail with the statement below.
Please send following information to Michiko Masutani
(michiko.masutani@noaa.gov).
your name,
Email address,
Affiliation,
Title of the project (or how NR will be used)
Then your name will be added to the user list and sent to ECMWF. The usage
permission will be given to individual not to the institute. Therefore, everyone
has to submit above information.
"I agree not to copy the ECMWF data for the use of other persons, and I
agree not to use these data and/or software for commercial purposes.
ECMWF will be given credit in any publications in which these data and/or
software are used. I understand that if other persons in my organization wish
to use these data and/or software, they must also sign a copy of this
agreement."
Evaluation of the Nature run
Utilize Goddard’s cyclone tracking
software. - By J. Terry(NASA/GSFC)
Comparison between
NR
the ECMWF T511
Nature Run against
climatology
MODIS
20050601-20060531, exp=eskb,
cycle=31r1
Adrian Tompkins, ECMWF
THE SOUTH AMERICAN
LOW LEVEL JET
Juan Carlos Jusem
(NASA/GSFC)
NRMODIS
Tropics by Oreste Reale (NASA/GSFC/GLA)
Vertical structure of a HL
vortex shows, even at the
degraded resolution of 1 deg,
a distinct eye-like feature and a
very prominent warm core.
Structure even more
impressive than the system
observed in August. Low-level
wind speed exceeds 55 m/s.
Time series showing the night
intensification of the LLJ at
the lee of the Andes in the
simulation.
Gridpoint at 18 S / 63 W
Total Cloud Cover (Land and Ocean)
100
Evaluation of
T511(1°) clouds
by SWA
90
80
Total Cloud Cover (%)
70
60
50
40
30
- NR
- ISCCP
- WWMCA
-- HIRS
20
10
0
-90
-60
-30
0
Latitude
30
60
90
M.Masutani (NOAA/NCEP)
Seasonal mean zonal mean zonal
wind jet maximum strength and
latitude of the jet maxima for the
ECMWF reanalysis (1989-2001,
blue circles) and the Nature Run
(), northern hemisphere. (N.
Prive.)
16
T511 Nature Run is found to be representative of the real
atmosphere and suitable for conducting reliable OSSEs for
midlatitude systems and tropical cyclones. (Note: MJO in T511
Nature Run is still weak.)
There are significant developments in high resolution forecast
models at ECMWF since 2006 and a more realistic tropics for
T799 Nature Run is expected with a newer version of the ECMWF
model.
ECMWF agreed to generate a new T799 NR, when the Joint
OSSE team has gained enough experience in OSSEs with
T511NR and is ready to make the best use of the high resolution
Nature Run.
For the time being, the Joint OSSE team will concentrate on
OSSEs using the T511 Nature Run.
17
Andersson, Erik and Michiko Masutani 2010: Collaboration on
Observing System Simulation Experiments (Joint OSSE), ECMWF
News Letter No. 123, Spring 2010, 14-16.
http://www.ecmwf.int/publications/newsletters/pdf/123.pdf
Reale O., J. Terry, M. Masutani, E. Andersson, L. P. Riishojgaard, J.
C. Jusem (2007), Preliminary evaluation of the European Centre for
Medium-Range Weather Forecasts' (ECMWF) Nature Run over the
tropical Atlantic and African monsoon region, Geophys. Res. Lett., 34,
L22810, doi:10.1029/2007GL031640.
http://www.agu.org/pubs/crossref/2007.../2007GL031640.shtml
Simulated of radiance
Michiko Masutani and Jack Woollen
NOAA/NWS/NCEP/EMC
Tong Zhu, Haibing Sun, Tom Kleespies, Yong Han, Fuzhong Weng
NOAA/NESDIS
Lars Peter Riishojgaard
JCSDA
For full OSSE, existing observation has to
be simulated. This is an initial investment.
Flexile Radiance data Simulation strategies
at NCEP
Flexible Radiance data Simulation
strategies at NCEP
Experts for data handling and experts of RTM
are different people.
Content of DBL91
DBL91
Nature Run data at foot print
91 level 3-D data (12 Variables)
2-D data (71 Variables)
Climatological data
All information to simulate Radiances
The DBL91 also used for development of RTM.
DBL91 can be processed for other sampling such as GMAO sampling
DBL91 can be processed for new observation
It is an option whether DBL91 to be saved and exchange among various
project, or DBL91 to be treated as temporary file produced in simulation
process. This depends on size of DBL91 compare to the Nature Run.
20
Observation template
Geometry
Location
Mask
Nature Run
(grib1 reduced Gaussian)
91 level 3-D data (12 Variables)
2-D data (71 Variables)
Climatological data
Need complete NR (3.5TB)
Random access to grib1 data
Need Data Experts
Decoding grib1
Horizontal Interpolation
Need large cpu
Need Radiation experts
DBL91
Running Simulation program (RTM)
Need Data Experts but this
will be small program
Post Processing (Add mask for channel, Packing to BUFR)
Simulated Radiance Data
21
Compare Tb(NR) with Tb(OBS)
AMSU-A 20050502 00Z
Old NCEP.v2 simulation
Observation
NOAA-17 HIRS3 Ch-07
Observation
CRTM Simulation
IASI simulation for 00Z May 2nd 2005
Using template based on usage on 00Z may 2nd 2009
Compared with observation at 00Z May 2nd 2005
IASI simulation Evolution at
Windows channel
IASI simulation over ocean( Clear atmosphere)
NCEP-NESDIS data posted in December 2009
http://portal.nccs.nasa.gov/josse
Dataset
Originating Institute
Contact
NCEP Obs
NOAA/NCEP
Michiko Masutani (Michiko.Masutani@noaa.gov)
NCEP-NESDIS
NOAA/NCEP
NOAA/NESDIS
Michiko Masutani (Michiko.Masutani@noaa.gov)
Subdirectories under each data set
NCEP_Obs
thinsats_rad.gdas.mask.v0909
thinsats_rad.gdas.v0905
NCEP-NESDIS
osbuvb.n_t511.v0906
prepbufr.n_t511.v0903
thinsats.n_t511.dbl91.v0909
[Interpolation program by Jack Woollen]
Horizontal interpolation code by Jack Woollen is posted at
http://www.emc.ncep.noaa.gov/research/JointOSSEs/Manuals/NCEP_SimObs
/JW-mkthinrad/
The directory contains software by Jack to do sampling
and horizontalinterpolation. He is using random access to grib data and that
made the program verysimple. Please contact Jack Woollen
Jack.Woollen@noaa.gov for the detail.
[Libraries used in the programs]
grib decorder from ECMWF can be downloaded from
http://www.ecmwf.int/products/data/software/download/gribex.html
Manual of gribx is posted from
http://www.ecmwf.int/publications/manuals/libraries/gribex/subroutineGribex.ht
ml
If you are interested in more recent version of ECMWF gribdecorder
grib API is available from
http://www.ecmwf.int/publications/manuals/grib_api/
w3lib and bacio lib are available from
http://www.nco.ncep.noaa.gov/pmb/codes/GRIB2/
Preliminary simulated radiances
are being updated at
ftp://ftp.emc.ncep.noaa.gov/exper/mmasutani/SIMOBS/NOAA_RAD/
The data need to be evaluated
Plans
The simulated radiance using CRTM1.2.2, at foot print based on usage by
2005 GDAS is reasonable. The simulated is continued to complete entire
period of T511 Nature run.
Observational error will be added based on method developed by T. J.
Kleespies.
GMAO also provided softwarre to add random error.
Calibration will be performed for observational errors
At NOAA currently OSSE experiments are conducted for July 2005 using
data provided by GMAO. Preliminary calibrated data are produced for this
period.
Remarks
Simple description is provided but these are posted to help people who are
interested in simulating observation and not design to be potable. Please
contact Jack.Woollen@noaa.gov and Michiko.masutani@noaa.govfor the
detail.We appreciate any comments and improvement to the code.
Simulation of radiance is done using CRTM REL-1.2.2 but CRTM REL-2.0.2 is
available from ftp://ftp.emc.ncep.noaa.gov/jcsda/CRTM/ We appreciate if
anyone can upgrade these code to REL-2.0.2 and share with Joint OSSE.
NCEP will post simulated observation as progress. First we post from NCEP ftp
site to be evaluated. After the data is evaluated it will be transferred to NASA
NCCS portal. We appreciate any help in evaluation of the simulated
observations.
GMAO is also working on simulation of radiance. Lars Peter Riishojgaard is the
contact person for GMAO OSSE. Please contact him if you have any questions
regarding GMAO OSSEs. We hope all efforts may be merged in some stage.
Simulated TC vital
[Simulation of TC vital]
TC vital was simulated using software originally written by Tim Marchock and
currently developed by Guan Ping Lou of NCEP.
The simulated observation has not been evaluated.
TC-vital for 13 month
http://www.emc.ncep.noaa.gov/research/JointOSSEs/Manuals/NCEP_SimObs/TC/
The software are posted from
http://www.emc.ncep.noaa.gov/research/JointOSSEs/Manuals/NCEP_SimObs/
TC/Software/
The simulated data is also posted from
ftp://ftp.emc.ncep.noaa.gov/exper/mmasutani/SIMOBS/TC-vital/T511-v3
Testing Line-Of Sight (LOS) analysis code
FY10 GDAS implemented on December 15, 2009
Jack Woollen, Michiko Masutani,
Lars Peter Riishojgaard, Zaizhong Ma
Acknowledgement
Dave Emmitt, Sid Wood, Steve Greco, Daryl Kleist, John Derber
Back ground
Single Obs test using (U,V) and two LOS are suggested by Dave
Emmitt to test
Early test was conducted in 2006 by
Jack Woollen, Yuanfu Xie, Joe Terry, Yucheng Song, Genia Brin and M.
Masutani but the results was not conclusive and the summary was not
produced.
Assimilation code for DWL was originally written by John Derber in
1997. Further development added by Weiyu Yang and other people at
NCEP.
Single
obs test
Observation
exactly same as the Nature Run
Date:
Position:
Wind:
July 3rd 6z ,2005
96W 29S 200mb height 11991m
U=46.00 V=19.18
U and V observation as pibal
Position: 96W 29S 200mb height 11991m, Wind:U=46.00 V=19.18
Representativeness Error =3.0
U200
Shading: anal-ges
V200
Input as two LOS
DATA(ExactNR) (96W 29S) Pressure level=200 HGT=11991 Representativeness Error =3.0
LOS 34.51 BEARAZ=90 ELEV= 41.38deg, and LOS 14.39 BEARAZ=0 ELEV=41.38deg
U200
V200
Observing Systems Simulation Experiments
For Global Wind Observing Sounder
Global Wind Observing
Sounder (GWOS)
34
Dual Technology Sampling
• The coherent subsystem provides very accurate (< 1.5m/s)
observations when sufficient aerosols (and clouds) exist.
• The direct detection (molecular) subsystem provides
observations meeting the threshold requirements above
2km, clouds permitting.
• When both sample the same volume, the most accurate
observation is chosen for assimilation.
• The combination of direct and coherent detection yields
higher data utility than either system alone. Note that in the
background aerosol mode, the combination of the coherent
and direct provide ~ 20 % more coverage near 3 -5 km
than could either technology by itself.
GWOS Sampling
Hybrid Doppler Wind Lidar
Measurement Geometry: 400 km
350 km/217 mi
53 sec
Along-Track Repeat
“Horiz. Resolution”
36
586 km/363 mi
First look of Impact of GWOS with Hybrid DWL
Very Initial results
Preliminary initial condition and control data
Reduction of RMSE from NR compared to CTL
Average between 00 ZJuly 3 -18Z July 3
Red: Positive impact
Blue: negative impact
U200
V200
U500
U850
V500
V850
Reduction of RMSE from the Nature run by GWOS
Averaged between July 3rd 00Z and July 3rd 18z
Area averaged between 60S to 60N
U
V
First look for three day experiments for three days.
Hybrid GWOS showed positive strong impact in analysis
The positive analysis impact is centered over tropics and spread to
mid latitude. Negative impact over both poles.
Further investigation to improve LOS data assimilation code may
increase the impact of DWL data.
Simulated observation require more realistic observational error.
Further calibration is required to gain the confidence in results.
These results do not guarantee positive impact GWOS DWL in
forecast. Longer time experiments are required for forecast
impact.
Further work
The experiments are done using preliminary initial condition and
simulated observation. Need to be repeated with clean initial
condition and control data.
Complete longer time period (minimum 6 week) to study forecast
verification.
The experiments for different season particularly NH winter
More metric
reduction of failed forecast (See slides 41-43)
Cyclone track forecasts.
Investigate the cause of the negative impact.
Test using flow dependent error covariance being developed at
NCEP.
Evolution of GFS Forecast
Skill
S. Lord and Fanglin Yang
5/20/2010
500 hPa Anomaly Correlation
• “But these are just ‘width of the line’ improvements…”
• Why are these changes important to users?
• How can you justify $M for new computing power based
on these anticipated improvements?
Does It Make a Difference to
How Forecasters Use Product?
Percent Poor Forecasts
NCEP
Percent Good Forecasts
NCEP
Useful References
Basic guide lines for Full OSSE
Masutani, M., T. W. Schlatter, R. M. Errico, A. Stoffelen, E. Andersson, W. Lahoz,
J. S. Woollen, G. D. Emmitt,L.-P. Riishøjgaard, S. J. Lord : Observing System
Simulation Experiments. The final draft of the chapter which describe OSSE is
posted at
http://www.emc.ncep.noaa.gov/research/JointOSSEs/references/Springer_Data_Assimilation_Ch24_OSSE_Draft.pdf
This is Chapter 24 of Data assimilation:Making Sense of Observation
Lahoz, William, Khattatov, Boris, Menard, Richard (Eds.) 2010: Data
Assimilation, Making Sense of Observation.. Springer, 732 p., Hardcover ISBN:
978-3-540-74702-4
http://www.springer.com/earth+sciences+and+geography/computer+%26+mathematical+applications/book/978-3-54074702-4
Introductory presentation about data assimilation.
Jones, Andrew S. 2008:What is Data Assimilation? A Tutorial, Presentation at AMS data
assimilation forum. AMS annual meeting, New Orleans, LA, January 20-24,2008
http://www.emc.ncep.noaa.gov/research/JointOSSEs/references/Jones_Data_Assimilation_Ed_Forum.ppt
Publication about Joint OSSE Nature Run
Andersson, Erik and Michiko Masutani 2010: Collaboration on Observing
System Simulation Experiments (Joint OSSE), ECMWF News Letter No.
123, Spring 2010, 14-16. http://www.ecmwf.int/publications/newsletters/pdf/123.pdf
Reale O., J. Terry, M. Masutani, E. Andersson, L. P. Riishojgaard, J. C.
Jusem (2007), Preliminary evaluation of the European Centre for MediumRange Weather Forecasts' (ECMWF) Nature Run over the tropical Atlantic
and African monsoon region, Geophys. Res. Lett., 34, L22810,
doi:10.1029/2007GL031640.
http://www.agu.org/pubs/crossref/2007.../2007GL031640.shtml
Publication for OSSEs with T213 Nature Run
Masutani, M, John S. Woollen, Stephen J. Lord, G. David Emmitt, Thomas J. Kleespies, Sidney
A. Wood, Steven Greco, Haibing Sun, Joseph Terry, Vaishali Kapoor, Russ Treadon, Kenneth A.
Campana (2010), Observing system simulation experiments at the National Centers for
Environmental Prediction, J. Geophys. Res., 115, D07101, doi:10.1029/2009JD012528.
http://www.agu.org/pubs/crossref/2010/2009JD012528.shtml
Masutani, M. K. Campana, S. Lord, and S.-K. Yang 1999: Note on Cloud Cover of the ECMWF
nature run used for OSSE/NPOESS project. NCEP Office Note No.427
http://wwwt.ncep.noaa.gov/officenotes/NOAA-NPM-NCEPON-0006/01408B76.pdf
Becker, B. D., H. Roquet, and A. Stoffelen 1996: A simulated future atmospheric observation
database including ATOVS, ASCAT, and DWL. BAMS, 77, 2279-2294.
http://ams.allenpress.com/archive/1520-0477/77/10/pdf/i1520-0477-77-10-2279.pdf
Errico, R.M., R. Yang, M. Masutani, M., and J. Woollen, 2007: Estimation of some characteristics
of analysis error inferred from an observation system simulation experiment. Meteorologische
Zeitschrift, 16, 695-708.
http://www.emc.ncep.noaa.gov/research/JointOSSEs/references/Errico_GB_071005.ps
Stoffelen, A., G. J. Marseille, F. Bouttier, D. Vasiljevic, S. De Haan And C. Cardinali 2006:ADMAeolus Doppler wind lidar Observing System Simulation Experiment, Quar.J.Roy. Metorol. Soc. ,
619, 1927-1948
Other references related to OSSE and simulation experiments for DWL
Arnold, C. P., Jr. and C. H. Dey, 1986: Observing-systems simulation experiments: Past, present, and future. Bull. Amer.,
Meteor. Soc., 67, 687-695.
Atlas, R. 1997: Atmospheric observations and experiments to assess their usefulness in
data assimilation. Journal of the Meteorological Society of Japan, 75, 111-130.
http://www.emc.ncep.noaa.gov/research/JointOSSEs/references/Beesley_ECMWF_SHEBA_Arctic_2000JD900079.pdf
Halem, H. and R. Dlouhy, 1984: Observing system simulation experiments related to space-borne lidar
wind profiling. Part 1: Forecast impacts of highly idealized observing systems. Preprints,
Conference on Satellite Meteorology/ remote Sensing and Applications, Clearwater, Fla., Americal
Meteorological Society, 272-279.
Kalnay, E, Jusem, J C and J Pfaendtner, 1985. The relative importance of mass and wind data in the
FGGE observing system. Proceedings of the NASA Symposium on Global Wind Measurements,
Clumbia, MD, NASA, 1-5.
Lorenc,A.C., Graham,R.J., Dharssi,I., MacPherson,B., Ingleby,N.B., Lunnon,R.W.,1992, Preparation for the use of a
Doppler wind lidar information in meteorological assimilation systems, ESA-CR(P)-3454
http://www.emc.ncep.noaa.gov/research/osse/NR/references/Lorenc.1992.TIDCCR4129.pdf
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