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Summary of full OSSEs at NCEP and

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Summary of full OSSEs at NCEP and Emerging Internationally Collaborative Joint OSSEs Joint OSSE Team http://www.emc.ncep.noaa.gov/research/JointOSSEs – PowerPoint PPT presentation

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Title: Summary of full OSSEs at NCEP and


1
Summary of full OSSEs at NCEP and Emerging
Internationally Collaborative Joint OSSEs
Joint OSSE Team
http//www.emc.ncep.noaa.gov/research/JointOSSEs h
ttp//www.emc.ncep.noaa.gov/research/THORPEX/osse
2
Speakers
Introduction to OSSEs and Summary of NCEP
Michiko Masutani (EMC) Introduction to Joint
OSSEs Michiko Masutani (EMC) Joint OSSE NR Erik
Andersson (ECMWF) Evaluation of Joint OSSE Nature
Run Oreste Reale (NASA/GSFC), Erik Andersson
(ECMWF) Strategies for Simulation of
Observations Jack Woollen (EMC) Simulation of
Dopler Wind Lidar (DWL) Dave Emmitt(Simpson
Weather Associates) Progress and Plans in Joint
OSSE Michiko Masutani (EMC) OSSEs at the Joint
Center for Satellite Data Assimilation Lars Peter
Riishojgaard (Director of Joint Center for
Satellite Data Assimilation)
3
Introduction to OSSE and Summary of NCEP OSSEs
Michiko Masutani
NOAA/NWS/NCEP/EMC RSIS/Wyle Information Systems
http//www.emc.ncep.noaa.gov/research/OSSE
4
Co-Authors and Contributors to NCEP OSSEs
Michiko Masutani, John S. Woollen, Stephen J.
Lord, Yucheng Song, Zoltan Toth,Russ
Treadon NCEP/EMC Haibing Sun, Thomas J.
Kleespies NOAA/NESDIS G. David Emmitt , Sidney
A. Wood, Steven Greco Simpson Weather
Associates Joseph Terry NASA/GSFC
Acknowledgements John C. Derber, Weiyu Yang,
Bert Katz, Genia Brin, Steve Bloom, Bob Atlas, V.
Kapoor, Po Li, Walter Wolf, Jim Yoe, Bob Kistler,
Wayman Baker, Tony Hollingsworth, Roger Saunder,
and many moor
5
Nature Run
Nature Run
Nature Run Serves as a true atmosphere for
OSSEs Observational data will be simulated from
the Nature Run
6
Nature Run
Existing data Proposed data DWL, CrIS, ATMS,
UAS, etc
Current observing system
DATA PRESENTATION
OSSE DATA PRESENTATION
OSSE Quality Control (Simulated conventional
data)
Quality Control (Real conventional data)
Real TOVS AIRS etc.
Simulated TOVS AIRS etc.
OSSE DA
DA
GFS
OSSE
NWP forecast
NWP forecast
7
Need for OSSEs
?Quantitativelybased decisions on the design and
implementation of future observing systems ?
Evaluate possible future instruments without the
costs of developing, maintaining using
observing systems.
Benefit of OSSEs
  • ? OSSEs help in understanding and formulating
    observational errors
  • ? DA (Data Assimilation) system will be prepared
    for the new data
  • ? Enable data formatting and handling in advance
    of live instrument
  • ? OSSE results also showed that theoretical
    explanations will not be satisfactory when
    designing future observing systems.

8
Full OSSE There are many types of simulation
experiments. We have to call our OSSE a Full
OSSE to avoid confusion. ? Nature run (proxy
true atmosphere) is produced from a free forecast
run using the highest resolution operational
model. ? Calibration to compare data impact
between real and simulated data will be
performed. ? Data impact on forecast will be
evaluated. ? A Full OSSE can provide detailed
evaluations of the configuration of observing
systems.
9
Summary of results from NCEP OSSE
  • Wintertime Nature run (1 month, Feb5-Mar.7,1993)
  • NR by ECMWF model T213 (0.5 deg)
  • 1993 data distribution for calibration
  • NCEP DA (SSI) withT62 2.5 deg, 300km and
  • T170 1 deg, 110km
  • Simulate and assimilate level1B radiance
  • Different method than using interpolated
    temperature as retrieval
  • Use line-of- sight (LOS) wind for DWL
  • not u and v components
  • Calibration performed
  • Effects of observational error tested
  • NR clouds are evaluated and adjusted

at NCEP
10
OSSE Calibration
11
Results from OSSEs forDoppler Wind Lidar (DWL)
  • All levels (Best-DWL) Ultimate DWL that provides
    full tropospheric LOS soundings, clouds
    permitting.
  • DWL-Upper An instrument that provides mid- and
    upper- tropospheric winds down only to the
    levels of significant cloud coverage.
  • DWL-PBL An instrument that provides wind
    observations only from clouds and the PBL.
  • Non-Scan DWL A non-scanning instrument that
    provides full tropospheric LOS soundings, clouds
    permitting, along a single line that parallels
    the ground track.
  • (ADM-Aeolus like DWL)

Zonally and time averaged number of DWL
measurements in a 2.5 degree grid box with 50km
thickness for 6 hours. Numbers are divided by
1000. Note that 2.5 degree boxes are smaller in
size at higher latitudes.
Estimate impact of real DWL from combination.
12
Atmospheric Dynamics Mission ADM-Aeolus
  • ADM-Aeolus with single payload Atmospheric
    LAser Doppler INstrument
  • ALADIN
  • Observations of Line-of-Sight LOS wind profiles
    in troposphere to lower stratosphere, up to 30 km
    with vertical resolution from 250 m - 2 km,
    horizontally averaged over 50 km every 200 km
  • Vertical sampling with 25 range gates can be
    varied up to 8 times during one orbit
  • High requirement for random error of HLOS
    lt1 m/s (z0-2 km, for ?z0.5 km) lt2
    m/s (z2-16 km, for ?z 1 km), unknown bias lt
    0.4 m/s and linearity error lt 0.7 of actual
    wind speed HLOS projection on horizontal of LOS
    gt LOS accuracy 0.6HLOS
  • Operating _at_ 355 nm with spectrometers for
    molecular Rayleigh and aerosol/cloud Mie
    backscatter
  • First wind lidar and first High Spectral
    Resolution Lidar HSRL in space to obtain
    aerosol/cloud optical properties (backscatter and
    extinction coefficients)

13
Doppler Wind Lidar (DWL) Impactwithout TOVS
(using 1999 DAS)
3
3
Scan_DWL
Scan_DWL_upper
Scan_DWL_Lower
Non_scan_DWL
8
8
No Lidar (Conventional NOAA11 and NOAA12 TOVS)
Time averaged anomaly correlations between
forecast and NR for meridional wind (V) fields at
200 hPa and 850 hPa. Experiments are done with
1999 DAS. CTL assimilates conventional data only.
14
Doppler Wind Lidar (DWL) ImpactWith TOVS (using
2004 DAS)
Scan_DWL
1.2
1.2
Scan DWL Uniformly thinned to 5
Scan DWL_upper
Scan DWL_Lower
3
3
Non-scan_DWL
No Lidar (Conventional NOAA11 and NOAA12 TOVS)
Forecast hour
Dashed green line is for scan DWL with 20 times
less data to make observation counts similar to
non-scan DWL. This experiment is done with 2004
DAS.
15
Data Impact of scan DWL1999 DAS vs. 2004 DAS
Impact of 2004 DAS
Scan DWL with 2004 DAS
Scan DWL with 1999 DAS
Impact of DWL better DAS
Impact of DWL
16
Data Impact of scan DWL vs. T170
Impact of DWL T170
Impact of T170 model
Impact of DWL
T62 CTL with Scan DWL
T170 CTL with Scan DWL
At planetary scale increasing the resolution is
more important than adding DWL with scan
At smaller scales, adding DWL with scan is more
important than increasing the model resolution
17
Effect of Observational Error on DWL Impact
  • Percent improvement over Control Forecast
    (without DWL)
  • Open circles RAOBs simulated with systematic
    representation error
  • Closed circles RAOBs simulated with random
    errors
  • Green Best DWL
  • Blue Non-Scan DWL

3
3
8
8
Time averaged anomaly correlations between
forecast and NR for meridional wind (V) fields at
200 hPa and 850 hPa. Experiments are done with
1999 DAS. CTL assimilates conventional data only.

18
Targeted DWL experiments(Technologically
possible scenarios)
Combination of two lidars
19
200mb
(Feb13 - Mar 6 average )
10 Upper Level Adaptive sampling (based on the
difference between first guess and NR, three
minutes of segments are chosen the other 81 min
are discarded)
Doubled contour
100 Upper Level
10 Uniform DWL Upper
Non-Scan DWL
20
Anomaly correlation difference from
control Synoptic scale Meridional wind
(V) 200hpa NH Feb13-Feb28
DWL-Lower is better than DWL-Non-Scan only With
100 DWL-Lower DWL-Non-Scan is better than
uniform 10 DWL-Upper Targeted 10 DWL-Upper
performs somewhat better than DWL-Non-Scan in the
analysis DWL-Non-Scan performs somewhat better
than Targeted 10 DWL-Upper in 36-48 hour forecast
CTL
21
Data and model resolution
OSSEs with Uniform Data
More data or a better model?
Fibonacci Grid used in the uniform data coverage
OSSE
40 levels of equally-spaced data 100km, 500km,
200km are tested
Skill is presented as Anomaly Correlation The
differences from selected CTL are presented
- Yucheng Song
Time averaged from Feb13-Feb28 12-hour
sampling 200mb U and 200mb T are presented
22
U 200 hPa
Benefit from increasing the number of levels
5
500km Raob T62L64 anal T62L64 fcst
L64 anl fcst
500km Raob T62L64 anal T62L28 fcst
500km RaobT62L28 anal fcst
L64 anl L28 fcst
1000km RaobT170L42 analT62L28 fcst
CTL
1000km RaobT62L28 anal fcst
T 200 hPa
L64 anl fcst
500km obs
T170 L42 model
High density observation give better analysis but
it could cause poor forecast
High density observations give a better analysis
but could cause a poor forecast.
Increasing the vertical resolution was important
for high density observations.
L64 anl L28 fcst
23
Summary
The current NCEP OSSEs have demonstrated that
OSSEs can provide critical information for
assessing observational data impacts.
The results also showed that theoretical
explanations will not be satisfactory when
designing future observing systems.
We also found that conducting reliable OSSEs are
much more work than expected.
The nature run used was too short and T213 was
too coarse to continue OSSEs.
24
Introduction to Joint OSSEs and Joint OSSE
Nature Runs
Michiko Masutani NOAA/NWS/NCEP/EMC Erik
Andersson ECMWF
http//www.emc.ncep.noaa.gov/research/JointOSSEs
25
Need for collaboration
  • Need one good new Nature Run which will be used
    by many OSSEs, including regional data
    assimilation.
  • Share the simulated data to compare the OSSE
    results from various DA systems to gain
    confidence in results.
  • OSSEs require many experts and require a wide
    range of resources.

Extensive international collaboration within the
Meteorological community is essential for timely
and reliable OSSEs to influence decisions.
26
Beginning to receive funds but mostly volunteers
NCEP Michiko Masutani, John S. Woollen, Yucheng
Song, Stephen J. Lord, Zoltan Toth JCSDA Lars
Peter Riishojgaard (NASA/GFSC), Fuzhong Weng
(NESDIS) NESDIS Haibing Sun, Tong Zhu SWA G.
David Emmitt, Sidney A. Wood, Steven
Greco NASA/GFSC Ron Errico, Oreste Reale, Runhua
Yang, Harpar Pryor, Alindo Da Silva, Matt McGill,
Juan Juseum, Emily Liu, NOAA/ESRLTom Schlatter,
Yuanfu Xie, Nikki Prive, Dezso Devenyi, Steve
Weygandt ECMWF Erik Andersson KNMI Ad
Stoffelen, Gert-Jan Marseille MSU/GRI Valentine
Anantharaj, Chris Hill, Pat Fitzpatrick,
More people are getting involved or considering
participation. T. Miyoshi(JMA), Z. Pu(Univ.
Utah), Lidia Cucil (EMC, JCSDA), G. Compo(ESRL),
Prashant D Sardeshmukh(ESRL), M.-J. Kim(NESDIS),
T. Enomoto(JEMSTEC), Jean Pailleux(Meteo France),
Roger Saunders(Met Office), C. OHandley(SWA), E
Kalnay(U.MD), A.Huang (U. Wisc), Craig Bishop(NRL)
People who helped or advised Joint OSSEs. Joe
Terry, K. Fielding (ECMWF), S. Worley (NCAR),
C.-F., Shih (NCAR), Y. Sato (NCEP,JMA), M.
Yamaguchi (JMA), Lee Cohen(ESRL), David
Groff(NCEP), Daryl Kleist(NCEP), J Purser(NCEP),
Bob Atlas(NOAA/AOML), C. Sun (BOM), M.
Hart(NCEP), G. Gayno(NCEP), W. Ebisuzaki (NCEP),
A. Thompkins (ECMWF), S. Boukabara(NESDIS), John
Derber(NCEp), X. Su (NCEP), R. Treadon(NCEP), P.
VanDelst (NCEP), M Liu(NESDIS), Y Han(NESDIS),
H.Liu(NCEP),M. Hu (ESRL), Chris Velden (SSEC),
George Ohring(JCSDA), Hans Huang(NCAR), Many more
people from NCEP,NESDIS, NASA, ESRL
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