Michiko Masutani

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Progress in Joint OSSEs Internationally
collaborative Full OSSEs sharing the same Nature
Runs Progress in simulation of observations
Michiko Masutani
NOAA/NWS/NCEP/EMC RS Information Systems
http//www.emc.ncep.noaa.gov/research/JointOSSEs h
ttp//www.emc.ncep.noaa.gov/research/THORPEX/osse
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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, Joe
Terry, Juan Juseum, Gail McConaughy , Runhua
Yang, 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,
People who helped or advised Joint OSSEs K.
Fielding (ECMWF), S. Worley (NCAR), C.-F., Shih
(NCAR), Y. Sato (NCEP,JMA), M. Yamaguchi (JMA), J
Purser(NCEP), Daryl Kleist(NCEP), Bob
Atlas(NOAA/AOML), C. Sun (GSFC), M. Hart(NCEP),
G. Gayno(NCEP), W. Ebisuzaki (NCEP), A. Thompkins
(ECMWF), S. Boukabara(NESDIS), X. Su (NCEP), R.
Treadon(NCEP), H.Liu(NCEP),M. Hu (ESRL) Many
more people from NCEP,NESDIS, NASA, ESRL
More people are getting involved. A. Da
Silva(NASA), M. J. McGill(NASA), T. Miyoshi(JMA),
Z. Pu(Univ. Utah), A.Huang (U. Wisc), David
Groff(NCEP), G. Compo(ESRl),M.-J. Kim(NESDIS), T.
Enomoto(JEMSTEC), Hans Huang(NCAR), Jean
Pailleux(Meteo France), Roger Saunders(Met
Office), Chris OHandley(SWA), Harper
Pryor(NASA/GSFC)
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Full OSSE There ae many types of simulation
experiments. We have to call our OSSE as 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 impact will be
performed. Data impact on forecast will be
evaluated Full OSSE can provide detail
evaluation about configuration of observing
systems.
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Benefit of OSSEs and need for collaboration

• ? OSSEs help understanding and formulation of
  observational errors
• ? DA (Data Assimilation) system will be prepared
  for the new data
• ? Enable data formatting and handling in advance
  of live instrument
• ? The OSSE results also showed that theoretical
  explanations will not be satisfactory when
  designing future observing systems.

?OSSEs require many experts and requires wide
range of resources ? Effective collaboration and
effective distribution of resources will
significantly reduce the cost of OSSEs. ? This
will also speed up the performance and enhance
the credibility of the results.
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Nature Run Serves as a true atmosphere for
OSSEs Preparation of the Nature Run and
simulation of basic observations consume a
significant amount of resources. If different
NRs are used by various DAs, it is hard to
compare the results.

• 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 by various DA systems to gain confidence
  in results.

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New Nature Run by ECMWF Based on
Recommendations by JCSDA, NCEP, GMAO, GLA, SIVO,
SWA, NESDIS, ESRL
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 Completed in July
2006, rerun October 2006
Two High Resolution Nature Run 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
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Archive and Distribution
To be archived in the MARS system on the THORPEX
server at ECMWF Accessed by external users.
Currently available internally as expveretwu
Copies for US are available to designated users
for research purpose users known to ECMWF
Saved at NCEP, ESRL, and NASA/GSFC Complete data
available from portal at NASA/GSFC ConctactMichik
o Masutani (michiko.masutani_at_noaa.gov),
Harper.Pryor_at_nasa.gov
Supplemental low resolution regular lat lon data
1degx1deg for T511 NR, 0.5degx0.5deg for T799 NR
Pressure level data 31 levels, Potential
temperature level data 315,330,350,370,530K Selec
ted surface data for T511 NR Convective precip,
Large scale precip,

MSLP,T2m,TD2m, U10,V10, HCC, LCC, MCC, TCC, Sfc
Skin Temp Complete surface data for T799
NR Available from NCAR CISL Research Data
Archive. Data set ID ds621.0 Currently NCAR
account is required for access.
Note This data must not be used for commercial
purposes and re-distribution rights are not
given.
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Initial Diagnostics of the Nature run
Study of drift in NR Michiko Masutani (NCEP)
Area averaged precipitation
Tropics
Zonal wind June 2006 By Juan Carlos Jusem
(NASA/GSFC)
NCEP reanalysis
Nature Run
Convective precipitation Large Scale
precipitation Total precipitation
It takes about two to three weeks to settle
tropical precipitation. - Michiko Masutani
(NCEP/EMC)
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Tropics 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
HL vortices vertical structure
These findings, albeit preliminary, are
suggestive that the ECMWF NR simulates a
realistic meteorology over tropical Africa and
nearby Atlantic and may prove itself beneficial
to OSSE research focused over the AMMA or the
Atlantic Hurricane regions. 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, doi10.1029/2007GL031640.
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Extratropical Cyclone StatisticsJoe Terry
NASA/GSFC
1) Extract cyclone information using Goddards
objective cyclone tracker

• Nature Run
• One degree operational NCEP analyses (from
  several surrounding years)
• NCEP reanalysis for specific years (La Nina, El
  Nino, FGGE)

2) Produce diagnostics using the cyclone track
information
(comparisons between Nature Run and NCEP analyses
for same month)

• Distribution of cyclone strength across pressure
  spectrum
• Cyclone lifespan
• Cyclone deepening
• Regions of cyclogenesis and cyclolysis
• Distributions of cyclone speed and direction

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Comparison between the ECMWF T511 Nature Run
against climatology of observation
20050601-20060531, expeskb, cycle31r1 Adrian
Tompkins, ECMWF
Total Precip NR vs. Xie Arkin
NR
Xie Arkin
Red NR BlackXie Arkin
NR-Xie_Arkin
TechMemo 452 Tompkins et al. (2004) Plot files
are also posted at http//www.emc.ncep.noaa.gov/re
search/osse/NR/ECMWF_NR_Diag/ECMWF_T511_diag The
description of the data http//www.emc.ncep.noaa.g
ov/research/osse/NR/ECMWF_T511_diag/climplot_READM
E.html
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Comparison of zonal mean zonal wind jet maxima,
NR and ECMWF analysis, Northern Hemisphere By
Nikki Prive, ESRL
blue ECMWF green star Nature Run
Nikki Prive also presented realistic Rossby wave
and many good storm to test T-PARC experiments
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Evaluation of Cloud Simpson weather associates
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Two T799 91 level Nature run 35 day long
hourly dump
1) Hurricane season T799OCT05 Initial
condition from T511 at 12z September 27,2005 End
at 12 z November 1st, 2005 One strong hurricane
over Atlantic. Another one in central Pacific
2) Spring thunderstorm season
T799APR06 Initial condition from T511 at 12z
April 10, 2006 End at 12 z May 15, 2006 Good
storm over Japan Several thunder storms over
US Four TC in Southern hemisphere
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Quick look using 1degree data
Min MSLP T799 APR06 period
T511
T799
By Michiko Masutani
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Case Events Identified from ECMWF HRNR(Plotted
from 1x1 data)

• May 2-4 squall line affecting all points along
  US Gulf coast

MSLP (hPa) 3-h convective precipitation (mm)
.
May 7-8 decaying squall line over TX Oct
10-11 squall line / tropical wave
Christopher M. Hill, Patrick J. Fitzpatrick,
Valentine G. Anantharaj Mississippi State
University
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Simulation of Observation
Simulation of Conventional ObservationsJack
Woollen (NCEP/EMC)
Sat wind was included to provide reasonable
fields for SH Radiation data are not included
Considerations Data distribution depends on
atmospheric conditions Cloud and Jet location,
Surface orography, RAOB drift
Precursor run with Conventional DataYuanfu Xie
(NOAA/ESRL)

• T62L64 is used in the experiment for entire
  period for T511 NR
• This will to test the OSSE system.
• The results could provide initial condition for
  other OSSEs.

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OBS91L Jack Woollen (NCEP/EMC)
For development purposes, 91-level NR variables
are processed at NCEP and interpolated to
observational locations with all the information
need to simulate data (OBS91L). OBS91L for all
foot prints of HIRS, AMSU, GOES are produced for
a few weeks of the T799 period in October
2005. Thinned foot prints for the entire period.
Thinning of the foot print is based on
operational use of radiance data. The OBS91L
are also available for development of a Radiative
Transfer Model (RTM) for development of other
forward model.
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Radiance Simulation System for OSSEGMAO, NESDIS,
NCEPTong Zhu, Haibing Sun, Fuzhong
Weng(NOAA/NESDIS)Jack Woollen(NOAA/NCEP)Ron
Errico, Runhua Yang, Emily Liu, Lars Peter
Riishojgaard (NASA/GSFC/GMAO)
Other resources and/or advisors David Groff ,
Paul Van Delst (NCEP) Yong Han, Walter Wolf, Cris
Bernet,, Mark Liu, M.-J. Kim, Tom Kleespies,
(NESDIS) Erik Andersson (ECMWF) Roger Saunders
(Met Office)
OBS91L is produced by Jack Woollen at
NCEP NASA/GMAO is developing best strategies to
simulate and work on complete foot
prints. NESDIS and NCEP are working on thinned
data. Full resolution data for GOESR.
Existing instruments experiments must be
simulated for control and calibration and
development of DAS and RTM Test GOESR,NPOESS, and
other future satellite data
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Simulation of GOES-R ABI radiances for OSSE Tong
Zhu et al. 5GOESR P1.31
Simulated from T511 NR. GOES data will be
simulated to investigate its data impact
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Simulation of DWL
KNMI is funded by ESA to simulate ADM and ADM
follow-on concepts SWA investigating cloud
effect in simulating DWL SWA is simulating
GWOS and NWOS as well as other AND follow-on
mission concepts NASA GSFC is working on Global
Wind Observing Sounder (GWOS) DWL More groups
may participate to verify the simulated data All
use common BUFR table and definitions when it is
used for data assimilation.
Unmanned Air Craft System (UAS)
Nikki Prive and Yuanfu Xie (NOAA/ESRL)
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Scatterometer KNMI is seeking resources to
simulate scatterometer data
SWA will simulate Cloud Motion Vectors - Advised
by Chris Velden -
Uniform Raob for testing Michiko Masutani(NCEP)
In US, Data assimilation will be conducted mainly
at NCEP/EMC, NASA/GMAO, and NOAA/ESRL
Calibration coordinator Michiko Masutani
(NCEP/EMC)
In calibrations of the OSSE, similarity in the
amount of impact from existing data in the real
and simulated atmosphere needs to be achieved.
Data assimilation Grid point Statistical
Interpolation (GSI) Various Forecast model
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OSSEs planned or considered
OSSEs to investigate data impact of GOES and
prepared for GOES-R Tong Zhu, Fuzhon Weng, J.
Woollen (NCEP) M.Masutani(NCEP) and more
OSSE to evaluate UAS N. Prive(ESRL), Y.
Xie(ESRL) NCEP and more
OSSE to evaluate DWL M.Masutani(NCEP), GMAO,
NOAA/ESRL, more
OSSEs for THORPEX T-PARC Evaluation and
development of targeted observation Z. Toth,
Yucheng Song (NCEP) and other THORPEX team
Regional OSSEs to Evaluate ATMS and CrIS
Observations Cris M. Hill, Pat. J. Fitzpatrick,
Val. G. Anantharaj GRI- Mississippi State
University (MSS) Lars-Peter Riishojgaard
(NASA/GMAO, JCSDA)
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Identical twin experiments It is worth while to
try identical twin experiments to understand
model error. Identical twin OSSEs can be only
used for illustration only . ECMWF offered to
perform identical twin OSSEs if there is specific
goals. (Erik Andersson)
Comparison between 4D-Var and LETKF T.
Miyoshi(JMA) and T. Enomoto(JEMSTEC)
Regional OSSEs to evaluate DWL X. Pu (Univ. Ytah)
Analysis with surface pressure Gil Compo, P. D.
Sardeshmukh (ESRL)
Visualization of the Nature run Jibo Sanyal
(MSS), O. Reale (NASA/GSFC/GLA), H.
Mitchell(NASA/GSFC/SIVO)
Targeted Observation using LETKF E. Kalnay (UMD)
Sensor Web NASA/GSFC/SIVO, SWA
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Integrations of meso/regional OSSE effort into
Joint OSSEs
Note There are global meso-scale model (NICAM,
GFDL, ESRL) and relatively low resolution
regional OSSEs are considered.
Good hurricanes and storms in T799 run even for
meso scale OSSEs. Before producing regional
NR, it is highly recommended to perform regional
OSSEs (40-60km resolusiton) with T799 global
NR. Mesoscale NR must be another Joint OSSE NR
which will be shared within Joint OSSE Regional
OSSEs are affordable to Universities. Simulation
of observations may be difficult. Regional OSSE
must present evaluation of effect of lateral
boundary conditions.
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