Wind Profile from Space based DWL

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Wind Profile from Space based DWL Evaluation
using OSSEs
Michiko Masutani
http//www.emc.ncep.noaa.gov/research/JointOSSEs h
ttp//www.emc.ncep.noaa.gov/research/THORPEX/osse
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DWL from ESA in 2009
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GWOS Mission Concept from NASA
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Co-Authors and Contributors to NCEP OSSEs
Evaluation of DWL in 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
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Nature Run
Nature Run
Nature Run Serves as a true atmosphere for
OSSEs Observational data will be simulated from
the Nature Run
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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
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NCEP OSSE To be upgrade to Joint OSSEs

• 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
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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.
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Doppler Wind Lidar (DWL) Impact
Scan_DWL
1.2
1.2
Scan DWL Uniformly thinned to 5
Scan DWL_upper
Scan DWL_Lower
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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. The results show definite
advantage of scanning. This experiment is done
with T62 model and SSI.
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Targeted DWL experiments
Combination of lidars Technologically possible
scenarios
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200mb
(Feb13 - Mar 6 average )
Doubled contour
100 Upper Level
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)
Non-Scan DWL
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Anomaly correlation difference from control 200V
Zonal wave 10-20
Zonal wave 1-20
CTL
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Anomaly correlation difference from control 200V
Zonal wave 10-20
Zonal wave 1-20
DWL-Lower with scan is better than DWL-Non-Scan
only even in upper atmosphere With 100
DWl-Lower, targeted 10 DWL-Upper performs
somewhat better than DWL-Non-Scan in the
analysis DWL non-Scan become better in 36-48
hour forecast
CTL
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One or Four non-scan-DWL
D2D3 Red Scan upperscan Lower D1 Light
Blue closed circle Best DWL (D1) with scan Rep
Error 1m/s R45 Cyen dotted line triangle D1
with rep error 4.5m/s (4.5x4.520) U20 Orange
D1 uniformly thinned for factor 20 (Note this
is technologically difficult) N4 VioletD1
Thinned for factor 20 but in for direction
45,135,225,315 (mimicking GWOS) S10 Green
dashed Scan DWL 10min on 90 min off. No other
DWL D4 Dark Blue dashed non scan DWL
NH V500 Zonal wave number 10-20
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D2D3 Red Scan upperscan Lower D1 Light Blue
closed circlescal-DWL repE1m/s R45 Cyen dotted
line triangle repE 4.5m/s U20 Orange uniformly
thinned to 5 N4 VioletFour direction S10
Green dashedScan DWL 10min on 90 min off. D4
Dark Blue dashed non scan DWL
Difference in AC from CTL (ConventionalTOVS) U
and V at 500hPa, top NH Bot SH, left 1-3, right
10-20
Zonal wave 1-3
Zonal wave 10-20
V500
U500
U500
V500
NH
V500
V500
U500
U500
SH
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D2D3 Red Scan upperscan Lower D1 Light Blue
closed circlescal-DWL repE1m/s R45 Cyen dotted
line triangle repE 4.5m/s U20 Orange uniformly
thinned to 5 N4 VioletFour direction S10
Green dashedScan DWL 10min on 90 min off. D4
Dark Blue dashed non scan DWL
Difference in AC from CTL (ConventionalTOVS) T
and W at 500hPa, top NH Bot SH, left 1-3, right
10-20
Zonal wave 10-20
Zonal wave 1-3
W500
T500
T500
W500
NH
W500
W500
T500
T500
SH
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D2D3 Red Scan upperscan Lower D1 Light Blue
closed circlescal-DWL repE1m/s R45 Cyen dotted
line triangle repE 4.5m/s U20 Orange uniformly
thinned to 5 N4 VioletFour direction S10
Green dashedScan DWL 10min on 90 min off. D4
Dark Blue dashed non scan DWL
GWOS is better than ADM-Aeolus in the SH, Zonal
wave 1-3
T
W
GWOS
V
U
ADM
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Scan-DWL has much more impact compared to non-
scan-DWL with same amount of data. If the data
is thinned uniformly 20 times thinned data (U20)
produce 50-90 of impact. 20 times less
weighted 100 data (R45) is generally slightly
better than U20 (5 of data) In fact U20 and
R45 perform better than D1 time to time. This is
more clear in 200hPa. Simple GWOS type
experiment (N4) showed significant improvement to
D4 only in large scale over SH but not much
better over NH and synoptic scale. Without
additional scan-DWL,10min on 90 off (S10)sampling
is much worse than U20(5 uniform thinning) with
twice as much as data.
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Using T62 model forecast skill drop after 48 hr
but this will improved with T170 particularly in
wave number 10-20
The results are expected to change with GSI
particularly with flow dependent back ground
error covariance.
OSSE with one month long T213 nature run is
limited. Need better nature run.
Any comments and advice appreciated
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Joint OSSEs

• 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.
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Joint OSSEs http//www.emc.ncep.noaa.gov/research
/JointOSSEs April 2008
NCEP Michiko Masutani, John S. Woollen, Yucheng
Song, Stephen J. Lord, Zoltan Toth ECMWF Erik
Andersson KNMI Ad Stoffelen, Gert-Jan
Marseille JCSDA Lars Peter Riishojgaard
(NASA/GFSC), NESDIS Fuzhong Weng, Tong Zhu
Haibing Sun, SWA G. David Emmitt, Sidney A.
Wood, Steven Greco NASA/GFSC Ron Errico, Oreste
Reale, Runhua Yang, Emily Liu, Joanna Joiner,
Harpar Pryor, Alindo Da Silva, Matt McGill,
NOAA/ESRLTom Schlatter, Yuanfu Xie, Nikki
Prive, Dezso Devenyi, Steve Weygandt MSU/GRI
Valentine Anantharaj, Chris Hill, Pat
Fitzpatrick, JMA Takemasa Miyoshi , Munehiko.
Yamaguchi JAMSTEC Takeshi Enomoto So far most
of the work is done by volunteers.
People who helped or advised Joint OSSEs. Joe
Terry (NASA), K. Fielding (ECMWF), S. Worley
(NCAR), C.-F., Shih (NCAR), Y. Sato (NCEP,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),
William Lahoz (Reading), George Ohring(JCSDA),
Many more people from NCEP,NESDIS, NASA, ESRL
More people are working on proposal, getting
involved or considering participation. Z.
Pu(Univ. Utah), Lidia Cucil (EMC, JCSDA), G.
Compo(ESRL), Prashant D Sardeshmukh(ESRL), M.-J.
Kim(NESDIS), Jean Pailleux(Meteo France), Roger
Saunders(Met Office), C. OHandley(SWA), E
Kalnay(U.MD), A.Huang (U. Wisc), Craig
Bishop(NRL), Hans Huang(NCAR),
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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
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
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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 Posted from NCAR CISL Research Data Archive.
Data set ID ds621.0 Currently NCAR account is
required for access. (Also available from NCEP
hpss, NASA/GSFC Portal, ESRL, NCAR/MMM, NRL/MRY,
Univ. of Utah, JMA)
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