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EGU2008-A-10290. OSSEs: Observing Systems Simulation Experiments JCSDA: Joint Center for Satellite Data Assimilation SWA: Simpson Weather Associates ESRL: Earth System Research Laboratory ( formerly FSL, CDC, ETL). Progress in Joint OSSEs
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OSSEs: Observing Systems Simulation Experiments
JCSDA: Joint Center for Satellite Data Assimilation
SWA: Simpson Weather Associates
ESRL: Earth System Research Laboratory(formerly FSL, CDC, ETL)
Progress in Joint OSSEs
- Evaluation of the New Nature runs. Progress in Simulation of Observation-
EGU 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/ESRL:Tom 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), 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. O’Handley(SWA),
E Kalnay(U.MD), A.Huang (U. Wisc), Craig Bishop(NRL), Hans Huang(NCAR),
Benefit of OSSEs
♦Quantitatively–based decisions on the design and implementation of future observing systems
♦ Evaluate possible future instruments without the costs of developing, maintaining & using observing systems.
●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.
If we cannot simulate observations, how could we assimilate observations?
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.
New Nature Run by ECMWF Based on discussion with JCSDA, NCEP, GMAO, GLA, SIVO, SWA, NESDIS, ESRL, and ECMWF
To be archived in the MARS system on the THORPEX server at ECMWF
Accessed by external users. Currently available internally as expver=etwu
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
Conctact:Michiko Masutani (firstname.lastname@example.org), Harper.Pryor@nasa.gov
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
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
Selected 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
T511 verification data is posted from NCAR CISL Research Data Archive. Data set ID ds621.0. Currently NCAR account is required for access.
T799 verification data are available from NASA/GSFC portal (Contact Harper.Pryor@nasa.gov)
(Also available from NCEP hpss, ESRL, NCAR/MMM, NRL/MRY, Univ. of Utah, JMA,Mississippi State Univ.)
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
20050601-20060531, exp=eskb, cycle=31r1
Adrian Tompkins, ECMWF
TechMemo 452 Tompkins et al. (2004) ftp://ftp.emc.ncep.noaa.gov/exper/mmasutani/ECMWF_NR_Diag/Tompkins_ECMWF_T511_diag/tm452.pdf
Jung et al. (2005) TechMemo 471
Plot files are also posted at
The description of the data
- These comparisons confirm the lack of rainfall over the
tropical land masses.
- We have an overestimation of precip over the high-SST regions in the tropics.
- There is a tendency for deep convection to become locked in with the highest SSTs, which in the east Pacific results in a narrow ITCZ.
- The TRMM NASDA-3b43 algorithm is presumed to be the most accurate of the two TRMM retrieval products.
Evaluation of the European Centre for Medium-Range Weather Forecasts’ (ECMWF) Nature Run: midlatitude cyclone activity; tropical Atlantic and African MonsoonOreste Reale NASA/GSFC/GLAJoe Terry NASA/GSFC/SIVO
Midlatitude Cyclone Diagnostics
Utilize Goddard’s cyclone tracking software (Terry and Atlas, AMS conf, Aug 1996):
Cyclogenesis density from NCEP reanalysis over Southern Hemisphere. (Simmonds and Keay, Journal of Climate, March 2000)
Nature Run diagnostics (J. Terry, NASA/GSFC/SIVO)
Nature Run Atlantic tropical cyclone seasonOreste Reale NASA/GSFC/GLA
Five`Early recurvers’ appear in the season. Three central-Atlantic TCs with convincing Extra-tropical transitions, and 3 systems of the Gulf. Overall, very realistic track variability. Early recurvers are more than climatology but not unseen.
Four early recurving systems in an active season, 2004 (NHC).
Wind speed (m/s)
Vertical structure of a TC 2 vortex shows, even at the degraded resolution of 1 deg,
a distinct eye-like feature and a very prominent warm core.
Singuarities, binary vortex
Interactions, Intensity fluctuations
Due to large-scale forcing fluctuations
Vertical structure of TC11 shows another example of eye-like feature and a very
prominent warm core. Structure even more impressive than the TC2. Low-level wind
speed exceeds 55 m/s; vorticity max In the lower levels.
Comparison with NCEP op. Analyses for Sep 2005
The AEJ has a realistic maximum of 11 m/s at 600 hPa but wind speed are too low at 750 and 700 hPa consistently with the altitude bias. Meridional shear of zonal wind is realistic and supportive of barotropic instability.
The AEJ has a perfectly realistic maximum of 11 m/s at 600 hPa in September and gradually weakens in October following climatology.
NCEP operational analyses
Tropical Easterly Jet (TEJ) at 150hPa
In the early stages, 850 hPa vort. increases and vort max becomes aligned with 650hPa circulation center. Eventually upper-level easterly shear suppresses development.
The potentially favorable situation induced by a vertically aligned structure between 800 and 500 hPa at 12-14N is counteracted by easterly vertical shear of the order of 20 m/s.
African Easterly Waves (AEWs)
CURRENT and FUTURE WORK at NASA GSFC (GLA, GMAO, JCSDA and SIVO) on the NR validation
AEWs show a realistic propagation speed of about 5-9 deg/day, comparable to analyses. Moreover, there is a period of about six weeks in which the majority of waves present signs of development. This is similar to what happens in very active seasons. The disappearance from the Hovm relates to changes in latitude.
Case Events Identified from ECMWF T799NRChristopher M. Hill, Patrick J. Fitzpatrick, Valentine G. Anantharaj Mississippi State University (Plotted from 1x1 data)
Comparison of zonal mean zonal wind jet maxima, NR and ECMWF analysis, Northern Hemisphere
By Nikki Prive, ESRL
blue – ECMWF
green star – Nature Run
Evaluation of Cloud
Simpson weather associates
Nikki Prive also presented realistic Rossby wave and many good storms to test T-PARC experiments
T511 vs T799 in 1deg
Michiko Masutan (NOAA/NCEP/EMC)
OBS91L Nature Run Model level profiles for simulating radiance obs
Jack Woollen (EMC)
Simulation of Conventional ObservationsJack Woollen (NCEP/EMC)
Sat wind was included to provide reasonable fields for SH
Radiation data are not included.
Initial data will have no error added and quality control is not necessary.
For development purposes, 91-level ML variables are processed at NCEP and interpolated to observational locations with all the information need to simulate radiance data (OBS91L).
OBS91L made for all foot prints of HIRS, AMSU, GOES are produced for a few weeks of the T799 period in October 2005
As well as for the month of May 2005.
OBS91L are produced for all radiance foot prints assimilated in operational GDAS as recorded in the archived “radstat” files.
The OBS91L are also available for development of a Radiative Transfer Model (RTM) for development of other forward model.
Data distribution depends on atmospheric conditions
Cloud and Jet location, Surface orography, RAOB drift
Simulation of Observaional Error
Ron Errico NASA/GSFC/GMAO
Precursor run with Conventional DataYuanfu Xie (NOAA/ESRL), Jack Woollen (EMC), Michiko Masutani(EMC), Yucheng Song(EMC)
T62L64 or T126 L64 is used in the experiment for entire period for T511 NR using perfect observation without quality control.
This will to test the OSSE system and provide initial condition for other OSSEs.
Radiance Simulation System for OSSEGMAO, NESDIS, NCEPRon Errico, Runhua Yang, Emily Liu, Lars Peter Riishojgaard, Ravi Govindaraju (NASA/GSFC/GMAO)Tong Zhu,Fuzhong Weng, Haibing Sun(NOAA/NESDIS)Jack Woollen(NOAA/NCEP)
Other possible 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)
NASA/GMAO is developing best strategies to simulate and work on more complete foot prints. This include development of cloud clearing algorithm.
NESDIS and NCEP are working on thinned data to perform precursor run for entire period. (seeking for resources) Sample full resolution data for GOES for cloudy radiance are also produced by Tong Zhu.
Existing instruments experiments must be simulated for control and calibration and development of DAS and RTM and to test GOESR,NPOESS, and other future satellite data
Tong Zhu et al. :5GOESR P1.31 at AMS annual meeting
Simulated from T511 NR. GOES data will be simulated to investigate its data impact
This list show the capabilities. Not all the activities are funded.
Simulation of DWLKNMI, SWA, NASA/GSFC, Ball(?)
Simulation Radiance data to be evaluated by OSSEsGMAO,NESDIS,MSU,JCSDA, UW/SSEC and more
Unmanned Air Craft System (UAS)
Cloud Motion Vectors
- Advised by Chris Velden -
Uniform Raob for testing DAS
Funded or seeking funding but starting with volunteers
OSSEs to investigate data impact of GOES
and preparation for GOES-R
OSSE to evaluate DWL
KNMI,SWA,EMC, GMAO, NOAA/ESRL, and more
OSSE to evaluate UAS
ESRL and NCEP
OSSEs for THORPEX T-PARC
EMC, FSU, ESRL
Regional OSSEs to Evaluate ATMS and CrIS Observations
GRI/Mississippi State Univ (MSU), JCSDA
Visualization of the Nature run
Jibo Sanyal (MSS),
O. Reale (NASA/GSFC/GLA),
Assimilation with LETKF possibly by 4D-var
T. Miyoshi(JMA) and T. Enomoto(JEMSTEC)
Analysis with surface pressure
Gil Compo, P. D. Sardeshmukh (ESRL)
Identical twin experiments
It is worthwhile 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)
and various possibilities
Regional OSSEs to evaluate DWL
Zhaoxia Pu,Univ of Utah
OSSE to design configuration of GPS
Evaluation data assimilation system
Ron Errico, GMAO
Simulation of ASCAT QuickScat
Ad Stoffelen, KNMI
Evaluation of RAOB Drift
Evaluation of Drift Buoy
Jean Pailleux, Meteo France
Evaluation of retrieval program
Simulating Gestational DWL, Ball
Use OSSEs for training and education.
Targeted Observation using LETKF
Eugenia Kalnay, UMCP
- Some suggestions -
We need to avoid many poorly planned and assessed nature run
♦ The NWP model must have good forecast skill.
Great visualization does not guarantee good forecast skill.
♦ At least a 3 month lower resolution run with the same model is required to provide a spin-up period for bias correction.
♦ Must have a good TC or a severe storm in the Nature Run period.
♦ Sufficient number of vertical levels: Minimum 91 levels
♦ Some degree of coupling with the ocean and land surface
♦ If it is regional, the effect of the lateral boundary must be evaluated.
♦ A list of verification methods must be produced by Joint OSSE.
♦ Need NR to be shared within Joint OSSE
♦ User friendly archive