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Michiko Masutani NOAA/NWS/NCEP/EMC RS Information Systems Progress in Joint OSSEs Internationally collaborative Full OSSEs sharing the same Nature Runs.

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Presentation on theme: "Michiko Masutani NOAA/NWS/NCEP/EMC RS Information Systems Progress in Joint OSSEs Internationally collaborative Full OSSEs sharing the same Nature Runs."— Presentation transcript:

1 Michiko Masutani NOAA/NWS/NCEP/EMC RS Information Systems Progress in Joint OSSEs Internationally collaborative Full OSSEs sharing the same Nature Runs Progress in simulation of observations

2 Joint OSSEs Team 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/ESRL:Tom 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), H Pryor(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 O’Handley(SWA)

3 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.

4 ● 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. Benefit of OSSEs and need for collaboration ●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.

5 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. 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.

6 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 1 st, 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

7 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 (, Archive and Distribution 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 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.

8 Area averaged precipitation Tropics Convective precipitation Large Scale precipitation Total precipitation It takes about two to three weeks to settle tropical precipitation. - Michiko Masutani (NCEP/EMC) Zonal wind June 2006 By Juan Carlos Jusem (NASA/GSFC) Nature Run NCEP reanalysis Initial Diagnostics of the Nature run Study of drift in NR Michiko Masutani (NCEP)

9 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 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, doi:10.1029/2007GL031640. HL vortices: vertical structure Tropics Oreste Reale (NASA/GSFC/GLA)

10 1) Extract cyclone information using Goddard’s 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 Extratropical Cyclone Statistics Joe Terry NASA/GSFC


12 Comparison between the ECMWF T511 Nature Run against climatology of observation 20050601-20060531, exp=eskb, cycle=31r1 Adrian Tompkins, ECMWF Total Precip NR vs. Xie Arkin TechMemo 452 Tompkins et al. (2004) Plot files are also posted at The description of the data NR Xie Arkin NR-Xie_Arkin Red: NR Black:Xie Arkin

13 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

14 Evaluation of Cloud Simpson weather associates

15 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

16 T511 T799 Min MSLP T799 APR06 period By Michiko Masutani Quick look using 1degree data

17 Case Events Identified from ECMWF HRNR (Plotted from 1x1 data) May 2-4: squall line affecting all points along US Gulf coast May 7-8: decaying squall line over TXOct 10-11: squall line / tropical wave MSLP (hPa)3-h convective precipitation (mm) MSLP (hPa)3-h convective precipitation (mm). Christopher M. Hill, Patrick J. Fitzpatrick, Valentine G. Anantharaj Mississippi State University Mississippi State University

18 Simulation of Observation Simulation of Conventional Observations Jack Woollen (NCEP/EMC) Considerations Data distribution depends on atmospheric conditions Cloud and Jet location, Surface orography, RAOB drift Precursor run with Conventional Data Yuanfu 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. Sat wind was included to provide reasonable fields for SH Radiation data are not included

19 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. OBS91L Jack Woollen (NCEP/EMC)

20 Radiance Simulation System for OSSE GMAO, NESDIS, NCEP Tong Zhu, Haibing Sun, Fuzhong Weng (NOAA/NESDIS) Jack Woollen(NOAA/NCEP) Ron Errico, Runhua Yang, Emily Liu, Lars Peter Riishojgaard (NASA/GSFC/GMAO) Existing instruments experiments must be simulated for control and calibration and development of DAS and RTM Test GOESR,NPOESS, and other future satellite data 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.

21 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

22 Simulation of DWL KNMI is funded by ESA to simulate ADM and ADM follow-on concepts SWA investigating cloud effect in simulating DWL SWA simulating GWOS and NWOS as well as other ADM 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)

23 SWA will simulate Cloud Motion Vectors - Advised by Chris Velden - Uniform Raob for testing Michiko Masutani(NCEP) Scatterometer KNMI is seeking resource to simulate scatterometer data. 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. In US, Data assimilation will be conducted mainly at NCEP/EMC, NASA/GMAO, and NOAA/ESRL Data assimilation: Grid point Statistical Interpolation (GSI) Various Forecast model

24 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 Christopher M. Hill, Patrick J. Fitzpatrick, Valentine G. Anantharaj GRI- Mississippi State University (MSS) Lars-Peter Riishojgaard (NASA/GMAO, JCSDA) OSSE to evaluate UAS Nikki Prive, Yuanfu Xie NOAA/ESRL, NCEP and more OSSEs planned or considered OSSEs to investigate data impact of GOES and prepared for GOES-R Tong Zhu, Fuzhon Weng, Michiko Masutani and more

25 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) Visualization of the Nature run Jibonananda Sanyal (MSS), Oreste Reale (NASA/GSFC/GLA), Horace Mitchell(NASA/GSFC/SIVO) Regional OSSEs to evaluate DWL X. Pu (Univ. Ytah) Sensor Web NASA/GSFC/SIVO, SWA Analysis with surface pressure Gil Compo, P. D. Sardeshmukh (ESRL)

26 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. 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.

27 END

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