Observing System Simulation Experiments: The New Nature Run

1. Observing System Simulation Experiments The New Nature run International Collaboration Michiko Masutani NOAA/NWS/NCEP/EMC http://www.emc.noaa.gov/research/osse

2. Contributors to NOAA-NASA and International OSSEs 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) NCEP: Michiko Masutani, John S. Woollen, Yucheng Song, Stephen J. Lord, Zoltan Toth, Russ Treadon JCSDA: John LeMarshal, Jim Yoe, Waymen Baker, NESDIS: Thomas J. Kleespies, Haibing Sun, SWA: G. David Emmitt, Sidney A. Wood, Steven Greco, Chris O’Handley, NASA/GFSC:Lars Peter Riishojgaard,Oreste Reale, Joe Terry, Ron Errico, Runhua Yang, Juan Juseum, Gail McConaughy NOAA/ESRL:Tom Schlatter, Yuanfu Xie,Steve Weygandt, Gil Compo ECMWF:Erik Andesrsson KNMI: Gert-Jan Marseille, Ad Stoffellen Japan: JMA, MRI and Earth Simulator Center

3. Existing data + Proposed data DWL, CrIS, ATMS, UAS, etc Nature Run Current observing system DATA PRESENTATION OSSE DATA PRESENTATION OSSE Quality Control (Simulated conventional data) Real TOVS AIRS etc. Quality Control (Real conventional data) Simulated TOVS AIRS etc. OSSE DA DA GFS OSSE NWP forecast NWP forecast

4. Need for OSSEs Quantitatively–based decisions on the design & implementation of future observing systems Evaluate possible future instruments without cost of developing, maintaining & using observing systems. There are significant time lags between instrument deployment and eventual operational NWP use.

5. OSSEs are a very labor intensive project. • DA (Data Assimilation) system will be prepared for the new data • OSSE helps understanding and formulation of observational errors • Enable data formatting and handling in advance of “live” instrument DA system will be different when the actual data become available If we cannot simulate observation, how could we assimilate observation? We need to present levels of confidence of the results from OSSEs. Comparison of OSSE by various DA system will be very important.

6. 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. Share the simulated data to compare the OSSE results by various DA systems to gain confidence in results.

7. Forecast run is used for the Nature Run Because the real atmosphere is a chaotic system governed mainly by conditions at its lower boundary, it does not matter that the Nature Run diverges from the real atmosphere. The Nature Run should be a separate universe, ultimately independent from but parallel to the real atmosphere. The Nature Run must have the same statistical behavior as the real atmosphere in every aspect relevant to the observing system under scrutiny. A succession of analyses is a collection of snapshots of the real atmosphere. Each analysis marks a discontinuity in model trajectory. Considering a succession of analyses as truth seems to be a serious compromise in the attempt to conduct a “clean” experiment. I favor a long, free-running forecast as the basis for defining “truth” in an OSSE. -- from Tom Schlatter Posted at http://www.emc.ncep.noaa.gov/research/osse/NR

8. 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 level: L91 3 hourly dump Initial condition: 12Z May 1st, 2005 End at: 0Z Jun 1,2006 Daily SST and ICE (Provided by NCEP) Model: Version cy31r1 High resolution Nature Run for a selected period T799 resolution, 91 levels, one hourly dump Get initial conditions from L-NR 1x1 degree 31 level pressure data Potential temperature level data Selected time series of 1x1 degree are also available Convective precipitation, Large scale precipitation, MSLP, Z1000, Z500, U500, V500, T2m,TD2m, U10,V10, HCC, LCC, MCC, TCC, Sfc Skin Temp More with requests To be archived in the MARS system on the THORPEX server at ECMWF Accessed by external users expver=etwu Copies for US is available to designated users and users known to ECMWF (Contact: Michiko Masutani) Nature Run home page http://www.emc.ncep.noaa.gov/research/osse/NR

9. Contacts for the New Nature Run • ECMWF Erik Andersson • NCEP Michiko Masutani • NASA/GSFC • Lars-Peter Riishojgaard(GMAO), Oreste Reale(GLA) • Joe Terry (SIVO) • JCSDA John LeMarshall • NESDIS Thomas J. Kleespies • SWA Steven Greco • ESRL Tom Schlatter • THORPEX • Pierre Gauthier(DAOS) • David Person(USA) • Zoltan Toth (GIFS) • Met Office Richard Swinbank • Meteo France Jean Pailleux • KNMI Gert-Jan Marseille • EUMETSAT Jo Schmetz • ESA Eva Oriol • JMA Munehiko Yamaguchi, • Kozo Okamoto • MRI Tetsuo Nakazawa, Masahiro Hosaka • ES Takeshi Enomoto Extended international collaboration within Meteorological community is essential for timely and reliable OSSEs JCSDA , NCEP, NESDIS,NASA, ESRL ECMWF, ESA, EUMETSAT THORPEX, IPO Operational Test Center OTC – Joint THORPEX/JCSDA Simulation of the data must be done from model levels and at full resolution. Pressure level data will be available for diagnostics and evaluation; only limited isentropic level data will become available. BUFR format will be used

10. Nature run home page http://www.emc.ncep.noaa.gov/research/osse/NR • Background • Progress • Discussion • Representativeness errors • Credible OSSE • Strategies for simulation of various observations • Evaluation metrics • Summary of some results • Diagnostics

11. The results depend on the representativeness error assigned. We have to assign representativeness error carefully. The discussions of representativeness error are posted at http://www.emc.ncep.noaa.gov/research/osse/NR/record/ Errico.reperror.061116.ppt RepE.Jun06-061116.doc Stoffelen.representation3.061116.pdf Lorenc,A.C et al 1992, Lorenc.1992.TIDCCR4129.pdf

12. Some initial diagnostics The SST, ice and Ts fields look OK, with the expected seasonal variations. The Z 500 also looks OK. Looking quickly at daily 1000 hPa Z maps for the Caribbean, I've been able to spot nine hurricanes between June and November. One made landfall in Florida (see attached ps-file). There might be some more hurricanes visible in the wind field? -- Erik Andersson

13. Tropical Cyclones in The Nature Run August 22-27

14. Cyclone tracks in the Nature Run Thomas Jung, ECMWF Annual total cyclone track

15. May February November August

16. Total precipitation By Juan Carlos Jusem. NASA/GSFC SON JJA MAM DJF

17. JJA Precipitation anomaly Nature run Observed

18. Comparison between the ECMWF T511 Nature Run against climatology. 20050601-20060531, exp=eskb, cycle=31r1 Adrian Tompkins, ECMWF TechMemo 452 Tompkins et al. (2004) http://www.emc.ncep.noaa.gov/research/osse/NR/ECMWF_T511_diag/ tm452.pdf Jung et al.  (2005) TechMemo 471 http://www.emc.ncep.noaa.gov/research/osse/NR/ECMWF_T511_diag/tm471.pdf Plot files are also posted at http://www.emc.ncep.noaa.gov/research/osse/NR/ECMWF_NR_Diag/ECMWF_T511_diag The description of the data http://www.emc.ncep.noaa.gov/research/osse/NR/ECMWF_T511_diag/climplot_README.html

19. Quickscat SFC wind SSMI 10m wind - Quikscat does not provide winds in rainy areas - Shows known bias in the W Pacific. Model winds are too low in deep convective areas.

20. **Total precipitation, against GPCP, SSMI, and XieArkin TRMM, NASDA and RSS - 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.

21. Area averaged precipitation Tropics NH midlatitude SH midlatitude Convective precipitation Large Scale precipitation Total precipitation It takes about one month to settle tropical precipitation.

22. More diagnostics are being conducted at NASA/GLA, ESRL/PSD, JMA, NCEP, etc.

23. Some recent results from OSSEs at NCEP andJCSDA (using T213 Nature Run)

24. DWL-Upper:An instrument that provides mid and upper tropospheric winds down only to the levels of significant cloud coverage. Operates only 10% (possibly up to 20%) of the time. DWL-Lower:An instrument that provides wind observations only from clouds and the PBL. Operates 100% of the time and keeps the instruments warm. DWL-NonScan: DWL covers all levels without scanning Targeted DWL experiments Combination of two lidar

25. 200mb V (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 discarded) Doubled contour 100% Upper Level 10% Uniform DWL Upper NonScan DWL

26. Target and 200U Target in Jet region Target and 200V Target in North America and Eurasia associated with Northerly wind

27. 850mb (Feb13 - Mar 6 average ) 10% Upper Level Adaptive sampling 100% Upper Level Doubled contour NonScan DWL 10% Uniform DWL Upper

28. 100% Lower + 100% Upper 100% Lower + 10% Targeted Upper 100% Lower + Non Scan 100% Lower + 10% Uniform Upper 100% Lower Non Scan only No Lidar (Conventional + NOAA11 and NOAA12 TOVS) Anomaly correlation difference from control Synoptic scale Meridional wind (V) 200hpa NH Feb13-Feb28 DWL-Lower is better than DWL-NonScan only With 100% DWL-Lower DWL-NonScan is better than uniform 10% DWL-Upper Targeted 10% DWL-Upper performs somewhat better than DWL-NonScan in the analysis DWL-NonScanperforms somewhat better than Targeted 10% DWL-Upperin 36-48 hour forecast CTL

29. Reduction of RMSE from NR for V by adding NonScan lidar to low level scan lidar. V850 V200 NonScan lidar by itself showed a reasonable impact but exhibited some negative impact with data from scanning lidar at lower levels. Note: the experiments are performed using old NCEP SSIDA system. This problem is expected to be resolved in the new GSIDA system. We have to work on a DA system for lidar and new instruments before the data become available. V500

30. Do not show the difference 100%L+ 0%U 100%L+100%U 100%L+10%U NODWL NODWL NOTOVS AC to Nature Run 500hPa height Total scale NH SH noDWL with TOVS noDWL noTOVS Z500 presents a very limited story 70% 90% 72 72

31. 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 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

32. 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

33. High density observations cannot help forecasts if the model does not have good resolution. Increasing vertical resolution in the analysis is important for high density observations. We have to work on a DA system for new instruments before the data become available. OSSE will be a very useful tool to prepare the DA system for new instruments.

34. Summary The current NCEP/JCSDA system has shown 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. The new Nature Run has been prepared with international teamwork: ECMWF, NOAA, NASA, THORPEX EUMETSAT, ESA

35. Summary Extended international collaboration within the Meteorological community is essential for timely and reliable OSSEs to influence decisions. OSSE and its evaluation will become affordable to the University and academic communities.

36. END http://www.emc.ncep.noaa.gov/research/osse