1. http:// www.emc.noaa.gov/research/osse OSSE at NCEP and JCSDA Michiko Masutani RSIS at EMC/NOAA/NWS/NCEP More Data or a Better Model

2. Authors and Contributors Michiko Masutani 1#, John S. Woollen 1 +, Yucheng Song 1@, Zoltan Toth 1, Russ Treadon 1, John LeMarshal 4, Stephen J. Lord 1, Haibing Sun 2 %, Thomas J. Kleespies 2, G. David Emmitt 3, Sidney A. Wood 3, Steven Greco 3, Chris O’Handley 3, Many other people of EMC, SWA, NESDIS, NASA/GSFC, JCSDA, and ECMWF Acknowledgement: Jim Yoe, Wayman Baker, Collaboration: Ron Errico and Runhua Yang (GMAO) OSSEs: Observing Systems Simulation Experiments

3. Withdraw or Add data for existing instruments Data assimilation Forecast impact test Control data Real observed data Simulation Real Control data + Simulated data for future instruments Forecast Calibration Analysis Analysis impact test Forecast impact test Analysis impact test Analysis Forecast Data assimilation Analysis and forecast are evaluate against analysis of control Simulation of data Nature Run Evaluation of New Instruments Simulated analysis and forecast are also evaluate against the Nature Run Experimental data Real observed data Simulated data Nature Run (Truth)

4. Comparison between impact of Adding best DWL and increase of model resolution from T62 to T170  The impact of DWL was not spectacular with T62 model  High resolution data may be analyzed better in high resolution model  OSSEs with T170 model were conducted Best DWL: DWL with scanning and sample from both low and upper level

5. Impact of DWL with Scanning T170 vs. T62 Analysis 48 hr T170 T62 T170T62 2510-2-5

6. T62 and T170 CTL (Conventional data only) Data Impact in T62 vs. T170 Total scaleSynoptic Scale T62 CTLwith Non Scan DWL T62 CTL with Scan DWL T170 CTL with Non Scan DWL T170 CTL with Scan DWL -T62 CTL -T170 CTL ◊ CTL ● Non-ScanDWL X Scan DWL Differences in anomaly correlation T62 T170 200 mb V Impact with T170 model look less than with T62 model

7. T62 CTL (reference) (Conventional data only) Data Impact of scan DWL vs. T170 T62 CTL with Scan DWL T170 CTL T170 CTL with Scan DWL ◊ CTL X CTL+Scan DWL - CTL Differences in anomaly correlation Synoptic ScaleTotal scale 200 mb V Impact of T170 model Impact of DWL T170 is better than adding DWL with scan Adding DWL with scan is more important than T170 model Impact of DWL and T170

8. Summary of NCEP OSSE A new nature run is needed for use by many OSSEs. Extended international collaboration within the Meteorological community is essential for timely and reliable OSSEs Operational Test Center OTC – Joint THORPEX/JCSDA JCSDA (NCEP, NESDIS,NASA), ESA, EUMETSAT THORPEX, NPOESS OSSE will be also used to design an ensemble system. The results also showed that theoretical explanations will not be satisfactory when designing future observing systems. The current NCEP system has shown that OSSEs can provide critical information for assessing observational data impacts.

9. Withdraw or Add data for existing instruments Data assimilation Forecast impact test Control data Real observed data Simulation Real Control data + Simulated data for future instruments Forecast Calibration Analysis Analysis impact test Forecast impact test Analysis impact test Analysis Forecast Data assimilation Analysis and forecast are evaluate against analysis of control Simulation of data Nature Run Evaluation of New Instruments Simulated analysis and forecast are also evaluate against the Nature Run Experimental data Real observed data Simulated data Nature Run (Truth)

10. Need one or two good new nature runs which will be used by many OSSEs.  Preparation of the nature run consumes a significant amount of resources.  If different NRs are used the results can not be compared. Search for the New Nature Run Many nature runs will delay the delivery of the OSSE results

11. New Nature Run (proposed) (ECMWF proposal modified by NCEP and NASA) Low resolution Nature Run (L-NR) Low resolution (T511, L91) with 3 hourly dump Two half year runs for summer and winter. Extra month to remove the drift. (Discard the first month) Daily SST and ICE (Provided by NCEP) Select one or two of the most interesting periods Get initial conditions from L-NR High resolution NR (T799, L91) with 30 min dump Archived in MARS system

12. Sample Data Set ECMWF pre-operational test-suite (expver=29) 24 hour forecast from 10 January 2006 T799, 91 model levels Surface data 1x1 degree lat lon pressure data Available from http://www.ecmwf.int/datasvc/download/d/download/044d12c4e5f17e76864ef3e030fba3ec Linked from http://www.emc.ncep.noaa.gov/research/osse

13. Period for the nature runs High resolution daily SST and ice available after 2001 Looking for an active year Summer Summer: 2005 was most active Winter (Various aspects discussed) Eastern Pacific Ocean was active during the winter of 2004-2005 Storm activities over the US main land may be different Winter 1997-98 was El Niño year and stormy (High resolution SST needs to be processed) A year with many storms many not be same as a year with many interesting weather systems We have to watch out for winter 2005-2006 Items needing to be discussed

14. Gale Storm Hurricane Force Total 2004-05 5051 2107 440 7598 2003-04 4741 2102 317 7106 2002-03 4299 2088 263 6704 Warnings issued by OPC/NCEP Courtesy of George Bancroft Jim Hoke http://data.giss.nasa.gov/stormtracks http://data.giss.nasa.gov/stormtracks / Storm track diagnostics By Mark Chandler and Jeffrey Jonas Items needing to be discussed (Cont.) (references)

15. Media for distribution THORPEX subdivision of MARS system Possible dedicated server Format of the data Spectral, Gaussian or Lat-Lon Model level or pressure level Low resolution data for a smaller computer Lat-Lon data in pressure levels for evaluation of NR Variables to be included The list of variables included in the T213 Nature run ftp://ftp.emc.ncep.noaa.gov/exper/mmasutani/ECMWF_NR_Sample/ECMWF_T213_NR_Content.html The list of variables included in the sample data set ftp://ftp.emc.ncep.noaa.gov/exper/mmasutani/ECMWF_NR_Sample/ECMWF_NR_TDS_Report.pdf Items needing to be discussed (Cont.)

16. NCEP Michiko Masutani NASA/GSFC Lars-Peter Riishojgaard GMAO), Oreste Reale (GLA) Joe Terry (SIVO) JCSDA John LeMarshall, Wayman Baker, James Yoe NESDIS Thomas J. Kleespies SWA G. David Emmitt, Steven Greco THORPEX Pierre Gauthier (DAOS), David Person (USA), Zoltan Toth (GIFS) Contacts for the new Nature Run ECMWF Erik Andersson, Philippe Bougeault List of interested people is being developed

17.  As a result a key program element for the Center is the conduct of OSSEs for advanced satellite sensors to be used for weather and climate (environmental ) analysis and prediction.  Instruments being currently assessed for such experiments are the CrIS, ATMS, GOES-R/GIFTS and the HyMS* – P and G %. * HyMS Hyperspectral Microwave Sounder % P- Polar, G Geostationary The Role of JCSDA in OSSEs Joint Center for Satellite Data Assimilation (JCSDA) Mission Accelerate and improve the quantitative use of research and operational satellite data in weather and climate analysis and prediction models

18. Using 1999 version of NCEP DA with T62 model DWL with scan: 12 hour improvement DWL non scan: 4 hour improvement Forecast hours % The diagram is anomaly correlations with nature run for 200mb V. Improvement in forecast skill with respect to forecasts with RAOB and surface data only. Skill for Northern Hemisphere synoptic scale events are presented. The resolution of DA is T62. NH synoptic scale

19. Doppler Wind Lidar (DWL) Impact in Synoptic Scale Time averaged anomaly correlations between forecast and NR for meridional wind (V) fields at 200 hPa and 850 hPa. Anomaly correlation are computed for zonal wave number from 10 to 20 components. Differences from anomaly correlation for the control run (conventional data only) are plotted. Forecast hour Non Scan Scan Lidar Upper lidar become more important in low level 8 8

20. Effect of Observational Error on DWL Impact Percent improvement over Control Forecast (without DWL) Open circles: RAOBs simulated with systematic representation error Closed circles: RAOBs simulated with random error Orange: Best DWL Purple: Non- Scan DWL Total Wave 10-20 Forecast length Significant impact in large scale by adding systematic large scale observational error DWL with large scale error DWL without large scale error