1. NCEP Status Use of Satellite Data and Other Topics Stephen J. Lord (NCEP/EMC) 17 th North America-Europe Data Exchange Meeting May 26-28, 2004, CMC Montreal Canada

2. Overview JCSDA Summary –Community RT model and data assimilation development –Observing system impact experiments –Applied Research Areas WSR & NATREC Results (preliminary) New NCEP Climate Forecast System Verification of Wave Guidance during Isabel with altimeter data North American Ensemble Forecast System development

3. JCSDA Summary FY03-04 John LeMarshall - JCSDA Director Stephen J. Lord (NCEP/EMC) Fuzhong Weng (NESDIS/ORA) L.P. Riishojgaard (NASA/GMAO) Pat Phoebus (NRL Monterey) 17 th North America-Europe Data Exchange Meeting May 26-28, 2004, CMC Montreal Canada

4. Establishing community models JCSDA RT models –Community support established Han, vanDelst, Yan –Strong demand and anticipated participation by JCSDA grantees and internal investigators –WRF data assimilation Unified (global, regional) analysis system at NCEP –Single analysis (Gridpoint Statistical Interpolation, GSI) –JCSDA satellite RT codes –Collaboration with NASA/GSFC –Opens way for flow-dependent background errors and unified NCEP analysis across global and regional applications –Advanced SST retrievals and analysis –2004 implementation

5. Data Assimilation Impacts in the NCEP GDAS Stephen Lord NCEP Environmental Modeling Center Tom Zapotocny and James Jung CIMSS/ Univ. of Wisconsin Tom.Zapotocny@ssec.wisc.edu Jim.Jung@noaa.gov Sponsored by JCSDA and NPOESS IPO

6. Data Assimilation Impacts in the NCEP GDAS (cont) AMSU and “All Conventional” data provide nearly the same amount of improvement to the Northern Hemisphere.

7. N. Hemisphere 500 mb ht anomaly correlation AMSU: 0.5 day improvement at 5 days

8. S. Hemisphere 500 mb ht anomaly correlation AMSU: 0.75 day improvement at 5 days

9. Tropics 850 mb Vector (F-A) RMS The REAL problem is Day 1

10. Satellite data ~ 10% impact Jung and Zapotocny JCSDA Funded by NPOESS IPO

11. Summary: Impacts of Current Instruments MW has largest impact on forecast scores IR useful in cloud free areas and for cloud top determinations Impact assessments lead to –Improved data sampling algorithms –Focused direction for future applied research –Improved knowledge of entire observing system and how to extract more information from all observations to improve forecasts Experiments ongoing with computing sponsored by NPOESS Program

12. Overview of JCSDA Applied Research Areas Advanced radiative transfer Improve sea surface temperature data and use of altimeter data Enhance land surface data sets (surface emissivity model) Observing System Simulation Experiments (OSSEs) Instrument specific development

13. Advanced Radiative Transfer [Tahara, VanDelst, McMillin, Han] Increased accuracy Improved computation efficiency –Required for huge increase in data volume Add effects of –Aerosols –Trace gases –Reflection, scattering and –absorption by clouds RMS=0.08 Mean=0.0017 OPTRAN fits to Line-by Line RT

14. Improve Sea Surface Temperature Data [X. Li & Derber] SST Difference 29-28 October 2003 - Experiment SST Difference 29-28 October 2003 - Control New physical retrieval from AVHRR data, cast as variational problem OPTRAN RTM & Linear Tangent Model Eventual direct use of AVHRR (and other) radiance data RMS and Bias Fits to Independent Buoy SST Data NOAA-16 AVHRR data only Northern Hemisphere Ex. Tropics

15. Improved Surface Emissivity Model for Snow [Yan, Okamoto and Weng) Annual Mean RMS T B Difference (Obs – Simulated) SnowEM Operational

16. Observing System Simulation Experiments (OSSEs) Prepare for advanced data –Formatting –Understanding and formulation of observational errors –Initial quality control algorithms Understanding and formulation of observational errors Assists requirements definition, instrument design and potential instrument impact

17. OSSEs Observational Error Formulation Surface & Upper Air [Woollen, Masutani] time With random error: Data rejection rate too small (top) Fit of obs too small (bottom) 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 4 0 -4

18. Doppler Wind Lidar (DWL) Impact 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

19. Examples of Instrument-Specific Development at the JCSDA GPS Occultation (COSMIC) – NCAR-sponsored Post-Doc at JCSDA – High vertical resolution, low horizontal res. (different from any other satellite data) – Forward model to derive index of refraction developed – Preparing for use of data within NCEP analysis – Preparing for COSMIC using CHAMP and SAC-C data AIRS

20. AIRS Testing at NCEP [Derber, Treadon] Red: control Black: AIRS Solid: cntl Dotted: AIRS Black: 12 h Red: 36 h Small Positive Impact T254/L64 Parallel testing has begun 254 out of 281 channels Initial results show small positive/neutral impact Testing will continue and additional improvements and uses (such as for SST and cloud analysis) will be developed Full data assimilation implementation scheduled for 1 st Quarter FY05 Neutral Impact

21. Results from the Winter Storm Reconnaissance (WSR) program 2004 Lacey Holland SAIC at EMC/NCEP Zoltan Toth EMC/NCEP/NWS Jon Moskaitis MIT Sharan Majumdar Univ. of Miami Craig H. Bishop NRL Roy Smith NCO/NCEP/NWS Acknowledgements NWS field offices, HPC/NCEP and SDMs NOAA G-IV and the USAFR C-130 flight crews CARCAH (John Pavone) Jack Woollen - EMC Russ Treadon - EMC Mark Iredell - EMC Istvan Szunyogh – Univ. of Maryland

22. About the Winter Storm Reconnaissance (WSR) Program 21 Jan – 17 March 2004 Dropwinsonde observations taken over the NE Pacific by aircraft operated by NOAA’s Aircraft Operations Center (G-IV) and the US Air Force Reserve (C-130s). Observations are adaptive – –Collected only prior to significant winter weather events of interest –Areas estimated to have the largest forecast impact Previous forecasts improved in 60-80% of targeted cases (in past studies) Operational at NCEP since January 2001 2004: 36 flights, around 720 dropsondes

23. Evaluation methodology Direct comparison with GFS –Cycled analysis and forecast –T126/L28 Contral uses all operationally available data (including dropsondes); Experiment excludes only dropsonde data Verify against observations over the pre- selected area of interest –Rawinsonde observations for surface pressure, 1000-250 hPa temperature, and other fields –Rain gauge data for precipitation

24. WSR 2004 Results Surface Pressure Temperature 21 improved 1 neutral 13 degraded 21 improved 1 neutral 13 degraded Wind 24 improved 1 neutral 10 degraded Humidity 21 improved 0 neutral 14 degraded

25. Individual Case Comparison 1 denotes positive effect 0 denotes neutral effect -1 denotes negative effect 2004012900 1 1 1 1 2004020100 -1 1 1 1 2004020200 1 1 1 1 2004020500 1 -1 1 1 2004020500 0 1 1 1 2004020800 -1 1 1 1 2004020900 1 1 1 1 2004021000 -1 1 1 1 2004021300 1 1 -1 1 2004021500 1 1 1 1 2004021600 1 1 0 1 2004021700 1 1 1 1 2004021800 1 -1 1 1 2004022100 -1 0 -1 -1 2004022200 1 1 1 1 2004022300 1 -1 -1 -1 2004022400 1 1 1 1 2004022500 1 -1 1 1 2004022600 1 1 1 1 2004022600 -1 -1 1 -1 2004022600 1 1 -1 1 2004022700 1 1 1 1 2004022800 -1 -1 1 -1 2004030200 1 1 1 1 2004030600 -1 -1 -1 -1 2004030700 -1 -1 1 -1 2004030700 -1 -1 -1 -1 2004031200 1 1 1 1 2004031300 1 1 1 1 2004031300 -1 -1 -1 -1 2004031500 -1 -1 -1 -1 2004031700 1 1 1 1 OBS. DATE P, T, V, OVERALL 24 OVERALL POSITIVE CASES. 0 OVERALL NEUTRAL CASES. 11 OVERALL NEGATIVE CASES. 69% improved 31% degraded

26. Future Work Examine the effect of dropsondes on precipitation Examine negative cases in detail Improve targeting method by reducing spurious or misleading guidance due to statistical sampling problems Investigate possibility of future NCEP Atlantic Winter Storm experiment

27. ATReC Prelim. Results Methodology similar to WSR

28. ATReC Results 35 improved 2 neutral 10 degraded Surface Pressure Temperature 42 improved 0 neutral 5 degraded Wind 37 improved 0 neutral 10 degraded Humidity 43 improved 0 neutral 4 degraded

29. Individual Case Comparison CASE P, T, V, Q, OVERALL 1 1 1 1 1 1 2 -1 1 -1 -1 -1 3 1 1 1 1 1 4 1 1 -1 1 1 5 1 1 1 1 1 6 -1 1 1 1 1 7 1 1 1 1 1 8 -1 1 1 1 1 9 1 1 1 1 1 10 1 1 1 1 1 11 1 1 1 1 1 12 1 1 1 1 1 13 1 1 1 1 1 14 1 1 1 1 1 15 1 1 -1 1 1 16 1 1 1 1 1 17 1 -1 1 1 1 18 -1 -1 -1 1 -1 19 1 1 1 1 1 20 1 1 1 1 1 21 1 -1 -1 1 0 22 1 1 1 1 1 23 1 1 1 1 1 24 0 1 1 -1 1 25 1 1 1 1 1 26 -1 1 1 1 1 27 1 1 1 1 1 28 1 1 1 1 1 29 1 1 1 1 1 30 1 1 1 1 1 31 1 -1 1 1 1 32 1 -1 1 -1 0 33 1 1 1 1 1 34 1 1 1 1 1 35 -1 1 -1 1 0 36 -1 1 1 1 1 37 -1 1 -1 1 0 38 1 1 1 1 1 39 0 1 -1 1 1 40 1 1 -1 1 1 41 -1 1 1 -1 0 42 1 1 1 1 1 43 1 1 1 1 1 44 -1 1 -1 1 0 45 1 1 1 1 1 46 1 1 1 1 1 47 1 1 1 1 1 1 denotes positive effect 0 denotes neutral effect -1 denotes negative effect 39 OVERALL POSITIVE CASES. 6 OVERALL NEUTRAL CASES. 2 OVERALL NEGATIVE CASES. 83% improved 39% neutral 4% degraded

30. Simulation of the Coupled Atmosphere- Ocean-Land Surface System and Hindcast Skill in SST Prediction with the New Coupled NCEP Ocean-Atmosphere Model Suranjana Saha*, Wanqiu Wang*, Hua-Lu Pan* and Huug van den Dool** *Environmental Modeling Center **Climate Prediction Center NCEP, NWS, NOAA

31. Global Coupled Forecast System for S/I Climate A new global Coupled atmosphere-ocean Climate Forecast System (CFS) has recently been developed at NCEP/EMC. Components a) T62/64-layer version of the current NCEP atmospheric GFS (Global Forecast System) model and b) 40-level GFDL Modular Ocean Model (MOM, version 3) c) Global Ocean Data Assimilation (GODAS) Notes: CFS has direct coupling with no flux correction GODAS –Implemented September 2003, runs daily –Salinity analysis, improved use of altimeter data –Real time global ocean data base in WMO standard format –Ready for GODAE

32. Coupled Red: monthly bias Observed

33. Examples of ENSO events Simulated El Nino 2015-2016Simulated La Nina 2017-18 Real El Nino 1982-1983Real La Nina 1988-1989

35. Maximum Significant Wave Heights: Model vs. JASON Direct hits: Altimeter through eye and maximum waves WNA (green), NAH (red): Good track of build-up, set-down and maximum Storm’s eye (lower panel) well captured by both models Early stages missed by WNA (green): weak GFS winds, small hurricanes

36. North American Ensemble Forecast System Project Joint Canadian-US project Goals –Accelerate improvements in operational weather forecasting through Canadian-US collaboration –Seamless (across boundary and in time) suite of ensemble products Planned activities –Ensemble data exchange (June 2004) –R&D (2003-2007) Statistical postprocessing New product development Verification and evaluation –Operational implementation (2004-2008)

37. North American Ensemble Forecast System Project (cont) Benefits –Improved ensemble composition Two independently developed systems using different –Analysis techniques –Initial perturbations –Models Enhanced quality –Development of generalized procedures applicable to other Centers’ ensembles, e.g. ECMWF JMA FNMOC –Broader researcher involvement –Shared development tasks (increased efficiency) –Seamless operation product suite

38. North American Ensemble Forecast System Project (cont) Potential expansion –Shared interest with THORPEX goals Improvements in operational forecasts International collaboration Entrain broader research community –Multi-center ensemble system NCEP, MSC, ECMWF, JMA, FNMOC