1. A Space and Time Multi-scale Analysis System (STMAS) for Tropical Cyclone Data Assimilation Yuanfu Xie Forecast Applications Branch Global Systems Division Earth System Research Laboratory OAR/ESRL/GSD/Forecast Applications Branch

2. Outline Introduction to STMAS; A multigrid implementation; Preliminary case studies and applications Current status Future work Observation Systems Simulation Experiments OAR/ESRL/GSD/Forecast Applications Branch

3. Introduction to STMAS Motivation: Difficulty for estimating a flow, location and case dependent error covariance for variational analysis; Approach: combining traditional objective analysis and modern variational analysis into a multiscale variational and ensemble method. Advantages: More effective for estimating error covariance after long wave information retrieved. OAR/ESRL/GSD/Forecast Applications Branch

4. Advantages of Objective Analysis Multiscale and inhomogeneous analysis Retrieval of resolvable information from observations without requiring accurate error covariance OAR/ESRL/GSD/Forecast Applications Branch Advantages of a 3DVAR/4DVAR Better handling remotely sensed data Possible dynamically balanced analysis Possible use of covariance

5. Limitations of an objective analysis Dynamic balance as a post-process (loss info) Inconvenient for remotely sensed data Incapable of handling cross error covariance OAR/ESRL/GSD/Forecast Applications Branch Limitations of a 3DVAR/4DVAR Heavily dependent of error covariance Extremely expensive to have an accurate error covariance

6. Relation between LAPS/3-4DVAR A response analysis of a recursive filter and a Barnes analysis demonstrates the relation OAR/ESRL/GSD/Forecast Applications Branch

7. A multiscale analysis system STMAS uses a multigrid technique and solves a variational analysis from coarse grids to fine grid by halving the grid sizes; From the coarsest grid to the finest grid, it can use error variance or covariance if available and act like a generalized LAPS analysis but with dynamic balances; At the finest grid, it becomes a 3-4DVAR; however, since longer waves retrieved at coarser grids, the covariance matrix is much less expensive to compute and even ensemble filters can be applied at this finest grid level. OAR/ESRL/GSD/Forecast Applications Branch

8. Background Observation Longer wave Background Observation Longer wave Resolvable Information for a Given Observation Network Difference on longer waveDifference on shorter wave OAR/ESRL/GSD/Forecast Applications Branch

9. A multiscale analysis system (cont’) OAR/ESRL/GSD/Forecast Applications Branch Sequence of 3-4DVARs with proper balances Similar to LAPS analysis with less requirement of covariance Standard 3-4DVAR With a band covariance Possible ensemble Filter application Long wavesShort waves

10. An idealized multiscale case Left: Mesonet surface stations; Right: An analysis function

11. An idealized multiscale case (cont.)

12. A Recursive Filter 3DVAR A single 3DVAR And B is approximated by a recursive filter (Hayden and Purser 95):

13.  =0.5 0.7 0.9 A single 3DVAR with different  These analyses tend to approximate the truth:

14. Different Implementations of STMAS Recursive filterWaveletMultigrid Since all implementations tend to capture long to short waves, STMAS yields similar results of multiscale analysis.

15. Applications Since 2004, STMAS surface analysis has been running at MIT/LL and GSD http://laps.noaa.gov/request/nph-laps.cgi supporting FAA applications, storm boundary detection and improving short term forecast.http://laps.noaa.gov/request/nph-laps.cgi Central Weather Bureau in Taiwan uses the surface analysis for reanalysis; is experimenting STMAS 3D for improving forecast. National Marine Data and Information Service in China uses STMAS for oceanic reanalysis. NOAA/MDL has running STMAS surface in real time for experimental nowcasting. OAR/ESRL/GSD/Forecast Applications Branch Publications: Li et al, 2008; He et al, 2008, Li et al, 2009 and Xie et al. 2010

16. Status of STMAS Surface Analysis for Storm/Gust Front Detection Space and Time Multiscale Analysis System (STMAS) is a 4-DVAR generalization of LAPS and modified LAPS is running at terminal scale for FAA for wind analysis. STMAS real time runs with 15 minute latency due to the observation data: STMAS surface analysis is running in real time over CONUS with 5-km resolution and 15-minute analysis cycle. It is so efficient that it runs on a single processor desktop; Real time 5-minute cycle run of STMAS assimilating 5-minute ASOS data also on single processor. Note: Modified LAPS is running at 40+ sites at terminal scales for FAA in real time. STMAS surface analysis with 5 minute latency with (MPI/SMS) and targeting at 2-km resolution; 5-minute cycle; over CONUS domain; assimilating 1-minute ASOS; using HRRR as background. CONUS 15-min cycle 5-min ASOS, 5-min cycle OAR/ESRL/GSD/Forecast Applications Branch

17. Storm Boundary Detection (1) OAR/ESRL/GSD/Forecast Applications Branch

18. Storm Boundary Detection (2) OAR/ESRL/GSD/Forecast Applications Branch

19. Preliminary case studies and applications for tropical cyclones STMAS is at its early stage of development and test; Case studies and applications are to identify issues and improve the analysis; Tropical cases: Dennis, Morakot and Katrina are selected in these studies; Some expected features (advantages) have been observed and some issues found. OAR/ESRL/GSD/Forecast Applications Branch

20. fdfdfd Dennis GOES/POES Soundings Cloud-Drift Winds ACARS Temperature ACARS/ Radar Wind

21. STMAS Dennis analysis (850mb): capable of relocate hurricane center STMAS (V)Background (V)

22. STMAS shows Strong ~35m/s S wind AUG 07 06UTC 1000mb

23. GFS Laps STMAS Wrf 24hrs 9km simulations with gfs, Laps, STMAS as an initial condition

24. Katrina Pre-balanced 950-mb wind speed and height. Balanced 950-mb wind speed and height.

25. KATRINA LAPS / HRS(2005)

26. Intensity: WRF Katrina forecast by STMAS Wind Barb, Windspeed image, Pressure contour at 950mb Surface pressure j

27. STMAS-WRF ARW: Hurricane Katrina experiment OAR/ESRL/GSD/Forecast Applications Branch STMAS balance produces no initialization shock waves No cycle: GFS 0.5 degree forecast as background Cycle run: WRF 5km cycle forecast as background

28. Current Status STMAS surface: – Real time run: univariate analysis; – Developing a multivariate analysis with some dynamic balances and a scheme handling complex terrain. STMAS 3D analysis: (prototype of 4DVAR) – Simple balances: continuity, geostrophic and hydrostatic; – Assimilating: radar, SFMR and all in-situ data. OAR/ESRL/GSD/Forecast Applications Branch

29. LAPS III Configuration Data Ingest Intermediate data files LAPSGSI Data STMAS3D Trans Post proc1Post proc2Post proc3 Model prep WRF-ARWMM5WRF-NMM Forecast Verification Error Covariance OAR/ESRL/GSD/Forecast Applications Branch

30. Future Developments Cycling LAPS/STMAS analysis and forecast at terminal scales (0.5-1 km) and 15-min to half hour cycle; Real time downscaling system with automatically handling terrain, stability, diabatic heating and so on; Ensemble estimates of background and observation error covariances; Parallelization of STMAS for 5-minute cycle 2-km resolution STMAS surface analysis over whole CONUS with 1-minute ASOS data; Incorporation of fine scale topography and land-water information into STMAS analysis; Verification of short-range forecasts and systematic inter-comparison to other DA schemes; Improved radar forward operator in STMAS for better use of reflectivity and radial wind; Porting CRTM LAPS/STMAS for improving cloud analysis using satellite data; Model constraints for STMAS to improve short-range forecast (adjoint development, 4DVAR); Fine scale, hotstarted, variational ensemble forecast system. OAR/ESRL/GSD/Forecast Applications Branch

31. Observation Systems Simulation Experiments (OSSEs) An OSSE is a modeling experiment used to evaluate the impact of new observing systems on operational forecasts when actual observational data is not available. OSSEs are data denial experiments - a forecast using an operational numerical weather prediction model is repeated with and without data from the new observing system. The forecasts are compared to determine the impact of the new data. OAR/ESRL/GSD/Forecast Applications Branch

32. Hurricane OSSE at GSD OSSE for hurricanes using both global (track) and regional (intensity) models Support for Unmanned Aircraft Systems (UAS) Program – Flight planning – Instrument choices – Data assimilation for future operations Potential for WISDOM OSSE OAR/ESRL/GSD/Forecast Applications Branch

33. Flight path scenarios Track A: UAS flies at 200-300 km Track E: 200, 300 and 400km Track F: 200, 400 and 600 km Track G: 400, 600, 800 km radii from the center of the storm OAR/ESRL/GSD/Forecast Applications Branch

34. Preliminary results 12Z Aug. 5 Track forecast errors in km for various trajectory senarios and control, where AA, AAA Stand for repetitions of flight A for twice or three times Forecast tracks for circumnavigation scenarios (red) vs Nature Run best track (blue). 12Z 5 August forecast initial time. Conclusion: Trajectories with larger radii of obs tend to show greater forecast improvement for this forecast period. Greatest reduction is in along-track error, minimal reduction in cross-track error. OAR/ESRL/GSD/Forecast Applications Branch

35. Observation Systems Simulation Experiments (OSSEs) At GSD/ESRL, there are two OSSE systems designed for tropical cyclone applications. Global OSSE: – Track forecast; Regional OSSE: – Intensity forecast; Currently, we focus on UAS flight patterns and payload. They will be used to evaluate data assimilation and numerical prediction models in the future. OAR/ESRL/GSD/Forecast Applications Branch