Improving High Impact Weather Forecasts by Adaptive Observing Methods

1. Improving High Impact Weather Forecasts by Adaptive Observing Methods Yucheng Song NOAA/NWS/NCEP/EMC Dave Novak NOAA/NWS/NCEP/HPC

2. Outline Concept Winter Storms Reconnaissance Program East Coast Reconnaissance Program Verification Future Directions A Typical Case (Sharan Majumdar – U of Miami)

3. Concept Take observations in key sensitive areas (target area) that will improve the forecast in the verification region. Target Obs. Area Verification Area Need additional observational platforms (primarily aircraft) Need method to determine where additional observations should be taken To Tv Observation errors taken from GDAS Ensemble forecast perturbation given

4. ta ta tv tv Poor Good ETKF Summary map of Signal Variance Sq, for many different q. Aim: to improve a 24-hr forecast of a frontal system on the West Coast How is the ETKF guidance constructed?

5. ECMWF/UKMO DTS system used as a reference in winter T-PARC Singular vector vs. ET KF method

6. NCEP Targeted Observation Programs Winter Storms Reconnaissance Program Time Period: January 15 – April 1 Operations Area: North Pacific to improve downstream forecasts Observations: Dropsondes from aircraft Method: Ensemble Transform Kalman Filter (ETKF) East Coast Winter Reconnaissance Program Time Period: November - March Operations Area: Gulf and western Atlantic to improve short range forecast Observations: Dropsondes from C-130 Method: Transitioning from subjective to ETKF Hurricane Synoptic Surveillance Time Period: June - November Operations Area: Atlantic Observations: Dropsondes from aircraft. CONUS Radiosondes Method: Combination of uniform sampling/subjective analysis/ensemble spread

7. Operational MethodEnsemble Transform Kalman Filter • Identify an area and forecast time we are trying to improve. • Uses the ECMWF (51 members), NCEP (4 cycles = 84 members), and Canadian (2 cycles = 42 members) ensembles for a total of 177 members, at 2.5 degree resolution. • Hypothetical observations of tropospheric wind and temperature are considered over every 9 grid points in the search domain.  The aim is to find the optimal "target area" in which the chosen forecast (over DCA in this case) is most expected to benefit from the targeted observations. • Guidance is quantified in terms of changes in the column energy norm, • defined by the temperature and wind at 250, 500, and 850 mb. • Guidance can be displayed on a summary map Majumdar, S. J., Bishop, C. H., Etherton, B. J., Toth, Z. 2002: Adaptive Sampling with the Ensemble Transform Kalman Filter. Part II: Field Program Implementation. Mon. Wea. Rev., 13, 1356-1369.

8. Example Summary Map for Feb 5-6 East Coast snowstormTarget Time: 12 UTC 4 Feb Verifying Time 12 UTC 6 Feb Summary map shows the expected forecast error reduction in the verification region due to hypothetical observations at all points Red circle shows our verification area Black contours show us the sensitivity – larger the values the more forecast spread reduction observations in those areas at the Target time would have on the key east coast area. Possible flight tracks are also overlaid.

9. Interpretation 5-6 Feb 2010 Obs taken at 00 UTC Feb 4 would be most effective over Mexico/southern Plains (the storm itself). The east coast forecast is also dependent on two Pacific storms, likely due to downstream development.

10. Winter Storm Reconnaissance Program Objective: Improve Forecasts ofSignificant Winter Weather Events Through TargetedObservations in Data Sparse Northeast Pacific Ocean in the 24-96 hour lead time range over CONUS Adaptive approach to collection of observational data: 1) Only prior to significantWinter Weather events of Interest 2) Only in areas that influence high impact event forecasts Results: 70+% of Targeted Numerical Weather Predictions Improve 10-20% error reduction for high impact event forecasts 12-hour gain in predicting high impact events – earlier warnings possible WSR data has 2.7 times more impact per obs than RAOB observations Operational since January 2001 2008 P-3 out of Portland, WA

11. Winter Storm Reconnaissance Program Operational Procedures: By 15 UTC: HPC, SPC, and EMC provide SDM points for storms of interest & priority From: Frank.Rosen@noaa.gov Date: 02/05/2010 08:33 AM To: Sdm@noaa.gov, Zoltan.Toth@noaa.gov, Yucheng.song@noaa.gov Good morning, Priority.........VT.........Verification Area.............Remarks High..........02/11/00Z....44N/74W......Major East-Central US Storm Mod...........02/11/12Z....56N134W......SE AK hvy pcpn Mod...........02/11/12z....27N81W.....Mdt/hvy central Fl rainfall 15-18 UTC: SDM selects points and runs ETKF method for those points. SDM, EMC, and others evaluate output and determine flight paths. By 18:30 UTC: SDM tasks flights taking off 1-2 days later in the future. Operational since January 2001 Can view real-time information on the web page: http://www.emc.ncep.noaa.gov/gmb/targobs/

12. Forecast uncertainties

13. Applying ETKF targeting technique- Dropsondes to be made by G-IV Storm The ETKF spotted the best targeting track Expected error reduction propagation based on the best flight track Evaluated by 3-level energy norm of u, v, T

14. East Coast Winter Reconnaissance Program Objective: Improve Forecasts ofSignificant East Coast Winter Storms Through TargetedObservations over the Gulf and Western Atlantic in the 12-60 hour lead time range. Time Period: November - March Operations Area: Gulf and western Atlantic to improve short range forecast Observations: Dropsondes from C-130 Method: Transitioning from subjective to ETKF Storm Selection: HPC Model Diagnostics Forecaster selects storms of interest Verification: To begin this season

15. Tracks modified – allows up to 10 drops per flight

16. Inclusion of C-130s from Gulf of Mexico An example for Winter 2010 East Coast snow storm on Feb 6

17. 2010 Targeted Eastern US Storms Jan 16 Jan 18 Jan 25 Jan 31 Medium Range Short Range Feb 5-6 Feb 10-11 Feb 22 March 2 March 8 March 22 March 30 Dec 19-20 Dec 31 Feb 5-6 Feb 10-11 Feb 22 Feb 24 March 2

18. Winter Storms Reconnaissance Program Verification

19. Verification Approaches Case studies Subjective comparison of analyses/forecasts with/without adaptively collected data Can we predict forecast impact of adaptive data? Compare spatiotemporal evolution of predicted and observed data impact How much forecasts improved? Compare errors in forecasts from analyses with/without targeted data Use both observations and analysis fields as proxy for truth Are benefits from targeted obs exceed its costs? Beyond scope of this study Must be evaluated for all components of observing network Importance of OSSE studies and socio-economic considerations

20. 0.5 0.4 Fraction of degraded analyses 0.3 0.2 0.1 0.0 0.01 0.1 1 10 100 How can ‘good observations’ have a negative impact? The fact that data assimilation relies on statistics of background and observation errors implies a distribution of beneficial and non-beneficial impacts… …the fact that we don’t know these statistics accurately increases the likelihood of there being non-beneficial impacts • A really accurate observation has a better chance of improving an analysis than a less accurate observation. • But degradation can still occur. Accurate Ob Calculation by Mike Fisher, ECMWF More accurate observations More accurate background

21. Typical adaptive observation data impact (2007) 24h moist total energy error norm (J kg-1) Error Reduction (x 1.0e5) Sum Impact Error Reduction (x 1.0e5) Forecast fits to RAOB (RMSE) verification summed over vertically (1000-100mb) and horizontally 1000km radius (CONUS) by NCEP/GFS Per-Ob 1-31 Jan 2007 (00UTC analyses) Rolf Langland

22. GFS data impact Signal variance from ET KF Forecastimprovement/ degradation 22

23. Targeted Observation Verification Winter T-PARC (2009) NCEP WSR Program (2004-2007) OVERALL 75% cases improved 0% cases neutral 25% cases degraded OVERALL 71% cases improved 1% cases neutral 28% cases degraded Better forecasts translate into a 12-hour gain in skill (48-hr forecast with targeted data as skillful as 36-hr forecast without) 23

24. T-PARC 2009 Extensive observational platforms during T-PARC winter phase allow us to track the potential storms and take additional observations as the perturbation propagate downstream into Arctic and North America Multi-agency coordination Lead by NCEP Day -4-6 RAWIN Russia Alaska VR Day -5-6 E-AMDAR CONUS VR Day -1-3 C-130 G-IV Day -3-5 G-IV Winter T-PARC 2009 – A THORPEX field campaign

25. Typical adaptive observation data impact W-TPARC error reduction is NEGATIVE, units are J/ kg Forecast fits to RAOB (RMSE) verification summed over vertically (1000-100mb) and horizontally 1000km radius (CONUS) by NCEP/GFS TARGETED DROPSONDE IMPACT ON 24H FORECAST ERROR IN NOGAPS/NAVDAS Rolf Langland

26. Strong Linkage with downstream wave packet development GDAS: 300mb v (ave:30-60N) Sampled Sampled 2009 Sampled Sampled

27. Large area of sensitivity – which may not be covered well by the limited dropsondes from manned aircraft Satellite data processing? Atlantic coast braces for biggest snowstorm of the season March 2, 2009, heavy rainfall near CA on the 27th, Feb

28. March 1, 2009 CA Storm • Weather event with a large societal impact • Each GFS run verified against its own analysis – 60 hr forecast • Impact on surface pressure verification • RMS error improvement: 35.2% • (7.07mb vs. 9.56mb) • Targeted in high impact weather area marked by the circle 2009030100 (+108) Surface pressure from analysis (hPa; solid contours) Forecast Improvement (hPa; shown in red) Forecast Degradation (hPa; blue) 28

29. March 1, 2009 CA Storm sequence Surface Pressure Overwhelming improvement! Forecast improvement Red: forecast improvement Blue: degradation Area averaged fit of surface pressure: Difference of forecast-analysis 29

30. 250 mb Height Improvement/Degradation ETKF signal variance

31. Future Directions Implement improved tracks and method for tasking short-range flights over Gulf and western Atlantic Run ETKF using mesoscale ensembles (SREF) Possibly improved estimates of the sensitivity of mesoscale features Explore adaptive satellite data thinning Process more satellite data in targeted regions Exploring use of Unmanned Aerial Vehicles (NASA Global Hawk) Longer duration Higher altitude Hazardous weather capable

32. Characteristics of ETKF guidance for medium-range forecasts of high-impact winter weather Sharan Majumdar (RSMAS/U.Miami) Kathryn Sellwood (ex RSMAS, now HRD) Dan Hodyss (ex RSMAS, now NRL) Zoltan Toth (ESRL) Yucheng Song (EMC) Project funded by NOAA THORPEX CSTAR Tutorial, 10/26/10

33. ta ta tv tv Poor Good ETKF Summary map of Signal Variance Sq, for many different q. Aim: to improve a 24-hr forecast of a frontal system on the West Coast How is the ETKF guidance constructed?

34. Typical Case Cold Pacific storm made landfall on west coast on 22 Feb 2007, then developed into a major snowstorm in the central high Plains. How to make a decision on temporally continuous deployments of G-IV, unmanned aircraft, or satellite rapid-scan winds 10 days in advance? Continuous series of ETKF guidance maps illustrating sensitivity between 0-7 day lead-time.

36. -7 days -3 days -6 days -2 days -5 days -1 day -4 days -0 days

37. ETKF Japan USA v300 Wavepacket amplitude

38. Q2: Can we trace upstream forecast sensitivity up to a week? A2: Yes, surprisingly so. For forecasts of high-impact weather over North America, the sensitivity can often be traced back to Japan at 4-7 days lead time, in zonal flows. Q1: Can we predict whether observations will be useful/useless for 3-7 day forecasts? A1: Yes, particularly in predominantly zonal flow regimes.

39. Relevant Publications • Petersen, Majumdar and Thorpe (QJRMS, 2007): Properties of ETKF targets in short-range. • Toth and Majumdar (BAMS, 2007): NOAA THORPEX Science Workshop summary. • Hodyss and Majumdar (QJRMS, 2007): ‘Contamination’ of data impact. • Sellwood et al. (QJRMS, 2008): Predicting ETKF signal variance out to 6 days. • Majumdar et al. (MWR, 2010): Properties of 3-7 day ETKF targets.