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Impacts of Rossby Wave Packets on Forecast Uncertainties and Errors

Impacts of Rossby Wave Packets on Forecast Uncertainties and Errors. Brian A. Colle, Edmund K.M. Chang, and Minghua Zheng School of Marine and Atmospheric Sciences Stony Brook University Stony Brook, New York, USA. NROW 14 10 December 2013. Outline. Motivation

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Impacts of Rossby Wave Packets on Forecast Uncertainties and Errors

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  1. Impacts of Rossby Wave Packets on Forecast Uncertainties and Errors Brian A. Colle, Edmund K.M. Chang, and MinghuaZheng School of Marine and Atmospheric Sciences Stony Brook University Stony Brook, New York, USA NROW 14 10 December 2013

  2. Outline • Motivation • Relationship between RWPs and Forecast Errors -- for relatively large East coast medium range errors -- evolution of errors relative to a moving RWP -- ensemble spread starting with RWP over Pacific • Ensemble Wave Packet Page and Updates to Ensemble Sensitivity Page • Summary and Ongoing Research

  3. What are Rossby Wave Packets? An example (adapted from Chang 1993) A B A B C B C D C D E Contours: 300 hPa Z D E

  4. A B A B C B C D C D E Contours: 300 hPa Z Shades: 300 hPa v D E

  5. Hovmoller (Time-Longitude) diagram A B C D E

  6. Climatology of RWPA(Souders et al. 2013 –submitted to MWR) Max: ~18m/s across NA Min: < 8m/s central Asia Unit: m/s 6

  7. From THORPEX International Science Plan (Shapiro and Thorpe, 2004) RWPs have frequently been linked to high impact weather downstream Cyclogenesis near Japan Flooding over Europe

  8. Initial analysis error structure 12-hr forecast uncertainty 24-hr forecast uncertainty Taken from U.S. TPARC science plan. Adopted from Hakim (2005)

  9. Another example showing the similarity between RWPs and ensemble sensitivity signals (2010 Christmas snowstorm) Sensitivity Magenta: region for calculating EOF of ensemble meridional wind v300. State field: v300 Wave packet Downstream development Group V > Phase V Unit: m/s From Zheng et al. (2013)

  10. Forecast Errors • Data 1: • GFS analysis and 7-day control run forecast for 5 cool seasons (2007-2012) • Parameters: geopotential height at 300 hPa (Z300) • Data 2: • RWP amplitude: calculated by Matthew Souders using u,v and Z at 300 hPa • Method • Composite method • RMSE of Z300 • N: total grids number • Absolute error

  11. Preliminary results: verification region and large error cases selections Verification region (VR): 22°N-63°N, 99°W-46°W Inter Quartile Range (IQR): Third Quartile - First Quartile Drop-out cases: RMSE>=mean+1.2*IQR (75 cases) Good forecasts: RMSE<=mean-1.0*IQR (79 cases)

  12. Composited RWPA anomaly for large error cases Initial Positive RWPA Anomaly Develop and Propagate into VR Unit: m/s Thepurplecontour corresponds to 95% significance level

  13. For 46 most robust wave packets over NAmer (2007-2013): Composited RWPA (shaded, [m/s]) and the normalized 300 Z forecast error (Magenta Contours, forecast error divided by maximum error,[%]). Errors at day -3 to +3 correspond to 0.5 day to 6.5 day forecast errors.

  14. For 46 most robust wave packets over NAmer (2007-2013): Composited RWPA (shaded, [m/s]) and the normalized 300 Z forecast error (Magenta Contours, forecast error divided by maximum error,[%]). Errors at day -3 to +3 correspond to 0.5 day to 6.5 day forecast errors.

  15. Wave packet relative errors for same 46 cases, which includes the composited RWPA and errors with the centers representing the weighted wave packet centers at each step. Wave packet amplitude (shaded, [m/s]) and forecast errors (magenta contour, [%]). The composites are centered at the weighted centers of RWPA at each step.

  16. 25 coherent RWPS starting from central Pacific are selected to calculate the RWPA mean and 300Z normalized spread divided by max spread (%) using the 20-member GEFS from day 0 to day 6.5

  17. Days 3-5

  18. Days 6-6.5

  19. Discussions: sensitivity to VR―composited RWPA anomaly for large error cases (EUROPE) Day 0 Day 4 Day 1 Day 5 Very strong Positive RWPA Anomaly Develop and Propagate into VR Day 2 Day 6 Unit: m/s The greencontour corresponds to 95% significance level Day 3 Day 7

  20. (http://wavy.somas.stonybrook.edu/wavepackets/home.html). Figure 8 shows the cover of the page, in which users can

  21. Updates to Ensemble Sensitivity Web Page http://dendrite.somas.stonybrook.edu/CSTAR/Ensemble_Sensitivity/EnSense_Main.html ). Figure 8 shows the cover of the page, in which users can

  22. Summary • Composited RWPA for GFS large error cases over U.S. East Coast shows a positive RWPA anomaly originating from eastern Asia and propagating across the Pacific into the verification box. Large error cases over Europe also show a propagating RWPA positive anomaly upstream. -- These results suggest that large error cases are associated with the presence of aenhanced RWPA upstream. • Early in the forecast over the Pacific, the largest errors tend to be in the middle of the RWP, but as the packet spreads across North America in the medium range, the largest errors (and ensemble spread) occur more around the leading edge of the packet (**Could be an important signal to look for while forecasting…)

  23. Ongoing Research • Examine ensemble forecasts using TIGGE data • To study the relationship between RWPs and forecast uncertainties • Study how forecast uncertainties and errors grow in multi-model • Compare ensemble outputs from different operational models

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