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1. Data preprocess and choose

1. Data preprocess and choose. 06 UTC. 09 UTC. 12 UTC. 15 UTC. 18 UTC. Observed Mosaic radar reflectivity data at 3km height level. 88D2WRF QC. RAW DATA. After manual QC. Radar radial velocity QC. Prepbufr: Mesonet site location. Stage II vs Stage IV.

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1. Data preprocess and choose

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  1. 1. Data preprocess and choose

  2. 06 UTC 09 UTC 12 UTC 15 UTC 18 UTC Observed Mosaic radar reflectivity data at 3km height level

  3. 88D2WRF QC RAW DATA After manual QC Radar radial velocity QC

  4. Prepbufr: Mesonet site location

  5. Stage II vs Stage IV

  6. 12 hour accumulated precipitation from06 UTC to 18 UTC, Aug 19,2007 Stage II Stage IV Stage IV – Stage II

  7. 2. Experiment design • 2.1 WRFV2.2 (0~2h 10m interval)

  8. 2.2 WRFV3 (4~6h 10m interval)

  9. 2.3 WRFV3 (different two-hour-long assimilation windows from 00 UTC to 06 UTC)

  10. 3. Simulation result and verification • path and strength • 12 hour accumulated precipitation and its verification (grid point) (stage IV as truth)(grid_stat, wavelet_stae, or mode?) • Hourly reflectivity at 3km height and its verification (grid point) (Mosaic reflectivity as truth) (grid_stat, wavelet_stae, or mode?) • Mesonet verification including surface temperature, sea level pressure and 10m wind vector (scatter point) (mesonet-data-web or mesonet-prepbufr as truth?)(point_stat) • Radar emulate and its verification ( radar coordinate)(single Doppler radar data as truth) • 1km Vs 3km

  11. 3.1 12 hour result

  12. 3.1.2 WRF result

  13. 12 hour accumulated precipitation from06 UTC to 18 UTC, Aug 19,2007 St2 St4 C00 C06 A02 A24 A46

  14. Moving Path and Min Sea level pressure

  15. 3.1.2 Grid stat a. Examine the different threshold b. Using different threshold for forecast and observation c. Examine the different neighborhood width d. Calculate various scores

  16. The same threshold C00 A02 A24 A46

  17. Different threshold C00 A02 A24 A46

  18. The same threshold C00 A02 A24 A46

  19. Different threshold C00 A02 A24 A46

  20. Conclusion The intensity of control experiment are more like the observation. This explain why using different threshold the FSS score drops. But since the position are a little too north and pattern are not like, it got the lowest value when compared to other experiments. The FSS score show a big improvement when using different threshold for the forecast (larger threshold) and observation(relative small threshold). That may due to the rainfall have been greatly enhanced after assimilation of reflectivity data. Although the pattern of experiment A24 are more like the observed. It didn’t got the highest value. That because the position are a little too south when compared to the experiment A02. Although the method of neighborhood consider the position deviation. It may still affected by the displacement error. Also, the highest score are more likely to show when the threshold are lower and the grid number of neighborhood are larger. That partly due to the forecast rain cover the entire Oklahoma region.

  21. Conclusion The score was affected by the width of neighborhood, especially the heavy rain. It may not obvious in this case when the threshold below 50, that may due to the rain cover the entire Oklahoma region. The score are more sensitive to the threshold. The control experiment shows highest score when the threshold are lower but lowest when the threshold are larger. That indicates the control experiment have capture the small rain better. The assimilation of radar data improve the result of heavy rain but due to introduce too much moist in the assimilation of radar reflectivity data, it produce too much rain.

  22. 3.1.2 Mode a. Examine the different convolution width b. Examine the different convolution threshold c. Calculate the interest value between objects

  23. 0.9851 R=5 T=0.0 0.9826 R=10 T=0.0 0.9866 R=15 T=0.0 0.9921 R=20 T=0.0 0.9960 R=25 T=0.0

  24. 0.9898 R=5 T=5.0 0.9799 R=10 T=5.0 0.9796 R=15 T=5.0 0.9846 R=20 T=5.0 0.9904 R=25 T=5.0

  25. 0.9967 R=5 T=10.0 0.9982 R=10 T=10.0 0.9929 R=15 T=10.0 0.9953 R=20 T=10.0 0.9946 R=25 T=10.0

  26. 0.9357 R=5 T=15.0 0.9367 R=10 T=15.0 0.9438 R=15 T=15.0 0.9513 R=20 T=15.0 0.9589 R=25 T=15.0

  27. 0.9210 R=5 T=25.0 0.9126 R=10 T=25.0 0.9065 R=15 T=25.0 0.9073 R=20 T=25.0 0.9038 R=25 T=25.0

  28. 0.9083 R=5 T=50.0 0.8873 R=10 T=50.0 0.8817 R=15 T=50.0 0.8734 R=20 T=50.0 0.8614 R=25 T=50.0

  29. 0.8467 R=5 T=100.0 NA R=10 T=100.0 NA R=15 T=100.0 NA R=20 T=100.0 NA R=25 T=100.0

  30. C00 A02 A24 A46

  31. Conclusion A24 got the highest interest vaule, although the position are a little to north when compared to the A02.

  32. 3.1.3 Wavelet a. Using different Wavelet method b. Using the same Wavelet method but with different k number c. Calculate the MSE and intensity skill score of different threshold

  33. HARR(2) 0.1070 Centered-HARR(2) 0.1877 Daubechies 0.1128 Centered-Daubechies(4) 0.1331 Bspline(103) -0.344 Centered-Bspline(103) -0.236

  34. Daubechies(4) 0.1128 Daubechies(8) 0.0718 Daubechies(12) 0.0043 Daubechies(14) 0.0300 Daubechies(16) -0.088 Daubechies(20) 0.0475

  35. Conclusion The centered method got a little higher intensity skill score value than the corresponding method. And Centered-Harr method shows the highest intensity skill score value of all the method.

  36. C00 A02 A24 A46

  37. OB C00 A02 A24 A46

  38. Conclusion When the threshold <= 25, the control experiment performance better than with the data assimilation. However, for the threshold larger than 50. The experiment A02 and A24 got the highest intensity skill score values. The more closer assimilation time windows to the verification period, the greater bias. We can see the from the pie picture, the small scale(3, 6 , 12km) are occupy more and more energy when the time assimilation windows get closer. That may due to the assimilation of radar data introduce small scale feature to the model. In all, the A02 get the highest intensity skill score when the threshold >=50. A24 performance better than without data assimilation. And A46 get the lowest.

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