1 / 49

Impact of AWS Data on May 28, 2010 MCV Case Simulation

Impact of AWS Data on May 28, 2010 MCV Case Simulation. Chong-Chi Tong, Ming Xue , Fanyou Kong Center for Analysis and Prediction of Storms June 2012. IA#24 - Task 2 deliverable. Due by 6/30/2012 Add auto-meteorological observations ( AWS ) for the Meiyu case of May 28-29, 2010

garret
Download Presentation

Impact of AWS Data on May 28, 2010 MCV Case Simulation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Impact of AWS Data on May 28, 2010 MCV Case Simulation Chong-Chi Tong, Ming Xue, Fanyou Kong Center for Analysis and Prediction of Storms June 2012

  2. IA#24 - Task 2 deliverable • Due by 6/30/2012 • Add auto-meteorological observations (AWS) for the Meiyucase of May 28-29, 2010 • This PPT summarizes the assimilation of AWS data set and the impact on model forecast reflectivity and hourly accumulated precipitation

  3. Experiment Design • MCV period: 0528 10Z ~ 0529 00Z (duration: 14 h) • Initial and assimilation time: 0528 12Z, 2010 • Length of forecast: 6 hours (12 - 18Z) • IC/LBC: WRF 0528 12Z (analysis), and 18Z (fcst) • Assimilated data: 4 CWB conventional radars, • Surface data (METAR, MESO, BUOY, SYNOP, SHIP), • Upper air data (AIREP, PILOT, TEMP, TEMPDROP), • and AWS data. 6-h forecast Data assimilated at

  4. Surface Observation Distribution Example: 1200 Z, May 28 Note: BUOY is not available on this time.

  5. Experiments and Comparisons • CNTL (no observation – CWB-WRF background only) • ExpR(radar) & ExpRA (radar+AWS) • ExpRS (radar+sfc) & ExpRSA (radar+sfc+AWS) • ExpRSU (radar+sfc+upp) & ExpRSUA (radar+sfc+upp+AWS) Assimilation pass order: Upp→ Sfc+AWS → Radar &adas_rangesfcqcrng = 50.E03,xyrange(1) = 100.E03,xyrange(2) = 80.E03,xyrange(3) = 60.E03, &adas_zrangezrange(1) = 300.,zrange(2) = 150.,zrange(3) = 100., &var_refilipass_filt = 5, 5, 5, hradius = 100.0, 40.0, 10.0, nradius_z = 3, 2, 2,

  6. Difference between ExpR & ExpRA(ExpRA-ExpR, valid 0528 12Z) qv diff (g/kg) Temp diff (°C) Wind spd diff (m/s) Note: All AWS station were marked with hollow circles.

  7. Reflectivity ExpR & ExpRA(initial cond: 12Z) Obs CNTL ExpR ExpRA

  8. Reflectivity ExpR & ExpRA(1-h fcst: 13Z) Obs CNTL ExpR ExpRA

  9. Reflectivity ExpR & ExpRA(2-h fcst: 14Z) Obs CNTL ExpR ExpRA

  10. Reflectivity ExpR & ExpRA(3-h fcst: 15Z) Obs CNTL ExpR ExpRA

  11. Reflectivity ExpR & ExpRA(6-h fcst: 18Z) Obs CNTL ExpR ExpRA

  12. Reflectivity ETS (20 dBZ)

  13. Reflectivity ETS (30 dBZ)

  14. Reflectivity ETS (40 dBZ)

  15. Difference between ExpRS & ExpRSA(ExpRSA-ExpRS, valid 0528 12Z) qv diff (g/kg) Temp diff (°C) Wind spd diff (m/s) Note: All AWS station were marked with hollow circles.

  16. Reflectivity ExpRS & ExpRSA(initial cond: 12Z) Obs CNTL ExpRS ExpRSA

  17. Reflectivity ExpRS & ExpRSA(1-h fcst: 13Z) Obs CNTL ExpRS ExpRSA

  18. Reflectivity ExpRS & ExpRSA(2-h fcst: 14Z) Obs CNTL ExpRS ExpRSA

  19. Reflectivity ExpRS & ExpRSA(3-h fcst: 15Z) Obs CNTL ExpRS ExpRSA

  20. Reflectivity ExpRS & ExpRSA(6-h fcst: 18Z) Obs CNTL ExpRS ExpRSA

  21. Reflectivity ETS (20 dBZ)

  22. Reflectivity ETS (30 dBZ)

  23. Reflectivity ETS (40 dBZ)

  24. Difference between ExpRSU & ExpRSUA(ExpRSUA-ExpRSU, valid 0528 12Z) qv diff (g/kg) Temp diff (°C) Wind spd diff (m/s) Note: All AWS station were marked with hollow circles.

  25. Reflectivity ExpRSU & ExpRSUA(initial cond: 12Z) Obs CNTL ExpRSU ExpRSUA

  26. Reflectivity ExpRSU & ExpRSUA(1-h fcst: 13Z) Obs CNTL ExpRSU ExpRSUA

  27. Reflectivity ExpRSU & ExpRSUA(2-h fcst: 14Z) Obs CNTL ExpRSU ExpRSUA

  28. Reflectivity ExpRSU & ExpRSUA(3-h fcst: 15Z) Obs CNTL ExpRSU ExpRSUA

  29. Reflectivity ExpRSU & ExpRSUA(6-h fcst: 18Z) Obs CNTL ExpRSU ExpRSUA

  30. Reflectivity ETS (20 dBZ)

  31. Reflectivity ETS (30 dBZ)

  32. Reflectivity ETS (40 dBZ)

  33. Hrly Rainfall ExpR & ExpRA(1-h fcst: 13Z) Obs CNTL ExpR ExpRA

  34. Hrly Rainfall ExpR & ExpRA(2-h fcst: 14Z) Obs CNTL ExpR ExpRA

  35. Hrly Rainfall ExpR & ExpRA(3-h fcst: 15Z) Obs CNTL ExpR ExpRA

  36. Hrly Rainfall ExpR & ExpRA(6-h fcst: 18Z) Obs CNTL ExpR ExpRA

  37. Hourly Rainfall ETS 2.5 mm 5.0 mm 10.0 mm 15.0 mm

  38. Hrly Rainfall ExpRS & ExpRSA(1-h fcst: 13Z) Obs CNTL ExpRS ExpRSA

  39. Hrly Rainfall ExpRS & ExpRSA(2-h fcst: 14Z) Obs CNTL ExpRS ExpRSA

  40. Hrly Rainfall ExpRS & ExpRSA(3-h fcst: 15Z) Obs CNTL ExpRS ExpRSA

  41. Hrly Rainfall ExpRS & ExpRSA(6-h fcst: 18Z) Obs CNTL ExpRS ExpRSA

  42. Hourly Rainfall ETS 2.5 mm 5.0 mm 10.0 mm 15.0 mm

  43. Hrly Rainfall ExpRSU & ExpRSUA(1-h fcst: 13Z) Obs CNTL ExpRSU ExpRSUA

  44. Hrly Rainfall ExpRSU & ExpRSUA(2-h fcst: 14Z) Obs CNTL ExpRSU ExpRSUA

  45. Hrly Rainfall ExpRSU & ExpRSUA(3-h fcst: 15Z) Obs CNTL ExpRSU ExpRSUA

  46. Hrly Rainfall ExpRSU & ExpRSUA(6-h fcst: 18Z) Obs CNTL ExpRSU ExpRSUA

  47. Hourly Rainfall ETS 2.5 mm 5.0 mm 10.0 mm 15.0 mm

  48. Summary • For ExpRA and ExpRSA, the AWS data significantly benefits the reflectivity forecasts in the first 2 hours. The benefits mitigate with larger thresholds. • For ExpRSUA,the benefits of AWS data only appear in high reflectivity (40 dBZ), but are extended to 4.5-hour long.

  49. Summary • No significant benefit were found for ExpRA and ExpRSA. Slightly higher skill for 5.0 mm/hr threshold during the first 1-2 hours • For forecasts of rainfall in moderate intensity (5.0 and 10.0 mm/hr), ExpRSUA gives the best ETS scores throughout the entire 6-hour forecast.

More Related