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ENHANCEMENTS OF THE NCAR AUTO-NOWCAST SYSTEM BY USING ASAP AND NRL SATELLITE PRODUCTS

ENHANCEMENTS OF THE NCAR AUTO-NOWCAST SYSTEM BY USING ASAP AND NRL SATELLITE PRODUCTS. Huaqing Cai, Rita Roberts, Cindy Mueller and Tom Saxen National Center for Atmospheric Research Boulder, Colorado USA. Acknowledgments. John Mecikalski and Kristopher Bedka Eric Nelson and Niles Oien.

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ENHANCEMENTS OF THE NCAR AUTO-NOWCAST SYSTEM BY USING ASAP AND NRL SATELLITE PRODUCTS

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  1. ENHANCEMENTS OF THE NCAR AUTO-NOWCAST SYSTEM BY USING ASAP AND NRL SATELLITE PRODUCTS Huaqing Cai, Rita Roberts, Cindy Mueller and Tom Saxen National Center for Atmospheric Research Boulder, Colorado USA

  2. Acknowledgments • John Mecikalski and Kristopher Bedka • Eric Nelson and Niles Oien

  3. How the NCAR Auto-Nowcaster (ANC) Works • Identify a set of predictor fields • Apply a membership function to each predictor field to obtain the interest field • Assign a weight to each interest field, add all interest fields together using fuzzy logic algorithm • A forecast is produced based on the final smoothed interest field

  4. NCAR Auto-Nowcast System Predictor Fields Cumulus development Boundary characteristics Satellite Cloud Typing B-L characteristics Storm motion and trends Large-Scale RUC Data

  5. How the Satellite Data Are Being Used in the Current Auto-Nowcaster • Satellite Cu field ( weight = 0.15) • Satellite clear field (weight = 0.4) • 15 min IR (10.7 µm) Rate of Change (ROC) (weight = 0.15)

  6. Why Injecting NASA ASAP (Advanced Satellite Aviation Weather Products) and NRL (Naval Research Lab) Satellite Data into ANC ? • The satellite algorithms in the current ANC are pretty basic • There are more sophisticated algorithms available which could potentially improve the performance of ANC, namely the NASA ASAP CI products (Mecikalski and Bedka) and NRL cloud classifier (Bankert)

  7. ASAP CI Products (Mecikalski and Bedka) • 10.7 Tb • 10.7 Tb 15 min ROC • 10.7 Tb 30 min ROC • (6.5 – 10.7) Tb • (6.5 – 10.7) Tb 15 min ROC • (13.3 – 10.7) Tb • (13.3 – 10.7) Tb 30 min ROC • Timing of 10.7 Tb drop below 0 degree • Satellite derived Atmospheric Motion Vectors (AMVs) • CI interest field ASAP CI interest Field

  8. ASAP CI interest Field

  9. NRL Cloud Classifier( Bankert) • A neural network-based cloud classifier • More detailed cloud types • Accuracy ~ 80 %

  10. Radar Reflectivity on 24 June 2004:A Case Study for ANC Performance with & without ASAP/NRL Data 2000 UTC 2101 UTC Human-entered boundary (yellow line) 60-min extrapolated boundary position (purple line)

  11. Experiment #1 • Replace the 15 min IR Rate of Change (ROC) field in the current ANC with the corresponding ASAP field • Keep everything else the same

  12. Visible Satellite Image

  13. Green Lines: radar reflectivity 60 min later White wind barbs: ASAP winds ASAP Cu Mask

  14. ASAP 10.7 Tb 15 Min ROC Field

  15. ASAP 10.7 Tb 15 Min ROC Interest Field

  16. Original 10.7 Tb 15 Min ROC Interest Field

  17. 60 Min ANC Forecasts With ASAP ROC Original ANC Green Line Contour: verification (radar reflectivity) Gray Shading: three levels of initiation forecasts Filled Color Contour: radar echo extrapolation

  18. Experiment #2 • Replace the cloud types in the current ANC with the NRL cloud types • ASAP satellite derived winds are used to advect the NRL data

  19. NRL Cloud Types

  20. NRL Sat Cu Interest Field

  21. NRL Sat CuC Interest Field

  22. 60 Min ANC Forecasts With NRL Cloud Types Original ANC Green Line Contour: verification (radar reflectivity) Gray Shading: three levels of initiation forecasts Filled Color Contour: radar echo extrapolation

  23. Experiment #3 • Using ASAP 10.7 Tb 15 min ROC • Using ASAP winds to advect the ROC field • Using NRL cloud type data advected by ASAP winds

  24. 60 Min ANC Forecasts ASAP ROC & NRL Cloud Types Original ANC Green Line Contour: verification (radar reflectivity) Gray Shading: three levels of initiation forecasts Filled Color Contour: radar echo extrapolation

  25. Discussions and Future work • A total of three cases (24 June, 23 July and 17 Aug of 2004 over IL/IN region) have been analyzed and similar conclusions as shown in this talk are obtained. • Advecting various fields 60 min into the future seems to be a problem. What kind of winds are better for advection is under investigation. • How to better use the ASAP CI products is still unclear at this point.

  26. Discussions and Future work (Continued) • The false alarm rate (FAR) could be reduced by using ASAP and NRL data, but the Probability of Detection (POD) could also be lowed as a result of that. Detailed initiation pattern seems to be improved when ASAP and NRL data were used. • The satellite predictor fields are only part of the ANC system. The reason that the ASAP and NRL data worked so well in ANC is because the satellite predictor fields are corresponding well in the right place at the right time with many other predictor fields in ANC.

  27. Discussions and Future work (Continued) • The NRL cloud type data are being used in Dallas/Ft Worth at real time. Both NRL and ASAP products are used in the FAA Demonstration Project in IL/IN this summer.

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