Presentation on theme: "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."— Presentation transcript:
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
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
NCAR Auto-Nowcast System Predictor Fields Large-Scale RUC Data B-L characteristics Satellite Cloud Typing Boundary characteristics Cumulus development Storm motion and trends
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)
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)
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
NRL Cloud Classifier ( Bankert) A neural network-based cloud classifier More detailed cloud types Accuracy ~ 80 %
Radar Reflectivity on 24 June 2004: A Case Study for ANC Performance with & without ASAP/NRL Data 2000 UTC2101 UTC Human-entered boundary (yellow line) 60-min extrapolated boundary position (purple line)
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
60 Min ANC Forecasts With ASAP ROCOriginal ANC Green Line Contour: verification (radar reflectivity) Gray Shading: three levels of initiation forecasts Filled Color Contour: radar echo extrapolation
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
60 Min ANC Forecasts With NRL Cloud TypesOriginal ANC Green Line Contour: verification (radar reflectivity) Gray Shading: three levels of initiation forecasts Filled Color Contour: radar echo extrapolation
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
60 Min ANC Forecasts ASAP ROC & NRL Cloud TypesOriginal ANC Green Line Contour: verification (radar reflectivity) Gray Shading: three levels of initiation forecasts Filled Color Contour: radar echo extrapolation
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.
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.
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.