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Composite Products And Nowcast Decision Support for the Beijing 2008 FDP

Composite Products And Nowcast Decision Support for the Beijing 2008 FDP. John Bally David Scurrah Beth Ebert Debin Su. Goals. Create consensus nowcast products Summarise FDP output for forecasters Deliver guidance directly to VIPS Test ability to manually add skill. Rainfall products.

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Composite Products And Nowcast Decision Support for the Beijing 2008 FDP

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  1. Composite Products And Nowcast Decision Support for the Beijing 2008FDP John Bally David Scurrah Beth EbertDebin Su

  2. Goals • Create consensus nowcast products • Summarise FDP output for forecasters • Deliver guidance directly to VIPS • Test ability to manually add skill

  3. Rainfall products • Generate probability of exceeding rainfall R • Logistic regression against ‘07 trial data • P(exceeding R) = 1 / (1 + (ec1 * QPFc2)) • Generate PoP 2 • Probability of exceeding warning thresholds • Combine systems by simple averaging

  4. Probability of Precipitation… 2mm • Example ensemble PoP2 including STEPS, GRAPES, MAPLE, SWIRLS and the BJANC • No calibration • Performance still good • Easily adapted to other thresholds

  5. PoP 2 • Systematic under-prediction • Excellent discrimination • Operationally robust

  6. Rainstorm warnings • First... measure accumulation from STEPS • Use 3hr accum, or 2 hr accum + 1hr forecast • Aim for 1 hr leadtime • Calculate R = threshold – 2hr accumulation • Again use P(exceeding R) = 1 / (1 + (ec1 * QPFc2)) • Thresholds for guidance were 40 (80mm) in 3hr for orange (red) warnings

  7. Rainstorm warnings Example “orange” rainstorm consensus product, using STEPS, GRAPES, MAPLE, SWIRLS, BJANC and CARDS. Contours of 20 and 50% chance of exceeding the threshold delivered to VIPS

  8. Thunderstormtrack error stats Histogram of storm detections wrt forecast (normalized speed and direction). after Dance, Ebert, Scurrah

  9. Standard deviation of velocity and direction errors (wrt forecast) was reasonably constant over forecast time (unlike most other measures). after Dance, Ebert, Scurrah

  10. Mathematics where (x, θ is a point in polar coordinates, t is forecast time, V is speed, σv is speed standard deviation, σ θ direction standard deviation. after Dance, Ebert, Scurrah Probability density function that thunderstorm centre is over a point, based on Gaussian distribution in polar coordinates. =

  11. To compute strike probability from probability density function: integrate along 'trailing edge' for each point in region. after Dance, Ebert, Scurrah

  12. Strike Probability • Use the THESPA (Dance, Ebert, Scurrah) algorithm to calculate Strike probability for each cell as Pn • Within a track set, each cell is independent: • Total Strike Prob = 1 - Πn (1- Pn)

  13. Strike Probability • Auto product combined strike prob from TITAN, SWIRLS, CARDS • Tracks used different reflectivity thresholds • Manual product had choice of TITAN thresholds (35, 40, 45dB) and could include tracks based on NIWOT & VDRAS advice

  14. Strike Probability • Un-calibrated • Over-prediction of probabilities above 0.4 • ROC curve shows skill

  15. Strike Probability • Manual vs Auto product for 60 forecasts • Manual products shows increased skill for these more difficult cases

  16. Conclusions • Robust and skillful consensus nowcast products created from ensemble of systems • Proof of concept for techniques used • Delivered to web page and VIPS • Manual strike probability showed more skill than Auto product

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