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 Generate probability of exceeding rainfall R Logistic regression against ‘07 trial data P (exceeding R) = 1 / (1 + (e c1 * QPF c2 )) Generate PoP 2 Probability of exceeding warning thresholds Combine systems by simple averaging

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

PoP 2 Systematic under-prediction Excellent discrimination Operationally robust

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 + (e c1 * QPF c2 )) Thresholds for guidance were 40 (80mm) in 3hr for orange (red) warnings

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

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

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

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.

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

Strike Probability Use the THESPA (Dance, Ebert, Scurrah) algorithm to calculate Strike probability for each cell as P n Within a track set, each cell is independent: Total Strike Prob = 1 - Π n (1- P n )

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

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

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

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