Composite Products And Nowcast Decision Support for the Beijing 2008

FDP

John BallyDavid Scurrah

Beth EbertDebin Su

Goals

Create consensus nowcast productsSummarise FDP output for forecastersDeliver guidance directly to VIPSTest ability to manually add skill

Rainfall products

Generate probability of exceeding rainfall R

Logistic regression against ‘07 trial dataP(exceeding R) = 1 / (1 + (ec1 * QPFc2)) Generate PoP 2 Probability of exceeding warning thresholdsCombine systems by simple averaging

Probability of Precipitation… 2mm

Example ensemble PoP2 including STEPS, GRAPES, MAPLE, SWIRLS and the BJANC

No calibrationPerformance 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 + (ec1 *

QPFc2)) 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 (normalizedspeed 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 Pn

Within a track set, each cell is independent:

Total Strike Prob = 1 - Πn (1- Pn)

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