Use of Probabilistic Forecasts

Ensembles These are a number of forecasts all run from similar, but slightly different initial conditions The same forecast model is run many times The resulting forecasts are grouped together to aid the forecaster Forecasts from several different models can also form an ensemble (“poor man’s ensemble”)

Displacement Time Small differences here

Displacement Time Small differences here BIG differences here

Ensembles - By running the model many times with small differences in initial conditions (and model formulation) we can: take account of uncertainty estimate probabilities and risks (eg. 30 members out of 51 = 60%)

Members or Resolution? Which is more important – to have as many ensemble members as possible or to have a higher resolution, and therefore fewer members? In practice it will be a compromise between the two!

Probabilistic input. Time evolution, one location. PLUMESMETEOGRAMS

EPS Plume diagram 850 hPa Temp 1 member Operational High Resolution model T799 (25 km) Control member

Range of temperatures indicated by the ensemble Probability density is shaded % of members have temperatures within this range (70-90% outwith). Thinner this band the higher the certainty.

EPS total precipitation rate (mm) in 12 hours EPS control 1 Ensemble member Operational High Resolution

Probabilistic input. Individual solutions, one time frame POSTAGE STAMPS

Interpreting Ensemble Data The presentation of results is important Need to reduce the different solutions to something manageable  Clustering - grouping solutions that are similar  Probability forecasting

Carlisle storm, Jan 05, from ECMWF 51-member medium-range ensemble

Cyclone plumes Forecasts starting from 00Z 06/01/05 OBSERVED

Ensembles – estimating risk By running model(s) many times with small differences in initial conditions (and model formulation) we can: take account of uncertainty estimate probabilities and risks –eg. 10 members out of 50 = 20%

National Warning - Issued on Monday, 2 October 2006 Heavy falls of rain are possible in places in the Overberg, Breede River Valley, Ruens, Garden Route and the Little Karoo, Eastern Cape coast and adjacent interior and KwaZulu- Natal.

GFS Ensemble T+96 for 00z Tuesday 3 rd October 2006

GFS 12-18z Mon 2 nd Oct 2006

ECMWF 12-18z Mon

GFS 12-18z Tue 3 rd Oct 2006

ECMWF 12-18z Tues

EPS Meteograms for Durban (00z Sat-00z Mon)

The main difficulties lie in … Knowing what to look at Knowing when to stop! Absorbing and retaining the information long enough to use it …

EC Total ppn prob > 20mm 12z Tue – 12z Wed

EFI ECMWF for Tue-Wed

What Happened? Margate 136mm. Monthly average 133mm East London 83mm. Monthly average 131mm

Impacts? Torrential rains cause havoc in the Eastern Cape October 04, 2006, 11:15 Torrential rains in the Transkei region of the Eastern Cape have caused major damage to homes, roads and schools. People in rural areas, who have mud houses, are the hardest hit. Two more schools have collapsed in Butterworth and some rural roads are flooded. Floods caused five deaths in the area last week near Lusikisiki. Near Butterworth, the low-lying Ceru bridge is overflowing. Bennet Malisana, from Butterworth lost his wife during earlier floods in 1993 when she was among 11 people swept away in a bakkie while crossing a river. He says his area has been hit again. "We have been greatly affected by the rain because we desperately need a bridge," he added.

TC Boloetse

Life history

Strike probability map 24 th Jan

Strike probability map 26th Jan

Strike probability map 29 th Jan

Strike probability map 1st Feb

Strike probability map 4th Feb

MOGREPS

Short-range Ensembles ECMWF EPS has transformed the way we do Medium-Range Forecasting Uncertainty also in short-range: –Rapid Cyclogenesis often poorly forecast deterministically (eg Dec 1999) –Uncertainty of sub-synoptic systems (eg frontal waves) –Many customers most interested in short-range Assess ability to estimate uncertainty in local weather –QPF –Cloud Ceiling, Fog –Winds etc

Initial conditions perturbations Perturbations centred around 4D-Var analysis Transforms calculated using same set of observations as used in 4D-Var (including all satellite obs) within +/- 3 hours of data time Ensemble uses 12 hour cycle (data assimilation uses 6 hour cycle)

Initial conditions perturbations Differences with ECWMF Singular Vectors: -It focuses on errors growing during the assimilation period, not growing period: - Suitable for Short-range! -Calculated using the same resolution than the forecast -ETKF includes moist processes -Running in conjunction with stochastic physics to propagate effect

Model error: parameterisations Random parameters ParameterSchememin/std/Max Entrainment rateCONVECTION2 / 3 / 5 Cape timescaleCONVECTION30 / 30 / 120 RH criticalLRG. S. CLOUD0.6 / 0.8 / 0.9 Cloud to rain (land)LRG. S. CLOUD1E-4/8E-4/1E-3 Cloud to rain (sea)LRG. S. CLOUD5E-5/2E-4/5E-4 Ice fallLRG. S. CLOUD17 / 25.2 / 33 Flux profile param.BOUNDARY L.5 / 10 / 20 Neutral mixing lengthBOUNDARY L.0.05 / 0.15 / 0.5 Gravity wave const.GRAVITY W.D.1E-4/7E-4/7.5E-4 Froude numberGRAVITY W.D.2 / 2 / 4  QUMP (Murphy et al., 2004)  Initial stoch. Phys. Scheme for the UM (Arribas, 2004)

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