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Consideration of Probability

Use of probabilities

Probabilities give no indication of the physical nature of the uncertainty.  A 25% probability of precipitation >5mm/24hr might be related to a showery regime or to the uncertainty of the arrival of a frontal rain band.  A 25% risk forecast for temperatures <0°C might be related to the possible early morning clearing of low cloud cover or the possible arrival of arctic air.

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In summary, forecasters should generally use the probability at a given time just as presented from the latest forecast.  They should not not try to change that probability based on any extrapolation procedures.  Sometimes some form of weighted average of the last two or three forecasts could be appropriate, particularly if the target day is more than a day or two away.

Probabilities over time intervals

Increased certainty in forecasting an occurrence is gained by sacrificing knowledge of exactly when the event will occur; the longer the time interval over which event probabilities are calculated, the higher will be their values on average.  The uncertainty in individualrain forecasts for days 5, 6 and 7 is always higher than for the whole three-day interval.  A statement of high probability over a few days (e.g. “70% risk of precipitation >40 mm/24hr any time during Friday - Sunday”) may convey a stronger message than a statement of lower probabilities for each day separately (e.g. "30% risk of precipitation >40mm/24hr on Friday, Saturday and/or Sunday").

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 Fig7.1.2:  If the events in the two adjacent time intervals are correlated, so that rain in the first interval is followed by rain in the second, the probability for rain at any time during the whole period is 40% (far left figure).  If they are anti-correlated (e.g. because of differing speeds of frontal passage), so that rain in the first period is followed by dry conditions in the second, and dry in the first period is followed by rain in the second, then the total probability is 60% (centre figure).  If the events in the two adjacent time intervals are non-correlated, the combined probability is (1 - (1 - 0.4) x (1 - 0.2)) =52% (far right figure).

Probabilities over areas

Probabilities are normally calculated for individual locations.  Calculating probabilities with respect to several grid points within a certain geographical area normally increases the event probability.   A statement of high probability over an area (e.g. “70% risk of precipitation >40 mm/24hr somewhere in Belgium”) may convey a stronger message than a statement of lower probabilities for an individual location (e.g. "10% risk of precipitation >40mm/24hr at Brussels Airport").

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Of course hydrology also involves further complexity, such as infiltration properties, and run-off lag times which will vary across each catchment.

Probabilities of combined events

Probabilities for combined events such as “cloud cover <6/8 and temperatures >20°C” or blizzards (heavy snowfall with strong wind) cannot be made from the separate probabilities.  They can, however, be calculated from the ensemble data.  ecCharts includes the capacity to compute several types of combined probabilities. (Input "combined" in the Add layers Filter box on ecCharts).

Modification of the probabilities

Event probabilities are calculated from the proportion of ensemble members exceeding a certain threshold (e.g. if 34% of ensemble members forecast 2mm/12hr or more, then the probability for this event is considered to be 34%).  The number of ensemble members is limited, so the probability of an event is not necessarily 0% just because no member has forecast it, nor is it necessarily 100% because all members have forecast the event.  Depending on the underlying mathematical-statistical assumptions and the size of the ensemble, probabilities such as 1-2% and 98-99% could be assigned to situations when no or all members forecast an event, with intermediate probabilities adjusted slightly upwards or downwards accordingly.  

Calibration of probabilities

Forecast probabilities often show systematic deviations from the observed frequencies.  Low probabilities are often too low, high probabilities are often too high.  Calibration of probabilities or statistical post-processing (MOS) can improve the reliability of the probability forecasts.  This might affect the internal consistency between parameters.  If an over-prediction of rain is coupled to an over-prediction of cloud and perhaps under-prediction of temperature, then ideally all the parameters should be calibrated jointly, in order to maintain a physical consistency.

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