Ensemble Forecasting

Ensemble forecasting means producing a set of forecasts, called members, instead of a single one. Each member uses slightly different starting conditions, input weather, model settings or even different models. The spread of the members shows how uncertain the forecast is, and the share of members crossing a threshold gives a rough probability. In hydrology, the most common setup is to drive a rainfall-runoff or river routing model with each member of an ensemble weather forecast from a numerical weather prediction centre. If 51 rainfall forecasts go in, 51 river flow forecasts come out. A flood forecaster can then say, for example, that 30 of 51 members exceed the warning level at a gauge in three days, rather than giving a plain yes or no. Large-scale flood awareness systems for Europe and for the globe work this way. Ensembles are also used for seasonal water supply outlooks, reservoir operation and drought planning, often by running a model with the weather from each past year, a method known as ensemble streamflow prediction. What it needs: ensemble weather inputs, a hydrologic model cheap enough to run many times, and a record of past forecasts and observations to check and correct the system. Raw ensembles are often biased or too narrow, so statistical post-processing is common. Strengths: it shows uncertainty openly, which supports risk-based decisions such as when to release water from a dam or issue a warning. The ensemble mean also tends to score better than a single run. Limits: the spread covers only the sources of uncertainty that were sampled. If all members share the same model error, they can agree and all be wrong. Many members cost more computing time, and users need some training to read probabilities correctly.