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Titel |
Comparison of three methods for the optimal allocation of hydrological model participation in an Ensemble Prediction System |
VerfasserIn |
D. Brochero, F. Anctil, C. Gagné |
Konferenz |
EGU General Assembly 2012
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Medientyp |
Artikel
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Sprache |
Englisch
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Digitales Dokument |
PDF |
Erschienen |
In: GRA - Volume 14 (2012) |
Datensatznummer |
250064529
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Zusammenfassung |
Today, the availability of the Meteorological Ensemble Prediction Systems (MEPS) and its
subsequent coupling with multiple hydrological models offer the possibility of building
Hydrological Ensemble Prediction Systems (HEPS) consisting of a large number of
members. However, this task is complex both in terms of the coupling of information and of
the computational time, which may create an operational barrier. The evaluation of the
prominence of each hydrological members can be seen as a non-parametric post-processing
stage that seeks finding the optimal participation of the hydrological models (in a fashion
similar to the Bayesian model averaging technique), maintaining or improving the quality of
a probabilistic forecasts based on only x members drawn from a super ensemble of d
members, thus allowing the reduction of the task required to issue the probabilistic
forecast.
The main objective of the current work consists in assessing the degree of simplification
(reduction of the number of hydrological members) that can be achieved with a
HEPS configured using 16 lumped hydrological models driven by the 50 weather
ensemble forecasts from the European Centre for Medium-range Weather Forecasts
(ECMWF), i.e. an 800-member HEPS. In a previous work (Brochero et al., 2011a, b), we
demonstrated that the proportion of members allocated to each hydrological model is a
sufficient criterion to reduce the number of hydrological members while improving
the balance of the scores, taking into account interchangeability of the ECMWF
MEPS.
Here, we compare the proportion of members allocated to each hydrological model
derived from three non-parametric techniques: correlation analysis of hydrological members,
Backward Greedy Selection (BGS) and Nondominated Sorting Genetic Algorithm (NSGA
II). The last two techniques allude to techniques developed in machine learning, in a
multicriteria framework exploiting the relationship between bias, reliability, and the
number of members of the probabilistic prediction. To compare the results of the
simplified scheme with respect to the 800 original members and the techniques
used in the selection criteria we use a weighted criterion in which each score in the
selected ensemble of hydrological members is normalized from the division by the
corresponding score in the initial 800-member HEPS, placing each component on the same
scale.
Performance is based on the application of the member selection to a neighboring basin,
providing a rigorous test. In general, we see that there is a relationship between the quality of
the ensemble prediction and the number of hydrological members that represent
different scenarios, as well as the complex balance between the bias represented by the
mean ignorance score and the reliability represented by the error in the reliability
diagram.
Although intuitively attractive and simpler to implement, member elimination based on
correlation is detrimental to the system reliability and consistency. Both BGS and NSGA II
provide high performance simplifications. However, the NSGA II is more descriptive than
BGS, since it provides the Pareto front of selections, which leaves the modeller the choice of
the weight allocated to the different characteristics assessed in the probabilistic
forecast. |
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