Classification of ensemble forecasts by means of an artificial neural network

Eckert, Pierre; Cattani, Daniel; Ambühl, Jacques

doi:10.1002/met.5060030207

Cited by 14 publications

(12 citation statements)

References 8 publications

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“…We also show that the similarity between an ensemble prediction and the climatology (the anomaly of the forecast), or between the predictions of different models, can be quantitatively measured using the relative entropy of the corresponding PDFs. Thus, this paper extends the work by Eckert et al (1996), who introduced the entropy for characterizing the dispersion, or spread, of the ensemble in a similar framework (medium‐range ensemble prediction systems).…”

Section: Introductionmentioning

confidence: 54%

“…On the one hand, Cavazos (1999, 2000) applies SOMs from a climatic perspective to classify circulation fields and derive relationships with daily precipitation at local scale (the National Center for Environmental Prediction/National Center for Atmospheric Research reanalysis is used in this case); however, no application to operative short‐range or ensemble forecast is performed. On the other hand, Eckert et al (1996) apply SOMs to characterize and group the ensemble members of a medium‐range ensemble forecast; however, in this case, no connection is made with local forecast (downscaling). The present paper shares a common methodology with the above described works, but applies it to a different framework, providing both operative local seasonal forecasts from a multi‐model ensemble (downscaling) and an estimation of the predictability of the resulting forecasts (ensemble analysis).…”

Section: Self‐organizing Maps For Data Analysismentioning

confidence: 99%

“…In particular, we have used an 8 × 8 lattice, which yields to a set of probabilities ( p 1 , …, p 64 ) =[ P ( c 1 ), …, P ( c 64 )]. In this case, the entropy is given by (see Eckert et al, 1996, for more details): The maximum value of the entropy is associated with a uniform distribution, whereas a minimum zero value corresponds to a delta‐like probability ( p i = 1, for some index i ). Note that a uniform distribution on the lattice corresponds to the climatology of the 20 yr used to train the SOM.…”

Section: Self‐organizing Maps For Data Analysismentioning

confidence: 99%

See 2 more Smart Citations

Analysis and downscaling multi-model seasonal forecasts in Peru using self-organizing maps

Gutiérrez¹,

Cano²,

Cofiño³

et al. 2005

Tellus A

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We present an application of self‐organizing maps (SOMs) for analysing multi‐model ensemble seasonal forecasts from the DEMETER project in the tropical area of Northern Peru. The SOM is an automatic data‐mining clustering technique, which allows us to summarize a high‐dimensional data space in terms of a set of reference vectors (cluster centroids). Moreover, it has outstanding analysis and visualization properties, because the reference vectors can be projected into a two‐dimensional lattice, preserving their high‐dimensional topology. In the first part of the paper, the SOM is applied to analyse both atmospheric patterns over Peru and local precipitation observations at two nearby stations. First, the method is applied to cluster the ERA40 reanalysis patterns on the area of study (Northern Peru), obtaining a two‐dimensional lattice which represents the climatology. Then, each particular phenomenon or event (e.g. El Niño or La Niña) is shown to define a probability density function (PDF) on the lattice, which represents its characteristic ‘location’ within the climatology. On the other hand, the climatological lattice is also used to represent the local precipitation regime associated with a given station. For instance, we show that the precipitation regime is strongly associated with El Niño events for one station, whereas it is more uniform for the other. The second part of the paper is devoted to downscaling seasonal ensemble forecasts from the multi‐model DEMETER ensemble to local stations. To this aim, the PDF generated on the lattice by the patterns predicted for a particular season is combined with the local precipitation lattice for a given station. Thus, a probabilistic or numeric local forecast is easily obtained from the resulting PDF. Moreover, a measure of predictability for the downscaled forecast can be computed in terms of the entropy of the ensemble PDF. We present some evidence that accurate local predictions for accumulated seasonal precipitation can be obtained some months in advance for strong El Niño episodes. Finally, we compare the multi‐model ensemble with single‐model ensembles, and show that the best results correspond to different models for different years; however, the best global performance over the whole period corresponds to the multi‐model ensemble.

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Section: Introductionmentioning

confidence: 54%

Section: Self‐organizing Maps For Data Analysismentioning

confidence: 99%

Section: Self‐organizing Maps For Data Analysismentioning

confidence: 99%

See 1 more Smart Citation

Analysis and downscaling multi-model seasonal forecasts in Peru using self-organizing maps

Gutiérrez¹,

Cano²,

Cofiño³

et al. 2005

Tellus A

View full text Add to dashboard Cite

show abstract

“…Tennant and Hewitson (2002) use SOMs to explore the dynamic controls on intra-seasonal variability of precipitation. Meanwhile, Eckert et al (1996) used SOMs to classify members of ensemble forecasts. In this approach multiple forecasts are generated with a climate model, each with slightly different initialization conditions to allow one to explore the envelope of possible climate evolutions inherent in a chaotic system.…”

Section: Discussionmentioning

confidence: 99%

Climate Analysis, Modelling, and Regional Downscaling Using Self‐Organizing Maps

Hewitson

2008

Self‐Organising Maps

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“…They have been used to improve temperature forecasts (e.g., Marzban 2003), thunderstorm forecasts (e.g., Manzato 2005), wind predictions (Kretzschmar et al 2004), quantitative precipitation forecasts (e.g., Kuligowski and Barros 1998;Hall et al 1999;Koizumi 1999), snowfall and snow density forecasts (Roebber et al 2003), rainfall-runoff processes (Hsu et al 1995), and quantitative precipitation estimation (Hsu et al 1997;Hsu et al 1999). However, applications of neural networks to classify (e.g., Eckert et al 1996;Scherrer et al 2004) ensemble members or calibrate (e.g., Mullen et al 1998;Mullen and Buizza 2004) ensemble forecasts appear relatively limited.…”

Section: Introductionmentioning

confidence: 99%

Calibration of Probabilistic Quantitative Precipitation Forecasts with an Artificial Neural Network

Yuan

Gao

Mullen

et al. 2007

Weather and Forecasting

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A feed-forward neural network is configured to calibrate the bias of a high-resolution probabilistic quantitative precipitation forecast (PQPF) produced by a 12-km version of the NCEP Regional Spectral Model (RSM) ensemble forecast system. Twice-daily forecasts during the 2002-2003 cool season (1 November-31 March, inclusive) are run over four U.S. Geological Survey (USGS) hydrologic unit regions of the southwest United States. Calibration is performed via a cross-validation procedure, where four months are used for training and the excluded month is used for testing. The PQPFs before and after the calibration over a hydrological unit region are evaluated by comparing the joint probability distribution of forecasts and observations. Verification is performed on the 4-km stage IV grid, which is used as "truth." The calibration procedure improves the Brier score (BrS), conditional bias (reliability) and forecast skill, such as the Brier skill score (BrSS) and the ranked probability skill score (RPSS), relative to the sample frequency for all geographic regions and most precipitation thresholds. However, the procedure degrades the resolution of the PQPFs by systematically producing more forecasts with low nonzero forecast probabilities that drive the forecast distribution closer to the climatology of the training sample. The problem of degrading the resolution is most severe over the Colorado River basin and the Great Basin for relatively high precipitation thresholds where the sample of observed events is relatively small.

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Classification of ensemble forecasts by means of an artificial neural network

Cited by 14 publications

References 8 publications

Analysis and downscaling multi-model seasonal forecasts in Peru using self-organizing maps

Analysis and downscaling multi-model seasonal forecasts in Peru using self-organizing maps

Climate Analysis, Modelling, and Regional Downscaling Using Self‐Organizing Maps

Calibration of Probabilistic Quantitative Precipitation Forecasts with an Artificial Neural Network

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