On the reliability of spatially disaggregated global ensemble rainfall forecasts

Gaborit, Étienne; Anctil, François; Fortin, Vincent; Pelletier, Geneviève

doi:10.1002/hyp.9509

Cited by 16 publications

(22 citation statements)

References 39 publications

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“…SAFRAN and CFSR come from a combination of meteorological simulations and observations and may therefore possess a different spatial variance than the network of ground stations. SAFRAN aggregation also results in a reduction in variance or, in other words, reduces its ability to describe irregular, non-uniform precipitation patterns [50].…”

Section: Climate Data Of Different Resolutionsmentioning

confidence: 99%

Testing the SWAT Model with Gridded Weather Data of Different Spatial Resolutions

Grusson

Anctil

Sauvage

et al. 2017

Water

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This study explored the influence of the spatial resolution of a gridded weather dataset when inputted in the soil and water assessment tool (SWAT) over the Garonne River watershed. Several datasets are compared: ground-based weather stations, the 8-km SAFRAN product (Système d'Analyse Fournissant des Renseignements Adaptés à la Nivologie), the 0.5 • CFSR product (Climate Forecasting System Reanalysis) and several derived SAFRAN grids upscaled to 16, 32, 64 and 128 km. The SWAT model, calibrated on weather stations, was successively run with each gridded weather dataset. Performances with SAFRAN up to 64 or 128 km were poor, due to a contraction of the spatial variance of daily precipitation. Performances with 8-km SAFRAN are similar to that of the aggregated 16-and 32-km SAFRAN grids. The~30-km CFSR product was found to perform well at some sites, while in others, its performance was considerably inferior because of grid points where precipitation was overestimated. The same problem was found in the calibration, where data at some weather stations did not appear to be representative of the subwatershed in which they are used to compute hydrology. These results suggest that the difference in the representation of the climate was more influential than its spatial resolution, an analysis that was confirmed by similar performances obtained with the SWAT model calibrated on the 16-and 32-km SAFRAN grids. However, the better performances obtained from these two weather datasets than from the ground-based stations' dataset confirmed the advantage of using the SAFRAN product in SWAT modelling.

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Section: Climate Data Of Different Resolutionsmentioning

confidence: 99%

Testing the SWAT Model with Gridded Weather Data of Different Spatial Resolutions

Grusson

Anctil

Sauvage

et al. 2017

Water

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“…These methods could hence be associated to disaggregation techniques, as discussed by Gaborit et al . ().…”

Section: The Selected Statistical Downscaling Techniquementioning

confidence: 97%

“…Finally, Zhang and Foufoula‐Georgiou () and Gaborit et al . () applied the technique developed by Perica and Foufoula‐Georgiou (), which also belongs to the fractal cascades.…”

Section: The Selected Statistical Downscaling Techniquementioning

confidence: 99%

“…Gaborit et al . () explored the statistical downscaling technique developed by Perica and Foufoula‐Georgiou () to spatially disaggregate ensemble rainfall forecasts issued by the Canadian GEPS. It consists of a multi‐fractal cascade belonging to the Full‐Field Generators (FFGs) described in the section ‘The selected statistical downscaling technique’.…”

Section: Introductionmentioning

confidence: 99%

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Hydrologic evaluation of spatially disaggregated global ensemble rainfall forecasts

Gaborit

Anctil

Fortin

et al. 2013

Hydrological Processes

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A statistic–stochastic multi‐fractal downscaling technique was evaluated from a hydrologic point of view. Ensemble hydrologic forecasts with a time step of 3 h were performed for original and disaggregated ensemble rainfall forecasts issued by the Canadian Global Ensemble Prediction System in its 2009 operational version. This hydro‐meteorological operational forecasting chain was conducted using the hydrological model SWMM5. The model was implemented on a small 6‐km2 urban catchment located in the Québec City region. The hydrological evaluation was based on the comparison of forecasted flows to the observed ones, calculating several deterministic and probabilistic scores, and drawing rank histograms and reliability diagrams. Disaggregated products led to a better representation of the ensemble members' dispersion. This disaggregation technique represents an interesting way of bridging the gap between the meteorological models' resolution and the high degree of spatial precision sometimes required by hydrological models in their precipitation representation. Copyright © 2013 John Wiley & Sons, Ltd.

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“…to scalar values and has been used in many hydrological and meteorological applications (Hamill 2001;Candille and Talagrand 2005;Aligo et al 2007;Brussolo et al 2008;Clark et al 2009;Gaborit et al 2013;Duda et al 2014). In this technique a set of bins is defined by distributing the ensemble replicates on the real axis.…”

Section: B Rank Histogrammentioning

confidence: 99%

Quantifying Precipitation Uncertainty for Land Data Assimilation Applications

Alemohammad

McLaughlin

Entekhabi

2015

Monthly Weather Review

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Ensemble-based data assimilation techniques are often applied to land surface models in order to estimate components of terrestrial water and energy balance. Precipitation forcing uncertainty is the principal source of spread among the ensembles that is required for utilizing information in observations to correct model priors. Precipitation fields may have both position and magnitude errors. However, current uncertainty characterizations of precipitation forcing in land data assimilation systems often do no more than applying multiplicative errors to precipitation fields. In this paper, an ensemble-based Bayesian method for characterization of uncertainties associated with precipitation retrievals from spaceborne instruments is introduced. This method is used to produce stochastic replicates of precipitation fields that are conditioned on precipitation observations. Unlike previous studies, the error likelihood is derived using an archive of historical measurements. The ensemble replicates are generated using a stochastic method, and they are intermittent in space and time. The replicates are first projected in a low-dimension subspace using a problem-specific set of attributes. The attributes are derived using a dimensionality reduction scheme that takes advantage of singular value decomposition. A nonparametric importance sampling technique is formulated in terms of the attribute vectors to solve the Bayesian sampling problem. Examples are presented using retrievals from operational passive microwave instruments, and performance of the method is assessed using ground validation measurements from a surface weather radar network. Results indicate that this ensemble characterization approach provides a useful description of precipitation uncertainties with a posterior ensemble that is narrower in distribution than its prior while containing both precipitation position and magnitude errors.

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On the reliability of spatially disaggregated global ensemble rainfall forecasts

Cited by 16 publications

References 39 publications

Testing the SWAT Model with Gridded Weather Data of Different Spatial Resolutions

Testing the SWAT Model with Gridded Weather Data of Different Spatial Resolutions

Hydrologic evaluation of spatially disaggregated global ensemble rainfall forecasts

Quantifying Precipitation Uncertainty for Land Data Assimilation Applications

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