Conditioning model output statistics of regional climate model precipitation on circulation patterns

Wetterhall, F.; Pappenberger, Florian; He, Yi; Freer, J. E.; Cloke, Hannah

doi:10.5194/npg-19-623-2012

Cited by 69 publications

(53 citation statements)

References 40 publications

(46 reference statements)

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“…Many studies have used (conditional) quantile mapping (QM) for pre-processing (Boé et al, 2007;Déqué, 2007;Kang et al, 2010;Kiczko et al, 2015;Verkade et al, 2013;Wetterhall et al, 2012) and post-processing (Hashino et al, 2007;Kang et al, 2010;Madadgar et al, 2014;Shi et al, 2008) to correct for bias and dispersion errors. According to Kang et al (2010), QM generally performs well in both pre-and post-processing.…”

Section: Pre-and Post-processingmentioning

confidence: 99%

See 1 more Smart Citation

Performance of ensemble streamflow forecasts under varied hydrometeorological conditions

Benninga

Booij

Romanowicz

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. The paper presents a methodology that gives insight into the performance of ensemble streamflowforecasting systems. We have developed an ensemble forecasting system for the Biała Tarnowska, a mountainous river catchment in southern Poland, and analysed the performance for lead times ranging from 1 to 10 days for low, medium and high streamflow and different hydrometeorological conditions. Precipitation and temperature forecasts from the European Centre for Medium-Range Weather Forecasts served as inputs to a deterministic lumped hydrological (HBV) model. Due to a non-homogeneous bias in time, pre-and post-processing of the meteorological and streamflow forecasts are not effective. The best forecast skill, relative to alternative forecasts based on meteorological climatology, is shown for high streamflow and snow accumulation lowstreamflow events. Forecasts of medium-streamflow events and low-streamflow events under precipitation deficit conditions show less skill. To improve performance of the forecasting system for high-streamflow events, the meteorological forecasts are most important. Besides, it is recommended that the hydrological model be calibrated specifically on lowstreamflow conditions and high-streamflow conditions. Further, it is recommended that the dispersion (reliability) of the ensemble streamflow forecasts is enlarged by including the uncertainties in the hydrological model parameters and the initial conditions, and by enlarging the dispersion of the meteorological input forecasts.

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Section: Pre-and Post-processingmentioning

confidence: 99%

“…Distributions may be different for different lead times and weather patterns or seasons (Boé et al, 2007;Wetterhall et al, 2012), so we tested three QM set-ups both with and without distinguishing lead times and seasons. Combining the options for pre-processing and post-processing results in four processing strategies.…”

Section: Pre-and Post-processingmentioning

confidence: 99%

Performance of ensemble streamflow forecasts under varied hydrometeorological conditions

Benninga

Booij

Romanowicz

et al. 2017

Hydrol. Earth Syst. Sci.

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“…It often includes simple corrections of the mean ('delta' approach), using cut-off thresholds, distribution corrections and increasing the variance (e.g. Yang et al, 2010;Wetterhall et al, 2012). MOS removes much of the model error in the precipitation, making it more useful in impact studies, which is considered by many to allow confidence in the examination of future changes in flow regimes in catchments from Europe and the wider world (e.g.…”

Section: Introductionmentioning

confidence: 99%

Modelling climate impact on floods with ensemble climate projections

Cloke

Wetterhall

et al. 2012

Quart J Royal Meteoro Soc

108

104

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The evidence provided by modelled assessments of future climate impact on flooding is fundamental to water resources and flood risk decision making. Impact models usually rely on climate projections from global and regional climate models (GCM/RCMs). However, challenges in representing precipitation events at catchment-scale resolution mean that decisions must be made on how to appropriately pre-process the meteorological variables from GCM/RCMs. Here the impacts on projected high flows of differing ensemble approaches and application of Model Output Statistics to RCM precipitation are evaluated while assessing climate change impact on flood hazard in the Upper Severn catchment in the UK. Various ensemble projections are used together with the HBV hydrological model with direct forcing and also compared to a response surface technique. We consider an ensemble of single-model RCM projections from the current UK Climate Projections (UKCP09); multi-model ensemble RCM projections from the European Union's FP6 'ENSEMBLES' project; and a joint probability distribution of precipitation and temperature from a GCM-based perturbed physics ensemble.The ensemble distribution of results show that flood hazard in the Upper Severn is likely to increase compared to present conditions, but the study highlights the differences between the results from different ensemble methods and the strong assumptions made in using Model Output Statistics to produce the estimates of future river discharge. The results underline the challenges in using the current generation of RCMs for local climate impact studies on flooding.

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“…For rainfall correction, the direct method (DM, sometimes referred to as delta method) was applied. (Lehner et al, 2006;Lenderink et al, 2007;Wetterhall et al, 2012)The direct method scales the simulated rainfall by multiplying it with the ratio of observed and RCM-simulated rainfall. Let , po t-, ̅ ob , and ̅̅̅̅̅̅̅ denote the raw MM5-RCM, adjusted or bias-corrected MM5-RCM, mean observed, and mean raw MM5-RCM rainfall, respectively.…”

Section: Rainfall Bias-correctionmentioning

confidence: 99%

Effects of climate change on future extreme rainfall indices over Thailand

2014

Global NEST Journal

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Extreme rainfall indices were calculated based on daily rainfall data derived from the outputs of MM5-RCM simulations. Projected changes of these indices in the future (2020s) under the IPCC Special Report on Emissions Scenarios (SRES) A1B compared to the reference period (1990s) were analyzed. Before calculating the indices, the raw outputs from the MM5-RCM were adjusted in order to reduce biases as part of the bias-correction process. The validation demonstrates that the average and cycle of annual rainfall in the reference period are reproduced reasonably in the bias-corrected MM5-RCM results. The projected changes of the rainfall indices suggest that in the future most areas of northern, western, and northeastern Thailand will become wetter in the wet season and drier in the dry season. Central and eastern Thailand will become drier in the wet season while in the dry season rainfall will become more intense, and rainy days will become more scattered with a higher number of heavy rainfall days. In the south, the length of the wet spell will be decreased in the Southern Andaman Sea area and increased in East-Southern Region. The dry spell will likely be shorter throughout the southern region for all seasons.

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Conditioning model output statistics of regional climate model precipitation on circulation patterns

Cited by 69 publications

References 40 publications

Performance of ensemble streamflow forecasts under varied hydrometeorological conditions

Performance of ensemble streamflow forecasts under varied hydrometeorological conditions

Modelling climate impact on floods with ensemble climate projections

Effects of climate change on future extreme rainfall indices over Thailand

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