High-resolution rainfall variability simulated by the WRF RCM: application to eastern France

Marteau, Romain; Richard, Yves; Pohl, Benjamin; Chateau-Smith, Carmela; Castel, Thierry

doi:10.1007/s00382-014-2125-5

Cited by 32 publications

(32 citation statements)

References 36 publications

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“…Note that Marteau et al (2014) draw the opposite conclusion from their 8-km ERAInterim-driven simulation for a region in East France. However, their analysis focusses on seasonal averages, for which we not find seasonality of the downscaling performance.…”

Section: Discussionmentioning

confidence: 83%

“…The specific spatial pattern of underrepresented heavy precipitation events points to problems with the convection scheme. Note that the ERA-Interim-driven simulations of Chan et al (2013) and Marteau et al (2014) document overestimation in summer. The fact that other RCM implementations of similar horizontal resolution (the primary differences between which concern the parameterisation of subgrid processes) show a reverse effect also supports this interpretation.…”

Section: Discussionmentioning

confidence: 93%

“…7b). Overprediction of wet days and light precipitation is a common problem of state-of-the-art mesoscale climate models and is known to be more pronounced in winter (Frei et al 2003;Feldmann et al 2008;Kjellström et al 2010;Heikkilä et al 2011;WarrachSagi et al 2013b;Marteau et al 2014). In winter, it occurs virtually everywhere, which points to a link to the large-scale scheme.…”

Section: Discussionmentioning

confidence: 99%

“…Indeed, Kendon et al (2012) compared a very high-resolution (1.5 km) and a state-of-the-art (12 km) RCM simulations over a region of the United Kingdom and showed that the overprediction of wet days and light precipitation is considerably reduced in the 1.5-km simulation. They attributed the issue to the convection parameterisation scheme, but, like Marteau et al (2014), recognised that the large-scale scheme also has a significant contribution. Over flat areas, in summer, large-scale biases do not leave a significant footprint on the simulated distributions because they should be masked by the larger variability.…”

Section: Discussionmentioning

confidence: 99%

“…6a, left panel). This is the spatial pattern of enhanced orographic forcing, known as the 'windward-lee effect', which is a common deficiency of state-of-the-art mesoscale weather and climate models (Feldmann et al 2008;Schwitalla et al 2008;Bauer et al 2011;Wulfmeyer et al 2011;Marteau et al 2014). As very highresolution models that explicitly resolve convection do not have this deficiency, convection schemes should be the cause.…”

Section: Discussionmentioning

confidence: 99%

See 4 more Smart Citations

Field significance of performance measures in the context of regional climate model evaluation. Part 2: precipitation

Ivanov

Warrach‐Sagi

Wulfmeyer

2017

Theor Appl Climatol

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A new approach for rigorous spatial analysis of the downscaling performance of regional climate model (RCM) simulations is introduced. It is based on a multiple comparison of the local tests at the grid cells and is also known as 'field' or 'global' significance. The block length for the local resampling tests is precisely determined to adequately account for the time series structure. New performance measures for estimating the added value of downscaled data relative to the large-scale forcing fields are developed. The methodology is exemplarily applied to a standard EURO-CORDEX hindcast simulation with the Weather Research and Forecasting (WRF) model coupled with the land surface model NOAH at 0.11 • grid resolution. Daily precipitation climatology for the 1990-2009 period is analysed for Germany for winter and summer in comparison with high-resolution gridded observations from the German Weather Service. The field significance test controls the proportion of falsely rejected local tests in a meaningful way and is robust to spatial dependence. Hence, the spatial patterns of the statistically significant local tests are also meaningful. We interpret them from a process-oriented perspective. While the downscaled precipitation distributions are statistically indistinguishable from the observed ones in most regions in summer, the biases of some distribution characteristics are significant over large areas in winter. WRF-NOAH generates appropriate stationary fine-scale climate features in the daily precipitation field over regions of complex topography in both seasons and appropriate transient fine-scale features almost everywhere in summer. As the added value of global climate model (GCM)-driven simulations cannot be smaller than this perfect-boundary estimate, this work demonstrates in a rigorous manner the clear additional value of dynamical downscaling over global climate simulations. The evaluation methodology has a broad spectrum of applicability as it is distribution-free, robust to spatial dependence, and accounts for time series structure.

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Section: Discussionmentioning

confidence: 83%

Section: Discussionmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Field significance of performance measures in the context of regional climate model evaluation. Part 2: precipitation

Ivanov

Warrach‐Sagi

Wulfmeyer

2017

Theor Appl Climatol

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Process‐based evaluation of the VALUE perfect predictor experiment of statistical downscaling methods

Soares

Maraun

Brands

et al. 2018

Intl Journal of Climatology

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Statistical downscaling methods (SDMs) are techniques used to downscale and/or bias‐correct climate model results to regional or local scales. The European network VALUE developed a framework to evaluate and inter‐compare SDMs. One of VALUE's experiments is the perfect predictor experiment that uses reanalysis predictors to isolate downscaling skill. Most evaluation papers for SDMs employ simple statistical diagnostics and do not follow a process‐based rationale. Thus, in this paper, a process‐based evaluation has been conducted for the more than 40 participating model output statistics (MOS, mostly bias correction) and perfect prognosis (PP) methods, for temperature and precipitation at 86 weather stations across Europe. The SDMs are analysed following the so‐called “regime‐oriented” technique, focussing on relevant features of the atmospheric circulation at large to local scales. These features comprise the North Atlantic Oscillation, blocking and selected Lamb weather types and at local scales the bora wind and the western Iberian coastal‐low level jet. The representation of the local weather response to the selected features depends strongly on the method class. As expected, MOS is unable to generate process sensitivity when it is not simulated by the predictors (ERA‐Interim). Moreover, MOS often suffers from an inflation effect when a predictor is used for more than one station. The PP performance is very diverse and depends strongly on the implementation. Although conditioned on predictors that typically describe the large‐scale circulation, PP often fails in capturing the process sensitivity correctly. Stochastic generalized linear models supported by well‐chosen predictors show improved skill to represent the sensitivities.

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Climatology and trend analysis (1987–2016) of fire weather in the Euro‐Mediterranean

Giannaros

Kotroni

Lagouvardos

2020

Intl Journal of Climatology

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High-resolution regional climate simulations were conducted for the past 30 years (1987-2016), focusing on the Euro-Mediterranean region. The numerical simulations were used for computing the Canadian Fire Weather Index, for deriving the climatology of fire weather in the study area and investigating the presence of long-term trends, with particular emphasis on extremes during the May to September period. Results suggest that the Euro-Mediterranean fire weather follows a zonal pattern, characterized by adverse conditions occurring most frequently in the southern parts. Air temperature and relative humidity are the key drivers of fire weather in the study area, with precipitation and wind exerting less influence. Based on the conducted spatial trend analysis, extreme fire weather conditions have become more prevalent in the Iberian Peninsula and eastern Balkans, whereas declining trends occur in the Southeast Mediterranean basin. For both cases, the trends in fire weather extremes appear to coincide with trends in the input meteorological variables. Overall, the results of this study provide a new insight on the recent climatology and trends of fire weather in the Euro-Mediterranean. Our dataset is publicly available on the Zenodo platform

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High-resolution rainfall variability simulated by the WRF RCM: application to eastern France

Cited by 32 publications

References 36 publications

Field significance of performance measures in the context of regional climate model evaluation. Part 2: precipitation

Field significance of performance measures in the context of regional climate model evaluation. Part 2: precipitation

Process‐based evaluation of the VALUE perfect predictor experiment of statistical downscaling methods

Climatology and trend analysis (1987–2016) of fire weather in the Euro‐Mediterranean

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