Statistical-dynamical downscaling of wind roses over the Czech Republic

Svoboda, Jaroslav; Chládová, Zuzana; Pop, L.; Hošek, Jiří

doi:10.1007/s00704-012-0759-y

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…However, our study confirms that even if windstorms are evaluated with respect to the size of the affected area, the set of maximum events also contains local windstorms particularly related to convective storms. This result is partly due to the use of return periods instead of wind gusts because higher wind gust values are generally reached in mountain areas where the orography enhances synoptic‐scale winds (Svoboda et al , ). Six events (12 %) of the 50 maximum EWEs were categorized as the convective‐scale type.…”

Section: Discussionmentioning

confidence: 99%

Relationship between Czech windstorms and air temperature

et al. 2016

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The strongest Czech windstorms from 1961 to 2010 were evaluated using a weather extremity index. The index combines the return periods of station wind gusts and the size of the affected area allowing determination of the duration of individual events. Of 50 extreme wind events (EWEs), most were synoptic‐scale windstorms that occurred between the end of October and the beginning of March. However, six EWEs were categorized as the convective‐scale windstorms. Four of the strongest Czech and European windstorms were related to one other (Kyrill, the November 1984 storm, Vivian and Wiebke, and Jeanette). Approximately 90% of the synoptic‐scale windstorms were characterized by strong westerly or northwesterly flow at the 850 hPa level and a north–south temperature gradient at the 500 hPa level. The remaining (rather weaker) synoptic‐scale events were characterized by southerly flow and a west–east temperature gradient at the 850 and 500 hPa levels, respectively. To determine the abnormality of windstorms by season, a weather abnormality index was used to evaluate 50 abnormal wind events (AWEs). Throughout the summer, as well as in half of May and September, AWEs were categorized as the convective‐scale windstorms accompanied by weak 850 hPa winds. Apart from two events with easterly winds, the remaining AWEs were accompanied by winds from the southwest and a strong west–east temperature gradient at the 850 hPa level. Ten of the latter events occurred in the last two ten‐day periods of July and first two ten‐day periods of August. All of the events followed hot episodes, and seven of them followed one of the 25 extreme high‐temperature events. A less pronounced relationship between wind and temperature anomalies was present in the colder half of the year from October to March: three of the strongest Czech EWEs were associated with abnormal high‐temperature events (AHEs).

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Section: Discussionmentioning

confidence: 99%

Relationship between Czech windstorms and air temperature

et al. 2016

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“…SDD has been applied frequently to wind (Mengelkamp et al 1997;Najac et al 2011;Martinez et al 2013;Svoboda et al 2013;Badger et al 2014) and precipitation in complex terrain such as that of the Alpine region (Frey-Buness et al 1995;Fuentes and Heimann 2000), central Asia (Reyers et al 2013), and Vietnam (Tran Anh and Taniguchi 2018). SDD has been further applied to winter storms (Pinto et al 2010), ocean modeling (Cassou et al 2011), and the urban heat island effect (Hoffmann et al 2018).…”

Section: A Backgroundmentioning

confidence: 99%

A Statistical–Dynamical Downscaling for the Urban Heat Island and Building Energy Consumption—Analysis of Its Uncertainties

Schoetter

Hidalgo

Jougla

et al. 2020

Journal of Applied Meteorology and Climatology

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High-resolution maps of the urban heat island (UHI) and building energy consumption are relevant for urban planning in the context of climate change mitigation and adaptation. A statistical-dynamical downscaling for these parameters is proposed in the present study. It combines a statistical local weather type approach with dynamical simulations using the mesoscale atmospheric model Meso-NH coupled to the urban canopy model Town Energy Balance. The downscaling is subject to uncertainties related to the weather type approach (statistical uncertainty) and to the numerical models (dynamical uncertainty). These uncertainties are quantified for two French cities (Toulouse and Dijon) for which long-term dense high-quality observations are available. The seasonal average nocturnal UHI intensity is simulated with less than 0.2 K bias for Dijon, but it is overestimated by up to 0.8 K for Toulouse. The sensitivity of the UHI intensity to weather type is, on average, captured by Meso-NH. The statistical uncertainty is as large as the dynamical uncertainty if only one day is simulated for each weather type. It can be considerably reduced if 3-6 days are taken instead. The UHI reduces the building energy consumption by 10% in the center of Toulouse; it should therefore be taken into account in the production of building energy consumption maps.

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“…As the focus of coordinated AOGCM experiments evolves to include decadal prediction (see, e.g., Meehl et al ), statistical downscaling will become better positioned to inform decision making in agricultural and hydrological applications. Recent work combining dynamical and statistical downscaling techniques (e.g., Chen et al , Svoboda et al ) suggests that even as model resolution increases and dynamical downscaling approaches evolve, statistical downscaling will continue to provide information to the impact community that cannot be provided by other methodological approaches. As better observed and reanalyzed data sets become available and AOGCM simulations continue to improve, there will be additional opportunities for the statistical downscaling community to evaluate the critical assumption of stationarity and better assess the scales at which statistical downscaling predictors are optimally simulated by AOGCMs.…”

Section: The Future Of Statistical Downscalingmentioning

confidence: 99%

“…Climate downscaling approaches can be broadly classified as dynamical or statistical with a small number of studies using dynamical–statistical approaches (e.g., Boé et al , Fuentes and Heimann , Svoboda et al ). Studies comparing statistical and dynamical downscaling approaches have generally found similar skill in reproducing historical climate statistics, although the ability of statistical downscaling to provide point estimates may be an addition consideration for some applications, such as those in hydrology (see, e.g., Chiew et al , Frost et al ).…”

Section: Introductionmentioning

confidence: 99%