Surface Wind Regionalization in Complex Terrain

Jiménez, Pedro A.; González‐Rouco, J. F.; Montávez, Juan Pedro; Navarro, Jorge; García‐Bustamante, Elena; Valero, Francisco

doi:10.1175/2007jamc1483.1

Cited by 50 publications

(53 citation statements)

References 39 publications

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“…As suggested by Jiménez et al (2008), in such areas the spatial resolution becomes a major challenge, and the conclusions drawn from coarser-resolution simulations cannot be generalised without caution. Thus, the present study aims at finding a model set-up that minimises systematic errors in hindcast simulations in storms with the purpose of reproducing mean and maximum surface winds in complex terrains.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of the WRF model to PBL parametrisations and nesting techniques: evaluation of wind storms over complex terrain

Gómez-Navarro

Raible

Dierer³

2015

Geosci. Model Dev.

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Abstract. Simulating surface wind over complex terrain is a challenge in regional climate modelling. Therefore, this study aims at identifying a set-up of the Weather Research and Forecasting Model (WRF) model that minimises systematic errors of surface winds in hindcast simulations. Major factors of the model configuration are tested to find a suitable set-up: the horizontal resolution, the planetary boundary layer (PBL) parameterisation scheme and the way the WRF is nested to the driving data set. Hence, a number of sensitivity simulations at a spatial resolution of 2 km are carried out and compared to observations. Given the importance of wind storms, the analysis is based on case studies of 24 historical wind storms that caused great economic damage in Switzerland. Each of these events is downscaled using eight different model set-ups, but sharing the same driving data set. The results show that the lack of representation of the unresolved topography leads to a general overestimation of wind speed in WRF. However, this bias can be substantially reduced by using a PBL scheme that explicitly considers the effects of non-resolved topography, which also improves the spatial structure of wind speed over Switzerland. The wind direction, although generally well reproduced, is not very sensitive to the PBL scheme. Further sensitivity tests include four types of nesting methods: nesting only at the boundaries of the outermost domain, analysis nudging, spectral nudging, and the so-called re-forecast method, where the simulation is frequently restarted. These simulations show that restricting the freedom of the model to develop large-scale disturbances slightly increases the temporal agreement with the observations, at the same time that it further reduces the overestimation of wind speed, especially for maximum wind peaks.The model performance is also evaluated in the outermost domains, where the resolution is coarser. The results demonstrate the important role of horizontal resolution, where the step from 6 to 2 km significantly improves model performance. In summary, the combination of a grid size of 2 km, the non-local PBL scheme modified to explicitly account for non-resolved orography, as well as analysis or spectral nudging, is a superior combination when dynamical downscaling is aimed at reproducing real wind fields.

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Section: Introductionmentioning

confidence: 99%

Sensitivity of the WRF model to PBL parametrisations and nesting techniques: evaluation of wind storms over complex terrain

Gómez-Navarro

Raible

Dierer³

2015

Geosci. Model Dev.

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“…The hierarchical method merges the groups that are close to one another until all of the groups are merged into one, whereas the non-hierarchical method divides the objects into K clusters by measure of similarity. Davis and Kalkstein (1990) and Davis and Walker (1992) developed a two-step cluster analysis method to classify weather patterns, and it was applied and reviewed in many studies (Jimenez et al, 2009;Jimenez et al, 2008;Kaufmann and Whiteman, 1999;Kaufmann and Weber, 1996). Based on their study results, the present study also applied the two-step cluster analysis method to surface wind regionalization.…”

Section: 2 Two-step Cluster Analysismentioning

confidence: 97%

“…Surface wind regionalization has been done by applying the principal component analysis (PCA) by Jimenez et al (2008). They derived wind regions based on two different methods according to temporal variability.…”

Section: Introductionmentioning

confidence: 99%

Surface Wind Regionalization Based on Similarity of Time-series Wind Vectors

Kim¹,

Kim²,

Park³

2016

Asian J. Atmos. Environ

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In the complex terrain where local wind systems are formed, accurate understanding of regional wind variability is required for wind resource assessment. In this paper, cluster analysis based on the similarity of time-series wind vector was applied to classify wind regions with similar wind characteristics and the meteorological validity of regionalization method was evaluated. Wind regions in Jeju Island and Busan were classified using the wind resource map of Korea created by a mesoscale numerical weather prediction modeling. The evaluation was performed by comparing wind speed, wind direction, and wind variability of each wind region. Wind characteristics, such as mean wind speed and prevailing wind direction, in the same wind region were similar and wind characteristics in different wind regions were meteor-statistically distinct. It was able to identify a singular wind region at the top area of Mt. Halla using the inconsistency of wind direction variability. Furthermore, it was found that the regionalization results correspond with the topographic features of Jeju Island and Busan, showing the validity.

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“…The procedure applied is similar to that employed by Jiménez et al (2008) and Lorente-Plazas et al (2014). First, a Principal Component Analysis (Storch and Zwiers, 1999) in Smode is applied to the correlation matrix calculated using daily anomalies of T x (obtained with respect the seasonal cycle).…”

Section: Clustering Procedures For Regionalizationmentioning

confidence: 99%

Attributing trends in extremely hot days to changes in atmospheric dynamics

García‐Valero

Montávez

Gómez-Navarro

et al. 2015

Nat. Hazards Earth Syst. Sci.

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Abstract. This paper presents a method for attributing regional trends in the frequency of extremely hot days (EHDs) to changes in the frequency of the atmospheric patterns that characterize such extraordinary events. The study is applied to mainland Spain and the Balearic Islands for the extended summers of the period 1958-2008, where significant and positive trends in maximum temperature (T x ) have been reported during the second half of the past century.First, the study area was split into eight regions attending to their different temporal variability of the daily T x series obtained from the Spain02 gridded data set using a clustering procedure. Second, the large-scale atmospheric situations causing EHDs are defined by circulation types (CTs). The obtainment of the CTs differs from the majority of CT classifications proposed in the literature. It is based on regional series and on a previous characterization of the main atmospheric situations obtained using only some days classified as extremes in the different regions. Three different atmospheric fields (SLP, T850, and Z500) from ECMWF reanalysis and analysis data and combinations of them (SLP-T850, SLP-Z500, and T850-Z500) are used to produce six different CT classifications. Subsequently, links between EHD occurrence in the different regions and CT for all days have been established. Finally, a simple model to relate the trends in EHDs for each region to the changes in the CT frequency appearance has been formulated.Most regions present positive and significant trends in the occurrence of EHDs. The CT classifications using two variables perform better. In particular, SLP-T850 is the best for characterizing the atmospheric situations leading to EHD occurrences for most of the regions. Only a small number of CTs have significant trends in their frequency and are associated with high efficiency causing EHD occurrences in most regions simultaneously, especially in the northern and central regions. Attribution results show that changes in circulation can only explain some part of the regional EHD trends. The percentage of the trend attributable to changes in atmospheric dynamics varies from 15 to 50 %, depends on the region and is sensitive to the selected large-scale variables.

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Surface Wind Regionalization in Complex Terrain

Cited by 50 publications

References 39 publications

Sensitivity of the WRF model to PBL parametrisations and nesting techniques: evaluation of wind storms over complex terrain

Sensitivity of the WRF model to PBL parametrisations and nesting techniques: evaluation of wind storms over complex terrain

Surface Wind Regionalization Based on Similarity of Time-series Wind Vectors

Attributing trends in extremely hot days to changes in atmospheric dynamics

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