scholarly journals Downslope windstorms and associated extreme wind speed statistics evaluation according to the COSMO-CLM Russian Arctic hindcast, ASR reanalysis and observations

2021 ◽  
Author(s):  
Vladimir Platonov ◽  
Anna Shestakova
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Temperature Fields ◽  
The Arctic ◽  
Russian Arctic ◽  
Novaya Zemlya ◽  
Wind Speeds ◽  
Extreme Wind ◽  
Extreme Wind Speed ◽  
Downslope Windstorms

<p>The number of severe weather events at the Arctic region increased significantly. Its formation related generally to the mesoscale processes including downslope windstorms over Novaya Zemlya, Svalbard, Tiksi bay accompanied by strong winds. Therefore, its investigation required detailed hydrometeorological and climatic information with a horizontal resolution of at least several kilometers. This work aims to investigate extreme wind speeds statistics associated with downslope windstorms and evaluate it according to the COSMO-CLM Russian Arctic hindcast, ASR reanalysis, stations and satellite data.</p><p>COSMO-CLM Russian Arctic hindcast created in 2020 covers the 1980–2016 period with grid size ~12 km and 1-hour output step, containing approximately a hundred hydrometeorological characteristics, as well at surface, as on the 50 model levels. The primary assessments of the surface wind speed and temperature fields showed good agreement with ERA-Interim reanalysis in large-scale patterns and many added values in the regional mesoscale features reproduction according to the coastlines, mountains, large lakes, and other surface properties.</p><p>Mean values, absolute and daily maxima of wind speed, high wind speed frequencies were estimated for the COSMO-CLM Russian Arctic hindcast and the well-known Arctic System Reanalysis (ASRv2) for a 2000-2016 period. COSMO-CLM showed higher mean and daily maximal wind speed areas concerned to coastal regions of Svalbard and Scandinavia, over the northern areas of Taymyr peninsula. At the same time, the absolute wind speed maxima are significantly higher according to ASRv2, specially over the Barents Sea, near the Novaya Zemlya coast (differences are up to 15-20 m/s). The same pattern observed by a number of days with wind speed above the 30 m/s threshold. Compared with station data, the ASRv2 reproduced mean wind speeds better at most coastal and inland station, MAE are within 3 m/s. For absolute wind speed maxima differences between two datasets get lower, the COSMO-CLM hindcast is quite better for inland stations.</p><p>Model capability to reproduce strong downslope windstorms evaluated according to the observations timeseries over Novaya Zemlya, Svalbard and Tiksi stations during bora conditions. Generally, the ASRv2 reproduced the wind direction closer to observations and the wind speed worser than COSMO-CLM. The extreme wind speed frequencies during bora cases have less errors according to COSMO-CLM hindcast (up to ~5%) compared to the ASRv2 data (up to 10%). At the same time, moderate wind speed frequencies are reproduced by ASRv2 better.</p><p>Five specific Novaya Zemlya bora cases were evaluated according to SAR satellite wind speed data. Both ASRv2 and COSMO-CLM overestimated mean wind speed (MAE 0.5-6 m/s), maximal wind speed bias has different signs, however, the COSMO-CLM is better in most cases. Extreme percentiles biases (99 and 99.9%), correlation, structure and amplitude (according to the SAL method) are closer to observations by the COSMO-CLM hindcast.</p>

scholarly journals Extreme wind speed prediction in mountainous area with mixed wind climates

2022 ◽  
Author(s):  
Teng Ma ◽  
Wei Cui ◽  
Lin Zhao ◽  
Yejun Ding ◽  
Genshen Fang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Mountainous Area ◽  
Construction Site ◽  
Wind System ◽  
Mountainous Areas ◽  
Wind Speeds ◽  
Extreme Wind ◽  
Synoptic Wind ◽  
Extreme Wind Speed ◽  
Extreme Wind Speeds

Abstract In addition to common synoptic wind system, the mountainous terrain forms a local thermally driven wind system, which makes the mountain wind system have strong terrain dependence. Therefore, in order to estimate the reliable design wind speeds for structural safety, the samples for extreme wind speeds for certain return periods at mountainous areas can only come from field measurements at construction site. However, wind speeds measuring duration is usually short in real practice. This work proposes a novel method for calculating extreme wind speeds in mountainous areas by using short-term field measurement data and long-term nearby meteorological observatory data. Extreme wind speeds in mountainous area are affected by mixed climates composed by local-scale wind and large scale synoptic wind. The local winds can be recorded at construction site with short observatory time, while the extreme wind speeds samples from synoptic wind climate from nearby meteorological station with long observatory time is extracted for data augmentation. The bridge construction site at Hengduan Mountains in southwestern China is taken as an example in this study. A 10-month dataset of field measurement wind speeds is recorded at this location. This study firstly provides a new method to extract wind speed time series of windstorms. Based on the different windstorm features, the local and synoptic winds are separated. Next, the synoptic wind speeds from nearby meteorological stations are converted and combined with local winds to derive the extreme wind speeds probability distribution function. The calculation results shows that the extreme wind speed in the short return period is controlled by the local wind system, and the long-period extreme wind speed is determined by the synoptic wind system in the mountain area.

COSMO-CLM Russian Arctic hindcast 1980 – 2016: experimental design and first evaluation results

2021 ◽  
Author(s):  
Vladimir Platonov ◽  
Mikhail Varentsov
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Western Coast ◽  
Russian Arctic ◽  
Eastern Siberia ◽  
High Wind ◽  
High Wind Speed ◽  
Novaya Zemlya ◽  
Severnaya Zemlya

<p>Detailed long-term hydrometeorological dataset for Russian Arctic seas was created using hydrodynamic modelling via regional nonhydrostatic atmospheric model COSMO-CLM for 1980 – 2016 period with ~12 km grid. Many test experiments with different model options for summertime and wintertime periods were evaluated to determine the best model configuration. Verification has showed that optimal model setup included usage of ERA-Interim reanalysis as forcing data, new model version 5.05 with a so-called ICON-based physics and spectral nudging technique. Final long-term experiments were simulated on the MSU Supercomputer Complex “Lomonosov-2” become more than 120 Tb data volume excluding many side files.</p><p>Primary evaluation of obtained dataset was done for surface wind and temperature variables. There are some mesoscale details in wind sped climatology reproduced by COSMO-CLM dataset including the Svalbard, Severnaya Zemlya islands, and the western coast of the Novaya Zemlya island. At the same time, high wind speed frequencies based on COSMO-CLM data increased compared to ERA-Interim, especially over Barents Sea, Arctic islands (Novaya Zemlya) and some seacoasts and mainland areas. Regional details are manifested in wind speed increase and marked well for large lakes and orography (Taymyr and Kola peninsulas, Eastern Siberia highlands).</p><p>Comparison of two periods (1980 ­­– 1990 and 2010 – 2016) has shown that spatial distributions of high wind speed frequencies are very similar, but there are some detailed differences. Wind speed frequencies above 20.8 m/s has been decreased in the last decade over the Novaya Zemlya, southwest from Svalbard, middle Siberia inlands; however, it has been increased over Franz Josef Land and Severnaya Zemlya.</p><p>Large-scale temperature climatology patterns have shown a good accordance between ERA-Interim and COSMO-CLM datasets. Significant temperature patterns are detailed relief and lakes manifestations, e.g., over Scandinavian mountains, Eastern Siberian and Taymyr highlands, Novaya Zemlya ranges. The added value in the 1% temperature percentile patterns is more pronounced, especially in the mountainous Eastern Siberia. Regional features are prominent over Onega and Ladoga lakes, and western Kara Sea. There is a remarkable warming over islands and Eastern Siberia valleys, and more clear temperature differentiation between ridges and valleys.</p><p>The nearest prospect of the COSMO-CLM Russian Arctic dataset application is its comparison with other appropriate datasets including reanalyses, satellite data, observations, etc. This will provide important and useful information about opportunities and restrictions of this dataset regarding different variables and specific regions, outline the limits of its applicability and get framework of possible tasks. The other important task is to share this dataset with scientific community.</p>

Extreme wind speed climatology over Greece

2021 ◽  
Author(s):  
Georgios Blougouras ◽  
Chris G. Tzanis ◽  
Kostas Philippopoulos
Keyword(s):  
Wind Speed ◽  
Confidence Intervals ◽  
Wind Turbines ◽  
Economic Losses ◽  
Return Periods ◽  
Wind Speeds ◽  
Return Levels ◽  
Extreme Wind ◽  
Extreme Wind Speed ◽  
Extreme Wind Speeds

<p>Extreme wind speeds are a multifaceted environmental risk. They may cause considerable damage to infrastructure (e.g., bridges, private property), they can jeopardize maritime and aviation activities, and sometimes even human safety. Furthermore, the design of wind turbines for on and off-shore wind farms requires a study of the return periods of extreme wind speeds in combination with the lifespan of the wind turbines. Windstorms also result in major economic losses and cause up to 80 % of the natural hazards' long term insurance loss in Europe. The scope of this work is to identify location-specific extreme wind speed thresholds and obtain accurate estimates of exceedances for multiple future horizons. In this context, the Extreme Value Analysis framework is used for providing the return periods and the respective return levels of extreme wind speeds. The Peaks Over Threshold method is utilized for the 10 m wind speed for a domain centered over Greece, in Southeastern Mediterranean. Wind speed data at 10 m are extracted from the ERA5 reanalysis dataset that provides hourly estimates of surface wind speed with a horizontal resolution of 0.25°x0.25°, from 1979/01/01 up to 2019/12/31 (i.e., 41 years). The thresholds are selected using the Mean Residual Life plots, which is the most reliable method for identifying accurate threshold values. The seasonal analysis of the exceedances is discussed in terms of the physical mechanisms in the region. The exceedances are modelled using the Generalized Pareto Distribution, whose shape and scale parameters (<em>ξ</em> and <em>σ</em>, respectively) are estimated using the Maximum Likelihood Estimation method. The return levels and their confidence intervals are estimated for return periods up to 100 years. Geographic Information Systems are used for mapping future projections of extreme wind speeds and the corresponding confidence intervals. The results are discussed in terms of identifying high-risk areas and the findings could assist in informed decision-making in the wind energy industry. The proposed methodological framework could be extended to other areas characterized by particularly high wind speeds and the results can contribute towards sustainable investments and support adaptation mechanisms.</p>

scholarly journals The Compound Inverse Rayleigh as an Extreme Wind Speed Distribution and its Bayes Estimation

2021 ◽  
Author(s):  
Elio Chiodo ◽  
Maurizio Fantauzzi ◽  
Giovanni Mazzanti
Keyword(s):  
Wind Speed ◽  
Prior Information ◽  
Extreme Values ◽  
Rayleigh Distribution ◽  
Bayes Estimation ◽  
Large Set ◽  
Wind Speeds ◽  
Extreme Wind ◽  
Extreme Wind Speed ◽  
Extreme Wind Speeds

The paper deals with the Compound Inverse Rayleigh distribution, shown to constitute a proper model for the characterization of the probability distribution of extreme values of wind-speed, a topic which is gaining growing interest in the field of renewable generation assessment, both in view of wind power production evaluation and the wind-tower mechanical reliability and safety. The first part of the paper illustrates such model starting from its origin as a generalization of the Inverse Rayleigh model - already proven to be a valid model for extreme wind-speeds - by means of a continuous mixture generated by a Gamma distribution on the scale parameter, which gives rise to its name. Moreover, its validity to interpret different field data is illustrated, also by means of numerous numerical examples based upon real wind speed measurements. Then, a novel Bayes approach for the estimation of such extreme wind-speed model is proposed. The method relies upon the assessment of prior information in a practical way, that should be easily available to system engineers. In practice, the method allows to express one’s prior beliefs both in terms of parameters, as customary, and/or in terms of probabilities. The results of a large set of numerical simulations – using typical values of wind-speed parameters - are reported to illustrate the efficiency and the accuracy of the proposed method. The validity of the approach is also verified in terms of its robustness with respect to significant differences compared to the assumed prior information.

Hurricane Surface Wind Measurements from an Operational Stepped Frequency Microwave Radiometer

2007 ◽  
Vol 135 (9) ◽  
pp. 3070-3085 ◽  
Author(s):  
Eric W. Uhlhorn ◽  
Peter G. Black ◽  
James L. Franklin ◽  
Mark Goodberlet ◽  
James Carswell ◽  
...  
Keyword(s):  
Wind Speed ◽  
Drag Coefficient ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Model Function ◽  
Near Surface ◽  
Wind Speeds ◽  
Extreme Wind ◽  
High Bias ◽  
Stepped Frequency

Abstract For the first time, the NOAA/Aircraft Operations Center (AOC) flew stepped frequency microwave radiometers (SFMRs) on both WP-3D research aircraft for operational hurricane surface wind speed measurement in 2005. An unprecedented number of major hurricanes provided ample data to evaluate both instrument performance and surface wind speed retrieval quality up to 70 m s−1 (Saffir–Simpson category 5). To this end, a new microwave emissivity–wind speed model function based on estimates of near-surface winds in hurricanes by global positioning system (GPS) dropwindsondes is proposed. For practical purposes, utilizing this function removes a previously documented high bias in moderate SFMR-measured wind speeds (10–50 m s−1), and additionally corrects an extreme wind speed (>60 m s−1) underestimate. The AOC operational SFMRs yield retrievals that are precise to within ∼2% at 30 m s−1, which is a factor of 2 improvement over the NOAA Hurricane Research Division’s SFMR, and comparable to the precision found here for GPS dropwindsonde near-surface wind speeds. A small (1.6 m s−1), but statistically significant, overall high bias was found for independent SFMR measurements utilizing emissivity data not used for model function development. Across the range of measured wind speeds (10–70 m s−1), SFMR 10-s averaged wind speeds are within 4 m s−1 (rms) of the dropwindsonde near-surface estimate, or 5%–25% depending on speed. However, an analysis of eyewall peak wind speeds indicates an overall 2.6 m s−1 GPS low bias relative to the peak SFMR estimate on the same flight leg, suggesting a real increase in the maximum wind speed estimate due to SFMR’s high-density sampling. Through a series of statistical tests, the SFMR is shown to reduce the overall bias in the peak surface wind speed estimate by ∼50% over the current flight-level wind reduction method and is comparable at extreme wind speeds. The updated model function is demonstrated to behave differently below and above the hurricane wind speed threshold (∼32 m s−1), which may have implications for air–sea momentum and kinetic energy exchange. The change in behavior is at least qualitatively consistent with recent laboratory and field results concerning the drag coefficient in high wind speed conditions, which show a fairly clear “leveling off” of the drag coefficient with increased wind speed above ∼30 m s−1. Finally, a composite analysis of historical data indicates that the earth-relative SFMR peak wind speed is typically located in the hurricane’s right-front quadrant, which is consistent with previous observational and theoretical studies of surface wind structure.

scholarly journals Future Trends of Arctic Surface Wind Speeds and their Relationship with Sea Ice in CMIP5 Climate Model Simulations

2021 ◽  
Author(s):  
Stephen Vavrus ◽  
Ramdane Alkama
Keyword(s):  
Surface Roughness ◽  
Wind Speed ◽  
Sea Ice ◽  
Surface Wind ◽  
Ice Cover ◽  
The Arctic ◽  
Future Trends ◽  
Surface Winds ◽  
Wind Speeds ◽  
Arctic Surface

Abstract Recent climate change in the Arctic has been rapid and dramatic, leading to numerous physical and societal consequences. Many studies have investigated these ongoing and projected future changes across a range of climatic variables, but surprisingly little attention has been paid to wind speed, despite its known importance for sea ice motion, ocean wave heights, and coastal erosion. Here we analyzed future trends in Arctic surface wind speed and its relationship with sea ice cover among CMIP5 global climate models. There is a strong anticorrelation between climatological sea ice concentration and wind speed in the early 21 st -century reference climate, and the vast majority of models simulate widespread future strengthening of surface winds over the Arctic Ocean (annual multi-model mean trend of up to 0.8 m s -1 or 13%). Nearly all models produce an inverse relationship between projected changes in sea ice cover and wind speed, such that grid cells with virtually total ice loss almost always experience stronger winds. Consistent with the largest regional ice losses during autumn and winter, the greatest increases in future wind speeds are expected during these two seasons, with localized strengthening up to 23%. As in other studies, stronger surface winds cannot be attributed to tighter pressure gradients but rather to some combination of weakened atmospheric stability and reduced surface roughness as the surface warms and melts. The intermodel spread of wind speed changes, as expressed by the two most contrasting model results, appears to stem from differences in the treatment of surface roughness.

scholarly journals Projected Changes to Mean and Extreme Surface Wind Speeds for North America Based on Regional Climate Model Simulations

Atmosphere ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 497
Author(s):  
Dae Il Jeong ◽  
Laxmi Sushama
Keyword(s):  
Wind Speed ◽  
Climate Model ◽  
Regional Climate ◽  
Surface Wind ◽  
Annual Maximum ◽  
Wind Speeds ◽  
Extreme Wind ◽  
Mean Wind Speed ◽  
Climate Model Simulations ◽  
Projected Changes

This study evaluates projected changes to surface wind characteristics for the 2071–2100 period over North America (NA), using four Global Environmental Multiscale regional climate model simulations, driven by two global climate models (GCMs) for two Representative Concentration Pathway scenarios. For the current climate, the model simulates well the climatology of mean sea level pressure (MSLP) and associated wind direction over NA. Future simulations suggest increases in mean wind speed for northern and eastern parts of Canada, associated with decreases in future MSLP, which results in more intense low-pressure systems situated in those regions such as the Aleutian and Icelandic Lows. Projected changes to annual maximum 3-hourly wind speed show more spatial variability compared to seasonal and annual mean wind speed, indicating that extreme wind speeds are influenced by regional level features associated with instantaneous surface temperature and air pressure gradients. The simulations also suggest some increases in the future 50-year return levels of 3-hourly wind speed and hourly wind gusts, mainly due to increases in the inter-annual variability of annual maximum values. The variability of projected changes to both extreme wind speed and gusts indicate the need for a larger set of projections, including those from other regional models driven by many GCMs to better quantify uncertainties in future wind extremes and their characteristics.

Sign in / Sign up

Export Citation Format

Share Document