scholarly journals Evaluation of an Air Pressure–Based Proxy for Storm Activity

2011 ◽  
Vol 24 (10) ◽  
pp. 2612-2619 ◽  
Author(s):  
Oliver Krueger ◽  
Hans von Storch
Keyword(s):  
Wind Speed ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Surface Wind ◽  
Ground Level ◽  
Geostrophic Wind ◽  
Air Pressure ◽  
Storm Activity ◽  
Wind Speeds

Abstract Yearly percentiles of geostrophic wind speeds serve as a widely used proxy for assessing past storm activity. Here, daily geostrophic wind speeds are derived from a geographical triangle of surface air pressure measurements and are used to build yearly frequency distributions. It is commonly believed, however unproven, that the variation of the statistics of strong geostrophic wind speeds describes the variation of statistics of ground-level wind speeds. This study evaluates this approach by examining the correlation between specific annual (seasonal) percentiles of geostrophic and of area-maximum surface wind speeds to determine whether the two distributions are linearly linked in general. The analyses rely on bootstrap and binomial hypothesis testing as well as on analysis of variance. Such investigations require long, homogeneous, and physically consistent data. Because such data are barely existent, regional climate model–generated wind and surface air pressure fields in a fine spatial and temporal resolution are used. The chosen regional climate model is the spectrally nudged and NCEP-driven regional model (REMO) that covers Europe and the North Atlantic. Required distributions are determined from diagnostic 10-m and geostrophic wind speed, which is calculated from model air pressure at sea level. Obtained results show that the variation of strong geostrophic wind speed statistics describes the variation of ground-level wind speed statistics. Annual and seasonal quantiles of geostrophic wind speed and ground-level wind speed are positively linearly related. The influence of low-pass filtering is also considered and found to decrease the quality of the linear link. Moreover, several factors are examined that affect the description of storminess through geostrophic wind speed statistics. Geostrophic wind from sea triangles reflects storm activity better than geostrophic wind from land triangles. Smaller triangles lead to a better description of storminess than bigger triangles.

German Bight Storminess over the Last Century

2020 ◽  
Author(s):  
Daniel Krieger ◽  
Oliver Krueger ◽  
Frauke Feser ◽  
Ralf Weisse ◽  
Birger Tinz ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
German Bight ◽  
Geostrophic Wind ◽  
Air Pressure ◽  
Offshore Wind ◽  
Pressure Data ◽  
Storm Activity ◽  
Wind Speeds ◽  
North East

<p>Assessing past storm activity provides valuable knowledge for economic and ecological sectors, such as the renewable energy sector, insurances, or health and safety. However, long time series of wind speed measurements are often not available as they are usually hampered by inhomogeneities due to changes in the surroundings of a measurement site, station relocations, and changes in the instrumentation. On the contrary, air pressure measurements provide mostly homogeneous time series as the air pressure is usually unaffected by such factors.</p><p>Therefore, we perform statistical analyses on historical pressure data measured at several locations within the German Bight (southeastern North Sea) between 1897 and 2018. We calculate geostrophic wind speeds from triplets of mean sea level pressure observations that form triangles over the German Bight. We then investigate the evolution of German Bight storminess from 1897 to 2018 through analyzing upper quantiles of geostrophic wind speeds, which act as a proxy for past storm activity. The derivation of storm activity is achieved by enhancing the established triangle proxy method via combining and merging storminess time series from numerous partially overlapping triangles in an ensemble-like manner. The utilized approach allows for the construction of robust, long-term and subdaily German Bight storminess time series. Further, the method provides insights into the underlying uncertainty of the time series.</p><p>The results show that storm activity over the German Bight is subject to multidecadal variability. The latest decades are characterized by an increase in activity from the 1960s to the 1990s, followed by a decline lasting into the 2000s and below-average activity up until present. The results are backed through a comparison with reanalysis products from four datasets, which provide high-resolution wind and pressure data starting in 1979 and offshore wind speed measurements taken from the FINO-WIND project. This study also finds that German Bight storminess positively correlates with storminess in the North-East Atlantic in general. In certain years, however, notably different levels of storm activity in the two regions can be found, which likely result from shifted large-scale circulation patterns.</p>

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.

scholarly journals A Weibull Approach for Improving Climate Model Projections of Tropical Cyclone Wind-Speed Distributions

2014 ◽  
Vol 27 (16) ◽  
pp. 6119-6133 ◽  
Author(s):  
Mari R. Tye ◽  
David B. Stephenson ◽  
Greg J. Holland ◽  
Richard W. Katz
Keyword(s):  
Wind Speed ◽  
Tropical Cyclone ◽  
Regional Climate Model ◽  
Climate Model ◽  
Probability Distributions ◽  
Regional Climate ◽  
Model Data ◽  
Maximum Wind ◽  
Wind Speeds ◽  
Climate Model Simulations

Abstract Reliable estimates of future changes in extreme weather phenomena, such as tropical cyclone maximum wind speeds, are critical for climate change impact assessments and the development of appropriate adaptation strategies. However, global and regional climate model outputs are often too coarse for direct use in these applications, with variables such as wind speed having truncated probability distributions compared to those of observations. This poses two problems: How can model-simulated variables best be adjusted to make them more realistic? And how can such adjustments be used to make more reliable predictions of future changes in their distribution? This study investigates North Atlantic tropical cyclone maximum wind speeds from observations (1950–2010) and regional climate model simulations (1995–2005 and 2045–55 at 12- and 36-km spatial resolutions). The wind speed distributions in these datasets are well represented by the Weibull distribution, albeit with different scale and shape parameters. A power-law transfer function is used to recalibrate the Weibull variables and obtain future projections of wind speeds. Two different strategies, bias correction and change factor, are tested by using 36-km model data to predict future 12-km model data (pseudo-observations). The strategies are also applied to the observations to obtain likely predictions of the future distributions of wind speeds. The strategies yield similar predictions of likely changes in the fraction of events within Saffir–Simpson categories—for example, an increase from 21% (1995–2005) to 27%–37% (2045–55) for category 3 or above events and an increase from 1.6% (1995–2005) to 2.8%–9.8% (2045–55) for category 5 events.

scholarly journals Evaluation of Arctic sea-ice drift and its dependency on near-surface wind and sea-ice concentration and thickness in the coupled regional climate model HIRHAM-NAOSIM

2019 ◽  
Author(s):  
Xiaoyong Yu ◽  
Annette Rinke ◽  
Wolfgang Dorn ◽  
Gunnar Spreen ◽  
Christof Lüpkes ◽  
...  
Keyword(s):  
Wind Speed ◽  
Sea Ice ◽  
Climate Model ◽  
Regional Climate ◽  
Surface Wind ◽  
Arctic Sea Ice ◽  
Near Surface ◽  
Drift Speed ◽  
Ice Drift ◽  
Sea Ice Drift

Abstract. We examine the simulated Arctic sea-ice drift speed for the period 2003–2014 in the coupled Arctic regional climate model HIRHAM-NAOSIM 2.0. In particular, we evaluate the dependency of the drift speed on the near-surface wind speed and sea-ice conditions. Considering the seasonal cycle of Arctic basin averaged drift speed, the model reproduces the summer-autumn drift speed well, but significantly overestimates the winter-spring drift speed, compared to satellite-derived observations. Also, the model does not capture the observed seasonal phase lag between drift and wind speed, but the simulated drift speed is more in phase with near-surface wind. The model calculates a realistic negative relationship between drift speed and ice thickness and between drift speed and ice concentration during summer-autumn when concentration is relatively low, but the correlation is weaker than observed. A daily grid-scale diagnostic indicates that the model reproduces the observed positive relationship between drift and wind speed. The strongest impact of wind changes on drift speed occurs for high and moderate wind speeds, with a low impact for calm conditions. The correlation under low-wind conditions is overestimated in the simulations, compared to observation/reanalysis. A sensitivity experiment demonstrates the significant effects of sea-ice form drag included by an improved parameterization of the transfer coefficients for momentum and heat over sea ice. However, this does not improve the agreement of the modelled drift speed/wind speed ratio with observations based on reanalysis for wind and remote sensing for sea ice drift. An improvement might be possible, among others, by tuning the open parameters of the parameterization in future.

scholarly journals The Informational Value of Pressure-Based Single-Station Proxies for Storm Activity

2012 ◽  
Vol 29 (4) ◽  
pp. 569-580 ◽  
Author(s):  
Oliver Krueger ◽  
Hans von Storch
Keyword(s):  
Ground Level ◽  
Air Pressure ◽  
Systematic Evaluation ◽  
Informational Content ◽  
Absolute Pressure ◽  
Storm Activity ◽  
Mean Values ◽  
Wind Speeds ◽  
Single Station ◽  
Informational Value

Abstract Air pressure readings and their variations are commonly used to make inferences about storm activity. More precisely, it is assumed that the variation of annual and seasonal statistics of several pressure-based proxies describes changes in the past storm climate qualitatively, an assumption that has yet to be proven. A systematic evaluation of the informational content of five pressure-based proxies for storm activity based on single-station observations of air pressure is presented. The number of deep lows, lower percentiles of pressure, the frequency of absolute pressure tendencies above certain thresholds, as well as mean values and high percentiles of absolute pressure tendencies is examined. Such an evaluation needs long and homogeneous records of wind speed, something that is not available from observations. Consequently, the proxies are examined by using datasets of ground-level wind speeds and air pressure from the NCEP-driven and spectrally nudged regional model, REMO. The proxies are gauged against the 95th and 99th percentile time series of ground-level wind speeds to quantify the relation between pressure-based proxies and storminess. These analyses rely on bootstrap and binomial hypothesis testing. The analyses of single-station-based proxies indicate that the proxies are generally linearly linked to storm activity, and that absolute pressure tendencies have the highest informational content. Further, it is investigated as to whether the proxies have the potential for describing storminess over larger areas, also with regard to surface conditions. It is found that absolute pressure tendencies have improved informational value when describing storm activity over larger areas, while low pressure readings do not show improved informational value.

scholarly journals The “urban meteorology island”: a multi-model ensemble analysis

2020 ◽  
Vol 20 (23) ◽  
pp. 15061-15077
Author(s):  
Jan Karlický ◽  
Peter Huszár ◽  
Tereza Nováková ◽  
Michal Belda ◽  
Filip Švábik ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Regional Climate Model ◽  
Cloud Cover ◽  
Climate Model ◽  
Regional Climate ◽  
Meteorological Variables ◽  
Layer Height ◽  
Boundary Layer Height ◽  
Induced Changes

Abstract. Cities and urban areas are well-known for their impact on meteorological variables and thereby modification of the local climate. Our study aims to generalize the urban-induced changes in specific meteorological variables by introducing a single phenomenon – the urban meteorology island (UMI). A wide ensemble of 24 model simulations with the Weather Research and Forecasting (WRF) regional climate model and the Regional Climate Model (RegCM) on a European domain with 9 km horizontal resolution were performed to investigate various urban-induced modifications as individual components of the UMI. The results show that such an approach is meaningful, because in nearly all meteorological variables considered, statistically significant changes occur in cities. Besides previously documented urban-induced changes in temperature, wind speed and boundary-layer height, the study is also focused on changes in cloud cover, precipitation and humidity. An increase in cloud cover in cities, together with a higher amount of sub-grid-scale precipitation, is detected on summer afternoons. Specific humidity is significantly lower in cities. Further, the study shows that different models and parameterizations can have a strong impact on discussed components of the UMI. Multi-layer urban schemes with anthropogenic heat considered increase winter temperatures by more than 2 ∘C and reduce wind speed more strongly than other urban models. The selection of the planetary-boundary-layer scheme also influences the urban wind speed reduction, as well as the boundary-layer height, to the greatest extent. Finally, urban changes in cloud cover and precipitation are mostly sensitive to the parameterization of convection.

scholarly journals Drifting snow climate of the Greenland ice sheet: a study with a regional climate model

2012 ◽  
Vol 6 (3) ◽  
pp. 1611-1635 ◽  
Author(s):  
J. T. M. Lenaerts ◽  
M. R. van den Broeke ◽  
J. H. van Angelen ◽  
E. van Meijgaard ◽  
S. J. Déry
Keyword(s):  
Wind Speed ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Scale ◽  
Winter Season ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Near Surface ◽  
Drifting Snow

Abstract. This paper presents the drifting snow climate of the Greenland ice sheet, using output from a high-resolution (~11 km) regional climate model (RACMO2). Because reliable direct observations of drifting snow do not exist, we evaluate the modeled near-surface climate instead, using Automatic Weather Station (AWS) observations from the K-transect and find that RACMO2 realistically simulates near-surface wind speed and relative humidity, two variables that are important for drifting snow. Integrated over the ice sheet, drifting snow sublimation (SUds) equals 24 ± 3 Gt yr−1, and is significantly larger than surface sublimation (SUs, 16 ± 2 Gt yr−1). SUds strongly varies between seasons, and is only important in winter, when surface sublimation and runoff are small. A rapid transition exists between the winter season, when snowfall and SUds are important, and the summer season, when snowmelt is significant, which increases surface snow density and thereby limits drifting snow processes. Drifting snow erosion (ERds) is only important on a regional scale. In recent decades, following decreasing wind speed and rising near-surface temperatures, SUds exhibits a negative trend (0.1 ± 0.1 Gt yr−1), which is compensated by an increase in SUs of similar magnitude.

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