Calculated wind climatology of the South-Saxonian/North-Czech mountain topography including improved resolution of mountains

Abstract. A mesoscale model has been applied to calculate climatological means of the surface wind. A reliable average requires more than 40 model runs, which are differentiated by the direction and speed of the geostrophic wind under the assumption of neutral stratification. The frequency distributions of the geostrophic wind have been taken from observations of the 850-hPa winds at the radiosonde station in Prague for a 10-year period. The simulation results have been averaged over all sectors and speed classes of the geostrophic wind according to their frequencies. A comparison of the calculated mean wind speeds with observed ones shows deviations of about 0.4 ms–1 outside the mountains. The representation of steep topography and isolated mountains on the basis of a 3-km horizontal resolution of the simulations needs special treatment in order to reduce the gap of up to 4 ms–1 between observed and simulated mean wind speeds over mountains. Therefore, an empiric speed-up formula has been applied to the isolated mountains that otherwise would fall through the 3-km meshes. The corresponding deviations have been reduced to 1.5 ms–1.

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A Reanalysis System for the Generation of Mesoscale Climatographies

Journal of Applied Meteorology and Climatology ◽

10.1175/2009jamc2351.1 ◽

2010 ◽

Vol 49 (5) ◽

pp. 954-972 ◽

Cited By ~ 51

Author(s):

Andrea N. Hahmann ◽

Dorita Rostkier-Edelstein ◽

Thomas T. Warner ◽

Francois Vandenberghe ◽

Yubao Liu ◽

...

Keyword(s):

Dynamical Downscaling ◽

Mesoscale Model ◽

Eastern Mediterranean ◽

Daily Rainfall ◽

Horizontal Resolution ◽

Monthly Rainfall ◽

Method Performance ◽

Diurnal Variability ◽

Frequency Distributions ◽

Downscaling Method

Abstract The use of a mesoscale model–based four-dimensional data assimilation (FDDA) system for generating mesoscale climatographies is demonstrated. This dynamical downscaling method utilizes the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), wherein Newtonian relaxation terms in the prognostic equations continually nudge the model solution toward surface and upper-air observations. When applied to a mesoscale climatography, the system is called Climate-FDDA (CFDDA). Here, the CFDDA system is used for downscaling eastern Mediterranean climatographies for January and July. The downscaling method performance is verified by using independent observations of monthly rainfall, Quick Scatterometer (QuikSCAT) ocean-surface winds, gauge rainfall, and hourly winds from near-coastal towers. The focus is on the CFDDA system’s ability to represent the frequency distributions of atmospheric states in addition to time means. The verification of the monthly rainfall climatography shows that CFDDA captures most of the observed spatial and interannual variability, although the model tends to underestimate rainfall amounts over the sea. The frequency distributions of daily rainfall are also accurately diagnosed for various regions of the Levant, except that very light rainfall days and heavy precipitation amounts are overestimated over Lebanon. The verification of the CFDDA against QuikSCAT ocean winds illustrates an excellent general correspondence between observed and modeled winds, although the CFDDA speeds are slightly lower than those observed. Over land, CFDDA- and the ECMWF-derived wind climatographies when compared with mast observations show similar errors related to their inability to properly represent the local orography and coastline. However, the diurnal variability of the winds is better estimated by CFDDA because of its higher horizontal resolution.

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The European wind from observational and simulated databases

10.5194/egusphere-egu2020-19846 ◽

2020 ◽

Author(s):

Elena García-Bustamante ◽

Jorge Navarro ◽

Jesús Fidel González-Rouco ◽

E. Etor Lucio- Eceiza ◽

Cristina Rojas-Labanda ◽

...

Keyword(s):

Wind Field ◽

Mesoscale Model ◽

Surface Wind ◽

Wrf Model ◽

Horizontal Resolution ◽

Spatial And Temporal Variability ◽

Model Simulations ◽

Wind Variability ◽

Observational Database ◽

Wind Velocities

The New European Wind Atlas (https://map.neweuropeanwindatlas.eu) is developed based on the simulated wind field over Europe from a mesoscale model coupled to a microscale component through a statistical downscaling approach. The simulation that provides mesoscale inputs within the model chain has been decided upon a careful sensitivity analysis of potential model configurations. In order to accomplish model resolutions of 3 km over Europe, the broader European domain is partitioned into a set of 10 partially overlapping tiles. The wind field is simulated with the WRF model over these tiles and finally blended into a single domain. The wind outputs from a reference simulation is evaluated on the basis of its comparison with an observational database specifically compiled and quality controlled for the purpose of validating the wind atlas over the complete European domain. The observational database includes surface wind observations at ca. 4000 sites as well as 16 masts datasets. The observational dataset of surface wind (WISED) is informative about the spatial and temporal variability of the wind climatology, punctuated with singular masts that provide information of wind velocities at height. The validation of the mesoscale simulation aims at investigating the ability of the high-resolution simulation to reproduce the observed intra-annual variability of daily wind within the entire domain.Observed and simulated winds are higher at the British, North Sea and Baltic shores and lowlands. Correlations are typically over 0.8. Surface wind variability tends to be overestimated in the northern coasts and underestimated elsewhere and inland. Mast wind variability tends to be overestimated except for some southern sites. Seasonal differences seem minor in these respects. Biases and RMSE can help identifying if systematic errors in specific tiles take place.Therefore, performing model simulations of a high horizontal resolution over the broader European domain is possible. We can learn about the variability of surface and height wind both from observations and model simulations. Model observations are not perfect, but observations also present uncertainties. Good quality wind data, both at the surface and in masts are a requisite for robust evaluation of models. European wide features of wind variability can be recognized both in observations and simulations.

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Statistical Downscaling of Gridded Wind Speed Data Using Local Topography

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0054.1 ◽

2017 ◽

Vol 18 (2) ◽

pp. 335-348 ◽

Cited By ~ 12

Author(s):

Adam Winstral ◽

Tobias Jonas ◽

Nora Helbig

Keyword(s):

Wind Speed ◽

Statistical Downscaling ◽

Surface Wind ◽

Horizontal Resolution ◽

Wind Data ◽

Wind Fields ◽

Forecast Performance ◽

Wind Speeds ◽

Mountain Environments ◽

High Winds

Abstract Winds, particularly high winds, strongly affect snowmelt and snow redistribution. High winds during rain-on-snow events can lead to catastrophic flooding while strong redistribution events in mountain environments can generate dangerous avalanche conditions. To provide adequate warnings, accurate wind data are required. Yet, mountain wind fields exhibit a high degree of heterogeneity at small spatial lengths that are not resolved by currently available gridded forecast data. Wind data from over 200 stations across Switzerland were used to evaluate two forecast surface wind products (~2- and 7-km horizontal resolution) and develop a statistical downscaling technique to capture these finer-scaled heterogeneities. Wind exposure metrics derived from a 25-m horizontal resolution digital elevation model effectively segregated high, moderate, and low wind speed sites. Forecast performance was markedly compromised and biased low at the exposed sites and biased high at the sheltered, valley sites. It was also found that the variability of predicted wind speeds at these sites did not accurately represent the observed variability. A novel optimization scheme that accounted for local terrain structure while also nudging the forecasted distributions to better match the observed distributions and variability was developed. The resultant statistical downscaling technique notably decreased biases across a range of elevations and exposures and provided a better match to observed wind speed distributions.

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Cloud-base vertical velocity statistics: a comparison between an atmospheric mesoscale model and remote sensing observations

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-11-9607-2011 ◽

2011 ◽

Vol 11 (3) ◽

pp. 9607-9633

Author(s):

J. Tonttila ◽

E. J. O'Connor ◽

S. Niemelä ◽

P. Räisänen ◽

H. Järvinen

Keyword(s):

Remote Sensing ◽

Standard Deviation ◽

Vertical Velocity ◽

Mesoscale Model ◽

Horizontal Resolution ◽

Cloud Base ◽

Special Treatment ◽

Grid Spacing ◽

Velocity Statistics ◽

Aerosol Cloud Interactions

Abstract. The statistics of cloud-base vertical velocity simulated by the non-hydrostatic mesoscale model AROME are compared with Cloudnet remote sensing observations at two locations: the ARM SGP site in Central Oklahoma, and the DWD observatory at Lindenberg, Germany. The results show that, as expected, AROME significantly underestimates the variability of vertical velocity at cloud-base compared to observations at their nominal resolution; the standard deviation of vertical velocity in the model is typically 4–6 times smaller than observed, and even more during the winter at Lindenberg. Averaging the observations to the horizontal scale corresponding to the physical grid spacing of AROME (2.5 km) explains 70–80% of the underestimation by the model. Further averaging of the observations in the horizontal is required to match the model values for the standard deviation in vertical velocity. This indicates an effective horizontal resolution for the AROME model of at least 4 times the physically-defined grid spacing. The results illustrate the need for special treatment of sub-grid scale variability of vertical velocities in kilometer-scale atmospheric models, if processes such as aerosol-cloud interactions are to be included in the future.

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Cloud base vertical velocity statistics: a comparison between an atmospheric mesoscale model and remote sensing observations

Atmospheric Chemistry and Physics ◽

10.5194/acp-11-9207-2011 ◽

2011 ◽

Vol 11 (17) ◽

pp. 9207-9218 ◽

Cited By ~ 17

Author(s):

J. Tonttila ◽

E. J. O'Connor ◽

S. Niemelä ◽

P. Räisänen ◽

H. Järvinen

Keyword(s):

Remote Sensing ◽

Standard Deviation ◽

Vertical Velocity ◽

Mesoscale Model ◽

Grid Point ◽

Horizontal Resolution ◽

Careful Consideration ◽

Cloud Base ◽

Special Treatment ◽

Velocity Statistics

Abstract. The statistics of cloud base vertical velocity simulated by the non-hydrostatic mesoscale model AROME are compared with Cloudnet remote sensing observations at two locations: the ARM SGP site in central Oklahoma, and the DWD observatory at Lindenberg, Germany. The results show that AROME significantly underestimates the variability of vertical velocity at cloud base compared to observations at their nominal resolution; the standard deviation of vertical velocity in the model is typically 4–8 times smaller than observed, and even more during the winter at Lindenberg. Averaging the observations to the horizontal scale corresponding to the physical grid spacing of AROME (2.5 km) explains 70–80 % of the underestimation by the model. Further averaging of the observations in the horizontal is required to match the model values for the standard deviation in vertical velocity. This indicates an effective horizontal resolution for the AROME model of at least 10 km in the presented case. Adding a TKE-term on the resolved grid-point vertical velocity can compensate for the underestimation, but only for altitudes below approximately the boundary layer top height. The results illustrate the need for a careful consideration of the scales the model is able to accurately resolve, as well as for a special treatment of sub-grid scale variability of vertical velocities in kilometer-scale atmospheric models, if processes such as aerosol-cloud interactions are to be included in the future.

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Evaluation of an Air Pressure–Based Proxy for Storm Activity

Journal of Climate ◽

10.1175/2011jcli3913.1 ◽

2011 ◽

Vol 24 (10) ◽

pp. 2612-2619 ◽

Cited By ~ 18

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.

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Atmospheric dust modeling from meso to global scales with the online NMMB/BSC-Dust model – Part 2: Experimental campaigns in Northern Africa

Atmospheric Chemistry and Physics ◽

10.5194/acp-12-2933-2012 ◽

2012 ◽

Vol 12 (6) ◽

pp. 2933-2958 ◽

Cited By ~ 53

Author(s):

K. Haustein ◽

C. Pérez ◽

J. M. Baldasano ◽

O. Jorba ◽

S. Basart ◽

...

Keyword(s):

Regional Scale ◽

Surface Wind ◽

Companion Paper ◽

Horizontal Resolution ◽

Northern Africa ◽

Saharan Dust ◽

Temperature Cycle ◽

Time Step ◽

Wind Speeds ◽

Dust Modeling

Abstract. The new NMMB/BSC-Dust model is intended to provide short to medium-range weather and dust forecasts from regional to global scales. It is an online model in which the dust aerosol dynamics and physics are solved at each model time step. The companion paper (Pérez et al., 2011) develops the dust model parameterizations and provides daily to annual evaluations of the model for its global and regional configurations. Modeled aerosol optical depth (AOD) was evaluated against AERONET Sun photometers over Northern Africa, Middle East and Europe with correlations around 0.6–0.7 on average without dust data assimilation. In this paper we analyze in detail the behavior of the model using data from the Saharan Mineral dUst experiment (SAMUM-1) in 2006 and the Bodélé Dust Experiment (BoDEx) in 2005. AOD from satellites and Sun photometers, vertically resolved extinction coefficients from lidars and particle size distributions at the ground and in the troposphere are used, complemented by wind profile data and surface meteorological measurements. All simulations were performed at the regional scale for the Northern African domain at the expected operational horizontal resolution of 25 km. Model results for SAMUM-1 generally show good agreement with satellite data over the most active Saharan dust sources. The model reproduces the AOD from Sun photometers close to sources and after long-range transport, and the dust size spectra at different height levels. At this resolution, the model is not able to reproduce a large haboob that occurred during the campaign. Some deficiencies are found concerning the vertical dust distribution related to the representation of the mixing height in the atmospheric part of the model. For the BoDEx episode, we found the diurnal temperature cycle to be strongly dependant on the soil moisture, which is underestimated in the NCEP analysis used for model initialization. The low level jet (LLJ) and the dust AOD over the Bodélé are well reproduced. The remaining negative AOD bias (due to underestimated surface wind speeds) can be substantially reduced by decreasing the threshold friction velocity in the model.

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Evaluating Terrain as a Turbulence Generation Method

Energies ◽

10.3390/en14216858 ◽

2021 ◽

Vol 14 (21) ◽

pp. 6858

Author(s):

Patrick Hawbecker ◽

Matthew Churchfield

Keyword(s):

Boundary Layer ◽

Mesoscale Model ◽

Boundary Layer Height ◽

Simulation Performance ◽

Wind Speeds ◽

Large Eddy ◽

Mean Wind Speed ◽

Speed Up ◽

Capping Inversion ◽

Turbulence Generation

When driving microscale large-eddy simulations with mesoscale model solutions, turbulence will take space to develop, known as fetch, on the microscale domain. To reduce fetch, it is common to add perturbations near the boundaries to speed up turbulence development. However, when simulating domains over complex terrain, it is possible that the terrain itself can quickly generate turbulence within the boundary layer. It is shown here that rugged terrain is able to generate turbulence without the assistance of a perturbation strategy; however, the levels of turbulence generated are improved when adding perturbations at the inlet. Flow over smoothed, but not flat, terrain fails to generate adequate turbulence throughout the boundary layer in all tests conducted herein. Sensitivities to the strength of the mean wind speed and boundary layer height are investigated and show that higher wind speeds produce turbulence over terrain features that slower wind speeds do not. Further, by increasing the height of the capping inversion, the effectiveness of topography alone to generate turbulence throughout the depth of the boundary is diminished. In all cases, the inclusion of a perturbation strategy improved simulation performance with respect to turbulence development.

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Stilling and Recovery of the Surface Wind Speed Based on Observation, Reanalysis, and Geostrophic Wind Theory over China from 1960 to 2017

Journal of Climate ◽

10.1175/jcli-d-19-0281.1 ◽

2020 ◽

Vol 33 (10) ◽

pp. 3989-4008 ◽

Cited By ~ 1

Author(s):

Zhengtai Zhang ◽

Kaicun Wang

Keyword(s):

Wind Speed ◽

Decadal Variability ◽

Surface Wind ◽

Geostrophic Wind ◽

Surface Wind Speed ◽

Strong Wind ◽

Wind Speeds ◽

The North ◽

Wind Theory ◽

Weak Winds

AbstractSurface wind speed (SWS) from meteorological observation, global atmospheric reanalysis, and geostrophic wind speed (GWS) calculated from surface pressure were used to study the stilling and recovery of SWS over China from 1960 to 2017. China experienced anemometer changes and automatic observation transitions in approximately 1969 and 2004, resulting in SWS inhomogeneity. Therefore, we divided the entire period into three sections to study the SWS trend, and found a near-zero annual trend in the SWS in China from 1960 to 1969, a significant decrease of −0.24 m s−1 decade−1 from 1970 to 2004, and a weak recovery from 2005 to 2017. By defining the 95th and 5th percentiles of daily mean wind speeds as strong and weak winds, respectively, we found that the SWS decrease was primarily caused by a strong wind decrease of −8% decade−1 from 1960 to 2017, but weak wind showed an insignificant decreasing trend of −2% decade−1. GWS decreased with a significant trend of −3% decade−1 before the 1990s; during the 1990s, GWS increased with a trend of 3% decade−1 whereas SWS continued to decrease with a trend of 10% decade−1. Consistent with SWS, GWS demonstrated a weak increase after the 2000s. After detrending, both SWS and GWS showed synchronous decadal variability, which is related to the intensity of Aleutian low pressure over the North Pacific. However, current reanalyses cannot reproduce the decadal variability and cannot capture the decreasing trend of SWS either.

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Stilling and Recovery of the Surface Wind Speed Based on Observation, Reanalysis, and Geostrophic Wind Theory over China from 1960 to 2017

10.5194/egusphere-egu2020-2501 ◽

2020 ◽

Author(s):

Zhengtai Zhang ◽

Kaicun Wang

Keyword(s):

Wind Speed ◽

Decadal Variability ◽

Surface Wind ◽

Geostrophic Wind ◽

Surface Wind Speed ◽

Strong Wind ◽

Wind Speeds ◽

The North ◽

Wind Theory ◽

Weak Winds

Surface wind speed (SWS) from meteorological observation, global atmospheric reanalysis, and geostrophic wind speed (GWS) calculated from surface pressure were used to study the stilling and recovery of SWS over China from 1960 to 2017. China experienced anemometer changes and automatic observation transitions in approximately 1969 and 2004, resulting in SWS inhomogeneity. Therefore, we divided the entire period into three sections to study the SWS trend, and found a near zero annual trend in the SWS in China from 1960 to 1969, a significant decrease of -0.24 m/s decade-1 from 1970 to 2004, and a weak recovery from 2005 to 2017. By defining the 95th and 5th percentiles of monthly mean wind speeds as strong and weak winds, respectively, we found that the SWS decrease was primarily caused by a strong wind decrease of -8 % decade-1 from 1960 to 2017, but weak wind showed an insignificant decreasing trend of -2 % decade-1. GWS decreased with a significant trend of -3 % decade-1 before the 1990s, during the 1990s, GWS increased with a trend of 3 % decade-1 whereas SWS continued to decrease with a trend of 10 % decade-1. Consistent with SWS, GWS demonstrated a weak increase after the 2000s. After detrended, both of SWS and GWS showed synchronous decadal variability, which is related to the intensity of Aleutian low pressure over the North Pacific. However, current reanalyses cannot reproduce the decadal variability, and can not capture the decreasing trend of SWS either.

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