scholarly journals A Synoptic Study of Low Troposphere Wind at the Israeli Coast

2018 ◽  
Vol 12 (1) ◽  
pp. 80-106
Author(s):  
Sigalit Berkovic ◽  
Pinhas Alpert
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Interim Analysis ◽  
Surface Wind ◽  
Radiosonde Data ◽  
Good Prediction ◽  
Wind Vector ◽  
The West ◽  
Resolution Model ◽  
High Resolution Model

Objective:This research is dedicated to the study of the feasibility of surface wind downscaling from 925 or 850 hPa winds according to synoptic class, season and hour.Methods:Two aspects are examined: low tropospheric wind veering and wind speed correlation and verification of the ERA-Interim analysis wind by comparison to radiosonde data at Beit Dagan, a station on the Israeli coast.Results:Relatively small (< 60°) cross angles between the 1000 hPa wind vector and the 925 hPa or 850 hPa wind vector at 12Z and high correlation (0.6-0.8) between the wind speed at the two levels were found only under winter lows. Relatively small cross angles and small wind speed correlation were found under highs to the west and Persian troughs.The verification of ERA-Interim analysis in comparison with radiosonde data has shown good prediction of wind direction at 12Z at 1000, 925 and 850 hPa levels (RMSE 20°-60°) and lower prediction quality at 1000 hPa at 0Z (RMSE 60°-90°). The analysis under-predicts the wind speed, especially at 1000 hPa. The wind speed RMSE is 1-2 m/s, except for winter lows with 2-3 m/s RMSE at 0Z, 12Z at all levels.Conclusion:Inference of surface wind may be possible at 12Z from 925 or 825 hPa winds under winter lows. Inference of wind direction from 925 hPa winds may be possible under highs to the west and Persian troughs. Wind speed should be inferred by interpolation, according to historical data of measurements or high resolution model.

The Effects of SST-Induced Surface Wind Speed and Direction Gradients on Midlatitude Surface Vorticity and Divergence

2010 ◽  
Vol 23 (2) ◽  
pp. 255-281 ◽  
Author(s):  
Larry W. O’Neill ◽  
Dudley B. Chelton ◽  
Steven K. Esbensen
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Spatial Scales ◽  
Kuroshio Extension ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Small Scale ◽  
Strong Wind ◽  
Sst Variability ◽  
Seawinds Scatterometer

Abstract The effects of surface wind speed and direction gradients on midlatitude surface vorticity and divergence fields associated with mesoscale sea surface temperature (SST) variability having spatial scales of 100–1000 km are investigated using vector wind observations from the SeaWinds scatterometer on the Quick Scatterometer (QuikSCAT) satellite and SST from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) Aqua satellite. The wind–SST coupling is analyzed over the period June 2002–August 2008, corresponding to the first 6+ years of the AMSR-E mission. Previous studies have shown that strong wind speed gradients develop in response to persistent mesoscale SST features associated with the Kuroshio Extension, Gulf Stream, South Atlantic, and Agulhas Return Current regions. Midlatitude SST fronts also significantly modify surface wind direction; the surface wind speed and direction responses to typical SST differences of about 2°–4°C are, on average, about 1–2 m s−1 and 4°–8°, respectively, over all four regions. Wind speed perturbations are positively correlated and very nearly collocated spatially with the SST perturbations. Wind direction perturbations, however, are displaced meridionally from the SST perturbations, with cyclonic flow poleward of warm SST and anticyclonic flow poleward of cool SST. Previous observational analyses have shown that small-scale perturbations in the surface vorticity and divergence fields are related linearly to the crosswind and downwind components of the SST gradient, respectively. When the vorticity and divergence fields are analyzed in curvilinear natural coordinates, the wind speed contributions to the SST-induced vorticity and divergence depend equally on the crosswind and downwind SST gradients, respectively. SST-induced wind direction gradients also significantly modify the vorticity and divergence fields, weakening the vorticity response to crosswind SST gradients while enhancing the divergence response to downwind SST gradients.

scholarly journals X-band COSMO-SkyMed wind field retrieval, with application to coastal circulation modeling

Ocean Science ◽  
2013 ◽  
Vol 9 (1) ◽  
pp. 121-132 ◽  
Author(s):  
A. Montuori ◽  
P. de Ruggiero ◽  
M. Migliaccio ◽  
S. Pierini ◽  
G. Spezie
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Surface Wind ◽  
Circulation Model ◽  
Coastal Circulation ◽  
Wind Fields ◽  
Sea Surface Wind ◽  
X Band ◽  
Sar Data ◽  
Ecmwf Model

Abstract. In this paper, X-band COSMO-SkyMed© synthetic aperture radar (SAR) wind field retrieval is investigated, and the obtained data are used to force a coastal ocean circulation model. The SAR data set consists of 60 X-band Level 1B Multi-Look Ground Detected ScanSAR Huge Region COSMO-SkyMed© SAR data, gathered in the southern Tyrrhenian Sea during the summer and winter seasons of 2010. The SAR-based wind vector field estimation is accomplished by resolving both the SAR-based wind speed and wind direction retrieval problems independently. The sea surface wind speed is retrieved by means of a SAR wind speed algorithm based on the azimuth cut-off procedure, while the sea surface wind direction is provided by means of a SAR wind direction algorithm based on the discrete wavelet transform multi-resolution analysis. The obtained wind fields are compared with ground truth data provided by both ASCAT scatterometer and ECMWF model wind fields. SAR-derived wind vector fields and ECMWF model wind data are used to construct a blended wind product regularly sampled in both space and time, which is then used to force a coastal circulation model of a southern Tyrrhenian coastal area to simulate wind-driven circulation processes. The modeling results show that X-band COSMO-SkyMed© SAR data can be valuable in providing effective wind fields for coastal circulation modeling.

scholarly journals Spatiotemporal Characteristics of Near-Surface Wind in Shenzhen

2020 ◽  
Vol 12 (2) ◽  
pp. 739 ◽  
Author(s):  
Cheng Liu ◽  
Qinglan Li ◽  
Wei Zhao ◽  
Yuqing Wang ◽  
Riaz Ali ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Surface Wind ◽  
Prevailing Wind ◽  
Average Wind Speed ◽  
Near Surface ◽  
Average Wind ◽  
Prevailing Wind Direction ◽  
Seasonal Wind ◽  
The Mean

The spatiotemporal characteristics of near-surface wind in Shenzhen were investigated in this study by using hourly observations at 92 automatic weather stations (AWSs) from 2009 to 2018. The results show that during the past 10 years, most of the stations showed a decreasing trend in the annual mean of the 10 min average wind speed (avg-wind) and the mean of the 3 s average wind speed (gust wind). Over half of the decreasing trends at the stations were statistically significant (p < 0.05). Seasonally, the decrease in wind speed was the most severe in spring, followed by autumn, winter, and summer. The distribution of wind speed tends to be greater in the east and coastal areas for both avg-wind and gust wind. From September to March of the following year, the prevailing wind direction in Shenzhen was northerly, and from April to August, the prevailing wind direction was southerly. The seasonal wind speed distribution exhibited two different types, spring–summer type and autumn–winter type, which may be induced by their different prevailing wind directions. The analysis by the empirical orthogonal function (EOF) method confirmed the previous findings that the mean wind speed was decreasing in Shenzhen and that two different seasonal wind speed spatial distribution patterns existed. Such a study could provide references for wind forecasting and risk assessment in the study area.

scholarly journals Diagnosing eclipse-induced wind changes

2012 ◽  
Vol 468 (2143) ◽  
pp. 1839-1850 ◽  
Author(s):  
S. L. Gray ◽  
R. G. Harrison
Keyword(s):  
Wind Speed ◽  
Maximum Temperature ◽  
Model Output ◽  
Transient Nature ◽  
Free Region ◽  
Resolution Model ◽  
High Resolution Model ◽  
A New Technique ◽  
Meteorological Models ◽  
Zone Temperature

Responses in surface winds to solar eclipses have an almost mystical status but are difficult to detect in observations because of their transient nature. High spatial resolution (approx. 1.5 km grid) meteorological models now provide a new technique for their investigation. Measurements from the southern UK meteorological network during the 11 August 1999 total solar eclipse are compared with a high-resolution model ignorant of the lunar shadow's influence. Differences between the model output and measurements at the eclipse time show transient eclipse zone temperature decreases of up to 3 ° C, which also depressed the day's maximum temperature compared with the model prediction. Coherent responses in temperature, and wind speed and direction measurements are detected in the inland cloud-free region (from 51 ° to 52 °  N and −2 ° to 0 °  E). A mean regional wind speed decrease of 0.7 m s −1 during the maximum eclipse hour is apparent with a mean anticlockwise wind direction change of 17 ° ; no such changes occurred in the model output. Such regional circulation changes are consistent with Clayton's 1901 cold-cored eclipse cyclone hypothesis, which may be related to the anecdotal ‘eclipse wind’.

scholarly journals Research on Wind Speed Inversion Method for X-Band Networked SAR Satellite

2020 ◽  
Vol 8 (9) ◽  
pp. 626
Author(s):  
Yong Wan ◽  
Xiaolei Shi ◽  
Yongshou Dai ◽  
Ligang Li ◽  
Xiaojun Qu ◽  
...  
Keyword(s):  
Wind Speed ◽  
Cost Function ◽  
Wind Direction ◽  
Incident Angle ◽  
Surface Wind ◽  
Sea Surface ◽  
Inversion Method ◽  
X Band ◽  
Direction Information ◽  
The Cost

Synthetic aperture radar (SAR) can extract sea surface wind speed information. To extract wind speed information through the geophysical model function (GMF), the corresponding wind direction information must be input. This article introduces some concepts about networked SAR satellites. The networked satellites enable multiple SARs to observe the same sea surface at different incidence angles at the same time. Aiming at the X-band networked SAR data with different incident angles, the cost function is established by using the GMF. By minimizing the cost function, accurate wind speed information can be extracted without inputting wind direction information. When the noise is small, the wind direction information is introduced, and the accuracy of the extracted wind speed will be improved. When the noise is less than 1 dB and the incident angle is greater than 30°, the root-mean-square error (RMSE) of the wind speed extracted by this method is basically less than 2 m/s.

scholarly journals On the Ability of the WRF Model to Reproduce the Surface Wind Direction over Complex Terrain

2013 ◽  
Vol 52 (7) ◽  
pp. 1610-1617 ◽  
Author(s):  
Pedro A. Jiménez ◽  
Jimy Dudhia
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Complex Terrain ◽  
Grid Point ◽  
Surface Wind ◽  
Winter Season ◽  
Wrf Model ◽  
Horizontal Resolution ◽  
Surface Flow ◽  
High Horizontal Resolution

AbstractThe ability of the Weather Research and Forecasting (WRF) model to reproduce the surface wind direction over complex terrain is examined. A simulation spanning a winter season at a high horizontal resolution of 2 km is compared with wind direction records from a surface observational network located in the northeastern Iberian Peninsula. A previous evaluation has shown the ability of WRF to reproduce the wind speed over the region once the effects of the subgrid-scale topography are parameterized. Hence, the current investigation complements the previous findings, providing information about the model's ability to reproduce the direction of the surface flow. The differences between the observations and the model are quantified in terms of scores explicitly designed to handle the circular nature of the wind direction. Results show that the differences depend on the wind speed. The larger the wind speed is, the smaller are the wind direction differences. Areas with more complex terrain show larger systematic differences between model and observations; in these areas, a statistical correction is shown to help. The importance of the grid point selected for the comparison with observations is also analyzed. A careful selection is relevant to reducing comparative problems over complex terrain.

scholarly journals Quality Control of Surface Wind Observations in Northeastern North America. Part I: Data Management Issues

2018 ◽  
Vol 35 (1) ◽  
pp. 163-182 ◽  
Author(s):  
Etor E. Lucio-Eceiza ◽  
J. Fidel González-Rouco ◽  
Jorge Navarro ◽  
Hugo Beltrami
Keyword(s):  
Quality Control ◽  
Wind Speed ◽  
North America ◽  
Data Management ◽  
Wind Direction ◽  
Measurement Errors ◽  
Surface Wind ◽  
Error Type ◽  
Northeastern North America ◽  
Management Issues

AbstractA quality control (QC) process has been developed and implemented on an observational database of surface wind speed and direction in northeastern North America. The database combines data from 526 land stations and buoys spread across eastern Canada and five adjacent northeastern U.S. states. It combines the observations of three different institutions spanning from 1953 to 2010. The quality of these initial data varies among source institutions. The current QC process is divided into two parts. Part I, described herein, is focused on issues related to data management: issues stemming from data transcription and collection; differences in measurement units and recording times; detection of sequences of duplicated data; unification of calm and true north criteria for wind direction; and detection of physically unrealistic data measurements. As a result, around ~0.1% of wind speed and wind direction records have been identified as erroneous and deleted. The most widespread error type is related to duplications within the same station, but the error type that entails more erroneous data belongs to duplications among different sites. Additionally, the process of data compilation and standardization has had an impact on more than 90% of the records. A companion paper (Part II) deals with a group of errors that are conceptually different, and is focused on detecting measurement errors that relate to temporal consistency and biases in wind speed and direction.

scholarly journals Evaluation and Projection of Wind Speed in the Arid Region of Northwest China Based on CMIP6

2021 ◽  
Vol 13 (20) ◽  
pp. 4076
Author(s):  
Yunxia Long ◽  
Changchun Xu ◽  
Fang Liu ◽  
Yongchang Liu ◽  
Gang Yin
Keyword(s):  
Wind Speed ◽  
Arid Region ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Northwest China ◽  
Tianshan Mountains ◽  
Model Ensemble ◽  
The West ◽  
The North ◽  
Temporal And Spatial

Near surface wind speed has significant impacts on ecological environment change and climate change. Based on the CN05.1 observation data (a gridded monthly dataset with the resolution of 0.25 latitude by 0.25 longitude over China), this study evaluated the ability of 25 Global Climate Models (GCMs) from Coupled Model Intercomparison Project phase 6 (CMIP6) in simulating the wind speed in the Arid Region of Northwest China (ARNC) during 1971–2014. Then, the temporal and spatial variations in the surface wind speed of ARNC in the 21st century were projected under four Shared Socioeconomic Pathways (SSPs), SSP1-2.6, SSP2-4.5, SSP3-7.0, and SP5-8.5. The results reveal that the preferred-model ensemble (PME) can fairly evaluate the temporal and spatial distribution of surface wind speed with the temporal and spatial correlation coefficients exceeding 0.5 at the significance level of p = 0.05 when compared to the 25 single models and their ensemble mean. After deviation correction, the PME can reproduce the distribution characteristics of high wind speed in the east and low in the west, high in mountainous areas, and low in basins. Unfortunately, no models or model ensemble can accurately reproduce the decreasing magnitude of observed wind speed. In the 21st century, the surface wind speed in the ARNC is projected to increase under SSP1-2.6 scenario but will decrease remarkably under the other three scenarios. Moreover, the higher the emission scenarios, the more significant the surface wind speed decreases. Spatially, the wind speed will increase significantly in the west and southeast of Xinjiang, decrease in the north of Xinjiang and the south of Tarim Basin. What’s more, under the four scenarios, the surface wind speed will decrease in spring, summer and autumn, especially in summer, and increase in winter. The wind speed will decrease significantly in the north of Tianshan Mountains in summer, decrease significantly in the north of Xinjiang and the southern edge of Tarim Basin in spring and autumn, and increase in fluctuation with high values in Tianshan Mountains in winter.

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