scholarly journals Surface wind at Leh

MAUSAM ◽  
2021 ◽  
Vol 43 (2) ◽  
pp. 155-162
Author(s):  
O. P. MADAN ◽  
N. NATARAJAN ◽  
H. C. SINGHAL ◽  
S. CHATURVEDI ◽  
V. THIYAGARAJAN
Keyword(s):  
Wind Energy ◽  
Surface Wind ◽  
Surface Winds ◽  
Contributory Factors ◽  
River Indus

Leh airfield normally experiences gale speed surface winds during the period from April to October. From November to March, the winds are relatively weak. The reason for the gale strength speed appears to be the channelling effect. Katabatlc /anabatic flows or  Foehn effects do not appear to be the significant contributory factors. There are numerous points along the river Indus where similar gale strength speeds are encountered and hence these appear to be good wind energy prospecting potential sites.

scholarly journals Remote Sensing of the Surface Wind Field over the Coastal Ocean via Direct Calibration of HF Radar Backscatter Power

2016 ◽  
Vol 33 (7) ◽  
pp. 1377-1392 ◽  
Author(s):  
Anthony Kirincich
Keyword(s):  
Wind Energy ◽  
Wind Field ◽  
Spatial Scales ◽  
Surface Wind ◽  
Coastal Ocean ◽  
Hf Radar ◽  
Offshore Wind ◽  
Surface Winds ◽  
Autonomous Surface Vehicle ◽  
Radar Systems

AbstractThe calibration and validation of a novel approach to remotely sense surface winds using land-based high-frequency (HF) radar systems are described. Potentially available on time scales of tens of minutes and spatial scales of 2–3 km for wide swaths of the coastal ocean, HF radar–based surface wind observations would greatly aid coastal ocean planners, researchers, and operational stakeholders by providing detailed real-time estimates and climatologies of coastal winds, as well as enabling higher-quality short-term forecasts of the spatially dependent wind field. Such observations are particularly critical for the developing offshore wind energy community. An autonomous surface vehicle was deployed within the Massachusetts Wind Energy Area, located south of Martha’s Vineyard, Massachusetts, for one month, collecting wind observations that were used to test models of wind-wave spreading and HF radar energy loss, thereby empirically relating radar-measured power to surface winds. HF radar–based extractions of the remote wind speed had accuracies of 1.4 m s−1 for winds less than 7 m s−1, within the optimal range of the radar frequency used. Accuracies degraded at higher winds due to low signal-to-noise ratios in the returned power and poor resolution of the model. Pairing radar systems with a range of transmit frequencies with adjustments of the extraction model for additional power and environmental factors would resolve many of the errors observed.

scholarly journals The Probability Distribution of Sea Surface Wind Speeds: Effects of Variable Surface Stratification and Boundary Layer Thickness

2010 ◽  
Vol 23 (19) ◽  
pp. 5151-5162 ◽  
Author(s):  
Adam Hugh Monahan
Keyword(s):  
Boundary Layer ◽  
Layer Thickness ◽  
Boundary Layer Thickness ◽  
Surface Wind ◽  
Sea Surface ◽  
Surface Winds ◽  
Free Atmosphere ◽  
Wind Speeds ◽  
Sea Surface Wind ◽  
Variable Surface

Abstract Air–sea exchanges of momentum, energy, and material substances of fundamental importance to the variability of the climate system are mediated by the character of the turbulence in the atmospheric and oceanic boundary layers. Sea surface winds influence, and are influenced by, these fluxes. The probability density function (pdf) of sea surface wind speeds p(w) is a mathematical object describing the variability of surface winds that arises from the physics of the turbulent atmospheric planetary boundary layer. Previous mechanistic models of the pdf of sea surface wind speeds have considered the momentum budget of an atmospheric layer of fixed thickness and neutral stratification. The present study extends this analysis, using an idealized model to consider the influence of boundary layer thickness variations and nonneutral surface stratification on p(w). It is found that surface stratification has little direct influence on p(w), while variations in boundary layer thickness bring the predictions of the model into closer agreement with the observations. Boundary layer thickness variability influences the shape of p(w) in two ways: through episodic downward mixing of momentum into the boundary layer from the free atmosphere and through modulation of the importance (relative to other tendencies) of turbulent momentum fluxes at the surface and the boundary layer top. It is shown that the second of these influences dominates over the first.

scholarly journals A Two-Season Impact Study of the WindSat Surface Wind Retrievals in the NCEP Global Data Assimilation System

2010 ◽  
Vol 25 (3) ◽  
pp. 931-949 ◽  
Author(s):  
Li Bi ◽  
James A. Jung ◽  
Michael C. Morgan ◽  
John F. Le Marshall
Keyword(s):  
Data Assimilation ◽  
Surface Wind ◽  
Temperature Fields ◽  
Experimental Simulation ◽  
Naval Research ◽  
Surface Winds ◽  
Control Simulation ◽  
Wind Retrievals ◽  
Global Data ◽  
Assimilation System

Abstract A two-season observing system experiment (OSE) was used to quantify the impacts of assimilating the WindSat surface winds product developed by the Naval Research Laboratory (NRL). The impacts of assimilating these surface winds were assessed by comparing the forecast results through 168 h for the months of October 2006 and March 2007. The National Centers for Environmental Prediction’s (NCEP) Global Data Assimilation/Global Forecast System (GDAS/GFS) was used, at a resolution of T382-64 layers, as the assimilation system and forecast model for these experiments. A control simulation utilizing all the data types assimilated in the operational GDAS was compared to an experimental simulation that added the WindSat surface winds. Quality control procedures required to assimilate the surface winds are discussed. Anomaly correlations (ACs) of geopotential heights at 1000 and 500 hPa were evaluated for the control and experiment during both seasons. The geographical distribution of the forecast impacts (FIs) on the wind field and temperature fields at 10-m height and 500 hPa is also discussed. The results of this study show that assimilating the surface wind retrievals from the WindSat satellite improve the NCEP GFS wind and temperature forecasts. A positive FI, which suggests that the error growth of the experiment is slower than the control, has been realized in the NCEP GDAS/GFS wind and temperature forecasts through 24 h. The WindSat experiment AC scores are similar to the control simulation AC scores until the day 6 forecasts, when the improvements in the WindSat experiment become greater for both seasons and in most of the cases.

scholarly journals Aerosol optical depth trend over the Middle East

2016 ◽  
Author(s):  
K. Klingmüller ◽  
A. Pozzer ◽  
S. Metzger ◽  
G. Stenchikov ◽  
J. Lelieveld
Keyword(s):  
Saudi Arabia ◽  
Soil Moisture ◽  
Middle East ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Surface Winds ◽  
Anthropogenic Aerosol ◽  
Dust Emissions

Abstract. We use the combined Dark Target/Deep Blue aerosol optical depth (AOD) satellite product of the Moderate-resolution Imaging Spectroradiometer (MODIS) collection 6 to study trends over the Middle East between 2000 and 2015. Our analysis corroborates a previously identified positive AOD trend over large parts of the Middle East during the period 2001 to 2012. We relate the annual AOD to precipitation, soil moisture and surface winds to identify regions where these attributes are directly related to the AOD over Saudi Arabia, Iraq and Iran. Regarding precipitation and soil moisture, a relatively small area in and surrounding Iraq turns out to be of prime importance for the AOD over these countries. Regarding surface wind speed, the African Red Sea coastal area is relevant for the Saudi Arabian AOD. Using multiple linear regression we show that AOD trends and interannual variability can be attributed to soil moisture, precipitation and surface winds, being the main factors controlling the dust cycle. Our results confirm the dust driven AOD trends and variability, supported by a decreasing MODIS-derived Ångström exponent and a decreasing AERONET-derived fine mode fraction that accompany the AOD increase over Saudi Arabia. The positive AOD trend relates to a negative soil moisture trend. As a lower soil moisture translates into enhanced dust emissions, it is not needed to assume growing anthropogenic aerosol and aerosol precursor emissions to explain the observations. Instead, our results suggest that increasing temperature and decreasing relative humidity in the last decade have promoted soil drying, leading to increased dust emissions and AOD; consequently an AOD increase is expected due to climate change.

scholarly journals An Assessment of NASA’s GMAO MERRA-2 Reanalysis Surface Winds

2019 ◽  
Vol 32 (23) ◽  
pp. 8261-8281 ◽  
Author(s):  
D. Carvalho
Keyword(s):  
Surface Wind ◽  
The Other ◽  
Surface Winds ◽  
Wind Fields ◽  
Near Surface ◽  
Wind Speeds ◽  
Wide Range ◽  
Ocean Surface Winds ◽  
The Tropics ◽  
Global Surface

Abstract The quality of MERRA-2 surface wind fields was assessed by comparing them with 10 years of measurements from a wide range of surface wind observing platforms. This assessment includes a comparison of MERRA-2 global surface wind fields with the ones from its predecessor, MERRA, to assess if GMAO’s latest reanalyses improved the representation of the global surface winds. At the same time, surface wind fields from other modern reanalyses—NCEP-CFSR, ERA-Interim, and JRA-55—were also included in the comparisons to evaluate MERRA-2 global surface wind fields in the context of its contemporary reanalyses. Results show that MERRA-2, CFSR, ERA-Interim, and JRA-55 show similar error metrics while MERRA consistently shows the highest errors. Thus, when compared with wind observations, the accuracy of MERRA-2 surface wind fields represents a clear improvement over its predecessor MERRA and is in line with the other contemporary reanalyses in terms of the representation of global near-surface wind fields. All reanalyses showed a tendency to underestimate ocean surface winds (particularly in the tropics) and, oppositely, to overestimate inland surface winds (except JRA-55, which showed a global tendency to underestimate the wind speeds); to represent the wind direction rotated clockwise in the Northern Hemisphere (positive bias) and anticlockwise in the Southern Hemisphere (negative bias), with the exception of JRA-55; and to show higher errors near the poles and in the ITCZ, particularly in the equatorial western coasts of Central America and Africa. However, MERRA-2 showed substantially lower wind errors in the poles when compared with the other reanalyses.

Comparison of AI-based approaches for statistical downscaling of surface wind fields in the North Atlantic

2021 ◽  
Author(s):  
Vadim Rezvov ◽  
Mikhail Krinitskiy ◽  
Alexander Gavrikov ◽  
Sergey Gulev
Keyword(s):  
Wind Speed ◽  
Statistical Downscaling ◽  
North Atlantic ◽  
Surface Wind ◽  
Surface Winds ◽  
Wind Fields ◽  
The North ◽  
The North Atlantic ◽  
Vector Wind ◽  
Resolution Data

<p>Surface winds — both wind speed and vector wind components — are fields of fundamental climatic importance. The character of surface winds greatly influences (and is influenced by) surface exchanges of momentum, energy, and matter. These wind fields are of interest in their own right, particularly concerning the characterization of wind power density and wind extremes. Surface winds are influenced by small-scale features such as local topography and thermal contrasts. That is why accurate high-resolution prediction of near‐surface wind fields is a topic of central interest in various fields of science and industry. Statistical downscaling is the way for inferring information on physical quantities at a local scale from available low‐resolution data. It is one of the ways to avoid costly high‐resolution simulations. Statistical downscaling connects variability of various scales using statistical prediction models. This approach is fundamentally data-driven and can only be applied in locations where observations have been taken for a sufficiently long time to establish the statistical relationship. Our study considered statistical downscaling of surface winds (both wind speed and vector wind components) in the North Atlantic. Deep learning methods are among the most outstanding examples of state‐of‐the‐art machine learning techniques that allow approximating sophisticated nonlinear functions. In our study, we applied various approaches involving artificial neural networks for statistical downscaling of near‐surface wind vector fields. We used ERA-Interim reanalysis as low-resolution data and RAS-NAAD dynamical downscaling product (14km grid resolution) as a high-resolution target. We compared statistical downscaling results to those obtained with bilinear/bicubic interpolation with respect to downscaling quality. We investigated how network complexity affects downscaling performance. We will demonstrate the preliminary results of the comparison and propose the outlook for further development of our methods.</p><p>This work was undertaken with financial support by the Russian Science Foundation grant № 17-77-20112-P.</p>

Observational Study of Wind Channeling within the St. Lawrence River Valley

2009 ◽  
Vol 48 (11) ◽  
pp. 2341-2361 ◽  
Author(s):  
Marco L. Carrera ◽  
John R. Gyakum ◽  
Charles A. Lin
Keyword(s):  
Surface Wind ◽  
Hudson River ◽  
Geostrophic Wind ◽  
The United States ◽  
River Valley ◽  
Quebec City ◽  
Surface Winds ◽  
Eastern Canada ◽  
St Lawrence River ◽  
Pressure Driven

Abstract The presence of orography can lead to thermally and dynamically induced mesoscale wind fields. The phenomenon of channeling refers to the tendency for the winds within a valley to blow more or less parallel to the valley axis for a variety of wind directions above ridge height. Channeling of surface winds has been observed in several regions of the world, including the upper Rhine Valley of Germany, the mountainous terrain near Basel, Switzerland, and the Tennessee and Hudson River Valleys in the United States. The St. Lawrence River valley (SLRV) is a primary topographic feature of eastern Canada, extending in a southwest–northeast direction from Lake Ontario, past Montreal (YUL) and Quebec City (YQB), and terminating in the Gulf of St. Lawrence. In this study the authors examine the long-term surface wind climatology of the SLRV and Lake Champlain Valley (LCV) as represented by hourly surface winds at Montreal, Quebec City, and Burlington, Vermont (BTV). Surface wind channeling is found to be prominent at all three locations with strong bidirectionalities that vary seasonally. To assess the importance of the various channeling mechanisms the authors compared the joint frequency distributions of surface wind directions versus 925-hPa geostrophic wind directions with those obtained from conceptual models. At YUL, downward momentum transport is important for geostrophic wind directions ranging from 240° to 340°. Pressure-driven channeling is the dominant mechanism producing northeasterly surface winds at YUL. These northeasterlies are most prominent in the winter, spring, and autumn seasons. At YQB, pressure-driven channeling is the dominant physical mechanism producing channeling of surface winds throughout all seasons. Of particular importance, both YUL and YQB exhibit countercurrents whereby the velocity component of the wind within the valley is opposite to the component above the valley. Forced channeling was found to be prominent at BTV, with evidence of diurnal thermal forcing during the summer season. Reasons for the predominance of pressure-driven channeling at YUL and YQB and forced channeling at BTV are discussed.

The Role of Small-Scale Vortices in Enhancing Surface Winds and Damage in Hurricane Harvey (2017)

2018 ◽  
Vol 146 (3) ◽  
pp. 713-722 ◽  
Author(s):  
Joshua Wurman ◽  
Karen Kosiba
Keyword(s):  
Surface Wind ◽  
Wind Damage ◽  
Small Scale ◽  
Surface Winds ◽  
Hurricane Wind ◽  
Wind Gusts ◽  
Significant Damage ◽  
Variable Wind ◽  
Potential Climate Change

Strong hurricanes cause severe, but highly variable, wind damage to homes and community infrastructure. It has been speculated, but not previously shown, that damage variability is caused by tornadoes or other small-scale phenomena. Here, the authors present the first mapping and tracking of persistent tornado-scale vortices (TSVs) in the eyewall and the first documentation of the likely role of eyewall mesovortices (MVs) and TSVs in enhancing surface winds and damage. Unprecedented finescale observations in the eyewall of Hurricane Harvey (2017) were obtained by a Doppler on Wheels (DOW) radar deployed inside the eye. These observations reveal several persistent eyewall MVs revolving about the eye, as well as superimposed subkilometer-scale TSVs. Wind field perturbations associated with TSVs and MVs are less than those typical in supercell tornadoes, but since they are embedded in strong background eyewall flow, they are likely responsible for the enhancement of surface wind gusts and significant damage, including destroyed buildings and lofted vehicles. Potential climate change may result in more frequent intense and/or rapidly intensifying hurricanes; thus, understanding and forecasting the causes of hurricane wind damage is a high priority.

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