scholarly journals Quality Control of Surface Wind Observations in Northeastern North America. Part II: Measurement Errors

2018 ◽  
Vol 35 (1) ◽  
pp. 183-205 ◽  
Author(s):  
Etor E. Lucio-Eceiza ◽  
J. Fidel González-Rouco ◽  
Jorge Navarro ◽  
Hugo Beltrami ◽  
Jorge Conte
Keyword(s):  
Quality Control ◽  
Wind Speed ◽  
North America ◽  
Wind Direction ◽  
Measurement Errors ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Eastern Canada ◽  
Error Type ◽  
Northeastern North America

AbstractA quality control (QC) process has been developed and applied to an observational database of surface wind speed and wind direction in northeastern North America. The database combines data from three datasets of different initial quality, including a total of 526 land stations and buoys distributed over the provinces of eastern Canada and five adjacent northeastern U.S. states. The data span from 1953 to 2010. The first part of the QC deals with data management issues and is developed in a companion paper. Part II, presented herein, is focused on the detection of measurement errors and deals with low-variability errors, like the occurrence of unrealistically long calms, and high-variability problems, like rapid changes in wind speed; some types of biases in wind speed and wind direction are also considered. About 0.5% (0.16%) of wind speed (wind direction) records have been flagged. Additionally, 15.87% (1.73%) of wind speed (wind direction) data have been corrected. The most pervasive error type in terms of affected sites and erased data corresponds to unrealistic low wind speeds (89% of sites affected with 0.35% records removed). The amount of detected and corrected/removed records in Part II (~9%) is approximately two orders of magnitude higher than that of Part I. Both management and measurement errors are shown to have a discernible impact on the statistics of the database.

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.

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.

Ocean Surface Wind Speed Retrieval From C-Band SAR Images Without Wind Direction Input

2014 ◽  
Vol 52 (2) ◽  
pp. 980-990 ◽  
Author(s):  
Alexander S. Komarov ◽  
Vladimir Zabeline ◽  
David G. Barber
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Surface Wind ◽  
Ocean Surface ◽  
Surface Wind Speed ◽  
Sar Images ◽  
Ocean Surface Wind

scholarly journals Changes in Surface Wind Speed over North America from CMIP5 Model Projections and Implications for Wind Energy

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Sujay Kulkarni ◽  
Huei-Ping Huang
Keyword(s):  
Wind Speed ◽  
North America ◽  
Greenhouse Gas ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Current Estimate

The centennial trends in the surface wind speed over North America are deduced from global climate model simulations in the Climate Model Intercomparison Project—Phase 5 (CMIP5) archive. Using the 21st century simulations under the RCP 8.5 scenario of greenhouse gas emissions, 5–10 percent increases per century in the 10 m wind speed are found over Central and East-Central United States, the Californian Coast, and the South and East Coasts of the USA in winter. In summer, climate models projected decreases in the wind speed ranging from 5 to 10 percent per century over the same coastal regions. These projected changes in the surface wind speed are moderate and imply that the current estimate of wind power potential for North America based on present-day climatology will not be significantly changed by the greenhouse gas forcing in the coming decades.

Retrieval of Ocean Surface Wind Speed and Wind Direction Using Reflected GPS Signals

2004 ◽  
Vol 21 (3) ◽  
pp. 515-526 ◽  
Author(s):  
Attila Komjathy ◽  
Michael Armatys ◽  
Dallas Masters ◽  
Penina Axelrad ◽  
Valery Zavorotny ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Surface Wind ◽  
Ocean Surface ◽  
Surface Wind Speed ◽  
Ocean Surface Wind
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