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 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 On the characteristics of ASCAT wind direction ambiguities

2013 ◽  
Vol 6 (4) ◽  
pp. 1053-1060 ◽  
Author(s):  
W. Lin ◽  
M. Portabella ◽  
A. Stoffelen ◽  
A. Verhoef
Keyword(s):  
Quality Control ◽  
Maximum Likelihood ◽  
Wind Speed ◽  
Wind Direction ◽  
Maximum Likelihood Estimators ◽  
High Rank ◽  
Low Rank ◽  
The Third ◽  
Low Probability

Abstract. The inversion of the Advanced Scatterometer (ASCAT) backscatter measurement triplets generally leads to two wind ambiguities with similar wind speed values and opposite wind directions. However, for up-, down- and crosswind (with respect to the mid-beam azimuth direction) cases, the inversion often leads to three or four wind solutions. In most of such cases, the inversion residuals or maximum likelihood estimators (MLEs) of the third and fourth solutions (i.e. high-rank solutions) are substantially higher than those of the first two (low-rank) ambiguities. This indicates a low probability for the high-rank solutions and thus essentially dual ambiguity. This paper investigates the characteristics of ASCAT high-rank wind solutions under different conditions with the objective of developing a method for rejecting the spurious high-rank solutions. The implementation of this rejection procedure improves the effectiveness of the ASCAT wind quality control (QC) and ambiguity removal procedures.

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 Quality control of the RMI's AWS wind observations

2016 ◽  
Vol 13 ◽  
pp. 13-19
Author(s):  
Cédric Bertrand ◽  
Luis González Sotelino ◽  
Michel Journée
Keyword(s):  
Quality Control ◽  
Wind Speed ◽  
Surface Wind ◽  
Temporal Consistency ◽  
Data Quality Control ◽  
Decision Algorithm ◽  
M 10 ◽  
Spatial Consistency ◽  
Wide Range ◽  
Automated Procedures

Abstract. Wind observations are important for a wide range of domains including among others meteorology, agriculture and extreme wind engineering. To ensure the provision of high quality surface wind data over Belgium, a new semi-automated data quality control (QC) has been developed and applied to wind observations from the automated weather stations operated by the Royal Meteorological Institute of Belgium. This new QC applies to 10 m 10 min averaged wind speed and direction, 10 m gust speed and direction, 2 m 10 min averaged wind speed and 30 m 10 min averaged wind speed records. After an existence test, automated procedures check the data for limits consistency, internal consistency, temporal consistency and spatial consistency. At the end of the automated QC, a decision algorithm attributes a flag to each particular data point. Each day, the QC staff analyzes the preceding day's observations in the light of the assigned quality flags.

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.

WiSED: A Quality-Controlled Surface Wind European Database

2020 ◽  
Author(s):  
Cristina Rojas-Labanda ◽  
J. Fidel González-Rouco ◽  
Elena García-Bustamante ◽  
Jorge Navarro ◽  
Etor E. Lucio-Eceiza ◽  
...  
Keyword(s):  
Quality Control ◽  
Data Storage ◽  
Measurement Errors ◽  
Climate Model ◽  
Surface Wind ◽  
Added Value ◽  
European Database ◽  
Spatial Coverage ◽  
Wind Behaviour ◽  
Three Phases

&lt;p&gt;Surface wind is a fundamental meteorological variable that is relevant for a wide array of topics (e.g., crop growth, extreme events, power generation). Yet, for many regions, there is still a scarcity of good quality observational datasets and the uncertainties within data sources like reanalysis products and between those and observational databases are large, limiting the understanding of this variable and hampering the accuracy of subsequent analyses.&lt;/p&gt;&lt;p&gt;In order to address this need and within the frame of the NEWA's (New European Wind Atlas) project, a quality-controlled Wind Surface European Database (WiSED) is created. WiSED feeds from eight different datasets, provided by different institutions and with varying levels of quality control. This initial version is then submitted to a Quality Control (QC) process structured into six phases that deal with the detection of various issues in data quality: 1) compilation; 2) duplication errors; 3) physical consistency in the ranges of recorded values; 4) temporal consistency, regarding abnormally high/low variability in the time series; 5) detection of medium-term biases; and 6) removal of isolated records. The first three phases deal with issues often related to data storage and management, whereas the last three phases deal with measurement errors related to problems in the instruments, calibration procedures or siting.&lt;/p&gt;&lt;p&gt;The improved quality of the data and the high temporal and spatial resolution, as well as its spatial coverage, represents an added value over previous products available for the same region.&amp;#160;&lt;/p&gt;&lt;p&gt;This work summarises the application of the quality control, showing the results of different steps throughout it. Additionally, a preliminary analysis of the surface wind behaviour over Europe is presented.&lt;/p&gt;&lt;p&gt;With a maximum timespan of about 100 years, the creation of such database will allow for analyzing different aspects of both wind speed and direction variability over Europe from intra-daily to multidecadal timescales. Within the potentially relevant applications, it is worth to mention: the identification of subregions in Europe with homogeneous wind behaviour (regionalization), statistical downscaling exercises, analyses of wind extremes, wind power assessment and evaluation of climate model, both global and regional, simulations.&lt;/p&gt;

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