scholarly journals Quality control and bias adjustment of crowdsourced wind speed observations

2021 ◽  
Author(s):  
Jieyu Chen ◽  
Kate Saunders ◽  
Kirien Whan
Keyword(s):  
Quality Control ◽  
Wind Speed ◽  
Bias Adjustment

scholarly journals Expanding HadISD: quality-controlled, sub-daily station data from 1931

2016 ◽  
Author(s):  
Robert J. H. Dunn ◽  
Kate M. Willett ◽  
David E. Parker ◽  
Lorna Mitchell
Keyword(s):  
Quality Control ◽  
Wind Speed ◽  
Time Range ◽  
Underlying Structure ◽  
Control Procedure ◽  
Future Projections ◽  
Quality Control Procedure ◽  
Temporal Coverage ◽  
Extremes Of Temperature ◽  
Selection Of

Abstract. HadISD is a sub-daily, station-based, quality-controlled dataset designed to study past extremes of temperature, pressure and humidity and allow comparisons to future projections. Herein we describe the first major update to the HadISD dataset. The temporal coverage of the dataset has been extended to 1931 to present, doubling the time range over which data are provided. Improvements made to the station selection and merging procedures result in 7677 stations being provided in version 2.0.0.2015p of this dataset. The selection of stations to merge together making composites has also been improved and made more robust. The underlying structure of the quality control procedure is the same as for HadISD.1.0.x, but a number of improvements have been implemented in individual tests. Also, more detailed quality control tests for wind speed and direction have been added. The data will be made available as netCDF files at www.metoffice.gov.uk/hadobs/hadisd and updated annually.

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.

A Stochastic Model of Surface Wind Speed for Air Quality Control Purposes

1984 ◽  
Vol 23 (9) ◽  
pp. 1354-1361 ◽  
Author(s):  
G. Finzi ◽  
P. Bonelli ◽  
G. Bacci
Keyword(s):  
Quality Control ◽  
Air Quality ◽  
Wind Speed ◽  
Stochastic Model ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Air Quality Control

scholarly journals Using ERA-Interim Reanalysis output for creating datasets of energy-relevant climate variables

2017 ◽  
Author(s):  
Philip D. Jones ◽  
Colin Harpham ◽  
Alberto Troccoli ◽  
Benoit Gschwind ◽  
Thierry Ranchin ◽  
...  
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Climate Variables ◽  
Climate Data ◽  
Near Surface ◽  
Bias Adjustment ◽  
Dewpoint Temperature ◽  
Data Store

Abstract. The construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim Reanalysis is presented. A number of different bias-adjustment approaches have been proposed. Here we modify the parameters of different distributions (depending on the variable), adjusting those calculated from ERA-Interim to those based on gridded station or direct station observations. The variables are air temperature, dewpoint temperature, precipitation (daily only), solar radiation, wind speed and relative humidity, available at either 3 or 6 h timescales over the period 1979-2014. This dataset is available to anyone through the Climate Data Store (CDS) of the Copernicus Climate Change Data Store (C3S), and can be accessed at present from (ftp://ecem.climate.copernicus.eu). The benefit of performing bias-adjustment is demonstrated by comparing initial and bias-adjusted ERA-Interim data against observations.

scholarly journals Statistical dynamics of equatorial waves in tropical radiosonde wind data

2010 ◽  
Vol 10 (7) ◽  
pp. 16345-16384
Author(s):  
T.-Y. Koh ◽  
Y. S. Djamil ◽  
C. K. Teo
Keyword(s):  
Quality Control ◽  
Wind Speed ◽  
Shape Parameter ◽  
Equatorial Waves ◽  
Strong Wind ◽  
Malay Peninsula ◽  
New Approach ◽  
Statistical Dynamics ◽  
The Central Limit Theorem ◽  
Global Datasets

Abstract. Weibull distributions were fitted to wind speed data from radiosonde stations in the global tropics. A statistical theory of equatorial waves was proposed to explain the shape parameter k obtained over Malay Peninsula and the wider Equatorial Monsoon Zone. The theory uses the (−5/3)-power law in quasi-2-D turbulence, classical Boltzmann statistics and the Central Limit Theorem. It provides a statistical dynamical basis for using empirical Weibull fits to derive wind speed thresholds for monitoring data quality. The regionally adapted thresholds retain more useful data than conventional ones defined from taking the regional mean plus three standard deviations. The new approach is shown to eliminate reports of atypically strong wind over Malay Peninsula which may have escaped detection in quality control of global datasets as the latter has assumed a larger spread of wind speed.

scholarly journals Using ERA-Interim reanalysis for creating datasets of energy-relevant climate variables

2017 ◽  
Vol 9 (2) ◽  
pp. 471-495 ◽  
Author(s):  
Philip D. Jones ◽  
Colin Harpham ◽  
Alberto Troccoli ◽  
Benoit Gschwind ◽  
Thierry Ranchin ◽  
...  
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Climate Variables ◽  
Climate Data ◽  
Near Surface ◽  
Bias Adjustment ◽  
Dewpoint Temperature ◽  
Data Store

Abstract. The construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim reanalysis is presented. A number of different, variable-dependent, bias-adjustment approaches have been proposed. Here we modify the parameters of different distributions (depending on the variable), adjusting ERA-Interim based on gridded station or direct station observations. The variables are air temperature, dewpoint temperature, precipitation (daily only), solar radiation, wind speed, and relative humidity. These are available on either 3 or 6 h timescales over the period 1979–2016. The resulting bias-adjusted dataset is available through the Climate Data Store (CDS) of the Copernicus Climate Change Data Store (C3S) and can be accessed at present from ftp://ecem.climate.copernicus.eu. The benefit of performing bias adjustment is demonstrated by comparing initial and bias-adjusted ERA-Interim data against gridded observational fields.

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.

Developing a Storm Severity Index

2005 ◽  
Vol 1911 (1) ◽  
pp. 143-148 ◽  
Author(s):  
Wilfrid A. Nixon ◽  
Lin Qiu
Keyword(s):  
Quality Control ◽  
Wind Speed ◽  
Severity Index ◽  
Department Of Transportation ◽  
Highway Maintenance ◽  
Winter Maintenance ◽  
The Matrix ◽  
Precipitation Type ◽  
Written Descriptions ◽  
Quality Process

A primary goal in winter highway maintenance is to develop various maintenance processes so that quality control can be measured. If actions can be measured, they can be improved. A difficulty with this approach is that winter maintenance addresses the impacts of winter weather on the transportation system and that weather is inherently uncontrollable. Consequently, for a quality process to be applied to winter maintenance, the severity of individual storms must be assessed. This paper presents one way in which the severity of a storm can be measured, specifically by an index. The first step in developing an index for individual storms is to develop a method of describing storms. The method here describes storms by using six factors, including prestorm and poststorm conditions and temperatures, wind speed, and precipitation type. The matrix created is a refinement of that presented in FHWA's manual of practice for effective anti-icing. With the use of a simplified variation of this matrix-based description of storms (more than 250 descriptions), a score is generated for each storm type. This score is then adjusted so that scores for all storms fall into a normal distribution between 0 and 1. This ranking of storms was evaluated by winter maintenance garage supervisors at the Iowa Department of Transportation. Supervisors were asked to rank 10 storms (presented as brief written descriptions) from easiest to hardest to handle. Results were compared with those of the initial storm severity index. From that comparison, numerical values for certain factors were adjusted so that storm severity index scores for these 10 storms agreed with rankings given by the garage supervisors.

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.

Quality Control and Bias Correction of Citizen Science Wind Observations

2021 ◽  
Author(s):  
Jieyu Chen ◽  
Kirien Whan ◽  
Kate Saunders
Keyword(s):  
Quality Control ◽  
Wind Speed ◽  
Citizen Science ◽  
Bias Correction ◽  
Temporal Consistency ◽  
Science Data ◽  
Wind Speeds ◽  
Systematic Biases ◽  
Official Data

<p>Wind observations collected at citizen science wind stations (CWS) could be an invaluable resource in climate and meteorology studies, yet these observations are underutilised because scientists do not have confidence in their quality. While a few studies have considered the quality of CWS wind speed observations, none have addressed the biases, likely caused by instrumentation biases and station placement errors. These systematic biases introduce spatial inconsistencies that prevent comparison of these stations spatially and limit the possible usage of the data. In this paper, we address these issues by improving and developing new methods for identifying suspect observations and calibrating systematic biases in the wind speed observations collected at CWS.</p><p>Our complete quality control system consists of four steps: (1) performing within-station quality controls to check the plausible range and the temporal consistency of observations; (2) correcting the bias, mainly caused by low sensor heights, using empirical quantile mapping; (3) implementing between-station quality control that compares observations from neighbouring stations to identify spatially inconsistent observations; (4) providing estimates of the true wind when CWS falsely report zero wind speeds, as a complement to bias correction.</p><p>We apply these methods to CWS from the Weather Observation Website (WOW) in the Netherlands, comparing the citizen science data with official data, and statistically assessing the improvements in data quality after each step. The results demonstrate that the citizen science wind data are comparable with official data after quality control checks and bias corrections. Our quality assessment methods therefore give confidence to CWS, converting their observations into a usable data product and an invaluable resource for applications in need of additional wind observations.</p>

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