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

Lucio-Eceiza, Etor E.; González-Rouco, J. Fidel; Navarro, Jorge; Beltrami, Hugo; Conte, Jorge

doi:10.1175/jtech-d-16-0205.1

Cited by 15 publications

(20 citation statements)

References 30 publications

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“…The EMD can be interpreted as the amount of physical work needed to move a pile of soil in the shape of one distribution to that of another distribution. More discussion about the EMD properties can be found in Lupu et al (2017). For one-dimensional distributions the EMD is equivalent to the area between two cumulative distribution functions, and this interpretation, with slight modifications, can also be applied to circular variables (Rabin et al, 2008).…”

Section: Model Evaluation Metricsmentioning

confidence: 99%

The making of the New European Wind Atlas – Part 1: Model sensitivity

et al. 2020

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Abstract. This is the first of two papers that document the creation of the New European Wind Atlas (NEWA). It describes the sensitivity analysis and evaluation procedures that formed the basis for choosing the final setup of the mesoscale model simulations of the wind atlas. The suitable combination of model setup and parameterizations, bound by practical constraints, was found for simulating the climatology of the wind field at turbine-relevant heights with the Weather Research and Forecasting (WRF) model. Initial WRF model sensitivity experiments compared the wind climate generated by using two commonly used planetary boundary layer schemes and were carried out over several regions in Europe. They confirmed that the most significant differences in annual mean wind speed at 100 m a.g.l. (above ground level) mostly coincide with areas of high surface roughness length and not with the location of the domains or maximum wind speed. Then an ensemble of more than 50 simulations with different setups for a single year was carried out for one domain covering northern Europe for which tall mast observations were available. We varied many different parameters across the simulations, e.g. model version, forcing data, various physical parameterizations, and the size of the model domain. These simulations showed that although virtually every parameter change affects the results in some way, significant changes in the wind climate in the boundary layer are mostly due to using different physical parameterizations, especially the planetary boundary layer scheme, the representation of the land surface, and the prescribed surface roughness length. Also, the setup of the simulations, such as the integration length and the domain size, can considerably influence the results. We assessed the degree of similarity between winds simulated by the WRF ensemble members and the observations using a suite of metrics, including the Earth Mover's Distance (EMD), a statistic that measures the distance between two probability distributions. The EMD was used to diagnose the performance of each ensemble member using the full wind speed and direction distribution, which is essential for wind resource assessment. We identified the most realistic ensemble members to determine the most suitable configuration to be used in the final production run, which is fully described and evaluated in the second part of this study (Dörenkämper et al., 2020).

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Section: Model Evaluation Metricsmentioning

confidence: 99%

The making of the New European Wind Atlas – Part 1: Model sensitivity

et al. 2020

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“…Some of these stations also have records for the earlier period 2000-2014, which were registered by the Parque Natural de Peñalara (PNP, Table 1). A quality control was applied to the GuMNet data for the period 2014-2018 in order to exclude unphysical values following guidelines in [39,40].…”

Section: Era Interim Reanalysis Datamentioning

confidence: 99%

An Assessment of Observed and Simulated Temperature Variability in Sierra de Guadarrama

et al. 2020

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This work provides a first assessment of temperature variability at interannual and decadal timescales in Sierra de Guadarrama, a high mountain protected area of the Central System in the Iberian Peninsula. Observational data from stations located in the area and simulated data from a high-resolution simulation (1 km) with the Weather Research and Forecasting (WRF) model, fed from ERA Interim reanalysis, are used in order to analyse the temperature variability in the period 2000–2018. Comparison among all datasets allows evaluation of the realism of the model simulations. The results show that the model tends to underestimate the observational mean temperatures and anomalies at high-altitude stations. A linear mean temperature vertical gradient of −5.81 °C/km is observed, but it is overestimated by the model (−6.56 °C/km). The variability of the daily temperature anomalies for both observations and, to a lesser extent, simulations increases with height. The added value that the WRF offers against the use of the ERA Interim is evaluated. The results show that the WRF provides a better performance than the reanalysis, as it shows smaller biases with respect to observational temperature anomalies. Finally, the study of temperature trends over the Sierra de Guadarrama and its surroundings for the period 2000–2018 shows a warming in the area, significantly pronounced in autumn. When extended to the last decades, observations show that this warming has been happening since the first half of the 20th century, especially during the period 1970–2018, but not as much as during 2000–2018.

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“…For official automatic weather stations, the QC of observations is common practice and there is a great deal of research into the QC methods for official meteorological observations (Shafer et al ., 2000; Durre et al ., 2010; Fiebrich et al ., 2010; Estévez et al ., 2011; Taylor and Loescher, 2013; Otop et al ., 2018). Some researchers studied the QC for official wind observations in detail (DeGaetano, 1997; Jiménez et al ., 2010; Lucio‐Eceiza et al ., 2018a; 2018b). In an early study, DeGaetano (1997) built a QC routine based on hourly wind speed and direction data which focused on examining the variation of wind with time, identifying excessively varying or inordinately constant observations.…”

Section: Introductionmentioning

confidence: 99%

Quality control and bias adjustment of crowdsourced wind speed observations

Chen

Saunders

Whan

2021

Quart J Royal Meteoro Soc

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Wind observations collected at citizen weather stations (CWSs) could be an invaluable resource in climate and meteorology studies, yet these observations are underutilised because scientists do not have confidence in their quality. These wind speed observations have systematic biases, likely caused by improper instrumentation and station sitings. Such 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 adjusting systematic biases. Our complete quality control and bias adjustment procedure consists of four steps: (a) performing within-station quality control tests to check the plausible range and the temporal consistency of observations, (b) adjusting the systematic bias using empirical quantile mapping, (c) implementing between-station quality control to compare observations from neighbouring stations to identify spatially inconsistent observations, and (d) providing estimates of the true wind when CWSs falsely report zero wind speeds, as a complement to the bias adjustment. We apply these methods to CWSs from the Weather Observation Website (WOW) in the Netherlands, comparing the crowdsourced data with official data, and statistically assessing the improvements in data quality after each step. The results demonstrate that the crowdsourced wind speed data are more comparable with official data after quality control checks and bias adjustment steps. Our quality assessment methods therefore give confidence in CWSs, converting their observations into a usable data product and an invaluable resource for applications in need of additional wind observations.

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

Cited by 15 publications

References 30 publications

The making of the New European Wind Atlas – Part 1: Model sensitivity

The making of the New European Wind Atlas – Part 1: Model sensitivity

An Assessment of Observed and Simulated Temperature Variability in Sierra de Guadarrama

Quality control and bias adjustment of crowdsourced wind speed observations

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