Fog and Low Stratus Obstruction of Wind Lidar Observations in Germany—A Remote Sensing-Based Data Set for Wind Energy Planning

Rösner, Benjamin; Egli, Sebastian; Thies, Boris; Beyer, Tina; Callies, Doron; Pauscher, Lukas; Bendix, J.

doi:10.3390/en13153859

Cited by 7 publications

(7 citation statements)

References 22 publications

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“…In the context of our model, we assume "perfect" conditions in the sense of ignoring factors like aerosol type, size, and density distribution and conditions like humidity, fog, or precipitation that can affect the quality of the return signal in the optical measurement of the radial velocity (Aitken et al, 2012;Boquet et al, 2016;Rösner et al, 2020). We similarly omit impacts of the carrier-to-noise ratio which can introduce additional uncertainty into the diagnosis of the radial velocity (Cariou and Boquet, 2010;Aitken et al, 2012).…”

Section: Sampling Along a Single Lidar Beammentioning

confidence: 99%

Behavior and mechanisms of Doppler wind lidar error in varying stability regimes

Robey

Lundquist

2022

Atmos. Meas. Tech.

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Abstract. Wind lidars are widespread and important tools in atmospheric observations. An intrinsic part of lidar measurement error is due to atmospheric variability in the remote-sensing scan volume. This study describes and quantifies the distribution of measurement error due to turbulence in varying atmospheric stability. While the lidar error model is general, we demonstrate the approach using large ensembles of virtual WindCube V2 lidar performing a profiling Doppler-beam-swinging scan in quasi-stationary large-eddy simulations (LESs) of convective and stable boundary layers. Error trends vary with the stability regime, time averaging of results, and observation height. A systematic analysis of the observation error explains dominant mechanisms and supports the findings of the empirical results. Treating the error under a random variable framework allows for informed predictions about the effect of different configurations or conditions on lidar performance. Convective conditions are most prone to large errors (up to 1.5 m s−1 in 1 Hz wind speed in strong convection), driven by the large vertical velocity variances in convective conditions and the high elevation angle of the scanning beams (62∘). Range-gate weighting induces a negative bias into the horizontal wind speeds near the surface shear layer (−0.2 m s−1 in the stable test case). Errors in the horizontal wind speed and direction computed from the wind components are sensitive to the background wind speed but have negligible dependence on the relative orientation of the instrument. Especially during low winds and in the presence of large errors in the horizontal velocity estimates, the reported wind speed is subject to a systematic positive bias (up to 0.4 m s−1 in 1 Hz measurements in strong convection). Vector time-averaged measurements can improve the behavior of the error distributions (reducing the 10 min wind speed error standard deviation to <0.3 m s−1 and the bias to <0.1 m s−1 in strong convection) with a predictable effectiveness related to the number of decorrelated samples in the time window. Hybrid schemes weighting the 10 min scalar- and vector-averaged lidar measurements are shown to be effective at reducing the wind speed biases compared to cup measurements in most of the simulated conditions, with time averages longer than 10 min recommended for best use in some unstable conditions. The approach in decomposing the error mechanisms with the help of the LES flow field could be extended to more complex measurement scenarios and scans.

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Section: Sampling Along a Single Lidar Beammentioning

confidence: 99%

Behavior and mechanisms of Doppler wind lidar error in varying stability regimes

Robey

Lundquist

2022

Atmos. Meas. Tech.

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“…The radial velocity, v r , is the dot product of the wind velocity vector, u = (u, v, w) with u along the zonal direction, and the beam unit direction vector b. The beam direction points along azimuthal angle, γ ∈ [0 In the context of our model, we assume 'perfect' conditions in the sense of ignoring factors like aerosol type, size, and density distribution and conditions like humidity, fog, or precipitation that can affect the quality of the return signal in the optical measurement of the radial velocity (Aitken et al, 2012;Boquet et al, 2016;Rösner et al, 2020). We instead focus on the representation of the averaging introduced by the sampling process.…”

Section: Sampling Along a Single Lidar Beammentioning

confidence: 99%

Behavior and Mechanisms of Doppler Wind Lidar Error in Varying Stability Regimes

Robey

Lundquist

2022

Preprint

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Abstract. Wind lidar are widespread and important tools in atmospheric observations. An intrinsic part of lidar measurement error is due to atmospheric variability in the remote sensing scan volume. This study describes and quantifies the distribution of measurement error due to turbulence in varying atmospheric stability. While the lidar error model is general, we demonstrate the approach using large ensembles of virtual WindcubeV2 lidar performing profiling doppler-beam-swinging (DBS) scans in quasi-stationary large-eddy simulations (LES) of convective and stable boundary layers. Error trends vary with the stability regime, time-averaging of results, and observation height. A systematic analysis of the observation error explains dominant mechanisms and supports the findings of the empirical results. Treating the error under a random variable framework allows for informed predictions about the effect of different configurations or conditions on lidar performance. Convective conditions are most prone to large errors, driven by the large vertical velocities in convective plumes and exacerbated by the high elevation angle of the scanning beams. The violations of the assumption of horizontal homogeneity due to filtered turbulent velocity variances dominate the error variance, with the vertical velocity variations of particular importance. Range gate weighting contributes little to the variability of the error, but induces an underestimating bias into the horizontal velocity near the surface shear layer. Error in the horizontal wind speed and direction computed from wind components is sensitive to the background wind speed but has negligible dependence on the relative orientation of the instrument. Especially during low winds and in the presence of large errors in the u and v velocity estimates, the reported wind speed is subject to a systematic positive bias. Vector time-averaged measurements can improve the behavior of the error distribution with a predictable effectiveness related to the number of decorrelated samples in the time window. The approach in decomposing the error mechanisms with the help of the LES flow field extends to more complex measurement scenarios and scans.

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“…The wavelength domain for PPFD in the FLUXNET data set is 400-700 nm (Olson et al, 2004) and has units of µmol m −2 s −1 . We convert PPFD to PAR irradiance in W m −2 through the relationship 4570 nmol m −2 s −1 = 1 W m −2 (Sager and McFarlane, 1997).…”

Section: Datamentioning

confidence: 99%

Climatic traits on daily clearness and cloudiness indices

Muñoz

Ochoa²

2021

Biogeosciences

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Abstract. Solar radiation has a crucial role in photosynthesis, evapotranspiration and other biogeochemical processes. The amount of solar radiation reaching the Earth's surface is a function of astronomical geometry and atmospheric optics. While the first is deterministic, the latter has a random behaviour caused by highly variable atmospheric components such as water and aerosols. In this study, we use daily radiation data (1978–2014) from 37 FLUXNET sites distributed across the globe to inspect for climatic traits in the shape of the probability density function (PDF) of the clear-day (c) and the clearness (k) indices. The analysis was made for shortwave radiation (SW) at all sites and for photosynthetically active radiation (PAR) at 28 sites. We identified three types of PDF, unimodal with low dispersion (ULD), unimodal with high dispersion (UHD) and bimodal (B), with no difference in the PDF type between c and k at each site. Looking for regional patterns in the PDF type, we found that latitude, global climate zone and Köppen climate type have a weak relation and the Holdridge life a stronger relation with c and k PDF types. The existence and relevance of a second mode in the PDF can be explained by the frequency and meteorological mechanisms of rainy days. These results are a frame to develop solar radiation stochastic models for biogeochemical and ecohydrological modelling.

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Fog and Low Stratus Obstruction of Wind Lidar Observations in Germany—A Remote Sensing-Based Data Set for Wind Energy Planning

Cited by 7 publications

References 22 publications

Behavior and mechanisms of Doppler wind lidar error in varying stability regimes

Behavior and mechanisms of Doppler wind lidar error in varying stability regimes

Behavior and Mechanisms of Doppler Wind Lidar Error in Varying Stability Regimes

Climatic traits on daily clearness and cloudiness indices

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