Long-Term Observations and High-Resolution Modeling of Midlatitude Nocturnal Boundary Layer Processes Connected to Low-Level Jets

Marke, Tobias; Crewell, Susanne; Schemann, Vera; Schween, Jan H.; Tuononen, Minttu

doi:10.1175/jamc-d-17-0341.1

Cited by 23 publications

(21 citation statements)

References 42 publications

(53 reference statements)

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“…As discussed earlier, in calculating turbulent parameters from Doppler lidar observations, accurate σ vr is needed to differentiate turbulence from instrumental noise (e.g. O'Connor et al, 2010;Vakkari et al, 2015;Newsom et al, 2017).…”

Section: Instrumentation and Measurementsmentioning

confidence: 99%

“…Wind retrievals at lower SNR will have higher uncertainty due to higher instrumental noise in radial velocity measurement; yet enhanced SNR will enable more accurate determination of the instrumental uncertainty in wind retrievals. However, as a major fraction of the uncertainty in retrieved winds arises in atmospheric turbulence (Newsom et al, 2017), the more accurate SNR will only have a limited effect on the overall uncertainty in the wind retrieval. Therefore, the uncertainty in each wind retrieval should be evaluated, e.g.…”

Section: Welgegund 6 September 2016mentioning

confidence: 99%

“…In addition to MLH, fibre-optic Doppler lidar systems have also enabled long-term monitoring of horizontal wind profiles within the PBL Päschke et al, 2015;Newsom et al, 2017;Marke et al, 2018). Together with vertical profiles of higher moments of the velocity distri-Published by Copernicus Publications on behalf of the European Geosciences Union.…”

Section: Introductionmentioning

confidence: 99%

“…The post-processing algorithm by Manninen et al (2016) has the added benefit of improving the accuracy of the signal-to-noise ratio (SNR), which leads to more accurate uncertainty estimates of the measured radial velocity (Rye and Hardesty, 1993;Pearson et al, 2009). This is especially important for the retrieval of turbulent properties under weak signal conditions, as uncertainty in instrumental noise level propagates into turbulent properties and wind retrievals (O'Connor et al, 2010;Vakkari et al, 2015;Newsom et al, 2017). Naturally, post-processing methods can be applied to historical data sets as well.…”

Section: Introductionmentioning

confidence: 99%

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A novel post-processing algorithm for Halo Doppler lidars

Vakkari

Manninen²,

O’Connor

et al. 2019

Atmos. Meas. Tech.

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Abstract. Commercially available Doppler lidars have now been proven to be efficient tools for studying winds and turbulence in the planetary boundary layer. However, in many cases low signal-to-noise ratio is still a limiting factor for utilising measurements by these devices. Here, we present a novel post-processing algorithm for Halo Stream Line Doppler lidars, which enables an improvement in sensitivity of a factor of 5 or more. This algorithm is based on improving the accuracy of the instrumental noise floor and it enables longer integration times or averaging of high temporal resolution data to be used to obtain signals down to −32 dB. While this algorithm does not affect the measured radial velocity, it improves the accuracy of radial velocity uncertainty estimates and consequently the accuracy of retrieved turbulent properties. Field measurements using three different Halo Doppler lidars deployed in Finland, Greece and South Africa demonstrate how the new post-processing algorithm increases data availability for turbulent retrievals in the planetary boundary layer, improves detection of high-altitude cirrus clouds and enables the observation of elevated aerosol layers.

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Section: Instrumentation and Measurementsmentioning

confidence: 99%

Section: Welgegund 6 September 2016mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A novel post-processing algorithm for Halo Doppler lidars

Vakkari

Manninen²,

O’Connor

et al. 2019

Atmos. Meas. Tech.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition to MLH, fibre-optic Doppler lidar systems have enabled also long-term monitoring of horizontal wind profiles within PBL (Hirsikko et al, 2014;Päschke et al, 2015;Newsom et al, 2017;Marke et al, 2018). Together with vertical profiles of higher moments of the velocity distribution (Lothon et al, 2009), e.g vertical wind speed variance and skewness, as well as turbulent kinetic energy dissipation rate, Doppler lidar measurements enable the diagnosis of the sources of turbulence within PBL (Hogan et al, 2009;Harvey et al, 2013;Tuononen et al, 2017;Manninen et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

A novel post-processing algorithm for Halo Doppler lidars

Vakkari¹,

Manninen²,

O’Connor³

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. Commercially available Doppler lidars have now been proven to be efficient tools for studying winds and turbulence in the planetary boundary layer. However, in many cases low signal-to-noise ratio is still a limiting factor for utilising measurements by these devices. Here, we present a novel postprocessing algorithm for Halo Streamline Doppler lidars, which enables an improvement in sensitivity of a factor of five or more. This algorithm is based on improving the accuracy of the instrumental noise floor and it enables using longer integration times or averaging of high temporal resolution data to obtain signals down to −32 dB. While this algorithm does not affect the measured radial velocity, it improves the accuracy of radial velocity uncertainty estimates and consequently the accuracy of retrieved turbulent properties. Field measurements with three different Halo Doppler lidars deployed in Finland, Greece and South Africa demonstrate how the new post-processing algorithm increases data availability for turbulent retrievals in the planetary boundary layer, improves detection of high-altitude cirrus clouds, and enables the observation of elevated aerosol layers.

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Linking Large-Eddy Simulations to local cloud observations

Schemann

Ebell

Pospichal

et al. 2020

Preprint

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In order to enhance our understanding of clouds and their microphysical processes, it is crucial to exploit both observations and models. Local observations from ground-based remote sensing sites provide detailed information on clouds, but as they are limited in dimension, there is no straightforward way to use them to guide large-scale model development. We show that large-eddy simulations (LES) performed on similar temporal and spatial scales as the local observations can bridge this gap. Recently, LES with realistic topography and lateral boundary conditions became feasible for domains spanning several 100 km. In this study, we show how these simulations can be linked to observations of the Jülich Observatory for Cloud Evolution (JOYCE) for a 9-day period in spring 2013. We discuss the advantages and disadvantages of very large versus small but more constrained domains as well as the differences compared to more idealized setups. The semi-idealized LES include time-varying forcing but are run with homogeneous surfaces and periodic boundary conditions. These assumptions seem to be the reason why they struggle to represent the observed varying conditions. The simulations using the "realistic" setup are able to represent the general cloud structure (timing, height, phase). It seems that the smaller and more constrained domain allows for a tighter control on the synoptic situation and is the preferred choice to ensure the comparability to the local observations. These simulations together with measures as the shown Hellinger distance will allow us to gain more insights into the representativeness of column measurements in the future.Plain Language Summary Clouds are still a cause for uncertainty in our understanding of climate and climate feedbacks. Due to the large range of involved scales-from small droplets up to storm systems-their representation in weather and climate models is an ongoing challenge. While new and sophisticated measurements of the atmospheric column could provide new insights into important processes, their linking to models is not trivial and is ongoing research. In this study, we are presenting and exploring different approaches to combine local observations of clouds with state-of-the-art high-resolution simulations. And we are presenting a setup, which shows a promising representation of the observed clouds and is constrained enough to be applicable for long-term statistics-one of the key requirements for improvements and evaluation clouds in of weather and climate models.

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Long-Term Observations and High-Resolution Modeling of Midlatitude Nocturnal Boundary Layer Processes Connected to Low-Level Jets

Cited by 23 publications

References 42 publications

A novel post-processing algorithm for Halo Doppler lidars

A novel post-processing algorithm for Halo Doppler lidars

A novel post-processing algorithm for Halo Doppler lidars

Linking Large-Eddy Simulations to local cloud observations

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