scholarly journals Inter-comparison of wind measurements in the atmospheric boundary layer and the lower troposphere with Aeolus and a ground-based coherent Doppler lidar network over China

2022 ◽  
Vol 15 (1) ◽  
pp. 131-148
Author(s):  
Songhua Wu ◽  
Kangwen Sun ◽  
Guangyao Dai ◽  
Xiaoye Wang ◽  
Xiaoying Liu ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Standard Deviation ◽  
Atmospheric Boundary Layer ◽  
Correlation Coefficient ◽  
Vertical Velocity ◽  
Lower Troposphere ◽  
Wind Data ◽  
Horizontal Line ◽  
Comparison Results ◽  
Wind Measurements

Abstract. After the successful launch of Aeolus, which is the first spaceborne wind lidar developed by the European Space Agency (ESA), on 22 August 2018, we deployed several ground-based coherent Doppler wind lidars (CDLs) to verify the wind observations from Aeolus. By the simultaneous wind measurements with CDLs at 17 stations over China, the Rayleigh-clear and Mie-cloudy horizontal-line-of-sight (HLOS) wind velocities from Aeolus in the atmospheric boundary layer and the lower troposphere are compared with those from CDLs. To ensure the quality of the measurement data from CDLs and Aeolus, strict quality controls are applied in this study. Overall, 52 simultaneous Mie-cloudy comparison pairs and 387 Rayleigh-clear comparison pairs from this campaign are acquired. All of the Aeolus-produced Level 2B (L2B) Mie-cloudy HLOS wind and Rayleigh-clear HLOS wind and CDL-produced HLOS wind are compared individually. For the inter-comparison result of Mie-cloudy HLOS wind and CDL-produced HLOS wind, the correlation coefficient, the standard deviation, the scaled mean absolute deviation (MAD) and the bias are 0.83, 3.15 m s−1, 2.64 m s−1 and −0.25 m s−1, respectively, while the y=ax slope, the y=ax+b slope and the y=ax+b intercept are 0.93, 0.92 and −0.33 m s−1. For the Rayleigh-clear HLOS wind, the correlation coefficient, the standard deviation, the scaled MAD and the bias are 0.62, 7.07 m s−1, 5.77 m s−1 and −1.15 m s−1, respectively, while the y=ax slope, the y=ax+b slope and the y=ax+b intercept are 1.00, 0.96 and −1.2 m s−1. It is found that the standard deviation, the scaled MAD and the bias on ascending tracks are lower than those on descending tracks. Moreover, to evaluate the accuracy of Aeolus HLOS wind measurements under different product baselines, the Aeolus L2B Mie-cloudy HLOS wind data and L2B Rayleigh-clear HLOS wind data under Baselines 07 and 08, Baselines 09 and 10, and Baseline 11 are compared against the CDL-retrieved HLOS wind data separately. From the comparison results, marked misfits between the wind data from Aeolus Baselines 07 and 08 and wind data from CDLs in the atmospheric boundary layer and the lower troposphere are found. With the continuous calibration and validation and product processor updates, the performances of Aeolus wind measurements under Baselines 09 and 10 and Baseline 11 are improved significantly. Considering the influence of turbulence and convection in the atmospheric boundary layers and the lower troposphere, higher values for the vertical velocity are common in this region. Hence, as a special note, the vertical velocity could impact the HLOS wind velocity retrieval from Aeolus.

scholarly journals Inter-comparison of wind measurements in the atmospheric boundary layer with Aeolus and a ground-based coherent Doppler lidar network over China

2021 ◽  
Author(s):  
Songhua Wu ◽  
Kangwen Sun ◽  
Guangyao Dai ◽  
Xiaoye Wang ◽  
Xiaoying Liu ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Standard Deviation ◽  
Atmospheric Boundary Layer ◽  
Correlation Coefficient ◽  
Vertical Velocity ◽  
European Space Agency ◽  
Wind Data ◽  
Horizontal Line ◽  
Comparison Results ◽  
Wind Measurements

Abstract. After the successful launch of Aeolus which is the first spaceborne wind lidar developed by the European Space Agency (ESA) on 22 August 2018, we deployed several ground-based coherent Doppler wind lidars (CDLs) to verify the wind observations from Aeolus. By the simultaneous wind measurements with CDLs at 17 stations over China, the Rayleigh-clear and Mie-cloudy horizontal-line-of-sight (HLOS) wind velocities from Aeolus in the atmospheric boundary layer are compared with that from CDLs. To ensure the quality of the measurement data from CDL and Aeolus, strict quality controls are applied in this study. Overall, 52 simultaneous Mie-cloudy comparison pairs and 387 Rayleigh-clear comparison pairs from this campaign are acquired. All of the Aeolus-produced L2B Mie-cloudy HLOS, Rayleigh-clear HLOS and CDL-produced HLOS are compared individually. For the inter-comparison result of Mie-cloudy HLOS wind and CDL-produced HLOS wind, the correlation coefficient, the standard deviation, the scaled MAD and the bias are 0.83, 3.15 m/s, 2.64 m/s and −0.25 m/s respectively, while the "y = ax" slope, the "y = ax + b" slope and the "y = ax + b" intercept are 0.93, 0.92 and −0.33 m/s. For the Rayleigh-clear HLOS wind, the correlation coefficient, the standard deviation, the scaled MAD and the bias are 0.62, 7.07 m/s, 5.77 m/s and −1.15 m/s respectively, while the "y = ax" slope, the "y = ax + b" slope and the "y = ax + b" intercept are 1.00, 0.96 and −1.2 m/s. It is found that the standard deviation, the scaled MAD and the bias on ascending tracks are slightly better than that on descending tracks. Moreover, to evaluate the accuracy of Aeolus HLOS wind measurements under different product baselines, the Aeolus L2B Mie-cloudy HLOS wind data and L2B Rayleigh-clear HLOS wind data under Baselines 07/08, Baselines 09/10, and Baseline 11 are compared against the CDL-retrieved HLOS wind data separately. From the comparison results, marked misfits between the wind data from Aeolus Baselines 07/08 and wind data from CDL in planetary boundary layer are found. With the continuous calibration and validation and product processor updates, the performances of Aeolus wind measurements under Baselines 09/10 and Baseline 11 are improved significantly. Considering the influence of turbulence and convection in the planetary boundary layers, higher values for the vertical velocity are common in this region. Hence, as a special note, the vertical velocity could impact the HLOS wind velocity retrieval from Aeolus.

Study of Thermal Activity in The Mixing Layer During First Generated Single Cloud by Using Combined Observation From Boundary Layer Radar, Doppler Lidar and Time Lapse Camera 

2021 ◽  
Author(s):  
Ginaldi Ari Nugroho ◽  
Kosei Yamaguchi ◽  
Eiichi Nakakita ◽  
Masayuki K. Yamamoto ◽  
Seiji Kawamura ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Time Series ◽  
Atmospheric Boundary Layer ◽  
Vertical Velocity ◽  
Time Lapse ◽  
Small Scale ◽  
Thermal Activity ◽  
Doppler Lidar ◽  
Cumulus Cloud ◽  
Scale Perturbation

<p>Detailed observation of small scale perturbation in the atmospheric boundary layer during the first generated cumulus cloud are conducted. Our target is to study this small scale perturbation, especially related to the thermal activity at the first generated cumulus cloud. The observation is performed during the daytime on August 17, 2018, and September 03, 2018. Location is focused in the urban area of Kobe, Japan. High-resolution instruments such as Boundary Layer Radar, Doppler Lidar, and Time Lapse camera are used in this observation. Boundary Layer Radar, and Doppler Lidar are used for clear air observation. Meanwhile Time Lapse Camera are used for cloud existence observation. The atmospheric boundary layer structure is analyzed based on vertical velocity profile, variance, skewness, and estimated atmospheric boundary layer height. Wavelet are used to observe more of the period of the thermal activity. Furthermore, time correlation between vertical velocity time series from height 0.3 to 2 km and image pixel of generated cloud time series are also discussed in this study.</p>

How is the marine atmospheric boundary layer turbulence organized in the trades ?

2021 ◽  
Author(s):  
Pierre-Etienne Brilouet ◽  
Marie Lothon ◽  
Sandrine Bony
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Vertical Velocity ◽  
Large Scale ◽  
Layer Structure ◽  
Potential Temperature ◽  
Surface Wind ◽  
Cloud Amount ◽  
Velocity Spectrum ◽  
Marine Atmospheric Boundary Layer

<p>Tradewind clouds can exhibit a wide diversity of mesoscale organizations, and the turbulence of marine atmospheric boundary layer (MABL) can exhibit coherent structures and mesoscale circulations. One of the objectives of the EUREC4A (Elucidating the role of cloud-circulation coupling in climate) field experiment was to better understand the tight interplay between the mesoscale organization of clouds, boundary-layer processes, and the large-scale environment.</p><p>During the experiment, that took place East of Barbados over the Western Tropical Atlantic Ocean in Jan-Feb 2020, the French ATR-42 research aircraft was devoted to the characterization of the cloud amount and of the subcoud layer structure. <span>During its 17 research flights, </span><span>it</span> <span>sampled a </span><span>large diversity of large scale conditions and </span><span>cloud patterns</span><span>. </span>Multiple sensors onboard t<span>he aircraft measure</span><span>d</span> <span>high-frequency </span><span>fluctuations of potential temperature, water vapour mixing ratio and wind , allowing </span><span>for </span><span>an extensive characterization </span><span> of</span><span> the turbulence </span><span>within</span><span> the subcloud layer. </span> <span>A </span><span>quality-controled and calibrated turbulence data</span><span>set</span><span> was produced </span><span>on the basis of these measurements</span><span>, which is now </span><span> available on the EUREC4A AERIS data portal.</span></p><p><span>The </span><span>MABL </span><span>turbulent </span><span>structure i</span><span>s</span><span> studied </span><span>using this dataset, </span><span>through a spectral analysis </span><span>of the vertical velocity</span><span>. Vertical profiles of characteristic length scales reveal a non-isotropic structure with a stretching of the eddies along the mean wind. The organization strength of the turbulent field is also explored </span><span>by defining</span><span> a diagnostic based on the shape of the vertical velocity spectrum. </span><span>The </span><span>structure and the degree of organization of the </span><span>subcloud layer </span><span>are</span><span> characterized for </span><span> different type</span><span>s</span><span> of mesoscale </span><span>convective </span><span>pattern </span><span>and </span><span>as a function of</span><span> the large-scale environment, </span><span>including</span> <span>near-</span><span>surface wind </span><span>and</span> <span>lower-</span><span>tropospheric</span><span> stability conditions.</span></p><p> </p>

scholarly journals Aircraft validation of Aura Tropospheric Emission Spectrometer retrievals of HDO / H<sub>2</sub>O

2014 ◽  
Vol 7 (9) ◽  
pp. 3127-3138 ◽  
Author(s):  
R. L. Herman ◽  
J. E. Cherry ◽  
J. Young ◽  
J. M. Welker ◽  
D. Noone ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Water Vapor ◽  
Standard Deviation ◽  
Lower Troposphere ◽  
Temporal Variations ◽  
Observation Error ◽  
Free Troposphere ◽  
Data Set ◽  
Airborne Measurements

Abstract. The EOS (Earth Observing System) Aura Tropospheric Emission Spectrometer (TES) retrieves the atmospheric HDO / H2O ratio in the mid-to-lower troposphere as well as the planetary boundary layer. TES observations of water vapor and the HDO isotopologue have been compared with nearly coincident in situ airborne measurements for direct validation of the TES products. The field measurements were made with a commercially available Picarro L1115-i isotopic water analyzer on aircraft over the Alaskan interior boreal forest during the three summers of 2011 to 2013. TES special observations were utilized in these comparisons. The TES averaging kernels and a priori constraints have been applied to the in situ data, using version 5 (V005) of the TES data. TES calculated errors are compared with the standard deviation (1σ) of scan-to-scan variability to check consistency with the TES observation error. Spatial and temporal variations are assessed from the in situ aircraft measurements. It is found that the standard deviation of scan-to-scan variability of TES δD is ±34.1‰ in the boundary layer and ± 26.5‰ in the free troposphere. This scan-to-scan variability is consistent with the TES estimated error (observation error) of 10–18‰ after accounting for the atmospheric variations along the TES track of ±16‰ in the boundary layer, increasing to ±30‰ in the free troposphere observed by the aircraft in situ measurements. We estimate that TES V005 δD is biased high by an amount that decreases with pressure: approximately +123‰ at 1000 hPa, +98‰ in the boundary layer and +37‰ in the free troposphere. The uncertainty in this bias estimate is ±20‰. A correction for this bias has been applied to the TES HDO Lite Product data set. After bias correction, we show that TES has accurate sensitivity to water vapor isotopologues in the boundary layer.

scholarly journals Distributed wind measurements with multiple quadrotor unmanned aerial vehicles in the atmospheric boundary layer

2021 ◽  
Vol 14 (5) ◽  
pp. 3795-3814
Author(s):  
Tamino Wetz ◽  
Norman Wildmann ◽  
Frank Beyrich
Keyword(s):  
Boundary Layer ◽  
Spatial Distribution ◽  
Wind Speed ◽  
Atmospheric Boundary Layer ◽  
Unmanned Aerial Vehicles ◽  
Horizontal Wind ◽  
Vertical Profiles ◽  
Aerial Vehicles ◽  
Wind Measurements ◽  
Quadrotor Unmanned Aerial Vehicles

Abstract. In this study, a fleet of quadrotor unmanned aerial vehicles (UAVs) is presented as a system to measure the spatial distribution of atmospheric boundary layer flow. The big advantage of this approach is that multiple and flexible measurement points in space can be sampled synchronously. The algorithm to obtain horizontal wind speed and direction is designed for hovering flight phases and is based on the principle of aerodynamic drag and the related quadrotor dynamics. During the FESST@MOL campaign at the boundary layer field site (Grenzschichtmessfeld, GM) Falkenberg of the Lindenberg Meteorological Observatory – Richard Assmann Observatory (MOL-RAO), 76 calibration and validation flights were performed. The 99 m tower equipped with cup and sonic anemometers at the site is used as the reference for the calibration of the wind measurements. The validation with an independent dataset against the tower anemometers reveals that an average accuracy of σrms<0.3 m s−1 for the wind speed and σrms,ψ<8∘ for the wind direction was achieved. Furthermore, we compare the spatial distribution of wind measurements with the fleet of quadrotors to the tower vertical profiles and Doppler wind lidar scans. We show that the observed shear in the vertical profiles matches well with the tower and the fluctuations on short timescales agree between the systems. Flow structures that appear in the time series of a line-of-sight measurement and a two-dimensional vertical scan of the lidar can be observed with the fleet of quadrotors and are even sampled with a higher resolution than the deployed lidar can provide.

scholarly journals A Marine Radar Wind Sensor

2007 ◽  
Vol 24 (9) ◽  
pp. 1629-1642 ◽  
Author(s):  
Heiko Dankert ◽  
Jochen Horstmann
Keyword(s):  
Standard Deviation ◽  
Correlation Coefficient ◽  
Wind Direction ◽  
Ocean Surface ◽  
Image Sequences ◽  
Wind Vector ◽  
Sea State ◽  
Wind Speeds ◽  
X Band ◽  
Wind Measurements

Abstract A new method for retrieving the wind vector from radar-image sequences is presented. This method, called WiRAR, uses a marine X-band radar to analyze the backscatter of the ocean surface in space and time with respect to surface winds. Wind direction is found using wind-induced streaks, which are very well aligned with the mean surface wind direction and have a typical spacing above 50 m. Wind speeds are derived using a neural network by parameterizing the relationship between the wind vector and the normalized radar cross section (NRCS). To improve performance, it is also considered how the NRCS depends on sea state and atmospheric parameters such as air–sea temperature and humidity. Since the signal-to-noise ratio in the radar sequences is directly related to the significant wave height, this ratio is used to obtain sea state parameters. All radar datasets were acquired in the German Bight of the North Sea from the research platform FINO-I, which provides environmental data such as wind measurements at different heights, sea state, air–sea temperatures, humidity, and other meteorological and oceanographic parameters. The radar-image sequences were recorded by a marine X-band radar installed aboard FINO-I, which operates at grazing incidence and horizontal polarization in transmit and receive. For validation WiRAR is applied to the radar data and compared to the in situ wind measurements from FINO-I. The comparison of wind directions resulted in a correlation coefficient of 0.99 with a standard deviation of 12.8°, and that of wind speeds resulted in a correlation coefficient of 0.99 with a standard deviation of 0.41 m s−1. In contrast to traditional offshore wind sensors, the retrieval of the wind vector from the NRCS of the ocean surface makes the system independent of the sensors’ motion and installation height as well as the effects due to platform-induced turbulence.

Distributed wind measurements with multiple quadrotor UAVs in the atmospheric boundary layer

2021 ◽  
Author(s):  
Tamino Wetz ◽  
Norman Wildmann ◽  
Frank Beyrich
Keyword(s):  
Boundary Layer ◽  
Spatial Distribution ◽  
Wind Speed ◽  
Atmospheric Boundary Layer ◽  
Horizontal Wind ◽  
Vertical Profiles ◽  
Sonic Anemometers ◽  
Meteorological Observatory ◽  
Wind Measurements ◽  
Quadrotor Uavs

&lt;p&gt;A swarm of quadrotor UAVs is presented as a system to measure the spatial distribution of atmospheric boundary layer flow. The big advantage of this approach is, that multiple and flexible measurement points in space can be sampled synchronously. The algorithm to obtain horizontal wind speed and direction is designed for hovering flight phases and is based on the principle of aerodynamic drag and the related quadrotor dynamics using only on-board sensors.&lt;/p&gt;&lt;p&gt;During the FESST@MOL campaign at the Boundary Layer Field Site (Grenzschichtmessfeld, GM) Falkenberg of the Lindenberg Meteorological Observatory - Richard-A&amp;#223;mann-Observatory (MOL-RAO), 76 calibration and validation flights were performed. The 99 m tower equipped with cup and sonic anemometers at the site is used as the reference for the calibration of the wind measurements. The validation with an independent dataset against the tower anemometers reveals that an average accuracy of &amp;#963;&lt;sub&gt;rms &lt;/sub&gt;&lt; 0.3 m s&lt;sup&gt;-1&lt;/sup&gt; for the wind speed and &amp;#963;&lt;sub&gt;rms&lt;/sub&gt;,&lt;sub&gt;&amp;#936;&lt;/sub&gt;&lt;sub&gt;&lt;/sub&gt;&lt; 8&amp;#176; for the wind direction was achieved.&lt;/p&gt;&lt;p&gt;Furthermore, we compare the spatial distribution of wind measurements with the swarm to the tower vertical profiles and Doppler wind lidar scans. We show that the observed shear in the vertical profiles matches well with the tower and the fluctuations on short time scales agree between the systems. Flow structures that appear in the time series of a line-of-sight measurement and a two-dimensional vertical scan of the lidar can be observed with the swarm and are even sampled with a higher resolution than the deployed lidar can provide.&lt;/p&gt;&lt;p&gt;In addition to the intercomparison of the mean wind velocity measurements, turbulence data of the UAV-swarm measurements are analyzed and a comparison to sonic anemometer measurements is provided.&lt;/p&gt;

scholarly journals Marine Atmospheric Boundary Layer Height over the Eastern Pacific: Data Analysis and Model Evaluation

2004 ◽  
Vol 17 (21) ◽  
pp. 4159-4170 ◽  
Author(s):  
Xubin Zeng ◽  
Michael A. Brunke ◽  
Mingyu Zhou ◽  
Chris Fairall ◽  
Nicholas A. Bond ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Correlation Coefficient ◽  
Climate Models ◽  
Eastern Pacific ◽  
Radiosonde Data ◽  
Climate System Model ◽  
Boundary Layer Height ◽  
Seasonal And Interannual Variations ◽  
Atmospheric Boundary Layer Height

Abstract The atmospheric boundary layer (ABL) height (h) is a crucial parameter for the treatment of the ABL in weather and climate models. About 1000 soundings from 11 cruises between 1995 and 2001 over the eastern Pacific have been analyzed to document the large meridional, zonal, seasonal, and interannual variations of h. In particular, its latitudinal distribution in August has three minima: near the equator, in the intertropical convergence zone (ITCZ), and over the subtropical stratus/stratocumulus region near the west coast of California and Mexico. The seasonal peak of h in the ITCZ zone (between 5.6° and 11.2°N) occurs in the spring (February or April), while it occurs in August between the equator and 5.6°N. Comparison of these data with the 10-yr monthly output of the Community Climate System Model (CCSM2) reveals that overall the model underestimates h, particularly north of 20°N in August and September. Directly applying the radiosonde data to the CCSM2 formulation for computing h shows that, at the original vertical resolution (with the lowest five layers below 2.1 km), the CCSM2 formulation would significantly underestimate h. In particular, the correlation coefficient between the computed and observed h values is only 0.06 for cloudy cases. If the model resolution were doubled below 2.1 km, however, the performance of the model formulation would be significantly improved with a correlation coefficient of 0.78 for cloudy cases.

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