Atmospheric Boundary Layer Processes, Characteristics and Parameterization (2nd Edition)

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".

Deadline for manuscript submissions: 31 March 2024 | Viewed by 4746

Special Issue Editors

School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044, China
Interests: tropical cyclones; atmospheric boundary layer; air-land-sea interaction; air pollution
Special Issues, Collections and Topics in MDPI journals
Shanghai Typhoon Institute of China Meteorological Administration, Shanghai 200030, China
Interests: tropical cyclone boundary layer; typhoon field experiment; extratropical transition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is the second volume in a series of publications dedicated to “Atmospheric Boundary Layer Processes, Characteristics and Parameterization” (https://www.mdpi.com/journal/atmosphere/special_issues/ABL_Processes).

The atmospheric boundary layer is distinguished from the rest of the atmosphere by its unique characteristics, i.e., direct interaction with the Earth’s surface and active turbulence. Understanding the dynamic and chemical processes in the boundary layer is of great importance in weather and air quality forecasting. Recently, with the improvements made in observation and simulation techniques, our understanding of atmospheric boundary layer processes and characteristics has significantly improved. For example, ultrasonic anemometers and large-aperture scintillometers can provide information on turbulent exchanges, while large eddy simulation techniques simulating the detailed structure of turbulent eddies. This Special Issue is dedicated to reporting new findings with regard to atmospheric boundary layer processes, characteristics, and parametrization methods. Potential topics include, but are not limited to, turbulent exchange, transportation, and their parametrization; boundary layer jet; local atmospheric circulation; surface energy partitioning; atmospheric stability condition; pollutant distribution and transportation; etc.

Prof. Dr. Yubin Li
Prof. Dr. Jie Tang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • atmospheric boundary layer
  • turbulent exchange
  • boundary layer jet
  • local atmospheric circulation
  • surface energy partitioning
  • pollutant transportation

Published Papers (5 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Other

13 pages, 5628 KiB  
Article
Understanding the Characteristics of Vertical Structures for Wind Speed Observations via Wind-LIDAR on Jeju Island
by Dong-Won Yi, Hee-Wook Choi, Sang-Sam Lee and Yong Hee Lee
Atmosphere 2023, 14(8), 1260; https://doi.org/10.3390/atmos14081260 - 08 Aug 2023
Viewed by 813
Abstract
Wind observations at multiple levels (40–200 m) have been conducted over a five-year time period (2016–2020) on Jeju Island of South Korea. This study aims to understand the vertical and temporal characteristics of the lower atmosphere. Jeju Island is a region located at [...] Read more.
Wind observations at multiple levels (40–200 m) have been conducted over a five-year time period (2016–2020) on Jeju Island of South Korea. This study aims to understand the vertical and temporal characteristics of the lower atmosphere. Jeju Island is a region located at mid-latitude and is affected by seasonal wind. The maximum wind speed occurs in the relatively lower altitudes during daytime and is delayed in the relatively higher altitude after sunset in a diurnal cycle. In the summer season, the altitudes appear earlier than in other seasons via the dominant solar radiation effect during daytime, and the altitude after sunset increases up to 160 m. However, the maximum wind speed in the winter season occurs irregularly among altitudes, and it is lower than that in the summer season. This can be attributed to the increase in the mean wind speed in the diurnal cycle caused by the strong northwestern wind in the winter season. These results imply that the relationship between near-surface and higher altitudes is primarily affected by solar radiation and seasonal winds. These results are expected to contribute to site selection criteria for wind farms. Full article
Show Figures

Figure 1

22 pages, 8464 KiB  
Article
Atmospheric Dynamic Response to Coupling Currents to Wind Stress over the Gulf Stream
by Jackie May and Mark Bourassa
Atmosphere 2023, 14(8), 1216; https://doi.org/10.3390/atmos14081216 - 28 Jul 2023
Viewed by 907
Abstract
Atmospheric near-surface stress and boundary layer wind responses to surface currents are examined with high resolution coupled atmosphere–ocean models over the Gulf Stream during winter. Because the ocean and atmosphere are linked through surface stress, the two fluids can cause dramatic changes through [...] Read more.
Atmospheric near-surface stress and boundary layer wind responses to surface currents are examined with high resolution coupled atmosphere–ocean models over the Gulf Stream during winter. Because the ocean and atmosphere are linked through surface stress, the two fluids can cause dramatic changes through feedback processes. When the current feedback is included, we find that the current gradient in the cross-wind direction drives the stress curl pattern and wind curl pattern to have minima and maxima at locations matching those of the ocean surface vorticity pattern. Furthermore, we find the large- (>30 km) and small-scale, or submesoscale (<30 km), stress curl and wind curl responses to ocean surface vorticity are complimentary; however, the large- and small-scale wind divergence responses are counteractive. These responses (commonly called coupling coefficients) are found to depend on the relative position to the Gulf Stream maximum current. Throughout the atmospheric boundary layer, we find including the current feedback also leads to changes in the atmospheric secondary circulation on either side of the Gulf Stream extension. The winter seasonal means suggest the current feedback will impact climate, and investigating individual events, such as an atmospheric front passing over the Gulf Stream, suggests the current feedback will also impact the intensity of weather. Full article
Show Figures

Figure 1

16 pages, 3810 KiB  
Article
On the Variability of In Situ Surface Layer Refractivity Measurements
by Douglas M. Pastore, Ryan T. Yamaguchi, Qing Wang and Erin E. Hackett
Atmosphere 2023, 14(7), 1085; https://doi.org/10.3390/atmos14071085 - 28 Jun 2023
Viewed by 790
Abstract
Direct measurements of profiles of atmospheric properties near the ocean surface and within the marine atmospheric surface layer often contain a large degree of variability. The variability observed can be explained by numerous technical and natural reasons such as the temporal variability over [...] Read more.
Direct measurements of profiles of atmospheric properties near the ocean surface and within the marine atmospheric surface layer often contain a large degree of variability. The variability observed can be explained by numerous technical and natural reasons such as the temporal variability over the time span a profile is measured (unsteadiness in the mean), spatial variations (inhomogeneity), turbulent fluctuations, and measurement uncertainty. In this study, we explored the observed variability in vertical distributions of refractive index measured with a tethered-balloon-based marine atmospheric profiling system (MAPS). MAPS profiled the atmosphere from approximately 0.5 to 50 m, with instantaneous (order 1 s) measurements performed at each profiled altitude. To explore whether the observed scatter could be largely explained by (inertial-scale) turbulent fluctuations, we simulated refractive index fluctuations with a spectral-based turbulent refractive index fluctuation (TRIF) model. TRIF was optimized based on the MAPS measurements to determine a vertical length scale of the turbulence. The scales computed in the optimization were reasonable based on other estimates in the literature under similar conditions. However, finer-scale trends of the length scale with atmospheric stability did not match expectations, and thus the estimated length scales may be considered more as an order-of-magnitude estimate rather than an exact measurement of this scale. The ability to match the observed variability in the MAPS data using a turbulence model with a reasonable choice of vertical length scale suggests that the MAPS variability is dominated by physical processes such as turbulence rather than being primarily driven by measurement uncertainty. Full article
Show Figures

Figure 1

12 pages, 2749 KiB  
Article
Investigating the Diurnal Variation in Coastal Boundary Layer Winds on Hainan Island Using Three Tower Observations
by Ziqiang Duan, Bingke Zhao, Shiwang Fu, Shuai Zhang, Limin Lin and Jie Tang
Atmosphere 2023, 14(4), 751; https://doi.org/10.3390/atmos14040751 - 21 Apr 2023
Viewed by 1037
Abstract
This study analyzes wind structures up to 509 m in the atmospheric boundary layer in the coastal area of Hainan Island, using a dataset obtained from ultrasonic anemometers housed in three towers. The wind profile, consisting of the measurements from the three towers, [...] Read more.
This study analyzes wind structures up to 509 m in the atmospheric boundary layer in the coastal area of Hainan Island, using a dataset obtained from ultrasonic anemometers housed in three towers. The wind profile, consisting of the measurements from the three towers, followed logarithmic law. In a diurnal variation, the maximum wind speed occurred at night, with a greater component of northerly wind, while the minimum wind speed was observed at noon, with a greater component of easterly wind. The variation in wind speed suggests that the measurements were representative of the wind field in the upper part of the atmospheric boundary layer, and the variation in wind direction might be affected by sea and land breezes, which can be induced by the different thermal conditions of underlying surfaces. The diurnal variation in average wind speed ranged from 0.5 to 1.5 m s−1, and the diurnal variation in wind direction was 10–20 degrees. In our measurements, the diurnal trajectory of the wind vector was observed to be counterclockwise, which differs from previous studies conducted over uniform and flat underlying surfaces. This is partially due to the different thermodynamic conditions of the underlying land and sea surfaces. The impact of topographic relief on wind measurement is also discussed. The measurements suggest that wind speeds at altitudes above 50 m are less influenced by terrain. The height of the reversal layer, which is generated by the different diurnal variations in wind speed in the upper and lower parts of the boundary layer, was estimated to be around 300 m. Full article
Show Figures

Figure 1

Other

Jump to: Research

11 pages, 15229 KiB  
Brief Report
Doppler LiDAR Observation of Subsidence in Synoptic Scale and Performance of a Global Numerical Weather Prediction Model in Capturing the Subsidence
by Pak-Wai Chan, Steve Hung-Lam Yim and Tao Huang
Atmosphere 2023, 14(11), 1686; https://doi.org/10.3390/atmos14111686 - 14 Nov 2023
Viewed by 587
Abstract
The vertical velocity data from a Doppler LiDAR situated at the centre of Hong Kong were examined to look for signature of subsidence within the atmospheric boundary layer against a synoptic background. Two case studies were performed, namely, stable atmospheric conditions in foggy [...] Read more.
The vertical velocity data from a Doppler LiDAR situated at the centre of Hong Kong were examined to look for signature of subsidence within the atmospheric boundary layer against a synoptic background. Two case studies were performed, namely, stable atmospheric conditions in foggy weather and possible “subsidence heating” at the periphery of the outer circulation of an intense tropical cyclone. The LiDAR’s Doppler velocity data were found to provide insights into the vertical motion of the air on the synoptic scale. They appear to confirm subsidence in foggy weather but provide new information about the mechanism for the occurrence of extremely hot weather. The data were also compared with vertical velocity forecasts from a numerical weather prediction model to assess the quality of the forecast. The Doppler LiDAR’s vertical velocity data were found to be useful in the verification of omega forecasts from the global numerical weather prediction model. They were found to provide further insights into the subsidence of the troposphere, particularly the atmospheric boundary layer, in certain synoptic patterns. Full article
Show Figures

Figure 1

Back to TopTop