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Characterizing Atmospheric and Marine Boundary Layer Processes by Remote Sensing

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 9620

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Special Issue Editors

Dr. Raghavendra Krishnamurthy

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Guest Editor

Pacific Northwest National Laboratory, Richland, WA 99354, USA
Interests: atmospheric fluid dynamics; Doppler and Raman lidars; marine atmospheric boundary; fog/cloud dynamics; wind energy

Dr. Ronald Calhoun

E-Mail Website
Guest Editor

School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ 85281, USA
Interests: fluid dynamics; atmospheric sciences; remote sensing; wind energy

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue in the journal Remote Sensing on Characterizing the Atmospheric and Marine Boundary Layer by Remote Sensing. Active and passive remote sensing instruments are routinely used to understand various dynamics within the atmospheric and marine boundary layers. Remote sensing systems such as lidars, radars, sodars, unmanned aircraft systems (UASs), satellites, scintillometers, radiometers, and spectrometers can measure various atmospheric processes with diverse spatial and temporal scales. This Special issue aims to compile the latest developments in the field of remote sensing applied to boundary layer studies. A representative selection of topics to be covered in the Special Issue are provided below:

Results from recent field experiments using remote sensing data;
Estimation of mean and turbulence parameters from remote sensing data;
Evaluations of novel remote sensing techniques against reference measurements;
Characterization of clouds and precipitation by radars;
Thermodynamic profiling of boundary layer by lidar, radiometry, and spectroscopy;
Use of satellite and UAS data for characterizing clouds and atmospheric/marine boundary layers;
Boundary layer climatology and boundary layer processes at long-term observatories;
Machine learning applications to remote sensing data.

Dr. Raghavendra Krishnamurthy
Dr. Ronald Calhoun
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

lidar/radar/sodar
satellite
UAS
field campaigns
clouds and precipitation
boundary layer turbulence
long-term observatories
machine learning

Published Papers (5 papers)

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Research

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13 pages, 4278 KiB

Open AccessArticle

Long-Term Measurements of the Atmospheric Boundary Layer Height in Central Amazonia Using Remote Sensing Instruments

by Carla Maria Alves Souza, Cléo Quaresma Dias-Júnior, Flávio Augusto F. D’Oliveira, Hardiney Santos Martins, Rayonil Gomes Carneiro, Bruno Takeshi Tanaka Portela and Gilberto Fisch

Remote Sens. 2023, 15(13), 3261; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15133261 - 25 Jun 2023

Viewed by 1004

Abstract

The height (zi) of the Atmospheric Boundary Layer (ABL) is a fundamental parameter for several areas of knowledge, especially for weather and climate forecasting, pollutant dispersion and air quality. In this work, we used data from a remote sensing instrument (ceilometer), located at the experimental site of the Amazon Tall Tower Observatory (ATTO) in the Central Amazonia rainforest, in order to obtain the height of the ABL. Data used were obtained from 2014 to 2020, with the exception of the year 2017. The results showed that the zi average varies from year to year (interannual variability) and the average of the maximum zi values (zi_max) was approximately 1400 ± 277 m, occurring at 15:00 local time. In addition, it was found that these maximum heights are higher in the dry season and during El Niño years (about 1741 ± 242 m) and they are lower during the wet period and in La Niña years (1263 ± 229 m). Taking into account all the years investigated, the month with the highest zi_max value is September (1710 ± 253 m), and the month with the lowest value is May (1108 ± 152 m). Finally, it was observed that the growth rate of the ABL during the early hours after sunrise varies from month to month (intraseasonal variability), reaching its maximum values in September and October (about 210 ± 53 m h

^{- 1}

and 217 ± 59 m h

^{- 1}

, respectively) and minimum values in April and July (approximately 159 ± 48 m h

^{- 1}

and 159 ± 50 m h

^{- 1}

, respectively). It is concluded that the values of zi in Central Amazonia are influenced by several seasonal factors (temperature, cloud cover, turbulent heat flux, etc.) which gives it a wide variability in terms of heights and growth rates. Additionally, a linear regression was proposed in order to model the maximum zi value as a function of its growth rate from 08:00 LT (Local Time) up to 10:00 LT. The results showed a good correlation compared with the experimental values. Full article

(This article belongs to the Special Issue Characterizing Atmospheric and Marine Boundary Layer Processes by Remote Sensing)

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Figure 1

19 pages, 3559 KiB

Open AccessArticle

Intercomparison of Planetary Boundary Layer Heights Using Remote Sensing Retrievals and ERA5 Reanalysis over Central Amazonia

by Cléo Quaresma Dias-Júnior, Rayonil Gomes Carneiro, Gilberto Fisch, Flávio Augusto F. D’Oliveira, Matthias Sörgel, Santiago Botía, Luiz Augusto T. Machado, Stefan Wolff, Rosa Maria N. dos Santos and Christopher Pöhlker

Remote Sens. 2022, 14(18), 4561; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14184561 - 13 Sep 2022

Cited by 9 | Viewed by 2034

Abstract

The atmospheric boundary layer height (

z i

) is a key parameter in the vertical transport of mass, energy, moisture, and chemical species between the surface and the free atmosphere. There is a lack of long-term and continuous observations of

z i

[...] Read more.

The atmospheric boundary layer height (

z i

) is a key parameter in the vertical transport of mass, energy, moisture, and chemical species between the surface and the free atmosphere. There is a lack of long-term and continuous observations of

z i

, however, particularly for remote regions, such as the Amazon forest. Reanalysis products, such as ERA5, can fill this gap by providing temporally and spatially resolved information on

z i

. In this work, we evaluate the ERA5 estimates of

z i

(

z i

-ERA5) for two locations in the Amazon and corrected them by means of ceilometer, radiosondes, and SODAR measurements (

z i

-experimental). The experimental data were obtained at the remote Amazon Tall Tower Observatory (ATTO) with its pristine tropical forest cover and the T3 site downwind of the city of Manaus with a mixture of forest (

63 %

), pasture (

17 %

), and rivers (

20 %

). We focus on the rather typical year 2014 and the El Niño year 2015. The comparison of the experimental vs. ERA5

z i

data yielded the following results: (i)

z i

-ERA5 underestimates

z i

-experimental daytime at the T3 site for both years 2014 (30%, underestimate) and 2015 (15%, underestimate); (ii)

z i

-ERA5 overestimates

z i

-experimental daytime at ATTO site (12%, overestimate); (iii) during nighttime, no significant correlation between the

z i

-experimental and

z i

-ERA5 was observed. Based on these findings, we propose a correction for the daytime

z i

-ERA5, for both sites and for both years, which yields a better agreement between experimental and ERA5 data. These results and corrections are relevant for studies at ATTO and the T3 site and can likely also be applied at further locations in the Amazon. Full article

(This article belongs to the Special Issue Characterizing Atmospheric and Marine Boundary Layer Processes by Remote Sensing)

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15 pages, 6343 KiB

Open AccessArticle

Ground-Based MAX-DOAS Measurements of Tropospheric Aerosols, NO_2, and HCHO Distributions in the Urban Environment of Shanghai, China

by Haoyue Wang, Wanlin Wei, Huizheng Che, Xiao Tang, Jianchun Bian, Ke Yu and Weiguo Wang

Remote Sens. 2022, 14(7), 1726; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071726 - 03 Apr 2022

Cited by 2 | Viewed by 1970

Abstract

Aerosol extinction profiles at 550 nm were retrieved by applying multi-axis differential optical absorption spectroscopy (MAX-DOAS) and lookup table. Then the tropospheric NO₂ and HCHO vertical column densities were retrieved using a two-step method from 28 July to 5 August of 2015 in Shanghai. The retrieved results were compared with the satellite products, and then their diurnal variation was observed. A consistency check was performed before the inversion to obtain a correction factor. Based on the sensitivity of geometric angles to oxygen dimer air mass factor (O₄ AMF, AMF is the ratio of the slanted column density to the vertical column density), the parameterization scheme of geometric angles in the lookup table is optimized. The results show that the aerosol increased significantly in the afternoon. The diurnal variation of tropospheric NO₂ and HCHO vertical column densities (VCDs) are bimodal and unimodal patterns respectively, and their values are higher than those of GOME-2 and OMI satellite products. A process of aerosol reduction and recovery are related to ground particulates and meteorological elements. The chemical sensitivity of local ozone production also has a clear diurnal variation. Full article

(This article belongs to the Special Issue Characterizing Atmospheric and Marine Boundary Layer Processes by Remote Sensing)

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Figure 1

19 pages, 26015 KiB

Open AccessArticle

Structure Analysis of the Sea Breeze Based on Doppler Lidar and Its Impact on Pollutants

by Jiaxin Liu, Xiaoquan Song, Wenrui Long, Yiyuan Fu, Long Yun and Mingdi Zhang

Remote Sens. 2022, 14(2), 324; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14020324 - 11 Jan 2022

Cited by 8 | Viewed by 2174

Abstract

The Doppler lidar system can accurately obtain wind profiles with high spatiotemporal resolution, which plays an increasingly important role in the research of atmospheric boundary layers and sea–land breeze. In September 2019, Doppler lidars were used to carry out observation experiments of the atmospheric wind field and pollutants in Shenzhen. Weather Research and Forecasting showed that the topography of Hongkong affected the sea breeze to produce the circumfluence flow at low altitudes. Two sea breezes from the Pearl River Estuary and the northeast of Hong Kong arrived at the observation site in succession, changing the wind direction from northeast to southeast. Based on the wind profiles, the structural and turbulent characteristics of the sea breeze were analyzed. The sea breeze front was accurately captured by the algorithm based on fuzzy logic, and its arrival time was 17:30 on 25 September. The boundary between the sea breeze and the return flow was separated by the edge enhancement algorithm. From this, the height of the sea breeze head (about 1100 m) and the thickness of the sea breeze layer (about 700 m) can be obtained. The fluctuated height of the boundary and the spiral airflow nearby revealed the Kelvin–Helmholtz instability. The influence of the Kelvin–Helmholtz instability could be delivered to the near-surface, which was verified by the spatiotemporal change of the horizontal wind speed and momentum flux. The intensity of the turbulence under the control of the sea breeze was significantly lower than that under the land breeze. The turbulent intensity was almost 0.1, and the dissipation rate was between 10⁻⁴ and 10⁻² m²·s⁻³ under the land breeze. The turbulent intensity was below 0.05, and the dissipation rate was between 10⁻⁵ and 10⁻³ m²·s⁻³ under the sea breeze. The turbulent parameters showed peaks and large gradients at the boundary and the sea breeze front. The peak value of the turbulent intensity was around 0.3, and the dissipation rate was around 0.1 m²·s⁻³. The round-trip effect of sea–land breeze caused circulate pollutants. The recirculation factor was maintained at 0.5–0.6 at heights where the sea and land breeze alternately controlled (below 600 m), as well as increasing with a decreasing duration of the sea breeze. The factor exceeded 0.9 under the control of the high-altitude breeze (above 750 m). The convergence and rise of the airflow at the front led to collect pollutants, causing a sharp decrease in air quality when the sea breeze front passed. Full article

(This article belongs to the Special Issue Characterizing Atmospheric and Marine Boundary Layer Processes by Remote Sensing)

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Other

Jump to: Research

14 pages, 2485 KiB

Open AccessTechnical Note

Study of the

C_{n}^{2}

Model through the New Dimensionless Temperature Structure Function near the Sea Surface in the South China Sea

by Feifei Wang, Kun Zhang, Gang Sun, Qing Liu, Xuebin Li and Tao Luo

Remote Sens. 2023, 15(3), 631; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15030631 - 20 Jan 2023

Viewed by 1536

Abstract

The refractive index structure constant

C_{n}^{2}

The refractive index structure constant

C_{n}^{2}

near the ocean surface is an important parameter for studying atmospheric optical turbulence over the ocean. The measured refractive index structure constant and meteorological parameters, such as temperature and three-dimensional wind speed, near the sea surface on the South China Sea during the period from January to November 2020 were analyzed. On the basis of Monin–Obukhov similarity theory, the dimensionless temperature structure parameter function

f_{T}

near the sea surface was established, and a new parameterized model of the near-sea surface was proposed. The new model improved the error of the widely used model proposed by Wyngaard in 1973 (W73) and better reproduced the daily variation in the measured

C_{n}^{2}

. Further analysis of the seasonal applicability of the new model indicated that the correlation coefficients between the estimated and measured

C_{n}^{2}

in the spring, summer, autumn, and winter were 0.94, 0.94, 0.95, and 0.89, respectively, and the root mean square errors were 0.32, 0.41, 0.46, and 0.40 m^−2/3, respectively. Compared with the

C_{n}^{2}

estimated by the W73 model, the correlation coefficient of

C_{n}^{2}

estimated by the new model and measured by the micro-thermometer increased by 0.05–0.27 and the root mean square error decreased by 0.04–0.56. The improved

f_{T}

demonstrated higher accuracy than the existing models, which can lay a foundation for estimating turbulence parameters in different sea areas. Full article

(This article belongs to the Special Issue Characterizing Atmospheric and Marine Boundary Layer Processes by Remote Sensing)

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