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25 pages, 23184 KiB  
Article
Doppler Sodar Measured Winds and Sea Breeze Intrusions over Gadanki (13.5° N, 79.2° E), India
by Potula Sree Brahmanandam, G. Uma, K. Tarakeswara Rao, S. Sreedevi, N. S. M. P. Latha Devi, Yen-Hsyang Chu, Jayshree Das, K. Mahesh Babu, A. Narendra Babu, Subrata Kumar Das, V. Naveen Kumar and K. Srinivas
Sustainability 2023, 15(16), 12167; https://doi.org/10.3390/su151612167 - 9 Aug 2023
Viewed by 2006
Abstract
Doppler sodar measurements were made at the tropical Indian station, i.e., Gadanki (13.5° N, 79. 2° E). According to wind climatologies, the wind pattern changes from month to month. In July and August, the predominant wind direction during the monsoon season was the [...] Read more.
Doppler sodar measurements were made at the tropical Indian station, i.e., Gadanki (13.5° N, 79. 2° E). According to wind climatologies, the wind pattern changes from month to month. In July and August, the predominant wind direction during the monsoon season was the southwest. In September, it was the northwest and south. While the winds in November came from the northeast, they came from the northwest and southwest in October. The winds in December were out of the southeast. The diurnal cycle of winds at 60-m above the ground was visible, with disturbed wind directions in September and October. This may be connected to the Indian subcontinent’s southeastern monsoon recession. To better understand the monsoon circulation on a monthly basis, the present work is innovative in that it uses high-resolution winds measured using the Doppler sodar at the atmospheric boundary layer. The convergence of a sea breeze and the background wind might result in a sudden change in wind direction, and forecasting such a chaotic atmospheric event is crucial in the aviation sector. As a result, the wind shear that is produced may pose a serious threat to airplanes that are landing. In the current study, we present a few cases of sea breeze intrusions. The physics underlying these intrusions may help modelers better understand these chaotic wind structures and use them as inputs in their models. Based on surface-based atmospheric characteristics, there have been two reports of deep sea breeze intrusions that we report in this research. The sea breeze days were marked by substantial (moderate) drops in temperature (dewpoint temperatures) and increased wind speed and relative humidity. The India Meteorological Department (IMD) rainfall data showed a rise in precipitation over this location on 23 July (4.8 mm) and 24 July (9.5 mm) when sea breeze intrusions over Gadanki were noticed. Sea breeze intrusions could have brought precipitation (intrusion-laden precipitation) to this area due to conducive meteorological conditions. A simple schematic model is proposed through a diagrammatic illustration that explains how a sea breeze triggers precipitation over adjacent locations to the seacoast. The skew-T log-P diagrams have been drawn using the balloon-borne radiosonde measured atmospheric data over Chennai (a nearby location to Gadanki) to examine the thermodynamic parameters to gain insights into the underlying mechanisms and meteorological conditions during sea breeze intrusion events. It is found that the convective available potential energy (CAPE), which is presented as a thermos diagram, was associated with large values on 23 July and 24 July (898 J/kg and 1250 J/kg), which could have triggered thunderstorms over Chennai. Full article
Show Figures

Figure 1

Figure 1
<p>The elevation map of the sodar, which is shown with a diamond symbol, and the blue area is the Bay of Bengal. This map is generated using Shuttle Radar Topography Mission (SRTM) Version 3.0 Global one arc second data in similar lines to Farr et al., 2007 [<a href="#B40-sustainability-15-12167" class="html-bibr">40</a>].</p>
Full article ">Figure 2
<p>(<b>a</b>) The NARL sodar (heavy white dot shown with a black arrow) in the Indian MST radar (see the erected antennae in the background) location (extracted from Anandan et al., 2008 [<a href="#B39-sustainability-15-12167" class="html-bibr">39</a>], @American Meteorological Society. Used with permission.) and (<b>b</b>) The topography map and note that the XY plane is tilted at a 40-degree angle to obtain a clear location of the site.</p>
Full article ">Figure 3
<p>Average trends of winds through wind roses at various altitudes, including (<b>a</b>) 90 m, (<b>b</b>) 270 m and (<b>c</b>) 450 m in July 2015. Source: Own elaboration.</p>
Full article ">Figure 4
<p>Average trends of winds through wind roses at various altitudes, including (<b>a</b>) 90 m, (<b>b</b>) 270 m, and (<b>c</b>) 450 m in August 2015. Source: Own elaboration.</p>
Full article ">Figure 5
<p>Average trends of winds through wind roses at various altitudes, including (<b>a</b>) 90 m, (<b>b</b>) 270 m, and (<b>c</b>) 450 m in September 2015. Source: Own elaboration.</p>
Full article ">Figure 6
<p>Average trends of winds through wind roses at various altitudes, including (<b>a</b>) 90 m, (<b>b</b>) 270 m, and (<b>c</b>) 450 m in October 2015. Source: Own elaboration.</p>
Full article ">Figure 7
<p>Average trends of winds through wind roses at various altitudes, including (<b>a</b>) 90 m, (<b>b</b>) 270 m, and (<b>c</b>) 450 m in December 2015. Source: Own elaboration.</p>
Full article ">Figure 8
<p>Daily course of average sodar measured (<b>a</b>) zonal wind (top left panel), (<b>b</b>) meridional wind (top right panel), (<b>c</b>) wind speed (bottom left panel), and (<b>d</b>) wind direction (bottom right panel) at different altitudes (60, 120, 210, 300, 390, 480, and 600 m) over Gadanki (13.5° N, 79.2° E) in July 2015. Double arrow vertical lines with blue (red) lines indicate sunrise (sunset) times. Source: Own elaboration.</p>
Full article ">Figure 9
<p>Daily course of sodar measured (<b>a</b>) zonal wind (top left panel), (<b>b</b>) meridional wind (top right panel), (<b>c</b>) wind speed (bottom left panel), and (<b>d</b>) wind direction (bottom right panel) at different altitudes (60, 120, 210, 300, 390, 480, and 600 m) in August 2015. Double arrow vertical lines with blue (red) lines indicate sunrise (sunset) times. Source: Own elaboration.</p>
Full article ">Figure 10
<p>Daily course of sodar measured (<b>a</b>) zonal wind (top left panel), (<b>b</b>) meridional wind (top right panel), (<b>c</b>) wind speed (bottom left panel), and (<b>d</b>) wind direction (bottom right panel) at different altitudes (60, 120, 210, 300, 390, 480, and 600 m) in September 2015. Double arrow vertical lines with blue (red) lines indicate sunrise (sunset) times. Source: Own elaboration.</p>
Full article ">Figure 11
<p>Daily course of sodar measured (<b>a</b>) zonal wind (top left panel), (<b>b</b>) meridional wind (top right panel), (<b>c</b>) wind speed (bottom left panel), and (<b>d</b>) wind direction (bottom right panel) at different altitudes (60, 120, 210, 300, 390, 480, and 600 m) in October 2015. Double arrow vertical lines with blue (red) lines indicate sunrise (sunset) times. Source: Own elaboration.</p>
Full article ">Figure 12
<p>Daily course of sodar measured (<b>a</b>) zonal wind (top left panel), (<b>b</b>) meridional wind (top right panel), (<b>c</b>) wind speed (bottom left panel), and (<b>d</b>) wind direction (bottom right panel) at different altitudes (60, 120, 210, 300, 390, 480, and 600) in November 2015. Double arrow vertical lines with blue (red) lines indicate sunrise (sunset) times. Source: Own elaboration.</p>
Full article ">Figure 13
<p>Daily course of sodar measured (<b>a</b>) zonal wind (top left panel), (<b>b</b>) meridional wind (top right panel), (<b>c</b>) wind speed (bottom left panel), and (<b>d</b>) wind direction (bottom right panel) at different altitudes (60, 120, 210, 300, 390, 480, and 600 m) in December 2015. Double arrow vertical lines with blue (red) lines indicate sunrise (sunset) times. Source: Own elaboration.</p>
Full article ">Figure 14
<p>Sea breeze intrusion observed from sodar measurements on (<b>a</b>) 23 July 2015, (<b>b</b>) 24 July 2015, and a non-sea breeze intrusion observed on (<b>c</b>) 25 July 2015. Source: Own elaboration.</p>
Full article ">Figure 14 Cont.
<p>Sea breeze intrusion observed from sodar measurements on (<b>a</b>) 23 July 2015, (<b>b</b>) 24 July 2015, and a non-sea breeze intrusion observed on (<b>c</b>) 25 July 2015. Source: Own elaboration.</p>
Full article ">Figure 15
<p>(<b>a</b>) Temperature (°C), Wind speed (m/s), and (<b>b</b>) Relative Humidity (%) and Dewpoint temperature (°C) variations observed from a co-located automated weather station (AWS) on 23 July 2015 (see breeze day), 24 July 2015 (sea breeze day), and 25 July 2015 (a non-sea breeze day) at Gadanki, India. Red color double arrows show the duration of sea breeze intrusions on 23 July and 24 July 2015; during these periods, a substantial decrease in temperature, a moderate decrease in dewpoint temperature, and a rise in wind speed and humidity can be seen. Source: Own elaboration.</p>
Full article ">Figure 16
<p>Contour plots of zonal wind (m/s) along with wind direction (black arrows) derived from ERA5 at (<b>a</b>) 1330 LT, (<b>b</b>) 1530 LT, (<b>c</b>) 1730 LT, and (<b>d</b>) 1930 LT on 23 July 2015. The location of Gadanki is shown with a closed red star, and the heavy black lines indicate boundaries. Source: Own elaboration.</p>
Full article ">Figure 17
<p>Contour plots of zonal wind (m/s) along with wind direction (black arrows) derived from ERA5 at (<b>a</b>) 1330 LT, (<b>b</b>) 1530 LT, (<b>c</b>) 1730 LT, and (<b>d</b>) 1930 LT on 24 July 2015. The location of Gadanki is shown with a closed red star, and the heavy black lines indicate boundaries. Source: Own elaboration.</p>
Full article ">Figure 18
<p>Contour plots of zonal wind (m/s) along with wind direction (black arrows) derived from ERA5 at (<b>a</b>) 1330 LT, (<b>b</b>) 1530 LT, (<b>c</b>) 1730 LT, and (<b>d</b>) 1930 LT on 25 July 2015. The location of Gadanki is shown with a closed red star, and the heavy black lines indicate boundaries. Source: Own elaboration.</p>
Full article ">Figure 19
<p>Daily accumulated precipitation (mm) at Gadanki, India, as provided by the India Meteorological Department. Source: Own elaboration.</p>
Full article ">Figure 20
<p>A simple schematic model depicts how sea breeze triggers precipitation over near and adjacent areas to the coast under amicable meteorological conditions. Source: Own elaboration.</p>
Full article ">Figure 21
<p>Skew-T Log-P diagrams over Chennai, (<b>a</b>) 0600 PM on 23 July 2015 and (<b>b</b>) 0600 PM on 24 July 2015. Source: <a href="https://weather.uwyo.edu/cgi-bin/sounding?region=seasia&amp;TYPE=PDF%3ASKEWT&amp;YEAR=2015&amp;MONTH=07&amp;FROM=2400&amp;TO=2600&amp;STNM=43279" target="_blank">https://weather.uwyo.edu/cgi-bin/sounding?region=seasia&amp;TYPE=PDF%3ASKEWT&amp;YEAR=2015&amp;MONTH=07&amp;FROM=2400&amp;TO=2600&amp;STNM=43279</a> (accessed on 9 June 2023).</p>
Full article ">Figure 21 Cont.
<p>Skew-T Log-P diagrams over Chennai, (<b>a</b>) 0600 PM on 23 July 2015 and (<b>b</b>) 0600 PM on 24 July 2015. Source: <a href="https://weather.uwyo.edu/cgi-bin/sounding?region=seasia&amp;TYPE=PDF%3ASKEWT&amp;YEAR=2015&amp;MONTH=07&amp;FROM=2400&amp;TO=2600&amp;STNM=43279" target="_blank">https://weather.uwyo.edu/cgi-bin/sounding?region=seasia&amp;TYPE=PDF%3ASKEWT&amp;YEAR=2015&amp;MONTH=07&amp;FROM=2400&amp;TO=2600&amp;STNM=43279</a> (accessed on 9 June 2023).</p>
Full article ">
42 pages, 220416 KiB  
Article
Characterization of Wind Resources of the East Coast of Maranhão, Brazil
by Felipe M. Pimenta, Osvaldo R. Saavedra, Denisson Q. Oliveira, Arcilan T. Assireu, Audálio R. Torres Júnior, Ramon M. de Freitas, Francisco L. Albuquerque Neto, Denivaldo C. P. Lopes, Clóvis B. M. Oliveira, Shigeaki L. de Lima, João C. de Oliveira Neto and Rafael B. S. Veras
Energies 2023, 16(14), 5555; https://doi.org/10.3390/en16145555 - 22 Jul 2023
Cited by 4 | Viewed by 1913
Abstract
The objective of this work is to assess the wind resources of the east coast of Maranhão, Brazil. Wind profilers were combined with micrometeorological towers and atmospheric reanalysis to investigate micro- and mesoscale aspects of wind variability. Field campaigns recorded winds in the [...] Read more.
The objective of this work is to assess the wind resources of the east coast of Maranhão, Brazil. Wind profilers were combined with micrometeorological towers and atmospheric reanalysis to investigate micro- and mesoscale aspects of wind variability. Field campaigns recorded winds in the dry and wet seasons, under the influence of the Intertropical Convergence Zone. The dry season was characterized by strong winds (8 to 12 m s1) from the northeast. Surface heat fluxes were generally positive (250 to 320 W m2) at midday and negative (−10 to −20 W m2) during the night. Convective profiles predominated near the beach, with strongly stable conditions rarely occurring before sunrise. Further inland, convective to strongly convective profiles occurred during the day, and neutral to strongly stable profiles at night. Wind speeds decreased during the rainy season (4 to 8 m s1), with increasingly easterly and southeasterly components. Cloud cover and precipitation reduced midday heat fluxes (77 W m2). Profiles were convective during midday and stable to strongly stable at night. Terrain roughness increased with distance from the ocean ranging from smooth surfaces (zo = 0.95 mm) and rough pastures (zo = 15.33 mm) to crops and bushes (zo = 52.68 mm), and trees and small buildings (zo = 246.46 mm) farther inland. Seasonal variations of the mean flow and sea and land breezes produced distinct diurnal patterns of wind speeds. The strongest (weakest) breeze amplitudes were observed in the dry (rainy) period. Daily changes in heat fluxes and fetch over land controlled the characteristics of wind profiles. During sea breezes, winds approached the coast at right angles, resulting in shorter fetches over land that maintained or enhanced oceanic convective conditions. During land breezes, winds blew from the mainland or with acute angles against the coastline, resulting in large fetches with nighttime surface cooling, generating strongly stable profiles. Coastal observations demonstrated that with increasing monopiles from 100 to 130 m it is possible to obtain similar capacity factors of beachfront turbines. Full article
(This article belongs to the Section A3: Wind, Wave and Tidal Energy)
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Brazil equatorial margin. The study region is indicated by a red square with an arrow. Within the equatorial coast, Rio Grande do Norte (RN) has the largest installed wind capacity: 6855 MW; followed by Piauí (PI): 3428 MW; Ceará (CE): 2568 MW; Pernambuco (PE): 1025 MW; Paraíba (PB): 672 MW and Maranhão (MA): 426 MW [<a href="#B31-energies-16-05555" class="html-bibr">31</a>]. (<b>b</b>) Eastern coast of Maranhão, Brazil. Barreirinhas and Paulino Neves counties are indicated. EOSOLAR study region is located in a region known as “little Lençóis”, east of the Preguiças River. Observation points are indicated by green squares, numbered from P0 to P5. Point P0 is located 1.5 km from the beach. Point P4 and point P5 are located 26 and 32 km, respectively, from P1. Turbine locations are identified by magenta dots. ERA5 refers to the grid point location (2.5° S, 42.5° W) derived from the atmospheric reanalysis, which is 26 km from P1. Stations’ geographical coordinates are P0 (2.694107° S, 42.554807° W), P1 (2.724877° S, 42.575182° W), P2 (2.725162° S, 42.606507° W), P3 (2.733535° S, 42.589530° W), P4 (2.759033° S, 42.807133° W) and P5 (2.787355° S, 42.855720° W). Image source: Google Earth.</p>
Full article ">Figure 2
<p>(<b>a</b>) Wind speed time series comparing ERA5 with observed winds derived from LIDAR and SODAR measurements at P1 location. All series are relative to the height of 100 m and averaged for a 6-hour resolution. A light red line indicates ERA5, dark blue represents the LIDAR and light blue the SODAR. Two-sided arrows on the top of the graph indicate the period of EOSOLAR field campaigns FC1 to FC6. (<b>b</b>) Wind speed climatology (1979–2021) at 100 m height derived from ERA5 monthly database. EOSOLAR observations are plotted as blue bullets (SODAR) and triangles (LIDAR). (<b>c</b>) Precipitation climatology (1979–2021) derived from ERA5. Bullets represent observations derived from micrometeorological towers. Box plot edges on panels (<b>b</b>,<b>c</b>) are the 25th and 75th percentiles. The central mark in each box represents the median. Whiskers extend to the most extreme data points not considered outliers. Outliers are plotted as empty circles. Although this article focuses on the field campaigns FC1 to FC6, up to 27 July 2022, data from August 2022 are included in panels (<b>b</b>,<b>c</b>) for completeness.</p>
Full article ">Figure 3
<p>Atmospheric conditions during EOSOLAR field campaigns. Maps are organized from top to bottom covering, respectively, the field campaigns FC1 to FC6. The left panels display mean wind speed and direction at 100 m height above the surface. Right panels refer to accumulated precipitation in each field campaign converted to mm month<math display="inline"><semantics><msup><mrow/><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math>. Fields were constructed based on ERA5 Atmospheric Reanalysis hourly fields. Labels refer to Intertropical Convergence Zone (ITCZ), northeast (NE) and southeast (SE) Trade winds. A black dot indicates the study region. <a href="#energies-16-05555-t002" class="html-table">Table 2</a> lists the timing of each field campaign.</p>
Full article ">Figure 4
<p>Time series of wind speed at the height of wind turbines (z = 100 m) derived from the LIDAR and SODAR wind profilers. A thin gray line illustrates the SODAR series at a 10 min time resolution. Thick red and blue lines, respectively, depict the LIDAR and SODAR averaged to 3 h time resolution. Panels refer to the EOSOLAR campaigns FC1 (top) to FC6 (bottom). Equipment locations are indicated in the legend of each panel. SODAR was installed on station P0 for FC1 but was repositioned to point P1 for all other campaigns. LIDAR started positioned on P1 for FC1 then moved to P0 for FC2. The equipment was reinstalled on points P2 to P5 for subsequent campaigns. In all panels, the <span class="html-italic">x</span>-axis is rescaled to represent the time covered for each campaign. Stations locations are indicated in <a href="#energies-16-05555-f001" class="html-fig">Figure 1</a>b.</p>
Full article ">Figure 5
<p>Statistical distributions of wind speed and direction. All panels refer to winds at the height of 100 m above the surface, derived from LIDAR and SODAR measurements at the monitoring point P1 (see <a href="#energies-16-05555-f001" class="html-fig">Figure 1</a>b for location). (<b>a</b>) Histograms of wind speed. Each field campaign (FC1 to FC6) is depicted by different line colors. The gray shading represents the distribution considering all campaigns and histogram bins are 0.5 m s<math display="inline"><semantics><msup><mrow/><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math> wide. (<b>b</b>) Histogram of wind direction for each field campaign (colored lines) and the entire period of observations (gray shading). Vertical bars are 15° wide and indicate the direction from which the wind blows. All analyses are based on 10 min time resolution dataset.</p>
Full article ">Figure 6
<p>Wind roses of each measurement campaign FC1 to FC6 at station P1. Direction bins have 5° increments and follow the meteorological convention, indicating the direction from which the wind blows. The radial distance indicates the percentage of occurrence of any particular direction, while the colors represent the intervals of wind speeds. A green shade indicates the coastline’s general orientation, considering a radius of 30 km from point P1 location.</p>
Full article ">Figure 7
<p>Average vertical wind speed profiles. Panels depict station locations from P0 (right) to P5 (left). Line colors represent the field campaigns covered by observations. Station P1 is the reference station, with data coverage for all field campaigns. Colored symbols indicate the wind profiler. Squares are used to represent the LIDAR and triangles for the SODAR. A gray line is drawn on panels P0 and P2 to P5 to facilitate comparison, based on SODAR observations at P1 during the same field campaign. Station locations are indicated in <a href="#energies-16-05555-f001" class="html-fig">Figure 1</a>b.</p>
Full article ">Figure 8
<p>Diurnal variability of wind speeds as a function of height, position and field campaign. Columns are organized according to field campaigns, with FC1 on the left and FC6 on the right. The top row refers to observations at the fixed station P1. Lower panels refer to positions P0 and P2 to P5. Line colors represent the height (m) of observations in reference to the surface. The title in each panel indicates the source of data (LIDAR or SODAR). Data loss on SODAR P0 (FC1) was substantial, so data above 130 m are not displayed. SODAR P1 (FC4) loss data for heights above 180 m.</p>
Full article ">Figure 9
<p>Local wind hodographs, depicting the sea and land breeze interaction with the mean winds. Columns represent field campaigns, top row depicts station P1 and the bottom row stations P0 and P2 to P5. Winds were vertically averaged between 100 and 130 m. Hodographs were obtained by computing the mean wind vectors for each hour of the day. Winds at the peak of the land breeze (8 h) and sea breeze (16 h) are indicated by light blue and orange vectors, respectively. For other hours, vectors are omitted and their heads are indicated as bullets. A color scale represents the local time of observations. A thick black arrow indicates the mean wind vector for each campaign and location. Concentric gray circles indicate the magnitude of the winds with 2 m s<math display="inline"><semantics><msup><mrow/><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math> increments. A dashed line represents the general coastline orientation. Data loss on SODAR P0 was substantial so only data up to 130 m are displayed. SODAR P1 loss data are for heights above 180 m.</p>
Full article ">Figure 10
<p>Roughness length <math display="inline"><semantics><msub><mi>z</mi><mi>o</mi></msub></semantics></math> and friction velocity <math display="inline"><semantics><msub><mi>u</mi><mo>∗</mo></msub></semantics></math> results obtained from the analysis of EOSOLAR micrometeorological towers. (<b>a</b>) Roughness length <math display="inline"><semantics><msub><mi>z</mi><mi>o</mi></msub></semantics></math> estimation, without accounting for atmospheric stability. (<b>b</b>) Same as (<b>a</b>) but accounting for the stability function <math display="inline"><semantics><mi>ψ</mi></semantics></math>. Line colors on panels (<b>a</b>,<b>b</b>) depict the different terrain locations P0 to P5. (<b>c</b>) Diurnal variability of the mean friction velocity <math display="inline"><semantics><msub><mi>u</mi><mo>∗</mo></msub></semantics></math> computed for points P0 and P2 to P5. Line colors indicate the field campaign. (<b>d</b>) Same as (<b>c</b>), but for station P1.</p>
Full article ">Figure 11
<p>Diurnal cycle of heat flux <math display="inline"><semantics><msub><mi>Q</mi><mrow><mi>h</mi><mi>b</mi></mrow></msub></semantics></math> computed from EOSOLAR micrometeorological towers. Panels are organized vertically following the field campaigns, with FC1 positioned at the top and FC6 at the bottom. The left column illustrates the distributions for station P1, while the right column depicts the moving tower for locations P0 and P2 to P5. <math display="inline"><semantics><msub><mi>Q</mi><mrow><mi>h</mi><mi>b</mi></mrow></msub></semantics></math> is given in W m<math display="inline"><semantics><msup><mrow/><mrow><mo>−</mo><mn>2</mn></mrow></msup></semantics></math> units and positive (negative) values indicate the surface heating up (cooling down) the atmosphere from below. Vertical bars illustrate <math display="inline"><semantics><mrow><mo>±</mo><mi>σ</mi></mrow></semantics></math> standard deviations. Red and blue labels indicate, respectively, the percentage of occurrence of positive <math display="inline"><semantics><mrow><msub><mi>Q</mi><mrow><mi>h</mi><mi>b</mi></mrow></msub><mo>&gt;</mo><mn>0</mn></mrow></semantics></math> and negative <math display="inline"><semantics><mrow><msub><mi>Q</mi><mrow><mi>h</mi><mi>b</mi></mrow></msub><mo>&lt;</mo><mn>0</mn></mrow></semantics></math> fluxes. Labels on the left represent the statistics before dawn (0 to 6 h) and labels on the right indicate after dusk hours (18 to 24 h).</p>
Full article ">Figure 12
<p>Frequency distributions of atmospheric stability based on Obukhov length <span class="html-italic">L</span>, estimated from micrometeorological tower data. Panels are organized vertically following the field campaigns, with FC1 positioned at the top and FC6 at the bottom. The left column illustrates the distributions for station P1, while the right column depicts the moving tower for locations P0 and P2 to P5. Limits used for stability classification are strongly stable (<math display="inline"><semantics><mrow><mn>0</mn><mo>≤</mo><mi>L</mi><mo>≤</mo><mn>40</mn></mrow></semantics></math>), stable (<math display="inline"><semantics><mrow><mn>40</mn><mo>&lt;</mo><mi>L</mi><mo>≤</mo><mn>200</mn></mrow></semantics></math>), neutral (<math display="inline"><semantics><mrow><mo>|</mo><mi>L</mi><mo>|</mo><mo>&gt;</mo><mn>200</mn></mrow></semantics></math>), convective (<math display="inline"><semantics><mrow><mo>−</mo><mn>200</mn><mo>≤</mo><mi>L</mi><mo>&lt;</mo><mo>−</mo><mn>40</mn></mrow></semantics></math>), strongly convective (<math display="inline"><semantics><mrow><mo>−</mo><mn>40</mn><mo>≤</mo><mi>L</mi><mo>&lt;</mo><mn>0</mn></mrow></semantics></math>).</p>
Full article ">Figure 13
<p>Frequency distributions of shear exponent computed from the LIDAR and SODAR wind profilers. Panels are organized vertically following the field campaigns, with FC1 positioned at the top and FC6 at the bottom. The left column illustrates the distributions for the fixed station P1, while the right column depicts the tower for P0 and P2 to P5 stations. Colors represent shear exponent classes following Wharton and Lundquist (2012) [<a href="#B36-energies-16-05555" class="html-bibr">36</a>]: strongly stable (<math display="inline"><semantics><mrow><mi>α</mi><mo>&gt;</mo><mn>0.3</mn></mrow></semantics></math>), stable (<math display="inline"><semantics><mrow><mn>0.2</mn><mo>&lt;</mo><mi>α</mi><mo>≤</mo><mn>0.3</mn></mrow></semantics></math>), neutral (<math display="inline"><semantics><mrow><mn>0.1</mn><mo>&lt;</mo><mi>α</mi><mo>≤</mo><mn>0.2</mn></mrow></semantics></math>), convective (<math display="inline"><semantics><mrow><mn>0.0</mn><mo>&lt;</mo><mi>α</mi><mo>≤</mo><mn>0.1</mn></mrow></semantics></math>), strongly convective (<math display="inline"><semantics><mrow><mi>α</mi><mo>≤</mo><mn>0</mn></mrow></semantics></math>).</p>
Full article ">Figure 14
<p>Shear exponent average distributions as a function of wind speed (m s<math display="inline"><semantics><msup><mrow/><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math>) and direction (degrees) at 100 m height. (<b>a</b>) Station P0 distribution based on LIDAR and SODAR observations obtained during FC1 and FC2 campaigns. (<b>b</b>) As in panel (<b>a</b>) but for station P0. (<b>c</b>) Station P2 distribution derived from LIDAR data for FC3. (<b>d</b>) Station P3 based on LIDAR data for FC4. Here, direction refers to the angle from which the wind blows. Average shear exponents are evaluated for a grid of 0.5 m s<math display="inline"><semantics><msup><mrow/><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math>× 6° bins, taking the average of at least 3 observations. A dashed line indicates the coastline’s general orientation.</p>
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<p>Resource variability as a function of time, location and height. (<b>a</b>) Average wind speeds (m s<math display="inline"><semantics><msup><mrow/><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math>) as function of field campaigns and the heights of 100, 130, 150, 200 and 260 m. (<b>b</b>) Capacity factor changes across field campaigns and the heights of 100, 130 and 150 m. The monitoring station P1 is illustrated with horizontal bars, whereas stations P0 and P2 to P5 are shown as a stem plot (vertical bars). Datasets are paired for same-time coverage. Heights with less than 50% coverage were not drawn.</p>
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20 pages, 2774 KiB  
Article
Assessing Economic Complementarity in Wind–Solar Hybrid Power Plants Connected to the Brazilian Grid
by Rafael B. S. Veras, Clóvis B. M. Oliveira, Shigeaki L. de Lima, Osvaldo R. Saavedra, Denisson Q. Oliveira, Felipe M. Pimenta, Denivaldo C. P. Lopes, Audálio R. Torres Junior, Francisco L. A. Neto, Ramon M. de Freitas and Arcilan T. Assireu
Sustainability 2023, 15(11), 8862; https://doi.org/10.3390/su15118862 - 31 May 2023
Cited by 2 | Viewed by 1650
Abstract
The share of electricity generation from Variable Renewable Energy Sources (VRES) has increased over the last 20 years. Despite promoting the decarbonization of the energy mix, these sources bring negative characteristics to the energy mix, such as power ramps, load mismatch, unpredictability, and [...] Read more.
The share of electricity generation from Variable Renewable Energy Sources (VRES) has increased over the last 20 years. Despite promoting the decarbonization of the energy mix, these sources bring negative characteristics to the energy mix, such as power ramps, load mismatch, unpredictability, and fluctuation. One of the ways to mitigate these characteristics is the hybridization of power plants. This paper evaluates the benefits of hybridizing a plant using an AI-based methodology for optimizing the wind–solar ratio based on the Brazilian regulatory system. For this study, the hybrid plant was modeled using data collected over a period of 10 months. The measurements were obtained using two wind profilers (LIDAR and SODAR) and a sun tracker (Solys 2) as part of the EOSOLAR R&D project conducted in the state of Maranhão, Brazil. After the power plant modeling, a Genetic Algorithm (GA) was used to determine the optimal wind–solar ratio, considering costs with transmission systems. The algorithm achieved a monthly profit increase of more than 39% with an energy curtailment inferior to 1%, which indicates economic complementarity. Later, the same methodology was also applied to verify the wind–solar ratio’s sensitivity to solar energy pricing. The results show that a price increase of 15% would change the power plant’s optimal configuration. Full article
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<p>Measurement site, with the station highlighted in red and the wind generators of the Delta III wind farm illustrated by blue dots.</p>
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<p>(<b>A</b>). Wind profiler SODAR MFAS, (<b>B</b>). Wind profiler LIDAR Windcube.</p>
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<p>LIDAR, SODAR and merged wind speed at 130 m height at a temporal resolution of 10 min.</p>
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<p>Solys 2 sun tracker at the measurement site.</p>
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<p>Monthly hourly average values of solar radiance and wind speeds, normalized to the maximum value for each respective month. The maximum hourly averages of wind speed in m s<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math> and radiation in W m<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> </semantics></math> for each month are, respectively, listed as follows: November: 10.38 and 934.36; December: 10.32 and 817.38; January: 8.53 and 802.28; February: 8.55 and 807.12; March: 7.77 and 721.46; April: 7.47 and 702.47; May: 6.77 and 677.36; June: 8.34 and 727.88; July: 9.16 and 801.27; August: 9.54 and 906.14.</p>
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<p>Monthly average normalized solar radiation and equivalent wind speed.</p>
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<p>Siemens 2.3-113 power curve.</p>
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<p>Energy annual P90 for wind generation and P50 for solar.</p>
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<p>Power plant sensibility to solar energy price increase.</p>
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15 pages, 2093 KiB  
Article
Influence of Meteorological Parameters on the Urban Heat Island in Moscow
by Mikhail A. Lokoshchenko and Lyubov I. Alekseeva
Atmosphere 2023, 14(3), 507; https://doi.org/10.3390/atmos14030507 - 6 Mar 2023
Cited by 8 | Viewed by 2114
Abstract
The urban heat island (UHI) intensity in Moscow and the influence of various meteorological parameters are discussed using weather station data. The maximal and average in-space UHI intensities, i.e., a comparison of air temperature T either in the city centre or in the [...] Read more.
The urban heat island (UHI) intensity in Moscow and the influence of various meteorological parameters are discussed using weather station data. The maximal and average in-space UHI intensities, i.e., a comparison of air temperature T either in the city centre or in the whole urban area together with rural zone have averaged 1.9 and 0.9 °C, respectively, in recent years. The UHI in Moscow has stabilized over the past decade and is not growing. Under conditions of a strong anticyclone, the maximal UHI intensity in space and time reaches 11–12 °C. Low cloudiness and amplitudes of diurnal air temperature, as well as surface temperature, demonstrate the closest relationship with the UHI intensity among other parameters with the correlation coefficient of up to −0.67 for low cloudiness and the maximal UHI intensity. The effect of wind speed, total cloudiness and relative humidity on the UHI is slightly weaker, but still significant. The relationships of all meteorological parameters with the maximal UHI intensity are closer than those with the average one. The multiple correlation coefficient between the maximal UHI intensity and both parameters (low cloudiness and average daily wind speed) is 0.76–0.82. The UHI intensity function of air temperature has a minimum in the range from −4 to 0 °C; its growth both at lower and higher T is due to the influence of anticyclonic weather. The UHI intensity function of wind speed decreases with wind strength. The threshold value at which this function asymptotically approaches its lower limit is 10 m/s in the 40–200 m air layer. The UHI intensity functions of both total and low cloudiness decrease with increasing cloudiness and the differences between them are significant if the cloud cover is more than 50%. Full article
(This article belongs to the Section Meteorology)
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<p>Statistical relation between the maximal and average UHI intensities in Moscow for every 3 h during 2018–2020.</p>
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<p>Air temperature maps over Moscow region. (<b>a</b>) Mean isotherms on average for the period 2018–2020; (<b>b</b>) Isotherms on 23 February 2018, at 06 a.m. Margins of Moscow city (until 2012) and Moscow region are shown by double black lines. Manned and automatic weather stations are shown by black and white circles, respectively. B—Balchug station; M—Mikhelson observatory; U—MSU MO; N—Nemchinovka station.</p>
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<p>Graphs of statistical relations between the maximal UHI intensity, low cloudiness (<b>left</b>) and daily air temperature amplitude (<b>right</b>). Moscow, 2018–2020.</p>
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<p>Empirical functions of the UHI intensity of daily averaged meteorological parameters in 2018–2020, Moscow. (<b>a</b>) Function of the air temperature; (<b>b</b>) functions of the wind velocity; (<b>c</b>) functions of the cloudiness. Confidence intervals are calculated with the 0.95 confidence probability.</p>
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17 pages, 4611 KiB  
Article
Observations of the Boundary Layer in the Cape Grim Coastal Region: Interaction with Wind and the Influences of Continental Sources
by Zhenyi Chen, Robyn Schofield, Melita Keywood, Sam Cleland, Alastair G. Williams, Stephen Wilson, Alan Griffiths and Yan Xiang
Remote Sens. 2023, 15(2), 461; https://doi.org/10.3390/rs15020461 - 12 Jan 2023
Cited by 1 | Viewed by 1748
Abstract
A comparative study and evaluation of boundary layer height (BLH) estimation was conducted during an experimental campaign conducted at the Cape Grim Air Pollution station, Australia, from 1 June to 13 July 2019. The temporal and spatial distributions of BLH were studied using [...] Read more.
A comparative study and evaluation of boundary layer height (BLH) estimation was conducted during an experimental campaign conducted at the Cape Grim Air Pollution station, Australia, from 1 June to 13 July 2019. The temporal and spatial distributions of BLH were studied using data from a ceilometer, sodar, in situ meteorological measurements, and back-trajectory analyses. Generally, the BLH under continental sources is lower than that under marine sources. The BLH is featured with a shallow depth of 515 ± 340 m under the Melbourne/East Victoria continental source. Especially the mixed continental sources (Melbourne/East Victoria and Tasmania direction) lead to a rise in radon concentration and lower BLH. In comparison, the boundary layer reaches a higher averaged BLH value of 730 ± 305 m when marine air is prevalent. The BLH derived from ERA5 is positively biased compared to the ceilometer observations, except when the boundary layer is stable. The height at which wind profiles experience rapid changes corresponds to the BLH value. The wind flow within the boundary layer increased up to ∼200 m, where it then meandered up to ∼300 m. Furthermore, the statistic shows that BLH is positively associated with near-surface wind speed. This study firstly provides information on boundary layer structure in Cape Grim and the interaction with wind, which may aid in further evaluating their associated impacts on the climate and ecosystem. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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<p>(<b>a</b>) Map of observation site Cape Grim at northwestern Tasmania, Australia. (<b>b</b>) Potential sources of radon in the observing period.</p>
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<p>The processing steps of IED.</p>
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<p>Mean sea-level pressure (MSLP) analyses show a high-pressure system west of observing site (yellow star) on (<b>a</b>) 3 June 2019 and on (<b>b</b>) 25 June 2019. Data are from the Australia Bureau of Meteorology (<a href="http://www.bom.gov.au/australia/charts/archive/index.shtml" target="_blank">http://www.bom.gov.au/australia/charts/archive/index.shtml</a>, accessed on 8 May 2022).</p>
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<p>An example illustrating the retrieval of BLH using the ceilometer measurements obtained on 3 June 2019 over Cape Grim. (<b>a</b>) Time-height cross section of the range-squared corrected ceilometer signal intensity during the entire diurnal cycle. Black lines and black stars indicate the BLH estimations from the IED based on ceilometer and ERA5 reanalysis, respectively. Vertically aligned color bar (in linear scale) on the right indicates the intensity in arbitrary units. (<b>b</b>) Wind direction form sodar, (<b>c</b>) wind speed from sodar, (<b>d</b>) near-surface temperature, and (<b>e</b>) radon concentration.</p>
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<p>Wind speed profiles measured from sodar on 3 June 2019.</p>
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<p>Wind direction profiles measured from sodar on 3 June 2019.</p>
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<p>(<b>a</b>) 48 h backward trajectory frequency (500 m) arriving at Cape Grim site on 3 June 2019. The back trajectories were calculated using NOAA HYSPLIT 4.8 model. (<b>b</b>) The fire spots are from level 2 Terra/MODIS thermal anomalies/fire product (MOD14).</p>
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<p>Another example illustrating the retrieval of BLH using the ceilometer measurements obtained on 25 June 2019 over Cape Grim. (<b>a</b>) Time-height cross section of the range-squared corrected ceilometer signal intensity during the entire diurnal cycle. Black lines and black stars indicate the BLH estimations from the IED based on ceilometer and ERA5 reanalysis, respectively. Vertically aligned color bar (in linear scale) on the right indicates the intensity in arbitrary units. (<b>b</b>) Wind direction form sodar, (<b>c</b>) wind speed from sodar, (<b>d</b>) near-surface temperature, and (<b>e</b>) radon concentration.</p>
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<p>Wind speed profiles measured from sodar on 25 June 2019.</p>
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<p>Wind direction profiles measured from sodar on 25 June 2019.</p>
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<p>(<b>a</b>) 48 h backward trajectory frequency (500 m) arriving at Cape Grim site on 25 June 2019. The back trajectories were calculated using NOAA HYSPLIT 4.8 model. (<b>b</b>) The fire spots are from level 2 Terra/MODIS thermal anomalies/fire product (MOD14).</p>
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<p>(<b>a</b>) Statistical result of BLH (retrieved by IED from the ceilometer) under different sources—Melbourne/Eastern VIC (WD: 0–60°), Northern Tasmania (WD: 120–180°), Baseline Sector (WD: 190–280°) and Western Australia (WD: 300–360°), and (<b>b</b>) under different wind speeds. The white rectangle and the white circle in the box show the mean and median values, and the upper and lower edges in the white box represent the upper and lower points in the data set. The distribution of observable numerical points is presented on the left half.</p>
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<p>(<b>a</b>) Statistical result of BLH (retrieved by IED from the ceilometer) under different sources—Melbourne/Eastern VIC (WD: 0–60°), Northern Tasmania (WD: 120–180°), Baseline Sector (WD: 190–280°) and Western Australia (WD: 300–360°), and (<b>b</b>) under different wind speeds. The white rectangle and the white circle in the box show the mean and median values, and the upper and lower edges in the white box represent the upper and lower points in the data set. The distribution of observable numerical points is presented on the left half.</p>
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17 pages, 3737 KiB  
Article
Performance Evaluation of LIDAR and SODAR Wind Profilers on the Brazilian Equatorial Margin
by Audalio R. Torres Junior, Natália P. Saraiva, Arcilan T. Assireu, Francisco L. A. Neto, Felipe M. Pimenta, Ramon M. de Freitas, Osvaldo R. Saavedra, Clóvis B. M. Oliveira, Denivaldo C. P. Lopes, Shigeaki L. de Lima, Rafael B. S. Veras and Denisson Q. Oliveira
Sustainability 2022, 14(21), 14654; https://doi.org/10.3390/su142114654 - 7 Nov 2022
Cited by 4 | Viewed by 2292
Abstract
This article seeks to compare the performance of a LIDAR Windcube V2, manufactured by Leosphere, with that of a SODAR MFAS, manufactured by Scintec, in evaluating wind speed at different altitudes. The data from these two sensors were collected at three locations on [...] Read more.
This article seeks to compare the performance of a LIDAR Windcube V2, manufactured by Leosphere, with that of a SODAR MFAS, manufactured by Scintec, in evaluating wind speed at different altitudes. The data from these two sensors were collected at three locations on the Brazilian equatorial margin in the state of Maranhão. The comparison of these sensors aims at their simultaneous use at different points. The horizontal velocity components, by altitude, showed Pearson correlation values above 0.9 and values for the vertical velocity component between 0.7 and 0.85. As for the sampling efficiency, the LIDAR had a performance slightly higher than that of SODAR, especially at the point closest to the coast. In general, both sensors showed similar values, despite the differences in sampling methods. The results showed that the joint performance of these sensors had good correlation, being reliable for application in estimating wind potential for power generation in coastal areas of the equatorial region. Full article
(This article belongs to the Section Sustainable Engineering and Science)
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<p>Eastern coast of Maranhão state in Brazil, showing the positions of the <math display="inline"><semantics> <mrow> <mi>U</mi> <mi>F</mi> <mi>M</mi> <mi>A</mi> </mrow> </semantics></math> (at UFMA’s Electric Energy Institute in the city of São Luís), <math display="inline"><semantics> <mrow> <mi>P</mi> <mi>r</mi> <mi>a</mi> <mi>i</mi> <mi>a</mi> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>G</mi> <mi>i</mi> <mi>b</mi> <mi>a</mi> </mrow> </semantics></math> (both in the city of Paulino Neves) campaigns.</p>
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<p><math display="inline"><semantics> <mrow> <mi>U</mi> <mi>F</mi> <mi>M</mi> <mi>A</mi> </mrow> </semantics></math> campaign (IEE-UFMA, 44.307° W, 2.560° S). (<b>upper</b>) LIDAR wind profiler installed 3.5 m from the ground on the roof. (<b>bottom</b>) SODAR installed at ground level in the parking lot.</p>
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<p><math display="inline"><semantics> <mrow> <mi>P</mi> <mi>r</mi> <mi>a</mi> <mi>i</mi> <mi>a</mi> </mrow> </semantics></math> campaign (42.555° W, 2.694° S), showing the SODAR and micrometeorological tower set-up. This point is located about 1300 m from the beach line and windward of the wind turbines of the Delta wind farm in Paulino Neves.</p>
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<p><math display="inline"><semantics> <mrow> <mi>G</mi> <mi>i</mi> <mi>b</mi> <mi>a</mi> </mrow> </semantics></math> campaign (−42.575° W, −2.725° S), showing the LIDAR installation. This point is located about 3.4 km from the <math display="inline"><semantics> <mrow> <mi>P</mi> <mi>r</mi> <mi>a</mi> <mi>i</mi> <mi>a</mi> </mrow> </semantics></math> point in a region with low vegetation and bushes.</p>
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<p>Geometries of the beams emitted by LIDAR and SODAR for the <math display="inline"><semantics> <mrow> <mi>U</mi> <mi>F</mi> <mi>M</mi> <mi>A</mi> </mrow> </semantics></math> campaign. LIDAR was positioned 3.5 m above the surface. <math display="inline"><semantics> <mi>α</mi> </semantics></math> and <math display="inline"><semantics> <mi>β</mi> </semantics></math> are the angles concerning the vertical beam emitted by LIDAR (28<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math>) and SODAR (from 29<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math> to −22<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math>), respectively.</p>
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<p>Schematic of the vertical overlap of the sample volume length at the 40-m measurement height for LIDAR and SODAR during the UFMA campaign (<a href="#sustainability-14-14654-f001" class="html-fig">Figure 1</a> and <a href="#sustainability-14-14654-t001" class="html-table">Table 1</a>). SFC indicates surface or ground level. Note that the LIDAR was at a 3.5-m height.</p>
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<p>The geometry used to estimate the superposition region of the horizontal layers sampled by LIDAR and SODAR.</p>
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<p>LIDAR and SODAR performance (% failure) based on height for all three campaigns: <math display="inline"><semantics> <mrow> <mi>U</mi> <mi>F</mi> <mi>M</mi> <mi>A</mi> </mrow> </semantics></math> (<b>A</b>), <math display="inline"><semantics> <mrow> <mi>P</mi> <mi>r</mi> <mi>a</mi> <mi>i</mi> <mi>a</mi> </mrow> </semantics></math> (<b>B</b>), and <math display="inline"><semantics> <mrow> <mi>G</mi> <mi>i</mi> <mi>b</mi> <mi>a</mi> </mrow> </semantics></math> (<b>C</b>). Note that LIDAR was 3.5 m from the surface in the UFMA campaign.</p>
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<p>Mean wind speed profiles of LIDAR (black dotted lines) and SODAR (red dotted lines) obtained in the three campaigns: <math display="inline"><semantics> <mrow> <mi>U</mi> <mi>F</mi> <mi>M</mi> <mi>A</mi> </mrow> </semantics></math> (<b>A</b>), <math display="inline"><semantics> <mrow> <mi>P</mi> <mi>r</mi> <mi>a</mi> <mi>i</mi> <mi>a</mi> </mrow> </semantics></math> (<b>B</b>), and <math display="inline"><semantics> <mrow> <mi>G</mi> <mi>i</mi> <mi>b</mi> <mi>a</mi> </mrow> </semantics></math> (<b>C</b>). The lines represent the variance in the average speed, with LIDAR in black and SODAR in red. Note that the LIDAR was 3.5 m from the surface in the UFMA campaign.</p>
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<p>Wind speed as a function of time and height for the <math display="inline"><semantics> <mrow> <mi>P</mi> <mi>r</mi> <mi>a</mi> <mi>i</mi> <mi>a</mi> </mrow> </semantics></math> campaign, estimated by LIDAR and SODAR.</p>
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<p>Pearson’s correlation coefficients (PCCs) between LIDAR and SODAR for the three campaigns: <math display="inline"><semantics> <mrow> <mi>U</mi> <mi>F</mi> <mi>M</mi> <mi>A</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>P</mi> <mi>r</mi> <mi>a</mi> <mi>i</mi> <mi>a</mi> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>G</mi> <mi>i</mi> <mi>b</mi> <mi>a</mi> </mrow> </semantics></math>. The panels refer to the correlation of the zonal (east-west) (<b>A</b>), meridional (north-south) (<b>B</b>), and vertical (<b>C</b>) components of the wind speed vector. Note that the LIDAR was 3.5 m from the surface in the UFMA campaign.</p>
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19 pages, 3559 KiB  
Article
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://doi.org/10.3390/rs14184561 - 13 Sep 2022
Cited by 12 | Viewed by 2578
Abstract
The atmospheric boundary layer height (zi) 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 zi [...] Read more.
The atmospheric boundary layer height (zi) 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 zi, 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 zi. In this work, we evaluate the ERA5 estimates of zi (zi-ERA5) for two locations in the Amazon and corrected them by means of ceilometer, radiosondes, and SODAR measurements (zi-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 zi data yielded the following results: (i) zi-ERA5 underestimates zi-experimental daytime at the T3 site for both years 2014 (30%, underestimate) and 2015 (15%, underestimate); (ii) zi-ERA5 overestimates zi-experimental daytime at ATTO site (12%, overestimate); (iii) during nighttime, no significant correlation between the zi-experimental and zi-ERA5 was observed. Based on these findings, we propose a correction for the daytime zi-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
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<p>(<b>a</b>) Geographic locations of the experimental sites T3 and ATTO in the Amazon basin. (<b>b</b>) Topography of the study region.</p>
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<p>Average daily cycle (data from entire month) of the PBL height at the T3 site near Manaus during the wet (<b>a</b>) and dry seasons (<b>b</b>) in 2014, derived from ceilometer (blue), SODAR (lilac), and radiosonde (green), as well as ERA5 retrieval (red). 2014 was a non-El Niño year. The shaded area represents the standard deviation.</p>
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<p>The same as <a href="#remotesensing-14-04561-f002" class="html-fig">Figure 2</a> for the El Niño-affected year 2015, during the wet (<b>a</b>) and dry seasons (<b>b</b>).</p>
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<p>Average diurnal cycle of the PBL height at ATTO in Nov 2015, obtained from ceilometer (blue) and radiosonde (green, circles, corresponding to the average of the period from 1–6 November) measurements, as well as ERA5 retrievals (red). The corrected ERA5 data (dark red) are shown for comparison, as discussed in detail in <a href="#sec3dot3-remotesensing-14-04561" class="html-sec">Section 3.3</a>.</p>
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<p>Sensible heat flux values through experimental data (black line) and ERA5 (red line) at the T3 site. The data correspond to the wet seasons of (<b>a</b>,<b>c</b>) 2014 and 2015, and the dry seasons of (<b>b</b>,<b>d</b>) 2014 and 2015.</p>
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<p>Potential temperature profile from experimental data (black line) and ERA5 (red line) at the T3 site. The data correspond to the wet seasons of (<b>a</b>,<b>c</b>) 2014 and 2015 and the dry seasons of (<b>b</b>,<b>d</b>) 2014 and 2015. The vertical profiles for both ERA5 and experimental data represent an average of all profiles made at 18:00 UTC (14:00 LT). The black and red circles correspond to the average height of the boundary layer estimated through experimental data and ERA5, respectively.</p>
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<p><math display="inline"><semantics> <msub> <mi>R</mi> <mi>i</mi> </msub> </semantics></math> values obtained through the potential temperature and wind profiles from ERA5 for the T3 site. The data correspond to the wet seasons of (<b>a</b>,<b>c</b>) 2014 and 2015 and the dry seasons of (<b>b</b>,<b>d</b>) 2014 and 2015.</p>
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<p>(<b>a</b>) Sensible heat flux values through experimental data (red line) and ERA5 (black line), and (<b>b</b>) <math display="inline"><semantics> <msub> <mi>R</mi> <mi>i</mi> </msub> </semantics></math> values obtained through the temperature and wind profiles from ERA5 for the ATTO site. The data correspond to November 2015.</p>
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<p>Scatterplots of daytime PBL heights derived from observational data (ceilometer) and model (ERA5) for before (<b>a</b>,<b>c</b>) and after correction (<b>b</b>,<b>d</b>) for wet (<b>a</b>,<b>b</b>) and dry (<b>c</b>,<b>d</b>) seasons for the year 2014 at the T3 site.</p>
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<p>Similar to <a href="#remotesensing-14-04561-f009" class="html-fig">Figure 9</a> for the year 2015: PBL heights derived from observational data (ceilometer) and model (ERA5) for before (<b>a</b>,<b>c</b>) and after correction (<b>b</b>,<b>d</b>) for wet (<b>a</b>,<b>b</b>) and dry (<b>c</b>,<b>d</b>) seasons.</p>
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<p>Average diurnal cycle (data from entire month) for the convective PBL estimated for different instruments: ceilometer (blue line), ERA5 (red line), and ERA5-corrected (Equation (<a href="#FD2-remotesensing-14-04561" class="html-disp-formula">2</a>)) for the wet (<b>a</b>) and dry (<b>b</b>) seasons for 2014 (non-El Niño year).</p>
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<p>Average diurnal cycle (data from entire month) for the convective PBL estimated for different instruments: ceilometer (blue line), ERA5 (red line), and ERA5-corrected (Equation (<a href="#FD2-remotesensing-14-04561" class="html-disp-formula">2</a>)) for the wet (<b>a</b>) and dry (<b>b</b>) seasons for 2015 (El Niño year).</p>
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<p>Linear regression between the <math display="inline"><semantics> <msub> <mi>z</mi> <mi>i</mi> </msub> </semantics></math>-ERA5 against (<b>a</b>) <math display="inline"><semantics> <msub> <mi>z</mi> <mi>i</mi> </msub> </semantics></math>-SODAR and (<b>b</b>) <math display="inline"><semantics> <msub> <mi>z</mi> <mi>i</mi> </msub> </semantics></math>-ceilometer for the wet season and <math display="inline"><semantics> <msub> <mi>z</mi> <mi>i</mi> </msub> </semantics></math>-ERA5 against (<b>c</b>) <math display="inline"><semantics> <msub> <mi>z</mi> <mi>i</mi> </msub> </semantics></math>-SODAR and (<b>d</b>) <math display="inline"><semantics> <msub> <mi>z</mi> <mi>i</mi> </msub> </semantics></math>-ceilometer for the dry season, during the nocturnal period of 2014.</p>
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<p>Time series of the nocturnal boundary layer of <math display="inline"><semantics> <msub> <mi>z</mi> <mi>i</mi> </msub> </semantics></math>-ERA5 (<b>a</b>,<b>b</b>), <math display="inline"><semantics> <msub> <mi>z</mi> <mi>i</mi> </msub> </semantics></math>-SODAR (<b>c</b>,<b>d</b>), and <math display="inline"><semantics> <msub> <mi>z</mi> <mi>i</mi> </msub> </semantics></math>-ceilometer (<b>e</b>,<b>f</b>) for the wet and dry seasons of 2014, respectively. The solid line is the mean value and the grey shaded area represents one standard deviation.</p>
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<p>Normal distribution of the differences between <math display="inline"><semantics> <mrow> <mi>z</mi> <mi>i</mi> </mrow> </semantics></math>-ERA5 and <math display="inline"><semantics> <msub> <mi>z</mi> <mi>i</mi> </msub> </semantics></math>-SODAR for (<b>a</b>) wet season and (<b>c</b>) dry season of 2014. Normal distribution of the differences between <math display="inline"><semantics> <msub> <mi>z</mi> <mi>i</mi> </msub> </semantics></math>-ERA5 and <math display="inline"><semantics> <msub> <mi>z</mi> <mi>i</mi> </msub> </semantics></math>-ceilometer for (<b>b</b>) wet season and (<b>d</b>) dry season of 2014.</p>
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24 pages, 18575 KiB  
Article
A Three-Year Climatology of the Wind Field Structure at Cape Baranova (Severnaya Zemlya, Siberia) from SODAR Observations and High-Resolution Regional Climate Model Simulations during YOPP
by Günther Heinemann, Clemens Drüe and Alexander Makshtas
Atmosphere 2022, 13(6), 957; https://doi.org/10.3390/atmos13060957 - 12 Jun 2022
Cited by 5 | Viewed by 2011
Abstract
Measurements of the atmospheric boundary layer (ABL) structure were performed for three years (October 2017–August 2020) at the Russian observatory “Ice Base Cape Baranova” (79.280° N, 101.620° E) using SODAR (Sound Detection And Ranging). These measurements were part of the YOPP (Year of [...] Read more.
Measurements of the atmospheric boundary layer (ABL) structure were performed for three years (October 2017–August 2020) at the Russian observatory “Ice Base Cape Baranova” (79.280° N, 101.620° E) using SODAR (Sound Detection And Ranging). These measurements were part of the YOPP (Year of Polar Prediction) project “Boundary layer measurements in the high Arctic” (CATS_BL) within the scope of a joint German–Russian project. In addition to SODAR-derived vertical profiles of wind speed and direction, a suite of complementary measurements at the observatory was available. ABL measurements were used for verification of the regional climate model COSMO-CLM (CCLM) with a 5 km resolution for 2017–2020. The CCLM was run with nesting in ERA5 data in a forecast mode for the measurement period. SODAR measurements were mostly limited to wind speeds <12 m/s since the signal was often lost for higher winds. The SODAR data showed a topographical channeling effect for the wind field in the lowest 100 m and some low-level jets (LLJs). The verification of the CCLM with near-surface data of the observatory showed good agreement for the wind and a negative bias for the 2 m temperature. The comparison with SODAR data showed a positive bias for the wind speed of about 1 m/s below 100 m, which increased to 1.5 m/s for higher levels. In contrast to the SODAR data, the CCLM data showed the frequent presence of LLJs associated with the topographic channeling in Shokalsky Strait. Although SODAR wind profiles are limited in range and have a lot of gaps, they represent a valuable data set for model verification. However, a full picture of the ABL structure and the climatology of channeling events could be obtained only with the model data. The climatological evaluation showed that the wind field at Cape Baranova was not only influenced by direct topographic channeling under conditions of southerly winds through the Shokalsky Strait but also by channeling through a mountain gap for westerly winds. LLJs were detected in 37% of all profiles and most LLJs were associated with channeling, particularly LLJs with a jet speed ≥ 15 m/s (which were 29% of all LLJs). The analysis of the simulated 10 m wind field showed that the 99%-tile of the wind speed reached 18 m/s and clearly showed a dipole structure of channeled wind at both exits of Shokalsky Strait. The climatology of channeling events showed that this dipole structure was caused by the frequent occurrence of channeling at both exits. Channeling events lasting at least 12 h occurred on about 62 days per year at both exits of Shokalsky Strait. Full article
(This article belongs to the Topic The Arctic Atmosphere: Climate and Weather)
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<p>Model domain of the COSMO-CLM (CCLM) model with a 5 km resolution with topography and sea ice concentration for 23 April 2020. The Russian observatories near Tiksi and at Cape Baranova (CB) are marked by red dots.</p>
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<p>(<b>a</b>) Map of the Severnaya Zemlya Archipelago with its topography. The position of Cape Baranova (CB) is marked. (<b>b</b>) Zoomed-in image for the Shokalsky Strait. White dots mark positions of selected model grid points: Bc—closest grid point to CB, Bm—closest grid point to CB over the ocean, Sn—Shokalsky north, Sw—Shokalsky west, Ss—Shokalsky south.</p>
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<p>Time series of the 2 m temperature (red line) and 10 m wind speed during the investigation period (3 h values).</p>
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<p>(<b>a</b>) Wind rose (15° bins) for the 10 m wind and (<b>b</b>) frequency distribution of the 10 m wind for October 2017–August 2020.</p>
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<p>Hourly mean values of the SODAR wind speed at 100 m (blue dots) and RASS temperature at 100, 200 and 300 m (red, blue and green lines) for 1 October 2017–1 September 2020.</p>
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<p>Wind roses (15° bins) for the 10 m wind for (<b>a</b>) grid point Bc and (<b>b</b>) grid point Bm for October 2017–August 2020.</p>
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<p>Wind speed (lower panel) and wind direction (upper panel) for 26 to 29 July 2018 for the SODAR measurements at about 100 m in height (black) and CCLM simulations at 96 m in height (time in UTC). The blue lines mark the sector characterizing channeled flow (205°–245°).</p>
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<p>Time–height cross-sections of the wind speed and direction for 26 to 29 July 2018 for the SODAR data (<b>a</b>,<b>b</b>) and CCLM simulations (<b>c</b>,<b>d</b>). The CLLM data are shown as interpolated fields, SODAR data are shown as pixels (not interpolated) and missing data are grey.</p>
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<p>CCLM simulation results with a 5 km resolution of the wind speed (shaded, m/s) and wind vectors (scale at the bottom) for 27 July 2018, 0600 UTC for the area of Shokalsky Strait: (<b>a</b>) 10 m wind, (<b>b</b>) wind at 96 m and (<b>c</b>) wind at 206 m. The position of Cape Baranova is marked with a white circle, vectors are shown at every grid point and the topography is shown as isolines every 200 m.</p>
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<p>CCLM simulation results with a 5 km resolution of the wind speed (shaded, m/s) and wind vectors (scale at the bottom) for 20 April 2019, 1200 UTC, for the area of Shokalsky Strait: (<b>a</b>) 10 m wind, (<b>b</b>) wind at 206 m and (<b>c</b>) wind at 443 m. The position of Cape Baranova is marked with a white circle, vectors are shown at every grid point and the topography is shown as isolines every 200 m.</p>
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<p>Time–height cross-sections of the wind speed (<b>a</b>) and direction (<b>b</b>) for 19 to 22 April 2019 for the CCLM simulations.</p>
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<p>Wind roses (15° bins) for the SODAR wind at 50 m (<b>a</b>) and 100 m (<b>c</b>) and the CCLM simulations at the times of available SODAR data at 50 m (<b>b</b>) and 100 m (<b>d</b>) based on 1 h values for October 2017–August 2020.</p>
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<p>(<b>a</b>) Bias (CCLM-SODAR, dots) and RMSE (squares) for the comparison of the wind speed from the SODAR and CCLM simulations at different heights (numbers of data points as labels). (<b>b</b>) Wind speed distributions of SODAR and simulations at the times of available SODAR data at 100 m (relative frequencies). (<b>c</b>) Wind speed distributions at 100 m for the CCLM data at the times of available SODAR data and for all the CCLM data (absolute frequencies). All statistics were based on 1 h values for October 2017–August 2020.</p>
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<p>Wind roses (15° bins) from the CCLM simulations at 205, 310 and 610 m based on 1 h values for October 2017–August 2020: (<b>a</b>–<b>c</b>) all data and (<b>d</b>–<b>f</b>) only for wind speeds ≥ 15 m/s.</p>
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<p>Distributions of the maximum wind speed for channeling events at 200 m in height from the CCLM simulations for October 2017–August 2020: (<b>a</b>) all events and (<b>b</b>) only for events with a duration ≥ 12 h.</p>
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<p>Wind roses (15° bins) for October 2017–August 2020 at the grid points Shokalsky south, Shokalsky west and Shokalsky north (see <a href="#atmosphere-13-00957-f002" class="html-fig">Figure 2</a>b for locations) for the 10 m wind (<b>a</b>–<b>c</b>) and the wind at 100 m for wind speeds ≥ 15 m/s (<b>d</b>–<b>f</b>).</p>
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<p>Mean sea level pressure difference (Ss-Sn, hourly values) for October 2017–August 2020 versus the 10 m wind component along the strait (vrel) at the grid points Shokalsky north (<b>a</b>) and Shokalsky south (<b>b</b>). The color code indicates the 10 m wind speed. Lines are the linear regressions using all data.</p>
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<p>Statistics (relative frequencies) for LLJs from the CCLM simulations at Cape Baranova of the (<b>a</b>) jet height, (<b>b</b>) jet speed, (<b>c</b>) jet direction and (<b>d</b>) directional shear (difference between the wind direction at the jet core and at a height of 5 m).</p>
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<p>Mean vertical profiles for all LLJs of the wind speed (<b>a</b>), wind direction (<b>b</b>) and potential temperature deviation (<b>c</b>) (see text).</p>
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<p>Mean vertical profiles of the wind speed for (<b>a</b>) strong LLJs, (<b>b</b>) strong LLJs below 300 m in height and (<b>c</b>) strong channeled LLJs. Strong—jet speed ≥ 15 m/s, labels indicate the number of profiles. The shaded regions mark the 25%- and 75%-tiles, respectively.</p>
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<p>Statistics (relative frequencies) for channeled LLJs from the CCLM simulations at Cape Baranova: (<b>a</b>) upstream mountain Froude number and (<b>b</b>) LLJ speeds for different mountain Froude number classes.</p>
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<p>Simulated 10 m wind speed (shaded) and mean wind vectors (scale at the bottom) for the area of Shokalsky Strait for 2017–2020. (<b>a</b>) Mean wind for the period October 2017–August 2020 based on hourly values; (<b>b</b>) mean wind for days with channeling events with a duration ≥ 12 h at 200 m in height at Cape Baranova (marked by a white circle, 190 days in total); (<b>c</b>) mean wind for days with channeling events with a duration ≥ 12 h at 200 m in height at grid point Shokalsky south (marked by a white circle, 193 days in total). Vectors are shown at every grid point and the topography is shown as isolines every 200 m.</p>
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<p>Simulated 99%-tile of the 10 m wind speed (shaded) and mean wind vectors for speeds exceeding 10 m/s based on hourly values for the area of Shokalsky Strait for 2017–2020. (<b>a</b>) For the period October 2017–August 2020, (<b>b</b>) December–February and (<b>c</b>) June–August. Vectors are shown at every grid point and the topography is shown as isolines every 200 m.</p>
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22 pages, 59035 KiB  
Article
EOSOLAR Project: Assessment of Wind Resources of a Coastal Equatorial Region of Brazil—Overview and Preliminary Results
by Arcilan T. Assireu, Felipe M. Pimenta, Ramon M. de Freitas, Osvaldo R. Saavedra, Francisco L. A. Neto, Audálio R. Torres Júnior, Clóvis B. M. Oliveira, Denivaldo C. P. Lopes, Shigeaki L. de Lima, Rafael B. S. Veras, Natália P. Saraiva, Luiz G. P. Marcondes and Denisson Q. Oliveira
Energies 2022, 15(7), 2319; https://doi.org/10.3390/en15072319 - 23 Mar 2022
Cited by 9 | Viewed by 2361
Abstract
The EOSOLAR project was designed to investigate the structure of the atmospheric boundary layer in an equatorial coastal zone, where the discontinuity of surface conditions induces non-stationarity gradients of wind speeds and the development of internal boundary layers. The proposed methodology considers several [...] Read more.
The EOSOLAR project was designed to investigate the structure of the atmospheric boundary layer in an equatorial coastal zone, where the discontinuity of surface conditions induces non-stationarity gradients of wind speeds and the development of internal boundary layers. The proposed methodology considers several aspects of the sea–land transition meteorology that are essential for precisely estimating wind–solar energy potential and assessment of structural loads on wind turbines. Infrared (LIDAR) and acoustic (SODAR) ground-based remote sensing instruments and micrometeorological towers were installed in a near-shore equatorial area of northeast Brazil, in order to provide a comprehensive view of meteorological processes. This paper reports a description of the project study area, methodology, and instrumentation used. Details of instruments configurations, a validation of micrometeorology towers, and a comparison between the LIDAR and SODAR are presented. Results of the first field campaign measuring the coastal flow, integrating the micrometeorological tower and LIDAR observations are described. Full article
(This article belongs to the Section A3: Wind, Wave and Tidal Energy)
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<p>Landsat image of the EOSOLAR Project study site. Blue dots represent wind turbine locations; green bullets represent stations with measurements in course; red bullets are proposed sampling locations. P0 is the reference point located 1.5 km from the beach. Points located inland from P0 will be referred to as P1, P2, P3, and so forth. Black dashed lines represent equally spaced distance lines with an interval of 5 km.</p>
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<p>Typical landscapes for the study site. (<b>a</b>) Region leeward of the first array of wind turbines. (<b>b</b>) A well-developed dune field. (<b>c</b>) Grass-covered terrain. (<b>d</b>) Vegetated terrain.</p>
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<p>Flowchart of EOSOLAR instruments and methods employed to obtain the necessary information for solar and wind resource assessments.</p>
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<p>EOSOLAR measuring stations. From left to right: LIDAR, SODAR, and micrometeorological towers. Trailers refer to the power stations solutions (solar panels with batteries). Photo illustrates instruments mounted on the UFMA campus for the testing and intercomparison campaign (<a href="#energies-15-02319-t002" class="html-table">Table 2</a>).</p>
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<p>Google Earth image showing the location of P0 and P1 sites for data collection.</p>
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<p>Comparison between 3-D sonic anemometers for tower 1 (black curves) and tower 2 (gray curves) during the EOSOLAR testing phase (<a href="#energies-15-02319-t002" class="html-table">Table 2</a>). The original dataset was sampled at 20 Hz and presented here at a 1 min time resolution.</p>
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<p>Differences in wind velocity measurements between the two towers normalized by the mean wind velocity versus the friction velocity (<b>a</b>) and wind direction (<b>b</b>). Arrow indicates the unobstructed direction.</p>
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<p>Comparison between 3-D sonic anemometers for micrometeorological tower 1 (black curves) and 2 (gray curves). Averaging time was 1.0 min with a sampling rate of 20 Hz.</p>
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<p>(<b>a</b>) Vertical profiles of wind speed measured by LIDAR and SODAR. (<b>b</b>) Vertical distribution of the Pearson’s correlation coefficient comparing LIDAR and SODAR in terms of their zonal and meridional wind components.</p>
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<p>Time series over the period of 19 to 23 of September for (<b>a</b>) wind speed; (<b>b</b>) wind direction; (<b>c</b>) air temperature; (<b>d</b>) relative humidity; and (<b>e</b>) atmospheric pressure. A vertical dashed bar indicates the meteorological phenomenon of interest (21 September, between 07:00 h and 08:30 h LT).</p>
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<p>Diurnal variability of the vertically averaged wind speed (<b>a</b>) and wind direction (<b>b</b>). Tower anemometers at 3, 5, 7, and 10 m were used for the vertical averages. A black line illustrates the time series for 21 September, with two vertical dashed lines indicating the meteorological phenomenon of interest (21 September, between 07:00 h and 08:30 h LT). A gray line represents the expected daily wind behavior (computed for 24 h with 1 min time resolution).</p>
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<p>Data time series: (<b>a</b>) Magnitude of wind speed (m s<sup>−1</sup>) based on 20 observations of LIDAR with height (40 to 260 m). Vertical profile of (<b>b</b>) wind speed (m s<sup>−1</sup>), and (<b>c</b>) direction (degrees). Here the angle represents the direction that the wind is blowing from (in reference to the true north). Time is expressed in local time.</p>
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15 pages, 4403 KiB  
Article
Investigation of the Mixing Height in the Planetary Boundary Layer by Using Sodar and Microwave Radiometer Data
by Sergey Odintsov, Eugene Miller, Andrey Kamardin, Irina Nevzorova, Arkady Troitsky and Mathias Schröder
Environments 2021, 8(11), 115; https://doi.org/10.3390/environments8110115 - 27 Oct 2021
Cited by 6 | Viewed by 3892
Abstract
The height of the mixing layer is a significant parameter for describing the dynamics of the planetary boundary layer (PBL), especially for air quality control and for the parametrizations in numerical modeling. The problem is that the heights of the mixing layer cannot [...] Read more.
The height of the mixing layer is a significant parameter for describing the dynamics of the planetary boundary layer (PBL), especially for air quality control and for the parametrizations in numerical modeling. The problem is that the heights of the mixing layer cannot be measured directly. The values of this parameter are depending both on the applied algorithms for calculation and on the measuring instruments which have been used by the data source. To determine the height of a layer of intense turbulent heat exchange, data were used from acoustic meteorological locator (sodar) and from a passive single-channel scanning microwave radiometer MTP-5 (MWR) to measure the temperature profile in a layer of up to 1 km. Sodar can provide information on the structure of temperature turbulence in the PBL directly. These data have been compared with the mixing layer height calculated with the Parcel method by using the MTP-5 data. For the analysis, July and September 2020 were selected in the city of Tomsk in Siberia as characteristic periods of mid-summer and the transition period to autumn. The measurement results, calculations and inter-comparisons are shown and discussed in this work. During temperature inversions in the boundary layer, it was observed that turbulent heat transfer (increased dispersion of air temperature) is covering the inversion layers and the overlying ones. Moreover, this phenomenon is not only occurring during the morning destruction of inversions, but also in the process of their formation and development. Full article
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<p>Photo of the meteorological temperature profiler MTP-5 (<b>left</b>) and the acoustic meteorological locator “Volna-4M” (sodar, (<b>right</b>)) on the territory of the Basic Experimental Complex IOA SB RAS, 2020.</p>
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<p>MTP-5 temperature profiles time series (each color for each height with step 50 m) in the 0–1 km layer in July (<b>a</b>) and September (<b>b</b>) 2020.</p>
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<p>Statistical parameters of temperature profiles values in the 0–1 km PBL altitude range for July (<b>a</b>) and September (<b>b</b>) 2020 by MTP-5 data.</p>
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<p>The original sodar echogram with the plotted height Hm graph (red line with dots). Times of sunrise and sunset were 04:36 and 22:11, respectively (indicated by blue arrows).</p>
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<p>The daily variation of the height <span class="html-italic">H<sub>m</sub></span> in July (<b>a</b>) and in September (<b>b</b>) 2020, indicating the average values (black asterisks) and its <span class="html-italic">RMSD</span> (black segments); (<b>c</b>) daily variation of mean and median values; (<b>d</b>) relative frequency of the <span class="html-italic">H<sub>m</sub></span>.</p>
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<p>Distribution of the temperature inversions by time of day (hh) and the number of surface based inversions (<span class="html-italic">H<sub>base</sub></span> = 0) in July and September 2020.</p>
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<p>Diurnal variation of the k<sub>ex</sub> overlap index (<b>a</b>) in July and (<b>b</b>) in September 2020; dependence of the overlap index on the time of day and the intensity of the inversion (<b>c</b>) in July and (<b>d</b>) in September 2020.</p>
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<p>Distribution of the temperature inversion power Δ<span class="html-italic">T<sub>i</sub></span> in July and September 2020.</p>
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<p>Distribution of the temperature inversion layer thickness Δ<span class="html-italic">H<sub>i</sub></span> in July and September 2020.</p>
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<p>Graphs of the Δ<span class="html-italic">H</span> = <span class="html-italic">H<sub>m</sub></span> − <span class="html-italic">H<sub>top</sub></span> in dependence of Δ<span class="html-italic">T<sub>i</sub></span> and Δ<span class="html-italic">H<sub>i</sub></span>: (<b>a</b>) July and (<b>b</b>) September 2020.</p>
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<p>Distribution of Δ<span class="html-italic">H</span> = <span class="html-italic">H<sub>m</sub></span> − <span class="html-italic">H<sub>top</sub></span> in dependence of the frequency of cases in July (<b>triangles</b>) and in September (<b>dots</b>) 2020.</p>
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<p>Δ<span class="html-italic">H</span> = <span class="html-italic">H<sub>m</sub></span> − <span class="html-italic">H<sub>top</sub></span> in the daily PBL cycle for (<b>a</b>) July and (<b>b</b>) September 2020. Δ<span class="html-italic">H</span> ≥ 0—red line, Δ<span class="html-italic">H</span> &lt; 0—blue line.</p>
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<p>Diagram of the height-time distribution of the potential temperature gradient (the scale of values is shown to the right of the graph), <span class="html-italic">H<sub>PC</sub></span>—asterisks, <span class="html-italic">H<sub>m</sub></span>—red dots.</p>
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<p>Diurnal variation (<b>a</b>) in July and (<b>b</b>) in September 2020 showing the mean values (asterisks) and <span class="html-italic">RMSD</span> (segments); (<b>c</b>) comparison of the daily variation of the mean values; and (<b>d</b>) cumulative frequency.</p>
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22 pages, 8016 KiB  
Article
Urban-Scale Computational Fluid Dynamics Simulations with Boundary Conditions from Similarity Theory and a Mesoscale Model
by Demetri Bouris, Athanasios G. Triantafyllou, Athina Krestou, Elena Leivaditou, John Skordas, Efstathios Konstantinidis, Anastasios Kopanidis and Qing Wang
Energies 2021, 14(18), 5624; https://doi.org/10.3390/en14185624 - 7 Sep 2021
Cited by 9 | Viewed by 1830
Abstract
Mesoscale numerical weather prediction models usually provide information regarding environmental parameters near urban areas at a spatial resolution of the order of thousands or hundreds of meters, at best. If detailed information is required at the building scale, an urban-scale model is necessary. [...] Read more.
Mesoscale numerical weather prediction models usually provide information regarding environmental parameters near urban areas at a spatial resolution of the order of thousands or hundreds of meters, at best. If detailed information is required at the building scale, an urban-scale model is necessary. Proper definition of the boundary conditions for the urban-scale simulation is very demanding in terms of its compatibility with environmental conditions and numerical modeling. Here, steady-state computational fluid dynamics (CFD) microscale simulations of the wind and thermal environment are performed over an urban area of Kozani, Greece, using both the k-ε and k-ω SST turbulence models. For the boundary conditions, instead of interpolating vertical profiles from the mesoscale solution, which is obtained with the atmospheric pollution model (TAPM), a novel approach is proposed, relying on previously developed analytic expressions, based on the Monin Obuhkov similarity theory, and one-way coupling with minimal information from mesoscale indices (Vy = 10 m, Ty = 100 m, L*). The extra computational cost is negligible compared to direct interpolation from mesoscale data, and the methodology provides design phase flexibility, allowing for the representation of discrete urban-scale atmospheric conditions, as defined by the mesoscale indices. The results compared favorably with the common interpolation practice and with the following measurements obtained for the current study: SODAR for vertical profiles of wind speed and a meteorological temperature profiler for temperature. The significance of including the effects of diverse atmospheric conditions is manifested in the microscale simulations, through significant variations (~30%) in the critical building-related design parameters, such as the surface pressure distributions and local wind patterns. Full article
(This article belongs to the Section G: Energy and Buildings)
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<p>3D representation of urban area being studied. Measurement positions are also indicated (1) for temperature measurements and (2) for wind measurements. (Satellite view of the city from Google Earth).</p>
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<p>Cartesian grid for the computational domain 700 × 100 × 750 with a total of ~1.6Μ cells.</p>
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<p>Flow chart of data for derivation of vertical profiles of wind speed, temperature and turbulence kinetic energy, necessary as boundary condition for CFD microscale simulation.</p>
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<p>CFD calculations (“-X387-Z289_00”), mesoscale calculations (“-tapm”) and measurements (“-sodar”) of vertical profiles of wind speed over position 2 (<a href="#energies-14-05624-f001" class="html-fig">Figure 1</a>) for the following: (<b>a</b>) unstable (B), (<b>b</b>) unstable (C), (<b>c</b>) neutral (D) and (<b>d</b>) stable (E) conditions (<a href="#energies-14-05624-t001" class="html-table">Table 1</a>). COST 710 inlet profiles (“-Inlet_”B, C, D, E) are also shown.</p>
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<p>CFD calculations (“-X387-Z289_00”), mesoscale calculations (“-tapm”) and measurements (“-sodar”) of vertical profiles of wind speed over position 2 (<a href="#energies-14-05624-f001" class="html-fig">Figure 1</a>) for the following: (<b>a</b>) unstable (B), (<b>b</b>) unstable (C), (<b>c</b>) neutral (D) and (<b>d</b>) stable (E) conditions (<a href="#energies-14-05624-t001" class="html-table">Table 1</a>). COST 710 inlet profiles (“-Inlet_”B, C, D, E) are also shown.</p>
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<p>Vertical profiles of wind speed over position 2 (<a href="#energies-14-05624-f001" class="html-fig">Figure 1</a>) at four stability conditions (<b>a</b>) D, (<b>b</b>) C, (<b>c</b>) B, (<b>d</b>) E (<a href="#energies-14-05624-t001" class="html-table">Table 1</a>). Comparison of CFD results using two different turbulence models (k-ε and SST) and SODAR measurements. Mesoscale results (“TAPM-inlet”) are also shown.</p>
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<p>Vertical profiles of wind turbulence kinetic energy over position 2 (<a href="#energies-14-05624-f001" class="html-fig">Figure 1</a>), corresponding to four stability conditions (<b>a</b>) D, (<b>b</b>) C, (<b>c</b>) B, (<b>d</b>) E (<a href="#energies-14-05624-t001" class="html-table">Table 1</a>). Comparison of CFD results using two different turbulence models (k-ε and SST). Mesoscale results (“TAPM-inlet”) are also shown.</p>
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<p>Vertical profiles of air temperature over position 1 (<a href="#energies-14-05624-f001" class="html-fig">Figure 1</a>) at four stability conditions (<b>a</b>) D, (<b>b</b>) C, (<b>c</b>) B, (<b>d</b>) E (<a href="#energies-14-05624-t001" class="html-table">Table 1</a>). Comparison of CFD results using two different turbulence models (k-ε and SST). Mesoscale results (“TAPM-inlet”) are also shown.</p>
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<p>(<b>a</b>) Vertical profiles of wind speed and (<b>c</b>) wind turbulence kinetic energy over position 2 and (<b>b</b>) air temperature over position 1 (<a href="#energies-14-05624-f001" class="html-fig">Figure 1</a>). CFD results, neutral atmosphere (D in <a href="#energies-14-05624-t001" class="html-table">Table 1</a>), SST turbulence model and inlet profiles from mesoscale results (“TAPM-inlet”) and from the COST 710 recommendations (“COST-inlet”).</p>
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<p>(<b>a</b>) Vertical profiles of wind speed and (<b>c</b>) wind turbulence kinetic energy over position 2 and (<b>b</b>) air temperature over position 1 (<a href="#energies-14-05624-f001" class="html-fig">Figure 1</a>). CFD results, neutral atmosphere (B in <a href="#energies-14-05624-t001" class="html-table">Table 1</a>), SST turbulence model and inlet profiles from mesoscale results (“TAPM-inlet”) and from the COST 710 recommendations (“COST-inlet”).</p>
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<p>(<b>a</b>) Vertical profiles of wind speed and (<b>c</b>) wind turbulence kinetic energy over position 2 and (<b>b</b>) air temperature over position 1 (<a href="#energies-14-05624-f001" class="html-fig">Figure 1</a>). CFD results, neutral atmosphere (C in <a href="#energies-14-05624-t001" class="html-table">Table 1</a>), SST turbulence model and inlet profiles from mesoscale results (“TAPM-inlet”) and from the COST 710 recommendations (“COST-inlet”).</p>
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<p>(<b>a</b>) Vertical profiles of wind speed and (<b>c</b>) wind turbulence kinetic energy over position 2 and (<b>b</b>) air temperature over position 1 (<a href="#energies-14-05624-f001" class="html-fig">Figure 1</a>). CFD results, neutral atmosphere (E in <a href="#energies-14-05624-t001" class="html-table">Table 1</a>), SST turbulence model and inlet profiles from mesoscale results (“TAPM-inlet”) and from the COST 710 recommendations (“COST-inlet”).</p>
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<p>Contours of constant pressure (in Pa on XZ ground plane and building surfaces) and contours of constant temperature (in K on YZ plane). Insert is the region around the building where wind speed measurements were performed (point 2 in <a href="#energies-14-05624-f001" class="html-fig">Figure 1</a>). Atmospheric conditions corresponding to Pasquil D category (<a href="#energies-14-05624-t001" class="html-table">Table 1</a>).</p>
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<p>Contours of constant pressure (in Pa on XZ ground plane and building surfaces) and contours of constant temperature (in K on YZ plane). Insert is the region around the building where wind speed measurements were performed (point 2 in <a href="#energies-14-05624-f001" class="html-fig">Figure 1</a>). Atmospheric conditions corresponding to Pasquil B category (<a href="#energies-14-05624-t001" class="html-table">Table 1</a>).</p>
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<p>Contours of constant pressure (in Pa on XZ ground plane and building surfaces) and contours of constant temperature (in K on YZ plane). Insert is the region around the building where wind speed measurements were performed (point 2 in <a href="#energies-14-05624-f001" class="html-fig">Figure 1</a>). Atmospheric conditions corresponding to Pasquil E category (<a href="#energies-14-05624-t001" class="html-table">Table 1</a>).</p>
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16 pages, 24806 KiB  
Article
Wind Speed Profile Statistics from Acoustic Soundings at a Black Sea Coastal Site
by Damyan Barantiev and Ekaterina Batchvarova
Atmosphere 2021, 12(9), 1122; https://doi.org/10.3390/atmos12091122 - 31 Aug 2021
Cited by 2 | Viewed by 2129
Abstract
More than seven years of remote sensing data with high spatial and temporal resolution were investigated in this study. The 20-min moving averaged wind profiles form the acoustic sounding with Scintec MFAS sodar were derived every 10 min. The profiles covered from 30 [...] Read more.
More than seven years of remote sensing data with high spatial and temporal resolution were investigated in this study. The 20-min moving averaged wind profiles form the acoustic sounding with Scintec MFAS sodar were derived every 10 min. The profiles covered from 30 to 600 m height with vertical resolution of 10 m. The wind speed probability and the Weibull distribution parameters were calculated by the maximum likelihood method at each level and then the profiles of the Weibull scale and shape parameters were analyzed. Diurnal wind speed at heights above 200 m has shown a well-expressed increase in the averaged values during the night hours, while during the day lower wind speeds were observed. The reversal height was explored from spatially and temporally homogenized diurnal wind speed data with applied quadratic functions for better interpretation of the results. In addition, analyses by type of air masses (land or sea air mass) were performed. One of the outcomes of the study was assessment of the internal boundary layer height, which was estimated to 50–80 m at the location of the sodar. The obtained information forms the basis for climatological insights on the vertical structure of the coastal boundary layer and is unique long-term data set important not only for Bulgaria but for coastal meteorology in general. Full article
(This article belongs to the Section Meteorology)
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Figure 1

Figure 1
<p>Location of Meteorological Observatory (MO) Ahtopol in southeastern Bulgaria (<b>left</b>) with views of the terrene (middle—Google Earth) and sodar system on the roof of the administrative building of MO Ahtopol (<b>right</b>). The maps are elaborated based on Google Earth.</p>
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<p>Wind speed histograms (green bars) and data availability (pink bars) at four different measurement levels ((<b>a</b>)—100 m, (<b>b</b>—300 m, (<b>c</b>)—400 m, (<b>d</b>)—600 m) with statistics values and fitted Weibull distributions (red curves) for a measurement period from 20.07.2008 to 31.01.2016 in MO Ahtopol (interval coverage: 82.2%—327,009 data sets).</p>
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<p>Variation of the wind speed probability distribution in height (color cross-section of height and wind speed intervals) with the processed data availability (green bars) for the study period.</p>
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<p>Shape parameter “<span class="html-italic">k</span>” (<b>a</b>) and scale parameter “<span class="html-italic">c</span>” (<b>b</b>) profiles of the two-parameter Weibull distribution applied to measured wind speed from 30 to 600 m above the ground at every 10 m.</p>
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<p>Averaged diurnal wind speed variation in height (<b>a</b>) diurnal data availability (<b>b</b>) for continuous profiles from 100 m above the ground.</p>
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<p>Average (of 2357 days) diurnal variation of wind speed at different heights (colors of dashed lines: 30 m—brown, 40 m—red, 50 m—green, 60 m—pink, 70 m—black, 80 m—blue, 90 m–100 m—orange, 110 m—grey) with applied quadratic functions (<b>a</b>) and display of the quadratic functions only (<b>b</b>).</p>
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<p>Only temporally homogenized diurnal data availability variation ((<b>a</b>)—availability up to 1538 days out of a total of 2357) and simultaneously spatially (up to 180 m above the ground) and temporally homogenized diurnal data availability variation ((<b>b</b>)—availability up to 456 days) for the study period.</p>
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<p>Diurnal variation of wind speed at different heights (colors of dashed lines: 30 m—brown, 40 m—red, 50 m—green, 60 m—pink, 70 m—black, 80 m—blue, 90 m–100 m—orange, 110 m—grey) from spatially and temporally homogenized data with applied quadratic functions (<b>a</b>) and individually displayed quadratic functions (<b>b</b>) out of a total of 456 days retrieved from the study period.</p>
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<p>Spatially and temporally diurnal data availability of marine air masses (<b>a</b>) and land air masses (<b>b</b>) of profiles with data availability at 100 m above the ground and continuity data after this height.</p>
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<p>Shape parameter “<span class="html-italic">k</span>” (left—(<b>a</b>,<b>c</b>)) and scale parameter “<span class="html-italic">c</span>” (right—(<b>b</b>,<b>d</b>)) profiles of the two-parameter Weibull distribution applied to measured wind speeds from 30 to 600 m above the ground at every 10 m height for marine (up—(<b>a</b>,<b>b</b>)) and land (down—(<b>c</b>,<b>d</b>)) air masses.</p>
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<p>Quadratic functions fit to the averaged diurnal variation of wind speed at different heights from spatially and temporally inhomogeneous data of marine (<b>a</b>) and land (<b>b</b>) air masses.</p>
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20 pages, 6099 KiB  
Article
Evaluation of Profiles of Standard Deviation of Vertical Wind in the Urban Area of Rome: Performances of Monin–Obukhov Similarity Theory Using Different Scaling Variables
by Armando Pelliccioni, Livia Grandoni and Annalisa Di Bernardino
Sustainability 2021, 13(15), 8426; https://doi.org/10.3390/su13158426 - 28 Jul 2021
Cited by 1 | Viewed by 2599
Abstract
The parametrizations of meteorological variables provided by the Monin–Obukhov similarity theory (MOST) is of major importance for pollutant dispersion assessment. However, the complex flow pattern that characterizes the urban areas limits the applicability of the MOST. In this work, the performance of different [...] Read more.
The parametrizations of meteorological variables provided by the Monin–Obukhov similarity theory (MOST) is of major importance for pollutant dispersion assessment. However, the complex flow pattern that characterizes the urban areas limits the applicability of the MOST. In this work, the performance of different existing parametrizations of the standard deviation of vertical wind velocity were tested in the city of Rome. Results were compared with experimental data acquired by a sonic detection and ranging (SODAR) and a sonic anemometer. Different scaling variables estimated from the anemometer data by considering two coordinate systems—one aligned with the geodetic reference frame and the other following the flow streamlines—were used to evaluate the effects of flow distortion due to the presence of buildings. Results suggest that the MOST parametrizations perform better if the scaling variables obtained using the coordinate system following the flow streamlines are used. This estimation of the scaling variables would make it possible to overcome the difficulties in conducting measurements of turbulent fluxes, either at different altitudes or even in the constant flux layer. Full article
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Figure 1
<p>Vertical structure of the ABL in urban environment.</p>
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<p><math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>w</mi> </msub> <mo>/</mo> <msub> <mi>u</mi> <mo>*</mo> </msub> </mrow> </semantics></math> as a function of z/L modelled by means of Equation (5) and the parameters listed in <a href="#sustainability-13-08426-t002" class="html-table">Table 2</a>. <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">z</mi> <mrow> <mi>ref</mi> </mrow> </msub> <mo>=</mo> <mn>3</mn> <msub> <mi mathvariant="normal">h</mi> <mi mathvariant="normal">b</mi> </msub> </mrow> </semantics></math> was been fixed, while 1/L ranged between −0.15 and 0.15.</p>
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<p>(<b>Left</b>): Aerial view (source: Google Earth) of the Sapienza University Campus. The blue circle indicates the sampling site (”Fermi”). The red polygon indicates the borders of the Sapienza University Campus of Rome. The red circle depicts the area of 500 m in radius. (<b>Right</b>): Enlargement of the region surrounding the “E. Fermi” building. In the right panel, the yellow and green circles refer to the sonic anemometer and the SODAR locations, respectively. For ease of interpretation, the wind sectors have been also depicted.</p>
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<p>Planar area fraction versus direction.</p>
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<p>(<b>a</b>) Tilt angle <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="sans-serif">φ</mi> <mrow> <mi>tilt</mi> </mrow> </msub> </mrow> </semantics></math> (light red circles), its moving average over 20 values (red line), and edge-anemometer distance (blue points) as a function of wind direction. (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="sans-serif">φ</mi> <mrow> <mi>tilt</mi> </mrow> </msub> </mrow> </semantics></math> as a function of edge–anemometer distance; the orange lines represent the mean <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="sans-serif">φ</mi> <mrow> <mi>tilt</mi> </mrow> </msub> </mrow> </semantics></math> for edge &lt; 5 m and for edge &gt; 5 m.</p>
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<p>(<b>a</b>) Daily cycles of friction velocity in the normal (black) and tilted (red) coordinate systems for April 2018. (<b>b</b>) Mean value of friction velocity in the normal (orange) and tilted (red) coordinate systems per each stability class.</p>
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<p>(<b>a</b>) Daily trend of sensible heat flux in the normal (black) and tilted (red) coordinate system (<b>b</b>) Mean value sensible heat flux in the normal (orange) and tilted (red) coordinate system per each atmospheric stability class.</p>
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<p>Frequency of occurrence of each stability class, considering the classification based on either <math display="inline"><semantics> <mrow> <msup> <mi mathvariant="normal">L</mi> <mrow> <mi>tilt</mi> </mrow> </msup> </mrow> </semantics></math> (red) or <math display="inline"><semantics> <mrow> <msup> <mi mathvariant="normal">L</mi> <mrow> <mi>nrm</mi> </mrow> </msup> </mrow> </semantics></math> (orange).</p>
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<p>Friction velocities estimated by SODAR data per atmospheric stability class (blue tones), along with friction velocities obtained by sonic anemometer measurements (red tones).</p>
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<p>Mean relative difference between modelled and experimental <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="sans-serif">σ</mi> <mi mathvariant="normal">w</mi> </msub> <mo>/</mo> <msub> <mi mathvariant="normal">u</mi> <mo>*</mo> </msub> </mrow> </semantics></math> ratios, for each stability class.</p>
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<p>Mean relative difference between modelled and experimental <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="sans-serif">σ</mi> <mi mathvariant="normal">w</mi> </msub> <mo>/</mo> <msub> <mi mathvariant="normal">u</mi> <mo>*</mo> </msub> </mrow> </semantics></math> ratios (average value for all the stability classes).</p>
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19 pages, 18033 KiB  
Article
Pannonian Basin Nocturnal Boundary Layer and Fog Formation: Role of Topography
by Joan Cuxart, Maja Telisman Prtenjak and Blazenka Matjacic
Atmosphere 2021, 12(6), 712; https://doi.org/10.3390/atmos12060712 - 31 May 2021
Cited by 4 | Viewed by 3321
Abstract
Under high-pressure systems, the nocturnal atmospheric boundary layer in the Pannonian Basin is influenced by gravity flows generated at the mountain ranges and along the valleys, determining the variability of wind and temperature at a local scale and the presence of fog. The [...] Read more.
Under high-pressure systems, the nocturnal atmospheric boundary layer in the Pannonian Basin is influenced by gravity flows generated at the mountain ranges and along the valleys, determining the variability of wind and temperature at a local scale and the presence of fog. The mechanisms at the mountain foothills are explored at Zagreb Airport using data from a sodar and high-resolution WRF-ARW numerical simulations, allowing identification of how the downslope flows from the nearby Medvednica mountain range condition the temperature inversion and the visibility at night and early morning. These flows may progress tens of kilometres away from the mountain ranges, merging with valley flows and converging in the central areas of the basin. The ECMWF model outputs allow us to explore the mesoscale structures generated in form of low-level jets, how they interact when they meet, and what is the effect of the synoptic pressure field over eastern Europe, to illustrate the formation of a basin-wide cold air pool and the generation of fog in winter. Full article
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Figure 1
<p>(<b>a</b>) The Pannonian Basin with indication of the 3 inner embedded domains of the WRF-ARW simulation, with the main topographical features (PP: Pannonian Plain, CM: Carpathian Mountains), some river names in blue, and Zagreb (Z) and Szeged (S). (<b>b</b>) The Zagreb Airport area, with the city to the NNW and the Medvednica mountain range to the NW–NNE. The orange bar indicates the runway of the airport and the blue circle the location of the sodar.</p>
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<p>Summer intermittent fog on the Medvednica foothills, 17–18 September 2017. Top row: (<b>a</b>) half-hourly 24 h series of wind speed and direction at 10 m, T and dew point at 2 m, and cloud cover from ZGB METAR reports; (<b>b</b>) 15 min time series of wind direction at 10 m from METAR and 30 and 90 m from sodar; and (<b>c</b>) sodar wind speed at 60 m and speed difference between 90 and 30 m. Bottom row: (<b>d</b>) a zoom of ECMWF model centred in Zagreb (indicated by a rhombus) at 05 UTC showing fields of integrated liquid water contents 1 km above the surface; (<b>e</b>) 10 m wind, T2m and mean sea level pressure; and (<b>f</b>) geopotential height, wind, and T at 950 hPa.</p>
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<p>Sodar and METAR data at Zagreb Airport. For 27 to 28 January 2018: (<b>a</b>) half-hourly 24 h series of wind speed and direction at 10 m, T and dew point at 2 m, and cloud cover from Zagreb Airport METAR reports; (<b>b</b>) 15 min time series of wind direction at 10 m from METAR and 30 m and 90 m from sodar; (<b>c</b>) sodar wind speed at 60 m and speed difference between 90 and 30 m. In the lower row, the same figures (<b>d</b>–<b>f</b>) are shown for 28 to 29 January 2018.</p>
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<p>Katabatic event developing during the clear-skies interval of night 27–28 January 2018. Top: horizontal vector and cloud water (colour) at the first model level ((<b>a</b>) 00 UTC, (<b>b</b>) 01 UTC, (<b>c</b>) 02 UTC). Bottom: wind direction (colours) and cloud water (dashed lines) along the same line ((<b>d</b>) 00 UTC, (<b>e</b>) 01 UTC, (<b>f</b>) 02 UTC).</p>
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<p>Deep fog layer on 28–29 January 2018: vertical cross-sections perpendicular to the Medvednica slope towards Zagreb Airport of wind vectors and cloud water at (<b>a</b>) 18 UTC, (<b>b</b>) 00 UTC, (<b>c</b>) 06 UTC, and (<b>d</b>) 11 UTC.</p>
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<p>Different wind patterns when high pressure dominates over the Pannonian Basin (<b>a</b>) in the absence of well-defined pressure gradients (3 September 2016, 00 UTC), with red arrows in the Alpine-Drava region, green arrows in the Carpathian-Tisza region, blue arrows in the Dinarides-Sava region, and the dotted orange line roughly indicating the limits between these regions; (<b>b</b>) for lower pressure at the north (29 August 2016, 00 UTC); (<b>c</b>) for lower pressure at the south (16 December 2016, 00 UTC); and (<b>d</b>) for lower pressure regions at the north and south (22 December 2016, 00 UTC)—black arrows within-basin circulation, red arrows air that passes through outside the basin.</p>
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<p>Summer clear skies cold air pool (2–3 September 2016): (<b>a</b>) radiosounding (blue) and ECMWF profile (red) at Szeged Airport at 00 UTC (blue) for potential temperature, (<b>b</b>) water vapour mixing ratio, (<b>c</b>) wind speed, and (<b>d</b>) wind direction; (<b>e</b>) temperature at 2 m and wind at 10 m in ECMWF output at 05 UTC, near sunrise. The black line indicates the location of the vertical cross-sections in <a href="#atmosphere-12-00712-f008" class="html-fig">Figure 8</a> and <a href="#atmosphere-12-00712-f009" class="html-fig">Figure 9</a>.</p>
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<p>Winter clear skies cold air pool (3–4-December 2016): (<b>a</b>) radiosounding (blue) and ECMWF profile (red) at Szeged Airport at 00 UTC (blue) for potential temperature, (<b>b</b>) water vapour mixing ratio, (<b>c</b>) wind speed, and (<b>d</b>) wind direction; (<b>e</b>) potential temperature in the lower atmosphere near sunrise (06 UTC) along the Tisza river valley reaching the Dinarides.</p>
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<p>Winter stratocumulus and fog-covered cold air pool (22–24 December 2016): (<b>a</b>) radiosounding (blue) and ECMWF profile (red) at Szeged Airport at 00 UTC (blue) for potential temperature, (<b>b</b>) water vapour mixing ratio, (<b>c</b>) wind speed at 00 UTC of 22 December; (<b>d</b>–<b>f</b>) similarly for 00 UTC of 23 December; (<b>g</b>) maximal extension of the Sc deck at the end of the fog event (24 December 2016, 06 UTC) showing the integrated cloud water for the lower 600 m of the atmosphere; (<b>h</b>) vertical cross-sections of cloud water along the Tisza valley reaching the Dinarides of cloud water on 23 December at 00 UTC; (<b>i</b>) similarly for temperature.</p>
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19 pages, 9005 KiB  
Article
Observations of Wintertime Low-Level Jets in the Coastal Region of the Laptev Sea in the Siberian Arctic Using SODAR/RASS
by Günther Heinemann, Clemens Drüe, Pascal Schwarz and Alexander Makshtas
Remote Sens. 2021, 13(8), 1421; https://doi.org/10.3390/rs13081421 - 7 Apr 2021
Cited by 7 | Viewed by 2034
Abstract
In 2014/2015 a one-year field campaign at the Tiksi observatory in the Laptev Sea area was carried out using Sound Detection and Ranging/Radio Acoustic Sounding System (SODAR/RASS) measurements to investigate the atmospheric boundary layer (ABL) with a focus on low-level jets (LLJ) during [...] Read more.
In 2014/2015 a one-year field campaign at the Tiksi observatory in the Laptev Sea area was carried out using Sound Detection and Ranging/Radio Acoustic Sounding System (SODAR/RASS) measurements to investigate the atmospheric boundary layer (ABL) with a focus on low-level jets (LLJ) during the winter season. In addition to SODAR/RASS-derived vertical profiles of temperature, wind speed and direction, a suite of complementary measurements at the Tiksi observatory was available. Data of a regional atmospheric model were used to put the local data into the synoptic context. Two case studies of LLJ events are presented. The statistics of LLJs for six months show that in about 23% of all profiles LLJs were present with a mean jet speed and height of about 7 m/s and 240 m, respectively. In 3.4% of all profiles LLJs exceeding 10 m/s occurred. The main driving mechanism for LLJs seems to be the baroclinicity, since no inertial oscillations were found. LLJs with heights below 200 m are likely influenced by local topography. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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Graphical abstract

Graphical abstract
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<p>(<b>a</b>) Map of the Laptev Sea with topography and bathymetry. The Russian observatories near Tiksi and at the Cape Baranov are marked by red dots. (<b>b</b>) Map of the Tiksi area with topography. The town of Tiksi and the location of the Sound Detection and Ranging (SODAR) at the Tiksi observatory are marked.</p>
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<p>Time series of the 2 m-temperature (red line), 10 m-wind speed, and mean sea level pressure (MSLP) during the investigation period.</p>
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<p>(<b>a</b>) Wind rose for the 10 m-wind during the investigation period (15° bins). (<b>b</b>) Frequency distribution of the 2 m-tem­perature during the investigation period.</p>
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<p>3D view from the south of the investigation area (Google Earth 2021). The SODAR and Radio Acoustic Sounding System (RASS) was installed south of the clean air facility (CAF). The Tiksi micro-meteorological tower (20 m height) is located approximately 315 m south-west of the CAF (see maps in <a href="#app1-remotesensing-13-01421" class="html-app">Figures S1 and S2</a> in the supplement). The scintillometer measured between the observatory and CAF. Standard meteorological observations are taken at the weather station (WS). The elevations of some hill tops are indicated.</p>
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<p>(<b>a</b>) Setup of the SODAR/RASS (view in south-west direction, photo: P. Schwarz). (<b>b</b>) Maintenance of the SODAR after snowfall (photo: C. Drüe).</p>
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<p>Hourly values of the SODAR wind speed at 100 m (black line) and at 300 m (green line) and RASS temperature at 100 m (red line) for 1 October 2014–31 March 2015.</p>
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<p>Wind rose for the wind at 100 m (<b>a</b>), at 400 m (<b>b</b>) and (<b>c</b>) for LLJs at the height of the wind maximum from SODAR data during the investigation period (15° bins).</p>
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<p>(<b>a</b>) Observations for 16 and 17 December 2014. Lower panel: time series of 1-hourly measurements of net radiation (Q, light blue diamonds), and 3-hourly measurements of 2 m-temperature (T) as well as observations of cloudiness in 1/10 (total NT, blue dots, and low clouds, green dots); upper panel: 10 m-wind speed (black line) and direction (black dots), jet speed (green line) and direction (green triangles), and temperature difference between 200 and 40 m (red line, scale as for wind speed). (<b>b</b>)–(<b>d</b>) Time-height cross-sections from SODAR/RASS data of the (<b>b</b>) temperature, (<b>c</b>) wind speed, and (<b>d</b>) wind direction from 14–21 December 2014. SODAR/RASS data are shown as pixels (not interpolated), missing data are grey. All times are in UTC.</p>
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<p>COSMO-CLM (CCLM) simulations with 15 km resolution for 17 December 2014, 0600 UTC: (<b>a</b>) mean sea-level pressure, 2 m-temperature and 10 m-wind vectors (scale in the lower right corner); (<b>b</b>) temperature and wind vectors at 850 hPa. The position of Tiksi is marked by an open circle, vectors are shown every 8th grid point.</p>
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<p>Vertical profiles from SODAR/RASS of the wind speed (<b>left</b>), wind direction (<b>middle</b>) and temperature (<b>right</b>) for 17 December 2014.</p>
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<p>Vertical profiles from radiosondes of the wind speed (<b>left</b>), wind direction (<b>middle</b>) and temperature (<b>right</b>) for 17 December 2014 0000 UTC (black) and 1200 UTC (green) and 18 December 0000 UTC (red). The data of the SODAR/RASS for 1000 UTC are shown as open dots (only every second point). Note that the radiosondes are launched about 1–2 hours before their nominal time.</p>
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<p>(<b>a</b>) Hodograph at the height 100 m for 17 December 2014. (<b>b</b>) Hodograph at the height 300 m for 31 December 2014. (<b>c</b>) Hodograph at the height 300 m for 1 January 2015. Numbers denote the time in UTC, triangles are plotted for every hour.</p>
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<p>(<b>a</b>) Observations for 31 December 2014 and 1 January 2015. Lower panel: time series of 1-hourly measurements of net radiation (Q, light blue diamonds), and 3-hourly measurements of 2 m-temperature (T) as well as observations of cloudiness in 1/10 (total NT, blue dots, and low clouds, green dots); upper panel: 10 m-wind speed (black line) and direction (black dots), jet speed (green line) and direction (green triangles), and temperature difference between 200 and 40 m (red line, scale as for wind speed). (<b>b</b>)–(<b>d</b>) Time-height cross-sections from SODAR/RASS data of the (<b>b</b>) temperature, (<b>c</b>) wind speed, and (<b>d</b>) wind direction from 31 December 2014 to 1 January 2015. SODAR/RASS data are shown as pixels (not interpolated), missing data are grey. All times are in UTC.</p>
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<p>Vertical profiles from SODAR/RASS of the wind speed (<b>left</b>) and wind direction (<b>right</b>) for 1 January 2015.</p>
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<p>Statistics (relative frequencies) for all LLJs of the (<b>a</b>) jet height, (<b>b</b>) jet speed, (<b>c</b>) jet direction and (<b>d</b>) directional shear (difference between the wind direction at the jet core and at a height of 30 m).</p>
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<p>Statistics (relative frequencies) for strong LLJs (exceeding 10 m/s) of the (<b>a</b>) jet height, (<b>b</b>) jet speed, (<b>c</b>) jet direction and (<b>d</b>) directional shear (difference between the wind direction at the jet core and at a height of 30 m).</p>
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<p>Statistics (relative frequencies) for (<b>a</b>) the height of the inversion base and (<b>b</b>) the temperature difference between 200 m and 40 m for all LLJs.</p>
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<p>Statistics for (<b>a</b>) the duration of jet events (absolute frequencies) and for (<b>b</b>) the change in jet direction during the jet events for jets with a duration of at least 3 h (relative frequencies).</p>
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<p>left (<b>a</b>): Scatter plot of the jet direction against jet speed with jet height color coded. Right (<b>b</b>): Scatter plot of the directional shear against jet height with jet speed color coded.</p>
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