Data from: Physical mechanisms of deep convective boundary layer leading to dust emission in the Taklimakan desert
Data files
Apr 15, 2024 version files 11.10 MB
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Fluctuations.mat
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Hodograph.mat
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Meteorol_elements_10m.mat
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Net_radiation.mat
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Radiosonde.mat
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RCS_ceilometer.mat
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README.md
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Tower_profile.mat
Abstract
Deserts play an important role in the climate system, which is closely associated with the emission and transport of dust aerosols. Based on the intensive observation experiment in the Taklimakan Desert, the potential physical processes between the deep convective boundary layer (CBL) and dust emission are revealed in this study. Deep CBL enables the formation of clouds in the late afternoon, leading to significant cooling of surface. Large-scale buoyant coherent structures thereby transform into the mechanical coherent structures confined near the surface. The responses promote the earlier occurrence of low-level jet (LLJ) than in cloudless conditions, which allows the downward transport of LLJ momentum and substantially increases surface wind. Therefore, dust emission is initiated by strong wind at dusk and lasts for several hours. The results are useful to predict dust emissions and improve our understanding of distinctive boundary-layer processes in desert regions.
README: Physical Mechanisms of Deep Convective Boundary Layer Leading to Dust Emission in the Taklimakan Desert
https://doi.org/10.5061/dryad.vt4b8gv06
The intensive atmospheric boundary layer experiment was conducted from 1 to 31 May 2022 at Tazhong meteorological station (39° 00’ N, 83° 40’ E), situated in the hinterland of the Taklimakan Desert. The station is surrounded by shifting sand and dunes. The intensive observations include an 80-m tower, a ceilometer, and the Global Positioning System (GPS) radiosonde. Mean temperature, humidity, and wind were measured at 10 levels (0.5, 1, 2, 4, 10, 20, 32, 47, 63, and 80 m) on the tower. Sonic anemometer (CSAT-3, Campbell Scientific, Inc., USA) turbulence measurement, i.e., 20-Hz wind components and temperature, was obtained at 10 m on the tower. Radiation components were measured using radiometers (Hukseflux, Netherlands) above the surface. The ceilometer (CHM 15k, Lufft, Inc., Germany) receives backscatter signals of clouds and aerosols from the zenith direction, with a spatiotemporal resolution of 15 m × 15 s. Its laser wavelength is 1064 nm. GPS radiosondes were launched at 05:15, 11:15, 17:15, and 23:15 LST (UTC+6 hr) during the observation period, to obtain the profile of temperature, humidity, pressure, wind speed, and wind direction. This study focuses on the cases that the deep CBL is followed by dust emissions at dusk, so three days (i.e., 16 May, 27 May, and 28 May) were selected for the following analysis.
Description of the data and file structure
1. RCS_ceilometer.mat includes the normalized range-corrected signals measured by the ceilometer. For each case/day, the data were sampled every 1 minute from 06:00 to 24:00 LST and every 15 m from the surface to 6,000 m. Variables are named as beta_mmdd, where mm and dd represent month and day, respectively.
2. Net_radiation.mat includes the net radiation measured from 06:00 to 24:00 LST with a resolution of 30 min for each case/day. Variables are named as Rn_mmdd, where mm and dd represent month and day, respectively.
3. Meteorol_elements_10m.mat includes mean air pressure (pres_10m_mmdd, hPa), temperature (ta_10m_mmdd, K), and specific humidity (qv_10m_mmdd, kg/kg) measured at 10 m with a resolution of 30 min, where mm and dd represent month and day, respectively.
4. Fluctuations.mat includes the fluctuations of streamwise wind component (u_fluct_HHMM_mmdd), vertical velocity component (w_fluct_HHMM_mmdd), and potential temperature (t_fluct_HHMM_mmdd) measured at 10 m with a sample frequency of 20 Hz, where HH, MM, mm, and dd represent hour, minute, month, and day, respectively.
5. Radiosonde.mat includes the profile of potential temperature (theta_HHMM_mmdd) and wind speed (ws_HHMM_mmdd) measured by GPS radiosonde, where HH, MM, mm, and dd represent hour, minute, month, and day, respectively. The vertical resolution is 10 m (see the variable named height).
6. Tower_profile.mat includes the profile of temperature (ts_tower_HHMM) and wind speed (ws_tower_HHMM) measured on the 80-m tower (see the variable named levels), where HH and MM represent hour and minute, respectively.
7. Hodograph.mat includes the east-west and north-south wind components, u_80m_mmdd and v_80m_mmdd, respectively, measured at 80 m with a resolution of 30 min, where mm and dd represent month and day, respectively.
Methods
The intensive atmospheric boundary layer experiment was conducted from 1 to 31 May 2022 at Tazhong meteorological station (39° 00’ N, 83° 40’ E), situated in the hinterland of the Taklimakan Desert. The station is surrounded by shifting sand and dunes. The intensive observations include an 80-m tower, a ceilometer, and the Global Positioning System (GPS) radiosonde. Mean temperature, humidity, and wind were measured at 10 levels (0.5, 1, 2, 4, 10, 20, 32, 47, 63, and 80 m) on the tower. Sonic anemometer (CSAT-3, Campbell Scientific, Inc., USA) turbulence measurement, i.e., 20-Hz wind components and temperature, was obtained at 10 m on the tower. Radiation components were measured using radiometers (Hukseflux, Netherlands) above the surface. The ceilometer (CHM 15k, Lufft, Inc., Germany) receives backscatter signals of clouds and aerosols from the zenith direction, with a spatiotemporal resolution of 15 m × 15 s. Its laser wavelength is 1064 nm. GPS radiosondes were launched at 05:15, 11:15, 17:15, and 23:15 LST (UTC+6 hr) during the observation period, to obtain the profile of temperature, humidity, pressure, wind speed, and wind direction. This study focuses on the cases that the deep CBL is followed by dust emissions at dusk, so three days (i.e., 16 May, 27 May, and 28 May) were selected for the following analysis.
The raw data observed from the sonic anemometer were processed over 30-min intervals using EddyPro v7.0.6 (LI-COR Inc., USA) software, to obtain fluctuations and turbulent statistics. The depth of CBL was determined based on GPS radiosondes and ceilometer observations, respectively. For the former, the top of CBL is identified as the peak height of potential temperature gradient, which is more accurate but only available at 11:15, 17:15 LST. For the latter, the top of CBL is defined as the height where the normalized range corrected signals rapidly decrease, accompanied by increased noise. This approach is available throughout the fair-weather daytime.