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  • View in gallery

    (a) The study area, showing the location of the equipment and the location of features and places mentioned in the text. The contours are at 100-m intervals. The yellow line indicates the route followed by the pickup trucks carrying the mobile sensors. The blue line indicates the 1650-m contour that encloses the basin. The rock dumps are 1 km to the north of Sierra Gorda. (b) A general view of the observational site. The map in the lower-right corner indicates the location of the study area within South America. Sierra Gorda (red point) is 100 km east of the sea and 100 km west of the Andes, which reach an altitude of ~5000 m at that latitude. (c) Heights of the temperature and wind sensors.

  • View in gallery

    (a) Cross comparison between the temperature data obtained by the HOBO HB installed in the drone flight location (blue line) and the data of the iMet-XQ sensor mounted on the drone (red points). Each iMet-XQ data point represents a 1-min average of data, with the drone hovering over ground (approximately 1.5 m above ground, in order to adjust to the HOBO installation height). (b) Detailed view. The red line is only to connect the iMet-XQ data points to improve visualization (each point is linked to one flight of the drone, while it was hovering over the ground).

  • View in gallery

    Cross comparison between the temperature data obtained by the different HOBO sensors (blue line) and the data of the iMet-XQ sensors mounted on the pickup trucks (red circles). Each iMet-XQ data point represents a single pass of the pickup truck within 50 m of the HOBO location. (top left) An example of the mobile iMet-XQ measurements included in the cross comparison. For each HOBO location (HOBO H1 in the example figure), all of the measurement points within 50 m of the HOBO location (green points in the figure) are included in the cross comparison.

  • View in gallery

    Data from the grid of temperature sensors located at different heights in and around the Sierra Gorda Valley (according to Fig. 1). To calculate the potential temperature, we used the pressure data from Sierra Gorda weather station and the iMet-XQ sensors deployed during the campaign. HOBOs HA and HB (in blue) are inside the basin. HOBOs H3, H4, H5, and H6 (in red) are outside the basin. HOBOs H1 and H2 are in between. The area between the potential temperature measured at the locations outside and inside the basin is colored in lighter blue to highlight the accumulation of cold air in the lowest areas of the basin during the night. During the coldest night (17 Aug 2017), a difference of 10°C in potential temperature was measured between the coldest location inside the basin (HOBO HB), and the locations outside the basin.

  • View in gallery

    Spatial distribution of potential temperature along the valley, according to the data of iMet-XQ sensors mounted on the pickup trucks: (a) 0400–0500 LT 16 Aug 2017, (b) 0700–0800 LT 17 Aug 2017, (c) 0400–0500 LT 18 Aug 2017, and (d) 0400–0500 LT 19 Aug 2017. The color scale represents the potential temperature (°C). The start and the end of the circuit are labeled.

  • View in gallery

    Wind (a) speed and (b) direction during the campaign according to three ground stations. Ground stations E2 and E1 are at a height of 1965 and 1720 m, respectively, on the slope at the east of the basin. Ground station SG is inside the basin, in Sierra Gorda, at a height of 1626 m. The shaded area indicates the nocturnal period.

  • View in gallery

    Vertical profiles of potential temperature (°C), measured by the iMet-XQ sensor mounted on the drone. Each night several profiles were made, one each hour (there are missing cases, when the drone could not fly because of wind conditions). In each case only the ascending profile is included.

  • View in gallery

    Potential temperature gradient between 0 and 200 m (°C m−1). Each point represents one vertical profile performed by the drone. Each color indicates a different day.

  • View in gallery

    Vertical profiles of potential temperature (°C) illustrating temperature profile evolution during the night. The data were measured by the iMet-XQ sensor mounted on the drone. In each case both the ascending and descending profiles are included.

  • View in gallery

    Basin heat deficit calculated from ground up to 200 m AGL, using the data captured by the iMet-XQ sonde mounted on top of the drone, for each day and hour.

  • View in gallery

    (a) Mean basin heat deficit calculated from ground up to 200 m AGL, using the data captured by the iMet-XQ sonde mounted on top of the drone, for each day. (b) Mean wind speed difference between the stations on the slope at the east of the basin [E1 (1720 m) − E2 (1965 m), in Fig. 1, between 0100 and 0700 LT for each day].

  • View in gallery

    Vertical profile of potential temperature, drone (free air, in blue) vs HOBOs (near surface, in red), for two days: 15 Aug (strongest cold pool) and 19 Aug (weaker cold pool), at two different hours (0330 and 0800 LT). The HOBOs included are HB (1630 m), HA (1636 m), H1 (1650 m), H2 (1700 m), H3 (1798 m), and H4 (1900 m).

  • View in gallery

    Mobile ceilometer measurements. The ceilometer route is included in yellow in the figure. The green and red triangles indicate the start and the end of the route, respectively. The small red arrow indicates the location of the drone profile.

  • View in gallery

    Mobile ceilometer measurements and drone vertical profile of potential temperature. The ceilometer route for each case is included in Fig. 13. The green and red triangles indicate the start and the end of the route, respectively. The red arrow indicates the location of the drone profile. The color scale represents the laser ceilometer backscatter coefficient (arbitrary scale). In (e) there is a large separation between the drone profile location and the ceilometer measurement route.

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Investigation of a Nocturnal Cold-Air Pool in a Semiclosed Basin Located in the Atacama Desert

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  • 1 Meteodata, Santiago, Chile
  • | 2 Departamento de Geofísica, Universidad de Chile, Santiago, Chile
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Abstract

In desert environments, intense radiative cooling of the surface during the night leads to rapid cooling of the adjacent air, resulting in a strong temperature inversion conducive to cold-air-pool formation. In this work observations are analyzed to investigate the structure of a nocturnal cold-air pool inside a semiclosed basin located near Sierra Gorda in the Atacama Desert in Chile and its effect on dust dispersion in the area. The measurement campaign was conducted over a 5-day period (14–19 August) in 2017 and included ceilometer data, vertical profiles of temperature, a grid of fixed ground stations, and mobile temperature sensors. We focus our attention on the conditions during periods of high levels of dust pollution, in order to understand the atmospheric conditions that contribute to these episodes. The analysis of the available data confirms the development of an intense nocturnal cold-air pool, which is reflected in a strong nocturnal potential temperature inversion (18 K in 150 m) and a 30°C diurnal temperature range. A comparison of the vertical distribution of dust and temperature shows that the capping inversion controls the location of the dust cloud. As a consequence, the highest dust concentrations were observed inside the cold pool, below the capping inversion, proving that within the basin the dust is confined to the layer where its source is located.

Current affiliation: Centro de Estudios Científicos, Valdivia, Chile.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JAMC-D-19-0237.s1.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Federico Flores, fed.flores@gmail.com

Abstract

In desert environments, intense radiative cooling of the surface during the night leads to rapid cooling of the adjacent air, resulting in a strong temperature inversion conducive to cold-air-pool formation. In this work observations are analyzed to investigate the structure of a nocturnal cold-air pool inside a semiclosed basin located near Sierra Gorda in the Atacama Desert in Chile and its effect on dust dispersion in the area. The measurement campaign was conducted over a 5-day period (14–19 August) in 2017 and included ceilometer data, vertical profiles of temperature, a grid of fixed ground stations, and mobile temperature sensors. We focus our attention on the conditions during periods of high levels of dust pollution, in order to understand the atmospheric conditions that contribute to these episodes. The analysis of the available data confirms the development of an intense nocturnal cold-air pool, which is reflected in a strong nocturnal potential temperature inversion (18 K in 150 m) and a 30°C diurnal temperature range. A comparison of the vertical distribution of dust and temperature shows that the capping inversion controls the location of the dust cloud. As a consequence, the highest dust concentrations were observed inside the cold pool, below the capping inversion, proving that within the basin the dust is confined to the layer where its source is located.

Current affiliation: Centro de Estudios Científicos, Valdivia, Chile.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JAMC-D-19-0237.s1.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Federico Flores, fed.flores@gmail.com

1. Introduction

Clear nights are a typical condition in the Atacama Desert of Chile. In fact, as stated in Muñoz et al. (2013, p. 2735), the climate of the Atacama Desert is characterized by “low synoptic variability, very infrequent precipitation, extremely clear skies (except near the coast where stratocumulus are common), intense solar radiation, large diurnal thermal amplitudes, strongly sloping topography, and a very arid surface with little vegetation and negligible moisture availability.” As a result, intense radiative cooling of the surface during the night [according to Mattar et al. (2018), the emissivity of the Atacama Desert soil is ~0.95] leads to rapid cooling of the adjacent air (Stearns 1969; Warner 2009), resulting in a strong temperature inversion near the ground (Sheridan et al. 2013; André and Mahrt 1982).

The development of the surface inversion is composed by three main processes (André and Mahrt 1982): first, radiative cooling of the ground surface. Second, shear-generated turbulence leads to downward heat flux to the cooled surface and corresponding upward extension of the cooling. Finally, horizontal advection, particularly over sloped terrain, can modify the local depth of the inversion layer. This leads to the progressive buildup of the nocturnal boundary layer (NBL) where temperature increases with height (Tjemkes and Duynkergke 1989; De Wekker and Whiteman 2006; Krishna et al. 2003; Garratt 1994).

In regions of complex topography, the interaction of airflow with topography modifies the NBL development (Fernando and Weil 2010; Gustavsson et al. 1998). First, as the air close to an inclined surface cools relative to the air at the same elevation away from the slope, gravity induces descending flows (katabatic winds; Skyllingstad 2003; Axelsen 2010). There are references that confirm the existence of such slope flows in Atacama Desert (Muñoz et al. 2013; Jacques-Coper et al. 2015; Muñoz et al. 2018). Second, obstacles (e.g., hills or basins) modify the slope flows, giving the flow a distinctive character that depends on the local geography. In particular, in the case of closed basins or valleys, the lack of an along-valley wind system allows the basin air to cool more rapidly, reaching minimum temperatures lower than those in adjacent valleys (Clements et al. 2003). This leads to the accumulation of cold air at the bottom of the basin, an atmospheric structure known as a cold-air pool (CAP). As reported by Lareau et al. (2013), CAPs can be defined as a topographic depression filled with cold air that occurs when atmospheric processes favor cooling of the air near the surface, warming of the air aloft, or both. Both the stable stratification and the surrounding topography contribute to reinforce the process, preventing the air within the basin from mixing with the atmosphere aloft and favoring air stagnation by the inhibition of lateral displacement of the air, respectively (Lareau et al. 2013).

Because of the particular features that characterize them, and their effect on pollution dispersion, CAPs are responsible for many wintertime and nighttime air-pollution episodes (Clements et al. 2003) and have been a focus of interest for a number of researchers in recent years.

The mechanisms by which cold pools form are studied numerically in Vosper et al. (2014), while nocturnal boundary layer evolution on a slope at the foot of a desert mountain is analyzed in Lehner et al. (2015). Dorninger et al. (2011) and Bigg et al. (2012) studied the meteorological events affecting the CAP. Numerical simulations were used to investigate the meteorological characteristics of the CAP in different locations: the Uintah Basin, Utah (Neemann et al. 2015), the Arizona Meteor Crater (Fritts et al. 2010), and the Salt Lake Valley, Utah (Crosman and Horel 2017). Also, several campaigns were performed to understand the effect that the CAP has over pollution (Chemel et al. 2016; Sheridan et al. 2013; Whiteman et al. 2014; Silcox et al. 2012). Recently, a network of measurements was used to study CAP distribution/extent in the northeast of the Iberian Peninsula (Pagès et al. 2017; Miró et al. 2017). The temperature structure of the CAP inside Arizona Meteor Crater is investigated by Whiteman et al. (2008, 2010), while large temperature fluctuations due to cold-pool displacement are studied in Jeglum et al. (2017). Multiday (persistent) CAPs that affect air quality in Utah’s Salt Lake Valley are the focus of the Persistent Cold-Air Pool Study (PCAPS), summarized by Lareau et al. (2013). The results of using a ceilometer to continuously measure aerosol-layer characteristics during PCAPS are included in Young and Whiteman (2015). Whiteman et al. (1999) studied the wintertime evolution of the temperature inversion in the Colorado Plateau Basin. The influence of topography and ambient stability on the characteristics of CAPs is investigated numerically in Katurji and Zhong (2012), while CAPs over complex terrain in the United Kingdom are investigated in the Cold-Air Pooling Experiment (COLPEX) (Jemmett-Smith 2014). CAP breakup by turbulent erosion is studied in Zhong et al. (2003).

The present work describes a short field campaign in a small semiclosed basin in the Atacama Desert. The basin chosen, in the vicinity of Sierra Gorda, Chile (1600 m MSL), is surrounded by mining activity. Numerical simulations (WRF Model) and satellite data (MODIS temperature) suggested that a nocturnal cold pool occurs in the site. The campaign was designed to determine whether this is the case, by performing spatially distributed measurements and observing the evolution of the vertical temperature structure in the basin. Since an additional objective was to determine the effect that the CAP has on dust dispersion in the area, a mobile, vehicle-mounted laser ceilometer was used to continuously measure aerosol-layer characteristics. One of the goals was to assess the utility of the mobile ceilometer in providing spatially distributed data and test instrumentation for drone vertical profiles measurements. To our knowledge, this is the first time that a partial man-made CAP is documented.

Following the introduction in section 1, in section 2 we introduce the field campaign, describing the topography of the site and general background relative to the cold-pool development in the area. This is followed by a detailed description of the equipment used, the processing of the data and the method used during the campaign. Section 3 presents the analysis results, including several validation experiments performed comparing the measurements of different sensors. This is followed in section 4 by conclusions.

2. Site and measurements

a. Site

A map describing the location of the Sierra Gorda valley is presented in Fig. 1 (1200 km north of Santiago, the capital of Chile, and 100 km east of the coast). Figure 1 shows the topography of the study region and the locations of the equipment. This section of valley includes one small town, Sierra Gorda (population of ~1000). The valley floor elevation decreases gradually down valley from 2100 m at location H6 to 1600 m at Sierra Gorda over a distance of 23 km, yielding an average slope of 2.2% between these two points. The basin is surrounded by higher terrain of approximately 200-m relief at the north and west. Vegetation in the basin is nonexistent. There are several possible aerosol source locations in the area, in particular, some large waste rock dumps, and obviously, the desert soil (an aerial video is included in the online supplemental material).

Fig. 1.
Fig. 1.

(a) The study area, showing the location of the equipment and the location of features and places mentioned in the text. The contours are at 100-m intervals. The yellow line indicates the route followed by the pickup trucks carrying the mobile sensors. The blue line indicates the 1650-m contour that encloses the basin. The rock dumps are 1 km to the north of Sierra Gorda. (b) A general view of the observational site. The map in the lower-right corner indicates the location of the study area within South America. Sierra Gorda (red point) is 100 km east of the sea and 100 km west of the Andes, which reach an altitude of ~5000 m at that latitude. (c) Heights of the temperature and wind sensors.

Citation: Journal of Applied Meteorology and Climatology 59, 12; 10.1175/JAMC-D-19-0237.1

Previous numerical simulations (WRF) and satellite data (MODIS temperature), not included in this work, suggested that a nocturnal cold pool occurs in the site.

b. Measurements

Instrument locations are shown in Fig. 1. Further details on the sites, instruments, and instrument characteristics are provided in Table 1. The instrumentation was installed primarily on the eastern and northern slopes of the valley.

Table 1.

Instruments. Here, ID indicates site identifier.

Table 1.

The instrumentation included ceilometer data, vertical profiles of temperature (drone), a grid of fixed ground stations, and several mobile sensors.

1) Ground stations

Two ground stations were installed exclusively for the campaign (E1 and E2 in Fig. 1), while another, SG, was already available in Sierra Gorda village. Its data, belonging to a mining company, were available to us to download.

The stations E1 and E2 included particulate matter sensors (Met One Instruments, Inc., E-Sampler) and wind measurements (Met One 034B Wind Sensor anemometer and wind vane). Their locations follow the main slope of the topography to the east of Sierra Gorda. SG station included meteorological variables [temperature T, pressure P, relative humidity (RH, or H), and wind], and particulate matter measurements. Its location is at the basin floor. (Sensors details are available in Table 1.)

2) Mesh of temperature probes

A grid of 8 HOBO temperature sensors (Onset Computer Corp. HOBO UA-002; details are in Table 1) were installed inside and outside the basin (eastern slope). As depicted in Fig. 1, HOBO HA and HB are at the bottom of the eastern slope (inside the basin defined by the 1650-m contour), while HOBO 1 is at the edge of the basin, and the rest are distributed up the eastern slope.

3) Drone vertical profiles

Three drones were used during the campaign: the DJI Co. Phantom 4 Pro Plus, DJI Inspire 2, and UAV Systems International, Inc., Tarot X6. However, because of the harsh desert conditions and to simplify the nocturnal measurements, almost all of the vertical profiles were performed using the small and simple-to-fly Phantom 4 Pro Plus. The sensor used was the InterMet Systems, Inc., iMet-XQ UAV (details in Table 1), mounted on top of the Phantom drone. During each night of the campaign (from 0300 to 0900 LT), a 300-m vertical profile was performed every hour, if meteorological conditions allowed it (wind below 10 m s−1).

4) Mobile ceilometer

A laser ceilometer, originally produced to determine height estimates of cloud bases, is designed to sense the presence of cloud particles via their backscattering cross section (Young and Whiteman 2015). Because particulate aerosols also have backscatter cross sections, their presence can also be detected by ceilometers (Emeis 2011).

A Vaisala, Inc., CL31 laser ceilometer was used. McKendry et al. (2009) showed that, under clear-sky conditions (conditions that usually apply to the Atacama Desert), the CL31 is able to “detect detailed aerosol layer structure (such as fire or dust plumes) in the lower troposphere.” We used a computer program written in the Python programming language to decode the ceilometer backscatter profiles to obtain backscatter coefficients.

To perform spatially distributed measurements, we implemented a vehicle-mounted system composed of the following components:

  • a ceilometer Vaisala CL31 mounted in the pickup bed of a pickup truck,

  • a battery bank,

  • an inverter to power the ceilometer using the battery bank,

  • a GPS system to retrieve accurate location data,

  • an iMet-XQ sounding system to retrieve temperature, pressure, and humidity,

  • a particulate matter sensor (Met One E-Sampler), and

  • a laptop computer running a Python code to decode and store ceilometer backscatter information and GPS data.

The whole system allowed us to obtain horizontally distributed vertical profiles of the ceilometer backscatter data, following the route depicted in Fig. 1 (yellow line). For a detailed review of CL31 operating features refer to Kotthaus et al. (2016), and Muñoz et al. (2020) is an example of its use as a mobile sensor.

5) Mobile sensors mounted on two pickup trucks

To retrieve horizontally distributed data, we mounted iMet-XQ sensors in two pickup trucks. One of the trucks was the one carrying the ceilometer and a particulate matter sensor (Met One E-Sampler). The other was also equipped with a handheld particle counter (Met One AEROCET 531S). Both pickup trucks followed the route included in yellow in Fig. 1 during each night of the campaign. Mobile thermal mappings were used in several studies, and they are found to be useful for analyzing the thermal pattern in complex terrain (Gustavsson et al. 1998).

3. Results and discussion

a. Verification of nocturnal cold-pool occurrence

Because of the multiplatform feature of the campaign implementation (fixed and mobile sensors), our first step in analyzing the results was to perform a cross validation between the different sensors. In particular, to account for its importance for the cold-pool characterization, we opted to focus our attention on comparing the HOBO (fixed) and iMet-XQ (mobile) temperature data.

Figure 2 compares the temperature data obtained by the HOBO HB (drone flight location, Fig. 1) and the data of the iMet-XQ sensor mounted on the drone. Each iMet-XQ data point represents a 1-min average of data, with the drone hovering over ground (approximately 1.5 m above ground, in order to adjust to the HOBO installation height; the iMet-XQ sonde was mounted on top of the drone in order to prevent turbulence below the drone while hovering). As seen in Fig. 2, there is good agreement between the HOBO and the iMet-XQ data [RMSE = 0.71°C; mean bias error (MBE) = 0.28°C; HOBO temperature is higher than the iMet-XQ data]. A closer look (Fig. 2b) reveals that the iMet-XQ readings are usually ahead of those of the HOBO. This can be easily explained by the response time of each sensor (10 min for HOBO, and 2 s for iMet-XQ; see Table 1).

Fig. 2.
Fig. 2.

(a) Cross comparison between the temperature data obtained by the HOBO HB installed in the drone flight location (blue line) and the data of the iMet-XQ sensor mounted on the drone (red points). Each iMet-XQ data point represents a 1-min average of data, with the drone hovering over ground (approximately 1.5 m above ground, in order to adjust to the HOBO installation height). (b) Detailed view. The red line is only to connect the iMet-XQ data points to improve visualization (each point is linked to one flight of the drone, while it was hovering over the ground).

Citation: Journal of Applied Meteorology and Climatology 59, 12; 10.1175/JAMC-D-19-0237.1

A second cross validation was performed by comparing the temperature data of the eight fixed HOBO sensors and the data of the iMet-XQ mounted on the pickup trucks. Figure 3 (top left) shows an example of the procedure used: for each HOBO sensor (HOBO 1 in the example) we retrieved the data of the iMet-XQ on the pickup trucks (green points in top-left panel) only at a distance of less than 50 m away from the HOBO location. Figure 3 includes the results of this comparative testing for the eight HOBOs. As seen in the figure, in each case both data agree very well (RMSE = 0.25°C; MBE = 0.03°C).

Fig. 3.
Fig. 3.

Cross comparison between the temperature data obtained by the different HOBO sensors (blue line) and the data of the iMet-XQ sensors mounted on the pickup trucks (red circles). Each iMet-XQ data point represents a single pass of the pickup truck within 50 m of the HOBO location. (top left) An example of the mobile iMet-XQ measurements included in the cross comparison. For each HOBO location (HOBO H1 in the example figure), all of the measurement points within 50 m of the HOBO location (green points in the figure) are included in the cross comparison.

Citation: Journal of Applied Meteorology and Climatology 59, 12; 10.1175/JAMC-D-19-0237.1

By virtue of the results mentioned above, and considering that there is no reason to believe (or assume) that the iMet-XQ measurements are good only near the HOBO locations, and not far from them, it is possible to conclude that the mobile iMet-XQ data are useful to describe the temperature distribution during the campaign.

Data from the grid of temperature sensors (located at different heights in and around the Sierra Gorda Valley; Fig. 1) document the occurrence of a well-defined nocturnal CAP inside the basin (Fig. 4). To calculate the potential temperature included in Fig. 4 we used the pressure data from Sierra Gorda weather station and the iMet-XQ sensors deployed during the campaign. While during the day the potential temperature measured at the eight locations is similar (well mixed diurnal boundary layer; Garratt 1994), during the night the two locations inside the basin (HOBO HA and HB; blue lines in Fig. 4) show significantly lower temperatures. In this sense, as stated by Whiteman et al. (1999), the blue shading between the potential temperature time series indicates the occurrence of a basin potential temperature inversion, while the vertical distance between curves provides a measure of the inversion strength. During the coldest night (17 August 2017), a difference of 10°C in potential temperature was measured between the coldest location inside the basin (HOBO HA), and the locations outside the basin. The graph also shows that the night with the least cooling was the 19 August 2017, and the occurrence of temperature oscillations inside the CAP (at least two local minima each night).

Fig. 4.
Fig. 4.

Data from the grid of temperature sensors located at different heights in and around the Sierra Gorda Valley (according to Fig. 1). To calculate the potential temperature, we used the pressure data from Sierra Gorda weather station and the iMet-XQ sensors deployed during the campaign. HOBOs HA and HB (in blue) are inside the basin. HOBOs H3, H4, H5, and H6 (in red) are outside the basin. HOBOs H1 and H2 are in between. The area between the potential temperature measured at the locations outside and inside the basin is colored in lighter blue to highlight the accumulation of cold air in the lowest areas of the basin during the night. During the coldest night (17 Aug 2017), a difference of 10°C in potential temperature was measured between the coldest location inside the basin (HOBO HB), and the locations outside the basin.

Citation: Journal of Applied Meteorology and Climatology 59, 12; 10.1175/JAMC-D-19-0237.1

b. Cold-pool location

To analyze the horizontal distribution of the cold pool we used the data from the iMet-XQ sensors mounted on the pickup trucks to calculate the potential temperature distribution along the valley. Figure 5 shows four circuits (0400–0500 LT 16 August 2017, 0700–0800 LT 17 August 2017, 0400–0500 LT 18 August 2017, and 0400–0500 LT 19 August 2017, respectively). In all figures, the start and the end of each circuit is included. As expected, in all days the coldest area is located in the lowest area inside the basin (to the north inside the blue line in Fig. 1). The measurement path took one hour to cover, which corresponds to approximately 2°C cooling in open, flat terrain (Gustavsson et al. 1998). As this cooling is relatively small when compared with the cooling in the basin, no correction has been made in the figures for temperature change during the time that it took to cover the route (although since cooling is not linear this is a rather crude estimation). Also, the measurements were made during the second half of the night when the CAP was well established and did not change very much. The temperature difference between the warmest and coldest areas along the measuring route can amount to 15°C. As it will be shown in next sections, this horizontal gradient agrees with the vertical gradient inside the basin. The temperature distribution also agrees with the one sensed by the HOBO (in general, HA is cooler than HB). This is especially evident on 17 August 2017. The inversion location also matches the altitude of the CAP detected by the drone measurements (see section 3c). From the potential temperature distribution, it is possible to estimate the extension of the cold pool: ~10 km north–south, and ~3 km west–east.

Fig. 5.
Fig. 5.

Spatial distribution of potential temperature along the valley, according to the data of iMet-XQ sensors mounted on the pickup trucks: (a) 0400–0500 LT 16 Aug 2017, (b) 0700–0800 LT 17 Aug 2017, (c) 0400–0500 LT 18 Aug 2017, and (d) 0400–0500 LT 19 Aug 2017. The color scale represents the potential temperature (°C). The start and the end of the circuit are labeled.

Citation: Journal of Applied Meteorology and Climatology 59, 12; 10.1175/JAMC-D-19-0237.1

Figure 6a includes the wind speed during the campaign according to three ground stations (see Fig. 1). Ground stations E2 and E1 are at a height of 1965 and 1720 m, respectively, on the slope at the east of the basin. Ground station SG is inside the basin, in Sierra Gorda, at a height of 1626 m. In each case the shaded area indicates the nocturnal period. There is a clear difference between the wind on the slope at the east of the basin (stations E2 and E1), and the wind inside the basin (station SG): the nocturnal wind on the slope is intense (~10 m s−1) while the atmosphere inside the basin is calm (generally < 3 m s−1). The wind directions included in Fig. 6b indicate that the diurnal cycle controls the wind direction on the eastern slope: during the day the air ascends, following the terrain slope (northwest wind), while during the night a more-intense flow descends (southeast wind). This diurnal cycle is not recognizable inside the basin (Sierra Gorda station), possibly because the descending flow is unable to completely penetrate to the basin floor. As will be seen further on, E1 and E2 are outside the lower layer of the cold pool (both are above an intense capping inversion). It can be argued that this strong capping inversion interrupts the airflow that descends down the eastern slope.

Fig. 6.
Fig. 6.

Wind (a) speed and (b) direction during the campaign according to three ground stations. Ground stations E2 and E1 are at a height of 1965 and 1720 m, respectively, on the slope at the east of the basin. Ground station SG is inside the basin, in Sierra Gorda, at a height of 1626 m. The shaded area indicates the nocturnal period.

Citation: Journal of Applied Meteorology and Climatology 59, 12; 10.1175/JAMC-D-19-0237.1

c. Cold-pool structure

Figure 7 includes the vertical profiles of potential temperature measured by the iMet-XQ sensor mounted on the drone. As seen in the figure, in all cases the potential temperature increases between the ground and 200 m. In fact, if we calculate the potential temperature gradient, between 0 and 200 m, for each profile, its value remains between 0.050° and 0.100°C m−1 (Fig. 8). This value reveals a highly stable atmosphere (Garratt 1994; Whiteman et al. 2010).

Fig. 7.
Fig. 7.

Vertical profiles of potential temperature (°C), measured by the iMet-XQ sensor mounted on the drone. Each night several profiles were made, one each hour (there are missing cases, when the drone could not fly because of wind conditions). In each case only the ascending profile is included.

Citation: Journal of Applied Meteorology and Climatology 59, 12; 10.1175/JAMC-D-19-0237.1

Fig. 8.
Fig. 8.

Potential temperature gradient between 0 and 200 m (°C m−1). Each point represents one vertical profile performed by the drone. Each color indicates a different day.

Citation: Journal of Applied Meteorology and Climatology 59, 12; 10.1175/JAMC-D-19-0237.1

To analyze the vertical structure of the atmosphere inside the basin, and its evolution during the night, Fig. 9 includes the vertical profile of potential temperature measured by the iMet-XQ sensor mounted on the drone during the early morning of the 17 August 2017 (at 0330, 0600, and 0800 LT). In each case both the ascending and descending profiles are included. The top of the artificial rock dumps that surround Sierra Gorda is included as a reference in the graph (approximately 75 m above ground).

Fig. 9.
Fig. 9.

Vertical profiles of potential temperature (°C) illustrating temperature profile evolution during the night. The data were measured by the iMet-XQ sensor mounted on the drone. In each case both the ascending and descending profiles are included.

Citation: Journal of Applied Meteorology and Climatology 59, 12; 10.1175/JAMC-D-19-0237.1

The vertical structure of the CAP shows the following features (each letter indicates one profile section as labeled in Fig. 9):

  • Profile section A—Temperatures near ground are several degrees lower than above, confirming that in the Sierra Gorda basin the intense radiative cooling of the surface during the night leads to rapid cooling of the adjacent air (Stearns 1969; Warner 2009), resulting in a strong temperature inversion near the ground (Sheridan et al. 2013; André and Mahrt 1982). The intense surface-based inversion at the basin floor is seen in all the drone profiles, and is confined below 10 m. In fact, the temperature gradient near ground is so strong that in several cases the temperature measured by the iMet-XQ on the drone while it was on the ground was several degrees Celsius lower than the temperature measured by the HOBO at 1.5 m above ground. Late in the early morning (0800–0900 LT), the surface-based inversion disappears, probably due to the rising sun (0804 LT).

  • Profile section B—The surface-based, intense, inversion sublayer on the basin floor is surmounted by a near-isothermal layer. This layer extends into the top of the artificial rock dumps that surround Sierra Gorda (70–80 m high). Basins are generally characterized by inversions with distinct tops whose altitudes are found below the surrounding topography (Whiteman et al. 1999). In the case of the Meteor Crater Experiment (METCRAX; Whiteman et al. 2008), this results in the development of a deep near-isothermal layer in the upper 75%–80% of the crater atmosphere overlying an intense near-surface inversion, similar to that observed here. This structure persisted through the night even as the atmosphere continued to cool, as seen in Whiteman et al. (2010). Vosper et al. (2014) suggests that, under clear-sky conditions, the reduction of turbulent mixing of warmer air from aloft due to the sheltering mechanism of the surrounding topography leads to a higher rate of cooling of the air close to the radiatively cooling surface. The spatial differences in cooling rates lead to localized cold pools. As previously discussed (in section 3b, Fig. 6), in this case the flow that descends the eastern slope is unable to completely penetrate to the basin floor, probably due to the intense capping inversion above the lower layer of the cold pool. The trapping of air in the basin, caused by the intense stability of the capping inversion (Zhong et al. 2003), will prevent turbulent mixing of the cold surface air with the warmer air above, thereby leading to a higher cooling rate near the surface (Gustavsson et al. 1998). It is highly probable that the cold pool near Sierra Gorda formed as a result of reduced downward turbulent heat flux when the flow within the valley was sheltered relative to that outside. However, due to the lack of vertical wind profiles, this hypothesis cannot be proved in this case (although the comparison between the basin and slope wind stations in Fig. 6 certainly supports this hypothesis). Whiteman et al. (1999) suggests that a continuous destabilization process must be present in the basin atmosphere in order to explain the persistence of the deep near-isothermal layer. Because winds were light in the basin atmosphere, we reject the hypothesis that the destabilization was produced by vertical shear of horizontal winds within the basin, as seen in Whiteman et al. (2010). It is probable that the weak north wind measured by the station at the bottom of the basin (HA) during the night represents a regional cold-air drainage flow that arrives from higher terrain to the north and reaches Sierra Gorda, at the exit of the basin running from about 12 km to its north (main south–north valley). Intrusion of air from such flows into the basin atmosphere and its horizontal mixing through the nocturnal inversion could destabilize the basin atmosphere, producing a near-isothermal atmosphere inside the basin, as seen in METCRAX (Whiteman et al. 2010). This argument is supported by the fact that temperatures to the north of the basin are colder than at the south. As in METCRAX (Whiteman et al. 2010), we hypothesize then that the isothermal atmosphere inside the crater is caused by the detrainment and horizontal mixing into the crater atmosphere of cold air that comes over the rim of the basin and flows down the inner sidewalls. This conceptual model (Haiden et al. 2011) states that cold air that forms on the slightly sloping mesoscale plain outside the basin interacts with the basin topography. Cold air flows down the inner sidewalls of the basin and leads to a cooling pattern that is different from what would be expected from purely local cooling. Just like the Meteor Crater, Sierra Gorda is located on a regional-scale sloping surface that puts it in the path of a regional-scale downslope cold-air drainage flow. During the course of the night, the near-isothermal layer becomes more stable, mainly as a result of a decrease in near ground temperatures (below 30 m).

  • Profile section C—In general, a CAP is capped along ridge heights by a temperature inversion with varying intensity and depth (Katurji and Zhong 2012). In this particular case, because of the more rapid cooling inside the basin compared to that of the atmosphere above it, an intense temperature jump developed at the level of the surrounding rock dumps, as seen in Whiteman et al. (2008). The capping inversion is intense and fully developed early in the morning (0330 LT in Fig. 9), with a strong potential temperature inversion (10°C in 20 m). Also, as seen in the METCRAX II Field Experiment (Lehner et al. 2016), early in the morning the temperature jump location coincides exactly with the surrounding topography (rock dumps). Later in the morning (0600 and 0800 LT in Fig. 9), the capping inversion becomes less intense, probably caused by wind-induced mixing.

  • Profile section D—A second near-isothermal layer develops above the capping inversion. This layer extends from the top of the artificial rock dumps that surround Sierra Gorda (70–80 m high) to 180 m above ground. Neff and King (1989) proposed that the pooling inside a basin resulted in elevated drainage flows entering the basin, several hundred meters above the surface from major tributary drainages. These elevated flows overlay a stronger surface-based inversion and lighter winds. Because of the lack of supporting instrumentation to measure vertical wind profiles, we were not able to determine if the drainage flow is responsible for this layer. However, despite our lack of data, the drone flying attitude (inertial measurements) showed us the existence of more intense winds above the capping inversion. During the course of the night, this second near-isothermal layer becomes more stable, mainly due to a decrease in near-ground temperatures and a less-intense capping inversion (the inversion actually seems to descend slightly). It becomes clear that in fact the layering of the atmosphere becomes generally more indistinct, as a result of vertical mixing, in part because of the shear between the differentiated vertically stacked flows, and the accumulation of drainage flows below.

  • Profile section E—A second capping inversion develops above the second near-isothermal layer. In this case, the temperature jump is much less intense than in the first inversion: only 3°C in 20 m. However, it is patently clear that the temperature jump breaks the temperature profile, particularly late in the morning (0800 LT in Fig. 9). This second capping inversion is even more evident on other days (for instance, 15 August in Fig. 7). Despite our lack of understanding of the details of the mechanisms generating this second capping inversion it is clear that the surrounding topography may play an important role: the natural hills surrounding Sierra Gorda are 150–200 m high.

  • Profile section F—above the second capping inversion, a new near-isothermal layer develops. The air temperature at this height hardly change along the night (see Fig. 7), suggesting that the profile is probably reaching the residual layer (Stull 1988).

As stated in Whiteman et al. (1999), “a basin potential temperature inversion is a surface-based layer of potentially cold air or, stated alternately, a surface-based layer in which potential temperature increases with height.” To make a quantitative assessment of the inversion evolution in the Sierra Gorda basin, we will use the heat deficit in the basin inversion, defined by Whiteman et al. (1999) as the heat Q required to destroy an inversion of depth h:
Q=cp0hρ(z)[θhθ(z)]A(z)dz(J),
where ρ is air density, cp is the specific heat of air at constant pressure, θ(z) is potential temperature within the inversion, θh is the potential temperature at the top of the pool, A(z) is the horizontal area of the integration volume, and z is height. The heat deficit of the pool Q is the heat that must be added to obtain an atmosphere with a height-independent potential temperature θh. Temporal changes in the heat deficit calculated at regular intervals in this way provide a means of determining if the basin inversion is growing, being maintained, or being destroyed.

The basin heat deficit was calculated from ground up to 200 m AGL, using the data captured by the iMet-XQ sonde mounted on top of the drone (highest point of the drone profile with data for each day and hour). Because the height of the volume considered is small and a detailed topography of the area is not available, we assumed a fixed horizontal area of integration and calculated a normalized heat deficit, with units of joules per square meter [the heat (energy) per unit area, in joules per meter squared; Chemel et al. 2016]. The result is plotted in Fig. 10. For each day, the inversion buildup is before the beginning of the measurements. For three days, 16, 17, and 18 August, there are no big changes in the heat deficit during the night. However, during 15 and 19 August, the heat deficit increases strongly during the night. Vertical profiles in Fig. 7 allow us to speculate that during the 15 and 19 August the cold pool was not yet fully developed when the drone measurements began, probably due to large-scale conditions not included in this work.

Fig. 10.
Fig. 10.

Basin heat deficit calculated from ground up to 200 m AGL, using the data captured by the iMet-XQ sonde mounted on top of the drone, for each day and hour.

Citation: Journal of Applied Meteorology and Climatology 59, 12; 10.1175/JAMC-D-19-0237.1

From the data it is also clear that during the campaign the basin heat deficit was stronger during the first day (15 August) and weaker during the last (19 August). The weakening of the heat deficit during the campaign, and consequently that of the cold-pool intensity, is made evident in Fig. 11a, which includes the mean heat deficit during the second half of each night from 0300 to 0900 LT. This is also patent in the intensity of the cooling at the bottom of the basin (Fig. 4). This link seems a consequence of the bond between the cooling of the internal atmosphere of the basin (vertical profile), and the temperature at the bottom. Also, Fig. 11b, which includes the mean wind speed difference between the stations on the slope to the east of the basin [E1 (1720 m) − E2 (1965 m), in Fig. 1, between 0100 and 0700 LT for each night], shows an interesting link between the airflow in the slope and the intensity of the cold pool. As the mean heat deficit decreased progressively over the observing period, the magnitude of the wind speed difference increases. Further analysis is needed to better understand the precise mode of action of this relationship, especially considering our lack of vertical wind profiles. High-resolution numerical simulations could be a good alternative.

Fig. 11.
Fig. 11.

(a) Mean basin heat deficit calculated from ground up to 200 m AGL, using the data captured by the iMet-XQ sonde mounted on top of the drone, for each day. (b) Mean wind speed difference between the stations on the slope at the east of the basin [E1 (1720 m) − E2 (1965 m), in Fig. 1, between 0100 and 0700 LT for each day].

Citation: Journal of Applied Meteorology and Climatology 59, 12; 10.1175/JAMC-D-19-0237.1

To investigate the layering perpendicular to the slopes, as well as the vertical layering discussed, it is useful to compare the “pseudoprofiles” derived from near-surface temperatures by the surface measurements (HOBOs) and the “free air” profiles at the same height taken using the drone. Figure 12 includes this comparison for two days: 15 August (strongest cold pool) and 19 August (weakest cold pool), at two different hours (0330 and 0800 LT). Below 1700 m, inside the first layer of the cold pool, both temperatures coincide almost in every case (even in the cases not included in the figure), except at 0330 LT 19 August. This may be a positive indication that this day, when the basin heat deficit was the weakest, the cold pool inside the basin developed lately, probably due to synoptic effects (clouds, humidity, etc.). The intense temperature gradient perpendicular to the slope below 1700 m in that case may indicate that the bottom of the basin was still rapidly cooling at that hour, with cold air in the slope entering into the warmer atmosphere inside the basin. Late in the morning (0800 LT), both profiles coincide below 1700 m. In all cases, the pseudoprofile of temperature measured by the HOBOs above 1700 m is almost neutral, with minimal changes in potential temperature. This may indicate a well-mixed layer close to the slope surface outside the lower layer of the cold pool. The capping inversion is not distinguishable near the surface of the slope. High-resolution numerical simulations could potentially elucidate the structuring and transport processes in this configuration.

Fig. 12.
Fig. 12.

Vertical profile of potential temperature, drone (free air, in blue) vs HOBOs (near surface, in red), for two days: 15 Aug (strongest cold pool) and 19 Aug (weaker cold pool), at two different hours (0330 and 0800 LT). The HOBOs included are HB (1630 m), HA (1636 m), H1 (1650 m), H2 (1700 m), H3 (1798 m), and H4 (1900 m).

Citation: Journal of Applied Meteorology and Climatology 59, 12; 10.1175/JAMC-D-19-0237.1

d. Effect on dust dispersion

The effect that the cold-pool structure has over the dust dispersion can be studied by comparing the data retrieved by the ceilometer and the vertical profiles of temperature captured by the iMet-XQ sonde mounted on top of the drone. Figure 13 includes the ceilometer route for each day, while Fig. 14 shows the vertical profile of potential temperature on the left (temporally at the closest hour to the passage of the pickup truck with the equipment), and the laser ceilometer backscatter coefficient on the right, for each route in Fig. 13.

Fig. 13.
Fig. 13.

Mobile ceilometer measurements. The ceilometer route is included in yellow in the figure. The green and red triangles indicate the start and the end of the route, respectively. The small red arrow indicates the location of the drone profile.

Citation: Journal of Applied Meteorology and Climatology 59, 12; 10.1175/JAMC-D-19-0237.1

Fig. 14.
Fig. 14.

Mobile ceilometer measurements and drone vertical profile of potential temperature. The ceilometer route for each case is included in Fig. 13. The green and red triangles indicate the start and the end of the route, respectively. The red arrow indicates the location of the drone profile. The color scale represents the laser ceilometer backscatter coefficient (arbitrary scale). In (e) there is a large separation between the drone profile location and the ceilometer measurement route.

Citation: Journal of Applied Meteorology and Climatology 59, 12; 10.1175/JAMC-D-19-0237.1

At the beginning of the cold-pool episode (Fig. 14a, 0850–0900 LT 15 August 2017), the vertical profile of potential temperature shows three clear layers, defined by two strong capping inversions. However, the backscatter from the ceilometer shows no dust within the basin. This counterexample is important, because while it may seem obvious, it demonstrates that the vertical structure of the cold pool is not directly influenced by the dust distribution above the basin.

In all other cases, the dust seems to be trapped inside the layers defined by the capping inversions: the dust distribution sensed by the ceilometer matches the changes in the vertical profile of temperature measured by the drone (capping inversions). In general, the dust seems to be a maximum just under the second capping inversion. There also seems to be dust trapped in the lowest layer below 1675 m, with a dust free area in the deep capping inversion in between. Dust concentration drops away in the top layer but is still relatively significant.

This pattern of the nonlocally sourced dust cloud lying above the first capping inversion, and below the second if present, appears to carry through to the figures from 17 August, with the exception of Fig. 14f, where a third capping inversion appears in between the two, corresponding to the base of the dust layer. In Fig. 14e the dust is distributed in two well defined layers: one above the first capping inversion (~1700 m), and one below it. This lower dust layer, also present in Fig. 14f, is only present in the lower area of the basin and can be attributed to local sources of dust.

The same behavior previously discussed, with the capping inversion controlling the location of the dust cloud, is present in Figs. 14g–i (18 August), where the dust layer seems to coincide roughly with an elevated inversion at around 1800 m. In this case the effect is even more evident, because the dust layer evolves according to the evolution of the capping inversion: early in the morning (Fig. 14g), the capping inversion is low (1700 m), and the dust layer is low located above it. One hour later (Fig. 14h), the capping inversion is higher (1800 m), and the layer is higher (although not the temperature profile nor the dust distribution are well defined). Three hours later (Fig. 14i), both the capping inversion and the dust distribution are well defined and high.

Figures 14c and 14i show that the dust cloud does not ascend following the eastern slope of the basin, probably due to the intense descending flows sensed near the slope surface. We suppose that the dust sources are the rock dumps, of different heights, that are inside the basin and the mining activity around Sierra Gorda, in particular, at the bottom of the basin. It accumulates during the day from local basin sources and then subsequently confined by the layers that form due to the vertical nocturnal structuring of temperature.

This work also includes a set of animations that illustrates the entire operation of the mobile ceilometer, along with the dust distribution around Sierra Gorda (see the online supplemental material). The height of the data in the animations is 1000 m. One of the interesting features of these videos is the clear visualization of dust distribution: almost all the dust is at the bottom of the basin, and no dust is sensed in the slope. Also, dust concentration varies from hour to hour, and different layers are recognizable, as described previously in this work.

To summarize, comparing the data retrieved by the ceilometer and the vertical profiles of temperature proves that the cold-pool vertical structure has a clear effect on the dust distribution inside the basin. In particular, the capping inversion inhibits both the descent and ascent of the dust cloud. This effect is particularly important because it implies that the dust inside the atmosphere of the basin remains at the layer where its source is located. It also implies that the knowledge of the vertical profile of temperature is useful to predict the dust distribution inside the basin (see ceilometer route, Fig. 13).

4. Conclusions

The analysis of the data collected as part of the Sierra Gorda field campaign during 15–19 August 2017 led to the following main conclusions.

  • The iMet-XQ sensor proved to be very useful to generate spatially distributed data. Both of the configurations used, mounted on top of the drone for vertical profiles, or mounted in the pickup trucks for horizontal transects, generated thermal mappings that proved to be useful to describe the temperature distribution during the campaign.

  • The mobile system implemented to perform spatially distributed measurements with the Vaisala CL31 laser ceilometer also proved to be very useful to generate spatially distributed dust mappings. Over 1000 km of measuring routes were done during the campaign, without interruptions, errors, or malfunctions.

  • The use of drones allowed us to perform vertical profiles of air temperature in an easy and cost-effective way.

  • The lack of supporting instrumentation to measure vertical wind profiles limited our ability to understand the drainage flows affecting the basin, and the turbulent exchanges between the different layers inside the basin atmosphere.

  • The data from the grid of temperature sensors allowed us to prove the occurrence of a well-defined nocturnal CAP inside the basin. While during the day the potential temperature measured at all the locations was similar (well mixed during the diurnal boundary layer), during the night the locations inside the basin showed significantly lower potential temperatures (during the coldest night, a difference of 10°C in potential temperature was measured between the coldest location inside the basin and the locations outside the basin).

  • The data of the iMet-XQ sensors mounted on the pickup trucks showed that the cold pool is located inside the 1650 m contour that closes the basin around Sierra Gorda. In particular, the coldest area of the pool is located at the north of the basin. The temperature difference between the warmest and coldest areas along the measuring route can amount to 15°C. During the night there are also clear differences in the wind patterns inside and outside the basin: while the atmosphere inside the basin is calm (wind generally ≤ 3 m s−1), the nocturnal wind in the eastern slope is rather intense (~10 m s−1). This is an indication that the downslope flow is unable to completely penetrate to the basin floor, probably due to the intense stability of the atmosphere inside the cold pool (strong capping inversions).

  • The vertical structure of the CAP shows several layers, separated by strong capping inversions. Near the ground, there is an intense surface-based inversion (below 10 m from the ground). This near-surface pattern is surmounted by a near-isothermal layer (as seen in Whiteman et al. 2008) that extents into the top of the artificial rock dumps that surround Sierra Gorda (70–80 m high), where an intense capping inversion exists (strong potential temperature inversion of 10°C in 20 m). It is probable that the intrusion of air from regional-scale cold-air drainage flow that arrives down a mean slope from higher terrain following the main south–north valley induces horizontal mixing through the nocturnal inversion, destabilizing the basin atmosphere and producing the near-isothermal atmosphere inside the basin. A second near-isothermal layer develops above the capping inversion. This layer extends from the top of the artificial rock dumps that surround Sierra Gorda (70–80 m high) to 180 m above ground. The lack of supporting instrumentation to measure vertical wind profiles prevented us to determine if the drainage flow is responsible of this second near-isothermal layer. A second capping inversion, less intense than the first one, develops above the second near-isothermal layer. The surrounding topography may be responsible for this because the natural hills surrounding Sierra Gorda are at the same height (150–200 m). Above the second capping inversion the air temperature hardly changes during the night, suggesting that the profile reaches the residual layer.

  • The evolution of the heat deficit in the basin reveals different features of the cold pool around Sierra Gorda during the observing period. First of all, the heat deficit is positive at all times during every night, revealing the recurrence of the nocturnal cold pool during the campaign (though recurring, this cold pool is diurnal, not persistent). Second, the CAP was strongest during the first day of the campaign (15 August), and weakest during the last (19 August), with a nearly constant weakening on the days in between. Third, the intensity of the heat deficit does not change dramatically during each night, revealing that the start of the cold pool is before the start of the measurements (0300 LT).

  • The analysis of the backscatter from the ceilometer and the vertical profile of potential temperature identifies the effect that the vertical structure of the cold pool has on the dust distribution inside the basin. The capping inversion inhibits both the descent and ascent of the dust cloud, causing the dust inside the atmosphere of the basin to remain inside the layer where its source is located.

In summary, we learned from the investigation that during the night an intense cold pool develops in the basin around Sierra Gorda. This cold pool has several layers and capping inversions and is partially man-made because the waste rock dumps around the city affect its structure. The intense descending flows observed near the eastern slope surface seem to play an important role controlling the formation and evolution of the cold pool. We also confirmed the utility of mobile sensors in providing spatially distributed data, which are crucial to the understanding of this type of meteorological phenomenon.

High-resolution numerical simulations may be an interesting approach for future work. They could potentially provide insight into the structuring and transport processes, while the detailed observational dataset offers a great opportunity for the verification of such modeling.

Acknowledgments

Three anonymous reviewers have provided very useful comments that have contributed to the improvement of the paper. The financial support of Minera Centinela for the campaign is appreciated.

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