As part of the EUREC
The interaction of trade-wind clouds with their environment is at the center of fundamental questions such as the role of clouds in climate sensitivity. The EUREC
While HALO was flying at an altitude near 10 km to observe the cloud field from above and to document the environment of clouds with dropsondes, the ATR was flying in the lower troposphere to characterize clouds and their environment through in situ and remote sensing measurements. To help understand the physical processes that control the climate change cloud feedbacks and the mesoscale organization of shallow convection, the primary mission of the ATR was to measure the cloud fraction near cloud base and the dynamical and thermodynamical environment of clouds from the turbulent scale to the mesoscale
Due to the nature of the trade-wind regimes, fulfilling this mission constitutes an experimental challenge. First of all, the cloud field in these regimes is composed of very small and thin broken clouds, with an expected cloud fraction at cloud base of only a few percent. Accurate measurements of the cloud base cloud fraction therefore require both a good sensitivity of the instruments to the presence of clouds and an adequate sampling of the cloud field. Secondly, the humidity field is associated with extremely large and steep vertical gradients, ranging from 80 % near the surface to 100 % within clouds and to less than 5 % above the trade inversion
These challenges were met by fitting the aircraft with a wealth of instrumentation, which, in some cases, was used in an airborne configuration for the very first time. The instrumentation was also chosen to promote redundancy or complementarity of sensors and measurement techniques. This redundancy was not only important for the post-processing and calibration of the data, it was also essential to assess the robustness of the ATR measurements of cloud fraction, humidity and winds.
The goal of this paper is to provide an overview of the operations and measurements of the ATR during EUREC
The SAFIRE ATR42 (F-HMTO) is a turbo-propeller aircraft flying in the lower troposphere (its ceiling is at about 7.5 km) which has been modified in many ways to fit scientific research purposes. The preparation of the ATR for the EUREC
First of all, the ATR home base is in Toulouse (in the south of France), and to join the Caribbean during boreal winter, the aircraft had to follow the historical route of the Aerospostale through Tenerife (Canary Islands), Prahia (Cape Verde) and Fortaleza (Brazil). As the crossing of the Atlantic Ocean required an exceptionally long flight (8 h) compared to the maximal autonomy of the aircraft (8.5 h), the ATR had to be kept as light as possible during the transit. For this purpose, most of the EUREC
Second, extraordinary circumstances independent of SAFIRE and EUREC
Despite these challenges to prepare the aircraft for the campaign, the ATR conducted 19 research flights on 11 operation days from 25 January to 13 February 2020 (totaling approximately 82 flight hours; Table
The ATR coming back from its successful EMI flight in Barbados on 23 January 2020.
List of ATR flights with a brief description of the main flight patterns: the mean approximate height (and number) of rectangles flown around cloud base (
The ATR generally performed two flights per day in coordination with the other aircraft. Each research flight was typically 4.5 or 5 h long, including a transit time from the airport to the EUREC
The ATR's mission was primarily focused on characterizing the cloud base cloudiness, subcloud-layer properties and their signals of spatial organization at the turbulent scale and at the mesoscale. For this purpose, each flight was composed of a basic set of patterns near cloud base and within the subcloud layer that were repeated independent of meteorological conditions. This repetition was motivated by the wish to sample the diversity of boundary layer conditions without any bias and to compare the flights with each other. Then, depending on flight and weather conditions, a few additional patterns were flown near cloud top, at cloud base and/or near the sea surface. Owing to the sharp vertical humidity gradients of the atmosphere and the need to minimize the instruments' memory effects, and due to the abundant presence of sea salt near the ocean surface, which can dirty the instruments'optics, the patterns were preferentially flown from top to bottom.
Shortly after takeoff, the ATR ferried towards the EUREC
Schematic representation of the trade-wind layer with the different levels considered as part of the ground support to determine the cloud base level of the ATR, plus sometimes the cloud top level. The subcloud-layer top, referred to as
Then, to characterize the turbulent and mesoscale organization of the subcloud layer, the ATR flew two L-shape patterns within the subcloud layer, one near the top of the subcloud layer (generally around 600 m) and the other near the middle of the subcloud layer (around 300 m). As the organization of the boundary layer can be anisotropic and dependent on the wind direction, each L pattern was composed of two straight legs perpendicular to each other (each leg being about 60 km long): one along-wind and one cross-wind. Finally, in daylight conditions a near-surface leg of about 40 km was performed at an altitude of about 60 m before returning to the Grantley Adams International Airport (BGI) in Barbados through another ferry leg in the free troposphere.
A few flights were associated with particular features:
During RF06 (30 January), from 11:42 to 12:32 UTC, HALO flew (twice as fast as the ATR) two racetrack patterns above the ATR rectangle at an altitude of about 10 km; two dropsondes were dropped at the extremities of the HALO racetrack. This coordinated flight will help compare the cloud detection and characterization performed with the HALO and ATR measurements. During RF16 (9 February), the ATR flew within the field of view of the RSS aircraft, which was flying parallel to the ATR at about the same altitude. On this occasion, the ATR flew four rectangles around cloud base. The coordination between the two aircraft will help compare the cloud detection performed with the ATR instruments with the high-resolution pictures taken by the visible camera of the RSS aircraft. During RF17 (13 February), the ATR flew during nighttime. This flight was coordinated with the P-3 aircraft
The main role of the ATR during EUREC
As illustrated by Fig.
The evening before the flight, and again 2 h before takeoff, a pre-flight estimation of the flight levels was performed based on near-real-time cloud radar, ceilometer, radiosonde and surface weather data from the BCO and R/V
During the flights, real-time ATR lidar backscatter quick-looks and visual impressions from the pilots, as well as real-time information from the HALO dropsondes and lidar quick-looks
At the beginning of the last rectangle of each flight, the level of the L patterns to be flown within the subcloud layer was determined. The first L pattern was flown near the top of the subcloud layer, about 150–200 m below the lowest cloud base leg (to make sure no cloud is present), and the second L pattern was flown near the middle of the subcloud layer. Finally, shortly before the ferry back to Barbados and when daylight was still present, the ATR flew short straight legs near the sea surface (S pattern).
Over the campaign, the cloud base flight level ranged from about 600 to 850 m; the L pattern near the top and the middle of the subcloud layer were flown around 500–600 and 200–400 m, respectively; and S patterns were flown about 60 m above the sea surface (Table
Segmentation of the ATR flights into patterns (“kind”), flown at different levels (“note”). Each segment is associated with a “name”, where
Vertical distribution of the number of R, L, S and ferry patterns flown by the ATR during the whole EUREC
To aid in the analysis of the flight data, each flight is segmented into non-exclusive timestamps summarized in a set of Yet Another Markup Language (YAML) files (Table
Segmentation of the R and L patterns into straight and stabilized segments of equal duration and length for turbulence studies (T-shortlegs: 30 km/5 min in red, referred to as
The characterization of the turbulence (“T”) requires the consideration of straight and stabilized legs of at least 30 km
Meteorological conditions associated with each ATR flight and their average over all flights. All quantities are computed from the JOANNE dropsonde dataset
Cloud, aerosol and precipitation conditions associated with ATR flights.
Through the combined analysis of Fig.
To aid in the analysis of the ATR data, we summarize in Tables
Daily reanalyses from the ECMWF Reanalysis 5th Generation (ERA5)
Consistently with these contrasted environmental conditions, the most prominent cloud types and mesoscale cloud patterns encountered during each flight also varied (Table
Finally, episodes of dust occurred from about 31 January to 5 February and on 11 February (Table
The ATR instrumentation used for EUREC
Location on the ATR of the main instruments discussed in this paper. The panels show the aircraft from different viewpoints: right, left, bottom and top, respectively. The exact positions of each instrument are given in Tables
The quality control, the calibration and the processing of the datasets derived from the core instrumentation of the ATR (referred to as SAFIRE-CORE, SAFIRE-RADIATION, SAFIRE-CLIMAT and SAFIRE-CAMERA), from the microphysical probes (UHSAS, ultra-high-sensitivity aerosol spectrometer, and PMA, Microphysics Airborne Platform), from the Doppler cloud radars (BASTA, Bistatic Radar System for Atmospheric Studies, and RASTA, RAdar SysTem Airborne) and from the combined radar–lidar dataset (BASTALIAS) are presented below. The processing of the lidar dataset (ALiAS, Airborne Lidar for Atmospheric Studies), the turbulence dataset (SAFIRE-TURB) and the isotopic dataset (Picarro) is fully described in separate papers (respectively
The ATR inertial navigation system, also named AIRINS, is an iXblue inertial navigation system using a fiber-optic gyroscope. By construction, an inertial unit is drifting, and the position needs to be reset by a global positioning system (GPS) position to provide accurate parameters. It is done by using a Trimble BX992 GPS. The AIRINS-GPS positioning system then provides groundspeed, acceleration, attitudes angles and speed platform components in an Earth-based coordinate system.
During EUREC
Core instrumentation of the ATR for pressure and temperature measurements. See Appendix
The ATR is equipped with a five-hole radome that measures the distribution of pressure around the nose of the aircraft (Table
The wind is then inferred from the difference between the speed of the aircraft relative to the Earth and the true air speed
During EUREC
From RF09 to RF20, fast (turbulent) temperature fluctuations were also measured at 200 Hz (and averaged at 25 Hz) with a fine-wire temperature sensor. The fine wire is a 5
The Rosemount temperature data are processed at 1 and at 25 Hz, and the fine-wire temperature data are processed at 25 Hz.
From RF09 to RF20, the turbulence dataset (SAFIRE-TURB) uses the fine-wire data as the best estimate for fast fluctuations and the Rosemount data as a spare
Humidity sensors. Note that the cavity ring-down spectrometer (whose inlet is shown here) is represented in Table
No fewer than five instruments measured humidity in situ on board the ATR (Table
A chilled mirror dew point hygrometer (Buckresearch 1011C) measured the atmospheric dew and frost points. This measurement, made by cooling a reflective condensation surface until an optical system detects the presence of condensation, is traditionally considered as a reference measurement for humidity. However, this type of hygrometer can have limitations when the aircraft undergoes large changes in altitude, passes through a cloud or samples environments with high humidity contrasts. This sensor also has a slow response time and shows limitations in very dry conditions such as those encountered above the trade inversion.
A Humicap 180C enviscope–Vaisala capacitive sensor was placed inside a non-deiced Rosemount E102 housing. This sensor is made of a hygroscopic dielectric material whose capacitance is dependent on humidity. After correcting for the effects of aircraft speed, it measures relative humidity directly with a short response time. However, the sensor is sensitive to the presence of cloud droplets, and it can report relative humidities above 100 %. Its measurements are thus considered only in unsaturated environments, and under these conditions they help assess the robustness or even calibrate the measurements of other sensors mentioned above.
Unlike previous sensors, the Water Vapor Sensing System version two (WVSS-II;
Finally, two additional instruments were used to measure rapid fluctuations in humidity: a Licor LI-7500A and a Campbell Scientific krypton hygrometer (KH20).
The Licor LI-7500A is a near-infrared gas analyzer originally designed to measure eddy-covariance fluxes on ground towers, which has been adapted by SAFIRE to perform airborne measurements
The KH20 uses the absorption of the UV light emitted at 123.58 and 116.49 nm by a krypton lamp to estimate the water vapor density
Based on the processing of these different measurements, two humidity datasets have been produced: one at 1 Hz, included in the SAFIRE-CORE dataset, and another at 25 Hz, which is included in the SAFIRE-TURB dataset. Note that in the SAFIRE-TURB dataset, the calibration of the humidity measurements is performed on a leg-by-leg basis, for both the Licor 7500A and the KH20 sensors.
Core instrumentation of the ATR for radiative measurements. See Appendix
Kipp & Zonen sensors mounted at the top and at the bottom of the ATR measured upwelling and downwelling broadband radiative fluxes, respectively (Table
Measuring upwelling and downwelling radiative fluxes requires the aircraft to be in a plane and stable position. For this reason, the SAFIRE-RADIATION dataset includes two sets of variables for each radiative flux: raw fluxes and fluxes corrected for the attitude of the aircraft. In the time series of corrected fluxes, whenever the roll or pitch of the aircraft was greater than
All pyrgeometers and pyranometers worked properly during the campaign except one: the CMP21 pyranometer (red dome) at the top of the aircraft. Because of this malfunctioning, the downwelling 0.75–2.7
In addition to broadband radiometers, the ATR carried a nadir-viewing multispectral radiometer, the CLIMAT CE332 instrument, developed by the Laboratoire d'Optique Atmosphérique (LOA) in collaboration with CIMEL
To visualize the context of the data acquired by in situ measurements or remote sensing, two high-resolution cameras were mounted on the aircraft. One camera, an AV GT 1920C model with a resolution of 1936
Three types of products are derived from these cameras: movies (in avi format) are produced for each camera (“window” or “ground”) and for each flight, and high-resolution images (in bmp format) are produced for the window camera for R and L patterns.
The five-hole nose radome and specific temperature and humidity sensors mounted on the ATR (Rosemount and fine-wire thermometers, Licor and KH20 hygrometers; see Tables
The dataset includes two kinds of products: “turbulent fluctuations” and “turbulent moments”. The “turbulent fluctuations” include time series of high-frequency fluctuations in the dynamical and thermodynamical variables over straight and stabilized segments of T-shortlegs, T-longlegs or T-longestlegs (Table
The “turbulent moments” include means, variances and covariances of dynamical and thermodynamical variables, turbulent kinetic energy and dissipation rate, third-order moments and skewnesses of wind components, potential temperature, and water vapor mixing ratio. They also include characteristic length scales such as the integral length scale or the wavelength of the vertical velocity density energy spectrum peak, error estimates on the turbulent moments, and quality flags on the wind, temperature and humidity measurements. These diagnostics are produced for each type of segment (T-shortlegs, T-longlegs and T-longestlegs).
This dataset is produced for two levels of data processing. In the Level 2 dataset, the turbulent moments and fluctuations are calculated for each humidity sensor and each temperature sensor, and a quality flag is associated with each sensor. In the Level 3 dataset, a “best estimate” of the turbulent moments and fluctuations is provided, together with a quality flag; for each segment, the best estimate corresponds to the moments and fluctuations computed from the sensor that has the best quality flag over this segment. The dataset is distributed in NetCDF files whose nomenclature is summarized in Table 3 of
Microphysical probes mounted on the ATR for EUREC
The ATR payload included a suite of six instruments to measure in situ aerosol and cloud properties (Table
A UHSAS-A probe (airborne version, serial no: 1303-007) was mounted on the lower left-hand pod on the fuselage section (Fig.
The operating principle is as follows: the external air drawn at a controlled flow rate (about 50 sccm) enters the instrument optical detector, where it is aerodynamically focused and brought through a laser beam (Nd3+:YLiF4 laser operating at 1053 nm). The laser light scattered by each aerosol particle is collected by two pairs of Mangin collection optics, and the scattered intensity is measured with a dual Avalanche photodiode low-gain PIN photodiode detection system. The size of each particle is derived from the scattered intensity by using Rayleigh (40–300 nm) or Mie (300–1000 nm) scattering models implemented in the instrument (they are not corrected for variations in particle refractive index or non-sphericity).
The UHSAS-A used in EUREC
According to the manufacturer, UHSAS operation is limited to a non-condensing environment.
The total concentration of aerosol particles and the particle size distribution measured by UHSAS during the different ATR flights are shown in Fig.
Cloud microphysical measurements were made with two instruments: the CDP-2
The CDP-2 (serial no. 1711-111, equipped with anti-shatter tips) is a cloud particle spectrometer that counts and sizes cloud droplets in the 2–50
Measurements in the subcloud layer reveal that the CDP-2 can detect non-cloud droplet particles such as large and ultra-large aerosols. Although these particles may not satisfy the underlying assumption of the CDP-2 sizing algorithms, it was decided not to filter out these measurements in the CDP-2 files so that further investigations of large aerosols may be conducted, at least qualitatively. However, the response of the CDP-2 to such aerosol particles being unknown, the data taken in non-cloudy areas are subject to unquantified errors.
The 2D-S (serial no: 006) is an optical array probe imaging cloud, drizzle and rain particles in the range 10–1280
The raw data (from either the vertical or horizontal channel, whichever worked best during the flight) are processed using the LaMP in-house processing routines, which stem from the early release of the SPEC 2DSView software and are continually updated to integrate state-of-the-art corrections.
The calculation of the sample volume takes into account the decrease in depth of field with particle size and follows the manufacturer's formula given in
Once most of the artifacts have been corrected, a series of geometrical descriptors, e.g., size (defined here as the diameter of a circle having an area equal to the projected area of the particle, often referred to as surface-equivalent diameter in the literature,
As the cloud drop size distribution is broad, a combined PMA dataset is produced that merges the CDP-2 and 2D-S data into a single composite spectrum that ranges from 2
As the CDP and 2D-S were mounted on two different wings about 10–15 m apart (Table
From the composite size distributions we calculate microphysical quantities such as the total concentration of particles (
We define a cloud mask and a drizzle mask based on the LWC and the particle size (diameter
The cloud LWC was inferred from the size distribution of cloud particles measured by the CDP-2 and 2D-S probes. It was also measured independently by a hot-wire probe (DMT LWC-300) that was part of the core instrumentation of the ATR (Table
Cloud droplet number concentrations at cloud base and their relationships with aerosol number concentrations (derived from UHSAS) are shown in Fig.
The distribution along all the R patterns of the droplet number concentration, MVD and LWC values of the clouds derived from the PMA composite dataset is shown in Fig.
An aerosol dataset was produced on the basis of UHSAS measurements. It is distributed as an ensemble of NetCDF files (one file per flight) that include products such as the PSD and NT, all processed at a frequency of 1 Hz.
A cloud dataset was produced on the basis of CDP-2 and 2D-S measurements (future versions of the dataset might include data from the FSSP-300 and FCDP probes). It is distributed as a set of NetCDF files (one file per flight) which include products such as PSD, NT and LWC (assuming that particles are spherical with a density of 1 g cm
The data are distributed for two levels of processing: the Level 2 dataset is associated with single instruments (either 2D-S or CDP-2), while the Level 3 dataset corresponds to a combined PMA dataset that merges CDP-2 and 2D-S data into a single composite spectrum that spans the range 2
The LWC measurements from the LWC-300 are included in the SAFIRE-CORE dataset at 1 Hz.
Lidar–radar remote sensing and stable isotopologue measurements. See Appendix
To characterize the presence of clouds and aerosols in the lower troposphere, the ATR was equipped with a lightweight backscatter lidar named ALiAS (Airborne Lidar for Atmospheric Studies) emitting at the wavelength of 355 nm and detecting polarization (Table
The native resolution of the lidar backscatter profile along the line of sight is 0.75 m. However, to improve the signal-to-noise ratio, a low-pass filter has been applied, and the resolution was downgraded to 15 m. In addition, the backscatter profile was averaged over 50 consecutive shots during the acquisition, which corresponds to approximately one recording every 5 s (averaging time of 2.5 s and recording time of 2.5 s). The backscatter lidar observations are used to define a cloud mask in the direction perpendicular to the aircraft trajectory. In this direction, the signal was distinguishable from noise up to a distance of about 8 km in clear-sky conditions. However, this range was reduced in the presence of strong scattering, for instance from thick clouds. It means that during the R patterns, as the aircraft was flying rectangles of about 120 km (along track)
Both aerosol and cloud products have been derived from the ALiAS observations, and the data are distributed as a set of NetCDF files (one per flight) for different levels of processing. Level 1 provides the raw profiles at native resolution recorded by the acquisition system. Level 1.5 data are geolocated, calibrated and corrected for geometric factors and molecular transmission, and time series of the apparent backscatter coefficient (ABC) and volume depolarization ratio (VDR) are produced with a resolution of 15 m along the lidar line of sight. Level 2 provides cloud and aerosol detection information and products, including a cloud mask and an aerosol extinction coefficient (AEC) along the horizontal line of sight. Level 3 provides statistics about the length of the cloud chords inferred from the lidar cloud detection. The ALiAS dataset is described in detail in
Figure
Relationship between the total concentration of aerosols measured by UHSAS at cloud base and the aerosol extinction coefficient measured by the horizontally pointing ALiAS lidar at cloud base. Horizontal and vertical bars represent the standard deviation of measurements across the different R patterns of each flight.
To characterize the cloudiness in synergy with the lidar, a horizontally staring cloud radar named BASTA (Bistatic Radar System for Atmospheric Studies) was mounted on the right-hand side of the ATR (Table
The Level 1 of the BASTA product contains, for both modes, the calibrated and range-corrected radar reflectivity, the Doppler velocity, and a mask distinguishing the meteorological target from background noise and surface echoes. The calibration of the radar has been derived from other field campaigns and confirmed using in situ data by calculating a reflectivity from the CDP and 2D-S cloud particle data and comparing it with radar measurements in cloudy conditions. The sensitivity of the radar is estimated at around
Cloud detection algorithm applied to BASTA and ALiAS data to detect hydrometeors (clouds, drizzle and rain).
Based on ALiAS and BASTA data, a combined dataset was developed that takes advantage of the lidar–radar synergy and complementarity to improve the detection of clouds, drizzle and rain (Fig.
For this purpose, the two modes of the BASTA radar products are merged on a single horizontal grid (resolution of 12.5 m within the first 200 m from the aircraft and 25 m beyond this distance) and a single time resolution (1.5 s). Then the reflectivity is corrected for liquid and gas attenuation, and the radar sensitivity is defined as a function of the distance from the aircraft. A first classification of hydrometeors is then made on the basis of radar observations. As the reflectivity associated with the presence of a remote hydrometeor depends on the drop diameter, reflectivity thresholds can be used to distinguish cloud droplets from drizzle or rain. The definition of these thresholds differs across ground-based radar studies; the threshold distinguishing clouds from drizzle (
Probability distribution function of equivalent radar reflectivities calculated for each ATR flight from the PMA particle size distribution for situations defined as cloud-only, drizzle-only or rain-only by PMA masks (based on LWC and cloud drop diameter; Sect.
To assess the ability of these reflectivity thresholds to distinguish between cloud, drizzle and rain situations, we calculate the reflectivity
Since the sensitivity of the radar decreases as the distance from the aircraft increases, BASTA can only detect clouds within a limited distance from the aircraft; beyond this point, the radar can only detect drizzle or rain. The range over which the radar can possibly detect clouds (
In parallel, the ALiAS lidar data at their original resolution (Level 1.5 data from
Finally, the lidar and radar cloud masks are analyzed jointly to make a final classification of hydrometeors and a lidar–radar cloud mask (Fig.
Illustration of cloud, drizzle and rain detection by horizontal remote sensing using lidar–radar synergy. The maximum distances
RASTA (RAdar SysTem Airborne) is an up-looking pulsed 95 GHz Doppler cloud radar with two antennas (zenith and up-backward, with an elevation of 66.7
Level 2 data are distributed as a set of NetCDF files for the flights during which the radar was operating, and clouds were detectable with the radar (RF03, RF04, RF11 and RF12 plus all flights from RF13 to RF19). For all these flights but two (RF11 and RF12), two antennas were working (zenith and up-backward), which allows us to derive wind information (its radial component) in addition to cloud information. For RF11 and RF12, only one antenna (zenith) was working, and therefore the wind information is not available.
Figure
In addition to characterizing the meteorological, turbulent, microphysical, cloud and radiative properties of the atmosphere, the ATR measured the water-isotopic composition of the atmosphere using a customized fast-response cavity ring-down spectrometer from Picarro (version L2130-i). This effort took place as part of a wider EUREC
The isotopic composition of atmospheric water vapor on board the ATR was measured with a sampling frequency of 1 Hz (Table
In the ATR, a rearward-facing 30 cm long stainless-steel inlet with
To assess the instrument's precision and drift, calibration gases were measured on the ground pre- and post-flight using a Picarro Standards Delivery Module (SDM). The high-precision liquid pumps of the SDM deliver a thin stream of liquid water of known isotopic composition into a vaporizer heated to 140
Recent studies have indicated that the precision of laser spectrometers in laboratory settings is comparable to the one of conventional isotope ratio mass spectrometer systems. However, for atmospheric field applications, the overall measurement uncertainty can result from a range of factors such as calibration, sensitivity to variations in water concentration and retention effects from the tubing
The ATR measured humidity, winds and clouds with multiple instruments based on different observation techniques. This redundancy and/or complementarity is an opportunity in several respects. It is an asset for the quality control of the data from each instrument and for the processing of combined datasets taking advantage of the complementarity of the instruments. It also allows the robustness and the statistical representativeness of the measurements to be assessed. This last point is particularly important for EUREC
The objective of this section is to verify this premise by comparing some of the main ATR measurements made by different instruments using different techniques and/or samplings. We also assess the consistency between the ATR measurements and the simultaneous dropsonde measurements
On board the ATR, humidity was measured by several instruments, but the WVSS-II sensor was considered as a reference for the calibration of the SAFIRE-CORE and SAFIRE-TURB datasets
Comparison for each ATR flight of the water vapor mixing ratio inferred (blue) from the SAFIRE-CORE dataset, (turquoise) from the Picarro dataset and (black) from the JOANNE dropsonde dataset. Panels
The comparison between these different measurements is presented in Fig.
For each flight and each pattern, the ATR measurements (SAFIRE-CORE and Picarro) generally exhibit a good agreement, in terms of both mean humidity and standard deviation: over the cloud base rectangles (R patterns), the mean discrepancy between the two datasets is 0.084 g kg
The ATR measurements are also in good agreement with the dropsonde data, including when all the measurements show a small variability. Even the standard deviations show good agreement, which is somewhat surprising given the much larger domain sampled by the HALO measurements. However, ATR and HALO measurements disagree more during RF09 (on 2 February) and RF14 (on 7 February), when the cloud organization was very heterogeneous at the mesoscale (Table
This comparison suggests that despite their different sampling and observing techniques, the ATR and HALO generally measured statistically consistent variabilities in humidity around cloud base, within the subcloud layer and near the surface. The main discrepancies occurred when the scale of the cloud field organization was much larger than the scale of the area probed by the ATR. In these cases, the differences are likely to be representative of real spatial differences associated with different samplings.
The wind was measured both in situ using the aircraft probes (Sect.
Figure
The two Doppler radars also measured the radial component of the horizontal wind along their line of sight. For BASTA, this information is retrieved on every flight, but for RASTA it is available only for RF03, RF04 and RF13 to RF19, when backward and zenith antennas were operating simultaneously. The radial component of the wind (perpendicular to the aircraft trajectory) derived from BASTA and RASTA along R patterns is compared to the horizontal wind measured by the aircraft probes and projected along the radars' line of sight (Fig.
In summary, the horizontal wind measurements of the ATR are consistent with each other and with the dropsonde measurements made along the EUREC
One of the original motivations for the EUREC
Reflecting the view that clouds are bodies interacting with radiation, collections of particles and a particular state of atmospheric water
Using horizontal lidar–radar measurements from ALiAS and BASTA together with the BASTALIAS cloud detection algorithm described in Sect.
Most of the cloud fraction (from 60 % to 100 %) is composed of clouds which were detected by the lidar only (Fig.
Comparison of the ATR isotope measurements below 400 m (in blue) with the ground-based measurements from the Barbados Cloud Observatory (BCO) during each ATR flight (in black). The comparison is done (from top to bottom) for
Mean values and standard deviations over all flights for the measurements made by the Picarro onboard the ATR and at the BCO during the ATR flights, as well as the Pearson correlation between the two datasets.
Using reflectivities at the fifth gate (i.e., about 90 m above the aircraft), and a threshold of
A cloud fraction estimate can also be diagnosed from in situ measurements of cloud microphysics (Sect.
Finally, recognizing that clouds occur in saturated (or, in the presence of sea salt, nearly saturated with respect to pure water saturation) conditions, it is possible to define a pseudo cloud fraction from the high-frequency (25 Hz), small-scale (4 m) measurements of relative humidity: using the SAFIRE-TURB dataset, we reconstruct high-resolution time series of humidity mixing ratio, temperature and then relative humidity by adding the turbulent fluctuations in each variable measured over stabilized segments (T-shortlegs) to the mean of each segment (Fig.
The values of CF
To assess the consistency of isotopic data between the ATR Picarro dataset and the ground-based measurements from the BCO (Fig.
All the datasets discussed in this paper are available on the EUREC
List of ATR datasets derived from EUREC
The EUREC
The ATR mission focused on characterizing the thermodynamic, dynamical, microphysical, turbulent and cloud properties of the lower atmosphere. One of its specific roles was to measure the cloud fraction around cloud base to help test low-cloud feedback mechanisms. For this and other purposes, the ATR was equipped with a rich and extensive instrumentation composed of in situ sensors, radiometers and active remote sensing. Eighteen coordinated research missions followed a repeated flight plan consisting of rectangles (or R patterns) flown at cloud base or cloud top, L legs flown within the subcloud layer (L patterns), straight legs flown 60 m above the sea surface (S patterns), and ferry legs flown in the lower free troposphere above clouds.
The first part of this paper presents the ATR operations, the flight patterns and the flight segmentation (summarized in a collection of YAML files). It also shows that during its 19 missions, the ATR sampled very contrasted environmental conditions.
The second part of this paper presents the ATR instrumentation used during EUREC
Finally, the paper assesses the consistency among the different ATR measurements and between the ATR measurements and those performed by other instruments on different platforms such as HALO or the BCO.
The large variability in the aerosol load in the atmosphere (ranging from 50 to more than 500 cm
The ATR measurements of humidity and winds exhibit a good consistency with HALO dropsonde measurements: the wind measurements from the ATR and from HALO are highly correlated (
These results thus verify two premises which were at the basis of the EUREC
Satellite animations were made to visualize the cloud scenes sampled by the ATR and other platforms during the campaign. Geostationary images shown here are from the GOES-16 visible channel during daytime (channel 2) and infrared channel during nighttime (channel 13). They are retrieved at 1 min time increments and 500 m resolution during daytime and 2 km resolution during nighttime. The code is modular
Figures
Vertical trajectories of each ATR flight, with the main patterns highlighted in color. R patterns at cloud base and/or cloud top (orange), L patterns in the subcloud layer (blue), S patterns near the sea surface (red), ferry legs (black), and upward and downward profiles (turquoise). Also reported (dashed line) is the subcloud-layer top height diagnosed from dropsondes (Table
Longitude–latitude trajectories of the ATR colored by the flight altitude. For repetitive flight patterns (e.g., the rectangles), only the last repetition is visible due to the overlap. The dashed circle shows the EUREC
Figure
Instrumental configuration of the ATR showing the nomenclature used in Tables
The people responsible for the processing of the different ATR datasets are listed in Table
The contact author has declared that neither they nor their co-authors have any competing interests.
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of the special issue “Elucidating the role of clouds–circulation coupling in climate: datasets from the 2020 (EUREC4A) field campaign”. It is not associated with a conference.
The Caribbean Regional Security System (RSS), especially George Harris and Calvert Herbert, are gratefully acknowledged for hosting the ATR and the ATR team in Barbados in the best conditions during the EUREC
This project was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (EUREC
This paper was edited by Silke Gross and reviewed by Alan Blyth and one anonymous referee.