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 EUREC4A, three problems occurred that impacted the measurements and the data processing. (1) A failure in the internet output of the AIRINS-GPS system prevented us from recording the data at 100 Hz as usual; the data were recorded instead at 50 Hz on a serial output, and then they were synchronized and averaged at 25 and at 1 Hz. (2) During RF03, the GPS was rejected by AIRINS, which resulted in an incorrect position (true heading and attitude) and thus unreliable horizontal wind measurements for this flight; a corrected position (derived from the GPS only) was used in the V2 version of the SAFIRE-CORE dataset as well as in the RF03 files of other ATR datasets. (3) For RF20, the inertial and GPS data are available at 1 Hz only.

The ATR is equipped with a five-hole radome that measures the distribution of pressure around the nose of the aircraft (Table ): the difference in pressure measured between two holes in the vertical or horizontal planes informs about the attack angle and sideslip angle, respectively . The static and dynamic pressures are measured by Rosemount or Thales transducers connected to Pitot tubes on both sides of the radome. The static pressure, which corresponds to the pressure corrected from the airflow disturbance produced by the aircraft, is determined using a pre-established calibration based on specific flights and maneuvers. The dynamical pressure is obtained by subtracting the static pressure from the total pressure measured at the central radome hole. The true air speed (TAS), which is the speed of the aircraft relative to the air mass through which it is flying, is calculated from the dynamical and static pressures.

The wind is then inferred from the difference between the speed of the aircraft relative to the Earth and the true air speed . The high-rate wind measurements of the ATR have been very robust since its first field campaign in 2006 . Unfortunately, because of a hose leak between a hole of the radome and a pressure transducer inside the radome, the measurement of the vertical wind is not reliable from RF02 to RF08. The horizontal wind measurements were not significantly affected by this problem.

During EUREC4A, the air temperature was measured by two Rosemount sensors E102AL (Table ). The first one is located on the nose of the aircraft, inside a non-deiced housing, and the second one is located on the fuselage inside a deiced housing (Fig. ). The static temperature, which is the temperature corrected for aircraft speed and recovery factor of the housing, is calculated as Ts=Tt1+rf1+ΔPPsRa/cpa-1 if ΔP>6, where Tt is the measured total temperature (∘C), ΔP the dynamic pressure (hPa), Ps the static pressure (hPa) and rf the recovery factor (rf=0.98)

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 µm platinum wire soldered on a support and mounted inside a SFIM T4113 housing. Despite its fragility (a fine wire can easily break during takeoff or landing when the aircraft encounters particles or insects), it remained intact during the whole campaign. Despite its housing, the response time of the Rosemount sensor can sometimes be affected by the presence of cloud droplets . The fine wire can also be affected by this problem, but it recovers much more quickly, emphasizing the complementarity of the two sensors . The total temperature from the fine wire is derived by fitting and calibrating its raw measurements against the total temperature measured by the non-deiced Rosemount sensor. The resistance of the fine wire being subject to oxidation, this calibration is performed for each individual flight. The static temperature is estimated using the same method as for the Rosemount sensor, using (for the lack of a better estimate) the same recovery factor.

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 .

No fewer than five instruments measured humidity in situ on board the ATR (Table ), in addition to the cavity ring-down spectrometer (CRDS) presented in another section of this paper (Sect. ). Each instrument is based on a particular measurement principle or technology and therefore exhibits specific strengths and limitations in terms of stability, response rate, sensitivity to the presence of condensation or measuring range. The comparison and fine analysis of the different measurements makes it possible to calibrate and correct or bypass the shortcomings of each measurement so as to produce high-quality humidity datasets. The main features associated with these instruments and the processing of their measurements are outlined below.

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; ) designed by SpectraSensors for use on commercial aircraft can measure humidity with a good reliability and regularity, without being affected by the presence of cloud droplets or very dry air . This is due to its particular technology, based on tunable diode laser absorption spectroscopy in the near-infrared (1.37 µm), and to the fact that its sampler has been designed to minimize the biases associated with the presence of cloud droplets or aerosols. Therefore, for this campaign it is considered as a reference for slow humidity measurements, and it is used to adjust or calibrate humidity measurements from other sensors. The WVSS-II measures the mixing ratio of water vapor relative to dry air in parts per million volume (ppmv). The volumic concentration is converted to a mass concentration to provide absolute humidity measurements in grams per cubic meter.

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 in replacement of the historical reference Lyman-alpha instrument . present a comparison of the two sensors. Its strength lies in its short time response, but its main limitation is its high sensitivity to the presence of liquid water (its performance can be affected even a few seconds after leaving a cloud). Periods when the humidity measurement is affected by condensation (typically inside clouds) are detected on the basis of the strength of CO2 measurements made by the same sensor. The Licor performance can also be affected by the presence of sea salt, particularly when the aircraft is flying low near the sea surface. To minimize this problem, the lowest legs were performed at the end of the flight, and the Licor window was cleaned before each subsequent flight. The Licor humidity measurements (g m-3) are calibrated against the WVSS-II absolute humidity measurements of RF13, and the same calibration coefficients are used in all flights (note however that the calibration of humidity in the SAFIRE-TURB dataset is performed leg by leg as described by ). As the Licor clock is initialized manually, it is sometimes delayed by a few seconds. This delay is subsequently corrected during post-processing. The corrected and synchronized time parameter of the Licor instrument is also used to correct a delay of 3 s of the WVSS-II sensor induced by the interface of the instrument. Note that Licor data were not recorded during the flights RF05 and RF06.

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 . This instrument has also been originally designed for eddy-covariance measurements on ground towers, but had reported its use on an aircraft. The instrument has been heavily modified by SAFIRE to be operated on the ATR : the housing of an older humidity sensor (a Lyman-alpha hygrometer) was used to install the source lamp and detector, and the electronic box was installed inside the cabin. This sensor was less sensitive to the presence of cloud droplets than the Licor, but it was more affected by sea salt. Therefore, as the Licor it was cleaned before each subsequent flight. The KH20 measures rapid fluctuations in humidity but not absolute humidity. Absolute values (g m-3) are obtained by calibration against the slow (1 Hz) humidity measurements of the WVSS-II .

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.

Kipp & Zonen sensors mounted at the top and at the bottom of the ATR measured upwelling and downwelling broadband radiative fluxes, respectively (Table ): CGR4 pyrgeometers measured hemispheric longwave fluxes in the 4.5–42 µm spectral range, CMP21 pyranometers measured hemispheric shortwave radiation in the 0.75–2.7 µm spectral range (red dome), and CMP22 pyranometers measured hemispheric shortwave radiation in the 0.2–3.6 µm spectral range (clear dome).

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 ±5∘ the radiative measurements were considered to be “undefined”, and otherwise the downwelling shortwave measurements were corrected for the attitude of the aircraft. This correction requires knowledge of the offset of the sensor installation, which corresponds to the bias associated with the potential tilt of the mechanical installation of the sensors relative to their support. This offset must be estimated every time the sensor has been remounted on the aircraft (as done at the arrival of the ATR in Barbados; Sect. ). It was determined through specific maneuvers performed during the test flight RF02.

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 µm irradiance measurements were either absent or invalidated during the campaign. However all other upward and downward longwave and shortwave fluxes, including the downwelling shortwave measurements over the 0.2–3.6 µm spectral range, are available and distributed in the SAFIRE-RADIATION dataset at 1 Hz.

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 . This radiometer measures infrared radiances and brightness temperatures at three wavelengths: 8.7, 10.6 and 12 µm (Table ). It is done by comparing the radiances measured on the observed target with that measured by looking at a reference cavity maintained at a given temperature. During the post-processing, the measurements performed at 6 Hz are synchronized and averaged at 1 Hz. They are included in the SAFIRE-CLIMAT dataset. It is planned to estimate the sea surface temperature from these measurements.

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 × 1456 pixels and a wide angle (focal length of 4.8 mm), took high-frequency images (10 frames per second) through the ATR window on the side of the horizontally staring lidar and radar instruments. The other camera, a Mako G-223 model with a resolution of 2048 × 1088 pixels and a focal length of 16 mm, looked down towards the sea surface at a moderate frequency (1 frame per second). The images taken through the aircraft windows often appear dark because the choice of exposure time was made to avoid saturation due to the brightness of the clouds as much as possible, especially when the sun is behind the aircraft (Fig. a). The downward-looking camera can detect the presence of clouds below the aircraft and can help characterize the state of the ocean surface (Fig. b).

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.

A UHSAS-A probe (airborne version, serial no: 1303-007) was mounted on the lower left-hand pod on the fuselage section (Fig. ). This probe is an optical-scattering aerosol particle spectrometer developed and commercialized by Droplet Measurement Technologies (DMT) that counts and sizes particles in the 0.06 to 1 µm range. The sizes are then sorted into 99 linearly spaced size bins of fixed width (9.7 nm).

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 EUREC4A was last maintained and calibrated by DMT in December 2018 (using National Institute of Standards and Technology traceable polystyrene spheres of nominal diameter 100 nm), and a calibration check (using polystyrene beads of various sizes, e.g., Thermo Fisher Scientific 3150A) was performed at SAFIRE prior to the campaign in May 2019.

According to the manufacturer, UHSAS operation is limited to a non-condensing environment. reported that UHSAS measurements are subject to water contamination when performed in a cloudy area, which is also visible in our data. Therefore, UHSAS measurements made in cloudy areas (determined by liquid water content >1 mg m-3 using CDP and 2D-S data, as in the case of ) are rejected. Moreover, the UHSAS has a maximum count rate of 3000 s-1, and have shown that the detection efficiency decreases when the particle concentration exceeds 3000 cm-3 due to a coincidence effect. Therefore, points where the total count exceeds 3000 s-1 are removed from the data. According to , particle concentrations in the small size range come with a caveat that the detection efficiency of a UHSAS (lab version) tends to decrease for particles smaller than 100 nm. Finally, inspection of the housekeeping data revealed erratic variations in the sample flow rate between 32 and 50 sccm, caused by a loose electrical connection at a mass flow controller. Periods of large sample flow variation are manually identified and discarded. The aerosol concentration is calculated from the probe counts per second and the sample flow rate converted from mass (sccm) to volumetric flow rate (cm-3) using temperature and pressure measurements from the aircraft core instruments (Sect.  and ).

The total concentration of aerosol particles and the particle size distribution measured by UHSAS during the different ATR flights are shown in Fig. . The concentrations in the subcloud layer and out of the cloud at the cloud base level are generally similar, although a few flights (RF06, RF11, RF15 and RF17) show a slightly reduced concentration at the cloud base level. In every case, the concentration is highly variable (Table  and Sect. ), with two main regimes: average aerosol concentrations are about 100 cm-3 in half of the flights and about 300–400 cm-3 in the other half. The particle size distribution also varies among flights, with the highest variability occurring in the frequency of large particles (diameters larger than 300 nm).

Cloud microphysical measurements were made with two instruments: the CDP-2 , which counts and sizes cloud droplets in the 2–50 µm size range, and the 2D-S , which images cloud, drizzle and raindrops in the 10–1280 µm nominal size range (Table ). Both instruments were mounted under the wings of the ATR, one on the right side and the other on the left side (Fig. ). Throughout the campaign, the optics of the 2D-S and CDP-2 (and FCDP) probes were cleaned after each flight to remove traces of dust and salt. At low altitudes, where the air is warm, the temperature of the CDP-2 and 2D-S lasers increased rapidly, and therefore the instruments were often switched off by the operator to avoid damaging the probe. As a result, few CDP-2 and 2D-S measurements are reported along the subcloud-layer legs.

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 µm range and sorts them into 30 size categories with a resolution of 1–2 µm. The 1 Hz raw data (histograms of counts per second) are processed using DMT's built-in counting and sizing algorithms based on the Mie scattering model, assuming that droplets are spherical with a refractive index of 1.33, and converted to concentrations with the probe sample volume. The sample volume is calculated using the true air speed of the aircraft from SAFIRE-CORE data and the calibrated sample area (0.292 m2) determined prior to the campaign by mapping the probe's response to calibrated water microdroplets injected across the laser beam with an apparatus similar to . At 100 m s-1, which was the typical ATR airspeed during the scientific flights, the sample volume was about 30 cm3 s-1. The calibration of the CDP-2 with respect to particle size was regularly monitored during the campaign by means of calibrated glass bead injection tests.

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 µm (the stereo capability of the probe is not used here): an array of 128 photodiodes is illuminated by a laser sheet; when a hydrometeor crosses the sample area (about 0.128 cm × 6.3 cm, located between a pair of emitting and receiving arms), it shades some of the photodiodes. The binary state (occulted, non-occulted) of the photodiodes is recorded at high frequency (up to 17 MHz for this probe), producing time-discretized black-and-white slices of the particle's silhouette, which are subsequently concatenated to reconstruct a projected 2D black-and-white image of the hydrometeor with a resolution of 10 µm. The calibration of the 2D-S probe was tested before the campaign with opaque calibrated features printed on glass spinning disks.

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 and the overload periods of the probe. Artifacts due to noisy or dead pixels are identified and removed using the pixel analysis described in . This probe is equipped with anti-shattering arm tips (K-tip; ) designed to prevent ice and droplet fragments from falling into the probe sample volume and contaminating the measurement at the lower end of the size spectra (note that no ice was sampled along the ATR flights of EUREC4A). In addition to the K-tip, a splash and shatter detection and removal algorithm based on arrival time analysis is applied (e.g., ). The size of particles seen out of focus is corrected using the diffraction correction. Despite these efforts to clean artifacts, the concentration in the first few bins remains questionable for reasons described in and (the contribution of remnant noisy events is amplified in the concentration calculation due to the small sample volume). The size of truncated particles (partial images) is corrected according to , and the nominal size range (10–1280 µm) is extended to 2.56 mm in post-processing.

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, Deq), area or perimeter, are retrieved from each individual 2D image. Statistical properties are then calculated at 1 Hz, such as the particle size distribution (PSD) or the total concentration (NT; calculated as the sum of bin concentrations). The mass size distribution (MSD) is computed from the PSD assuming that the particles are spherical with a liquid water density of 1 g cm-3.

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 µm to 2.55 mm, at the native size resolution of the CDP-2 up to 43 µm, the 10 µm size resolution of the 2D-S up to 1 mm and a coarser resolution of 100 µm from 1.05 up to 2.55 mm. To do so, the 2D-S size distributions are interpolated to match the CDP-2 bin resolution on the 10 to 50 µm overlap region. When data from both probes are available, we use CDP-2 data up to 31 (±1) µm; between 33 and 43 (±1) µm, we use the average of CDP-2 and 2D-S concentrations; and beyond 50 (±5) µm, we use 2D-S data. When CDP-2 data are not available (data are set to NaN (not a number, i.e. undefined value) values whenever a probe does not operate), the first two bins of the 2D-S are omitted such that the composite spectra start at 30 (±10) µm. Note that a ±1 s offset was added to the 2D-S data whenever it improved the correlation between the LWC retrieved from the CDP and 2D-S data in the 25–45 µm overlap region.

As the CDP and 2D-S were mounted on two different wings about 10–15 m apart (Table ), it could happen that only one of the two wings crossed a cloud, thus generating some inconsistency between the measurements of the two probes. Therefore, the composite product comes with a variable (compo_index) that describes the composite and qualifies the overlap between the two sondes (1: CDP data only; 2: 2D-S data only; 3: CDP and 2D-S in good agreement; and 4: CDP and 2D-S in poor agreement). In the future, inconsistencies could be limited by producing another composite that would use the 2–50 µm measurements from the FCDP probe (rather than the CDP) as this probe was mounted just below the 2D-S.

From the composite size distributions we calculate microphysical quantities such as the total concentration of particles (NT); liquid water content (LWC; third moment of the distribution assuming a density of 1 g cm-3); median volume diameter (MVD; defined as the median of the cumulative mass size distribution); and a series of masks that indicate the presence of cloud, drizzle or rain drops.

We define a cloud mask and a drizzle mask based on the LWC and the particle size (diameter D): a cloud particle is identified when the LWC of droplets smaller than D0 exceeds LWC0, where LWC0 and D0 are specified thresholds of LWC and D, respectively. There is no simple definition of cloud situations, and therefore the values of these thresholds remain uncertain. Here, we use LWC0 =0.010 g m-3 (which is consistent with other observational and modeling studies of trade-wind clouds such as , or ) and D0 =100 µm (which is consistent with the AMS glossary definition of cloud drops as water particles between 1 and 100 µm in diameter). We assume that drizzle occurs (drizzle mask is set to 1) when 100≤D<500 µm, and rain occurs when D≥500 µm.

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 ; note that the LWC-300 sensor broke during RF14 and was immediately replaced by a new one). The hot-wire estimates the LWC by measuring the heat released by the vaporization of water droplets on a heated cylinder exposed to the airstream. This calculation is made with the Particle Analysis and Display System (PADS) software, using the aircraft airspeed, pressure and deiced temperature measured by the ATR and the formulas given in the DMT PADS Manual Hot Wire Module 3.5.0 DOC-0290 Rev A. However, the collection efficiency of the sensor is limited for small droplets (<10 µm), and the evaporation of large drops (>50 µm) can be incomplete, which can underestimate the LWC measurement in drizzle and rain conditions when such large drops are present (DMT LWC-300 LWC operator's manual DOC-0361 Rev C). The LWC estimate derived from the CDP-2 and 2D-S probes (distributed in the PMA composite dataset) is thus considered to be more precise than that derived from the LWC-300 (distributed in the SAFIRE-CORE dataset).

Cloud droplet number concentrations at cloud base and their relationships with aerosol number concentrations (derived from UHSAS) are shown in Fig. a. Cloud droplet number concentrations tend to be about 2/3 of the aerosol concentrations, with a strong case-to-case co-variability (correlation of 0.94). At larger aerosol concentrations, the cloud droplet concentrations are disproportionally less in cases with larger aerosol concentrations. This could be indicative of a lower maximum of cloud base supersaturations in an aerosol-rich environment or a less cloud-active aerosol in conditions when the concentrations are high. However, such a sublinear relationship between cloud condensation nuclei (CCN) and cloud drop concentration is not uncommon, and different interpretations have been proposed for this feature, including a measurement artifact known as “coincidence” . Since the CDP-2 probe is prone to coincidence errors at concentrations as low as 200 cm-3 , in this case an instrumental artifact cannot be ruled out without further investigation.

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. b–d. Cloud particle concentrations are very variable, but, on average, they tend to be much larger at cloud base (median of 128 cm-3) than in the stratiform layers of trade cumuli detraining near the inversion level (median of 46 cm-3); on the other hand, cloud particle sizes and cloud liquid water contents tend to be much smaller at cloud base (about 10 µm and 50 mg m-3, respectively) than at cloud top (about 24 µm and 200 mg m-3 near the inversion level). The range of MVD values measured near cloud base and cloud top during EUREC4A are similar to those measured in trade cumuli over the Indian Ocean or in cumulus clouds over the sea around the UK .

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-3), all processed at a frequency of 1 Hz.

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 µm to 2.55 mm. The composite dataset includes additional products such as a cloud mask, a drizzle mask and a rain mask (defined in Sect. ) as well as the sixth moment of the particle size distribution to ease the comparison with radar reflectivities. The periods of flight when the probes are switched off are filled with NaN values. All datasets also include the time and aircraft position from the SAFIRE-CORE dataset.

The LWC measurements from the LWC-300 are included in the SAFIRE-CORE dataset at 1 Hz.

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 main role of this lidar was to measure, together with the BASTA radar described next, the fractional area covered by the cloud field near the cloud base level. For this purpose, the line of sight of the lidar was oriented horizontally, looking through one of the ATR windows (UV fused silica glass) on the right side of the aircraft (Fig. ).

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) ×20 km (cross track), the lidar was able to sample most of the rectangle area unless thick clouds within the rectangle extinguished the lidar signal at some distance of the aircraft.

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  shows the relationship between the total concentration of particles measured by the UHSAS microphysical probe (Sect. ) and the aerosol extinction coefficient retrieved from ALiAS lidar data. Although the two instruments sample the atmosphere very differently (the lidar probes the atmosphere horizontally perpendicular to the aircraft trajectory over a range of several kilometers, while the UHSAS probes the atmosphere in situ along the aircraft trajectory), the two measurements are highly correlated (R=0.89). It shows the consistency of the two measurements and confirms the strong variability in the atmospheric load in aerosols during the campaign (Fig. ).

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 ). BASTA is a 1 W bistatic FMCW (frequency-modulated continuous-wave) 95 GHz Doppler cloud radar developed from the ground-based BASTA system . It was used in an aircraft for the first time during EUREC4A, with two antennas of 20 cm (0.95∘ beamwidth) installed in back lateral windows of the ATR (Table ). The radar was operated in two modes, one after the other, at 12.5 and 25 m range resolutions with 0.5 and 1 s time resolutions, respectively. It led to a measurement in one mode every 1.5 s. The maximum range was 12 km with an ambiguous velocity of 9.85 m s-1 for both modes. The minimum detection range is about 80 m from the aircraft due to coupling between the two antennas.

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 -35 dBZ at 1 km. Level 2 data are the most elaborated product, the two modes being combined to optimize the advantages of each range resolution. Within the first 250 m, the 12.5 m mode is used, while the 25 m mode covers the rest of the profile. The combined reflectivity and Doppler profiles are available every 1.5 s. The radar gates are geolocated in latitude, longitude and altitude in order to derive maps. The reflectivity is corrected for gaseous attenuation using colocated information from dropsonde temperature, humidity and pressure. A parameterization of liquid attenuation for both cloud and precipitation as a function of reflectivity was derived thanks to in situ data and applied to correct reflectivity for liquid attenuation. The corrected reflectivity is then used to distinguish cloud areas from drizzle or rain (Sect. ). The radar Doppler velocity is corrected for aircraft motion and folding using gate-to-gate correction. All files are available in a self-documented NetCDF file.

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 (Zd) is often set at -20 dBZ (e.g., ), but it can also be set at -17 or -15 dBZ. The BASTALIAS dataset thus considers three options for the definition of cloud droplets, associated with each of these thresholds. The threshold distinguishing drizzle from rain is set at Zr=0 dBZ.

To assess the ability of these reflectivity thresholds to distinguish between cloud, drizzle and rain situations, we calculate the reflectivity ZPMA that would correspond to the drop size distribution of the PMA dataset. It is done using the T-matrix approach and accounting for the beam orientation and for the non-sphericity of large particles. For the smallest particles, ZPMA follows Rayleigh theory and is equal to 10log⁡10(M6), where M6 is the sixth moment of the drop size distribution. The distribution of ZPMA values for situations classified by the PMA microphysical masks (based on LWC and D measurements; Sect. ) as cloud-only, drizzle-only or rain-only shows that clouds and rain are mainly associated with reflectivities lower than -20 dBZ and larger than 0 dBZ, respectively (Fig ), which supports the Zd and Zr thresholds used in the BASTALIAS dataset. Reflectivities between -20 and 0 dBZ are predominantly associated with drizzle. However, drizzle is associated with a broader range of reflectivities, and therefore its identification from reflectivity thresholds remains imperfect.

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 (Dcloudradar) is determined by the distance at which the expected radar sensitivity corrected for attenuation equals Zd. In the trade-wind boundary layer conditions of EUREC4A, the radar could detect clouds over a maximum horizontal distance ranging from 1.5 to 3.5 km (2.2 km on average) for Zd=-20 dBZ and from 2.9 to 6.2 km (3.8 km on average) for Zd=-15 dBZ. On the other hand, drizzle and rain could be detected at any distance up to 12 km if there is no rain in the vicinity of the aircraft.

In parallel, the ALiAS lidar data at their original resolution (Level 1.5 data from ) are analyzed to determine the horizontal lidar profile that corresponds to the molecular or aerosol backscatter, to estimate the noise level, and to detect the presence of clouds. The lidar cloud detection methodology used in the BASTALIAS dataset is inspired from that developed for the CALIPSO spaceborne lidar and the airborne LNG (Leandre Nouvelle Génération) lidar . Although derived from a different methodology, the lidar-only cloud mask of the BASTALIAS dataset is very consistent with that proposed by , showing the robustness of the cloud detection from lidar measurements. This information is then used to define a lidar pseudo cloud mask at the same space and time resolution as the radar information (for this purpose, each radar time is associated with the closest lidar observation in time). The cloud detection by the lidar is considered impossible beyond the distance from the aircraft (Dcloudlidar) at which the lidar backscatter signal is completely extinguished or undistinguishable from noise. During EUREC4A, Dcloudlidar ranged from 0 to 8 km and was about 5 km on average.

Finally, the lidar and radar cloud masks are analyzed jointly to make a final classification of hydrometeors and a lidar–radar cloud mask (Fig. ). The synergy between lidar and radar is illustrated by two examples of individual radar and lidar profiles along their horizontal line of sight (Fig. ). In the first one, derived from a flight (RF05) associated with small very shallow clouds (“Sugar”; Table ), the lidar detects three clouds in a row which are not detected by the radar; beyond Dcloudlidar (6.3 km), the backscatter signal is extinguished, and the cloud detection becomes impossible. In the second example, from RF11, the radar detects a cloud within the first kilometer, and then drizzle and rain. Wherever the radar detects drizzle or rain, the hydrometeors detected by the lidar are not considered to be a cloud in the cloud mask.

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∘, 30 cm large; Table ). The radar was dedicated to the characterization of cloud microphysics and dynamics. The radar was operated at 30 m resolution with a maximum range of 6 km at 1 s integration. Both Doppler moments (reflectivity and velocity) and spectrum are available. As for BASTA, the radar reflectivity is range-corrected and calibrated, and the background noise is removed using a thresholding technique based on the background noise characteristics. The derived mask is refined thanks to some image processing. The reflectivity is corrected for gaseous attenuation using colocated information from dropsonde temperature, humidity and pressure. Once the Doppler velocity is unfolded and corrected from the aircraft's motion and when backward and zenith antennas are simultaneously available, the vertical velocity and the along-track wind components of the cloud and precipitation wind are retrieved. Two antennas allow us to retrieve the two components of the wind in the plane defined by the two antennas.

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  shows the reflectivity and Doppler velocity measured in the vertical and radial directions by the RASTA and BASTA radars during a leg of RF17. During this flight, the height of precipitating cloud tops could exceed 3 km. The vertical and radial reflectivity structures tend to reflect each other, suggesting a well-defined geometry (or aspect ratio) of the clouds. The vertical structure of the Doppler velocity from RASTA exhibits a maximum positive velocity near cloud top and negative velocities in the parts of clouds that are associated with falling hydrometeors (rain or drizzle).