dataesr/scientific-paragraphs-categorization huggingface dataset:
The 89 GHz channel is especially sensitive to the surface emission and the emission by liquid clouds. Over the open ocean, where the emissivity of the surface is low, this channel can be used to retrieve the LWP  40. Note, that the passive observations are for a slanted path of 25° and that no correction for the attitude and viewing geometry has been applied. As for the active measurements, the time resolution of the brightness temperature datasets is approximately 1 s. The MiRAC-A datasets are available in netcdf format on PANGAEA for 14 flights of AFLUX  37  and seven flights of MOSAiC-ACA  38  in 1 s resolution. For both data, reflectivities and brightness temperatures, a flag indicating the instrument status is provided. Microwave radiometers. During AFLUX and MOSAiC-ACA, passive microwave radiometers have been operated on board Polar 5 in addition to the passive channel at 89 GHz of the MiRAC-A radar.
In addition to atmospheric parameters, the passive microwave radiometers can be used to derive ocean surface as well as sea ice emissivities. For MiRAC-P, data are available for all 14 flights of AFLUX, whereas HATPRO measured during nine flights of MOSAiC-ACA. The final datasets  44, 45  for both instruments have been corrected for non-physical brightness temperatures by hand and doubled time stamps have been removed. The uploaded datasets have a 1 s resolution and are available in netcdf format. AMALi. The Airborne Mobile Aerosol Lidar (AMALi; AWI Potsdam, Germany) system  46  has been operated onboard Polar 5 in both campaigns installed inside the cabin pointing nadir through the floor, thus, probing the atmosphere between the flight level and the surface. It is a backscatter lidar having three channels: one unpolarized channel in the ultraviolet at 355 nm and two channels in the visible spectral range at 532 nm (perpendicular and parallel polarized).
For eye safety reasons, AMALi was operated at flight altitudes above 9000 ft only. Overlap between the transmitted laser beam and the receiving telescope is achieved for ranges larger than 235 m  46. Data are recorded with 7. 5 m vertical and 1 s temporal resolution. For consistency to the radar profiles, the AMALi data were converted into "altitude above sea level" by using the GPS altitude. To improve the signal-to-noise ratio, the profiles were averaged for 5 s temporal resolution, which yields a horizontal resolution of approximately 350 m for typical aircraft speed during measurements. The backscattered intensities can be converted into attenuated backscatter coefficients, depolarization ratio at 532 nm, and the color ratio (532 to 355 nm) to analyze cloud and aerosol particles (not provided in the dataset).
The data processing eliminated the background signal, which mainly results from scattered sunlight and electronic noise. Additionally, a drift of the so-called baseline of each channel was corrected for. Neglecting aerosol extinction, the attenuated backscatter coefficients for each channel were calculated from the background-corrected signals by normalizing the measurements to a typical air density profile  47. This is done by using data from the AWIPEV  24  station in Ny-Ålesund. The published dataset provides cloud top heights derived from the lidar profiles in 1 s resolution and by that as well the cloud mask. Clouds below the aircraft were identified from the attenuated backscatter coefficients in the 532 nm parallel channel. Each height bin of the profile, which exceeds the backscatter coefficients of a reference cloud-free section of five bins above the possible cloud detection by a factor of five, was labeled as a cloud.
Cloud top height was then defined as the highest altitude, which meets the above criterion for consecutive altitude bins. In the published datasets, cloud tops in close distance to the aircraft (less than 100 m below the flight level) and low clouds (below 30 m above the ground) are excluded. The datasets are available in netcdf format and 1 s resolution on PANGAEA for twelve flights of AFLUX  48  and five of MOSAiC-ACA  49. SMART. The Spectral Modular Airborne Radiation measurement sysTem (SMART; Leibniz-Institute for Tropospheric Research, Germany and Enviscope GmbH, Germany) is configured to measure the spectral solar irradiance and radiance. It is equipped with four optical inlets mounted at the fuselage of the Polar 5 and connected via optical fibers to grating spectrometers. These spectrometers disperse the incident radiation on a single-line photodiode array.
Dark measurements are conducted with optical shutters. The upward-looking optical inlets are actively horizontally stabilized with respect to aircraft movement within pitch and roll angles of 5°5 0. Irradiance measurements by SMART cover a spectral range between 300 and 2200 nm, while spectral radiance is measured between 300 and 1000 nm only  16, 51. Both are sampled with a frequency of 2 Hz. Due to an increase of noise at the edges of the measured spectra, the final data are provided for 400 and 1800 nm wavelength, only. The measurement uncertainties are related to the radiometric and spectral calibration and to the correction of the cosine response which sum up to a total wavelength-dependent uncertainty ranging between 3 and 14%  52. During AFLUX, only the upward facing optical inlets for the observation of the downward radiance and irradiance could be installed.
However, for most of the time, the measured signal was either contaminated by condensation on the inside of the optical inlets or the stabilization platform did not work properly. Therefore, the SMART dataset providing quality checked, radiometrically calibrated, and cosine corrected solar spectra (400-1800 nm) along the flight tracks, is only available in netcdf format with a 2 Hz resolution on PANGAEA for eight flights of MOSAiC-ACA  53. Spectral imager. The Airborne Imaging Spectrometer for Applications (AISA) Hawk  16, 54  and AISA Eagle  16, 55  (designed by Specim Ltd. , Finland) were operated onboard the Polar 5 during AFLUX and MOSAiC-ACA. AISA Hawk consists of a downward-viewing push-broom sensor aligned across the flight track to measure 2-dimensional (2D) fields of upward radiance.
It contains 384 across-track pixels, where each pixel delivers a whole spectrum in a wavelength range between 930 and 2500 nm in 288 channels with an average spectral resolution of 5. 6 nm. AISA Eagle is the second imager and uses a similar measurement technique like AISA Hawk, but covers a shorter wavelength range with a higher spectral and spatial resolution. In comparison to AISA Hawk, it has 1024 across-track pixel and 504 spectral channels to cover a wavelength range between 400 and 970 nm with 1. 2 nm spectral resolution. AISA Hawk and AISA Eagle have a field of view of 36° and used a sampling frequency of 20 Hz during both campaigns. However, the data were not recorded continuously throughout the whole flight. Measurement sequences of approximately 10 min duration were performed, whenever the conditions were appropriate (no in-cloud measurements, no measurements in too close distance to the cloud top or surface).
The quality checked and radiometrically calibrated datasets contain 2D fields of cloud top and surface spectral radiance observed along the flight track and are available on PANGAEA in netcdf format with 20 Hz resolution for thirteen flights of AFLUX  56  and seven flights for AISA Eagle during MOSAiC-ACA  57. Due to condensation on the quartz window of AISA Hawk in cold environments (high flight altitude), these data are only available for five flights during MOSAiC-ACA. Nikon. To measure the directional distribution of upward radiance in the full lower hemisphere, a commercial digital camera (Nikon D5; Nikon Inc. , Japan) was mounted at the bottom of the fuselage. The camera was equipped with a fish-eye lens during the campaigns, with the exception of the first half of MOSAiC-ACA, where a wide-angle lens was used.
Following the method described by  58  , the HDRFs of sea ice and open-ocean surfaces can be separated employing a sequence of surface images. Further, the Nikon data were used to classify the sea ice and ocean surface into open water, sea ice, and melt ponds based on color thresholds. Depending on the illumination conditions, these thresholds were determined using color intensity histograms which were created for training samples  59. The datasets are available on PANGAEA in netcdf format in a 4 to 6 s resolution for thirteen flights of AFLUX  60  and eight of MOSAiC-ACA  61  , respectively. Broadband radiation. Solar and terrestrial broadband irradiances were measured by a pair of upward-and downward-looking CMP22 pyranometers (spectral range of 0. 2 to 3. 6 m) and CGR4 pyrgeometers (4. 5 to 42 m), respectively (both designed by Kipp & Zonen B.
V. , Netherlands). The sampling frequency of the radiometer is 20 Hz. Unlike SMART, the sensors are fixed to the aircraft frame. Therefore, the data processing includes a correction for the aircraft attitude and accounts for the sensor inertia. In order to reconstruct fast changes of irradiance time series despite the slow sensor response, a deconvolution method was applied  62. During AFLUX, time constants (e-folding time) of 1. 4 s for the pyranometer and 3. 6 s for the pyrgeometer were used as determined in the laboratory. For MOSAiC-ACA, the time constants were adjusted from in flight maneuvers of known irradiance changes (e. g. , turns). The adjusted time constants amount to 1. 8 s for the pyranometer and 3. 4 s for the pyrgeometer. Remaining dynamic effects of the pyrgeometer may results from rapid changes of the ambient temperature, when the temperature of the silicon dome adapts faster than the sensor temperature  63.
Therefore, sections with a change of air temperature larger than 0. 5 K min -1 were flagged, as indicated in the dataset. These data, which often refer to ascents and descents needs to be analyzed with care. The downward solar irradiance was corrected for the aircraft attitude following a common geometric post-processing procedure  64. This correction holds only for direct solar radiation and was applied only for manually identified sections that were dominated by direct illumination (e. g. , cloud-free above the aircraft). In these conditions, the fraction of the direct downward solar radiation was be determined by radiative transfer simulations. The selection of cloud-free conditions might be uncertain. To allow user of the data to make their own decision, both uncorrected data (referring to cloudy conditions) and corrected data (referring to cloud-free conditions) are provided in the published solar downward irradiance dataset of MOSAiC-ACA  65.
KT-19. The brightness temperature below Polar 5 was measured by an infrared radiation thermometer (KT-19. 85II, short KT-19; Heitronics Infrarot Messtechnik GmbH, Germany) looking into nadir-direction. The instrument operates in a spectral range between 9. 6 and 11. 5 m where the impact of atmospheric absorption is negligible. Thus, the brightness temperature measured in flight altitude is assumed to equal either the cloud top or the surface brightness temperature to a good approximation  68. The brightness temperatures of the KT-19 are measured at 20 Hz resolution and published in a joint dataset with the broadband irradiances measured by the pyranometers and the pyrgeometers and is available for the same flights, 14 AFLUX 66 and eight MOSAiC-ACA  67  , respectively. Sun photometer. The airborne Sun photometer with an active tracking system (SPTA; Dr.
The final processed dataset has a resolution of 15 s and is available in ascii format for eleven flights of AFLUX  71  on PANGAEA. For MOSAiC-ACA the data have not been processed yet. Scattering cloud probes. Data recorded by the Cloud Aerosol Spectrometer (CAS; Droplet Measurement Technologies, Longmont, CO, USA) and the Cloud Droplet Probe (CDP; Droplet Measurement Technologies, Longmont, CO, USA) give the droplet size distribution from 3 to 50 m  [72] [73] [74] [75]. Both instruments determine the particle drop size by the intensity of forward scattered laser light (4-12°, given by the manufacturer) underlying Mie theory. Standard methods for size calibration using mono-disperse glass beads have been applied  76. For both instruments, the size of the sample area was determined by using a piezoelectric droplet generator setup similar to the design of Lance et al.
77. For sizing the particles the Mie binning approach  78  was applied with the refraction index of water (n = 1. 333), including a distinct choice of bin edges to avoid ambiguities due to Mie resonances in the size range below 10 m. Here the range of diameter can vary by a factor of two while data above 10 m have reduced Mie oscillation and their uncertainty drops to ~30%  79. The final datasets include the total include the total particle number concentration, effective diameter, and liquid water content (LWC) in addition to the particle number concentration in each size bin. These datasets are published in netcdf format and 1 Hz resolution for thirteen flights of AFLUX  80  (CAS) and eight flights of MOSAiC-ACA  81. The Polar Nephelometer (designed by Laboratoire de Météorologie Physique, France) measures the angular scattering coefficients (ASC, i.
ASC can be used to discriminate spherical from non-spherical cloud particles, as well as the dominant cloud thermodynamical phase  84, 85. In addition, the extinction coefficient and the asymmetry parameter g can be derived from these measurements  86, 87  with uncertainties of 25% and 0. 04, respectively. The ASCs are provided with a temporal resolution of 1 Hz in netcdf format for 14 flights of AFLUX  88  and seven of MOSAiC-ACA  89  on PANGAEA. Optical array probes. The basic measurement of optical array probes (OAP) is shadowgraphs of water and ice particles. Two-dimensional images of hydrometeors are reconstructed from individual slices, where a slice is the state (shadowed or non shadowed) of a linear multi element photo diode array at a given moment in time. The data recorded by the Cloud Imaging Probe (CIP; Droplet Measurement Technologies, Longmont, CO, USA), the Precipitation Imaging Probe (PIP; Droplet Measurement Technologies, Longmont, CO, USA)  72  , and the 2D Stereo Imaging Probe (2D-S; Stratton Park Engineering Company, Boulder, CO, USA)  90  differ in pixel quantity and resolution (64 diode array with 15 m resolution for CIP, 64 diode array with 103 m resolution for PIP, and 128 diode array with 10 m resolution for 2D-S).
From the individual CIP and PIP datasets, a combined dataset including CAS/CDP was created, which contains a continuous size spectrum of hydrometeors from 3-6400 m and microphysical properties including total number concentration, ED, MVD and an estimated cloud water content (CWC) derived from it. Here, for the CWC calculations particles smaller 50 m are assumed as droplets and larger 50 m as ice crystals, which is appropriate for Arctic mixed-phase clouds  94, 95. Raw data recorded by the 2D-S were processed using code described in  96. The particle number size distribution (PNSD) of hydrometeors are computed from the 2D-S images following the procedure similar as the one for the CIP and PIP during ACLOUD  16. Two sets of PNSD are calculated for each probe channel (horizontal and vertical) using two different definitions of the cloud particle diameter: Circumpolar diameter (Dcc), the diameter of the circle encompassing the particle image (equivalent to circle-fit sizing in SODA) commonly used with the Brown and Francis  93  (labeled with BF95) mass diameter relationship and equivalent diameter (Deq), the diameter of a circle which has the same surface as the particle image.
25)  99. Total number concentrations and effective diameters of the sampled hydrometeors are derived from each PNSD whereas ice crystal effective diameter, ice median mass diameter, and IWC are calculated using the BF95 mass-diameter relationship. The slight deviation in the PNSDs between the 2D-S (horizontal channel, using all images and Dcc as particle sizing) and the combined spectra of CAS, CIP, and PIP in Fig. 6a ,b result from the different processing algorithms explained above  100. Besides processing, matching measurements from different instruments with overlapping sizes is challenging because of their respective uncertainties  78. However, the deviations here are within the scope of the uncertainties of the instruments. The data of the OAPs are published on PANGAEA in netcdf format in 1 Hz resolution for 14 flights for 2D-S 88 and thirteen for CIP and PIP  80  of AFLUX, respectively.
For MOSAiC-ACA, seven flights for 2D-S  89  and eight for CIP and PIP  81  are available. The combined dataset of CAS/CDP, CIP, and PIP is available with the same resolution and format have been published for thirteen AFLUX  80  and seven MOSAiC-ACA 81 flights, respectively. Nevzorov. During the flight campaigns, a Nevzorov probe  101, 102  (designed by Sky Physics Technology, Woodbridge, ON, Canada) was installed on the fuselage of Polar 5 aircraft. The Nevzorov probe is a constant-temperature, hot-wire probe designed for the airborne bulk measurements of the LWC and total water content (TWC) of clouds in 1 Hz resolution. It has to be noted, that data recorded during a large temperature gradient, respectively during ascent and descent, might be inaccurate. Due to instrumental limitations ice fractions smaller 0.
1 are difficult to resolve. As a result, it is challenging to retrieve both LWC and IWC in liquid dominated Arctic mixed-phase clouds  103. Due to an incorrect setting during AFLUX, only MOSAiC-ACA  81  data for eight flights are published on PANGAEA in netcdf format and 1 Hz resolution. Dropsondes. In total, 93 dropsondes were released from the Advanced Vertical Atmosphere Profiling System (AVAPS) installed on Polar 5 during both campaigns (33 during AFLUX, 60 during MOSAiC-ACA). The dropsondes measured vertical profiles of pressure, temperature, humidity, and the horizontal wind vector. The vertical resolution of the measurements was 5 to 6 m. With a sampling frequency of 2 Hz, this corresponds to a fall velocity of about 10 to 12 m s -1. The dropsonde type RD94 used during AFLUX was replaced by the new type RD41 during MOSAiC-ACA, which contains improved temperature and humidity sensors.
3 and 1. 6 s, respectively. The humidity profiles show a dry-bias as they never reach a relative humidity of 100% inside clouds, which could be due to increasing contamination of the polymer film of the humidity sensor as the dropsondes age  106. A reconditioning procedure aiming to correct for this bias has not been performed before each launch during the campaigns. The dropsonde data obtained during MOSAiC-ACA were thus corrected for the dry bias. An individual correction factor was applied to each humidity profile such that the saturation level of 100% is reached inside clouds. The correction factor is in the range of 1. 025 for the majority of the sondes. This correction has not been done on the dropsonde data for AFLUX uploaded to PANGAEA. The published datasets contain both the temperature and humidity data processed and corrected by ASPEN and the manually corrected data.
Data Records All datasets are published in PANGAEA with open access. Table  3  lists the corresponding dataset identifiers. Dataset collections of all corresponding datasets have been compiled, for both campaigns, AFLUX  26  and MOSAiC-ACA  27. With the exception of the nose boom and sun photometer data, that are available in compressed ascii format, all datasets have been converted to and are available in NetCDF4 file format. In general, each data file contains the data for one research flight. The files are identified by date and research flight number according to Table  1. An exception are the data of the fish-eye camera. With several gigabytes per hour, these data are very large and therefore provided in hourly files. The datasets available on PANGAEA contain all necessary information needed to work with the data.
technical Validation The quality of the datasets has been assured by multiple steps. First, the instruments have been calibrated either before the installation into the aircraft in a laboratory, on ground during flight preparation before take-off, by specific flight patterns under well defined conditions during the research flights, or by cross-calibration with well calibrated instruments. Second, each instrument team conducted quality control by applying methods based on their respective user community standards. Most of the calibration procedures and the methods applied to ensure the quality of the collected data are described in the respective section, the data publication for the ACLOUD campaign  16  , in other peer-reviewed publications given in Table  2 , or have been operated as supplied by the manufacturer.
AISA Eagle and Nikon data were spatially averaged to match the size of the SMART footprint of 2°. Figure  7  displays scatterplots of the radiance data using SMART as a reference. The correlation between SMART and AISA Eagle data (orange dots) is consistent for all three channels with a correlation coefficient (R) of about 0. 97 and an offset of 6%, which falls within the measurement uncertainty of the two instruments. The correlation coefficient between SMART and Nikon data is slightly lower with R = 0. 94. The best agreement was found for the red and the green channels, while a significant offset of about 22% was derived for the blue channel. Finally, these findings were used to calibrate the Nikon camera in order to provide a consistent dataset.
Usage Notes During the field campaigns MOSAiC-ACA and AFLUX, a suite of remote sensing and in-situ instruments has been successfully operated on board the Polar 5 research aircraft to perform measurements of clouds, precipitation, and the structure of the lower Arctic atmosphere. The datasets collected can be used for a wide range of studies and are especially well suited for studies on Arctic mixed-phase clouds and boundary-layer processes, to derive higher level products by appropriate retrieval algorithms, or to perform model or satellite validation studies. Along with the measurement campaigns conducted in the past years, the python package ac3airborne 23 has been compiled to make the airborne data more visible and more readily usable. ac3airborne is a simple python module that follows the idea of the EUREC4A 108,109 community.
It is publicly available on github. The module makes use of the intake 110 python library, that contains drivers for loading different file formats, cataloging system for specifying the sources of datasets as machine-readable YAML (YAML Ain't Markup Language) files, and a server-client architecture to share the catalog meta data over the network. By that, all datasets of each instrument for every flight performed in (AC)  3  are easily accessible without knowing their storage location or format. No additional information is needed. Everything else is handled by the package. Within the ac3airborne package, scripts are included that have been used to perform conversions on the publicly available datasets on PANGAEA for a better integration into the structure. A central part of the package is the flight segmentation  111  , where each research flight has been split up into logical parts like ascends, descends, specific patterns for in-situ probing, high, mid, or low level legs, and patterns for calibration purposes.
By making use of this information defined by start and end time stamp of the specific section, it is easy to extract the data of interest. The usage of the package together with a collection of example scripts is presented on How to ac3airborne  112  , an online and interactive jupyter book  113. There, an overview of the data availability for each instrument on each flight is shown for all AC3 campaigns. The sections of the jupyter book describe simple usage cases of the datasets from reading procedures to quicklook production or more complex scripts for combining datasets from the different instruments. The use of the information provided by the flight segmentation is explained in more detail and its application shown along with the different code examples. For example, the data presented in Figs.