Continuous, semi-long-term, ground-based in situ cloud
measurements were conducted during eight Pallas Cloud Experiments (PaCEs)
held in autumn between 2004 and 2019. Those campaigns were carried out in
the Finnish sub-Arctic region at the Sammaltunturi station
(67∘58′24′′ N,
24∘06′58′′ E;
560 m a.m.s.l.), the part of the Pallas Atmosphere–Ecosystem Supersite and Global
Atmosphere Watch (GAW) program. Two cloud spectrometer ground setups and a
weather station were installed on the roof of the station to measure in situ
cloud properties and several meteorological variables. Thus, the obtained
data sets include the size distribution of cloud droplets as a measured cloud
parameter along with the air temperature, dew point temperature, humidity,
pressure, horizontal wind speed and direction, (global solar) sun radiation,
and visibility at the station. Additionally, the number concentration,
effective diameter, median volume diameter, and liquid water content from
each instrument were derived. The presented data sets provide a insight into
microphysics of low-level clouds in sub-Arctic conditions over a wide range
of temperatures (-25.8 to 8.8 ∘C). The data are available in the Finnish Meteorological
Institute (FMI)
open data repository for each campaign and each cloud spectrometer ground
setup individually: https://doi.org/10.23728/FMI-B2SHARE.988739D21B824C709084E88ED6C6D54B
(Doulgeris et al., 2021).
Introduction
Clouds are considered a major component of both the climate system and
the hydrological cycle. Nevertheless, our level of understanding of the
fundamental details of the cloud microphysical processes is still very
limited (Boucher et al., 2013). To gain a deeper knowledge of the formation
and development of the clouds, more in situ measurements are needed
(Morrison et al., 2019). In addition, a correct representation of cloud
microphysics in general circulation models for numerical weather and climate
prediction is of great importance (Guichard and Couvrex, 2017; Morrison et
al., 2020). Despite the fact that cloud processes can now be studied with
much more confidence (Bony et al., 2015), representing the formation and
evolution of cloud droplets and the effects of aerosols on clouds at various
meteorological conditions remains a challenge (Grabowski et al., 2019). The
number concentration and size distribution of cloud droplets are considered key parameters for a quantitative microphysical description of clouds
(e.g., Rosenfeld and Ulbrich, 2003; Komppula et al., 2005; Lihavainen et
al., 2008; Pruppacher and Klett, 2010; Chang et al., 2019) and are
connected with the cloud lifetime and radiative effects as well as
precipitation (e.g., Albrecht, 1989; Devenish et al., 2012; McFarquhar et
al., 2020).
Three general approaches were used in previous studies of cloud
microphysical properties: in situ sampling through airborne measurements by
aircraft (e.g., Heymsfield et al., 2011; Craig et al., 2014; Petäjä
et al., 2016; Nguyen et al., 2021) and recently by unmanned aerial systems
(UASs) (e.g., Girdwood et al., 2020; Brus et al., 2021; Harrison et al.,
2021), in situ sampling by using laboratory cloud chambers (e.g., Möhler
et al., 2003; Stratmann et al., 2004; Nichman et al., 2017; Doulgeris et
al., 2018), and in situ ground-based measurements (e.g., Guyot et al., 2015;
Lloyd et al., 2015; Lowenthal et al., 2019; Doulgeris et al., 2020). In
situ airborne and ground measurements (Wandinger et al., 2018) using cloud
spectrometers are considered fundamental as they offer instrumental access
to individual hydrometeors within a sampling volume. Unfortunately, each of
the aforementioned approaches has inherent limitations.
Data sets that have been obtained from measurements in sub-Arctic clouds are
significant as cloud processes are of high value since cloud processes are
considered an important component of climate change in the Arctic region
(Wendisch et al., 2019). Pallas Cloud Experiments (PaCEs) took place in the
Finnish sub-Arctic. The main objective during PaCE was to study low-level
clouds and their microphysical properties in a background sub-Arctic
environment. In this work, we present a unique data set of ground in situ
cloud measurements along with several meteorological variables collected at
the Sammaltunturi station in eight autumn campaigns conducted between 2004
and 2019. This data set can be used in studies of cloud microphysics,
climate change in the sub-Arctic, and performance evaluation and improvement of
existing models, in particular at higher altitudes. In the next section, we
provide a description of the sampling location, instrumentation, and the
measurement methodology we used for sampling, data processing, and quality
control.
MethodsMeasurement site and PaCE campaigns overview
The Sammaltunturi station (67∘58′24′′ N, 24∘06′58′′ E) is hosted by the Finnish Meteorological
Institute (FMI) and is located on a top of an arctic fell (560 m a.m.s.l.)
in the Finnish sub-Arctic region inside the Pallas–Yllästunturi
National Park (Fig. 1). The Pallas area is located around 180 km above the
Arctic circle, and it has no significant local or regional air pollution
sources. Thus, the Sammaltunturi station provides an excellent location for
the monitoring of background air composition in northern Europe. The station
is about 100 m above the tree line, and the vegetation around it consists
mainly of low vascular plants, mosses, and lichen. There is a long history
of atmospheric data collection in the area (see Lohila et al., 2015).
Monitoring activities of atmospheric composition at Sammaltunturi started in
1991 in a building that originally served the Finnish Broadcasting Company.
The new station (102 m2) opened in July 2001. Since 1994, Sammaltunturi
has been established as a node of the Pallas–Sodankylä supersite that
contributes to the GAW program of the World Meteorological Organization. The
site was described in detail in Hatakka et al. (2003). The main research
measurements focus on greenhouse gas concentration, climate effects of
atmospheric aerosols, aerosol cloud interaction, and air quality (e.g.,
Komppula et al., 2005; Lihavainen et al., 2008; Asmi et al., 2011; Backman
et al., 2017; Doulgeris et al., 2020). The predominant origin of air masses
arriving at Sammaltunturi is from the Arctic (Asmi et al., 2011).
The main motivation to perform in situ cloud measurements at the
Sammaltunturi was that the station was occasionally immersed in a cloud.
Based on analytical data the most suitable time of the year for in situ
cloud measurements was autumn when the horizontal visibility drops below 1 km around 40 % of the time (Hatakka et al., 2003). Once the preferable
time of the year was identified, we started to conduct ground-based in situ
measurements and study cloud formation. The Pallas Cloud Experiments
were, usually, 6–8 weeks long and lasted approximately from the beginning of
September until the end of November, occasionally extended to the beginning
of December. The first attempt at measuring in situ cloud properties was
made in 2004 using the forward-scattering spectrometer probe (FSSP-100)
ground setup that was the only available cloud spectrometer at that time.
The next campaigns, in 2005 and 2009, were done using the same instrument
setup (Lihavainen et al., 2008). Later, in 2011 the cloud, aerosol, and
precipitation spectrometer (CAPS) ground setup was added. In January and
February 2012, it was tested for the first time for two short periods during
winter at the Sammaltunturi site. In 2012, 2013, and 2015 both instruments
were installed and used during PaCE (Doulgeris et al., 2020). In 2017 and
2019, only CAPS was used (Girdwood et al., 2020). An overview of each year's
campaign duration and the cloud spectrometer ground setups' availability is
presented in Fig. 2. Instruments that were used for measuring the
meteorological variables and the solar radiation were operating continuously
during all PaCE years. The instrumentation used during PaCE campaigns is
described in detail in the following section.
Cloud spectrometer ground setups' availability during PaCE is
presented for each year.
Instrumentation
In order to monitor meteorological variables, the station was equipped with
an automatic weather station (Milos 500, Vaisala Inc.). A weather sensor
(model FD12P, Vaisala Inc.) was used for measuring the horizontal
visibility, the Vaisala HUMICAP was used for measuring the relative
humidity, BAROCAP sensors were used for measuring the barometric pressure,
and PT100 sensors were used to measure temperature at 570 m. Global
radiation and photosynthetically active radiation were measured with a
pyranometer and a photovoltaic detector, respectively. Additionally, the wind speed was measured with a heated cup and the wind
direction with a heated wind vane. All the above meteorological variables
were saved as 1 min averages. A detailed description of the weather
sensors can be found in Hatakka et al. (2003).
In order to conduct in situ cloud ground-based measurements, we deployed two
instruments. The cloud, aerosol, and precipitation spectrometer (CAPS) and
the forward-scattering spectrometer probe (FSSP-100; Droplet Measurement
Technologies (DMT), Boulder, CO, USA) (Fig. 3). The FSSP (model SPP-100,
DMT) was originally manufactured by Particle Measuring Systems (PMS Inc.,
Boulder CO, USA). Both instruments were originally developed for airborne
measurements but modified as ground setups by the manufacturer (DMT, USA).
They were installed on the rooftop of the Sammaltunturi station. The CAPS
was fixed with a heading always to the main wind direction of the station
southwest, ∼ 225∘, while the FSSP-100 was installed on a
rotating platform to continuously face the wind. The CAPS had a total height
of 0.6 m above the roof where it was installed and a height of 4.5 m from
the ground. FSSP had a total height of 0.6 m above the roof where it was
installed and a height of 5.5 m from the ground. The two setups had a
horizontal distance of ∼ 10 m and vertical distance of
∼ 1 m between them. From 2004 until 2012 a flow laminator
was used inside the FSSP inlet (Lihavainen et al., 2008). However, the flow
laminator was often blocked by freezing or supercooled cloud droplets at
sub-zero temperatures, and for this reason it was cleaned every hour if
occurrence of subcooled water was detected. The laminator blockage was
evident both during everyday instrument inspection and from the raw data.
Only data cleaned of this artifact were used in the FSSP data set. However,
even without placing the laminator, the Reynolds number indicated that the
flow inside the inlet was still laminar. As a result, in 2012 we decided
that the laminator would not be used in the FSSP setup anymore. Thus, the
number of data after 2012 were more extensive, and the number of cases when
the FSSP would have been blocked was significantly reduced. A detailed
description of both ground setups and the methodology we used for obtaining
the ground-based cloud microphysical properties with in situ method was
documented in Doulgeris et al. (2020). Only a short overview is given here.
CAPS (a) and FSSP-100 (b) ground setups as installed on the
roof of Sammaltunturi station.
The CAPS has been widely used in airborne measurements of the microphysical
properties in clouds (e.g., Baumgardner et al., 2001, 2011; Droplet Measurement Technologies Manual, 2011; Lachlan-Cope et al., 2016). The CAPS probe includes three
instruments: the cloud and aerosol spectrometer (CAS) which measures smaller
particles, the cloud imaging probe (CIP), and the hot-wire liquid water
content (LWChw) sensor. For the ground setup we deployed, the hot-wire
LWC faced difficulties operating in such extreme conditions; after
operating in supercooled liquid clouds (even for a short time) the sensor
was accreting ice. In addition, the lifetime of the sensor is limited and
significantly shorter than the duration of the campaign. The FSSP-100 was
widely used for measuring droplet size distribution (e.g., Brenguier, 1989;
Lihavainen et al., 2008; Lloyd et al., 2015; Doulgeris et al., 2020). CAS and
FSSP-100 derive the size of the particle from the intensity of the
scattered light, using the Mie theory (Mie, 1908). Furthermore, backscatter
optics measure light intensity in the 168 to 176∘ range. This
allows the determination of the real component of a particle's refractive
index for spherical particles. The CIP is a single particle optical array
probe. Its design is based on optical measurement techniques whereby single
particles pass through a collimated laser beam and their shadow is projected
onto a linear array of 64 photodetectors. The count of the particle is
dependent on the change in the light intensity of each diode.
All the instruments were calibrated before and after each campaign. Until
2011, we relied on the manufacturer calibration that was done at DMT. After
2011, we also started to perform calibration at the FMI, on top of
manufacturer calibration, to ensure the quality of the collected data. For
the calibration of the CAS and FSSP-100, glass beads in the diameter size
range 2–40 µm and polystyrene latex sphere (PSL) standards in the
diameter size range 0.74–2 µm were used. Cloud spectrometers (in
our case CAS and FSSP-100) are calibrated for size measurements but not for
number concentration measurements. The instruments faced extreme conditions
during the whole campaign, in terms of frequent changes in wind direction,
wind speed and sub-zero temperatures. Despite the calibration procedures we
should always keep in mind that extreme meteorological conditions could
possibly lead to unexpected performance. To calibrate the CIP, a spinning
glass disk with opaque dots of known size was used.
The CAPS ground setup included a high-flow pump (Baldor, Reliance, USA)
which worked as an aspiration system. The aspiration system was made
and provided by the manufacturer (DMT). A custom aspiration system with a high-flow ventilator was also made by the manufacturer (PMS) and employed through
FSSP-100 inlet to ensure constant flow through it. A digital
thermo-anemometer (model 471, Dwyer Inc.) was used in each campaign for
checks of daily cloud spectrometers' air speed. The FSSP air speed inside
the inlet was calculated from the measured air speed in front of the inlet,
except in 2004 and 2005 when the air speed was calculated with measured
volume flow rate through the inlet. A necking inside the inlet led the flow
from inner diameter 3.8 to 2.0 cm. Both spectrometers were equipped with
anti-ice systems as they were modified by the manufacturers (DMT for CAPS
and PMS for FSSP-100) for ground-based use. Despite the existing anti-ice
features, due to the subzero temperatures that they were facing, snow or ice
could accrete and affect the air speed inside the probe inlets. For this
reason, to ensure the proper operation of the instruments, they were
inspected and cleaned twice per day, every morning and evening
(approximately every 12 h).
The ground-based in situ cloud measurements provided the cloud and
precipitation size distribution. The PADS 2.5.6 software that was used for
the data acquisition of CAPS measurements (Droplet Measurement Technologies Manual, 2009) provided the
number concentration (Nc, cm-3), liquid water content (LWC, g cm-3), median volume diameter (MVD, µm), and effective
diameter (ED, µm). For the FSSP-100, Nc, LWC, MVD, and ED were
also derived using the same equations (Doulgeris et al., 2020), since we
used an older software for data acquisition (PACS 2.2, DMT).
The major sources of uncertainties of the cloud spectrometers can be
coincidence, dead-time losses, and changing velocity ratio (Guyot et
al., 2015). The uncertainty of estimation of sizing at the cloud
spectrometers was 20 % and that of the number concentration was 16 %
(Baumgardner, 1983; Dye and Baumgardner, 1984; Baumgardner et al., 2017).
According to Lance (2012), it was observed that for CAS at ambient droplet
concentrations of 500 cm-3 there was 27 % undercounting and a 20 %–30 % oversizing bias. In our case, during PaCE campaigns the droplet
number concentration values we monitored were in the majority of cases less
than 300 cm-3. These number concentration values lead us not to take
coincidence, dead-time losses, and velocity acceptance ratio (VAR) uncertainties into consideration in
this analysis. LWC has a significant uncertainty of 40 % (Droplet Measurement Technologies Manual,
2009). The FSSP-derived ED and LWC had an uncertainty of 3 µm and 30 % in mixed-phase clouds (Febvre et al., 2012). An overview of the
instrumentation and their operational characteristics we used for cloud
measurements are summarized in Table 1.
An overview of instrumentation and their operational
characteristics provided by manufacturer.
InstrumentOperating rangeNumber of binsSampling frequencyAir speed rangeAccuracyUncertaintiesCloud instrumentsCAS, DMT0.51 to 50 µm10, 20, 30, or 400.05 to 40 Hz10–200 ms-1upper Nc>1000 cm-3 aftercorrections for coincidence that are about25 % at 800 particles cm-3 and 30 % at1000 particles cm-3 sizing accuracy: 20 %ambient Nc of 500 cm-3: 27 % undercounting and 20 %–30 % oversizing bias Lance (2012) LWC: 40 % (DMT Manual)CIP, DMT12.5 µm to 1.55 mm620.05 to 40 Hz10–300 ms-1upper Nc range up to 500 particles cm-3 for a CIP with standard tips and arm width sizing accuracy: 1 µmdigitization uncertainty of approximately ±1 size resolution that depends upon where the particle passes across the array Baumgardner et al. (2017)FSSP-100, PMS0.5 to 47 µm15, 30 or 400.05 to 40 HzNc accuracy: 16 % sizing accuracy: ±3µm LWC accuracy: 30 %–50 % Baumgardner (1983); Baumgardner et al. (2017)derived ED: 3 µm derived LWC: 30 % Febvre et al. (2012)Meteorological instrumentsRangeResolutionSensitivityAccuracyPT100 sensor,Vaisala-70 to +180 (∘C)0.01 (∘ C)±0.1 (∘C)HUMICAP sensor,Vaisala0–100 (%) RH<0.01 (%) RH±0.8 (%) RHBAROCAP sensor,Vaisala500–1000 (hPa)0.01 (hPa)±0.15 (hPa)heated cup and wind vane, Vaisala0.4–75 (ms-1) 0 – 360∘0.1 (ms-1) 1∘±0.17 (ms-1) ±3∘Pyranometer,Vaisala305–2000 (W m-1)9–15 (µV W m-2)<±20 W m-2 at1000 W m-2FD12P, Vaisala10–50 000 (m)±10 %,10–10 000 m ±20 %,10 000–50 000 mOverview of data set and quality control description
The current data set contains only in-cloud measurements when the station was
immersed in a cloud. Data from each cloud probe and the weather station were
quality controlled and unified in a common format for release and further
analysis. The presence of a cloud at the station was identified with three
different factors. First, we checked the droplet size distribution measured
in both of the cloud spectrometers. This was the main parameter to consider
that the station was inside a cloud. Then, to confirm this assumption, we
cross-checked the droplets counts with two meteorological variables – the
relative humidity at the measurement site which was expected to be ∼ 100 % and the horizontal visibility which should be less than 1 km, when
the Sammaltunturi station is in the cloud. In the event that one of the factors
was not fulfilled, a final inspection was done visually using pictures
recorded by an automatic weather camera installed on the roof of the
station.
During PaCE 2004 and 2005 the sampling time of the FSSP-100 was 15 s. During
PaCE 2009 the instrument was set to sample at 10 s. From 2009 until 2019 the
sampling time was set to sample each 1 s (1 Hz) for both instruments. The PT100
sensor, Vaisala HUMICAP and BAROCAP sensors, the pyranometer, and the heated cup
and wind vane were also set to sample to 1 s. The FD12P Vaisala weather sensor
sampling time was 15 s. For every year, 1 min averages were calculated
for each cloud spectrometer and each meteorological variable. As a result,
we obtained the cloud droplet size distribution and several meteorological
variables for each minute and as derived parameters the Nc (cm-3), LWC (g cm-3), MVD (µm), and ED (µm). All data sets were converted to NetCDF format. All times in this work are given in
UTC time. Our data set includes a separate NetCDF and CVS file for each
cloud spectrometer and for each year under the file name
PACE.yyyy.cloud_spectrometer.nc and
PACE.yyyy.cloud_spectrometer.cvs (example names). For every
file, the sampling area (mm2) and the probe air speed (ms-1) that
were used to derive each parameter are provided. In addition, it includes the
cleaned timeline data set of the following cloud properties and
meteorological variables: year (YYYY), day (DD), month (MM), hour (HH), min
(MN), size bin lower limit, size bin higher limit, number concentration
(cm-3), liquid water content (g cm-3), effective diameter (µm), median volume diameter (µm), the calculated MSD (cm-3)
values in each bin, temperature at 570 m (∘C), dew point
(∘C), humidity at 570 m (%), pressure (hPa), wind speed (m s-1), horizontal wind direction (degrees), global solar radiation
(W m-2), photosynthetically active radiation (µmol m-2 s-1), and the horizontal visibility (m). The derived cloud parameters
– number concentration (cm-3), liquid water content (g cm-3),
effective diameter (µm), and median volume diameter (µm) – were not
included in the CIP files. The number of cloud droplets per minute in CIP
size range leads to statistically biased values, and for this reason we
decided to exclude them. The variables, naming abbreviations, and units are
summarized in Table 2.
Cloud properties and meteorological variables along with
abbreviations and units as they are included in each data set.
Variable nameAbbreviationsUnitsCommentsCloud properties Number concentrationNccm-3derived parameterLiquid water contentLWCg cm-3derived parameterEffective diameterEDµmderived parameterMedian volume diameterMVDµmderived parameterSize distributionMSDcm-3µm-1calculated from min averages counts per binMeteorological variables Temperature at 570 mT∘CPT100 sensorDew point temperatureTDP∘CRelative humidity at 570 mRH%Vaisala HUMICAP sensorPressurePhPaVaisala BAROCAP sensorWind speedWsms-1measured with a heated cupWind directionWdir∘measured with a heated wind vaneGlobal solar radiationSradW m-2PyranometerPhotosynthetically active radiationPARµmol m-2 s-1Photovoltaic detectorHorizontal visibilityVmFD12P Vaisala weather station
The CAS contains 30 size bins with a forward-scattering upper bin size of
0.61, 0.68, 0.75, 0.82, 0.89, 0.96, 1.03, 1.1, 1.17, 1.25, 1.5, 2, 2.5, 3,
3.5, 4, 5, 6.5, 7.2, 7.9, 10.2, 12.5, 15, 20, 25, 30, 35, 40, 45, and 50 µm, and the CIP contains 62 size bins with a bin size of 15, 30, 45, 60,
75, 90, 105, 120, 135, 150, 165, 180, 195, 210, 225, 240, 255, 270, 285,
300, 315, 330, 345, 360, 375, 390, 405, 420, 435, 450, 465, 480, 495, 510,
525, 540, 555, 570, 585, 600, 615, 630, 645, 660, 675, 690, 705, 720, 735,
750, 765, 780, 795, 810, 825, 840, 855, 870, 885, 900, 915, and 930 µm. For the FSSP-100 two different bin size ranges were used. During 2004 and
2005 the instrument was set up to use 30 size bins with a forward-scattering
upper bin size of 3.0, 4.5, 6.0, 7.5, 9.0, 10.5, 12.0, 13.5, 15.0, 16.5,
18.0, 19.5, 21.0, 22.5, 24.0, 25.5, 27, 28.5, 30.0, 31.5, 33.0, 34.5, 36.0,
37.5, 39.0, 40.5, 42.0, 43.5, 45.0, and 47.0. From 2009 until 2015, the FSSP was
set up to use 40 size bins with a forward-scattering upper bin size of 1.2,
2.4, 3.5, 4.7, 5.9, 7.1, 8.2, 9.4, 10.6, 11.8, 12.9, 14.1, 15.3, 16.5, 17.6,
18.8, 20, 21.2, 22.3, 23.5, 24.7, 25.9, 27, 28.2, 29.4, 30.6, 31.7, 32.9,
34.1, 35.3, 36.4, 37.6, 38.8, 40, 41.1, 42.3, 43.5, 44.7, 45.8, and 47 µm.
Measurements of each year were inspected to ensure a good quality of the
data set. First, the raw data set was checked in order to eliminate and
exclude from further analysis cases in which one of the cloud probes was
partially or fully blocked. Partially or fully blocked probes were also
visible in raw data. To detect blocked probes, Nc was carefully investigated
for the whole data set. When a sudden decrease just before a sudden increase
in droplet number concentration was occurring, we had a clear sign of probe
inlet freezing. This behavior was observed due to the opening of the probe
inlet becoming smaller (from the accumulation of snow/ice) and resulted in
a raised probe air speed. During data evaluation we considered that the
probe air speed was constant. This abnormality in the Nc was happening due
to the underestimation of the probe air speed. Then, we applied the
suggested corrections due to limitations (Doulgeris et al., 2020) for the
data analysis of the CAS and FSSP-100 ground setups. Doulgeris et al. (2020)
demonstrated that the CAPS (that was fixed to one direction) showed
significant sampling losses when it was not facing the wind direction since
it was not sampling isokinetically. For this reason, the data that were
obtained in the wind iso-axial conditions were considered to have the best
quality. Thus, regarding CAPS, only the measurements when the instrument was
facing the wind direction were included. The FSSP-100 ground setup was always
directed against the wind direction, and as a result we provided measurements
from all wind sectors. Missing data points were marked as -9999.9.
As it is shown in Fig. 4, the observation hours after PaCE 2013 when the
campaigns had longer duration are significantly higher. The number of data
in these years is excessive, serving as an important source of information
for Arctic studies. An overview of meteorological variables is presented for
each campaign when the FSSP-100 and CAPS ground setups were operational. In
Fig. 5, a statistical description of the temperature at 570 m a.m.s.l. for each
campaign is illustrated. Each PaCE year the temperature trends and ranges
were similar (around -10.0 to 8 ∘C). In Fig. 6, we show the percentage
of the data set for each year in which the global solar radiation was higher
than 0. It was used to estimate the number of data collected in each
campaign in daylight. In addition, an overview of the microphysical derived
cloud properties data from each campaign is presented. Thus, in Figs. 7, 8,
and 9, the number concentration, the effective diameter, the medium
volume diameter, and the liquid water content are presented for each campaign
and for the FSSP-100 and CAS ground setups, respectively. Number
concentration averaged values were similar for every year of the
measurements and reach scales around 100 cm-3. However, there were some
cloud cases during each campaign that number concentration had values around
300 cm-3. The averaged ED and MVD values were ranging approximately
from 10 to 20 µm. The liquid water content was less than 0.2 g cm-3 in most cases.
Hours of observation data collected for each PaCE campaign when
the FSSP-100 and CAPS ground setups were operational.
Statistical description of the temperature at 570 m a.m.s.l. for
each PaCE campaign when the FSSP-100 and CAS ground setups were operational.
The percentage of the global solar radiation that was higher than
0 during each campaign when the FSSP-100 and CAS ground setups were
operational.
Statistical description of Nc for each PACE campaign during
which the FSSP-100 and CAS ground setups were operational.
Statistical description of ED (a) and MVD (b) for each
PACE campaign during which the FSSP-100 and CAS ground setups were operational.
Statistical description of LWC for each PaCE campaign when the
FSSP-100 and CAS ground setups were operational.
Code availability
Software developed to process and display the data from the cloud ground-based spectrometers is not publicly available and leverages licensed data
analysis software (MATLAB). This software contains intellectual property
that is not meant for public dissemination.
Data availability
Each described data set was collected by Finnish Meteorological Institute
during PaCE campaigns and was published in the described form at the FMI open
data repository. All data sets have undergone thorough quality control, and
false readings were eliminated. Data sets can be all found at
https://doi.org/10.23728/FMI-B2SHARE.988739D21B824C709084E88ED6C6D54B
(Doulgeris et al., 2021). When the CIP was operational, we also collected the
CIP images. However, we did not include the raw images in the data set for
two reasons. First, they were in binary format. To read them, we used a
proprietary image analysis software that was provided by DMT. Secondly, the
upper limit of the open data repository is 10 GB, which was not enough to
include the CIP raw images which were approximately 0.5 GB per case per day.
However, RAW CIP images could be provided by the authors upon request.
Summary
In this study we produced and summarized data sets obtained from two cloud
ground-based spectrometers (CAPS and FSSP-100 ground setups) owned by the FMI
during 8 years of PaCE campaigns conducted in autumn from 2004
until 2019 along with several meteorological variables. PaCE campaigns took
place in the Finnish sub-Arctic region in a clear environment in
temperatures that were usually below zero. In Sect. 2, we describe the
measuring site where PaCE campaigns took place and the cloud ground
spectrometers setups that were used to obtain the cloud data along with the
instrumentation that was used to monitor the weather conditions. In Sect. 3 an overview of the data set is presented.
These observations gathered in sub-Arctic conditions are a unique source of
in situ cloud measurements, which can contribute to the understanding of the
cloud dynamics and formation in a sub-Arctic environment in different
meteorological conditions. Such semi-long observations are difficult to
obtain in similar environments due to current lack of instrumentation which
would allow continuous unattended operation at temperatures below 0 ∘C.
Cloud droplet spectrometers with surface installation has been identified as
a potential method for continuous cloud in situ measurements (Wandinger et
al., 2018). Thus, due to the increased demand for long-term continuous
ground-based in situ cloud measurements, we provide a data set of in situ
cloud measurements in a harsh sub-Arctic environment. Each data set includes
a combination of cloud microphysical properties along with several
meteorological variables. Even though the data set includes measurements
from eight campaigns, we would propose a case-by-case cloud investigation.
Due to the inhomogeneity of the presented cloud cases, it is challenging to
retrieve any trend that can be unambiguously connected to changes in the
atmosphere. Also, the quality of data set may differ for each campaign due to
the different number of observations per year and operators' experience
running the ground-based spectrometers through the years. In addition, each
cloud case could be of different mass origin. We therefore discourage from
any trend analysis based only on the presented data set. At least thorough
back-trajectory analysis and subsequent segregation of data set
according to air mass origin is recommended. However, this was not an
objective of this paper. The data set in current form provides a helpful
contribution to cloud microphysics processes on shorter timescales.
Microphysical processes can strongly influence cloud–climate feedbacks in
global climate models (Bodas-Salcedo et al., 2019). Furthermore, it can
be used as complementary in model development. Representation of cloud
microphysics is considered significant for large eddy simulation (LES) models (Morrison et al., 2020). There is a need for in situ cloud data sets
due to two significant problems that the modeling community is facing: the
representation of the population of the cloud and precipitation particles
and the uncertainties due to fundamental gaps in knowledge of cloud physics
(Morrison et al., 2020). In this data set, the cloud size distribution was
monitored in different stages of its evolution.
Abbreviations
PaCEPallas Cloud ExperimentGAWGlobal Atmosphere WatchUASUnmanned aerial systemFMIFinnish Meteorological InstituteCAPSCloud, aerosol, and precipitation spectrometerCASCloud and aerosol spectrometerCIPCloud imaging probeLWChwHot-wire liquid water content sensorFSSP-100Forward-scattering spectrometer probeDMTDroplet Measurement TechnologiesPMSParticle Measuring SystemsPSLPolystyrene latex sphereNcNumber concentrationLWCLiquid water contentEDEffective diameterMVDMedian volume diameterTTemperature at 570 m a.m.s.l.TDPDew point temperatureRHRelative humidity at 570 m a.m.s.l.PPressureWsWind speedWdirWind directionSradGlobal solar radiationPARPhotosynthetically active radiationVHorizontal visibility
Author contributions
KMD wrote the paper with contributions from all co-authors. HL planned and
coordinated PaCE 2004, 2005, and 2009. HL and DB planned and coordinated PaCE
2012 and 2013. KMD and DB planned and coordinated PaCE 2015, 2017, and 2019.
KMD and DB processed, analyzed, and quality controlled the data set. VMK and
APH reviewed and edited the manuscript.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
This work was supported by the Koneen Säätiö (grant no.
46-6817), NordForsk (grant no. 26060), the Academy of Finland (grant no.
269095), the Academy of Finland Center of Excellence program (grant no.
307331), Academy of Finland Flagship funding (grant no. 337552), and the Natural
Environment Research Council (NERC, grant no. NE-L011514-1). This project
has received funding from the European Union Seventh Framework Programme
(BACCHUS) (grant no. 603445)) and the Horizon 2020 (H2020) research and innovation program
(ACTRIS-2, the European Research Infrastructure for the observation of
Aerosol, Clouds, and Trace gases) (grant agreement no. 654109).
Financial support
This research has been supported by the Koneen Säätiö (grant no. 46-6817), NordForsk (grant no. 26026), the Academy of Finland Center of Excellence program (grant nos. 269095 and 307331), Academy of Finland Flagship funding (grant no. 337552), Horizon 2020 (ACTRIS-2, grant no. 654109), the European Union Seventh Framework Programme
(BACCHUS (grant no. 603445)), and the Natural Environment Research Council (grant no. NE-L011514-1).
Review statement
This paper was edited by Nellie Elguindi and reviewed by two anonymous referees.
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