Long-Term Retrievals of Cloud Type and Fair-Weather Shallow Cumulus Eventsat the ARM SGP Site
KYO-SUN SUNNY LIM,a LAURA D. RIIHIMAKI,b,f YAN SHI,b DONNA FLYNN,b JESSICA M. KLEISS,c
LARRY K. BERG,b WILLIAM I. GUSTAFSON JR.,b YUNYAN ZHANG,d AND KAREN L. JOHNSONe
a School of Earth System Sciences, Kyungpook National University, Daegu, South KoreabPacific Northwest National Laboratory, Richland, Washington
cLewis and Clark College, Portland, OregondLawrence Livermore National Laboratory, Livermore, California
eBrookhaven National Laboratory, Upton, New York
(Manuscript received 27 November 2018, in final form 15 July 2019)
ABSTRACT
A long-term climatology of classified cloud types has been generated for 13 years (1997–2009) over the
Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site for seven cloud categories:
low clouds, congestus, deep convection, altocumulus, altostratus, cirrostratus/anvil, and cirrus. The classifi-
cation was based on the cloud macrophysical quantities of cloud top, cloud base, and physical thickness of
cloud layers, as measured by active sensors such as the millimeter-wavelength cloud radar (MMCR) and
micropulse lidar (MPL). Climate variability of cloud characteristics has been examined using the 13-yr cloud-
type retrieval. Low clouds and cirrus showed distinct diurnal and seasonal cycles. Total cloud occurrence
followed the variation of low clouds, with a diurnal peak in early afternoon and a seasonal maximum in late
winter. Additionally, further work has been done to identify fair-weather shallow cumulus (FWSC) events for
9 years (2000–08). Periods containing FWSC, a subcategory of clouds classified as low clouds, were produced
using cloud fraction information from a total-sky imager and ceilometer. The identified FWSC periods in our
study show good agreement with manually identified FWSC, missing only 6 cases out of 70 possible events
during the spring to summer seasons (May–August).
1. Introduction
Various types of clouds have different radiative
forcing (Chen et al. 2000); thus, an accurate cloud-type
classification is necessary to understand the role of
clouds on the energy budget and the regional/global
hydrological cycle. Mace et al. (2006) and McFarlane
et al. (2013) categorized cloud types based on typical
values of cloud top, cloud base, and physical thickness
of cloud layers, over the U.S. Department of Energy’s
Atmospheric RadiationMeasurement (ARM) Southern
Great Plains (SGP) and tropical western Pacific Ocean
atmospheric observatory sites. An advantage of using a
simple definition of cloud types relying on cloud mac-
rophysical quantities, such as cloud height and thick-
ness, is that it can be easily duplicated in large-eddy
simulation (LES) models. However, classifying cloud
types using this method will be sensitive to predefined
threshold values. Another classification method seen in
the literature utilizes a trained network based on ex-
pertly categorized samples according to different char-
acteristics of cloud types (Penaloza and Welch 1996;
Wang and Sassen 2004). The trained network is then
applied to unknown cloud samples to categorize them
into desired cloud types. A trained network method can
better handle ambiguous situations, but it does not
guarantee improved performance versus a simple
method using threshold values (Tovinkere et al. 1993).
The ARM SGP site, established in 1993, is suitable
for studying a continental climate in midlatitudes.
Since 1997, this site has provided continuous mea-
surements of cloud vertical distribution using active
Denotes content that is immediately available upon publica-
tion as open access.
f Current affiliation: Cooperative Institute of Research in Envi-
ronmental Sciences, NOAA/Earth System Research Laboratory,
Boulder, Colorado.
Corresponding author: Laura D. Riihimaki, Laura.Riihimaki@
noaa.gov
OCTOBER 2019 L IM ET AL . 2031
DOI: 10.1175/JTECH-D-18-0215.1
� 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS CopyrightPolicy (www.ametsoc.org/PUBSReuseLicenses).
sensors such as the millimeter-wavelength cloud ra-
dar (MMCR) (Moran et al. 1998) and the micropulse
lidar (MPL) (Spinhirne 1993) as well as radiation,
aerosols, and vertically integrated cloud properties.
Previous studies of cloud characteristics over the
ARM SGP have shown a distinct seasonal cycle of
cloud fraction (CF) with a maximum during late
winter and a minimum during summer (Dong et al.
2006; Kennedy et al. 2014; Wang and Sassen 2001; Xie
et al. 2010). The diurnal cycle, another fundamental
mode of climate variability, commonly showed an
increase of low-level clouds in early afternoon over the
SGP site (Dong et al. 2006; Mace et al. 2006; Wang and
Sassen 2001; Xie et al. 2010). The diurnal cycle of high-
level clouds differed between studies, which could be
due to different analysis periods, seasons, definitions
of high-level clouds, or different measurements used
to detect clouds.
Recently, the Department of Energy (DOE) ARM
facility has expanded its activities to include routine
LES modeling through the LES ARM Symbiotic
Simulation and Observation (LASSO) data stream
to complement high-density observations at the SGP
site (Gustafson et al. 2017b, 2018). Shallow cumulus
clouds at the SGP have been the initial focus of the
LES modeling efforts because they are an important
part of the radiation budget, having an average short-
wave radiative forcing of 245.5Wm22 (Berg et al.
2011), and are challenging to simulate accurately using
climate models. This is partly due to the small spatial
scale of these clouds compared to model grid spacing
and due to complicated interactions between micro-
physical and boundary layer processes (Gustafson et al.
2017a). To study climatological changes of cloud types
and to give guidance in choosing shallow cumulus
events for the routine LES modeling, we developed
a cloud-type classification algorithm based on pre-
defined values of cloud base, top, and thickness over
the SGP site. We used this algorithm to create a data-
base of classified cloud types as an initial step for fur-
ther categorization of low clouds into fair-weather
shallow cumulus (FWSC).
In this study, a 13-yr (1997–2009) climatology of the
classified cloud types was produced. Further, a 9-yr
(2000–08) dataset of automatically identified FWSC
periods was generated and compared with manually
determined FWSC (Berg and Kassianov 2008, here-
after BK08; Zhang and Klein 2013, hereafter ZK13)
during the spring to summer seasons from May to
August. Details of the algorithms developed to classify
cloud types and select FWSC events are explained in
section 2. Sections 3 and 4 present results and a sum-
mary with discussion, respectively.
2. Methods
a. Classified cloud types
Cloud top, cloud base, and thickness of cloud layers
were calculated from the active remote sensing of
clouds (ARSCL) (Johnson and Jensen 2009) data
product at the SGP Central Facility. ARSCL data
include the top and bottom heights of each cloud
layer for up to 10 layers, detected by either the MMCR
orMPL (Clothiaux et al. 1998). Cloud boundaries and
thickness derived from the combination of these two
instruments provides more reliable cloud layer iden-
tification because of the complementary capabil-
ities of the two active sensors (i.e., MMCR and MPL)
(Clothiaux et al. 2000; Uttal et al. 1995). Lidar can
detect most mid- and/or high-level clouds, but strong
optical signal attenuation prevents penetration of
thick low and midlevel clouds with high hydrometeor
concentrations. In contrast, radar often fails to detect
clouds containing small particles, yet can effectively
detect mid- and/or high-level clouds above lower cloud
layers because it penetrates low clouds that do not
contain significant precipitation.
The ARSCL data are given at a high vertical reso-
lution (30m). When selecting cloud layers from the
ARSCL data, we eliminated thin layers and merged
layers that are separated by a small vertical distance to
simplify the cloud classification and reduce false cloud
layers that result from lidar or radar artifacts. Figure 1a
illustrates the screening method used to remove thin
cloud layers using a hypothetical column of clear and
cloudy retrievals. Since the vertical resolution of
ARSCL data is 30m, the depth of the first cloud layer
in this scenario is 150m. The top and bottom of the first
cloud layer were denoted cB1 and cT1, respectively. We
required cloud layers to be contiguous over 120m to be
retained for further analysis. Therefore, the first cloud
layer was retained. The depth of the second layer was
60m, so this section was not retained. In addition to
removing small cloud layers (Fig. 1a), a second screen
was applied to merge two cloud layers separated by less
than 120m (Fig. 1b).
Each cloud layer was assigned to one of seven cloud
types based on the top height, base height, and physi-
cal thickness of each layer. Table 1 shows the cloud
base, top, and thickness criteria used to define cloud
types. A threshold value of 3.5 km was used to differ-
entiate low clouds from other cloud types, as previous
studies had shown a minimum in cloud amount at SGP
between 3 and 4 km (e.g., Naud et al. 2005; Mace and
Benson 2008). Ka-band cloud radars, like the MMCR,
attenuate during heavy rainfall periods; thus, high-
level clouds can be missed, and the detected cloud top
2032 JOURNAL OF ATMOSPHER IC AND OCEAN IC TECHNOLOGY VOLUME 36
can be underestimated (Wang and Sassen 2001). There-
fore, we did not retrieve cloud types during times when
the precipitation rate was larger than 1mmh21, which
might reduce the frequency of deep convection and
congestus in our retrieval. Surface precipitation from
the ARM surface meteorology system (MET) (Cialella
et al. 1990) was used for this procedure. Cloud-type
retrievals were generated for 13 years from 1997 to
2009, with a temporal resolution of 1min (Riihimaki
and Shi 2018).
b. Detection of fair-weather shallow cumulus
Single-layer low-cloud layers detected by the cloud-
type algorithm were further processed to select
FWSC events (Sivaraman et al. 2018). This was done
by incorporating additional CF information from the
total-sky imager (TSI; Morris 2005, 1994) and the
ceilometer (Morris 2016; Ermold and Morris 1996)
located at the SGP site to complement and check
the low cloud layers detected by the ARSCL-based
cloud types from the MPL and MMCR. Partially
cloudy conditions were the main criteria used to dis-
tinguish FWSC from other low cloud types. The TSI
gives CF with a hemispheric field of view, providing a
broader contextual view of the sky than the narrow,
zenith-pointing field of view of the ARSCL-derived
cloud types. The TSI processing software analyzes
charge-coupled device (CCD) images to determine
the fraction of opaque and optically thin clouds over a
1808 field of view centered on zenith. The ceilometer
provides a narrow, zenith-pointing field of view like
the ARSCL cloud boundaries; however, the infrared
wavelength and proprietary processing software is
optimized to accurately identify low cloud base. We
used the ceilometer cloud fraction to check that the
ARSCL-based cloud-type product was accurately
detecting the presence of low clouds, since the in-
struments used in the ARSCL-based cloud-type re-
trieval can sometimes misclassify aerosol layers as
FIG. 1. Schematic explanation of a cloud-layer screeningmethod and its order. (a) Removal
of thin cloud layer and (b) merging of thin clear layer into cloud layers. During screening
(a) will be followed by (b). Subscripts 1, 2, and 3 represent different cloud layers from the
active remote sensing of clouds (ARSCL). cT and cB represent the cloud top and cloud
bottom for each cloud layer. Cloud layer is 30-m depth. Blue squares indicate a cloud layer
and white squares indicate a clear layer.
TABLE 1. Cloud-type definition over the ARM SGP site.
Cloud type
Cloud base
(km)
Cloud top
(km)
Cloud thickness
(km)
Low clouds ,3.5 ,3.5 ,3.5
Congestus ,3.5 3.5–6.5 $1.5
Deep convection ,3.5 .6.5 $1.5
Altocumulus 3.5–6.5 3.5–6.5 ,1.5
Altostratus 3.5–6.5 3.5–6.5 $1.5
Cirrostratus/anvil 3.5–6.5 .6.5 $1.5
Cirrus .6.5 .6.5 No restriction
OCTOBER 2019 L IM ET AL . 2033
low cloud (MPL) or miss the small cloud droplets of
shallow convection (MMCR).
The detailed procedure for automated identifica-
tion of FWSC is illustrated in Fig. 2. In addition to
single-layer FWSC, FWSC cases with overlying cirrus
were also identified using the same procedure. Because
FWSC only partially covers the sky, CF is required to
reside within a certain range to be classified as FWSC.
Hourly averaged opaque CF from the TSI (CFTSI) and
retrieved hourly CF from the ceilometer lowest cloud
base (CFceilometer) were used.
Figure 2b shows an example of the procedure used to
identify FWSC events. First, single-layer low clouds,
noted in groups B, C, and D, or low clouds with over-
lying cirrus, noted in group A, in Fig. 2b are determined
from the cloud-type data product. The algorithm then
requires hourly CFTSI to be between 0.5% and 80%,
CFceilometer to be greater than 0%, and at least 2min
of the hour to be identified as low clouds by the cloud-
type algorithm. If CFTSI or CFceilometer do not satisfy
the criteria during a given low cloud event, the event
is rejected as an FWSC event (e.g., CFTSI for the fourth
low cloud in B is 90% and is now marked in a gray
color in second panel of Fig. 2b). Note that FWSC is
characterized by smaller CF and composed of a smaller
number of larger-size individual cloud cells, relative to
altocumulus (Ac). To be defined as an FWSC event, the
low cloud occurrence during 1 h should be greater than
2min and the duration of the FWSC must be longer
than 1.5 h. The low cloud event in period D does not
meet this length criteria; thus, clouds in D were rejected
and noted in a gray color (see third panel of Fig. 2b). If
there is a time gap longer than 2.5 h between each cloud
among the identified events of FWSC, the algorithm
separates the events into two different FWSC events.
Clouds in A and B represent one FWSC event because
the time between the two cloud events was shorter than
2.5 h. By contrast, clouds in A and C represent separate
FWSC events.
FWSC events were further subclassified into iso-
lated FWSC and transition cases where FWSC was
proceeded or followed by other mid- and upper-level
cloud types. Specifically, FWSC transitions to and from
cirrus/cirrostratus (Ci/Cs), low-level stratus (St), and
Ac/altostratus (As) were identified separately from FWSC
in isolation from other cloud types. Layers classified as
low clouds were defined as St when CFTSI was greater
than 80%. Ci, Cs, Ac, and As were defined using the
FIG. 2. (a) Schematic diagram and (b) the corresponding example of the procedure to identify FWSC periods. In (b) C (A) represents
the identified single-layer FWSC event (the identified FWSC event with overlying cirrus). CFTSI is the 1-h opaque cloud fraction from a
total-sky imager and CFceilometer is the retrieved cloud fraction using the frequency of occurrence of detected lowest cloud base from a
ceilometer measurement during 1 h.
2034 JOURNAL OF ATMOSPHER IC AND OCEAN IC TECHNOLOGY VOLUME 36
cloud-type classification (Table 1) as cloud types 7, 6, 4,
and 5, respectively. If any of these cloud types (Ci/Cs, St,
or Ac/As) existed with a duration exceeding 2h during
the 3-h period preceding the start time of FWSC, we
identified the corresponding FWSC events as transi-
tion events from this cloud type to FWSC. The opposite
transition cases from FWSC to Ci/Cs, St, or Ac/As were
identified in an analogous way but using the 3-h period
after the FWSC ending time.
3. Results
a. Classified cloud types
Figure 3b shows an example of the classified-cloud
types for 24 May 2008 and Fig. 3a shows the corre-
sponding radar reflectivity and the best estimate of
lowest cloud base from the lidar. During this day,
well-organized convection developed to the north-
west of the SGP site, moved toward the site, and
produced an abundant amount of rainfall, with a
maximum rate of 1.3mmmin21 at 0900 UTC. After
1800 UTC on the same day, a lightly precipitating con-
vective cloud with precipitation rates of 0.3mmmin21,
formed by locally driven conditions around the SGP
site. The algorithm categorized the cloud types on
this day, such as deep convection during the period
between 0800 and 1130 UTC and the subsequent low-
cloud period. This simple algorithm can be duplicated
for other ARM sites by adjusting the threshold values
in accordance with different cloud characteristics in the
corresponding regions.
Diurnal and seasonal frequencies of cloud types at
SGP were examined over 13 years (1997–2009) to
provide a consistent and long-term assessment of the
variability of cloud characteristics over these time scales
(Fig. 4). Seasonally, the low cloud-type maximum fre-
quency was found in February and minimum in July
(Fig. 4a). Cirrus cloud occurrence, the most frequent
cloud type over the SGP site, peaked in May followed
by a decrease until September. Seasonal variation of
total cloud occurrence followed the variation of low
clouds (black and blue solid lines in Fig. 4a). A maxi-
mum in total cloud occurrence was seen during late
winter and a minimum in summer. Other cloud types
(congestus, deep convection, alto cumulus/stratus, cir-
rostratus) did not substantially contribute to the total
cloud occurrence. Each of these categories occurred less
than 10% of the time. In addition to cirrus and low cloud
types, altocumulus and cirrostratus also had distinct
seasonal cycles (see green and orange lines in Fig. 4a).
Altocumulus frequency was at a maximum during sum-
mer and minimum during winter. The opposite trend is
seen in the seasonal cycle of cirrostratus.
Cirrus and low clouds had a clear diurnal cycle
(Fig. 4b). Low cloud occurrence increased until the
early afternoon. The total cloud occurrence was also
slightly higher in early afternoon, mainly due to the
increase in low cloud occurrence (black line in Fig. 4b).
By contrast, the minimum frequency of cirrus was in the
afternoon and the maximum at night. The diurnal cycle
of deep convention/congestus and alto cumulus/stratus,
showed little variability compared to that of low cloud
and cirrus. Our findings for the seasonal and diurnal
cycle (Figs. 4a,b) are consistent with previous studies
(Dong et al. 2006; Kennedy et al. 2014;Wang and Sassen
2001; Xie et al. 2010).
FIG. 3. Example of (a) time–height evolution of radar reflectivity from millimeter-wavelength
cloud radar (MMCR) (shaded) and cloud-base best estimate retrievedusingmicropulse lidar (MPL)
and ceilometer (black dots) and (b) classified cloud types at the ARMSGPC1 site on 24May 2008.
OCTOBER 2019 L IM ET AL . 2035
Segele et al. (2013) analyzed warm boundary layer
clouds during 4 years (1997–2000) over the SGP site and
showed no significant interannual differences in cloud-
base heights of boundary layer clouds. However, our
climatology showed distinct diurnal and seasonal
variations of cloud height with higher cloud-base height
during daytime and summer (Fig. 5). Del Genio and
Wolf (2000) also found similar seasonal patterns as in
our study. To examine how the seasonal and diurnal
variation of classified cloud types were sensitive to the
predefined threshold values, the threshold value of
3.5 km (Table 1) was increased to 4.5 km. As expected,
low cloud amount increased because of the increased
depth over which low clouds can reside when using the
4.5-km threshold (cf. Figs. 4a,c and4b,d). However,
even though the amount of Ac, As, and Cs decreased
FIG. 4. (a)Monthly averaged and (b) hourly averaged percentage of cloud occurrence for seven cloud types during
13 years (1997–2009). (c),(d) As in (a) and (b), but cloud types in Table 1 are classified using a different threshold,
which is changed from 3.5 to 4.5 km. Black solid lines show the percentage of total cloud occurrence (right axis).
FIG. 5. (a) Monthly averaged and (b) hourly averaged cloud-base height for the low cloud type during 13 years
(1997–2009).
2036 JOURNAL OF ATMOSPHER IC AND OCEAN IC TECHNOLOGY VOLUME 36
and that of deep convection increased, the overall effect
of changing the threshold did not change the features
of diurnal and seasonal variations of the cloud types
(Figs. 4c,d).
b. Detection of fair-weather shallow cumulus
The identified FWSC periods in our study were com-
pared with manually selected periods from previous
studies by BK08 and ZK13. Using ARSCL data
(Clothiaux et al. 1998) and TSI (Morris 2005) movies,
BK08 manually selected FWSC periods during the spring
to summer seasons from May to August for 5 years
(2000–04) to study the climatology of cloud macroscale
properties over the SGP site. Their study focused on
identifying cases with single-layer shallow cumuli, so it
excluded cases that appeared to have multilayer clouds.
That 5 years of data from BK08 was extended to 9 years
(2000–08) by Berg et al. (2011). ZK13 also manually
identified FWSC events over the SGP during 13 years
(1997–2009) and examined the factors controlling
the vertical extent of FWSC. Besides ARSCL and TSI
data, precipitation from the Arkansas Red Basin River
Forecast Center and data from Geostationary Opera-
tional Environmental Satellites (GOES) were incorpo-
rated in the study by ZK13 to identify and exclude both
precipitation days and FWSC impacted by large-scale
phenomena. ZK13 also required that the observed
lowest cloud base had to rise during the daytime, hinting
at the link to boundary layer development and including
only locally generated FWSC. The coincident 9-yr
dataset of FWSC periods (2000–08) from both ZK13
and BK08 are compared with the FWSC climatology
generated using our automated algorithm.
Table 2 shows the results of comparing our automated
dataset, generated following the schematic diagram in
Fig. 2, with manually determined FWSC periods from
BK08 and ZK13. Because of inconsistencies between
the datasets from BK08 and ZK13, we evaluated our
dataset with the FWSC periods identified by both BK08
and ZK13 studies. One of the main differences between
the two datasets is the exclusion of FWSC affected by
large-scale phenomena in ZK13. A total of 81 FWSC
cases were identified by both BK08 and ZK13 during
nine years, though 11 of these cases were missing some
of the input data used in our automated algorithm, so we
used the 70 cases with all available data as listed in the
‘‘total man. w/all data’’ column as the reference dataset.
Our algorithm, labeled LR18 in Table 2, identified 40
of the remaining 70 FWSC validation cases as single
layer, isolated FWSC, as listed in the ‘‘hit’’ column. An
additional 24 of those cases were identified as FWSC
by our algorithm but labeled as cases with overlying
cirrus, transition cases of FWSC from or to other cloud
types, or both (Table 2, overlap only, transition only, and
overlap and transition, respectively). Six cases were re-
jected by our algorithm because the duration of low
clouds was too short to be classified as a shallow cumulus
event (Table 2, miss).
Figure 6 shows examples of hit, miss, and overlap
cases. In Fig. 6a, LR18 identifies a FWSC period from
1700 to 2300 UTC, similar to BK08 and ZK13. The TSI
image at 2200 UTC confirms the occurrence of FWSC
during the identified period. It is interesting to note that
the time periods identified as containing FWSC are
slightly different between the three datasets (BK08,
ZK13, LR18) for this hit case (1600–2500 UTC in BK08
vs 1600–2400 UTC in ZK13 vs 1700–2300 UTC in
LR18). The TSI image at 1900 UTC in Fig. 6b assures us
that FWSC existed on 27 August 2000. The reason our
algorithm missed this FWSC event was due to the re-
striction that low cloud occurrence must be greater than
or equal to 2minh21 (Fig. 2). During the time between
1930 and 2030 UTC, the frequency of low cloud occur-
rence does not meet this criterion. Relaxing this criteria
about the frequency of low clouds, however, increased
the number of false positive cases.
A total of 14 cases identified as FWSC in both BK08
and ZK13 were categorized as FWSC overlapping
TABLE 2. Evaluation of the classified fair-weather shallow cumulus (FWSC) events from our study (LR18) with datasets fromBK08 and
ZK13 during 9 years (2000–08). The total number of cases found by both of these two studies during that time period is given in ‘‘total
manual’’ column, and the number of those cases that had all needed datasets to run our automated algorithm is given in ‘‘total man. w/all
data’’ column. The number of cases that were found (hit) and missed (miss) by the automated algorithm, along with cases found by the
algorithm that were classified as having overlying cirrus (overlap only), were transition cases (transition only), or both (overlap and
transition) add up to the total number of manually identified cases with all needed data. The last column (false positives) indicates shallow
cumulus events found by the automated algorithm but not manually identified. The values in parentheses in the ‘‘false positives’’ column
show the numbers of false positive events caused by large-scale weather phenomena, smoke, and altocumulus clouds. The first and second
rows show results from the control (LR18) and sensitivity test (LR18_TSI50).
Expt
Total
manual
Total man.
w/all data Hit Miss
Overlap
only
Transition
only
Overlap and
transition
False
positives
LR18 81 70 40 6 4 10 10 43 (11, 1, 27)
LR18_TSI50 81 70 37 9 4 10 10 35 (11, 1, 19)
OCTOBER 2019 L IM ET AL . 2037
with cirrus clouds in LR18. In Fig. 6c, classified cloud
types using ARSCL and the TSI image at 2000 UTC
verified the existence of high-level cirrus clouds above
FWSC. For some purposes, we wish to be able to sepa-
rate single-layered FWSC from those with overlying
cloud layers; thus, these cases were labeled separately.
Another situation that we classified into categories other
than single-layered FWSC was the transition between
other cloud types (St, Ci/Cs, and Ac/As) and FWSC.We
note that BK08 and ZK13 identified many of these
transition cases as FWSC. Table 2 shows that 20 cases
fromBK08 and ZK13 were confirmed as transition cases
by TSI movie inspection. Among the 20 cases, change
from Ci/Cs to FWSC happened the most frequently
(9 cases). Figure 7 shows six examples of transition cases
that our algorithm distinguished from FWSC. All these
cases were confirmed as transition cases through in-
spection of TSI movies.
The biggest challenge for improving our algorithm
is to reduce the number of false positive cases. To
give modelers maximum flexibility to choose cases of
interest, we designed our algorithm to prioritize cap-
turing all possibly relevant cases over excluding false
positives. As a result, LR18 produced 43 more cases of
single-layer FWSC than identified by both BK08 and
ZK13 (Table 2). Through inspection of TSI movies and
GOES data, we classified 39 of the 43 events into three
categories. The other four cases could not be judged
because of an absence of TSI movies on the corre-
sponding dates. False positive cases in LR18 could be
categorized into three groups according to their causes:
smoke, Ac, and FWSC impacted by large-scale weather
phenomena. It would be advantageous to be able to
separate locally driven FWSC from FWSC created
by large-scale phenomena, consistent with the ZK13
method. While FWSC events influenced by large-scale
weather patterns are still shallow cumulus clouds and
are not precisely false positives, they are more chal-
lenging to simulate well in the limited domain of an LES
model. For the LR18 false positive cases impacted by
large-scale phenomena, the classified cloud types and
opaque CF from TSI (or detected cloud base from
FIG. 6. Examples of (a) hit, (b) miss, and (c) overlap cases. (left) Classified cloud types and
(right) TSI images at a certain time for the corresponding cases. Hours in UTC on each figure
represent the selected FWSCperiods fromBK08 andZK13 and identified FWSCperiod from
our study (LR18).
2038 JOURNAL OF ATMOSPHER IC AND OCEAN IC TECHNOLOGY VOLUME 36
ceilometer) of these false positive cases did not show
any differences from the true FWSC case. TSI images
alone could not distinguish cases impacted by large-
sale phenomena from locally driven FWSC cases (cf.
Figs. 8a and 6a).
To investigate how to distinguish this type of FWSC
from locally generated FWSC, we utilized cloud data
from visible infrared solar-infrared split-window tech-
nique (VISST)-retrieved satellite products, which are
based on 4-km resolution data from the 0.65-, 3.9-, 11-,
and 12-mm channels of theGOES-8 imager since March
2000 (Minnis et al. 2008). Eleven false positive cases
(Table 2) were confirmed as FWSC impacted by large-
scale weather phenomena from inspection of visible images
of GOES data (http://www.aviationweather.gov/adds/
satellite) and surface weather charts from the Weather
Prediction Center of the National Weather Service
(http://www.wpc.ncep.noaa.gov). Time-averaged CF
(.25%) and cloud-top height (.7.5 km), calculated
from three snapshots at 0830, 1130, and 1430 LST of
VISST data, over a region (338–418N, 1028–938W) cen-
tered in the SGP site were used to identify cloud systems
impacted by large-scale phenomena. Four out of the 11
cases were classified as impacted by large-scale weather
using the above criteria. A cloud system on 11 August
2006 is an example (Fig. 8a). Another six cases could not
be detected using these criteria because there were
squall-like narrow clouds with insignificant CF. A cloud
system on 4 July 2008 is an example of these squall-like
clouds. VISST data problems on 7August 2011, the final
case, made it difficult to examine whether the FWSC
event was impacted by large-scale weather on that date.
For the smoke and Ac false positive cases, TSI images
showed the features of Ac and smoke (see right column
in Figs. 8b,c). We conducted additional sensitivity tests
to distinguish characteristics of false positive cases from
true FWSC cases. From the sensitivity test results
(LR18_TSI50 in Table 2), in which we reduced the
maximum threshold value of CFTSI from 80% to 50%
(see Fig. 2), we did see that we could eliminate some Ac
false positives cases with larger opaque TSI CF. How-
ever, by reducing the maximum threshold value of
CFTSI, several FWSC cases were also missed. Because
we did not want to exclude true FWSC cases, we kept
the maximum threshold value of CFTSI at 80%.
Only one false positive case was found due to the
misclassification of smoke as a cloud and is shown in
Fig. 8c. The ceilometer can detect the backscatter signal
from clouds but does not detect smoke as cloud; thus, a
ceilometer is a critical instrument to distinguish clouds
from smoke. However, the presented false positive case
could not be eliminated even with the incorporation
of ceilometer measurements because the smoke plume
existed with FWSC (note the FWSC on the left-top side
of the TSI image in Fig. 8c).
The seasonal variation of detected FWSC events de-
rived using the LR18 method over the 9-yr (2000–08)
period is shown in Fig. 9. FWSC events occurred most
FIG. 7. Examples of six different transition cases.
OCTOBER 2019 L IM ET AL . 2039
frequently during the summer season. The number of
FWSC events decreased dramatically in October and
remained low through March. Even though changing
the threshold value from 3.5 to 4.5 km in the definition of
the low cloud type in Table 1 increased the number of
FWSC events except in January (cf. solid and dotted
lines in Fig. 9), it did not affect the pattern of the FWSC
seasonal cycle, with the majority of FWSC found during
the warm season.
4. Summary and discussion
An algorithm that can classify cloud type based on
predefined threshold values of cloud-top height, cloud-
base height, and cloud thickness has been developed for
the SGP site. Cloud layers were detected from surface-
based active remote sensors, specifically millimeter-
wavelength cloud radar (MMCR) and micropulse lidar
(MPL). This classification was based on the method
by Mace et al. (2006). However, differently from
Mace et al. (2006), in which cloud optical depth from
the multifilter rotating shadow-band radiometer (MFRSR)
was used to give information on cloud thickness to
define the cloud types, the cloud thickness in our study
was directly calculated from cloud-top and cloud-base
heights following Burleyson et al. (2015) andMcFarlane
et al. (2013). Even though the cloud classification al-
gorithm was sensitive to threshold values, this simple
definition of cloud types had the advantage of easy
duplication using a large-eddy simulation model.
Cloud-type classification using simple cloud boundary
threshold values can be duplicated for other ARM sites
by adjusting the threshold values according to different
cloud characteristics in the corresponding regions. In
addition to the general cloud classification, an auto-
mated method has been devised to select fair-weather
shallow cumulus (FWSC) periods. FWSC is a sub-
category of the cloud layers identified as low clouds by
the cloud classification algorithm using opaque cloud
fraction from a total-sky imager (TSI) and detected
cloud-base information from a ceilometer.A 13-yr (1997–
2009) climatology of the classified cloud types and a 9-yr
(2000–09) dataset of FWSC periods were produced.
The variability of cloud characteristics, including di-
urnal and seasonal variations of cloud types, was ex-
amined over 13 years. Low-level and cirrus cloud types
FIG. 8. Examples of false positive cases caused by (a) large-scale impacted FWSC, (b) Ac, and (c) smoke. (left)
Classified cloud types and (right) TSI images for the corresponding cases.
2040 JOURNAL OF ATMOSPHER IC AND OCEAN IC TECHNOLOGY VOLUME 36
had distinct diurnal and seasonal cycles, and the varia-
tion of total cloud occurrence followed the variation
of the low cloud type. Similar to low clouds, the diurnal
cycle of total cloud occurrence peaked in the early af-
ternoon and the seasonal cycle peaked during late
winter.
Periods of FWSC identified by the automated iden-
tification algorithm were compared with manually se-
lected FWSC periods from previous studies (BK08;
ZK13). Our algorithm subset FWSC events into iso-
lated FWSC, those that transition between FWSC and
other cloud types, and FWSC overlapping with cirrus
clouds. Of the 70 cases selected as FWSC in the studies
of both BK08 and ZK13 that included all data needed
for our algorithm, 24 cases were judged as overlap and
transition cases using our algorithm, 40 were identified
as isolated FWSC events, and only 6 FWSC cases were
missed.
Our automated algorithm found 43 additional FWSC
events that were not identified in the manually selected
datasets. Three main causes were found for these false
positive cases including smoke, large-scale weather
phenomena, and altocumulus. Sensitivity tests showed
that some altocumulus false positive cases had larger
opaque TSI cloud fractions than true FWSC cases and
could be eliminated by reducing themaximum threshold
value of CFTSI. However, reducing the maximum
threshold value of CFTSI below 80% also caused the
algorithm to miss true FWSC cases, so this change was
not made. Altocumulus showed distinct features with
a greater number of more closely spaced individual
cloud cells compared to features shown from FWSC
(cf. differences in Figs. 6a and 8b). Thus, we hope that
future work incorporating advanced techniques to iden-
tify the visual patterns of clouds from TSI images will
improve our automated algorithm to identify FWSC
events. More efforts should be pursued to eliminate
the contamination of ARSCL by insect clutter and to
identify FWSC related to large-scale phenomena as
well. From tests incorporating VISST satellite data, we
saw the possibility to detect some cases when FWSCwas
created by large-scale phenomena. Being able to sepa-
rate locally forced FWSC events from those influenced
by large-scale weather phenomena could help better
separate cases that we expect an LESmodel to be able to
simulate well from those that require a larger domain,
and is a subject for future work.
Acknowledgments. We greatly express our thanks to
the ARM value-added products (VAP) science spon-
sors and scientists, Andrew M. Vogelmann, Jennifer
M. Comstock, Chitra Sivaraman, Michael Jensen, and
Justin W. Monroe, for their helpful discussions and
contributions to VAP development. This research was
supported by the Office of Biological and Environ-
mental Research (BER) of the U.S. Department of
Energy (DOE) as part of the Atmospheric Radiation
Measurement (ARM) facility, an Office of Science user
facility and by the National Research Foundation of
Korea (NRF) grant funded by the South Korean gov-
ernment (MSIT) (2019R1C1C1008482). Data were ob-
tained from the ARM facility, a U.S. DOE Office of
Science user facility sponsored by the Office of BER.
All data, including active remote sensing and TSI used
in this study, are freely downloadable online (https://
www.arm.gov/). Larry Berg and Yunyan Zhang were
supported by Atmospheric System Research (ASR)
program in the Office of Biological and Environmen-
tal Research, Office of Science, DOE. Lawrence
Livermore National Laboratory is operated for the
DOE by Lawrence Livermore National Security,
LLC, under Contract DE-AC52-07NA27344. The Pacific
Northwest National Laboratory is operated for DOE
by Battelle Memorial Institute under Contract DE-
AC05-76RLO 1830.
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