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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