Characteristics of cloud occurrence using ceilometer measurements and its relationship to precipitation over Seoul

Accepted Manuscript Characteristics of cloud occurrence using ceilometer measurements and its relationship to precipitation over Seoul

Sanghee Lee, Seung-On Hwang, Jhoon Kim, Myoung-Hwan Ahn PII: DOI: Reference:

S0169-8095(17)30510-0 doi:10.1016/j.atmosres.2017.10.010 ATMOS 4086

To appear in:

Atmospheric Research

Received date: Revised date: Accepted date:

8 May 2017 6 October 2017 10 October 2017

Please cite this article as: Sanghee Lee, Seung-On Hwang, Jhoon Kim, Myoung-Hwan Ahn , Characteristics of cloud occurrence using ceilometer measurements and its relationship to precipitation over Seoul. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Atmos(2017), doi:10.1016/j.atmosres.2017.10.010

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Characteristics of cloud occurrence using ceilometer measurements and its relationship to precipitation over Seoul

Meteorological Observation Laboratory, Weather Information Service Engine, Hankuk

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Sanghee Lee1,2*, Seung-On Hwang1, Jhoon Kim2, and Myoung-Hwan Ahn3

University of Foreign Studies, Yongin-si, Gyeonggi-do 17035, Korea

Department of Atmospheric Science, Yonsei University, Yonsei-ro 50, Seodaemun-gu, Seoul

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03722, Korea

Department of Atmospheric Science Engineering, Ewha Womans University, Ewha-Yeodae-

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Gil 52, Seodaemun-gu, Seoul, Korea

Corresponding author: Sanghee Lee Meteorological Observation Laboratory, Oedae-ro 81, Mohyeon-myeon, Cheoin-gu, Yongin-si, Gyeonggi-do 17035, Korea. Tel.: +82-70-4617-4769, E-mail: [email protected]

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Abstract Clouds are an important component of the atmosphere that affects both climate and weather, however, their contributions can be very difficult to determine. Ceilometer measurements can provide high resolution information on atmospheric conditions such as

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cloud base height (CBH) and vertical frequency of cloud occurrence (CVF). This study presents the first comprehensive analysis of CBH and CVF derived using Vaisala CL51

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ceilometers at two urban stations in Seoul, Korea, during a three-year period from January

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2014 to December 2016. The average frequency of cloud occurrence detected by the ceilometers is 54.3%. It is found that the CL51 is better able to capture CBH as compared to

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another ceilometer CL31 at a nearby meteorological station because it could detect high

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clouds more accurately. Frequency distributions for CBH up to 13,000 m providing detailed vertical features with 500-m interval show 55% of CBHs below 2 km for aggregated CBHs.

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A bimodal frequency distribution was observed for three-layers CBHs. A monthly variation

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of CVF reveals that frequency concentration of lower clouds is found in summer and winter, and higher clouds more often detected in spring and autumn. Monthly distribution features of cloud occurrence and precipitation are depending on seasons and it might be easy to define

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their relationship due to higher degree of variability of precipitation than cloud occurrence.

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However, a fluctuation of cloud occurrence frequency in summer is similar to precipitation in trend, whereas clouds in winter are relatively frequent but precipitation is not accompanied. In addition, recent decrease of summer precipitation could be mostly explained by a decrease of cloud occurrence. Anomalous precipitation recorded sometimes is considerably related to corresponding cloud occurrence. The diurnal and daily variations of CBH and CVF from ceilometer observations and the analysis of microwave radiometer measurements for two typical cloudiness cases are also reviewed in parallel. This analysis in finer temporal scale exhibits that utilization of ground-based observations together could help to analyze the cloud

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behaviors.

Keywords: cloud base height, cloud occurrence, cloud vertical frequency, ceilometer,

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microwave radiometer

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1. Introduction Clouds play a significant role in both climate and weather (Boucher et al., 2013). To understand their full impact, accurate information regarding cloud properties such as cloud base height (CBH), cloud vertical structure (CVS), and vertical frequency of cloud

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occurrence (CVF) is essential. Historically, humans obtained cloud information directly by visual observations. However, many automated instruments including ceilometers,

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rawinsonde, sky imagers, infrared cloud imagers, and satellites were developed to observe

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cloud properties in real-time (Cazorla et al., 2008; Gaumet et al., 1998; Shields et al., 2013; Sun et al., 2008; Wiegner et al., 2014). Studies using datasets from rawinsonde observations

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or the International Satellite Cloud Climatology Project (ISCCP) have mainly derived cloud

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top height, CBH, and CVS on global and long-term scales (e.g., Poore et al., 1995; Probst et al., 2012; Rossow and Schiffer, 1991; Wang and Rossow, 1995; Wang et al., 2000). In

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addition, field experiments have been carried out to produce datasets from a combination of

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radar, lidar, and ceilometer (e.g., Allwine et al., 2002; Clothiaux et al., 2000; Randall et al., 1996; Wang et al., 1999).

Among the various instruments available to observe cloud properties, ceilometers provide

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a continuous record of cloud characteristics with high temporal and spatial variability. Studies

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from the World Meteorological Organization and other institutions have claimed that the laser ceilometer is one of the most accurate instruments for measuring CBH, cloud occurrence, and cloud cover fraction (Jarraud, 2008; Wiegner et al., 2014). Recently, several short-term studies using ceilometer measurements analyzed cloud characteristics at individual stations (e.g., Costa-Surós et al., 2013; Liu et al., 2015). Probst et al. (2012) compared model simulations and the ISCCP D2 dataset to determine the global and zonal monthly mean of cloud cover fraction (CF), and determined that the average CF by ISCCP cloud analysis was approximately 66% in the mid-latitude location including Korea. Wang et al. (1999) studied

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the CVS over Porto Santo Island (Portugal) during the Atlantic Stratocumulus Transition Experiment using ceilometer, rawinsonde, radar, and satellite data. Their results indicated that low clouds (<2000 m) constituted the dominant distribution (59% and 74% for radar– ceilometer and rawinsonde observations, respectively) and high clouds (approximately 7000–

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8000 m) had the second peak of approximately 20%. A number of studies have constructed details of basic cloud climatology based on model research and various observations,

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however, no comprehensive analysis of CBH and CVF using ground-based observations had

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been conducted for Seoul.

Currently, ceilometers are in use at 92 stations operated by the Korea Meteorological

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Administration (KMA) in Korea and only one of these stations is in Seoul. During November

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2013, two stations in Seoul installed improved CL51 ceilometers as part of the Weather Information Service Engine (WISE) project that was launched by the KMA in 2012. The

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WISE project aims to provide high resolution meteorological information to reduce the

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damage caused by extreme weather phenomena in the Seoul metropolitan area (Choi et al., 2013). Ceilometers are commonly for operational cloud observation. In particular, the CL51 ceilometer has improved performance over earlier CL31 and CT25K models. This

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improvement is due to an enhanced algorithm resulting in less divergence and better

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detection of CBHs, up to an altitude of 13,000 m (Liu et al., 2015; Morris et al., 2014; Wiegner et al., 2014). Data obtained from the WISE project CL51 ceilometer has been accumulated only for three years (2014–2016) because its operation began on November 20, 2013. The period of three years is not enough to analyze the climatology or decadal trend of cloud properties but it may be possible to explain three years’ interannual variability of cloud occurrence from ceilometer measurements and its link to precipitation for Seoul. In this study, CBH and CVF from Vaisala CL51 ceilometer observations are analyzed to further the understanding of the cloud characteristics over Seoul, a complex urban area.

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This study has three purposes. The main purpose is to explore cloud behavior such as CBH, CVF, and vertical variability of CBH over Seoul using CL51 ceilometers for an observational period of three years. In general, most ground-based instruments for clouds (e.g., CL31, CT25K, LD-40, and CHM15k) can provide cloud information below 7500 m.

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Many studies have tried to provide various cloud information such as CBH, horizontal and vertical distribution, cloud fraction, vertical occurrence, and type classifications (e.g., Costa-

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Surós et al., 2013; Fontana et al., 2013; Mace and Benson, 2008; Martucci et al., 2010; Probst

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et al., 2012; Sharma et al., 2016; Wang et al., 1999). Most of them have focused on clouds below middle altitude. However, CL51 ceilometer is an up-to-date instrument to detect

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additional high clouds above 7500 m. This study would be the first one of long-term steps to

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investigate detailed cloud characteristics using CL51 measurements over Seoul. The second one is to present monthly variations of cloud characteristics in relation to precipitation. To

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tackle this purpose, annual and monthly variations of cloud occurrence and precipitation for

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three years were compared. Despite the monthly trend of cloud occurrence in each year is similar, seasonal and interannual variations of cloud occurrence shows different features. These different variations were discussed in terms of its relationship to precipitation. The last

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goal is to investigate CBHs detected by CL51 ceilometer in finer temporal scale and to

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review cloud occurrence and its link to precipitation in cases of rainy events. To do this, diurnal and daily variations of cloud for two rainy cases in summer and winter seasons were chosen. Ceilometer is so limited in providing vertical structures such as cloud top and thickness that information including relative humidity and liquid water content retrieved by microwave radiometer installed at same stations where CL51 ceilometer are operated and satellite images are utilized as a complementary tool. In section 2, brief descriptions of the instruments and data used in this analysis are presented. The results of monthly cloud occurrence, vertical frequency, and the case studies

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of typical cloudiness in winter and summer are discussed in section 3. Finally, a summary of

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the research and concluding remarks are presented in section 4.

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2. Data 2.1 Ceilometer measurements Vaisala CL51 ceilometers have been installed on the roofs of buildings at the Jungnang (hereafter, WISE201) located at 37.59°N, 127.08°E, 34 m above sea level (ASL) (14 m ASL

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+ 20 m building height) and Gwanghwamun (hereafter, WISE202) located at 37.58°N, 126.89°E, 103 m ASL (32 m ASL + 71 m building height) stations (Fig. 1). These devices

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have been in operation since November 20, 2013, continuously monitoring the sky above

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Seoul, Korea. The lidar-based CL51 ceilometer measures the optical backscatter intensity of the air at wavelengths of 910 ± 10 nm with a vertical resolution of 10 m (Münkel and

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Roininen, 2010; Münkel et al., 2011; Vaisala, 2006, 2012). The general expression of

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backscattering height (z) by lidar is z = ct/2

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where c is the speed of light and t is the round-trip time (Eberhard, 1986). The equation for

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the instantaneous return signal power from Lidar equation can be expressed as 𝑃𝑟 (𝑧) = 𝐸0

𝑧 𝑐𝐴 𝛽(𝑧)exp (−2 ∫ 𝜎 (𝑧 ′ ) 𝑑𝑧′) 2 𝑧2 0

(2)

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where Pr(z) is instantaneous power received from distance z, E0 is the effective pulse energy, A is the receiver aperture, β(z) is the volume backscatter coefficient at distance z, and the

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exponential term is two-way atmospheric transmittance (Van Tricht et al., 2014). The volume backscatter coefficient, β(z), can be expressed as follows β(z) = kσ(z)

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where k is the proportionality constant, which is called the Lidar Ratio, and σ(z) is the extinction coefficient. The extinction coefficient can be related to visibility (V) as σ = 3/V

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The WISE201 and WISE202 ceilometers observe backscatter profiles up to three CBHs every minute. CBH is retrieved based on the time delay between the launch of the laser pulse

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and the detection of the backscattered signal (Vaisala, 2012). The CL51 ceilometer has higher accuracy and better performance than the CL31 model because of its improved algorithms and more powerful laser source, which has less divergence (Liu et al., 2015; Morris et al., 2014). To improve the accuracy, Vaisala CL51

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ceilometer has adopted the enhanced Sky Condition algorithm. In the normal full-range operation, CL51 digitally samples the return signal from 0 to 100 100 µs from ground level

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up to 15,000 m while CL31 samples 0 to 50 µs from ground level up to 7,700 m (Vaisala,

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2006, 2012). According to the CL51 demonstration field campaign report, the CL51 provides six times greater signal-to-noise ratio than the CL31, and thus can provide improved

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information regarding aerosol layers and boundary layer heights with clouds (Morris and

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Winston, 2016). Whereas the CL31 can detect CBHs up to an altitude of 7500 m, the CL51 can detect clouds including cirrus up to 13,000 m and backscatter profiles can be retrieved up

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to 15,000 m (Liu et al., 2015; Vaisala, 2006, 2012). In this study, the maximum CBH

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retrieved by Vaisala’s algorithm was detected at 13,000 m for both stations. The overall availability of measurements during the analysis period was approximately 92%. In the available dataset, 53 of a total of 72 months had up to 99% data retrieval.

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Unfortunately, datasets from WISE201 during November 10-31, 2014, February 27 to May

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14, 2015, and August 20 to 22, 2016 are not available because of a software malfunction and module upgrade, i.e., data is the 30%, 92%, 55%, and 80% available on November 2014, on February and May 2015, and on August 2016, respectively. At WISE202, datasets for the period of December 2014, July 2015, January 2016, and August 2016 had lower availability (71%, 60%, 65%, and 84% per month, respectively) because of electricity outbreaks caused by lightning, software malfunctions, and module upgrade. Monthly data availabilities at WISE201 and WISE202 were shown in Fig. 2. Statistics during periods where observation rate were low need to be used with caution.

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For example, the WISE201 ceilometer was inoperable only from 04:31 to 24:00 (approximately 20 hours) LST on April 28, 2014. Fortunately, the adjacent observation site WISE202 (9.8 km apart) was operational during this time. From the monthly statistics, the frequency of cloud occurrence at WISE202 during this short inoperable period was found to

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reach up to 18.8% of the total monthly records. This shows that missing observations on cloudy days can significantly distort the result, and that two nearby observation sites can

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complement each other.

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The Vaisala CL31 ceilometer at the KMA Seoul station (hereafter, KMA108: 37.57°N, 126.96°E, 86 m ASL; Fig. 1) is used to compare with the CBHs obtained from the WISE201

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and WISE202 ceilometers. It is located within 1.1 km of the WISE202 station, and has

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provided heights for three cloud base layers and cloud coverage for the first layer since

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October 12, 2011.

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2.2 Other measurements for analysis

KMA has a network of Automated Weather Stations including approximately 520 sites throughout the Korean peninsula, of which KMA108 is one of them (Sohn et al., 2010). A

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dataset of total monthly precipitation recorded by using a rain gauge, which measures

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automatically every minute with a measuring unit of 0.5 mm per hour, at the KMA108 was used (KMA). We also use monthly climatology of precipitation for the period of 30 years (1981–2010) at KMA108. To better understand the cloud characteristics during the precipitation events, the vertical profiles of relative humidity and liquid water content along with the integrated water vapor and integrated cloud liquid contents obtained from a microwave radiometer are utilized in this study. Previous studies show that the microwave retrievals are especially efficient to monitor the convective weather conditions due to its high temporal resolution with acceptable

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accuracy (e.g., Chan and Lee, 2015; Gascón et al., 2015; Knupp et al., 2009; Ware et al., 2013). Although there are limitations in the vertical information contents in the radiometer observation (Hewison, 2007), especially vertical profiles of the liquid content (Crewell et al., 2009), the uncertainties associated with the radiometer installed at the WISE stations are

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known to comparable with the radiosonde observation (Balaji et al., 2017; Chan and Lee, 2015; Cimini et al., 2011; Cossu et al., 2015; Crewell et al., 2009; Knupp et al., 2009; Matzler

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and Morland, 2009; Peter and Kämpfer, 1992; Sánchez et al., 2013; Ware et al., 2003; 2013).

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Furthermore, the uncertainties of integrated water vapor and integrated cloud liquid water are known to 3% to 10–20% of the measured value, respectively (Cossu et al., 2015; Peter and

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Kämpfer, 1992).

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The microwave radiometer at WISE stations has been operated in conjunction with the ceilometer since January 1, 2015. The retrieval products are produced every 10 minutes with

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the vertical resolution of 25–300 m from the surface to 10 km. To measure the uncertainty of

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WISE instruments including microwave radiometer and wind lidar, intensive in-situ observations were conducted during WISE Urban Summer Observation Campaign (WUSOC2016) from 19 September to 7 October 2016. Radiosonde was launched at every 3

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or 6 hours during this period. Comparisons of relative humidity between radiometer and 86

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radiosonde observations show that the uncertainty is 15 ± 10% depending on the altitude. During the convective cloud conditions, the uncertainty of relative humidity is decreased similar to the previous studies (Balaji et al., 2017; Gascón et al., 2015; Sánchez et al., 2013; Ware et al., 2013). Furthermore, the comparison of the liquid water information with the liquid water content of European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis meteorological database (ERA5) shows that the distribution of liquid water is quite similar each other (not shown) (e.g., Iassamen et al., 2009). Thus, the addition of products from the microwave radiometer strengthen the understanding of the ceilometer data.

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At WISE201 and WISE202, no ground-based instrument (i.e., sky-viewer) to monitor the sky condition continuously was available. Hence, to visualize the sky conditions with true color images over the Korean peninsula, MODerate resolution Imaging Sensor onboard Aqua satellite (Aqua/MODIS) collection 6 Level 1 products (MYD021KM and MYD03) from the

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National Aeronautics and Space Administration/Goddard Space Flight Center are used (http://ladsweb.nascom.nasa.gov/). The Aqua has overpass time of approximately 1330 local

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time with ascending (daytime) mode, and radiances and geolocation of MODIS are calibrated

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with a 1 km resolution.

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3. Results

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3.1 Cloud occurrence

Cloud occurrence is a suitable parameter for evaluating the characteristics of clouds. Here,

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cloud occurrence is defined as the ratio between the number of at least one detected CBH and

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the total number of available observations, i.e.: the number of CBH at first layer Cloud Occurrence = ( ) x 100 [%] total available record

(5)

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This definition has been used in previous studies on cloud occurrence and cloud layers

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derived from radiosondes and ceilometers (e.g., Costa-Surós et al., 2013; Liu et al., 2015; Poore et al., 1995; Rossow and Schiffer, 1991; Wang et al., 2000). Figure 2 shows the percentage of monthly distributions of cloud occurrence at the three stations in Seoul during 2014–2016. Monthly data availability is denoted with bar graph so that it is easy to find low-availability month and its availability rate. The average frequency of cloud occurrence detected by the ceilometers at the WISE stations was 54.3% for the entire period. The monthly distributions in Fig. 2 show similar features in 2014, 2015 and 2016 when those months are not considered because of the lower data availability. The maximum

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cloud occurrence is during summer (June–August) (approximately 73%) and the minimum occurs usually in spring (March–May) or autumn (September–November) (approximately 34%). However, seasonal variations are slightly different; the maximum shows a constant value of 71.5 ± 1.7% during summer in 2014, whereas a maximum peak occurs in July with a

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value of 69.4 ± 3.4% in 2015 and 66.7 ± 5.7% in 2016. When interannual variation of cloud occurrence for the three years are compared, it is found that the variation in 2016 is lower

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than that of 2014 and 2015.

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Frequencies of cloud occurrence at KMA108 are lower than two WISE observations by 11.2 ± 7.6% over all of the years, which is attributable to the relative performance of CL51

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and CL31. The CL51 ceilometer can detect clouds up to 13,000 m, whereas the vertical range

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of the CL31 ceilometer is 7620 m (Costa-Surós et al., 2013; Liu et al., 2015). Therefore, high clouds (>7620 m) possibly account for about 10–20% of the total record during each month.

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In particular, the monthly difference in cloud occurrence between WISE stations and

high cloud or clear sky.

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KMA108 was up to 25% in spring and autumn that is known as a dry season with prevailing

It would be meaningful to investigate the case of anomalous cloud occurrence. On

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November 2015, the maximum peak of cloud occurrence appeared in autumn as opposed to

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the climatology. The monthly frequencies of cloud occurrence is 5.3% greater than one in July 2015 at WISE202 and it reaches the highest of the year; 75.3%, 76.0%, and 66.0% at WISE201, WISE202, and KMA108, respectively (Fig. 2(b)). There were no clear days and CBH was detected on every day in the month. There were only four days on which CBHs were detected for less than 75% of the day (3, 4, 21, and 30). It is found that anomalous precipitation was recorded along with highest cloud occurrence. According to the KMA report (KMA, 2015), on average precipitation in the Korean peninsula was recorded the 127.8 mm (104.6 mm in Seoul) on November 2015, and the number of rainy days was the 15 days,

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which is the second highest since 1973. The high pressure flow formed around the Philippine Sea due to the influence of El Niño, and then the southern wind with warm air and many water vapors come into the Korean peninsula, resulting in frequent rainfall in the month (up to 257% from climatology).

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Diurnal and monthly variations of cloud occurrence for WISE201 and WISE202 are much similar. It is probably because the observations at WISE stations have been carried out

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in a similar way and two stations are too closely installed in an east-west direction. Westerly

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wind is dominant in Seoul throughout the year and consequently cloud is generally moving eastward. This indicates that it may be difficult to distinguish between the results of the two

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stations in terms of synoptic scale or daily and monthly scale. Meanwhile, in case one

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observation site is out of order, the other site could provide cloud information instead.

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3.2 Vertical frequency of cloud occurrence (CVF)

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CVF can be derived from vertical distribution statistics of CBHs measured from ceilometer. Vertical distribution of CBHs according to single-layered, multilayered, and aggregated layered clouds, and distance statistics between CBHs observed over Seoul for

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three years are introduced in this Section.

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Figure 3 shows the frequency distributions of CBH at WISE202 for the entire period. The histograms for WISE201 depict similar features to those for WISE202, and as such are not shown. Total number of CBHs found in each 500-m vertical interval was counted, and there were 26 bins within the detection range of the CL51 ceilometer from 0 to 13,000 m. The frequency distribution of the aggregated CBH for all layers over each year shows that on average 55% of CBHs are found below 2000 m (Fig. 3(a)). CBHs for each year are similar in vertical distribution and the peak of the distributions occurs 1000– 1500 m high in 2014 and 2015, whereas 500–1000 m high in 2016 by narrow margin. In addition, another second peak

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is observed near 7500 m. The frequency of cloud occurrence above 7500 m, the threshold altitude beyond which only the CL51 ceilometer can detect clouds, is 10.1% for aggregated layered CBH on average. To analyze the vertical distribution for multilayered clouds, the frequency of the lower (1st

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CBH), middle (2nd CBH), and higher (3rd CBH) layers is investigated when one, two, and three layers were detected (Fig. 3(b, c, and d)). When multilayered clouds are detected, two-

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layered are predominately. For the entire period, the estimated values of single-, double-, and

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triple-layered cases in relation to the total number of CBHs were 65.9%, 26.9%, and 7.2%, respectively. These results are similar to the values of approximately 60% for single-layered

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and 26% multilayered clouds, based on a 20 year (1976–1995) rawinsonde dataset (Wang et

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al., 1999; Wang et al., 2000), and to the value of approximately 79% when single-layer cloud was detected, which is determined by analyzing combined ceilometer-radar measurements

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(Wang and Rossow, 1995). CVF for all of single-, two-, and three-layered cases shows

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analogous vertical distribution pattern; higher distribution below 2000 m, a gradually decreasing frequency with height, and another frequency peak around 7500 m. The frequency of CBHs for single- and two-layered clouds decreases rapidly from about 2000 m high, while

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for the three-layered clouds the lowest proportion below 2000 m of all clouds cases and

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relatively even frequency distribution are exhibited. For three-layered CBHs detected, the frequencies clearly show bimodal distributions for all lower (1st CBHs), middle (2nd CBHs), and upper (3rd CBHs) layers, as shown in Fig. 3(b). Whereas the first peak in the three-layered CBH system tends to be escalated vertically at a bin centered at 750, 750, and 1250 m for the 1st, 2nd, and 3rd CBHs, respectively, the secondary peak represents a bin centered at 8250 m for each layer. Furthermore, the secondary peak in each layer has a smooth distribution with an average value of 4.9% from 6500 to 8500 m. For two-layered cloud systems, the frequency in 1000–1500 m bin is the

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highest of all cloud layers and nearly half of total frequency appears below 2000 m for all 1st and 2nd CBHs (Fig. 3(c)). For single-layered clouds, 60% of total frequency appears below 2000 m indicating the highest concentration of frequency of all cloud systems. For multilayered cloud systems, the distances between the CBHs of the adjacent layers

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are analyzed to determine the cloud characteristics, as shown in Fig. 3(e). To examine the distances between each adjacent CBH in more detail, bins with 250-m intervals instead of

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500-m intervals are applied. Distance between consecutive CBHs for two-layered clouds is

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less than 250 m with 44%. Distance of 250–500 m is as high as 31%. This indicates that the majority of two-layered cloud systems locates within the distance of 500 m. On the contrary,

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when three CBHs are detected, distance between adjacent CBHs is more frequent in 250–500

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m than in under 250 m. There are approximately 70% for 2nd - 1st CBH and 77% for 3rd - 1st CBH in three-layered cases. As mentioned in Section 2, the CL51 ceilometer was designed to collect light backscattered from clouds, precipitation, fog, and mist (Vaisala, 2012). Although

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their signals are distinguished by Vaisala’s ceilometer algorithm, the retrieved CBH might be affected by signals other than those from clouds. This implies that even if only the characteristics of ceilometer measurements are considered, most of the distance between

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adjacent CBHs are within 1000 m (average 87.1%) in multilayered cloud systems. In

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particular, the variability of cloud occurrence below 3000 m found in this study is very similar to a previous study using radar and ceilometer data acquired during the Atlantic Stratocumulus Transition Experiment (Wang et al., 1999).

3.3 Monthly vertical variability of cloud occurrence Temporal variation of CVF is analyzed. Figure 4 shows the monthly distributions of CBH with 250-m vertical bins for all retrieved layers and single-layer clouds. They are spatiotemporally averaged over three years and two WISE stations. The monthly vertical

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distribution shows that more than half of the observed CBHs are located below 2000 m, except in October (46.2%). It depicts the higher frequency below 2000 m as shown in Fig. 3(a) and 3(d) as a detailed monthly distribution. The accumulated CBHs from the surface to 2000 m have a maximum frequency in July; 73.3% for single-layer and 70.6% for all-layer

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cloud. This indicates that low clouds appear 1.5 times more frequently in July than in other months.

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The vertical distributions of cloud occurrence for single- and all-layer cloud are similar

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features. However, the frequency of CBHs above 4000 m is slightly different; CBHs for aggregated clouds were detected more frequently at higher altitudes than single layer. The

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maximum CVF tends to be formed lower for single layer clouds than aggregated clouds; e.g.,

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in April, the maximum CVF for single layer clouds appears at 250-500 m with the frequency of 14.7%. On the other hand, the frequency of each bin above 4000 m has significantly

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smaller values of less than 0.5%.

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The vertical frequency of monthly cloud occurrence shows different variabilities by season. For instance, the maximum CVF for aggregated layers appears at 1000–1250 m bin (15.6%) in February while for single layers at 500–750 m bin (16.2%) in July. In May and

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September, CBH is observed more frequently in the upper layer (>6000 m) than in other

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months, and the frequency of low cloud (<2000 m) occurrence is relatively less than in other months. In comparison with other months, September has the maximum frequency of cloud occurrence between 6000 and 8000 m. These results show good agreement with previous studies that reported a tendency for frequent occurrence of clear and blue skies during autumn because of low aerosol optical depth (Kim et al., 2010). Furthermore, clouds over 7500 m comprise only a small fraction for aggregated layer (2.9% in May on average) and single layer (2.1% in September on average) for the entire CBH observations. However, if both lower and upper clouds exist, CBH of the upper layer might not be detected by the limitations

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of the ceilometer, because the device only provides the lowest three CBHs.

3.4 Relationship between cloud occurrence and precipitation, and case studies As cloud information is generally related to estimating precipitation, longer-term

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precipitation for the KMA108 station are used to compare with cloud occurrence from the WISE stations. It would be difficult to compare fairly due to no climatological information of

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cloud occurrence, but the comparison was tried to make using available information. Figure 5

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shows the monthly precipitation based on observations at KMA108 station for the period of 2014, 2015, 2016, and climatology (See on KMA, web site). Monthly variations of

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precipitation from 2014 to 2016 as in Fig. 5 are compared with the cloud occurrence at WISE

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stations as in Fig. 2. The monthly variations of precipitation amount in 2014, 2015, 2016, and climatology show similar patterns in that precipitation is concentrated in July. Precipitation

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amount is the highest in summer and the lowest in winter with enormous monthly variation.

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On the other hand, cloud occurrence is highest in summer and not the lowest in winter, with moderate monthly variation. The lowest frequency of cloud occurrence is generally observed in spring and autumn season when a moderate rainfall is accompanied. The relationship

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between cloud occurrence and precipitation in Seoul differs in summer and winter. Cloud

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occurrence at WISE stations in winter is as high as 53%. However, precipitation reaches a minimum in a year with 32 mm. Clouds over Seoul that are affected by the dry winter monsoon are frequently formed, but they do not produce considerable precipitation. In contrast, the highest cloud occurrence (77%) and the highest annual precipitation are recorded during summer, which is supported by the fact that the frequency of CBHs below 2000 m is the highest in July as analyzed in Section 3.3. The monthly trend of cloud occurrence does not exactly correspond to that of precipitation amounts. It is probably because precipitation amount is related to not only cloud occurrence but also vertical growth

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and convective strength of cloud. The most salient feature of precipitation comparison between climatology and 2014–2016 is that the total amount of rainfall for July-August of summer season has decreased approximately 51% of the climatology, which indicates that summers in recent years have

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been dry. In addition, following September shows unprecedented low precipitation of 20% when compared to the climatology. During the summer of 2015–2016, the long-standing high

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pressure over North Pacific and China influenced continuously over Korean peninsula

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resulting in temperature of +1.5ºC greater than the climatology (KMA). In August 2016, the maximum number of heat wave of 16.7 days was recorded, which is the highest number since

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1973. Furthermore, typhoons, affecting Korean peninsula two times on average, influenced

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only once during this period. As a result, both dry summer monsoon and infrequent typhoon have played a role to reduce precipitation in August and September. This unusual low

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precipitation in August 2015 and 2016 might be connected to corresponding lower cloud

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occurrence shown in Fig. 2 is relatively same as the annual averaged frequency, indicating no close connection to precipitation. This suggests that clouds in September during recent years do not bring out about rainfall and consequently are not closely related to precipitation.

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cloud occurrence.

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However, September in 2015 shows close connection between lower precipitation and lower

Additional comparisons in other months are briefly introduced. Anomalous abundant rainfall and exceptional higher cloud occurrence in November 2015 are found. Surprisingly, trend of cloud occurrence in WISE stations shown in Fig. 2 and precipitation in 2015 is very similar in shape – not in magnitude. Relative maximum precipitation appears together with the highest cloud occurrence. In November unusual frequent and sustained cloud occurrence could be related to precipitation. Highest precipitation in July considerably corresponds to higher level of cloud occurrence. Precipitation and cloud occurrence seem to be well

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correlated each other with lower in March and higher in April. However, sudden increase in precipitation in May 2016 dose not correspond to cloud occurrence, as shown in Fig. 2. According to a previous study using rawinsonde datasets covering 14 years (1975–1988) (Poore et al., 1995), the annual mean frequency of occurrence of single-layer clouds was

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found to be approximately 88% at Osan station (37.10°N, 127.03°E, approximately 46 km away from the WISE stations). However, the annual mean frequency of cloud occurrence

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detected by the ceilometers is found to be 54.3% for the entire period of this study. This result

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indicates that cloud occurrence in recent years considerably decreased as compared to the late 20 century, although the methods of measurements for calculating cloud occurrence are

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different and averaging period for study is considerably shorter than Poore et al. (1995).

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Therefore, it can be estimated that the decrease in the frequency of cloud occurrence would impact the decrease of precipitation over the Korean peninsula, as shown in Fig. 5.

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In this section, we describe the diurnal and daily variations of CBH distributions for a

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typical cloudiness cases during winter and summer associated with precipitation over Korea. Case studies of precipitation show some characteristics of CBHs detected by ceilometer along with observations of microwave radiometer and satellite. Most of the cloudiness in winter is

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related synoptically to the outbreak of coldness from the Siberia High pressure system. In

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contrast, cloudiness in summer is associated with the stationary front with warm and moist air from South-Pacific High pressure system, called the Asian summer monsoon.

3.4.1 A winter case study Figure 6 shows an example of ceilometer measurements for rainy days at WISE202 during December 19–26, 2015. Figures 6(a) and 6(b) show daily variations of the backscatter profiles and CBH observations up to 15,000 m from the CL51 ceilometer, respectively. Distinct signals can be identified as cloud layers in the backscatter profile and the CBH

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variations, where the cyan, purple, and red triangles represent the 1st, 2nd, and 3rd CBHs, respectively. Light blue shadings of Fig. 6(b) represent the aperiodic rainfall time. The relative humidity and liquid water contents up to 7000 m high retrieved by the microwave radiometer at the same station are shown in Figs. 6(c) and 6(d). Integrated liquid water from

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the microwave radiometer is shown in Fig. 6(e). The diurnal variation of cloud formation shows typical patterns at Seoul in winter, i.e.

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middle-level (alto-type) clouds approaching after high-level (cirrus-type) clouds pass in the

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upper layer above 6000 m on December 20, 22, and 24. After the middle-level clouds, the showers came down due to approaching the low-level clouds on December 21, 23, and 25.

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This type of weather is created by the flow of cold and dry air from Siberian High pressure

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systems. Therefore, these systems lead to relatively low precipitation despite the great cloud occurrence of 50–60% in winter, as shown in Fig. 2. Total precipitation of 3.1 mm was

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recorded during this period (Fig. 6). On the other hand, clouds in winter frequently appear as

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multi-layered systems in the high-middle layer (>2000 m) without precipitation. It is confirmed that the cloud amount at that altitude is relatively uniform as compared to other seasons, as shown in Fig. 4(a).

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Microwave radiometers can be used to figure out the behavior of clouds by providing

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information of relative humidity and liquid water content along with the integrated cloud liquid contents throughout continuous measurements. The high percentage of relative humidity and liquid water contents derived from microwave radiometers is observed vertically (Fig. 6(c) and 6(d)). These measurements correspond to clouds appearing in the low troposphere on rainy days, as shown in Fig. 6(b). Integrated cloud liquid contents values also appeared a maximum of 10 g at each rainy time (Fig. 6(e)). However, the atmospheric environment generally is not sufficient to induce continuous precipitation due to the influence of the Siberia High pressure system in winter. In conclusion, it is difficult to derive a vigorous

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convection or massive advection of a humid air mass to trigger rainfall in winter.

3.4.2 A summer case study Figure 7 shows another rainy case occurred at WISE201 during July 20–30, 2015 which

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is associated with the stationary Jangma front this is known in Korea of the East Asian monsoon that persists over the Korean peninsula during summer monsoon. In general, the

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Jangma front brings precipitation over East Asia (including Korea) from June to July under

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the influence of the Asian–Pacific summer monsoon (Wang, 2002).

According to the weather reports issued by the KMA for this period, the southern pressure

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trough on July 20 gradually influenced to extend wet bands over the Korean peninsula. On

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the following day, it was located at the edge of the high pressure system, providing rainfall in the morning. On July 23–26, it was raining because the wet air mass was located from the

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north-west side to the south coast caused by the North Pacific high pressure. On July 29,

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while maintaining the high pressure system in the North Pacific in the south, a trough passed from the northeastern part of China, and a strong convergence flow induced instability. At

147.0 mm).

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that time, the total amount of heavy rainfall reached 180.5 mm (in particular, days 24–26:

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Figures 7(a) and 7(b) show the detail of backscatter signals and CBHs from ceilometer observations. On July 20, cirrus and altostratus clouds passed over Korea and the following day cumulonimbus clouds developed from near the surface to 6000 m. There was a rainfall of 11.0 mm on July 21, CBHs went up and then again cumulonimbus clouds were well developed vertically with heavy precipitation during July 23–26. Backscatter signals and CBHs represent a dominant spread of clouds in the lower troposphere (< 2000 m) for the entire period. It also exhibits the aggregated CBHs around lower levels and the distribution of rare signals above that level for rainy days. Backscatter signals of this summer case for rainy

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days generally show sparse area of signals above the first CBH, which is different from dense backscatter signals above the cloud base in the winter case described in section 3.4.1. It indicates that cloud depth for the winter case may be not as deep as that of this summer case. For non-rainy days, the vertical distribution of multiple CBHs up to the middle or higher

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troposphere and frequent signals are observed. From these observations, it is assumed that there are several shallow clouds formed on non-rainy days.

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Figures 7(c), 7(d), and 7(e) show the relative humidity, liquid water contents (up to 7000

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m high), and integrated cloud liquid contents measured by a microwave radiometer. The vertical profiles of relative humidity are similar with the liquid water contents and integrated

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cloud liquid contents shows the peak values on rainy times, which help to infer the existence

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of convective cloud conditions (Fig. 7(d) and 7(e)). During the rainfall days, integrated cloud liquid contents varied ranged from 0 to 28 g, the maximum corresponds the relative humidity

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on 24-26 July (Fig. 7(e)). The interpretation of data from in-situ ceilometer with a microwave

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radiometer would be helpful for analyzing a cloud system during the convective rainfall event. Figure 7(f) shows the Aqua/MODIS true color images that can be used to verify the cloud observations from the ceilometer and radiometer. Yellow circles represent the area over Seoul.

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These observations match well with satellite images showing a wide range of horizontal

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cloud distribution.

4. Summary

For the first time, the cloud base height and vertical frequency of cloud occurrence were analyzed using high resolution data (a temporal resolution of 1 min and a vertical resolution of 10 m) acquired from two improved ceilometers (CL51) installed as a part of the WISE project for a period of three years (2014–2016) in Seoul metropolitan area. The evaluation of the high-resolution data show that the overall frequency of cloud

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occurrence measured by the ceilometers was 54.3% for three years; the annual frequency of cloud occurrence during this period ranged between 50–56%. Although annual variabilities varied during the measurements period, overall monthly cloud occurrence distributions showed a similar seasonal cycle, with maximum values of 70–77% in summer. The

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evaluation also shows that the monthly cloud occurrence detected at two WISE stations in Seoul reveals very similar frequencies due to their adjacent location Because of that, it could

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be thought to be redundant installation of ceilometer, but they have been used together for

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useful study on an urban meteorology in Seoul megacity. Comparison study using measurements from CL31 at a nearby meteorological station reveals that the temporal trends

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of cloud occurrence obtained from CL51 are consistent with measurements obtained using

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the CL31 ceilometer at the in-situ Seoul meteorological station. However, the frequency of cloud occurrence measured by the CL51 is greater than the CL31 measurements because

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clouds above 7620 m are not retrieved by the CL31. Hence, it is determined that CL51 is able

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to detect CBH with greater accuracy as compared to CL31, and it is found the frequency of high clouds (>7620 m) measured by the CL51 ceilometer is approximately 10–25% of the total records for each month.

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The vertical characteristics of cloud occurrence from high temporal resolution products

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through continuous observations indicated that the peak of CBHs with 16–23% was represented mostly between 1000 and 1500 m at both stations for the entire period, and the bimodal frequency distribution was observed for all CBH layers. In particular, more than half of CBHs are detected below 2000 m, which is the level of interest altitude in the urban model for forecasting. The frequency of monthly cloud occurrence shows different vertical seasonal variabilities, although CBH above 50% was mostly below 2000 m. In May and September, CBH was detected more frequently in the upper layers above 6000 m as compared to other months, and the frequency of clouds below 2000 m was relatively less than in other months.

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When multilayered clouds occur, the first peak of each layer tends to be escalated vertically and the secondary peak occurs between 8000 and 8500 m. It indicates that the frequency of cloud occurrence at the upper layer (>7500 m) is also not negligible. However, this is an altitude where most ceilometers cannot measure, resulting in missing approximately 10–25%

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of the cloud information at the upper layer. Currently, ceilometer products with high temporal resolution at WISE stations are used as input for numerical weather forecast model in Seoul

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metropolitan area, forecast skills are expected to be further improved.

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The monthly and seasonal patterns of cloud occurrence at WISE stations were compared with the variation of precipitation at the Seoul. The variation of cloud occurrence does not

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exactly correspond to but it is partially related to that of precipitation. Generally, precipitation

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in Seoul has a distinct pattern showing more rainfall in summer than other seasons. Associated maximum of cloud occurrence and precipitation occur in July, which indicates a

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close correspondence between precipitation and cloud occurrence in summer. On the contrary,

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cloud occurrence in winter can be as high as 53% on average even though averaged precipitation during this season is only 32 mm – the lowest in year. This is because the dry winter monsoon frequently forms clouds during the winter in Seoul, but hardly produces any

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precipitation. Meanwhile, it is found that cloud occurrence and precipitation are mostly

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related each other when some cases unusually different from climatology happened. As examples, frequent cloud occurrence (approximately 77%) leads to more precipitation in November 2015 and infrequent cloud occurrence (approximately 58% and 50%) appeared with less precipitation in August 2015 and 2016. As the relation between precipitation amount and cloud occurrence was seen, it is not easy to find exact mutual relevance due to their different degree of variability. However, the result revealed the fact that their relationship is different in summer and winter, and cloud distribution with altitude and season is related to precipitation. In addition, they show a considerable relationship when some unusual weather

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condition occurred. According to the KMA annual report and Jung et al. (2011), the trend of annual mean temperature and total annual precipitation is increasing for a century (1910-2010) in Korea. The annual mean temperature in the period of three years (2014–2016) increased average

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0.85°C compared to climatology, but total amounts of annual precipitations in the same period decreased by 15% on average than climatology. In particular, the amounts of

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precipitation in summer tend to decrease significantly as compared with climatology

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(approximately 51%) due to dry summer monsoon and infrequent typhoon. In addition, the precipitation in September during recent years was recorded in less than 20% of the

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climatology while the cloud occurrence shows 53.4% close to yearly-averaged 54.3%. This

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means that clouds present in September do not lead rainfall. Characteristics of clouds, captured by ceilometers in winter and summer, were discussed

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by means of two case studies. In winter and summer, different precipitation patterns appeared.

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For the analysis of vertical cloud distribution during periods of rain, the vertical profiles of liquid water contents and relative humidity from a microwave radiometer were also considered with the analysis of CBH and CVF from ceilometer measurements. The liquid

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water contents, which can account for the vertical distribution of clouds, of microwave

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radiometer also has a large uncertainty in the lower layer (<2000 m), except for strong precipitation events. An addition of the retrieval products from the microwave radiometer including vertical profiles of relative humidity and liquid water content along with integrated cloud liquid content help understand the vertical characteristics of clouds, especially under the convective weather condition. This is the first evaluation on cloud analyses using ceilometers in Seoul for a three-year that provide a high resolution cloud distribution for urban meteorology. This data could be used for the analysis and modeling of urban meteorological characteristics and data

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assimilation. We hope to provide further analysis to enhance understanding of the complex

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urban area after more long-term data is accumulated.

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Acknowledgements This work was funded by the Weather Information Service Engine Program of the Korea Meteorological Administration under Grant KMIPA-2012-0001-1. We thank the KMA for providing access to the products. The data from Jungnang and Gwanghwamun stations,

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analyzed in this study, were also produced under the WISE program.

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Figures

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Figure 1. Distribution of ceilometer stations prior to 2016 on the Korean peninsula. Blue boxes are stations operated by the KMA. Red boxes are stations operated as part of the WISE

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stations, respectively.

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project. The numbers 108, 201, and 202 refer to the Seoul, Jungnang, and Gwanghwamun

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Figure 2. Monthly cloud occurrence derived from ceilometer data at Jungnang (WISE201),

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Gwanghwamun (WISE202), and Seoul (KMA108) stations: (a) 2014, (b) 2015, and (c) 2016. White and black bars represent the monthly data availability at WISE201 and WISE202,

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respectively.

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Figure 3. Frequency distributions of aggregated CBHs for all layers retrieved by the CL51 ceilometers at (a) WISE202 stations in 2014, 2015, and 2016. (b) Distribution of CBHs at WISE202 for the lower and higher layers when two layers are detected, (c) distribution of CBHs for the first, second, and third layers when three layers are detected, for three years

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with 500-m interval bins. (d) Frequency distribution of aggregated CBH for all layers and for single layer at WISE stations for the entire period. (e) Frequency distributions of the

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distances between the lower and higher layers when two CBHs are detected (black), between

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the lower and middle layers (gray), and middle and higher layers when three CBHs were

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detected (white) with 250-m interval bins.

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Figure 4. Monthly distributions of cloud occurrence in 250-m vertical bins averaged at two

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stations for the entire period: (a) aggregated layers and (b) single-layer clouds.

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Figure 5. Monthly precipitation at Seoul (KMA108) station in 2014, 2015, and 2016. Black

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dashed line represents monthly climatology of precipitation during 30-years (1981–2010) at

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Seoul station, respectively.

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Figure 6. An example of (a) backscatter profiles and (b) CBHs detected by the CL51 ceilometer on December 19–26, 2015 at WISE202. The cyan, purple, and red triangles represent the 1st, 2nd, and 3rd CBHs, respectively. Blue shade represents precipitations. (c) Relative humidity, (d) liquid water contents, and (e) Integrated cloud liquid contents retrieved

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by the microwave radiometer during the same period at WISE202.

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Figure 7. An example of (a) backscatter profiles and (b) CBHs detected by the CL51

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ceilometer on July 20–30, 2015 at WISE201. The cyan, purple, and red triangles represent the

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1st, 2nd, and 3rd CBHs, respectively. Blue shade represents precipitations. (c) Relative humidity, (d) liquid water contents, and (e) Integrated cloud liquid contents retrieved by the

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microwave radiometer during the same period at WISE202. (f) Aqua/MODIS true color images around 0500 UTC on July 20–31, 2015 over Korea. Yellow circles represent the area

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over Seoul.

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Highlights ▶ CL51 ceilometer can retrieve Cloud base height and backscatter profiles up to 13,000 and 15,000m.

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▶ the first study of a high-resolution cloud distribution derived from ceilometer measurements over Seoul, Korea for urban meteorology.

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▶ Comparison of the results obtained from two different models (CL51 and CL31) of ceilometer.

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▶ Characteristics of clouds were discussed through a typical cloudiness case in winter and

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summer from ceilometer and microwave radiometer measurements