AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-9-711-2016Evaluation of cloud base height measurements from Ceilometer CL31 and MODIS
satellite over Ahmedabad, IndiaSharmaSomsomkumar@prl.res.inVaishnavRajeshShuklaMunn V.KumarPrashantKumarPrateekThapliyalPradeep K.LalShyamAcharyaYashwant B.Physical Research Laboratory, Ahmedabad,
IndiaSpace Applications Centre, ISRO, Ahmedabad, IndiaSom Sharma (somkumar@prl.res.in)29February2016927117192October201511November20154February20164February2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/9/711/2016/amt-9-711-2016.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/9/711/2016/amt-9-711-2016.pdf
Clouds play a tangible role in the Earth's atmosphere and in particular, the
cloud base height (CBH), which is linked to cloud type, is one of the most
important characteristics to describe the influence of clouds on the
environment. In the present study, CBH observations from Ceilometer CL31 were extensively studied during May 2013 to January 2015 over Ahmedabad
(23.03∘ N, 72.54∘ E), India. A detailed comparison has
been performed with the use of ground-based CBH measurements from Ceilometer
CL31 and CBH retrieved from MODIS (Moderate Resolution Imaging
Spectroradiometer) onboard Aqua and Terra satellite. CBH retrieved from
MODIS is ∼ 1.955 and ∼ 1.093 km on 25 July 2014
and 1 January 2015 respectively, which matches well with ceilometer-measured CBH (∼ 1.92 and ∼ 1.097 km). Some
interesting features of cloud dynamics viz. strong downdraft and updraft
have been observed over Ahmedabad which revealed different cloud
characteristics during monsoon and post-monsoon periods. CBH shows seasonal
variation during the Indian summer monsoon and post-monsoon period. Results
indicate that the ceilometer is an excellent instrument to precisely
detect low- and mid-level clouds, and the MODIS satellite provides accurate
retrieval of high-level clouds over this region. The CBH algorithm used for
the MODIS satellite is also able to capture the low-level clouds.
Introduction
Clouds, visible masses of tiny water droplets or frozen ice crystals, are one
of the most crucial parameters for weather and climate prediction (Bauer et
al., 2011; Errico et al., 2007; Shah et al., 2010). Kiehl and Trenberth (1997) showed the importance of clouds on the global energy budget. Accurate
information of cloud cover is essential for better understating of the
climate system (Fontana et al., 2013). Randall et al. (1984) observed that a
4 % increase in the cloud cover with stratocumulus can compensate the
global warming due to CO2 doubling. The types of low-level clouds and
their development are governed by meteorological conditions, especially in
the atmospheric boundary layer, such as vertical stability (Norris, 1998).
Koren et al. (2010) discussed that aerosols affect clouds, which
contributes to climate change. Andrejczuk et al. (2014) found that cloud albedo may
increase as a result of the seeding, if enough aerosols are delivered into
the cloud. Kokhanovsky et al. (2007) discussed that the global cloud top
height (CTH) is near to 6000 m. Li and Min (2010) showed the impact of
mineral dust on tropical clouds which is dependable on rain type. Varikoden
et al. (2011) studied cloud base height (CBH) over Thiruvananthapuram
(8.4∘ N, 76.9∘ E), India, during different seasons and
found diurnal and seasonal variations except rainy days. Zhang et al. (2010)
deployed AMF (ARM Mobile Facility) for radiosondes in Shouxian, China, and
showed that the diurnal variation in upper-level clouds thickness is larger
than that of low-level clouds over this region.
Space-based instruments are widely used to detect clouds globally at high
spatial and temporal resolution. Various scientific studies have been
performed to retrieve information on clouds, which needs further evaluation with
ground observations. At nighttime, CBH can be retrieved accurately using
Visible Infrared Imaging Radiometer Suite algorithms (Hutchison et
al., 2006). Meerkötter and Zinner (2007) used an adiabatic algorithm to find
CBH from satellite data for convective clouds. Weisz (2007) suggested various
algorithms and methods to measure cloud height from space-borne instruments.
The ability to determine the cloud top/bottom height is still limited due to
the nature of infrared-based passive measurements from satellites (Kim et
al., 2011). Bhat and Kumar (2015) used precipitation radar measurement to
detect vertical structure of cumulonimbus and convective clouds over the south
Asian region. Gu et al. (2011) used the Scale Invariant Feature Transform
algorithm to detect clouds from the MODIS (Moderate Resolution Imaging
Spectroradiometer) satellite without manual interference.
Lidars have been widely used for both atmospheric boundary-layer structure
and cloud-base detection (Mariucci et al., 2007; Albrecht et al., 1990). Liu
et al. (2015) used two ceilometers (CL31, CL51) and a whole-sky infrared
cloud-measuring system and found significant differences in CBH
due to the retrieval algorithm or measurement principle. Cloud-Aerosol Lidar
and Pathfinder Satellite Observations are used to understand the global clouds distribution, cloud statics, and the effect of
clouds on the radiation budget (Rasmussen et al., 2002; Wu et al., 2011;
Winker et al., 2003). Pal et al. (1992) demonstrated an algorithm to
retrieve CTH and CBH from Nd YAG (neodymium-doped yttrium aluminium garnet)
lidar. Duynkerke and Teixeira (2001) determined cloud cover with
stratocumulus using observations obtained from the Regional Experiment of
International Satellite Cloud Climatology Project. Clothiaux et al. (2000)
used multiple active remote sensors like the Belfort or Vaisala Ceilometer and a
micro-pulse lidar to find CBH.
Kotarba (2009) evaluated MODIS-derived cloud amount data with visual surface
observations over the Poland region. Forsythe et al. (2000) compared cloud
information retrieved from GOES-8 geostationary satellite with surface
observation. Stefan et al. (2014) used both ceilometer and satellite data to detect
clouds and found that low-level clouds are better captured by the ceilometer, and
for high-level clouds, satellites provide better information. Albrecht et al. (1990) used a
sodar, ceilometer, and microwave radiometer all together to
estimate cloud thickness. Kassianov et al. (2005) estimated CBH from
hemispherical surface observations and validated these against micro-pulse lidar
(MPL) observations.
Recently, Physical Research Laboratory (PRL) installed Ceilometer CL31 over
Ahmedabad, India. The objective of this study is to evaluate the performance
of satellite-derived cloud features with these ground-based cloud
measurements. Detailed investigations of cloud base retrieved from the MODIS
satellite are compared with ceilometer measurements during the years 2013 to 2015. Brief details about ceilometer observations and MODIS data are
discussed in Sect. 2. The methodology and results are discussed in Sects. 3
and 4 respectively. Conclusions of the paper are given in Sect. 5.
Technical specification of Ceilometer CL31.
PropertyDescription/valueLaser sourceIndium gallium arsenide (InGaAs) diode laserCenter wavelength910 ± 10 nm at 25 ∘C (77 ∘F)Operating modePulsedEnergy1.2 µWs ±20 % (factory adjustment)Width, 50 %110 ns typicalRepetition rate10.0 kHzAverage power12.0 mWMax irradiance760 W cm-2 measured with 7 mm apertureLaser classificationClassified as class 1M laser deviceBeam divergence±0.4 mrad ×±0.7 mradReceiver detectorSilicon avalanche photodiodeData usedGround observations by the ceilometer
The ceilometer lidar set up at PRL, Ahmedabad (23.03∘ N,
72.54∘ E; 55 m a.m.s.l.; Fig. 1), consists of a vertically pointing
laser and a receiver at the same location. Ceilometer CL31 employs pulsed
diode laser InGaAs (indium gallium arsenide) lidar technology. The
transmitter is an InGaAs pulsed laser diode, operating at a wavelength of
910 nm (±10 nm), typically with a peak power of 11 W. The receiving
unit is a silicon avalanche photodiode with an interference filter with a
center wavelength of 915 nm and a surface diameter of 0.5 mm. The receiver bandwidth
is 3 MHz and 80 % of transmissivity at 913 nm. The focal length of the
optical system is 300 mm with a lens diameter of 96 mm. The model CL31 has the
maximum reportable cloud base detection range of 7500 m above the
surface, with the reporting interval of a minimum 2 s to a maximum 120 s. It
can be used in the temperature range of -40 to +60 ∘C. The
technical specifications of the system are provided in Table 1. The single
lens eye-safe lidar ceilometer reported CBH at three layers and vertical
visibility at lower altitudes regularly. To obtain the height of the cloud base,
a laser pulse is sent through the atmosphere. This light pulse is scattered
by aerosol particles. A component of this scattered light is received back by
the lidar receiver. The received backscattered profile is used to detect the CBH.
CL View is an interface software which is a graphical
presentation program for cloud height and backscatter profile
information. CL view software is used here for data handling and
visualization purposes.
(a) Location
of Ahmedabad (23.03∘ N, 72.54∘ E; 55 m a.m.s.l.), where Ceilometer CL31 is installed, and (b) a photograph of the
Vaisala Ceilometer.
MODIS-retrieved clouds
The MODIS is a scientific instrument launched by NASA (National Aeronautics
and Space Administration) into the Earth's orbit on board two satellites:
Terra, in the year 1999, and Aqua, in the year 2002. It uses 36 spectral bands between
wavelengths of 0.41 and 14.2 µm (Xiong et al., 2004) and scans a
cross-track swath of 2330 km. These bands are divided into four separate
focal plane assemblies viz. visible, near-infrared, shortwave infrared,
mid-wave infrared, and long-wave infrared. MODIS provides measurements of
large-scale global dynamics, including cloud cover, radiation budget, and the
processes occurring in the lower atmosphere at 5 km spatial resolution. The
cloud detection algorithm is mainly based on the multispectral analysis of
clouds. Reflectance and radiation of clouds are different from the earth's
surface in visible and infrared band spectra. The following five bands viz. CH1
(0.620–0.670 µm), CH2 (0.841–0.876 µm), CH26 (1.360–1.390 µm),
CH29 (8.400–8.700 µm), and CH31 (10.780–11.280 µm) in the near
infrared/visible and thermal infrared are used for the cloud spectrum (Gu et
al., 2011).
Methodology
The present study focuses on the most important features of temporal
variability of cloudiness over Ahmedabad during May 2013 to January 2015,
using cloud data retrieved from the MODIS satellite, in conjunction with cloud
observations by the ceilometer. The location map of the Ahmedabad region and a
photograph of the Ceilometer CL31 are shown in Fig. 1. The ceilometer data
set contains three consecutive heights of multilayer clouds and backscatter
coefficients (Martucci et al., 2007, 2010). The MODIS satellite products
MOD06_L2 (Hirsch et al., 2011) contain the data from the
Terra satellite, and the “MYD06_L2” files contain data from
the Aqua satellite platform that are used in this study. Only the daytime passes
of the MODIS satellite over the Ahmedabad region are used in this study. For
comparison purposes, MODIS satellite data are used directly, if data lie
within a 0.1∘ radius of the in situ location. Ceilometer data have very high temporal
frequency; because of this suitability, ceilometer data that lie near the MODIS pass
are used for comparison purposes.
CBH detection algorithm
For water clouds, CBH is measured using CTH and cloud geometrical thickness
(CGT; Meerkötter and Bugliaro, 2009). CGT is derived from two parameters,
liquid water path (LWP) which is obtained from the cloud optical thickness
(t) and cloud effective radius (reff; gm-2),
and liquid water content (LWC), where LWC is the integration of cloud size
distribution over droplet size and has units of gm-3 (Hutchison, 2002). The value of LWC varies
according to the types of cloud.
CBH=CTH-CGT,
where
CGT=LWPLWC,LWP=2×t×reff3.
Here, t is cloud optical depth and reff is the cloud droplet effective radius.
The value of LWC varies between about 0.03 and 0.45gm-3 (Hess et al., 1998;
Rosenfeld and Lensky, 1998). This algorithm of CBH is restricted to daytime
data only, because the cloud optical thickness and effective radius are
available only in sunlit regions of the Earth (Hutchison, 2002).
Results and discussions
This study investigates cloud analysis over the Ahmedabad region using
ceilometer measurements and MODIS satellite-retrieved cloud parameters. The
scanning frequency of MODIS satellite above the Ahmedabad region is twice per
day, whereas the ceilometer provides ∼ 100 % monthly coverage
at high temporal resolution. The number of observations was 379 days during
the years 2013 to 2015. Figure 2 shows the sample vertical backscattering
profile for different days and times. In Fig. 2a, the maximum
backscattering is seen at 7.22 km on 6 June 2013 at 02:00:02 IST which
shows the availability of high-level clouds. Figure 2b shows the detection of
multilayer clouds in which low-level and mid-level clouds appear together.
The peak backscattering is at 4 km, which provides us information about
mid-level clouds, as found in Fig. 2c. In Fig. 2d, the maximum
backscattering is seen at 2 km, which gives information on low-level clouds.
Vertical profile of backscatter data for different days (a) 6 June 2013 at 02:00:02 IST, (b) 20 July 2013 at 04:19:20 IST, (c) 31 December 2014
at 23:48:06 IST, and (d) 1 January 2015 at 16:32:21 IST from Ceilometer
CL31 over Ahmedabad, India.
(a) Cloud intensity with range-corrected backscattering profile for
multilayer cloud detection on 25 July 2013 at 15:29:50 IST.
(b) Evolution of three layers of CBH measured by the ceilometer on 2 August 2014 (upper panel) along with strong updraft and downdraft (lower
panel) for the same day.
Figure 3a shows the detection of multilayer clouds using the ceilometer
instrument. In this figure, both the intensity and back scattering profile
and three layers of clouds with a corresponding height of 0.384, 1.8,
and 2 km are seen at 15:29:50 IST. Figure 3b shows the detection of multilayer
clouds for 2 August 2014. The strong updraft and downdraft can be seen in the
lower panel of Fig. 3b. Continuous updraft and downdraft can be found
from 1 km height to 3 km height till 18:00 IST. Strong downdraft was seen
from 13:44 to 13:51 IST with the velocity of 2.1 m s-1, and strong updraft
was observed from 16:36 to 16:51 IST with the velocity of 1.8 m s-1. On
22 July 2013 from 03:00 to 04:00 IST, the ceilometer detected multilayer
clouds, which move with almost constant velocity (figure not shown). At 03:21 IST,
the corresponding backscatter profile in which maximum backscattering seen
at 320 m and 3.520 km provides information about low-level and mid-level clouds. Similarly,
on 25 July 2015 (01:00 to 02:00 IST) and 1 August 2015 (16:00 to 18:00 IST), low-level clouds appear at 1 to 0.860 km
respectively and a second layer of clouds (CBH2) is seen from the
backscattering at 3.5 to 3.13 km respectively. These investigations from
continuous CBH measurements at high temporal resolution (every 2 s) show
that the ceilometer is able to capture the multilayer clouds, which may be an
important input for various meteorological applications. With the use of
very high temporal resolution CBH observations from ceilometers, CBH shows
an updraft over the Ahmedabad region on 1 January 2015 between 14:00 to 16:00 IST. The ceilometer also captured the two-layer low clouds
at 0.201 and 1.316 km on 25 July 2013, and corresponding backscatter values show peak at the same
heights. The ceilometers detect three layers of clouds on 30 October 2014 at
22:40 IST, and this shows the capability of the instrument to measure multilayer
clouds. From these experiences to detect multilayer clouds at different
altitudes, we can state that the ceilometer provides better information on the
low- and mid-level clouds. Recently, Stefan et al. (2014) used a similar
ground-based instrument to study cloud cover over Măgurele, Romania, and
compared these with the MODIS satellite. These results infer that ceilometer-observed
low- and mid-level clouds are very precise, and high-level clouds can be
accurately detected by the satellite. The comparison has been made between
the ceilometer and MODIS satellite in Fig. 4, which shows the cloud cover over
the Ahmedabad region for 3 different days.
MODIS satellite-retrieved cloud top height for (a) 21 July 2013,
(b) 20 July 2014, (c), 3 August 2014, and (d) 1 January 2015 over Ahmedabad,
India.
Comparison between ceilometer and MODIS satellite-measured
clouds.
Comparison between cloud top height and CBH derived from MODIS, and
base height measured by Ceilometer CL31 over the Ahmedabad region.
Comparison of cloud heights from the ceilometer and MODIS
In this section, the CTH retrieved from the passive remote sensor viz. MODIS and
active remote sensor viz. the ceilometer (Naud et al., 2003) are compared for
cloud detection (Fig. 5). In the last section, for comparing the
accuracy of the ceilometer retrievals, the CBHs derived from the active
remote sensor ceilometer are presented. The ceilometer has confirmed its ability
to operate throughout the year, taking continuous measurements of the lowest
CBH as found by Costa-Surós et al. (2013). The cloud detections from
MODIS and the ceilometer are compared to show the difference between the passive
remote sensor and the active remote sensor. The ceilometer can detect three
cloud layers simultaneously. As found in Table 2, the different measurements
are used for the comparison between the satellite and the ceilometer. Figure 5a shows
that on 20 July 2013 between 14:00 to 15:00 IST, the CBH is 1 km. At
14:40 IST the ceilometer detects clouds at 0.786 km and MODIS at 11.25 km.
This indicates that MODIS provides the information about high-level cirrus
clouds and the ceilometer provides the information about low-level clouds. Figure 5b shows that
cloud moved with almost constant velocity from 14:20 to
14:30 IST on 25 July 2014 and the CBH detected by the ceilometer is 1.92 km. The CTH
from the MODIS satellite is 4.25 km which shows the mid-level clouds and by
applying the algorithm, the CBH is calculated as 2.2 km. So, the difference between
the base height measured by the ceilometer and by MODIS is ∼ 130 m.
Multilayer clouds appear in Fig. 5c measured by the ceilometer from 02:00 to
04:00 IST. It shows the beauty of this instrument to detect the three layers
of clouds, and MODIS provides CTH at 3.4 km. Here, the CBH algorithm for the MODIS
satellite is not applicable due to the non-availability of cloud optical
thickness and effective radius. Figure 5d shows that on 1 January 2015
from 14:00 to 16:00 IST, multilayered clouds appeared at a height of
around 1 km and the second layer appeared at around 1.5 km for the first 15 min. The
continuous updraft of cloud from 1 to 2 km till 16:00 IST was observed.
At a common point (at 14:25 IST), the CBH by the ceilometer is 1.097 km and CTH
provided by MODIS is 2 m, and from the algorithm, CBH is calculated as 1.093 km, which is almost the same as the CBH measured by the ceilometer. Therefore, it can be
concluded that for low-level clouds, this algorithm is fine. The cloud cover
for monsoon and post-monsoon periods during the year 2014 was also studied, and
the variation of CBH with rain and without rain was found.
Cloud characteristics during monsoon
Rainy clouds:
on 5 September 2014 from 11:00 to 12:00 IST, the ceilometer detected low-level
clouds which move with almost constant velocity. At 11:55 IST, the
ceilometer detects the CBH at 0.82 km, which shows the availability of low-level clouds, and
MODIS detected CTH as 4.25 km, which provides information about
mid-level clouds. On that day, rainfall amount was reported as 21 mm, shown in Fig. 6a.
Heavy rain:
on 30 July 2014, low-level clouds were detected which move with almost
constant velocity. At 11:35 IST, CBH measured by the ceilometer is 0.4 km and
CTH retrieved by MODIS is 10.9 km, which provides information on high-level
clouds. On that day, rainfall amount was 207 mm which is the maximum, as
shown in Fig. 6b.
Non-rainy clouds:
on 15 September 2014 from 10:00 to 11:00 IST, cloud over the Ahmedabad region
detected by the ceilometer is shown in Fig. 6c. It detects the CBH at 0.9 km, which
provides information on low-level clouds, and the CTH retrieved from the MODIS satellite is 1.25 km.
Comparison between cloud top height derived from MODIS, and CBH
observed by the ceilometer during the monsoon season over the Ahmedabad region
during sample days for (a) normal rain, (b) heavy rain, and (c) no rain
cases.
Cloud characteristics during post-monsoon
Rainy clouds:
on 15 November 2014 strong updraft and downdraft were observed. Clouds moved
downward at a velocity of 14.79 m s-1 from 16:51 to 16:56 IST and
moved upward at a velocity of 15.13 m s-1 from 17:08 to 17:15 IST, as shown in Fig. 7a.
Non-rainy clouds:
Fig. 7b shows that on 30 October 2014 from 02:00 to 03:00 IST high-level
clouds are detected by the ceilometer over the Ahmedabad region. Between 02:26 and 02:41 IST, the
ceilometer shows clear sky, and the CTH detected by MODIS is 9 km.
Higher level clouds are much better detected in the satellite data than
by the ceilometer due to a power limitation; therefore, the ceilometer can detect a maximum up to 7.5 km.
Comparison between cloud top height derived from MODIS, and CBH
observed by the ceilometer during the monsoon season over the Ahmedabad region
during sample days for (a) rain, and (b) no rain cases.
Conclusions
For the first time, cloud characteristics have been produced over Ahmedabad
for the total cloudiness as a physical parameter, using observations from
Ceilometer CL31 and the MODIS satellite. The study of cloud types and cloud
cover fraction (total cloudiness) at Ahmedabad during May 2013–January 2015
has shown the following findings. (1) Some strong downdraft and updraft were found.
Clouds moved downward at a velocity of 14.8 m s-1 and upward at a velocity
of 15.1 m s-1 on 15 November 2014. (2) CBH shows variations during the
southwest monsoon and the post-monsoon period. (3) The ground-measured
cloudiness due to low-level and mid-level clouds is obviously higher than
the one determined by the satellite. Overall, the ceilometer provides
information on up to three layers of clouds, which are not possible to detect by the MODIS
satellite. The satellite only provides the CTH; moreover, the satellite gives
information about cloud height twice in a day when it passes over the
Ahmedabad region, but the ceilometer provides regular (high temporal frequency)
and real-time information. The low-level clouds are not accurately detected by
the satellite as shown in the observation table, whereas the satellite provides
information about high-level clouds. The high-level clouds are accurately
captured by satellite data compared to ceilometer measurements due to the
power limitation of the ceilometer; because of that it can measure up to 7.5 km.
The comparison of the cloud cover from satellite observations with that of
the ground-based observations suggests that the low- and mid-level clouds are
much better and accurately detected by the Ceilometer CL31 ground-based
instrument than the satellite, and the satellite provides better information about
high-level clouds. Also, it is important to note here that the CBH algorithm
is valid for low-level clouds but mostly fails due to the absence of cloud
optical thickness and effective radius. Finally, the cloud detection can be
obtained by the combination of ground-based observations and satellite
observations, which can be used for further weather modeling purposes which
need accurate cloud information to initialize numerical models.
Acknowledgements
Authors are thankful to the Indian Space Research Organization (ISRO)
Geosphere-Biosphere Program (GBP) for financial support for instruments.
Authors are also grateful to NASA for MODIS-retrieved products; these
satellite data are available from http://ladsweb.nascom.nasa.gov/ and
http://modis.gsfc.nasa.gov/. This work is supported by PRL, Department of Space,
government of India. The Indian Meteorological Department is
acknowledged for rainfall reports over India.
Edited by: A. Kokhanovsky
References
Albrecht, B. A., Fairall, C. W., Thomson, D. W., White, A. B., Snider, J.
B., and Schubert, W. H.: Surface-based remote sensing of the observed and
the adiabatic liquid water content of stratocumulus clouds, Geophys. Res.
Lett., 17, 89–92, 1990.
Andrejczuk, M., Gadian, A., and Blyth, A.: Numerical simulations of
stratocumulus cloud response to aerosol perturbation, Atmos. Res., 140,
76–84, 2014.
Bauer, P., Auligné, T., Bell, W., Geer, A., Guidard, V., Heilliette, S.,
Kazumori, M., Kim, M.J., Liu, E. H. C., McNally, A. P., and Macpherson, B.:
Satellite cloud and precipitation assimilation at operational NWP centres,
Q. J. Roy. Meteor. Soc., 137, 1934–1951,
2011.
Bhat, G. S. and Kumar, S.: Vertical structure of cumulonimbus towers and
intense convective clouds over the South Asian region during the summer
monsoon season, J. Geophys. Res.-Atmos., 120, 1710–1722, 2015.
Clothiaux, E. E., Ackerman, T. P., Mace, G. G., Moran, K. P., Marchand, R.
T., Miller, M. A., and Martner, B. E.: Objective determination of cloud
heights and radar reflectivities using a combination of active remote
sensors at the ARM CART sites, J. Appl. Meteorol., 39, 645–665, 2000.
Costa-Surós, M., Calbó, J., González, J. A., and Martin-Vide,
J.: Behavior of cloud base height from ceilometer measurements, Atmos. Res.,
127, 64–76, 2013.
Duynkerke, P. G. and Teixeira, J.: Comparison of the ECMWF reanalysis with
FIRE I observations: Diurnal variation of marine stratocumulus, J. Climate,
14, 1466–1478, 2001.
Errico, R. M., Bauer, P., and Mahfouf, J. F.: Issues regarding the
assimilation of cloud and precipitation data, J. Atmos. Sci., 64, 3785–3798, 2007.
Fontana, F., Lugrin, D., Seiz, G., Meier, M., and Foppa, N.: Intercomparison
of satellite-and ground-based cloud fraction over Switzerland (2000–2012),
Atmos. Res., 128, 1–12, 2013.
Forsythe, J. M., Vonder Haar, T. H., and Reinke, D. L.: Cloud-base height
estimates using a combination of meteorological satellite imagery and
surface reports, J. Appl. Meteorol., 39, 2336–2347, 2000.
Gu, L., Ren, R., and Zhang, S.: Automatic cloud detection and removal
algorithm for MODIS remote sensing imagery, Journal of Software, 6,
1289–1296, 2011.
Hess, M., Koepke, P., and Schult, I.: Optical properties of aerosols and
clouds: The software package OPAC, B. Am. Meteorol. Soc., 79, 831–844,
1998.Hirsch, E., Agassi, E., and Koren, I.: A novel technique for extracting
clouds base height using ground based imaging, Atmos. Meas. Tech., 4, 117–130, 10.5194/amt-4-117-2011,
2011.
Hutchison, K. D.: The retrieval of cloud base heights from MODIS and
three-dimensional cloud fields from NASA's EOS Aqua mission, Int. J. Remote
Sens., 23, 5249–5265, 2002.
Hutchison, K., Wong, E., and Ou, S. C.: Cloud base heights retrieved during
night-time conditions with MODIS data, Int. J. Remote Sens., 27,
2847–2862, 2006.
Kassianov, E., Long, C. N., and Christy, J.: Cloud-base-height estimation
from paired ground-based hemispherical observations, J. Appl. Meteorol.,
44, 1221–1233, 2005.
Kiehl, J. T. and Trenberth, K. E.: Earth's annual global mean energy
budget, B. Am. Meteorol. Soc., 78, 197–208, 1997.
Kim, S. W., Chung, E. S., Yoon, S. C., Sohn, B. J., and Sugimoto, N.:
Intercomparisons of cloud-top and cloud-base heights from ground-based
Lidar, CloudSat and CALIPSO measurements, Int. J. Remote Sens., 32,
1179–1197, 2011.
Kokhanovsky, A. A., Vountas, M., Rozanov, V. V., Lotz, W., Bovensmann, H.,
and Burrows, J. P.: Global cloud top height and thermodynamic phase
distributions as obtained by SCIAMACHY on ENVISAT, Int. J. Remote Sens.,
28, 4499–4507, 2007.Koren, I., Remer, L. A., Altaratz, O., Martins, J. V., and Davidi, A.:
Aerosol-induced changes of convective cloud anvils produce strong climate
warming, Atmos. Chem. Phys., 10, 5001–5010, 10.5194/acp-10-5001-2010,
2010.
Kotarba, A. Z.: A comparison of MODIS-derived cloud amount with visual
surface observations, Atmos. Res., 92, 522–530, 2009.Li, R. and Min, Q.-L.: Impacts of mineral dust on the vertical structure of
precipitation, J. Geophys. Res., 115, D09203, 10.1029/2009JD11925, 2010.Liu, L., Sun, X. J., Liu, X. C., Gao, T. C., and Zhao, S. J.: Comparison of
Cloud Base Height Derived from a Ground-Based Infrared Cloud Measurement and
Two Ceilometer, Advances in Meteorology, 2015, 853861,
10.1155/2015/853861, 2015.
Martucci, G., Matthey, R., Mitev, V., and Richner, H.: Comparison between
backscatter lidar and radiosonde measurements of the diurnal and nocturnal
stratification in the lower troposphere, J. Atmos. Ocean Tech., 24,
1231–1244, 2007.
Martucci, G., Milroy, C., and O'Dowd, C. D.: Detection of cloud-base height
using Jenoptik CHM15K and Vaisala CL31 ceilometer, J. Atmos. Ocean Tech., 27,
305–318, 2010.Meerkötter, R. and Zinner, T.: Satellite remote sensing of cloud base height
for convective cloud fields: A case study, Geophys. Res. Lett., 34, L17805,
10.1029/2007GL030347, 2007.Meerkötter, R. and Bugliaro, L.: Diurnal evolution of cloud base heights
in convective cloud fields from MSG/SEVIRI data, Atmos. Chem. Phys., 9,
1767–1778, 10.5194/acp-9-1767-2009, 2009.Naud, C., Muller, J.-P., and Clothiaux, E. E.: Comparison between active
sensor and radiosonde cloud boundaries over the ARM Southern Great Plains
site, J. Geophys. Res., 108, 4140, 10.1029/2002JD002887, 2003.
Norris, J. R.: Low cloud type over the ocean from surface observations. PART II:
Geographical and and seasonal variations, J. Climate, 11, 383–403, 1998.
Pal, S. R., Steinbrecht, W., and Carswell, A. I.: Automated method for lidar
determination of cloud-base height and vertical extent, Appl. Optics, 31,
1488–1494, 1992.
Randall, D. A.: Stratocumulus cloud deepening through entrainment, Tellus A,
36, 446–457, 1984.
Rasmusen, R. M., Geresdi, I., Thompson, G., Manning, K., and Karplus, E.:
Freezing drizzle formation in stably stratified layer clouds: The role of
radiative cooling of cloud droplets, cloud condensation nuclei, and ice
initiation, J. Atmos. Sci., 59, 837–860, 2002.
Rosenfeld, D. and Lensky, I. M.: Satellite-based insights into precipitation
formation processes in continental and maritime convective clouds, B. Am.
Meteorol. Soc., 79, 2457–2476, 1998.
Shah, S., Rao, B.M., Kumar, P., and Pal, P. K.: Verification of cloud cover
forecast with INSAT observation over western India, J. Earth Syst. Sci., 119, 775–781, 2010.
Stefan, S., Ungureanu, I., and Grigoras, C.: A survey of cloud cover over
Magurele, Romania, using ceilometer and satellite data, Rom. Rep. Phys.,
66, 812–822, 2014.
Varikoden, H., Harikumar, R., Vishnu, R., Sasi Kumar, V., Sampath, S.,
Murali Das, S., and Mohan Kumar, G.: Observational study of cloud base
height and its frequency over a tropical station, Thiruvananthapuram, using
a ceilometer, Int. J. Remote Sens., 32, 8505–8518, 2011.Weisz, E., Li, J., Menzel, W. P., Heidinger, A. K., Kahn, B. H., and Liu, C.
Y.: Comparison of AIRS, MODIS, CloudSat and CALIPSO cloud top height
retrievals, Geophys. Res. Lett., 34, L17811, 10.1029/2007GL030676,
2007.
Winker, D. M., Pelon, J. R., and McCormick, M. P.: The CALIPSO mission:
Spaceborne lidar for observation of aerosols and clouds, Proc. SPIE Int.
Soc. Opt. Eng., 4893, 1–11, 2003.Wu, L., Su, H. and J. H., Jiang H. J.: Regional simulations of deep convection
and biomass burning over South America: 2. Biomass burning aerosol effects
on clouds and precipitation, J. Geophys. Res., 116, D17209,
10.1029/2011JD016106, 2011.Xiong, X., Chiang, K. F., Sun, J., Che, N., and Barnes, W. L.: Aqua MODIS
first year on-orbit calibration and performance, Proceedings of SPIE – Sensors, Systems, and Next Generation of Satellites VII, 5234, 391–399,
10.1117/12.510580, 2004.
Zhang, J., Chen, H., Li, Z., Fan, X., Peng, L., Yu, Y., and Cribb, M.:
Analysis of cloud layer structure in Shouxian, China, using RS92 radiosonde
aided by 95 GHz cloud radar, J. Geophys. Res., 115, D00K30, 10.1029/2010JD014030, 2010.