Clouds are a key modulator of the Earth energy budget at the top
of the atmosphere and at the surface. While the cloud top height is
operationally retrieved with global coverage, only few methods have been
proposed to determine cloud base height (zbase) from satellite
measurements. This study presents a new approach to retrieve cloud base
heights using the Multi-angle Imaging SpectroRadiometer (MISR) on the Terra
satellite. It can be applied if some cloud gaps occur within the chosen
distance of typically 10 km. The MISR cloud base height (MIBase) algorithm
then determines zbase from the ensemble of all MISR cloud top
heights retrieved at a 1.1 km horizontal resolution in this area. MIBase
is first calibrated using 1 year of ceilometer data from more than 1500
sites within the continental United States of America. The 15th percentile of
the cloud top height distribution within a circular area of 10 km radius
provides the best agreement with the ground-based data. The thorough
evaluation of the MIBase product zbase with further ceilometer
data yields a correlation coefficient of about 0.66, demonstrating the
feasibility of this approach to retrieve zbase. The impacts of
the cloud scene structure and macrophysical cloud properties are discussed.
For a 3-year period, the median zbase is generated globally
on a 0.25∘× 0.25∘ grid. Even though overcast cloud
scenes and high clouds are excluded from the statistics, the median
zbase retrievals yield plausible results, in particular over ocean
as well as for seasonal differences. The potential of the full 16 years of
MISR data is demonstrated for the southeast Pacific, revealing interannual
variability in zbase in accordance with reanalysis data. The
global cloud base data for the 3-year period (2007–2009) are available
at 10.5880/CRC1211DB.19.
Introduction
As state in the IPCC Assessment Report 5, clouds and
aerosols continue to contribute the largest uncertainty to estimates and
interpretations of the Earth's changing energy budget. To describe the effect
of clouds on the radiation energy budget, the geometric thickness, the
vertical location of clouds and, therefore, the cloud base height
(zbase) are crucial parameters. Furthermore, long-term
observations of cloud heights would be beneficial to assess the contribution
and the response of clouds to climate change. zbase is a key
parameter for the radiative energy budget at the Earth surface.
zbase may also have an impact on ecosystems which are supplied
with water by the immersion of clouds . Aviation is
another field which benefits from information on zbase.
Various methods to retrieve the zbase have been proposed applying
different physical concepts, such as active measurements, spectral methods,
approaches using an adiabatic cloud model e.g., and
in situ measurements.
From the ground, the most accurate and well-established method to derive
zbase is the backscatter information from a lidar ceilometer,
also providing crucial information on visibility for aircraft safety. Thus,
ceilometers are employed at airports. Their number has increased in
particular in Europe and North America during the past couple of years. A
dedicated web page hosted by the Deutscher Wetterdienst shows the
distribution of ceilometer stations around the world
(http://www.dwd.de/ceilomap, last access: 13 March 2019). Radiosondes provide in situ measurements of thermodynamic
variables. compare different methods to infer
zbase from radiosonde data. For the best of these methods,
67 % of the considered profiles agree with the utilized reference data
regarding the number of cloud layers and height category (distinguished as low,
middle and high). Cloud radar transmits microwave radiation to derive
vertical profiles of radar reflectivity. However, this signal strongly
depends on the particle size. Therefore, the occurrence of a few drizzle
drops can mask the cloud base. Measurements with radiosondes and cloud radars are
even less common than ceilometers; global coverage cannot be achieved from
the ground today.
From space, active measurements are carried out by CALIOP (Cloud Aerosol
Lidar with Orthogonal Polarization) on the CALIPSO (Cloud Aerosol Lidar and
Infrared Pathfinder Satellite Observations) satellite . A
valid retrieval of the zbase can only be ensured if the signal of
CALIOP reaches the Earth's surface, which is only possible in the case of low
optical thickness. Optically thick clouds will lead to attenuation of the
signal. The spatial coverage is limited to the narrow laser beam of CALIOP.
The CALIOP cloud base determination has been revisited by
. They developed an algorithm to extrapolate cloud
base retrievals for thin clouds into locations where the CALIOP signal is
attenuated within a thicker cloud before it reaches the cloud base.
Passive measurements in the near-infrared exploiting spectral information
have been proposed by . They suggest an approach to infer
the cloud vertical extent from multi-angular POLDER (POLarization and
Directionality of the Earth's Reflectances) oxygen A-band measurements. As
they point out, the penetration depth of photons into a cloud, and, hence,
the height of the reflector, depends on the cloud vertical extent and the
viewing geometry. Exploiting the different viewing angles provided by POLDER,
apply this approach to infer the vertical position of
clouds. Their comparison to retrievals from the cloud profiling radar on
CloudSat and CALIOP shows that this method works best for liquid clouds over
ocean with a retrieval bias of 5m and a standard deviation of the
retrieval differences of 964m. However, this approach has not been
carried out operationally yet. Moreover, an estimate of the cloud top height
is required to retrieve the cloud base height from the cloud vertical extent,
which introduces additional uncertainty.
suggest a method to derive zbase of
convective clouds which are not affected by advective motion. An adiabatic
cloud model incorporating measurements of cloud optical depth and effective
radius is used to calculate the geometric extent of the cloud from the
retrieved cloud top height. By introducing a subadiabatic factor,
investigate the adiabatic assumption in more detail. By
additionally introducing a factor into the calculations, they account for
subadiabaticity due to entrainment of dry air through the cloud edges. As a
reference, the cloud vertical extent is derived as the difference between
ztop (radar) and zbase (ceilometer) from ground-based measurements. The authors conclude that for their 2-year data
set, neither the assumption of an adiabatic cloud nor the assumption of a
temporally constant subadiabatic factor is fulfilled.
suggest a new approach to determine zbase
utilizing the Multi-angle Imaging SpectroRadiometer (MISR) on the Terra
satellite. For a preliminary case study, they chose the observations from
Graciosa Island, Azores, Portugal, for which they compared cloud top height
(z) retrievals from MISR to collocated and coincidental lidar measurements.
Under the assumption that the cloud vertical extent varies horizontally
within the cloud, they retrieve zbase by identifying the lowest
cloud top height in the height profile provided by MISR. The reference cloud
base height (z^base) is retrieved from the lidar signal by
visual inspection of the backscatter coefficient in a time–height cross
section over a period of about 5 h. They selected 12 cases which show
a promising agreement between MISR and lidar retrievals.
We build on the approach proposed by Lau et al. and develop an automatic
retrieval method to derive zbase from MISR measurements.
Parameters employed in the retrieval scheme are derived from coincident
ceilometer measurements over 1 year in the continental United States of
America (USA). The performance of the zbase algorithm is
demonstrated through an evaluation with ceilometers over a longer time period, and
the potential for application on the global scale and for longer time series
is explored.
The paper is structured as follows. In Sect. , the utilized
data from MISR and from ceilometers are described. Section
introduces the new retrieval method along with a case study for illustration.
In Sect. , the evaluation of the algorithm against the
ceilometer measurements is shown, and the effect of the scene structure on the performance of the algorithm is discussed.
Section includes two applications of the algorithm: the
median zbase is presented globally for a 3-year period and
regionally over the southeast Pacific for a 16-year period. Finally, Sect. 6
concludes the study.
DataMISR cloud product
Schematic depiction of a cloud field observed from different viewing
angles during the satellite overpass. Ceilometers, here represented as a
cylindrical box, provide ground-based measurements of cloud base heights
which can be used as a reference.
MISR is carried on board the Terra satellite and provides sun-synchronous
(equatorial overpass at around 10:30 local solar time) global products of
cloud properties with a 1.1km horizontal resolution. With an
across-track swath width of 380km, MISR takes 2 (poles) to 9 (equator) days for repeated observations of the same site. The MISR Level 2TC
Cloud Product
MIL2TCSP; is
used in this study to provide retrievals of cloud top height z and a
stereo-derived cloud mask (SDCM). Three years of global data (2007–2009) are
utilized here. The MISR Ancillary Geographic Product is
additionally used to assign corresponding spatial coordinates and the average
scene elevation for each pixel. Here, we give a brief summary on how the
operational MISR z product is derived. More in-depth descriptions can be
found in and in .
A cloud field is schematically depicted in Fig. . MISR hosts
cameras providing a total of nine viewing angles. Besides the nadir viewing
camera (0∘), there are four forward and four aftward-viewing cameras
set up at 26.1, 45.6, 60.0 and 70.5∘ angles, respectively. During an
overpass, each MISR camera records the reflected radiances at its
particular viewing angle. A pattern matching routine which compares the
radiances recorded at a wavelength of 670nm identifies equal cloud
features in the images of the different viewing angles. Pixels with the least
deviation from each other are matched. This way, a detected cloud feature is
observed from multiple satellite positions with its respective time and
viewing angle. If at least three images can be attributed to the same cloud
feature, the cloud motion vector along with the horizontal and vertical
position of the cloud feature can be inferred geometrically. This process is
not sensitive to absolute values of the radiances; therefore, this retrieval
method is not sensitive to calibration.
The cloud motion vector is determined at a 17.6km resolution. For
each of these coarser grid boxes, the cloud motion vector is then used to
determine z at 1.1km resolution, which is carried out for two
camera pairs individually: one pair (FWD) consisting of the nadir and
26.1∘ forward-viewing cameras and the other (AFT) consisting of the
nadir and 26.1∘ aftward-viewing cameras. This way, two z values for
the same location are available, and the mean of the two values yields the
final z. In the case that only one camera pair provides a valid z, it is
taken as the final z at its specific location. To derive the stereo-derived
cloud mask, the two individual z values undergo the following comparison.
The retrieval of each camera pair is classified as surface or cloud retrieval
according to the threshold height hmin
(Eq. ). This is Eq. (59) in the Algorithm Theoretical
Basis documentation by , where the threshold height for
flat terrain HSDCM is 560m, H is the terrain
height and σh is the variance of the terrain height listed
in the Ancillary Geographic Product. Within the MISR Level 2TC Cloud Product,
the cloud top height and the stereo-derived cloud mask are also provided
without wind correction. Here, we use the wind-corrected data sets.
hmin=HSDCM+H+2σh
The use of two camera pairs allows attribution of a confidence level to the
retrieved z. If the mean of the two values is above or below the threshold,
the pixel will be classified as cloud or surface, respectively. If only one
camera pair provides a valid retrieval, it is tested against the threshold
and classified accordingly. In the case that only one camera pair provides a valid
retrieval and in the case of two valid retrievals which disagree upon their
individual classification, the z retrieval is marked as having low confidence. If two
retrievals are available which agree upon their individual classification,
the z retrieval is marked as having high confidence. Any other case leads to a
non-retrieval. Table summarizes possible
combinations of retrievals from the two camera pairs and their corresponding
attribution within the stereo-derived cloud mask.
MISR z is given in meters above the World Geodetic System 1984 (WGS 84)
surface. To calculate the height above ground level, we subtract the average
scene elevation, which is provided within the Ancillary Geographic Product for
each pixel.
The MISR z product is expected to be superior to z products from other
passive instruments. It does not depend on any auxiliary data, and it is not
sensitive to calibration. Therefore, it is not granted that the application
of MIBase to z retrieved using techniques other than the geometric approach
would yield similar results.
Classification scenarios of MISR retrievals. The cloud height
obtained using the nadir and the 26.1∘ forward-viewing camera pair
(denoted by FWD) and the cloud height obtained using the nadir and the
26.1∘ aftward-viewing camera pair (AFT) are tested against the
threshold height hmin (Eq. ) individually
and then compared to one another to determine the stereo-derived cloud mask
(SDCM) attribute.
ConditionSDCM attributeFWD and AFT above thresholdhigh confidence cloudFWD and AFT disagree, mean (FWD, AFT) above thresholdlow confidence cloudonly one camera pair, retrieval above thresholdlow confidence cloudFWD and AFT below thresholdhigh confidence surfaceFWD and AFT disagree, mean (FWD, AFT) below thresholdlow confidence surfaceonly one camera pair, retrieval below thresholdlow confidence surfaceMETAR data
Aerodrome routine meteorological reports (METARs)
WMO; contain weather observations at airports
worldwide, including measurements of zbase. METARs from airports
from the continental USA provide zbase determined by the
Automated Surface Observing System ASOS;. ASOS
utilizes lidar ceilometers which operate at a wavelength of
0.9µm and have a vertical range of 12000ft
(≈3700m). Cloud base heights are routinely retrieved by
evaluating the vertical gradient of the detected backscatter profile with a
temporal resolution of 30 s. These individual retrievals are stored in
different bins by rounding to the nearest 100ft
(≈30m) for heights between the surface and 5000ft
(≈1500m); to the nearest 200ft
(≈60m) for heights between 5000ft
(≈1500m) and 10000ft (≈3000m);
and to the nearest 500ft (≈150m) for heights
above 10000ft (≈3000m). If there are more than
five bins filled with measurements during a 30 min period, the cloud heights
are clustered into layers until only five clusters remain. Finally, all
cluster heights are rounded according to the rules given in
Table . The lowest three layers are passed on to the
METAR message.
The ceilometer z^base retrievals are rounded to
different values depending on their height window according to ASOS User
Guide . The values are originally given in feet and are
converted to meters here.
Height (ft)Rounded toRounded tonearest value (ft)nearest value (m)<500010030.55000 to 10 000500152> 10 0001000305
Locations of ceilometer stations utilized in this study across the
continental USA. Data from these stations for the years 2008 and 2007 are
used for the calibration of the zbase retrieval algorithm and a
subsequent evaluation, respectively. Blue shading indicates the number of
valid coincidental retrievals from MISR and ceilometers which were utilized for the calibration (year 2008) and are within the constraints
described in the text.
We extract the ceilometer cloud base height z^base from
METAR data for a total of 1510 ceilometer sites around the continental USA to
benefit from the homogeneity of the automated measurements and the
standardized reporting range. z^base serves as reference
data to which the zbase derived from the satellite cloud heights
is compared. First, METAR data from 2008 are used to estimate parameters used
in the zbase retrieval algorithm to create the MISR cloud base
height algorithm (MIBase). Second, to validate the “tuned” algorithm, METAR
data from 2007 are applied for a statistically independent comparison. For a
total of 1510 ceilometer stations, collocated and coincidental
satellite-based zbase retrievals could be found (see below for
exact definition). A distribution of the locations can be seen in
Fig. .
Cloud base height retrieval
The MISR cloud base height retrieval (MIBase) algorithm, which derives
zbase from the MISR z product, is developed and calibrated with
collocated METAR data to define the parameters and preconditions involved.
The first subsection of this section introduces the retrieval principle on the
basis of a case study. By comparison with METAR ceilometer measurements from
2008, parameters used within MIBase are estimated, namely the radius
Rc of the MIBase retrieval cell, the minimum number of valid
cloud pixel N and the percentile P of the z distribution.
Flow chart of the zbase retrieval algorithm. MISR's
MIL2TCSP cloud product provides z and the stereo-derived cloud mask (SDCM).
MISR's Ancillary Geographic Product (MISR-AGP) provides the average scene
elevation (ASE) and the longitude and latitude coordinates for each pixel.
Starting from these products, the processing steps depicted are undergone to
derive zbase. The parameters which were optimized during the
calibration are highlighted in orange.
Method
We assume that the information on the zbase is included in the
distribution of the z retrievals from the MISR cloud product for a specific
area of limited size. This assumption is valid in a cloud scene with a
homogeneous zbase and a heterogeneous z similar to the one
schematically depicted in Fig. . Especially at the edge of a cloud
where the cloud is thinner, z can serve as a proxy for zbase.
To ensure that the thinner edge of the cloud is within the observed MIBase
retrieval cell, the considered area needs to be large enough and the cloud
field needs to be broken. The inherent assumption of a homogeneous
zbase over a certain area presupposes a horizontally constant
lifting condensation level. This is a valid approximation in particular for a well-mixed
boundary layer or a homogeneous air mass away from the proximity of a frontal
zone, where advective motion could introduce temperature or humidity
gradients across the horizontal plane.
In order to derive zbase from the z product, the following
steps, which are outlined in Fig. , are undertaken. First, a
retrieval cell has to be defined. For the comparison to the ceilometer
measurements, we consider a circular area with the radius Rc
around its midpoint at a ceilometer station. In order to estimate the
magnitude of Rc, we consider the following: METAR
z^base retrievals are representative of a time window of
30 min. Within this time window, and at a typical wind speed of approximately
10ms-1, a cloud would shift its position about
20km in the wind direction. Therefore, the magnitude of
Rc should be on the order of kilometers. The impact of
Rc on the retrieved zbase and, therefore, the
deviation from the ceilometer z^base is discussed below.
When we apply the algorithm to retrieve a global estimate of
zbase, we use a regular lat–long. grid of 0.25∘ (see Sect. ). This grid size corresponds to a meridional
length of the grid boxes of about 28km and a zonal length ranging
between 25km (25∘ N) and 18km
(50∘ N), taking the continental USA as an example. A greater
MIBase cell increases the chance of seeing the thinner part of the cloud.
This could lead to a more realistic zbase retrieval. In turn, for
a smaller MIBase cell, the assumption of a homogeneous zbase is
more realistic.
For each grid cell or circular MIBase cell, the enclosed z retrievals from
the MISR cloud product are processed further. MIBase only selects those z
retrievals which are marked as high confidence cloud (hcc) according to the
stereo-derived cloud mask. A consideration of retrievals marked as low
confidence cloud (lcc) has shown a decrease of the correlation with the ceilometer
z^base. An example of a cloud field with z retrievals and
the corresponding stereo-derived cloud mask for 21 August 2015 at the
International Airport of Atlanta, Georgia, USA, is presented in
Fig. a and b.
MISR observations within a 20km radius within the
vicinity of Atlanta, Georgia, USA (ICAO: KATL), on 21 August 2015 at around
16:30 UTC. (a)z. (b) Corresponding stereo-derived cloud
mask (SDCM) distinguishing non-retrievals (NA), high confidence cloud (hcc),
low confidence cloud (lcc), low confidence surface (lcs) and high confidence
surface (hcs). (c) Density of z measurements with illustration of
certain parameters: height between two layers (hgap), which is
the height difference between the highest retrieval of the bottom layer and
the lowest retrieval of the next higher layer (dashed blue lines); upper
cutoff height (dashed orange) for zbase retrievals
(hmax), which is based on the ceilometer granularity; lower
cutoff height (dashed red), which is based on the MISR threshold height to
distinguish between cloud and surface retrieval (hmin); and the
ceilometer retrieval z^base from 16:52 UTC (dashed pink).
ztop and zbase (dashed purple) are inferred by
applying the 15th and 95th percentile to the distribution of z of the
lowest cloud layer, respectively. Heights are above sea level.
For some scenes, the distribution of z reveals extended height ranges with
no z retrievals between two or more local maxima. Such cases suggest
multilayer cloud scenes if the apparent gap between adjacent z retrievals
is of sufficient size. If such a gap hgap is greater than
500m, the algorithm distinguishes between the cloud layer above
and below the gap (see Fig. c for the aforementioned
example). The value for this threshold has been chosen to be close to the
specified accuracy of MISR (560 m). By evaluating different vertical cloud
layers individually, a zbase retrieval for each layer can be
derived. Since for most applications the lowest zbase is of
interest, the lowest detected cloud layer is processed here. For the
comparison with z^base, we restrict ourselves to scenes for
which MISR detects only one cloud layer.
The occurrence of a broken cloud field is a basic requirement of MIBase.
Therefore, at least one z retrieval marked as high confidence surface needs to
be within the MIBase cell. A complete cloud cover or a high rate of
non-retrievals can prevent this criterion from being met. Both scenarios
suggest doubtful zbase retrievals. Hence, they are not
considered.
For each grid cell or circular cell surrounding the ceilometer station,
zbase is diagnosed from the height distribution of z using a
certain percentile P. In principle, P should be as low as possible.
However, as a certain measurement noise is expected and a robust result
should be achieved, a choice substantially larger than zero is necessary.
Another parameter which describes the distribution of z for each scene is
the number of valid z retrievals marked as high confidence cloud n. A higher
n implies a higher observed cloud cover within the MIBase cell. In order to
take a meaningful percentile of the z distribution, a minimum n>N is
required. A cloud which is horizontally more extended (higher cloud cover) is
more likely to pass over the ceilometer. Therefore, there is a higher chance
that both instruments observe the same cloud. Therefore, the deviation of
zbase from z^base is expected to decrease for a
higher n. The impact of the threshold for N is studied later on.
For certain applications, the cloud vertical extent Δz might be of
interest. Therefore, an estimate of the cloud top height ztop
is required. In principle, P=100 should yield the highest point of the
cloud. However, analogously to the retrieval of zbase, a certain
measurement noise is expected. Therefore, P is not chosen to be the extreme
value. Without further validation, we apply the 95th percentile rather than
the median, as we do not want a height which might be representative of the
whole area but rather an estimate of the highest top of the cloud, especially
for a heterogeneous cloud top height to estimate Δz at its most
extensive point.
Case study
One of the utilized ceilometer stations is located at the Hartsfield–Jackson
Atlanta International Airport. To illustrate the functionality of the
presented algorithm, we investigate a particular MISR overpass over this
station on 21 August 2015 at around 16:30 UTC. Figure shows the
z retrievals for all pixels which are within the circular MIBase cell
defined by Rc. Here, we exemplarily use
Rc=20km with its midpoint at the ceilometer station. z
is given above the WGS 84 surface, which is approximately equal to sea level.
The spatial distribution shows a low cloud layer with z between 800 and
2000m, which covers most of the area. Another cloud layer appears
between 5 and 6km. Some pixels with heights above 7km
indicate the presence of a third layer (Fig. a). For a few
pixels, MISR was not able to determine z. This might be due to the viewing
geometry. A retrieval requires valid images from two different cameras, one
camera viewing nadir and the other viewing at a 26.1∘ angle. In the
case studied here, the most missing retrievals are closely attached to high
clouds which might lead to shading effects (Fig. b).
The density of the z distribution shows the aforementioned three cloud
layers. They are distinguished according to the threshold value for
hgap (Fig. c) as illustrated for the bottom and
middle layer. For the bottom layer, which is selected for further processing,
the number of z retrievals marked as high confidence cloud is determined to be
n=621. This number is well above the threshold N, which is defined later.
zbase is then calculated using P=15 as the preliminary
percentile of the z distribution. This yields zbase≈1160m above the WGS 84 surface. The mean average scene elevation
for the given area is subtracted from the retrieval to obtain
zbase≈927m above ground level. The closest METAR
report for this day is from 16:52 UTC. Three heights were reported at
2800ft (≈853m), 7500ft (≈2286m) and 23000ft (≈7010m) above
ground level. By adding the station elevation (315m), the
corresponding height above sea level is obtained. This yields
z^base≈1168m and is denoted in
Fig c. In conclusion, using the preliminary values for P,
the zbase retrieval from MISR is about 927m above
ground level, which is 74m higher than the ceilometer retrieval
(z^base=(853±15)m). The given uncertainty solely
represents the resolution of the METAR reports
(Table ). Note that the third layer detected around
7000m by MISR was also detected by the ceilometer.
Parameter optimization
For each considered ceilometer station (Fig. ), collocated and
coincidental MISR overpasses from the year 2008 are identified. The algorithm
is then applied as described in the case study
(Sect. ) to retrieve zbase. All
pairs of MIBase zbase and ceilometer z^base are
evaluated to investigate the influence of Rc, N and P on the
performance of the zbase retrieval algorithm and to estimate the
most suitable values. For this purpose, the following statistical measures
are considered: the slope and intercept of a linear regression, which are
ideally 1 and 0, respectively; the Pearson correlation coefficient r
(ideally unity); the root mean square error (RMSE) E, defined as
E=1n∑i=1nzbase,i-z^base,i2;
and the retrieval bias B, defined as
B=1n∑i=1nzbase,i-z^base,i.
Slope, intercept, correlation coefficient r, RMSE E, bias B
and number of samples ns resulting from comparing
zbase and z^base retrievals for different radii
of the MISR circular area around the ceilometer stations. These values are
obtained for the year 2008 applying a required minimum number of cloud pixels
of N=10 and the 15th percentile to the z distribution.
Evaluation of the minimum number of valid pixels N within a cloud
layer detected by MISR for the year 2008. (a) The normalized number
of events nsnmax for which
zbase and z^base could both be retrieved.
nmax is the maximum number of events, which is found for N=1.
(b) The linear correlation coefficient r between zbase
and z^base. (c) The RMSE between zbase
and z^base. MISR zbase is retrieved using the
15th percentile of the z distribution for a 10 km radius around the
individual ceilometer measurements. The chosen value for N is highlighted
in orange. For further details, see text.
MISR can only detect clouds above the threshold height according to
Eq. (). To prevent this obvious limitation from
introducing a bias into the statistics, we only consider cloud scenes for
which the ceilometer retrieval is above hmin. In addition, only
zbase retrievals below a maximum height hmax of
3000m are considered to focus on a cloud range for which the
ceilometer retrievals are more finely granulated (below 10 000 ft
according to Table ).
First, we investigate the influence of the size of the MIBase cell on the
comparison of MIBase and ceilometer retrievals. For this purpose,
Rc is varied between 5 and 30 km, while the other parameters are
set to the preliminary values P=15 and N=10. With a decreased
Rc, the correlation between zbase and
z^base increases and E decreases
(Table ). This is to be expected as the
representativity should increase. However, for a lower Rc, the
retrieval algorithm encounters more situations in which at least one of the
requirements (at least one high confidence surface pixel is visible and at
least 10 valid cloud pixels per layer) cannot be fulfilled, as the decrease
in the total number of retrievals indicates. The better agreement between
zbase and z^base for lower Rc might
be due to a relatively larger overlap of the measurement sampling areas of
the two instruments and to a better fulfilment of the assumption of a
homogeneous zbase over smaller areas. For further evaluation, a
radius of 10km is chosen as a compromise between a good agreement
in terms of r and E and without having to discard too many retrieval
scenes.
Evaluation of the percentile P which is applied to retrieve
zbase from the distribution of z for the year 2008, with N=10
and Rc=10km. (a) The linear correlation
coefficient r between zbase and z^base.
(b) The RMSE between zbase and z^base.
The chosen value for P is highlighted in orange.
Second, the effect of the minimum number of valid zbase
retrievals is studied, which strongly limits the number of samples for the
comparison (Fig. ). With increasing N, initially a slight
increase to N=10 improves the correlation between zbase and
z^base and E significantly to a correlation coefficient of
about 0.66. A further increase only yields slight improvement of the
correlation and E. This slight increase can be explained by the elimination
of more complex scenes from the comparison. However, for a higher N the
trade-off is a lower total number of zbase retrievals. For
instance, for N=50 only 80 % of possible retrievals yield a valid
zbase (Fig. a). Therefore, we select N=10.
(a, b) Joint density of zbase and
z^base for the year 2008 (a, c) which is used to
estimate parameters of the algorithm and for the year 2007 (b, d) which is used to validate the stability of the algorithm with the
estimated parameters. The value of the normalized density is indicated by
color (maximum values in light yellow) and contour lines with corresponding
values on them (linear scale). For each ceilometer height bin, the mean (red)
and median (blue) of the MISR zbase are shown. (c, d) Probability density functions of the residuals after a linear fit (red),
the retrieval differences (blue) and a normal distribution with a standard
deviation of 250m (black).
Finally, we consider the percentile threshold used to diagnose
zbase from the z distribution. Figure shows an
evaluation of different percentiles which are applied to derive
zbase. Percentiles between the 10th and the 15th give the best
correlation. The lowest E is achieved for percentiles between the 15th and
the 25th. Therefore, P=15 is chosen for further processing. The fact that
very clear and localized minima (maxima) for E (r) are found supports the
hypothesis that the z distribution contains information on
zbase.
In summary, the comparison yields the estimated parameters
Rc=10km, the minimum number N=10 and the percentile
P=15. While the last two are kept fixed in MIBase, Rc is
optimized for the intercomparison with point data, i.e., ceilometer
measurements. The algorithm can also be applied to larger grids. However, no
data for validating extended areas are available.
Slope, intercept, correlation coefficient r, RMSE E, bias B
and number of retrievals ns resulting from a comparison of
zbase and z^base for data obtained in 2008
(calibration) and 2007 (validation). These values are obtained with N=10
and P=15.
Number of cases for different conditions of the cloud field observed
by MISR and reported in METAR messages for the considered METAR sites. The
number of z retrievals labeled “high confidence cloud” (NHCC)
or “high confidence surface” (NHCS) according to MISR's
stereo-derived cloud mask is used to characterize the cloud field. The size
of the scene is defined by Rc=10 km.
Description of the situation2008200820072007MISRMETAR(%)(%)overpasses over METAR sites80 454154.189 782145.9valid z retrievalsmessage available52 215100.061 531100.0NHCC=0; NHCS>0 (clear sky*)19 50737.420 30033.0clear sky*26 98351.730 03748.8clear sky*clear sky*16 98232.517 37428.2NHCC=0; NHCS>0 (clear sky*)z^base retrieval25254.829264.8NHCC=0; NHCS>0 (clear sky*)z^base>hmin21064.025204.1NHCC>0; NHCS>0clear sky*680013.0851113.8NHCC>0; NHCS=0 (overcast*)15 94530.519 72532.1NHCC>0; NHCS=0 (overcast*)z^base retrieval12 76924.515 60025.4NHCC>0; NHCS=0 (overcast*)clear sky*31766.141256.7NHCC=0; NHCS=0510.1510.1NHCC>0; NHCS>0z^base retrieval991219.012 94421.0NHCC≥N=10; NHCS>0z^base retrieval860316.511 38718.5zbase retrievalz^base retrieval853516.311 31918.4zbase retrieval; single layerz^base retrieval786315.110 25116.7zbase<hmax=3 km; single layerz^base retrieval720613.8940715.3zbase<hmax; single layerz^base<hmax704313.5922715.0zbase<hmax; single layerhmin<z^base<hmax51209.8680111.1
* indicates apparent conditions. See text for details.
This section investigates the applicability of MIBase by quantifying the
number of cases for which the concurrent conditions allow the successful
derivation of a zbase retrieval. First, we filter for cases which
fulfill the following two conditions: (i) the number of valid z retrievals
within the MIBase cell Nval must be >0 and (ii) METAR data
must be available for the calibration and validation. These requirements are
fulfilled for about two-thirds of all considered MISR overpasses over the
ceilometer sites (Table ). Furthermore, there are two
main conditions which prevent the derivation of a zbase
retrieval. These are namely apparent clear sky conditions and apparent
overcast, which is only a limitation for MIBase. Here, we use the phrases
“apparent clear sky” and “apparent overcast” rather than “clear sky”
and “overcast”, respectively, to account for the fact that this attribution
is based on instrumental indications rather than actual known sky conditions.
For METAR, apparent clear sky is indicated if a METAR message is available
but does not provide a valid retrieval. Note that in the case that the lowest cloud is
above the METAR reporting range (typically 3700 m), it is possible that no
retrieval is issued. Here, such cases would also be attributed apparent clear
sky.
For MIBase, we attribute apparent clear sky to the following configuration of
the SDCM: MISR sees the surface with high confidence (NHCS>0)
and has no high confidence cloud in the view (NHCC=0). This does
not have to be an actual clear sky case since it could include low confidence
surface or low confidence cloud retrievals, for which the declaration is less
certain. In the case of invalid z retrievals, it is also uncertain whether
clouds are present or not.
Out of all MISR apparent clear sky cases, 87 % are also classified as
clear sky by METAR, while the remaining 13 % yield a METAR cloud height
retrieval. Mismatches in attributing apparent clear sky cases are due to
METAR retrievals below the threshold height hmin (17 %)
and other reasons, such as the temporal offset between MISR and METAR
measurement. The METAR reports comprise retrievals over a 30 min period.
During this time, cloud formation and cloud dissipation can alter the cloud
scene and cause mismatches between MISR and METAR retrievals.
Furthermore, for MIBase, we attribute apparent overcast to the following
configuration of the SDCM: MISR observes a cloud with high confidence
(NHCC>0) and does not observe any surface retrievals with high
confidence (NHCS=0). Again, the scene could include invalid
retrievals, or retrievals of low confidence. In about 20 % of all the
MISR apparent overcast cases, the corresponding METAR report yields an
apparent clear sky case. These could be cases in which the cloud cover is mainly
above the reporting range of the ceilometer.
Out of all cases with valid z retrievals within the MIBase cell
(Nval>0) and a corresponding METAR retrieval, 19 % are
processed further. The main reasons why cases are excluded are apparent clear
sky scenes for MISR (37.4 %), apparent overcast for MISR (30.5 %)
and apparent clear sky for METAR when valid z retrievals are within the
MIBase cell (13 %). Additional requirements, such as the minimum number
of z retrievals marked as high confidence cloud (NHCC>N), single
layer situations, zbase and z^base retrievals
below hmax and METAR retrievals above the MISR threshold height
(z^base>hmin), lead to a further reduction of
the number of cases which are used to derive the statistics. Further numbers
for specific cases are presented in Table .
From left to right: number of samples ns, RMSE, bias and
correlation coefficient r for the comparison of MIBase and ceilometer
retrievals as a function of the number of valid z retrievals
Nval (panels a–d), the number of retrievals marked as high confidence
surface NHCS (panels e–h) and the number of retrievals marked as high confidence cloud NHCC (panels i–l). Each data point is
calculated for a subsample which includes only Nval±δNval, NHCS±δNHCS and
NHCC±δNHCC, respectively. The various widths
of the considered Nval and NHCC windows are indicated
by the blue shading. All values are normalized by the total number of pixels
within the MIBase cell Ntot. Data are for the year 2008 with
Rc=10 km, P=15 and N=10.
MIBase evaluation
With the parameters Rc=10km, N=10 and P=15 derived
in the previous section, MIBase is applied to MISR retrievals which are
coincident with ceilometer retrievals from the year 2007. These data have not
been used for calibration. The joint density of zbase retrieved
from MISR and the ceilometer is shown in Fig. . For lower
zbase, MISR yields higher heights than the ceilometers. This can
possibly be attributed to the threshold height (Eq. )
constraining zbase retrievals at the lower end of the height
distribution. For zbase greater than 1000m, mean and
median MISR heights are lower than the ceilometer. Overall, the bias B is
slightly negative (about 60 m; cf. Table ) and the
density of the retrieval differences is shifted slightly towards negative
values (Fig. d). Thus, MISR zbase retrievals are
generally lower than the ceilometer retrievals. This could be due to the
different sample volumes. On the one hand, the ceilometer only records point
measurements over a period of time, so the measured sample of the cloud
depends on the velocity of the wind. On the other hand, MISR observes the
entire circular area defined by Rc around the ceilometer
location. Chances are that MISR can observe a cloud with a lower base which
does not pass over the ceilometer.
The joint density and the density of the retrieval differences appear similar
for both the 2007 and the 2008 data sets (Fig. ). Slope, intercept,
r2, E, and B resulting from the zbase retrieval
comparisons for the year 2008 (calibration) and the year 2007 (validation)
appear very similar, demonstrating the stability of the algorithm with the
chosen parameters (Table ), to interannual variability in
cloud properties. Changing the MIBase cell to a 0.25∘× 0.25∘ latitude–longitude grid results in a slightly lower correlation
coefficient accompanied by a higher E. An even coarser grid size of
0.75∘× 0.75∘, which is applied later for a comparison with
ERA-Interim cloud heights, results in an even lower correlation and higher
E. A decreasing agreement between zbase and
z^base for a larger MIBase cell has already been described
when studying the influence of Rc (see discussion in
Sect. ).
Scene structure influence
To estimate the influence of the scene structure on the performance of
MIBase, we further exploit the MISR cloud top height product and the MISR
Ancillary Geographic Product to investigate characteristics of the terrain
height and the cloud field.
To derive a quantity to estimate the variability of the terrain height, we
calculate the standard deviation of the average scene elevation, which is
provided by the ancillary product at 1.1km resolution. For each
METAR site, the standard deviation is calculated for an area defined by
different Rc (5, 10, 15, 20 and 30km). Typical
standard deviations range around a few tens of meters, with overall higher
standard deviations for greater Rc (Fig. S1a in the Supplement). When METAR sites
with a higher standard deviation of the average scene elevation are excluded
from the comparison of MIBase and METAR cloud base height retrievals, the
RMSE decreases slightly and the bias slightly increases (towards 0), while the
correlation is hardly affected (Fig. S1b, c, d). Thus, the variability of the
terrain height has a very small effect on the accuracy of the MIBase
algorithm, with a slightly better performance over more homogeneous terrain.
Global distribution of median cloud heights for a 3-year period
(2007–2009). Shown are zbase(a),
ztop(b) and cloud vertical extent (d) on a
0.25∘× 0.25∘ latitude–longitude grid.
zbase and ztop are above ground level (a.g.l.).
zbase and ztop retrievals are only included in the
statistic if zbase is below 5000m. The number of
retrievals ns(c) represents the number of valid
zbase retrievals within this 3-year period.
To further investigate the performance of the MIBase algorithm as a function
of parameters related to cloud types, we determine RMSE, bias and the
correlation coefficient as a function of ztop and the cloud
vertical extent Δz (Fig. S2). The best correlation is obtained for
cloud vertical extents up to 1000m. The RMSE is also smaller for
lower Δz and for lower ztop. However, the RMSE
increases with decreasing ztop below about 1000m. We
conclude that MIBase performs best for shallow low clouds. However, further
analyses are necessary to increase the sample size of thicker clouds and to
include more medium–high and high clouds for a more robust analysis of such
cloud types. Furthermore, the increased RMSE for very low ztop
indicates that, for very shallow low clouds in the proximity of the threshold
height, MIBase retrievals do not agree as well with the METAR retrievals.
This might be due to cases for which MIBase detects a shallow low cloud with
zbase and ztop close the hmin when,
in fact, the actual cloud base is below hmin. MIBase would miss
this actual cloud base height because the retrievals below hmin
would not be marked as high confidence cloud. For that matter, we require that
the ceilometer retrieval is above the threshold height
(z^base>hmin). However, if such a near-surface
cloud was not detected by the ceilometer, a mismatch would result leading to
a higher RMSE.
Relative occurrences of different stereo-derived cloud mask (SDCM)
configurations within the 3-year period (2007–2009). The reference
sample size ns given in (a) corresponds to 100 %
and includes all overpasses per grid cell which contain valid z retrievals.
(b) Relative number for which MIBase successfully retrieved
zbase. Panels (c) and (d) show the relative number
of occurrence of cloud scenes which include z retrievals of specific SDCM
labels within a grid cell. These configurations are (c) no high
confidence cloud (HCS), which are apparent clear sky cases, and (d) no high confidence cloud (HCS), which are apparent
overcast cases.
Additionally, we exploit the stereo-derived cloud mask as a proxy for the cloud
cover fraction to investigate the sensitivity of the MIBase performance to
the number of valid z retrievals Nval, the number of z
retrievals marked as high confidence surface NHCS and the number of
z retrievals marked as high confidence cloud NHCC within the
MIBase cell. We determine RMSE, bias and the correlation coefficient as a
function of Nval, NHCS and NHCC
normalized by the total number of pixels Ntot which the MIBase
cell encloses (Fig. ). For example, for
Rc=10km, a total of Ntot=265 pixels is
processed by MIBase to obtain a unique zbase retrieval. For the
continental USA, most cases comprise a high portion of valid z retrievals
within the MIBase cell. The RMSE, bias and the correlation coefficient are
robust under different choices of Nval and NHCS. This
suggests that MIBase generally does not depend much on cloud cover fraction.
However, for cases which suggest almost apparent clear sky, indicated by high
NHCS, RMSE increases and r decreases. This could be due to a
lower chance of observing the same cloud in the case of less extended clouds.
This bias appears to strongly depend on the portion of z retrievals marked
as high confidence cloud (Fig. ). The increased bias for higher
NHCC could be explained by the decreasing portion of the thin
edge of the cloud compared to the thicker part of the cloud with greater
horizontal extent. For instance, the edge of a larger cloud might only be
partly within the MIBase cell, whereas the edge of a smaller cloud might be
fully processed by MIBase. The clear increase of the bias with increasing
NHCC shows potential for a bias correction in the future after a
better understanding of the underlying reasons. The bias obtained in this
study can have different sources: the different sample volumes of the defined
MIBase cell and the ceilometer, biased MISR z retrievals and various scene
characteristics.
MIBase applicationGlobal cloud height distribution
MIBase has been applied for a 3-year period between 2007 and 2009 to
determine the zbase from MISR globally. Herein, z data from
each individual orbit have been sorted into a 0.25∘× 0.25∘ longitude by latitude grid. For each orbit and each grid
box, zbase has been retrieved as described above and the median over
the 3-year period has been calculated. Only cloud height retrievals below
5000m are considered to exclude cirrus clouds from the statistics.
ztop is retrieved analogously to zbase by applying
the 95th percentile on the z distribution. Taking the difference between
ztop and zbase for each observed cloud scene yields
Δz. The medians of these measures are shown in Fig. .
A sharp and steep gradient of the zbase can be seen for most coastlines with a higher zbase over land. This seems plausible as
boundary layers above oceans are known to be shallower. Exceptions to this
rule are the Congo Basin and the Amazon Basin. These regions are moisture
sinks characterized by high precipitation and excessive surface runoff. The
maritime stratus cloud regions are clearly visible at the subtropical eastern
boundaries of the Pacific, Atlantic and Indian oceans. These regions are
characterized by prevailing high pressure due to the location at the
subsiding branch of the Hadley circulation and cold ocean currents, creating a
temperature inversion on top of the boundary layer. For these regions, cloud
formation is limited to the well-mixed maritime boundary layer. The
intertropical convergence zone (ITCZ) is clearly visible, in particular for
the tropical Pacific Ocean with a higher zbase and even higher
ztop, yielding an overall higher Δz slightly north of
the Equator. Over land, this phenomenon is not as clear. There, the diurnal
cycle of surface heating becomes important. MISR on the Terra satellite has a
morning overpass over the Equator when cloud formation just begins.
show the diurnal cycle of cloud top temperature (CTT)
derived from SEVIRI measurements, indicating that the lowest
ztop occurs between 9:00 and 13:00 local time with the lowest
mean CTT at 11:00 and the lowest median CTT at 12:00, close to the overpass
time of MISR.
Global distribution of seasonal median cloud heights for a 3-year
period (2007–2009). Shown are ztop(a, b), and
zbase(c, d) for December, January and February (a, c) and June, July and August (b, d) on a
0.25∘× 0.25∘ latitude–longitude grid.
zbase and ztop are above ground level (a.g.l.).
zbase and ztop retrievals are only included in the
statistic if zbase is below 5000m. The red rectangle
in (d) frames the region for which results over a 16-year period are
presented in Fig. .
The sampling size varies spatially, with a higher number of retrievals in the
Arctic region. (Fig. c). This is expected for a polar orbiting
satellite with more frequent MISR overpasses in polar regions
(Fig. a). Generally, the causes for retrieval failure are
apparent clear sky and apparent overcast situations, as discussed in
Sect. . The frequency of occurrence of
such situations varies spatially. For continental dry regions in the
subtropics and continental polar regions, apparent clear sky conditions
predominantly limit the number of zbase retrievals
(Fig. c). The continental polar regions yield a high number of
cases for which the grid cell comprises only high confidence surface
retrievals (NHCS=Ntot, Fig. S3). This poses an even
more robust indication of apparent clear sky conditions. However, the
boundary layer is typically shallower in polar regions. Therefore, boundary
layer clouds occur likely below hmin, so zbase
cannot be retrieved by the MIBase algorithm. Predominant apparent overcast
conditions limit the number of zbase retrievals for midlatitude
regions over ocean and stratocumulus regions on the western boundaries of
continents in the subtropics. In midlatitude continental regions, a mix of
apparent clear sky and apparent overcast conditions limits the number of
zbase retrievals. In the trade cumulus regions within 30∘ N
and 30∘ S, very high success rates occur (Fig. b). A
visual comparison to the 2011 mean cloud cover fraction derived from MODIS
indicates the plausibility of the attribution of apparent
clear sky and apparent overcast.
To further investigate the plausibility of the seasonal variability of cloud
heights, composites over the 3-year period are presented in
Fig. . We distinguish the boreal winter season comprising December,
January and February (DJF) and the boreal summer season comprising June, July and
August (JJA). Over land and between 30 and 70∘ N,
zbase and ztop are lower during winter, when
stratiform clouds prevail. In contrast, zbase and
ztop are higher during summer, when more convective clouds are
typically present. Boundary layer clouds are also lower during the winter season
since the boundary layer is shallower during the cold season. Over ocean an
inverse pattern can be observed in both hemispheres. During winter,
zbase and ztop are higher than during the summer.
Sea surface temperatures show less seasonal variation than air temperatures
due to the higher heat capacity of the water. This causes additional
instability during winter, enhancing convective cloud formation, which can
result in higher cloud heights. Additionally, the instability during winter
can be attributed to storm tracks. During summer, the influence of high-pressure systems can limit convection to the maritime boundary layer, causing
cloud heights to be lower.
Southeast Pacific
The southeast Pacific hosts one of the largest and most persistent
stratocumulus cloud decks on Earth, as shown by using data
from the combined land–ocean cloud atlas database . In this
region, cloud cover and cloud thickness have major impacts on the net cloud
radiative effect, which raises the importance of studying the heights of
these clouds.
Median of ztop(a, b) and zbase(c, d) over a 16-year period (2001–2016) for austral summer (DJF,
a and c) and austral winter (JJA, b and
d) on a 0.25∘× 0.25∘ longitude by latitude
grid in the southeast Pacific. zbase and ztop are
given above ground level (a.g.l.). The red rectangle (d) frames the
region for which a time series of cloud heights is presented in
Fig. .
Orographically induced fog at the coastal cliff ranging from Peru to northern
Chile is the major source of moisture for this region .
zbase and ztop of the stratocumulus clouds near the
coast determine the areas where fog can provide water to the environment at
the coastal cliff. The cloud heights also affect the ability of the fog to be
advected further inland across the cliff. Here, we apply the
zbase retrieval algorithm to determine the spatial and seasonal
variability of zbase and ztop for the region (see
red rectangle in Fig. d). We extend the time window to
the full 16-year record of available MISR data (2001–2016). Furthermore, we
investigate how well the temporal changes are represented in the global
reanalysis ERA-Interim.
Spatial and seasonal variability of zbase and ztop
For the 16-year period, the medians of zbase and
ztop over the southeast Pacific are shown in Fig. .
The summer and winter season are distinguished. Over ocean the median
zbase ranges from 600m near the coast to about
1200m further west. During austral summer (DJF) the lowest
zbase is observed near the coast between 30 and
35∘ S. During austral winter the region of low zbase shifts
to the north between 20 and 30∘ S. This shift is in phase with
the direction of the seasonal shift of the Hadley cell. It appears that the
region of lowest zbase corresponds to the strongest subsidence.
During austral summer the highest zbase clearly appears in the
north, whereas during austral winter a north–south gradient is hardly
visible between 120 and 80∘ W. Over land, zbase is
generally higher except for the coastline north of 35∘ S, where
cloud heights are even lower than over ocean. There, the prevailing maritime
stratocumulus clouds form orographic fog as they reach the coastal cliff.
Similar spatial and seasonal patterns are apparent for ztop.
Over ocean, the highest ztop is about 2500m, which
is observed during austral summer in the northwest of the region. The lowest
ztop is about 1000m, which is observed during winter
and closer to the coast of northern Chile.
Cloud height comparison between MISR and ERA-Interim
Time series of deviations of sea surface temperature
ΔSST (a), cloud top height Δztop(b) and cloud base height Δzbase(c) from the corresponding mean over the entire
period from 2001 to 2016. Cloud heights are derived from MISR (green)
and ERA-Interim (orange). SST is derived from ERA-Interim.
In order to preliminarily assess how well clouds are represented in common
reanalysis, we compare MISR-derived zbase and ztop
to cloud heights derived from ERA-Interim which is provided by
the European Centre for Medium-Range Weather Forecasts (ECMWF). Cloud heights
are not a direct output variable of ERA-Interim. Therefore, the cloud liquid
water content is used to infer the cloud base height
z̃base and cloud top height z̃top. For
each grid point, the vertical column is scanned for model levels with a
specific cloud liquid water content greater than
10-18kgkg-1 (≈0). The bottom height of the
lowest of such levels is taken as z̃base. Moving higher in
the column, z̃top is given by the bottom height of the
next higher model level which has a cloud liquid water content equal to zero.
We use data with a 0.75∘× 0.75∘ resolution, which is
similar to the native grid of ERA-Interim, over a region between 20
and 23∘ S and 74 and 71∘ W, as indicated by the red
rectangle in Fig. . ERA-Interim data are provided 6-hourly. The
comparison is performed using the 18:00 UTC output, which corresponds to 14:00
Chile Standard Time (CLT). Note, MISR overpass times range around 10:51
to 11:29 CLT for this particular region.
For each MISR overpass and ERA-Interim 18:00 UTC output, the median cloud
heights are used to calculate the median cloud heights of each month over the
whole 16-year period. The mean difference of the monthly cloud heights is
roughly 500m for both cloud base height and cloud top height, with
ERA-Interim yielding lower cloud heights than MISR. The fact that
z̃base is lower than zbase could be due to the
threshold height used to determine the MISR stereo-derived cloud mask
(Eq. ), which leads to a cutoff of
zbase retrievals at hmin. At the same time the same
bias is found between ztop and z̃top. This
could be an indicator that clouds are systematically placed too low by
ERA-Interim. mentioned several studies which conclude that
models typically underestimate the height of the planetary boundary layer
(PBL) in the southeast Pacific area. This would cause boundary layer clouds
to appear lower than observed. Their study compares the PBL height retrieved
from in situ measurements and remote sensing to different models. While the
observations show a PBL height of 1100m, the models produce a PBL
height between 400 and 800m and hence an underestimation
of 700 to 300m. This is in accordance with the bias
found here.
To reveal the annual cycle of the cloud heights, we look at anomalies from
the 16-year mean of each time series (Fig. ). These anomalies of
zbase and z̃base as well as ztop
and z̃top from their respective mean values agree rather
well; thus the amplitude of the annual cycle appears very similar.
Figure also shows the anomaly of the sea surface temperature
(SST) from its 16-year mean value. SSTs are taken from ERA-Interim as well.
The peaks of the cloud heights correspond to the maxima of the SSTs. While
the highest SSTs coincide with the highest cloud heights during austral
summer, the lowest SSTs coincide with the lowest cloud heights during austral
winter.
Conclusions
Here, we present a new method to determine zbase over a spatial
region from satellite-based measurements. The MIBase algorithm derives
zbase from the high spatial resolution MISR cloud top height
product z if some preconditions, such as a broken cloud scene, are met.
Validation against 1510 ceilometer stations in the continental USA results
in a correlation coefficient of 0.66 and a RMSE of 385 m for the
validation data set (year 2007). The bias of -59 m even states that MISR
sees a slightly lower zbase on average. This is possibly due to
the larger retrieval cell which is set up for the retrievals from MISR as
opposed to the point measurements provided by the ceilometer.
Very few attempts to derive zbase from satellite have been
performed and evaluated before. retrieve Δz from
POLDER measurements. The standard deviation of the difference between their
Δz retrieval and reference data from CPR and CALIOP is about
964m. However, their method is hard to compare to the MIBase
algorithm, since they retrieve Δz and make a distinction of different
types of clouds which is not done in this study. The CBASE algorithm
derives zbase from CALIOP measurements,
even for optically thick clouds. Depending on the circumstances, different
retrieval uncertainties can be derived. Similar to the study presented here,
they compare their zbase retrievals with ceilometer data over the
continental USA. They obtain RMSEs between 404 and 720m,
depending on the concurrent local conditions of the individual retrievals.
The RMSE we obtain for the MIBase algorithm is slightly lower. Even though
the two studies make use of a similar reference database, they measure cloud
heights at different times of the day. While CALIOP has an afternoon
overpass, MISR has a morning overpass, when more clouds of lesser extent are
present. For a more in-depth comparison and validation of the presented
algorithm, more cloud height reference observations would be desirable,
including observations in different climate zones and especially over ocean.
Within Europe, the European Cooperation in Science and Technology (COST)
activity is expected to harmonize the networks of the different weather
services (e.g., ;
), enabling more intercomparisons in the
future.
An important strength of MIBase is the geometric approach, which is applied to
create the z product from MISR measurements. Neither a calibration nor
auxiliary data are necessary to obtain the z product, which is the starting
point for the zbase retrieval algorithm presented here. In
consequence, retrievals are possible over all kinds of terrain, even above
ice. A disadvantage is the threshold height which MISR requires to create the
stereo-derived cloud mask. Therefore, depending on the terrain variability in
the vicinity of the measurement, this new zbase retrieval method
is not capable of deriving zbase below at least 560m
(flat terrain). The algorithm requires a broken cloud scene. For complete
overcast within the chosen MIBase cell, zbase cannot be
retrieved. Therefore, climatologies derived from this algorithm would be
biased towards cloud types for which MISR is able to observe the surface
through cloud gaps.
Depending on the application, the MIBase uncertainty and the missing coverage
of the diurnal cycle can be a limitation. However, in combination with
ceilometer networks, both temporal and spatial patterns can be investigated.
The application of MIBase over a 3-year period reveals plausible patterns
in the global distribution and seasonal variability of zbase. A
first analysis over the 16-year MISR time series in the southeast Pacific
shows the potential to investigate the interannual variability of
zbase. This makes MIBase a promising tool for the evaluation of
climate models on seasonal and interannual time scales in data-sparse regions
if, for example, the climate model output is limited to clouds below
5km and cloud fractions below 1 and if a sufficient number of
MIBase retrievals is provided within the considered region and time period.
Data availability
Multiple archives providing METAR data are available. The
data utilized here were downloaded from the Weather Underground archive
(https://www.wunderground.com/history/airport/, last access: 4 December 2018). The MISR Level 2TC Cloud Product data were
downloaded from the NASA Langley Research Center Atmospheric Science Data
Center (ftp://l5ftl01.larc.nasa.gov/MISR/MIL2TCSP.001/, last access: 17 October 2017). ERA-Interim data were downloaded from the ECMWF data server via
Web-API. The MIBase cloud base data set is freely available
at the Collaborative Research Centre 1211 database at
10.5880/CRC1211DB.19. It comprises zbase retrievals
globally on a 0.25∘× 0.25∘ grid for a 3-year
period (2007–2009). Daily files include zbase retrievals derived
from the MISR MIL2TCSP product for about 14 respective Terra revolutions
around the Earth. Cloud base altitudes are given above the WGS 1984
ellipsoid. Furthermore, the surface altitude is provided to derive the cloud
base height above ground level.
Sensitivity to threshold height hmin
Joint density of zbase and z^base for
the year 2008 applying a lower threshold height hmin=300m+H+2σh (Eq. ) for
the distinction between surface and cloud pixels in contrast to
Eq. ().
The distinction between surface and cloud retrieval according to the
threshold height described by Eq. () introduces a
constraint to the zbase retrieval algorithm. Below a height of
560m for flat terrain, or higher for more complex terrain,
zbase retrievals are not possible. As an attempt to lower this
threshold height, we adjusted HSDCM in
Eq. () so that
hmin=300m+H+2σh.
This modification results in a bimodal retrieval density clearly showing a
mode consisting of surface retrievals (Fig. ). Therefore, the
original threshold height given by MISR has to be applied, in order to ensure
that only cloud retrievals are utilized during data processing.
The supplement related to this article is available online at: https://doi.org/10.5194/amt-12-1841-2019-supplement.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
The MISR Level 2TC Cloud Product data were obtained from the NASA Langley
Research Center Atmospheric Science Data Center . We
gratefully acknowledge financial support by the Deutsche
Forschungsgemeinschaft (DFG, German Research Foundation) – project number 268236062 – SFB 1211. We thank the seven
referees for their constructive feedback. Edited by:
Alexander Kokhanovsky
Reviewed by: seven anonymous referees
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