A technique is presented that uses attenuated backscatter profiles from the
CALIOP satellite lidar to estimate cloud base heights of lower-troposphere
liquid clouds (cloud base height below approximately 3

The base height

Multiple methods have been proposed for satellite determination of the cloud
base height.

For analyses wishing to combine cloud base information with other cloud
properties retrieved by A-Train satellites, these methods share the
disadvantage that the required instruments are not part of the A-Train.
Methods that are applicable to A-Train satellites are based on
Moderate-Resolution Imaging Spectroradiometer

In this paper, we revisit the CALIOP cloud base determination. We rely on
one central assumption, namely that, because the lifting condensation level is
approximately homogeneous within an air mass, the cloud bases retrieved by CALIOP
for thin clouds are a good proxy for the cloud base heights of an entire
cloud field, including the optically thicker clouds within the field. We have
designed an algorithm that extrapolates the CALIOP cloud base measurements into
locations where CALIOP attenuates before reaching cloud base. This algorithm is
called Cloud Base Altitude Spatial Extrapolator (CBASE). In this paper we
evaluate its performance by comparing CBASE

The cloud base of interest in this analysis is the base of the lowest cloud in each column. Even if CALIOP can also detect the base heights of other layers in multilayer situations, it is the base height of the lowest cloud that is of the greatest interest for many applications (e.g., surface radiation estimates).

Section

Data sources used in this analysis.

Two classes of data are used in this work: cloud lidar data, from which we
intend to derive a global

Table

The input satellite data to our analysis are from the Cloud–Aerosol Lidar with
Orthogonal Polarization

In the present analysis, we use VFM version 4.10

For optimizing several parameters of the algorithm, for determining the expected
cloud base uncertainty, and for evaluating the trained algorithm, reference
measurements of

In the US,

There are 1645 stations throughout the continental US that lie within 100 km
of a CALIOP footprint. In normal operation, the time resolution of

ASOS ceilometers used for CBASE

The CBASE algorithm and evaluation proceed in four steps.

We determine the cloud base height from all CALIOP profiles in which the
surface generates a return, indicating that the lidar is not completely
attenuated by cloud. We refer to this as the

Using ground-based ceilometer data, we determine the quality of cloud base
height depending on a number of properties of the CALIOP profile. Assuming
those properties suffice to determine the quality of the

Based on the predicted quality of each profile cloud base, we either reject
the column cloud base or combine it with other cloud bases within a
distance

Using a statistically independent validation dataset, we verify that the
predicted

This section is divided into four subsections, one for each algorithm step enumerated above.

Profile

We assess the quality of the CALIOP

Schematic of CALIOP cloud base determination and evaluation strategy.
In optically thick clouds

The following metrics, which are useful for a qualitative assessment of the
quality of the satellite cloud base, are also calculated but play no
quantitative role in the algorithm:

between the CALIOP cloud base and ground-based observation of the cloud base (we use the Pearson correlation coefficient, ideally unity);

(ideally 1 and 0, respectively);

defined as

Scatter plots of CALIOP versus ceilometer cloud base height faceted
by the CALIOP VFM QA flag; all CALIOP profiles meeting the temporal and
spatial collocation requirements with a METAR enter into this plot. Color
indicates the number of CALIOP profiles within each bin of ceilometer and
CALIOP

Statistics of the relationship between ceilometer and CALIOP cloud
base height faceted by the CALIOP VFM QA flag. Shown are the number of CALIOP
profiles

CALIOP's ability to detect cloud base depends on the properties of the cloud.
Therefore, we expect that the

horizontal distance

number of column cloud bases within horizontal distance

CALIOP VFM feature quality assurance flag,

geometric thickness of the lowest cloud layer,

CALIOP thermodynamic phase determination of the lowest cloud,

feature type, if any, detected between the lowest cloud and the surface, and

horizontal averaging distance required for CALIOP cloud feature detection.

As in Fig.

As in Table

Based on determining the retrieval quality as a function of one variable at a
time (integrating over the sample distribution of the remaining variables), the
following classes of CALIOP profiles are discarded:

CALIOP VFM quality assurance worse than “high”,

“invalid” or “no signal” layers between the surface and the lowest cloud layer (indicating that although the surface may generate a detectable return, the lidar is sufficiently attenuated that the cloud base, which scatters less strongly than the surface, is unreliable),

minimum CALIOP cloud detection horizontal averaging distance within the lowest cloud layer greater than 1 km (indicating that, although average cloud properties are known at the averaging length scale, those properties may not be representative of the particular CALIOP footprint under consideration), or

thermodynamic phase of the lowest layer determined to be other than liquid by the CALIOP VFM algorithm (the reason for this is that not enough such columns exist to determine the RMSE reliably in each of the categories defined below).

Density estimates of the projection of the SVM correction function. The training dataset (ceilometer overpasses in 2008) is used as the ensemble for performing the projection.

The remaining variables are discretized roughly into quintiles of their
distribution within the VFM dataset with the
following boundaries:

horizontal distance

number of CALIOP columns

geometric thickness

Density estimates of the projection of

We can now consider the joint distribution of CALIOP and ceilometer cloud bases for each combination of the above variables to derive the RMSE of each combination. Throughout this work, we use cloud base height above ground level (AGL); using height above mean sea level would introduce an intrinsic correlation between satellite and ceilometer cloud base height due to the varying terrain height, which would lead to an unrealistically positive assessment. To convert cloud base heights to AGL height, we subtract the surface elevation contained in the CALIOP VFM data files, which in turn comes from the CloudSat R05 surface digital elevation model.

Scatter plot of CBASE versus ceilometer

Distribution function of cloud base error divided by predicted
uncertainty; for the ideal case of unbiased

Scatter plot of 2B-GEOPROF-LIDAR versus ceilometer

When calculating aggregate statistics such as the RMSE, a further
consideration comes into play.

Following bias correction, the sample RMSE is calculated for each combination
of

CBASE cloud base statistics by decile of predicted uncertainty; see
Table

CALIOP

For each remaining column

Having trained the algorithm on data from the year 2008, we evaluate it using a
statistically independent dataset from the year 2007. In the evaluation
dataset, the true (i.e., ceilometer-measured)

For satellite-derived measurements of

As a further test of the reliability of the expected uncertainty, we divide the
validation dataset into deciles of the expected uncertainty.
Table

To check that the algorithm satisfies its design constraints (i.e., to ensure
that we made no methodological error when implementing the algorithm), we
have also verified that linear regression between

Scatter plot of 2B-GEOPROF-LIDAR lidar-only versus CBASE

Geographic distribution of mean

It is possible that

Comparison with 2B-GEOPROF-LIDAR cloud bases (version P2_R04_E02, based on the
2B-GEOPROF and CALIOP VFM products) is shown in Fig.

Lidar-only 2B-GEOPROF-LIDAR cloud base performs comparably to the CBASE cloud
base on average; this is to be expected, as the underlying physical measurement
(the CALIOP attenuated backscatter) is the same for all three products
considered (2B-GEOPROF-LIDAR, CALIOP VFM, and CBASE).
Figure

Distribution of predicted

Cloud base uncertainty quantiles. Statistics are calculated within
each

Unlike 2B-GEOPROF-LIDAR and the CALIOP VFM, CBASE provides a validated
point-by-point uncertainty estimate, which allows an analysis to select only
low-uncertainty cases or to statistically weight

Statistics of the relationship between ceilometer and
2B-GEOPROF-LIDAR

Uncertainty on the surface downwelling longwave radiation

Geographic distributions of the mean

As an example application, we consider the surface downwelling longwave
radiation

The source code used to produce the dataset and evaluation
plots is available from

The CBASE

We have presented the CBASE algorithm, which derives the cloud base height

CBASE

The performance of CBASE

The authors declare that they have no conflict of interest.

We thank Patric Seifert and Albert Ansmann for valuable suggestions on the
algorithm; the editor and two anonymous reviewers for comments that have
improved the paper; ICARE for hosting the CALIOP VFM dataset, which was
originally obtained from the NASA Langley Research Center Atmospheric Science
Data Center; DKRZ for computing and data hosting; and the R Foundation for
Statistical Computing for providing the open-source software used for this
analysis