Background
Parameterization of clouds and convection remains a vexing problem for global models. Convective updrafts and
downdrafts in the atmosphere have horizontal scales from
100s of meters to several kilometers. These motions are well
below the horizontal resolution of current global
atmospheric models and will probably not be resolvable in
global models for a decade or more. Nevertheless, convection
exerts a first order effect on the climate and atmospheric
circulation of Earth. This effect is currently included in
global models using simplified representations (or parameterizations) that are driven by the resolved flow features of the global model.
CloudSat
is a satellite-borne cloud profiling radar (CPR) launched by
NASA on April 28, 2006. CloudSat is designed to detect small
cloud particles. The instrument provides profiles of cloud
induced radar-reflectivity with a vertical resolution of 500
m. Profiles are measured approximately every 1-2 km along the
satellite ground track. These profiles provide an
unprecedented view of the vertical structure of clouds in
the atmosphere. This study will examine the relationships
between CloudSat derived cloud parameters and other
atmospheric variables. We will utilize global analyses of
atmosphere obtained from the new GEOS-5 Data assimilation
system (DAS) developed for NASA's Modern Era Retrospective-analysis for
Research and Applications (MERRA). Our focus here will be on
convective systems which are poorly represented in models
and which have so far eluded efforts to correlate
simulations with CloudSat observations.
The purpose of this study is twofold. First we would like
to place the huge CloudSat database in the correct
meteorological setting in the hope of shedding light on the
large scale dynamical controls exerted on convective systems
by the atmosphere, using a state-of-the-art high resolution
DAS. Second we would like to directly compare the output of
the current convective and cloud parameterizations operating
in the AGCM component of the GEOS-5 DAS with CloudSat
observations.
Objectives and Techniques
Cloud or convective depth scale is a critical parameter
for a number of atmospheric processes including tracer
transport, radiative forcing, and diabatic heating. The
depth of convection is typically underestimated in climate
models. Our first task is to determine climatologies of
cloud depth in CloudSat data and to relate them to
predictors of cloud depth based on local atmospheric
profiles of u, v, T and q. We use the following algorithm to
automate estimates of cloud depth from CloudSat data. Raw
GEOPROF-2B radar reflectivities are used as the starting
point. Each granule of radar reflectivity consists
of around 37000 profiles with 125 range bins (or levels) containing
returns from different altitudes. Centers of the altitude range bins
are spaced about 240 m apart although
vertical feature resolution is around 500 m. For the analysis, each granule is
subdivided into overlapping segments of 100 profiles, with
centers spaced 50 profiles apart. (See Figure
1. Correlation coefficients are then calculated between each
pair of vertical levels in the segment. Only those
between level 105, the typical surface return level, and level 45 at around
15000 m are used. This results in a 60x60 correlation matrix
for each segment (Figure 1c). A two-sided, one-parameter
Gaussian fit is then made to the
correlations from each level. This gives two correlation
length scales (up and down) for each level in the CloudSat
data (Figures 1d,e).
We use atmospheric data from the 0.5x0.666 degree
MERRA retrospective analysis interpolated onto CloudSat tracks in both
space and time to derive a number of predictors for
convective cloud depth. One of the simplest of these is the
height to which an undiluted surface parcel would rise
before losing buoyancy. This depends primarily on the moist
static energy near the surface and the temperature profile
aloft, as illustrated schematically in Figure 2.
The undiluted surface parcel height is a simple estimate
of the maximum possible convective cloud height for given
atmospheric profiles of T and q.
Results
Figure 3 shows a joint PDF of CloudSat vertical
correlation scale from the surface upwards and the undiluted
surface parcel height obtained from MERRA retrospective analysis for
July 2006. The plot shows a clear statistical relationship
between this simple predictor of convective cloud depth
and observed cloud vertical correlation scales from CloudSat.
Observed cloud vertical
scales rarely exceed the undiluted parcel height estimated by
MERRA. The shape of the PDF for a given value of the
undiluted parcel height is approximately exponential, with many
more observed correlation scales close to zero than to the maximum
possible height predicted by the analysis.
We believe the close match between the upper limit of the
PDF in the horizontal and the y=x line may be
fortuitous consequence of our analysis, but that the general
character of the joint PDF is not.
The result in Figure 3 is encouraging in many respects.
A simple analysis derived predictor for cloud height
yields a good and physically-reasonable description of
the statistics of vertical correlation scales in the CloudSat data.
The lack of a good deterministic relationship between our
simple analysis predictor and observed cloud vertical scales
is almost certainly due in part to sampling issues related to
CloudSat, but may also reflect a real underlying stochastic
character in atmospheric convection. It is also possible that
refinements in the analysis predictor could produce a tighter
relationship with the CloudSat observations. However, we believe
the first two possibilities, sampling and inherent stochasticity,
are likely to dominate in comparisons of CloudSat convective
cloud observations with large-scale models. Cloud resolving models
should prove useful in interpreting results such as those in
Figure 3.
Future Work
We have aleady begun to examine results from the
Goddard Cumulus Ensemble (GCE)
cloud resolving model. The GCE will be forced with analysis profiles and tendencies from MERRA to
extend the sample space of available GCE simulations to compare with CloudSat and other high resolution satellite
data sets. In addition to addressing issues that may arise from satellite sampling, the GCE results will also be used to
extrapolate knowledge gained about condensates and other satellite observables, to
quantities such as vertical velocities and mass fluxes, which are central to parameterization development.
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