One of the fundamental difficulties in modeling cloud fields is the large variability of cloud optical properties (liquid water content, reflectance, emissivity). The stratocumulus and cirrus clouds, under special consideration for FIRE, exhibit spatial variability on scales of 1 km or less. While it is impractical to model individual cloud elements, the research direction is to model a statistical ensembles of cloud elements with mean-cloud properties specified. The major areas of this investigation are: (1) analysis of cloud field properties; (2) intercomparison of cloud radiative model results with satellite observations; (3) radiative parameterization of cloud fields; and (4) development of improved cloud classification algorithms

Welch, R. M.

English

NASA Technical Reports Server

NASA-CR-199778
MODELING AND PARAMETERIZATION OF HORIZONTALLY
INHOMOGENEOUS CLOUD RADIATIVE PROPERTIES
Rnal Report Under Grant No. NAG -1-54 2
Principal Investigator: R. M. Welch
Period Covered: 6 December 1984 - 15 November 1995
Grantee's Address: Institute of Atmospheric Sciences
South Dakota School of Mines and Technology
501 E. Saint Joseph Street
Rapid City, SD 57701-3995
Tel: (605) 394-2291; Fax: (605) 394-6061
e-mail: welch@cloud.ias.sdsmt.edu
(NASA-CR-199778) MODELING AND N96-15861
PARAMETERIZATION OF HORIZONTALLY
INHOMOGENEOUS CLOUD RADIATIVE
PROPERTIES Final Report, 6 Dec. uncias
1984 - 15 Nov. 1995 (South Dakota
School of Mines and Technology)
10 p
https://ntrs.nasa.gov/search.jsp?R=19960008695 2020-06-16T06:01:00+00:00Z
OBJECTIVES
One of the fundamental difficulties in modeling cloud fields is the large
variability of cloud optical properties (liquid water content, reflectance,
emissivity). The stratocumulus and cirrus clouds, under special consideration
for FIRE, exhibit spatial variability on scales of 1 km or less. While it is
impractical to model individual cloud elements, the research direction is to
model a statistical ensembles of cloud elements with mean-cloud properties
specified. The major areas of this investigation are: 1) analysis of cloud field
properties; 2) intercomparison of cloud radiative model results with satellite
observations; 3) radiative parameterization of cloud fields; and 4) development
of improved cloud classification algorithms.
RESULTS OF COMPLETED WORK
The following results have been completed under the support of this
grant. These results have been published in refereed journals or presented at
science conferences.
1) Parameterization of Stratocumulus Cloud Fields. A radiative
parameterization scheme of stratocumulus cloud fields is developed based on
our Monte Carlo simulations. The parameterization includes Rayleigh scattering
and atmospheric absorption above the cloud layer. Zero surface reflection is
assumed below the cloud, roughly simulating an ocean or lake surface. The
parameterization is valid for cloud cover ranging up to 100%, for zenith angles
less than 72.5° and for cloud optical depths between t = 3 and i=49.
2) Structural Characteristics of Marine Stratocumulus Cloud. It is
found that stratocumulus cloud size distributions obey a power law, that cell
horizontal aspect ratio increases with cell diameter, that these clouds are bi-
fractal in nature, and that they appear to be homogeneous over regions of
about 100 km x 100 km. These are important constraints in our modeling of
these cloud fields.
3) Pattern Recognition Method for Cloud Classification. Texture has
been found to be a very powerful descriptor of cloud field structure. A new
texture-based pattern recognition method has been developed which is a hybrid
of Max-Min statistics and Generalized Co-occurrence Matrices (GCM). Using
these techniques, it is found that cloud fields may be classified to within 95%
accuracy using high spatial resolution single channel near-infrared data.
4) Effects of Broken Cloudiness Upon Surface Reflectance. Monte
Carlo simulations have been performed as a function of cloud cover, cloud
optical depth, solar zenith angle and surface albedo. The results show that
broken cumulus cloudiness of about 20% cloud cover may not be detectable
over reflecting surfaces with albedo as low as 0.25 - 0.30. Over high albedo
surfaces with albedos greater than about 0.40, partial cloudiness may actually
decrease the system albedo.
5) Comparison of Radiative Transfer Theory with Observations by
Satellite and Aircraft. Observations of cirrus and altocumulus clouds during
FIRE by Landsat and the King Air have been compared to theoretical models of
cloud radiative properties. Good agreement is found between observations and
theory when water droplets dominate. Poor agreement is found when ice
particles dominate.
6) Cumulus Cloud Spacing and Clustering. Detailed observations of
cumulus cloud size distributions, inhomogeneities, nearest-neighbor relation-
ships and cloud field scales of clustering have been determined for ten cloud
fields using Landsat data.
7) Cloud Base Height Determination. Cloud base heights are critical
for accurate radiative energy balance studies. A Hough Transform approach is
used to estimate cumulus cloud base height from hjgh spatial resolution
Landsat data. The approach employs a variety of image processing techniques
to match cloud edges with their corresponding shadow edges. Cloud base
height then is estimated by computing the separation distance between the
corresponding Generalized Hough Transform reference points. Another method
for determining cloud base height of cirrus clouds also was developed. The
cross correlation between a transparent cloud and its shadow is used to
determine their separation distance. It is estimated that cloud base height
accuracies of 50-70 m may be possible using HIRIS and ASTER instruments.
8) Automated Detection of Jet Contrails. AVHRR split window
imageries are used to examine cirrus and contrail signatures. A two step
approach is used for this purpose. A preliminary algorithm subtracts the
11.8 mm image from the 10.8 mm image to enhance contrails. Then a three-
stage algorithm searches the difference image for the nearly-straight line
segments which characterize contrails.
9) Cloud Classification Using Texture Analysis. We use textural
characteristics of cloud and ice covered surfaces as a tool of cloud
classification in polar regions. Neural network technique, an artificial
intelligence classifier, has been used by us as an approach to cloud
classification. We also examine the loss of cloud classification accuracy as a
function of spatial resolution by degrading the imagery through progressive
averaging. Significant improvement in cloud classification accuracy can be
obtained using 1/2 -km spatial resolution data rather than the current 1 km
resolution data available today from AVHRR and GOES. Cirrus classification
accuracy is especially compromised as the spatial resolution is degraded.
However, the use of texture measures defined at the combination of pixel
separations d = 1, 4 improves classification accuracies by several percent even
for 1 km spatial resolution data. Cirrus accuracy is significantly improved by
use of multiple distance features.
10) Developing Monte Carlo Radiative Transfer Code Under IDL. The
Monte Carlo radiative transfer code has been rewritten in IDL. The radiances
calculated by the Monte Carlo radiative transfer code has been verified against
the plane-parallel spherical harmonics and discrete ordinate models. The Monte
Carlo radiances are obtained in discrete angular bins. Since not all bins have
the same number of photons, the radiances agree better for bins with more
photons (higher radiance) and poorer for bins with fewer photons (lower
radiance). This behavior is characteristics of Monte Carlo simulations. A
number of simulations have been run, most notably to check Kobayashi's
results. We have run the case of a plane parallel lower cloud layer with a
checkerboard pattern on top. Our flux results are nearly identical to those
reported by Kobahashi.
11) Retrieval of Cloud Microphvsics. Using a radiative transfer model
to interpret AVHRR radiances, we have developed a practical method to
retrieve effective droplet radius for liquid water clouds on a global scale. We
performed sensitivity tests to investigate the possibility and the accuracy of
retrieving effective droplet radius and developed a method to remove the
thermal emission contribution to Channel 3 radiances. A methodology has also
been developed to assess instrument noise of all AVHRR channels under
operational conditions. We have performed a global survey of the effective
droplet radii of liquid water clouds.
12) Inferring Cloud LWP and Validation of Cloud Droplet Retrievals.
We developed a method using visible, infrared and near infrared bands to
retrieve cloud LWP in liquid water clouds using the simple relationship between
LWP, cloud optical thickness and effective droplet radius. Cloud optical
thickness is obtained from visible band and effective radius is retrieved from
visible, near-infrared and infrared radiances. Note that this analysis only
retrieves effective radius from the top of clouds, so the results may
overestimate or underestimate LWP if r9 is not a good approximation of
vertically mean effective radius over the whole cloud layer.
13) Coreaistration of AVIRIS and TIMS Imagery and Retrieval of Cloud
Properties. A technique was developed for coregistering AVIRIS and TIMS
imagery using both line by line adjustments and 2D scene adjustments. It was
found that the 3-band ratio method of Gao and Goetz (1991) applied to AVIRIS
imagery was superior in detecting cloud pixels on the cloud sunside whereas
the thermal IR bands of TIMS were superior in detecting pixels on the
antisunside. It was also shown that the shadows cast by the short-level and
relatively fast moving FWC clouds induced an average 1 °C temperature
difference.
14) Cumulus Three-Dimensional Cellular Structure. LANDSATTM
channel 6 images are used to obtain the statistics. A cell recognition algorithm
is developed to automatically examine the cloud cellular structures. This allows
both unicellular and multicellular analyses. A linear least squares method is
employed to model the shape of cloud cells with quadric surfaces. Clouds
smaller than 1 km in diameter generally are unicellular and have smaller fractal
dimensions. The larger clouds are mostly multicellular and have larger fractal
dimensions. The cloud cells exhibit characteristics similar to those of the small
unicellular clouds, with the exceptions: (1) that the cell size distribution
appears to be better modeled by an exponential distribution and (2) that the
larger cells have smaller horizontal aspect ratios, meaning that they have
increasing tendencies to be more circular. For the 3000+ cells examined, it is
found that 80% can be best approximated by the shape of a hyperboloid of one
sheet. About 15% of the cells are hyperboloids of two sheets with convex
domes in the positive z direction. The average number of cells in a cloud is
found to increase with increasing cloud size slightly faster than linearly. A
cloud with effective diameter of 5 km is composed of approximately 5 cells.
PUBLICATIONS UNDER GRANT NO. NAG-1-542
Refereed Publications:
Parker, L, R. M. Welch and D. J. Musil, 1986: Analysis of spatial
inhomogeneities in cumulus clouds using high spatial resolution
Landsat data. J. Climate App/. Meteor., 25, 1301-1314.
Welch, R. M., K. S. Kuo, B. A. Wielicki, S. K. Sengupta and L. Parker,
1988: Marine stratocumulus cloud fields off the coast of southern
California observed using Landsat imagery. Part I: Structural
characteristics. J. Appl. Meteor., 27, 341-362.
Welch, R. M., S. K. Sengupta and D. W. Chen, 1988: Cloud field
classification based upon high spatial resolution textural features.
Part I: Gray level cooccurrence matrix approach. J. Geophys. Res.,
93, 12663-12681.
Welch, R. M., S. K. Sengupta and K. S. Kuo, 1988: Marine stratocumulus
cloud fields off the coast of southern California observed using
Landsat imagery. Part II: Textural analysis. J. Appl. Meteor., 27,
362-378.
Welch, R. M., and B. A. Wielicki, 1989: Reflected fluxes for broken clouds
over a Lambertian surface. J. Atmos. Sci., 46, 1384-1395.
Welch, R. M., M. S. Navar and S. K. Sengupta, 1989: The effect of spatial
resolution upon texture-based cloud field classifications. J. Geophys.
Res., 94, No. D12, 14767-14781.
Chen, D. W., S. K. Sengupta and R. M. Welch, 1989: Cloud field
classification based upon high spatial resolution textural features.
Part 2: Simplified vector approaches. J. Geophys. Res., 94, No.
D12, 14749-14765.
Welch, R. M., K. S. Kuo and S. K. Sengupta, 1990: Cloud and surface
textural features in polar regions. IEEE Trans. Geosci. and Remote
Sens., 28, 520-528.
Lee, J., R. Weger, S. K. Sengupta and R. M. Welch, 1990: A neural
network approach to cloud classification. IEEE Trans. Geosci. and
Remote Sens., 28, 846-855.
Sengupta, S. K., R. M. Welch, M. S. Navar, T. A. Berendes and D. W. Chen,
1990: Cumulus cloud field morphology and spatial patterns derived
from high spatial resolution Landsat imagery. J. Appl. Meteor., 29,
1245-1267.
Berendes, T., S. K. Sengupta, R. M. Welch, B. A. Wielicki and M. Navar,
1992: Cumulus cloud base height estimation from high spatial
resolution Landsat data: A Hough transform approach. IEEE Trans.
Geosci. and Remote Sensing, 30, No. 3, 430-443.
Engelstad, M., T. Lee, S. K. Sengupta and R. M. Welch, 1992: Automated
detection of jet contrails using the AVHRR split window. Intnl. J.
Remote Sens., 13, 1391-1412.
Weger, R. C., J. Lee, T. Zhu and R. M. Welch, 1992: Clustering,
randomness and regularity in cloud fields: 1. Theoretical
considerations. J. Geophys. Res.f 97, No. D18, 20,519-20,536.
Zhu, T., J. Lee, R. C. Weger and R. M. Welch, 1992: Clustering,
randomness and regularity in cloud fields: 2. Cumulus cloud fields.
J. Geophys. Resf 97, D18, 20,537-20,558.
Kuo, K-S., R. M. Welch, R. C. Weger, M. A. Engelstad and S. K. Sengupta,
1993: The three-dimensional structure of cumulus clouds over the
ocean. Parti: Structural analysis. J. Geophys. Res.,
98(011), 20,685-20,711.
Weger, R. C., J. Lee and R. M. Welch, 1993: Clustering, randomness, and
regularity in cloud fields: 3. The nature and distribution of clusters.
J. Geophys. Res.f 98, 18,449-18,463.
Lee, J., J. Chou, R. C. Weger and R. M. Welch, 1994: Clustering,
randomness, and regularity in cloud fields: 4. Stratocumulus cloud
fields. J. Geophys. Res., 99, D7, 14,461-14,480.
Han, Q. Y., W. B. Rossow and A. A. Lacis, 1994: Near-global survey of
effective droplet radii in liquid water clouds using ISCCP data. J.
Climate, 7, 465-497.
Han, Q., W. Rossow, R. Welch, A. White and J. Chou, 1995: Validation of
satellite retrievals of cloud microphysics and liquid water path using
observations from FIRE. J. Atmos. Sci., 52, 4183-4195.
Papers Presented:
Welch, R. M.f S. K. Sengupta and D. W. Chen, 1987: Classification of
cloud fields based on textural characteristics. SPIE Conf. on Digital
Image Processing and Visual Communications Technologies in
Meteorology, 26-29 October, Cambridge, MA.
Welch, R. M., K. S. Kuo and S. K. Sengupta, 1989: Textural characteristics
of cloud and ice covered surfaces in polar regions. Presented at
IGARSS '89/12th Canadian Symposium on Remote Sensing,
10-14 July, Vancouver, BC.
Sengupta, S. K., R. M. Welch and M. S. Navar, 1989: Performance of two
texture-based classifiers of cloud fields using spatially averaged
Landsat data. Presented at IGARSS '89/12th Canadian Symposium
on Remote Sensing, 10-14 July, Vancouver, BC.
Welch, R. M., S. K. Sengupta, R. C. Weger, F. D. Carsey, S. A. Christopher
and K. S. Kuo, 1990: Cloud classification in polar regions using
AVHRR textural and spectral signatures. Preprints, 7th Conf. Atmos.
Rad., San Francisco, CA, Amer. Meteor. Soc., 411-415.
Welch, R. M., S. K. Sengupta, F. D. Carsey, C. A. Sundar, K. S. Kuo and
M. S. Navar, 1990: Pattern recognition of clouds and ice in polar
regions. Presented at the SPIE Conf., 16-20 Apr 1990, Orlando, FL.
Sengupta, S. K., T. Berendes, B. Wielicki, R. M. Welch and M. Navar, 1990:
Automated cloud base height determination from high resolution
Landsat data: A Hough transform approach. Preprints, 7th Conf.
Atmos. Rad., San Francisco, CA, Amer. Meteor. Soc., 303-308.
Kuo, K.-S., M. Englestad, S. K. Sengupta and R. M. Welch, 1992: The
three-dimensional structure of cumulus clouds over the ocean.
Extended Abstract, Intnl. WCRP Symposium on Clouds and Ocean in
Climate, 28 Sep-1 Oct 1992, Nagoya, Japan.
Weger, R. C., J. Lee and R. M. Welch, 1992: Clustering, randomness and
regularity in cloud fields. Extended Abstract, Intnl. WCRP Symposium
on Clouds and Ocean in Climate, 28 Sep-1 Oct 1992, Nagoya, Japan.
Kuo, K-S., R. M. Welch, R. C. Weger, M. A. Engelstad and S. K. Sengupta,
1993: The three-dimensional structure of cumulus clouds over the
ocean. Proc. IGARSS '93/URSI Symposium, 18-21 Aug 1993, Tokyo,
Japan.
Christopher, S. A., A. M. Vender Vorste, S. E. Watters and R. M. Welch,
1994: Cloud radiative forcing in the tropics: The effect of optical
property retrievals and scene identification. Proc. 8th Conf. on
Atmos. Radiation, Nashville, TN, Amer. Meteor. Soc., 451-452.
Han, Q., W. Rossow, R. Welch, A. White and J. Chou, 1995: Comparison
of cloud droplet radii and LWP retrieved by satellite data with obser-
vations from FIRE. Proc. Conf. on Cloud Physics, Dallas, TX, Amer.
Meteor. Soc., 65-69.
Han, Q., R. Welch and J. Chou, 1995: Retrieval of cirrus microphysics over
oceans on a near-global scale using ISCCP data. Proc. Conf. on
Cloud Physics, Dallas, TX, Amer. Meteor. Soc., 583-585.
Nair, U. S., M. R. Hjelmfelt and R. A. Pielke, 1995: Numerical simulation of
June 9-10, 1972 Black Hills storm using CSU RAMS. Proc. 7th Conf.
Mountain Meteor., Breckenridge, CO, Amer. Meteor. Soc., 51-56.
Han, Q, W. B. Rossow, J. Chou, and R. M. Welch, 1996: A near-global
survey of cirrus particle size using ISCCP. Preprints, 8th Conf. on
Satellite Meteor., Atlanta, GA, Amer. Meteor. Soc., -.
Theses: (Full or Partial Support)
Berendes, T. A., 1991: Remote sensing of cloud base height. M.S. Thesis,
Department of Meteorology, S.D. School of Mines and Technology,
Rapid City, SD. 52 pp.
Chen, D-W., 1988: Texture-based classification. M.S. Thesis, Department
of Computer Science, S.D. School of Mines and Technology, Rapid
City, SD. 101 pp.
Christopher, S.A., 1995: Cloud radiative forcing in the tropics. Ph.D.
Dissertation, Department of Atmospheric Sicence, Colorado State
University, Fort Collins, CO. 186 pp.
Kuo, K-S., 1995: The Picard iterative approximation to the solution of the
integral equation of radiative transfer. Ph.D. Dissertation, Department
of Atmospheric Science, Colorado State University, Fort Collins, CO.
101 pp.
Nair, U. S., Ph.D. in progress
8
Navar, M. S., 1989: Image segmentation and region classification of Arctic
scenes. M.S. Thesis, Department of Computer Science, S.D. School
of Mines and Technology, Rapid City, SD. 71 pp.
Rangaraj, N., 1991: Neural network classification of AVHRR data. M.S.
Thesis, Department of Computer Science, S.D. School of Mines and
Technology, Rapid City, SD. 67 pp.
Zhu, T., 1992: Clustering, randomness, and regularity in cumulus cloud
fields. M.S. Thesis, Department of Meteorology, S.D. School of
Mines and Technology, Rapid City, SD. 68 pp.


Modeling and parameterization of horizontally inhomogeneous cloud radiative properties

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