DTIC ADA444212: Multifrequency Retrieval of Cloud Ice Particle Size Distributions

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DISSERTATION
MULTIFREQUENCY RETRIEVAL OF CLOUD ICE
PARTICLE SIZE DISTRIBUTIONS
Submitted by
Brian D. Griffith
Department of Atmospheric Science
In partial fulfillment of the requirements
For the Degree of Doctor of Philosophy
Colorado State University
Fort Collins, Colorado
Fall 2005
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tDisiibution Unlimited
COLORADO STATE UNIVERSITY
September 20, 2005
WE HEREBY RECOMMEND THAT THE DISSERTATION PREPARED UNDER
OUR SUPERVISION BY BRIAN D. GRIFFITH ENTITLED MULTIFREQUENCY
RETRIEVAL OF CLOUD ICE PARTICLE SIZE DISTRIBUTIONS BE ACCEPTED
AS FULFILLING IN PART REQUIREMENTS FOR THE DEGREE OF DOCTOR OF
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MULTIFREQUENCY RETRIEVAL OF CLOUD ICE PARTICLE SIZE
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ABSTRACT OF DISSERTATION
MULTIFREQUENCY RETRIEVAL OF CLOUD ICE
PARTICLE SIZE DISTRIBUTIONS
There are many sources of uncertainty in remote sensing retrievals. This is
particularly true where complex parameters such as liquid or ice hydrometeors must be
retrieved. Many of the uncertainties are the direct result of assumptions made in the
retrieval process to address the ill-posed nature of the inverse problem - namely that
there are more variables than measurements. In this paper, an optimal estimation retrieval
technique is applied to a multi-frequency data set from the Wakasa Bay AMSR-E
validation experiment. First, airborne radar observations at 13.4, 35.6 and 94.9 GHz are
integrated to retrieve all three parameters of a normalized gamma ice particle size
distribution (PSD), No*, p, and Di. This retrieved PSD was validated against the near-
simultaneous coincident in situ cloud probe observations. The differences between the
retrieved and in situ measured PSDs were explored through sensitivity analysis and the
sources of uncertainty were found to be the ice particle density and the aspect ratio of the
nonspherical particles modeled as oblate spheroids in the forward radiative transfer
model. The optimal estimation technique was then applied to retrieve an optimal density
and aspect ratio for the cloud under study through integration of the in situ and remote
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sensing observations. The optimal particle size-density relationship was found to be
p(D) = 0.07* D-"5'8 and the oblate spheroid aspect ratio was found to be 0.53. The use of
these optimal values as improved assumptions in the PSD retrieval reduced the
uncertainty in the retrieved reflectivity of the three radars from +/- 6 dB to +/- 2 dB.
Next, the retrieval technique is expanded to include passive microwave observations and
retrieve a full atmospheric column vertical hydrometeor profile. Eleven airborne passive
microwave frequencies from 10.7 to 340 GHz are integrated with airborne radar
observations at 13.4, 35.6 and 94.9 GHz to retrieve all three parameters of a normalized
gamma ice particle size distribution (PSD): No*, ýt, and Din. The vertical profile retrieval
is validated against a clear sky scene before being applied to the horizontal extent of an
ice cloud. The PSD retrieval shows vertical structure consistent with cloud microphysical
processes. The default density and shape retrieval is used as a baseline for comparison
with the retrieval using the optimized model from the companion paper, which reveals an
order of magnitude difference in ice water path between the two retrievals. This
difference is explored and an information content analysis reveals that the optimized
model improves on the information content of the retrieval by 287 more states resolved
than the default model indicating a significant reduction in retrieval uncertainty.
Brian D. Griffith
Atmospheric Science Department
Colorado State University
Fort Collins, CO 80523
Fall 2005
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ACKNOWLEDGEMENTS
I would first like to thank my adviser, Professor Christian Kummerow for his guidance
and leadership throughout this process. I would also like to thank the United States Air
Force, Air Force Institute of Technology Civilian Institution program for allowing the
opportunity to complete this Ph. D. research program. Thanks to Dr. Masataka
Murakami, Japanese Meteorological Research Institute for supplying the Japanese Cloud
Probe data and his explanations of the measurements. Thanks also to Dr. Richard Austin,
Dr. Simone Tanelli, Dr. Jim Wang and Dr. Thomas T. Wilheit for their contributions to
the Wakasa Bay data set and my use of it. Thanks to Dr. Tristan L'Ecuyer for his
seemingly endless patience in discussing all aspects of the optimal estimation retrieval.
Thanks to Becky Burke and the rest of the Kummerow research group. Finally, I'd like to
thank my wife .for her unfailing support and encouragement. The views expressed
in this article are those of the author and do not reflect the official policy or position of
the United States Air Force, Department of Defense, or the U. S. Government.
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TABLE OF CONTENTS
Chapter Title Page
CHAPTER 1 INTRODUCTION 1
CHAPTER 2 WAKASA BAY EXPERIMENT 7
CHAPTER 3 FORWARD RADIATIVE TRANSFER MODEL 16
CHAPTER 4 OPTIMAL ESTIMATION METHODOLOGY 22
CHAPTER 5 INFORMATION CONTENT ANALYSIS 30
CHAPTER 6 SINGLE-LEVEL PSD RETRIEVAL 35
CHAPTER 7 VERTICAL PROFILE PSD RETRIEVAL 46
CHAPTER 8 DISCUSSION 62
8.1 Single-Level PSD Retrieval 62
8.2 Vertical Profile PSD Retrieval 65
CHAPTER 9 CONCLUSIONS 70
REFERENCES REFERENCES 74
APPENDIX A JPL CORRECTION 77
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LIST OF TABLES
Table Pao e
2.1. Goals of the AMSR-E precipitation team for the Wakasa Bay experiment. 8
2.2. Summary of P-3 flights for the Wakasa Bay experiment. 9
6.1. Forward modeled radar reflectivities using various in situ PSDs 39
6.2. Comparison of retrieved parameters for different retrieval methods 41
6.3. Variation in retrieved parameters and X2 with varying Sy 41
6.4. Comparison of F(x) and associated RMS for different retrieval methods 42
7.1. Bias between PSR observations and synthetic PSR Tbs 49
7.2. Comparison between MIR-only and PSR-only (w/o 89 GHz) retrieved states 50
7.3. Clear sky retrieval with diagnostics 51
7.4. Ice cloud default model retrieved emission parameters 52
7.5. Ice cloud default model retrieved PSD parameters 53
7.6. Ice cloud optimal model retrieved emission parameters 55
7.7. Ice cloud optimal model retrieved PSD parameters 56
7.8. Ice cloud default model information content 57
7.9. Ice cloud optimal model information content 58
7.10. Ice cloud optimal model observation subset information content 59
7.11. Ice cloud default model observation subset information content 60
7.12. Sensitivity of retrieval to changes in a priori and observations 60
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LIST OF FIGURES
Figure Page
2.1. Raw data flight line plot: radar + radiometer 11
2.2. Gridded data flight line: radar + radiometer 12
2.3. Gridded in situ PSD data for ice cloud 14
2.4. PSD bin plot (2DC v. 2DP) across ice cloud 15
6.1. Retrieved v. in situ PSD 36
6.2. Retrieved v. in situ IWC and D,, 37
6.3. Observations v. F(in situ) at A/C level 38
6.4. Forward model comparison 43
6.5. Retrieval parameters (single-grid v. all-grid) 44
6.6. Retrieved density 44
6.7. Retrieved v. in situ PSD using optimal density and a/b 45
7.1. MIR v. PSR 89 GHz TB 49
7.2. Default model retrieved PSD profiles 52
7.3. Optimal model retrieved PSD profiles 56
8.1. Retrieved IWC and D.. 63
8.2. Retrieved ice water path with uncertainty 66
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CHAPTER 1
INTRODUCTION
Clouds are a fundamental component of the water cycle in the atmosphere. They
dominate the planetary energy budget through cooling the earth by reflecting sunlight
back to space and warming the earth by absorbing and emitting thermal radiation. By
modulating the energy budget, clouds fundamentally alter the general atmospheric and
oceanic circulations.
Unfortunately, there are many sources of uncertainty in remote sensing retrievals.
Instruments, measurement techniques, and the physical models relating the measured
values to the parameters of interest and to each other all have intrinsic uncertainties.
Many of those uncertainties are the result of assumptions made in the retrieval process.
While these assumptions are necessary to solve the under-constrained retrieval problem,
they can affect not only the output uncertainty but also the character of the retrieved
solution itself.
Retrievals of cloud or rainfall parameters intrinsically have more free parameters
than are routinely observed. As a result, the largest retrieval uncertainty is generally not
in the observation (sensor noise) but in the forward model used for the inversion. These
forward model uncertainties are not well defined and little is known about them (AMSR
Rainfall Validation Implementation Strategy, 2001). Exploring and quantifying the
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