Retrieval of cloud liquid water distributions from a single scanning microwave radiometer aboard a moving platform - Part 1: Field trial results from the Wakasa Bay experiment

Tomographic methods o er a new promise for retrieving three-dimensional distributions of cloud liquid water from path-integrated radiometric measurements by passive sensors. A mobile cloud tomography system using only a single scanning microwave radiometer has many advantages over a fixed system using multiple distinctly-located 5 radiometers, e.g., e cient and flexible data collection. Part 1 (this paper) examines the results from a limited cloud tomography trial carried out during the 2003 AMSR-E validation campaign at Wakasa Bay of the Sea of Japan. During the tomographic test, the Polarimetric Scanning Radiometer (PSR) and Microwave Imaging Radiometer (MIR) aboard the NASA P-3 research aircraft scanned through a system of low-level clouds 10 and thus provided a useful dataset for testing the cloud tomography method. We conduct three retrieval runs with a constrained inversion algorithm using, respectively the PSR, MIR, and combined PSR and MSR data. The liquid water paths calculated from the PSR retrieval are consistent with that from the MIR retrieval. The retrieved cloud field based on the combined data appears to be physically plausible and consistent 15 with the cloud image obtained by a cloud radar. It is unfortunate that there were no in-situ cloud measurements during the experiment that can be used to quantitatively validate the tomographic retrievals. Nevertheless, we find that some vertically-uniform clouds appear at high altitudes in the retrieved fields where the radar image shows clear sky. This is likely due to flawed data collection geometry, which, in turn, is deter20 mined by the radiometer scan strategy, and aircraft altitude and moving speed. This sets the stage for Part 2 of this study that aims at possible improvements of the mobile cloud tomography approach by a group of sensitivity studies using observation system simulation experiments.

[1]  John N Tsitsiklis,et al.  Optimal margin and edge-enhanced intensity maps in the presence of motion and uncertainty , 2010, Physics in medicine and biology.

[2]  Dong Huang,et al.  Cloud tomography: Role of constraints and a new algorithm , 2008 .

[3]  Dong Huang,et al.  Determination of cloud liquid water distribution using 3D cloud tomography , 2008 .

[4]  Kazumasa Aonashi,et al.  Wakasa Bay : An AMSR precipitation validation campaign , 2007 .

[5]  Joel T. Johnson,et al.  An efficient two-scale model for the computation of thermal emission and atmospheric reflection from the sea surface , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Paul Racette,et al.  Remote measurements of snowfalls in Wakasa Bay, Japan with Airborne Millimeter-wave Imaging Radiometer and Cloud Radar , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[7]  T. Chan,et al.  Edge-preserving and scale-dependent properties of total variation regularization , 2003 .

[8]  Ignasi Corbella,et al.  On-board accurate calibration of dual-channel radiometers using internal and external references , 2002 .

[9]  Mark A. Miller,et al.  Diurnal Cloud and Thermodynamic Variations in the Stratocumulus Transition Regime: A Case Study Using In Situ and Remote Sensors , 1998 .

[10]  Stephen L. Durden,et al.  The NASA DC-8 airborne cloud radar: design and preliminary results , 1997, IGARSS'97. 1997 IEEE International Geoscience and Remote Sensing Symposium Proceedings. Remote Sensing - A Scientific Vision for Sustainable Development.

[11]  P. Lions,et al.  Image recovery via total variation minimization and related problems , 1997 .

[12]  Paul Racette,et al.  An Airborne Millimeter-Wave Imaging Radiometer for Cloud, Precipitation, and Atmospheric Water Vapor Studies , 1996 .

[13]  Albin J. Gasiewski,et al.  Polarimetric scanning radiometer for airborne microwave imaging studies , 1996, IGARSS '96. 1996 International Geoscience and Remote Sensing Symposium.

[14]  C. Fairall,et al.  Measurement of Stratus Cloud and Drizzle Parameters in ASTEX with a K , 1995 .

[15]  C. Vogel,et al.  Analysis of bounded variation penalty methods for ill-posed problems , 1994 .

[16]  B. Barkstrom,et al.  Cloud-Radiative Forcing and Climate: Results from the Earth Radiation Budget Experiment , 1989, Science.

[17]  J. Snider,et al.  Liquid Water Distribution Obtained from Coplanar Scanning Radiometers , 1986 .

[18]  P. Krehbiel,et al.  Determination of Cloud Liquid Water Distribution by Inversion of Radiometric Data , 1985 .

[19]  A. Kak,et al.  Simultaneous Algebraic Reconstruction Technique (SART): A Superior Implementation of the Art Algorithm , 1984, Ultrasonic imaging.

[20]  H Stark,et al.  Image restoration by convex projections in the presence of noise. , 1983, Applied optics.

[21]  G. Herman,et al.  Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and x-ray photography. , 1970, Journal of theoretical biology.

[22]  S. Twomey Introduction to the Mathematics of Inversion in Remote Sensing and Indirect Measurements , 1997 .

[23]  B. Albrecht,et al.  Surface‐based remote sensing of the observed and the Adiabatic liquid water content of stratocumulus clouds , 1990 .

[24]  D. Youla,et al.  Image Restoration by the Method of Convex Projections: Part 1ߞTheory , 1982, IEEE Transactions on Medical Imaging.

[25]  M. Sezan,et al.  Image Restoration by the Method of Convex Projections: Part 2-Applications and Numerical Results , 1982, IEEE Transactions on Medical Imaging.