A Fieldwise Retrieval Approach to the Noise Versus Resolution Tradeoff in Wind Scatterometry

This paper approaches the noise versus resolution tradeoff in wind scatterometry from a fieldwise retrieval perspective. Theoretical considerations are discussed, and a practical implementation is developed and applied to the SeaWinds scatterometer. The approach is compared with conventional approaches, as well as numerical weather predictions and buoys. The new method incorporates knowledge of the wind spectrum to reduce the impact of components of the wind signal that are expected to be noisy while enabling reconstruction of fine-scale features that are distinguishable from noise.

[1]  Marcos Portabella,et al.  On Bayesian scatterometer wind inversion , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Ad Stoffelen,et al.  Ambiguity removal and assimilation of scatterometer data , 1997 .

[3]  Ad Stoffelen,et al.  Validation of Two-Dimensional Variational Ambiguity Removal on SeaWinds Scatterometer Data , 2009 .

[4]  David G. Long,et al.  Wind field model-based estimation of seasat scatterometer winds , 1993 .

[5]  David G. Long,et al.  Model-based estimation of wind fields over the ocean from wind scatterometer measurements. II. Model parameter estimation , 1990 .

[6]  David G. Long,et al.  Estimation of Hurricane Winds From SeaWinds at Ultrahigh Resolution , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Marcos Portabella,et al.  A New Method for Improving Scatterometer Wind Quality Control , 2012, IEEE Geoscience and Remote Sensing Letters.

[8]  David G. Long,et al.  A median-filter-based ambiguity removal algorithm for NSCAT , 1991, IEEE Trans. Geosci. Remote. Sens..

[9]  David G. Long,et al.  The ultra high resolution QuikSCAT product , 2009, 2009 IEEE Radar Conference.

[10]  David G. Long,et al.  Reconstruction From Aperture-Filtered Samples With Application to Scatterometer Image Reconstruction , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[11]  B. Williams,et al.  Signal processing methods for ultra high resolution scatterometry , 2010 .

[12]  Ad Stoffelen,et al.  NWP Model Error Structure Functions Obtained From Scatterometer Winds , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[13]  K. Tung,et al.  The k−3 and k−5/3 Energy Spectrum of Atmospheric Turbulence: Quasigeostrophic Two-Level Model Simulation , 2001 .

[14]  Yongsheng Xu,et al.  The Global Characteristics of the Wavenumber Spectrum of Ocean Surface Wind , 2011 .

[15]  Thomas M. Cover,et al.  Elements of Information Theory: Cover/Elements of Information Theory, Second Edition , 2005 .

[16]  Robert Atlas,et al.  A Two-Dimensional Variational Analysis Method for Nscat Ambiguity Removal: Methodology, Sensitivity, and Tuning , 2013 .

[17]  C. Jauffret.  Observability and fisher information matrix in nonlinear regression , 2007, IEEE Transactions on Aerospace and Electronic Systems.

[18]  W. Timothy Liu,et al.  Equivalent Neutral Wind , 1996 .

[19]  Ad Stoffelen,et al.  ERS Scatterometer Ambiguity Removal Scheme Comparison , 2002 .

[20]  David G. Long,et al.  A Reconstruction Approach to Scatterometer Wind Vector Field Retrieval , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[21]  T. Moon,et al.  Mathematical Methods and Algorithms for Signal Processing , 1999 .

[22]  David G. Long,et al.  Model-based estimation of wind fields over the ocean from wind scatterometer measurements. I. Development of the wind field model , 1990 .

[23]  David G. Long,et al.  Identifiability in Wind Estimation From Scatterometer , 1991 .

[24]  David G. Long,et al.  Radar backscatter measurement accuracy for a spaceborne pencil-beam wind scatterometer with transmit modulation , 1997, IEEE Trans. Geosci. Remote. Sens..

[25]  Michael H. Freilich,et al.  Wavenumber Spectra of Pacific Winds Measured by the Seasat Scatterometer , 1986 .

[26]  Hans Hersbach,et al.  395 CMOD 5 An improved geophysical model function for ERS C-band scatterometry , 2003 .

[27]  Marcos Portabella,et al.  A probabilistic approach for SeaWinds data assimilation , 2004 .

[28]  W. Pierson,et al.  Correction to “Probabilities and statistics for backscatter estimates obtained by a scatterometer” , 1990 .

[29]  Marcos Portabella,et al.  Scatterometer Backscatter Uncertainty Due to Wind Variability , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[30]  Abhijit Sarkar,et al.  A New Algorithm for Wind-Vector Retrieval From Scatterometers , 2008, IEEE Geoscience and Remote Sensing Letters.

[31]  W. Timothy Liu,et al.  QuikSCAT geophysical model function for tropical cyclones and application to Hurricane Floyd , 2001, IEEE Trans. Geosci. Remote. Sens..