A Two-Dimensional Variational Analysis Method for Nscat Ambiguity Removal: Methodology, Sensitivity, and Tuning

Abstract In this study, a two-dimensional variational analysis method (2DVAR) is applied to select a wind solution from NASA Scatterometer (NSCAT) ambiguous winds. A 2DVAR method determines a “best” gridded surface wind analysis by minimizing a cost function. The cost function measures the misfit to the observations, the background, and the filtering and dynamical constraints. The ambiguity closest in direction to the minimizing analysis is selected. The 2DVAR method, sensitivity, and numerical behavior are described. 2DVAR is used with both NSCAT ambiguities and NSCAT backscatter values. Results are roughly comparable. When the background field is poor, 2DVAR ambiguity removal often selects low probability ambiguities. To avoid this behavior, an initial 2DVAR analysis, using only the two most likely ambiguities, provides the first guess for an analysis using all the ambiguities or the backscatter data. 2DVAR and median filter-selected ambiguities usually agree. Both methods require horizontal consistency...

[1]  Mark A. Bourassa,et al.  Objectively Derived Daily “Winds” from Satellite Scatterometer Data , 2000 .

[2]  J. O'Brien,et al.  Objective Analysis of Pseudostress over the Indian Ocean Using a Direct-Minimization Approach , 1989 .

[3]  Robert Atlas,et al.  A Multiyear Global Surface Wind Velocity Dataset Using SSM/I Wind Observations , 1996 .

[4]  David M. Legler,et al.  VARIABILITY OF SURF ACE FLUXES OVER THE INDIAN OCEAN; 1960-1989 , 1995 .

[5]  Robert Atlas,et al.  A comparison of a two-dimensional variational analysis method and a median filter for NSCAT ambiguity removal , 2003 .

[6]  Jun-Dong Park,et al.  Validation of QuikScat radiometric estimates of rain rate , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[7]  R. Hoffman.  SASS Wind Ambiguity Removal by Direct Minimization , 1982 .

[8]  J. Goerss,et al.  The Multivariate Optimum Interpolation Analysis of Meteorological Data at the Fleet Numerical Oceanography Center , 1993 .

[9]  Christopher Grassotti,et al.  Development and Application of a Visible–Infrared Rain Flag for Scatterometer Data , 1999 .

[10]  F. Wentz.  A Simplified Wind Vector Algorithm for Satellite Scatterometers , 1991 .

[11]  J. Szpunar,et al.  Part II applications , 2003 .

[12]  Michael H. Freilich,et al.  The accuracy of the NSCAT 1 vector winds: Comparisons with National Data Buoy Center buoys , 1999 .

[13]  William Bourke,et al.  Global Modeling of Atmospheric Flow by Spectral Methods , 1977 .

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

[15]  Jorge Nocedal,et al.  On the limited memory BFGS method for large scale optimization , 1989, Math. Program..

[16]  H. Charnock.  Wind stress on a water surface , 1955 .

[17]  G. Wahba,et al.  Some New Mathematical Methods for Variational Objective Analysis Using Splines and Cross Validation , 1980 .

[18]  F. J. Wentz,et al.  A model function for ocean radar cross sections at 14.6 GHz , 1984 .

[19]  Z. Janjic.  The Step-Mountain Eta Coordinate Model: Further Developments of the Convection, Viscous Sublayer, and Turbulence Closure Schemes , 1994 .

[20]  David G. Long,et al.  The design of an onboard digital Doppler processor for a spaceborne scatterometer , 1988 .

[21]  David L. T. Anderson,et al.  Scatterometer Data Interpretation: Measurement Space and Inversion , 1997 .

[22]  K. Conradsen,et al.  Review of Weibull Statistics for Estimation of Wind Speed Distributions , 1984 .

[23]  Robert E. Fischer,et al.  Standard Deviation of Scatterometer Measurements from Space , 1972 .

[24]  Ross N. Hoffman,et al.  SASS wind ambiguity removal by direct minimization. II - Use of smoothness and dynamical constraints , 1984 .

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

[26]  S. Thiria,et al.  Wind ambiguity removal by the use of neural network techniques , 1991 .

[27]  J. Louis,et al.  Distortion Representation of Forecast Errors , 1995 .

[28]  R. Hoffman,et al.  A Technique for Assimilating SSM/I Observations of Marine Atmospheric Storms: Tests with ECMWF Analyses , 1996 .

[29]  W. Alpers,et al.  An improved composite surface model for the radar backscattering cross section of the ocean surface 1. Theory of the model and optimization/validation by scatterometer data , 1997 .

[30]  H. Schultz.  A circular median filter approach for resolving directional ambiguities in wind fields retrieved from spaceborne scatterometer data , 1990 .

[31]  Christopher Grassotti,et al.  Feature calibration and alignment to represent model forecast errors: Empirical regularization , 2003 .

[32]  J. Louis.  A parametric model of vertical eddy fluxes in the atmosphere , 1979 .

[33]  Christopher Grassotti,et al.  Fusion of Surface Radar and Satellite Rainfall Data Using Feature Calibration and Alignment , 1999 .

[34]  David L. T. Anderson,et al.  Scatterometer data interpretation: Estimation and validation of the transfer function CMOD4 , 1997 .

[35]  Chong Gu,et al.  Minimizing GCV/GML Scores with Multiple Smoothing Parameters via the Newton Method , 1991, SIAM J. Sci. Comput..

[36]  Ad Stoffelen,et al.  On the assimilation of Ku-band scatterometer winds for weather analysis and forecasting , 2000, IEEE Trans. Geosci. Remote. Sens..

[37]  John C. Price.  The nature of multiple solutions for surface wind speed over the oceans from scatterometer measurements , 1976 .

[38]  Dick Dee,et al.  Maximum-Likelihood Estimation of Forecast and Observation Error Covariance Parameters. Part II: Applications , 1999 .

[39]  Dick Dee,et al.  Ooce Note Series on Global Modeling and Data Assimilation Maximum-likelihood Estimation of Forecast and Observation Error Covariance Parameters , 2022 .

[40]  Feng Gao,et al.  Adaptive Tuning of Numerical Weather Prediction Models: Randomized GCV in Three- and Four-Dimensional Data Assimilation , 1995 .

[41]  C. S. Jones,et al.  The sensitivity to parametric variation in direct minimization techniques , 1994 .

[42]  Philippe Courtier,et al.  Interactions of Dynamics and Observations in a Four-Dimensional Variational Assimilation , 1993 .

[43]  W. Linwood Jones,et al.  Erratum: ``NSCAT high-resolution surface wind measurements in Typhoon Violet'' , 1999 .

[44]  A. Simmons,et al.  Implementation of the Semi-Lagrangian Method in a High-Resolution Version of the ECMWF Forecast Model , 1995 .

[45]  Ross N. Hoffman,et al.  The influence of atmospheric stratification on scatterometer winds , 1990 .

[46]  Robert Atlas,et al.  Space-based surface wind vectors to aid understanding of air-sea interactions , 1991 .

[47]  A. Stoffelen.  Toward the true near-surface wind speed: Error modeling and calibration using triple collocation , 1998 .

[48]  Arlindo da Silva,et al.  Data assimilation in the presence of forecast bias , 1998 .

[49]  R. A. Brown.  On satellite scatterometer capabilities in air‐sea interaction , 1986 .

[50]  David G. Long,et al.  Spaceborne radar measurement of wind velocity over the ocean-an overview of the NSCAT scatterometer system , 1991, Proc. IEEE.

[51]  P. Courtier,et al.  Variational assimilation of conventional meteorological observations with a multilevel primitive-equation model , 1993 .

[52]  Heikki Järvinen,et al.  Variational quality control , 1999 .

[53]  W. Alpers,et al.  An improved composite surface model for the radar backscattering cross section of the ocean surface: 2. Model response to surface roughness variations and the radar imaging of underwater bottom topography , 1997 .

[54]  Christopher Grassotti,et al.  Physically based modeling of QuikSCAT SeaWinds passive microwave measurements for rain detection , 2002 .

[55]  D. Offiler.  The Calibration of ERS-1 Satellite Scatterometer Winds , 1994 .

[56]  Robert Atlas,et al.  Geophysical validation of NSCAT winds using atmospheric data and analyses , 1999 .

[57]  Gérald Desroziers,et al.  Diagnosis and adaptive tuning of observation‐error parameters in a variational assimilation , 2001 .

[58]  Ionel M. Navon,et al.  Conjugate-Gradient Methods for Large-Scale Minimization in Meteorology , 1987 .

[59]  J. Mitchell,et al.  Aircraft measurements of the microwave scattering signature of the ocean , 1977, IEEE Journal of Oceanic Engineering.

[60]  Patricia A. Phoebus,et al.  The navy's operational atmospheric analysis , 1992 .

[61]  D. Wylie,et al.  Removal of Ambiguous Wind Directions for a Ku-Band Wind Scatterometer Using Three Different Azimuth Angles , 1985, IEEE Transactions on Geoscience and Remote Sensing.

[62]  W. Pierson,et al.  Probabilities and statistics for backscatter estimates obtained by a scatterometer with applications to new scatterometer design data , 1989 .

[63]  Thomas Nehrkorn.  Analysis and Quality Control of Profiler Data Using Optimum Interpolation , 2000 .

[64]  Fuk K. Li,et al.  A comparative study of several wind estimation algorithms for spaceborne scatterometers , 1988 .

[65]  Frank J. Wentz,et al.  A model function for the ocean‐normalized radar cross section at 14 GHz derived from NSCAT observations , 1999 .

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

[67]  David G. Long,et al.  An assessment of NSCAT ambiguity removal , 1999 .

[68]  Carl A. Mears,et al.  Detecting rain with QuikScat , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).