Weather Radar Data Interpolation Using a Kernel-Based Lagrangian Nowcasting Technique

The Dynamic Radar Tracking of Storms (DARTS) model is a Lagrangian persistence-based nowcasting model that has previously shown utility in nowcasting a variety of weather radar data in severe weather and aviation decision support applications. DARTS is based on the discrete Fourier transform and thus provides an inherent means to perform interpolation. In this context, the model is modified such that interpolation can be accurately and efficiently performed by appropriately windowing the input data and evaluating an interpolating polynomial using the fast Fourier transform. The utility of this interpolation methodology for operational use is demonstrated, and its performance is compared with linear and cubic spline interpolation methods. The use of the original DARTS model to perform advection-based interpolation is also investigated. Rainfall rates derived from data collected by the Weather Service Radar-1988 Doppler S-band radar and the X-band radar at the Dallas-Fort Worth test bed were used for the analyses. The results show that the modified DARTS technique yielded normalized standard error values that were close to those of the forward-backward advection approach using the original DARTS model and ran about 2-4 orders of magnitude faster in terms of computation time. The error structure of the interpolation methods in the context of spatial variability and sampling of atmospheric scales represented by the data is also presented. In this sense, utility of the 1-2-km scales was shown, and the modified DARTS-based approach showed the ability to effectively utilize the value in these scales.

[1]  Kuldeep Kumar,et al.  Robust Statistics, 2nd edn , 2011 .

[2]  V. Chandrasekar,et al.  Algorithm for Estimation of the Specific Differential Phase , 2009 .

[3]  Filiberto Pla,et al.  Estimating Translation/Deformation Motion through Phase Correlation , 1997, ICIAP.

[4]  V. Chandrasekar,et al.  Recent updates to the CASA nowcasting system , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[5]  Chandra Thimmannagari,et al.  CPU Design: Answers to Frequently Asked Questions , 2004 .

[6]  Neil I. Fox,et al.  A Bayesian Quantitative Precipitation Nowcast Scheme , 2005 .

[7]  V. Chandrasekar,et al.  A New Dual-Polarization Radar Rainfall Algorithm: Application in Colorado Precipitation Events , 2011 .

[8]  Mark Z. Jacobson,et al.  Fundamentals of Atmospheric Modeling: Cloud thermodynamics and dynamics , 2005 .

[9]  Donald Fraser,et al.  Interpolation by the FFT revisited-an experimental investigation , 1989, IEEE Trans. Acoust. Speech Signal Process..

[10]  V. Chandrasekar,et al.  Quantitative Precipitation Estimation in the CASA X-band Dual-Polarization Radar Network , 2010 .

[11]  Dominique Pastor,et al.  Robust Estimation of Noise Standard Deviation in Presence of Signals With Unknown Distributions and Occurrences , 2012, IEEE Transactions on Signal Processing.

[12]  Mark Z. Jacobson,et al.  Fundamentals of atmospheric modeling , 1998 .

[13]  R. A. Kropfli,et al.  Part II: Experimental Design and Procedures. , 1980 .

[14]  Jesus Selva,et al.  Convolution-Based Trigonometric Interpolation of Band-Limited Signals , 2008, IEEE Transactions on Signal Processing.

[15]  M. J. Carpenter,et al.  Doppler Radar Sampling Limitations in Convective Storms , 1985 .

[16]  J. Janowiak,et al.  CMORPH: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution , 2004 .

[17]  J. D. Tarpley,et al.  Real‐time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project , 2003 .

[18]  P. Rousseeuw,et al.  Alternatives to the Median Absolute Deviation , 1993 .

[19]  V. Chandrasekar,et al.  Scale Filtering for Improved Nowcasting Performance in a High-Resolution X-Band Radar Network , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[20]  John W. Woods Multidimensional Signal, Image, and Video Processing and Coding, Second Edition , 2011 .

[21]  L. Rabiner,et al.  A digital signal processing approach to interpolation , 1973 .

[22]  Giovanni Battista Chirico,et al.  Sampling errors in rainfall measurements by weather radar , 2005 .

[23]  Kevin S. Paulson,et al.  Fractal interpolation of rain rate time series , 2004 .

[24]  G. McFarquhar,et al.  STEPS TOWARD IMPROVED RADAR ESTIMATES OF CONVECTIVE RAINFALL USING SPATIAL AVERAGES OBTAINED FROM RAIN GAUGE CLUSTERS , 1998 .

[25]  R. Sluiter,et al.  Interpolation methods for climate data Literature review , 2009 .

[26]  V. Chandrasekar,et al.  An Investigation of the Short-Term Predictability of Precipitation Using High-Resolution Composite Radar Observations , 2012 .

[27]  V. Chandrasekar,et al.  Short wavelength technology and the potential for distributed networks of small radar systems , 2009, 2009 IEEE Radar Conference.

[28]  A. Jann,et al.  ON THE USE OF COMPLEX EMPIRICAL ORTHOGONAL FUNCTIONS FOR THE TEMPORAL INTERPOLATION OF NWP , RADAR AND SATELLITE DATA , 2008 .

[29]  I. Zawadzki,et al.  Scale-Dependence of the Predictability of Precipitation from Continental Radar Images. Part I: Description of the Methodology , 2002 .

[30]  Abderrahim Bentamy,et al.  Gridded surface wind fields from Metop/ASCAT measurements , 2012 .

[31]  L. Li,et al.  Nowcasting of Motion and Growth of Precipitation with Radar over a Complex Orography , 1995 .

[32]  E. Meijering,et al.  A chronology of interpolation: from ancient astronomy to modern signal and image processing , 2002, Proc. IEEE.

[33]  Jian Zhang,et al.  Four-Dimensional Dynamic Radar Mosaic , 2004 .

[34]  Michael R. Rasmussen,et al.  Bias adjustment and advection interpolation of long-term high resolution radar rainfall series , 2014 .

[35]  V. Chandrasekar,et al.  The CASA Nowcasting System , 2011 .

[36]  David J. Fleet,et al.  Optical Flow Estimation , 2006, Handbook of Mathematical Models in Computer Vision.

[37]  E. Meijering A chronology of interpolation: from ancient astronomy to modern signal and image processing , 2002, Proc. IEEE.

[38]  M. R. Rasmussen,et al.  Improving Weather Radar Precipitation Estimates by Combining two Types of Radars , 2014 .

[39]  Cram,et al.  Discrete-time signal processing : Alan V. Oppenheim, 3rd edition , 2011 .

[40]  M. R. Rasmussen,et al.  A Numerical Method to Generate High Temporal Resolution Precipitation Time Series by Combining Weather Radar Measurements with a Nowcast Model , 2014 .