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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.
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.
Weather Radar Data Interpolation Using a Kernel-Based Lagrangian Nowcasting Technique
Ruzanski, Evan (author) / Chandrasekar, V
2015
Article (Journal)
English
Local classification TIB:
770/3710/5670
BKL:
38.03
Methoden und Techniken der Geowissenschaften
/
74.41
Luftaufnahmen, Photogrammetrie
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