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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #214712
Title: AN AUTOMATED METHOD FOR DETECTING PRECIPITATION AND CELL TYPE FROM RADAR PRODUCTS

Author
item LIMPERT, GEORGE - UNIVERSITY OF MISSOURI
item LACK, STEVEN - UNIVERSITY OF MISSOURI
item FOX, NEIL - UNIVERSITY OF MISSOURI
item Sadler, Edward

Submitted to: American Meteorological Society Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 1/20/2008
Publication Date: 1/20/2008
Citation: Limpert, G.L., Lack, S.A., Fox, N.I., Sadler, E.J. 2008. An automated method for detecting precipitation and cell type from radar products. In: American Meteorological Society Sixth Conference on Artificial Intelligence applications to Environmental Science. January 20-24, 2008, New Orleans, LA.J2.4.

Interpretive Summary: The purpose of this work is to identify precipitation structures at multiple scales and classify precipitation systems using data obtained from weather radar. The three scales at which convective structures are identified are clusters, segments, and cells. The terminology refers to typical structures that might be found within a squall line. The goal of the system would be to identify the entirety of the line as a convective cluster. However, the line may have segments within it, each of which may contain embedded convective cells. Although the levels of the hierarchy of structures may not always be as distinct, the system still is useful for identifying convective structures of different scales and distinguishing convection from stratiform precipitation. There are many potential applications to this including nowcasting, forecast verification, and the selection of an appropriate conversion from radar reflectivity to rainfall rate. The potential application to nowcasting is in applying a conceptual model to storm behavior to better predict motion and evolution. Identification of structures within weather radar is useful in verifying the number and structure of individual storms and clusters in forecasts. Improved estimates of rainfall rate and accumulation are useful in improving hydrologic models to better predict and simulate flooding, streamflow, and sediment transport due to runoff.

Technical Abstract: It is of interest for many purposes, including nowcasting, to evaluate the structure of radar images in an effort to produce more accurate estimates of rainfall totals from radar data. Although subjective analysis can reliably determine the structure of radar imagery, computational techniques exist to analyze a radar image using algorithms that can be automated. Many of these techniques use some form of multiresolution analysis or Fourier analysis to accomplish structure identification.   One method of identifying structures of differing sizes and scales within an image is to use high, low, and band pass filters to highlight features of interest. This is frequently accomplished by decomposing an image into the frequency domain using a transform and performing the filtering operation within the frequency domain. A variety of transforms exist for accomplishing this including the Wavelet Transform (WT) and many transforms related to the Fourier Transform (FT).   An algorithm, based on the FT and Gaussian filters, has been developed to analyze a radar image and identify structures within the image. A variety of cases are then presented to demonstrate the performance and robustness of this algorithm. Primarily this algorithm delineates regions of convective and stratiform precipitation and identifies convective structures on multiple scales. Properties of convective features are also determined by the algorithm including finding a centroid and attempting to fit an ellipse to define the structure. Additional properties of convective features are determined within the classification scheme.