The prediction of hail. Part I: radar quantities of hail intensity

Abstract The short-term prediction of hail has been investigated by a thorough analysis of high-resolution radar data. This contribution consists of two parts. Part I presents prediction models for radar parameters of hailfall intensity whereas Part II considers the prediction of the location of hailfall occurrence. The available data of 209 hail cells have been divided into two equally sized groups: one sample was used for estimates of the model coefficients whereas the second sample was used for an independent test about the performance of the prediction models. Three different statistical models for predictions of the hailfall parameters have been defined: a persistence model, an autoregressive time-series model and a more general regression model. The residual mean square of the prediction errors is about 20–30% smaller when using the regression model instead of the persistence model or the autoregressive model. A major part of the error reduction is attributed to predictor variables which characterize the height and the extent of the 50 dBZ contour. A secondary result of this study is the periodic behaviour of the maximum radar reflectivity of hail cells. A spectral analysis of the data has shown that the peaks of maximum radar reflectivity tend to appear in time intervals of about 15 min. This periodic property is attributed to a weak-evolution behaviour that is characteristic for “Swiss-type” hail cells.

[1]  J. Chalon,et al.  Structure of an Evolving Hailstorm, Part 1: General Characteristics and Cellular Structure , 1976 .

[2]  Robert B. Wilhelmson,et al.  A Simulation of the Development of Successive Cells Along a Cold Outflow Boundary , 1982 .

[3]  F. J. Anscombe,et al.  The Examination and Analysis of Residuals , 1963 .

[4]  G. Foote,et al.  Case Study of a Hailstorm in Colorado. Part III: Airflow From Triple-Doppler Measurements. , 1983 .

[5]  D. B. Preston.  Spectral Analysis and Time Series , 1983 .

[6]  N. Draper,et al.  Applied Regression Analysis , 1966 .

[7]  E. A. Zurndorfer,et al.  Automated Guidance for Predicting Quantitative Precipitation , 1979 .

[8]  J. W. Gorman,et al.  Fitting Equations to Data. , 1973 .

[9]  John A. Flueck,et al.  The Florida Area Cumulus Experiment's Second Phase (FACE-2). Part II: Replicated and Confirmatory Analyses , 1983 .

[10]  H. Schiesser.  Hailfall: the relationship between radar measurements and crop damage , 1990 .

[11]  John P. Pino,et al.  An Interactive Method for Estimating Maximum Hailstone Size from Forecast Soundings , 1990 .

[12]  V. N. Bringi,et al.  Potential Use of Radar Differential Reflectivity Measurements at Orthogonal Polarizations for Measuring Precipitation , 1976 .

[13]  W. Schmid,et al.  The Kinetic Energy of Hailfalls. Part I: Hailstone Spectra , 1978 .

[14]  W. Schmid,et al.  The prediction of hail. Part II: the movement of hail cells , 1992 .

[15]  Influence of Low-Level Environment on Severity of High-Plains Moist Convection , 1977 .

[16]  George E. P. Box,et al.  Time Series Analysis: Forecasting and Control , 1977 .

[17]  G. Austin,et al.  An evaluation of extrapolation techniques for the short‐term prediction of rain amounts , 1981 .

[18]  W. Schmid,et al.  The Kinetic Energy of Hailfalls. Part II: Radar and Hailpads , 1978 .

[19]  A. Waldvogel,et al.  Criteria for the Detection of Hail Cells , 1979 .

[20]  Carlton W. Ulbrich.  Relationships of Equivalent Reflectivity Factor to the Vertical Fluxes of Mass and Kinetic Energy of Hail. , 1978 .

[21]  A new parameter scheme for radar contours of hail cells , 1990 .

[22]  W. Stahel,et al.  Main Results of Grossversuch IV , 1986 .

[23]  Frederick Mosteller,et al.  Data Analysis and Regression , 1978 .

[24]  Y. Pointin,et al.  Discrimination between hail and rain precipitation types from dual polarization radar, raingage and hailpad data , 1989 .