Search Results

You are looking at 1 - 3 of 3 items for

  • Author or Editor: John A. Russo Jr. x
  • Refine by Access: All Content x
Clear All Modify Search
John A. Russo Jr.
Full access
Edwin Kessler III and John A. Russo Jr.

Abstract

The great information-processing capacity of modern digital computers is used to assemble radar data in a form suitable for its application to weather analysis and forecasting studies and for investigation of the data's physical and statistical properties. Data are collected by PPI photography at steps of the antenna-elevation angle and radar-system sensitivity and aye reduced manually to digital form. The data are registered on magnetic tape for entry to the digital computer, wherein they are edited, range-normalized, reassembled to produce plan distributions that refer to a constant altitude above the earth, and processed for the distributions of the heights of echo bases and tops. Any of a practically limitless number of processing combinations can be selected to portray the intensity, male, and vertical development characteristic of the echoes for a particular period and location. The outputs of the computer program are printed distributions, for visual inspection and manual processing, and magnetic tapes containing the data in a form suitable for analysis by other computer routines. This program provides practical means for developing knowledge of radar data; realization of the full benefits of radar under operational conditions will require instruments which quantize the data rapidly.

Full access
John A. Russo Jr., Isadore Enger, and Edna L. Sorenson

Abstract

The screening-multiple-regression technique is applied to predicting surface u- and v-wind components at Idlewild International Airport for periods of 2, 3, 5 and 7 hr. The predictors are variables from 11 synoptic stations, easily obtained or derivable from conventional service A teletype data. Additional predictors are used to account for diurnal and seasonal variations. In all, 141 predictors are screened and one prediction equation is obtained for each predictand. Each equation is applicable to any hour of the day and any day of the year.

The regression equations derived from a dependent sample selected randomly from 7 years of data proved significantly better at the 1-per cent level than both persistence and climatology for the 3-, 5- and 7-hr forecasts and at the 5 per cent level for the 2-hr forecasts when tested on 1387 independent cases. The screening-regression root-mean-square errors on this independent set ranged from 3.36 kt to 4.48 kt for the u-wind forecasts and from 3.69 kt to 5.57 kt for the v-wind forecasts.

Operational 3-, 5- and 7-hr surface-wind forecasts extracted from terminal forecasts made at Idlewild are compared both quantitatively and categorically with corresponding regression forecasts made on a new set of independent data. The screening-regression forecast errors are approximately ⅓ smaller than the subjective errors, and the improvements for all the predictands are statistically significant beyond the 1 per cent level. The categorical comparison concerning only categories of <10 kt and ≥10 kt (dictated by the format of the subjective data) resulted in Heidke skill scores of 0.399 for screening regression and 0.249 for the subjective forecasts when applied to 7-hr prediction of the surface-wind speed at Idlewild.

Full access