Accession Number : ADA289456
Title : Diagnosing Cloudiness from Global Numerical Weather Prediction Model Forecasts.
Descriptive Note : Final rept. Oct 91-Sep 93,
Corporate Author : PHILLIPS LAB HANSCOM AFB MA
Personal Author(s) : Norquist, Donald C. ; Muench, H. S. ; Aiken, Donald L. ; Hahn, Douglas C.
PDF Url : ADA289456
Report Date : 05 JUL 1994
Pagination or Media Count : 150
Abstract : We investigated the utility of any information derivable from noncloud numerical weather prediction (NWP) model forecasts in inferring layer cloud amount distributions. This effort involved identifying and preparing a suitable source of the predictand (cloud amount), generating and preparing a suitable source of the predictors (NWP variables and geographic information), and combining them to form diagnostic relationships in a model output statistics approach. Both AFGWC RTNEPH cloud analyses and Phillips Laboratory Global Spectral Model (PL GSM) NWP forecasts were rendered on a 125 km equal-area grid in three cloud deck regimes (high, middle, low). Two statistical methods CLOUD CURVE ALGORITHM (CCA), a univariate method, and multiple linear regression (MLR) were used to relate the cloud amount to relative humidity (CCA) and to relative humidity and a large number of other NWP variables (MLR). We found that the CCA method preserves the sharpness of the cloud distribution while sacrificing skill, while MLR produced cloud diagnoses that were more skillful but less sharp. The methods fall short of the error level standards established by Air Force requirements, but show potential for useful cloud forecast skill upon further refinement.
Descriptors : *WEATHER FORECASTING, *CLOUD COVER, COMPUTER PROGRAMS, MATHEMATICAL MODELS, ALGORITHMS, ATMOSPHERIC TEMPERATURE, VORTICES, METEOROLOGICAL DATA, WIND VELOCITY, BAROMETRIC PRESSURE, LINEAR REGRESSION ANALYSIS, HUMIDITY.
Subject Categories : Meteorology
Distribution Statement : APPROVED FOR PUBLIC RELEASE