Open AccessSaturated Pseudoadiabats—A Noniterative Approximation
Author(s) -
Atoossa Bakhshaii,
Roland B. Stull
Publication year - 2013
Publication title -
journal of applied meteorology and climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.079
H-Index - 134
eISSN - 1558-8432
pISSN - 1558-8424
DOI - 10.1175/jamc-d-12-062.1
Subject(s) - iterated function , thermodynamics , approximation error , mathematics , skew , mean squared error , bounded function , correlation coefficient , physics , mathematical analysis , statistics , astronomy
Two noniterative approximations are presented for saturated pseudoadiabats (also known as moist adiabats). One approximation determines which moist adiabat passes through a point of known pressure and temperature, such as through the lifting condensation level on a skew T or tephigram. The other approximation determines the air temperature at any pressure along a known moist adiabat, such as the final temperature of a rising cloudy air parcel. The method used to create these statistical regressions is a relatively new variant of genetic programming called gene-expression programming. The correlation coefficient between the resulting noniterative approximations and the iterated data such as plotted on thermodynamic diagrams is over 99.97%. The mean absolute error is 0.28°C, and the root mean square error is 0.44 within a thermodynamic domain bounded by −30° 20 kPa, and −60° ≤ T ≤ 40°C, where θ w , P , and T are wet-bulb potential temperature, pressure, and air temperature.