Data archive strategy for computing the long-term means of nonlinear functions in geophysical problems

Climate Research

Ben-Jei Tsuang¹ 

关键词: Data processing;    Nonlinear;    Transient and time domain;    Seasonal cycle;    Diurnal cycle;    Taichung;   

DOI  :  10.3354/cr027225

来源: Inter-Research Science Publishing

【 摘 要 】

ABSTRACT: An algorithm for computing the temporal mean of a nonlinear function using the temporal means, covariances and higher order statistical moments of the variables involved in the function is revisited. Furthermore, a pyramidal algorithm isderived, which hierarchically stores the statistical moments of a longer interval of a variable from those of its shorter subintervals. The 2 methodologies together presented here show a systematic way of data storage and show that the long-term mean of anonlinear process can be analyzed by decomposing it into various shorter sub-time scales such as diurnal and seasonal cycles. For example, the long-term mean of horizontal moisture flux can be decomposed into the product of the means of wind speed andhumidity observations, plus the covariance of daily means of the 2 variables, and plus the mean of the daily covariances of the 2 variables on each day, where the 3 mean values and the covariance are suggested for storage. The results are exactly the sameas those directly calculated from their hourly data. Since only 4 statistical moments are needed, significant data reduction for data distribution can be achieved. The error associated with the data archive replica has been discussed for a highlynonlinear function of which the statistical moments of its variables are only available up to a finite order. A case study using 41 yr of data taken on an urbanizing site on a subtropical island is illustrated.

【 授权许可】

Unknown