Prediction of Indian summer monsoon rainfall (ISMR) using canonical correlation analysis of global circulation model products

Meteorological applications

article

Ankita Singh¹  Makarand A. Kulkarni²  U. C. Mohanty¹  S. C. Kar³  Andrew W. Robertson⁴  G. Mishra⁵ 

[1] Centre for Atmospheric Sciences, Indian Institute of Technology;Department of Atmospheric and Space Sciences, University of Pune;National Center for Medium Range Weather Forecasting;International Research Institute for Climate and Society;Department of Statistics, Utkal University

关键词: Canonical correlation analysis;    prediction;    General Circulation Model;    season;    rainfall;   

DOI  :  10.1002/met.1333

学科分类：社会科学、人文和艺术（综合）

来源: Wiley

【 摘 要 】

The Canonical Correlation Analysis (CCA) method has been used in this study for improving General Circulation Model (GCM) predicted rainfall over India during the southwest monsoon season. Hindcast runs for 27 years (1982–2008) from six GCM outputs are used. This statistical technique relates the pattern of multivariate predictor field (model rainfall) to the pattern of predictand fields (observed rainfall). It is found that the CCA method improves the skill of three of the GCMs at the all-India level. A noticeable improvement is also observed in the composite prediction with CCA as compared to the simple mean of raw GCM products. The skill of the composite prediction after applying CCA is higher compared to the simple mean of raw model products in several homogeneous zones such as the hilly areas, west central area and over some parts of northwest India. The possible reason for the improvement in the skill of some of the GCMs may be the similarity between the loading patterns of model predictions and the observed rainfall.

【 授权许可】

CC BY|CC BY-NC|CC BY-NC-ND