Process / pipelineStatistical analysis
Maximum Covariance Analysis
Maximum covariance analysis (MCA) is a statistical technique that identifies coupled patterns of variability between two spatially distributed fields (e.g., sea surface temperature and precipitation). Unlike EOF analysis which focuses on variance in a single field, MCA identifies spatial patterns that are maximally correlated between two different fields.
Open in MethodMindSoonVideoSoon
Read the full method
Members only
Sign inSign in with a free account to read this section.
Sources
- Bretherton, C. S., Widmann, M., Dymnikov, V. P., Wallace, J. M., & Blade, I. (1992). The effective number of spatial degrees of freedom of a time-varying field. Journal of the Atmospheric Sciences, 49(11), 1063-1083. DOI: 10.1175/1520-0469(1992)049<1063:TENOSO>2.0.CO;2 ↗
- Newman, M., Sardeshmukh, P. D., & Penland, C. (2016). Relative Contributions to Subseasonal Predictability: Bridging Medium-Range and Climate Time Scales. Journal of Climate, 29(15), 5629-5647. DOI: 10.1175/JCLI-D-15-0541.1 ↗