Virtual Control Volume Approach to the Study of Climate Causal Flows: Identification of Humidity and Wind Pathways of Influence on Rainfall in Ecuador

• Main Authors: Angel Vázquez-Patiño, Lenin Campozano, Daniela Ballari, Mario Córdova, Esteban Samaniego
• Format: Article
• Language: English
• Published: MDPI AG 2020-08-01
• Series: Atmosphere
• Subjects: causal flows; control volume; control surface; climate networks; Granger causality; causal strength
• Online Access: https://www.mdpi.com/2073-4433/11/8/848

Unraveling the relationship between humidity, wind, and rainfall is vitally important to understand the dynamics of water vapor transport. In recent years, the use of causal networks to identify causal flows has gained much ground in the field of climatology to provide new insights about physical processes and hypothesize previously unknown ones. In this paper, the concept of a virtual control volume is proposed, which resembles the Eulerian description of a vector field, but is based on causal flows instead. A virtual control surface is used to identify the influence of surrounding climatic processes on the control volume (i.e., the study region). Such an influence is characterized by using a causal inference method that gives information about its direction and strength. The proposed approach was evaluated by inferring and spatially delineating areas of influence of humidity and wind on the rainfall of Ecuador. It was possible to confirm known patterns of influence, such as the influence of the Pacific Ocean on the coast and the influence of the Atlantic Ocean on the Amazon. Moreover, the approach was able to identify plausible new hypotheses, such as the influence of humidity on rainfall in the northern part of the boundary between the Andes and the Amazon, as well as the origin (the Amazon or the tropical Atlantic) and the altitude at which surrounding humidity and wind influence rainfall within the control volume. These hypotheses highlight the ability of the approach to exploit a large amount of scalar data and identify pathways of influence between climatic variables.