On the stability of the troposphere/lower stratosphere and its relationships with cirrus clouds and three mandatory levels over Buenos Aires

Abstract

An unrotated principal components analysis was carried out to establish the most representative modes for the joint variability between the heights of the upper and lower boundaries of cirrus clouds and three different mandatory levels (850, 500, and 100 hPa), and the associated stability of the troposphere over Buenos Aires. Discussion is limited to the first three most representative structures found, which consists of spatial patterns (or empirical orthogonal functions, EOFs) and their time-evolving coefficients (or principal components, PCs). EOF 1 shows a direct (indirect) mode that encompasses the cirrus slightly below (above) its mean position, with 500 and 100 hPa exhibiting a similar behaviour and 850 hPa acting the opposite way. EOF 1 is associated with above-normal stability (instability) for direct (indirect) modes (i.e. positive (negative) values of PC 1 ). On a monthly average, this occurs in the austral winter (summer) months. Regarding EOF 2 , all three mandatory levels experience positive (negative) height anomalies in direct (indirect) modes and cirrus goes up (down) under mild stability (instability). Monthly averages show that PC 2 is approximately positive in summer and in early fall and negative the rest of the year. As to EOF 3 , it is characterized by a stability similar to that of EOF 2 , with direct (indirect) modes showing lowered (raised) cirrus and all three mandatory levels above (below) normal conditions; on a monthly basis, PC 3 is best described as having a semi-annual evolution, with maxima (minima) in March and October (January and August). Overall, EOF 1 has the highest stability or instability, depending upon the sign of PC 1 . These results are the first of their kind worldwide.

A Cluster Approach to Cloud Cover Classification over South America and Adjacent Oceans Using a k-means/k-means++ Unsupervised Algorithm on GOES IR Imagery

“…Time series of CC data from conventional information collected by meteorological observers all around the world are prone to subjective errors [11] and cannot be automatically performed. Subjective observations supplemented by data from ground equipment measurements using various technologies that can be implemented in an automated fashion allows for more detailed studies, but they are usually limited spatially, temporally or both [12,13]. Identifying the presence and behavior of distinct clouds is a manifold task that requires the understanding of the different meteorological systems and their associated phenomena, and also involves the development of products and techniques that can help assessing their spatial and temporal behavior using remotely sensed data [14][15][16][17][18].…”