Detecting the ITCZ in Instantaneous Satellite Data using Spatiotemporal Statistical Modeling: ITCZ Climatology in the East Pacific

Abstract

A Markov random field (MRF) statistical model is introduced, developed, and validated for detecting the east Pacific intertropical convergence zone in instantaneous satellite data from May through October. The MRF statistical model uses satellite data at a given location as well as information from its neighboring points (in time and space) to decide whether the given point is classified as ITCZ or non-ITCZ. Two different labels of ITCZ occurrence are produced. IR-only labels result from running the model with 3-hourly infrared data available for a 30-yr period, 1980-2009. All-data labels result from running the model with additional satellite data (visible and total precipitable water), available from 1995 to 2008. IR-only labels detect less area of ITCZ than all-data labels, especially where the ITCZ is shallower. Yet, qualitatively, the results for the two sets of labels are similar.The seasonal distribution of the ITCZ through the summer half year is presented, showing typical location and extent. The ITCZ is mostly confined to the eastern Pacific in May, and becomes more zonally distributed toward September and October each year. Northward and westward shifts in the location of the ITCZ occur in line with the seasonal cycle and warm sea surface temperatures. The ITCZ is quite variable on interannual time scales and highly correlated with ENSO variability. When the ENSO signal was removed from labels, interannual variability remained high. The resulting IR-only labels, representing the longer time series, showed no evidence of a trend in location nor evidence of a trend in area for the 30-yr period.

Vertical and latitudinal variation of the intertropical convergence zone derived using GPS radio occultation measurements

“…In addition, the ITCZ can be identified by using visible and infrared satellite observations of high reflective cloud cover , by locating the convergence of wind fields (e.g., Žagar, Skok, & Tribbia, 2011;Zheng, Yan, Liu, Tang, & Kurz, 1997), by calculating the relative vorticity structures (e.g., Magnusdottir & Wang, 2008), or by using multiple variable criteria (e.g., Bain et al, 2010). Despite the availability of a number of methods for its identifications, some discrepancies in identifying the ITCZ are mentioned in the literature.…”

Globally Gridded Satellite Observations for Climate Studies

“…The latter has the disadvantage of including convection not associated with the ITCZ. Using GridSat data, Bain et al (2011) developed a statistical model for ITCZ detection that objectively identifies the envelope of convection. The method requires that the satellite dataset be reliably calibrated over long periods and available at high temporal resolution.…”

Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques

“…a Markov random field (MRF)-based approach (Fu et al, 2012); this type of MRF approach has been validated by automatically detecting the intertropical convergence zone from instantaneous satellite data (Bain et al, 2011). The algorithm was able to detect some of the major global droughts and proved to be efficient in detecting droughts as compared to fixed percentile-based approaches.…”