Automatic identification and classification of the northern part of the Red Sea trough and its application for climatological analysis

The Red Sea trough (RST) is a low-pressure trough extending from south towards the Levant. Unlike previous synoptic classifications covering all systems that affect the region, our algorithm focuses on the RST alone. It uses sea-level pressure (SLP) and relative geostrophic vorticity for identifying the existence of an RST and classifying it to one of three types, according to the location of the trough axis with respect to 35°E longitude. The following conditions were imposed to assure the existence of an RST: (a) north to south SLP drop across the Levant, (b) average positive sea-level relative vorticity over the region of interest, (c) existence of a distinct and continuous trough axis from the south towards the region of interest and (d) absence of any pronounced closed cyclone near the Levant. The algorithm was applied on the NCEP/NCAR reanalysis, 2.5° × 2.5° resolution and the ERA-Interim, 2.5° × 2.5° and 0.75° × 0.75° resolutions. An evaluation of the algorithm against subjective identification, based on the NCEP reanalysis, showed an agreement of 93% for RST identification and 79% for correct classification. The use of fine resolution data may insert noise that reduce the identification rate of RSTs but improves the axis locating. The autumn is the main season of RST, with a maximum in November and a consistent decrease towards the July minimum. The annual frequency varies among the data sources between 17.6 and 24.6%. The trough axis is shown to have a diurnal oscillation; towards the eastern coast of the Mediterranean at nighttime and eastward, inland, at noontime. No consistent long-term trend was found for the period 1979–2016, during which the global warming was persistent. This automated algorithm is flexible in the sense that it is not confined to any predetermined spatial resolution and is applicable to operational forecast model as well as to climate model outputs.