| ABSTRACT | The Red Sea Trough (RST) is a low-pressure system extending from south toward the Eastern Mediterranean (EM) and further to the Levant. The semi-objective synoptic classification of Alpert et al. (2004) for the Levant identified 19% of the days of the year as 'RST days'. They are most frequent in the fall and the winter, fading out by mid-spring. This system is the most frequent among all easterly troughs, which extend from the east African Monsoon toward the EM, and is attributed to the lee effect of mountain ridges east of the Red Sea. The identification according to the above approach compares each day to one of 19 predefined synoptic types. Unfortunately, this identification does not differentiate successfully the RST from other synoptic systems, such as 'high pressure to the west' and 'shallow low east of Israel'. Our approach aims to explicitly identify RST days and classify them as one of three types, according to the location of the trough axis. For that end, we use the sea level pressure (SLP) and the sea level relative geostrophic vorticity. These two fields are interpolated (cubic) to a 0.5°x0.5° resolution. The following conditions has to be met in order for a day to be regarded as an RST day: (i) a north to south SLP drop across the Levant area, (ii) average positive relative vorticity over that domain, (iii) the existence of a distinct trough axis from the low pressure toward the domain along a continuous line where the curvature of the isobars is maximum and (iv) the absence of a pronounced closed cyclone in the region of interest (which is not a meso-cyclone within the RST). The classification of the RSTs is done according to the location of the trough axis; to the west of Israel, to the east or within Israel. It is found that the trough axis has a diurnal oscillation, whereas it tends to be located near the eastern coast of the Mediterranean at nighttime (00UTC) and shifts eastward (inland) toward noontime (12UTC). An evaluation of the automatic classification, done for randomly selected 200 days, showed that in 96% of them the subjective classification is similar. However, for the location of the trough axis, the agreement is 89%. For 100 days, identified subjectively as RSTs, the automatic classification agreed on 85% (72% agreement on the trough axis location). The fully automated algorithm is not tailored to a predetermined spatial resolution, so it is applicable to a variety of reanalysis datasets, operational forecast model results and climate model outputs. |