Machine learning and dynamics based error‐index method for the detection of monsoon onset vortex over the Arabian Sea: Climatology and composite structures

Abstract

Monsoon onset vortex (MOV) forms over the Arabian Sea near the northern flank of the low-level jet during the monsoon onset over Kerala (MOK). The study concerns the development and evaluation of an algorithm for detecting and tracking MOVs in regional/global analyses. The first step involves preparing the first-guess database of MOV locations based on geopotential height, surface and 850 hPa wind magnitude and circulation from ERA5 reanalysis for 1982-2020. Three different approaches: (a) error-index of MOV, (b)machine-learning (ML), and (c) combination of error-index and ML models, are employed to detect MOV. The error-index method, in which the detected vortex is compared with the idealized vortex, achieves an accuracy of 0.6 with a 0.95 true-positive-rate and 0.55 false-positive-rate. The best ML models can identify the MOVs in the training samples with maximum accuracy of 0.99. However, their accuracy is limited in tracking the MOVs continuously in the global analyses as they are not trained with wind circulation. The combined error-index and ML models could detect all the 27 observed MOVs in the ERA5 reanalysis with 5 false-positives. This approach is tested on IMDAA reanalysis, and the success rate is 0.79 with 6 false-positives. Temporal analyses show that ∼95% of the MOVs occur during −10 to +20 days from MOK. Composite structures indicate that the MOVs exhibit higher sea-surface temperatures (>0.3 • C) in the forward sector with 85% cloud cover in the left-rear sector. Rainfall of 4-5 mm⋅hr −1 is seen in the left sector. Upper-level (700-200 hPa) warm core (>3.5 • C) and lower-level (1000-700 hPa) cold-core (<1 • C) is evident. The composite structures of MOVs are almost similar to that of monsoon depressions with higher asymmetry in the forward-rear sectors. This study may help explore future projections of MOV activity from climate models and its relationship with monsoon rainfall activity.

The impact of the Madden-Julian Oscillation on the formation of the Arabian Sea Monsoon Onset Vortex

“…Deepa and Oh (2014) presented a list of past MOVs from 1982-2011, but their rationale for identification was not clear. Recently, Sasanka et al (2023) classified cyclonic synoptic systems over the Arabian Sea within -10 days to +20 days of the monsoon onset over Kerala as MOVs. It is important that the definition of the MOV includes only those vortices which are associated with the onset and advance of the monsoon over the Arabian Sea and not the pre-monsoon or seasonal monsoonal disturbances.…”

The Impact of the Madden‐Julian Oscillation on the Formation of the Arabian Sea Monsoon Onset Vortex

“…Deepa and Oh (2014) presented a list of past MOVs from 1982 to 2011, but their rationale for identification was not clear. Recently, Sasanka et al (2023) classified cyclonic synoptic systems over the Arabian Sea within −10 days to +20 days of the monsoon onset over Kerala as MOVs. It is important that the definition of the MOV includes only those vortices which are associated with the onset and advance of the monsoon over the Arabian Sea and not the pre-monsoon or seasonal monsoonal disturbances.…”

Enhanced prediction of premonsoon thunderstorms over eastern India through assimilation of INSAT‐3D sounding data

“…During premonsoon, the eastern parts of India (Gangetic West Bengal, Assam, Odisha, Bihar, and Jharkhand) experience severe TS, locally known as Nor'westers or Kal-Baisakhi (Tyagi et al, 2012). This region is also identified as one of the most unstable (Convective Available Potential Energy, CAPE of ∼1500-3500 J⋅kg −1 ) and a suitable region to produce severe TS with intense rainfall (>7 mm⋅h −1 ) activity compared to other parts of India (Sahu et al, 2020;Sasanka et al, 2023;Tyagi et al, 2011Tyagi et al, , 2013Tyagi et al, 2012). The nonlinear nature of TS dynamics, coupled with their smaller spatial and temporal scales, makes predicting TS events a significant challenge (Osuri et al, 2017;Priya et al, 2021Priya et al, , 2022Rajeevan et al, 2010).…”