Day-to-day statistical and spatio-temporal analysis of cyclone Mocha (2023) induced cold wake and sea surface height anomalies through multi-resolution satellite data and cloud computing

Priyanka Puri

doi:10.26881/oahs-2026.1.06

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Day-to-day statistical and spatio-temporal analysis of cyclone Mocha (2023) induced cold wake and sea surface height anomalies through multi-resolution satellite data and cloud computing

Oceanological and Hydrobiological Studies

Volume 55 (2026): Issue 1 (March 2026)

By: Priyanka Puri

Open Access

|Mar 2026

Figures & Tables

Study area - Location. Source: Author (2025).

Track of cyclone Mocha (2023)—from pre-to post-landfall. (Track of cyclone Mocha from pre to post (pink color) to post-landfall (purple color). Source: Author, 2025 from ‘NOAA/IBTrACS/v4’ from GEE. GEE, Google Earth Engine.

SST anomalies associated with cyclone Mocha. Source: Author, 2025 from NOAA/CDR/OISST/V2_1 from GEE. GEE, Google Earth Engine; SST, sea surface temperature.

Day wise total area observed under cold wake for cyclone Mocha track in BoB. Source: Author, 2025 from NOAA/CDR/OISST/V2_1 from GEE. BoB, Bay of Bengal; GEE, Google Earth Engine.

Spatio-temporal extent of cold wake by cyclone Mocha (9th–15th May’23) against the baseline period (15th April–1st May’23). Source: Author, 2025 from NOAA/CDR/OISST/V2_1 from GEE. GEE, Google Earth Engine.

SSHA day-to-day spatial observations for Mocha (9th–15th May’23) against baseline of 15th April–1st May’23. Source: Author, 2025 from Copernicus Marine Physics 2D daily mean fields from GEE. GEE, Google Earth Engine.

SSHA day-to-day temporal observations for Mocha against baseline of 15th April–1st May’23. Source: Author, 2025, Derived from NOAA/CDR/OISST/V2_1 from GEE. GEE, Google Earth Engine.

SST and SSHA via baseline for cyclone Mocha. Source: Author (2025). SST, sea surface temperature.

Z-score for SST and SSHA observed for cyclone Mocha (15th April–31st May’23). Source: Author (2025). SST, sea surface temperature.

Details and applicability of statistical techniques used

Method	Formula	Purpose in general	Purpose in study
Pearson’s correlation	r=Σ[(X−X¯)(Y−Y¯)]/(Σ(X−X¯)2·Σ(Y−Y¯)2)r = \Sigma [(X - \bar X)(Y - \bar Y)]/\sqrt {} (\Sigma {(X - \bar X)^2}\cdot\Sigma {(Y - \bar Y)^2})	Linear association between two variables	Strength and direction of SST–SSHA coupling; evaluate how SST cooling aligns with SSHA changes (r ≈ 0.426, p = 0.001)
Linear trend (Regression slope)	Slope = (y₂ - y₁)/(x₂ - x₁)	Rate of change over time	Daily SST warming (0.00783°C/day) and SSHA rebound (0.00034 m/day) after the cyclone
CCF	CCF(τ) = Σ(X_t · Y_{t-τ})/(m · σ_X · σ_Y)	Lag relationships between time series	Whether SSHA responds to SST with a lag (SST leads SSHA by ~1–3 days)
Granger causality test	F = ((SSR_r − SSR_ur)/m)/(SSR_ur/(N − k))	Predictive influence of past values	Whether past SST improves the prediction of SSHA (SST → SSHA significant at lags 1–3)
Z-score standardization	z = (x - μ)/s	Raw values into standardized anomalies	Extreme cold-wake SST anomalies and comparing their magnitude against weaker SSHA Z-scores

Statistical relationship between SST and SSHA for cyclone Mocha

Parameter	Value observed	Interpretation
Correlation (SST vs SSHA)	r = 0.426, p = 0.001	Moderate, significant positive relationship, increase in SST is associated with increase in SSHA.
Linear trend/day (Regression Slope)	SSHA slope: 0.00034 m/day SST slope: 0.00783°C/day	To observe the rate of change between the two parameters
Baseline SSHA	0.081 m	Normal pre-cyclone SSH.
Cyclone-period SSHA	0.078 m	Slight drop, indicating initial setup + partial surge dissipation.
Post-cyclone SSHA	0.0814 m	Returns close to baseline with a rapid recovery of sea level.
Baseline SST	29.54°C	Warm pre-cyclone ocean; fuels cyclone intensification.
Cyclone-period SST	29.09°C	Noticeable drop with a strong cold wake formation
Post-cyclone SST	29.52°C	Re-warming shows restoration of surface stratification.
SSHA trend (post-cyclone)	0.00034 m/day	Very slow increase; structural ocean recovery.
SST trend (post-cyclone)	0.00783°C/day	Stronger warming trend; aligns with rapid recovery of thermal layer.
Granger causality (does SST cause SSHA?)	Significant at lags 1–3	SST changes precede and help predict SSHA variations as thermal forcing influences water column structure.
Z-score anomaly comparison	SST shows larger deviations than SSHA	Cyclone affects temperature more strongly than sea level.
Cold wake intensity (overall track)	~0.45°C cooling	Typical for BoB cyclones; confirms mixing-induced cooling.
Maximum cold wake SST cooling	≈ –1.5°C	Strongest localized SST drop observed in anomaly maps, typically on the right-hand side of the cyclone track where upwelling and mixing are highest.

Locational and meteorological details of cyclone Mocha’23_

Latitude	Longitude	Pressure_hPa	Wind_speed_knots
11.10	88.19	994	35
11.39	88.09	991	45
11.99	88.09	990	51
12.80	88.09	984	60
13.4	88.19	981	64
13.99	88.30	981	74
14.60	88.69	966	89
15.00	88.69	960	109
15.30	89.10	955	115
15.99	89.99	955	115
16.89	90.80	923	128
17.80	91.09	923	128
18.70	91.80	919	138
19.80	92.49	918	134
20.79	93.09	946	105
22.99	94.69	984	54
11.22	88.14	992	40
11.66	88.09	990	48
12.39	88.09	987	55
13.11	88.14	982	62
13.69	88.22	981	69
14.31	88.50	973	81
14.82	88.70	963	99
15.12	88.83	957	112
15.60	89.51	955	115
16.43	90.44	939	121
17.34	90.95	923	128
18.23	91.42	921	133
19.24	92.15	918	136
20.21	92.73	932	119
21.77	93.79	965	79