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Preprints
https://doi.org/10.5194/egusphere-2025-273
https://doi.org/10.5194/egusphere-2025-273
18 Feb 2025
 | 18 Feb 2025
Status: this preprint is open for discussion and under review for Weather and Climate Dynamics (WCD).

Probabilistic seasonal outlook for the rainy season over India by monitoring the onset dates using GPM IMERG satellite-based precipitation

Chempampadam Balasubramannian Jayasankar and Vasubandhu Misra

Abstract. We utilized the Integrated Multi-Satellite Retrievals for Global Precipitation Mission version 6 (IMERG) rainfall observation (available in real time) over India to determine the onset and demise of the rainy season. The annual mean climatology derived from IMERG observations over India aligned closely with the rain gauge-based India Meteorological Department observation. The IMERG rainfall time series was randomly perturbed to generate 101 ensemble members at every grid point of the rainfall analysis to obtain a corresponding ensemble of the onset and demise dates of the rainy season. The perturbations were designed to sample the uncertainty due to random synoptic or mesoscale rain events influencing the diagnosis of the onset/demise dates at the granularity of the IMERG observations (at 10 km grid). Following earlier studies, we find from the IMERG dataset that seasons with an earlier onset date are strongly related to a lengthier and wetter season, whereas seasons with a later onset date correspond to a shorter and drier season. In contrast, the connections between the onset, demise, seasonal length, and rainfall with ENSO and IOD were comparatively weaker over most of India. The generation of ensembles in this study underscores the potential for real-time application of generating reliable, probabilistic seasonal outlooks of the rainy season over India by leveraging the local links amongst onset date, seasonal length, and seasonal rainfall anomalies. This potential is further confirmed by the high probabilistic skill scores of the seasonal outlooks using the area under the relative operating characteristic curve method.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
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This study examines the potential of the seasonal predictably of the India's rainy season using...
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