Browsing by Author "Anand Singh Dinesh"

Now showing 1 - 2 of 2

Comparative changes in seasonal marine heatwaves and cold spells over the Tropical Indian Ocean during recent decades and disentangling the drivers of highly intense events
(John Wiley and Sons Ltd, 2023) Anand Singh Dinesh; Alok Kumar Mishra; Aditya Kumar Dubey; Suruchi Kumari; Akash Anand
The unprecedented increase in the Sea Surface Temperature (SST) in the warming climate yield stress to the system and pose severe threats to the marine ecosystem. Marine Heatwaves (MHWs) and Marine Cold Spells (MCSs) are two extreme events related to SST variability. For better management of ocean productivity, marine ecosystem, marine services, and fisheries, the understanding of seasonal discrepancies rather than annual documentation of MHWs and MCSs metrics is more utilitarian. This study documents the decadal changes in the MHWs and MCSs over the Tropical Indian Ocean (TIO) for all seasons. Additionally, highly intense events (based on intensity and duration) are identified and demonstrate the associated drivers. During the past two decades (1982–1990, 1991–2000), the MCSs were more frequent than MHWs in every season. However, in the recent two decades (2001–2010, 2011–2020), TIO become more prone to MHWs with considerably more frequent and prolonged events in JJAS months. Moreover, MCSs are disappearing from the TIO. It was noted that the choice of baseline period has an impact on the magnitude of MHWs and MCSs changes, but the spatial pattern (regions with high/low magnitude MHWs and MCSs) stays fairly constant in all baseline period sensitivity checks. The investigation of highly intense events reveals that MHWs and MCSs are produced and sustained by the same drivers when they are at their opposing edges. In general, the coherence effort from winds, net heat fluxes (shortwave radiation, longwave radiation, latent heat flux, and sensible heat flux), mixed level depth, and mean sea level pressure contribute to the genesis of seasonal MHWs or MCSs events. Additionally, in some cases, a single driver (e.g., wind) may also play a crucial role in these extreme events. The remote climate modes of variability, such as El Niño–Southern Oscillation, also contribute significantly to the MHWs and MCSs. El Niño (La Niña) events not only increase the spatial coverage of MHWs (MCSs) but also increases the intensity. © 2023 Royal Meteorological Society.
Rainfall rate estimation over India using global precipitation measurement’s microwave imager datasets and different variants of fuzzy information system
(Taylor and Francis Ltd., 2022) Akash Anand; Anand Singh Dinesh; Prashant K. Srivastava; Sumit Kumar Chaudhary; Atul Kumar Varma; Pavan Kumar
Effective rain rate estimation using satellite-based measurement is imperative for many hydro-meteorological applications. With the recent advancement in satellite products and retrieving algorithms, rain rate estimations are continuously improving. This study provides a comparative performance appraisal of three hybrid machine learning algorithms namely Adaptive Neuro-Fuzzy Inference System (ANFIS), Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS) and Hybrid Fuzzy Inference System (HYFIS) for rain rate estimation using the Global Precipitation Measurement (GPM)’s Microwave Imager (GMI) and ground-based Disdrometer data. The in situ sampling was conducted at four different locations (both land and ocean) across the Indian region and different statistical metrics were used to evaluate the performances of these models. The results showed that HYFIS algorithm has provided better rain rate estimation than ANFIS and DENFIS. The study endorses these neuro-fuzzy models for generating accurate precipitation products and can be considered as an alternative for future satellite retrieval algorithms. © 2021 Informa UK Limited, trading as Taylor & Francis Group.