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Your search keyword '"Joshi, Purvee"' showing total 3 results
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3 results on '"Joshi, Purvee"'

1. Recent glacier area changes in Himalaya–Karakoram and the impact of latitudinal variation

Author

Bahuguna, Ishmohan, primary, Rathore, Bhanu Prakash, additional, Jasrotia, Avtar Singh, additional, Randhawa, Surjeet Singh, additional, Yadav, Santosh Kumar Singh, additional, Ali, Sadiq, additional, Gautam, Nishtha, additional, Poddar, Joyeeta, additional, Srigyan, Madhukar, additional, Dhanade, Abhishek, additional, Joshi, Purvee, additional, Singh, Sushil Kumar, additional, Rajak, Dhani Ram, additional, and Sharma, Shashikant, additional

Published
2021
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2. Estimation of Sea Ice Concentration in the Arctic Using SARAL/AltiKa Data

Author

Thombson, Chungkham, Singh, Rajkumar Kamaljit, Oza, Sandip Rashmikant, Joshi, Purvee Nisarg, Singh, Sushil Kumar, and Bahuguna, Ishmohan

Abstract

Sea ice concentration (SIC) is important in determining important climate variables. Together with sea ice thickness, fluxes between air and sea as well as heat transfer through the atmosphere can be determined. We present here an adaptation of the NASA bootstrap (BT) algorithm with significant necessary modifications to estimate daily and monthly SIC in the Arctic for winter and summer seasons separately. Brightness temperature (Tb) data from the dual-frequency microwave radiometer (23.8 and 37 GHz) onboard the SARAL/AltiKa satellite for the period from April 2013 to December 2020 were used to estimate SIC. We compared our daily SIC product quantitatively with the daily National Snow Ice and Data Centre Climate Data Record (NSIDC CDR) SIC, the Ocean and Sea Ice Satellite Application Facility (OSISAF) SIC, and the ARTIST Sea Ice Algorithm SIC (ASI) (3.125 and 6.25 km resolutions). Root-mean-square errors (RMSEs) are 11% (versus NSIDC CDR), 11% (versus OSISAF), and 13% (versus ASI 3.125 and 6.25 km); for winter months about 9% (versus NSIDC CDR), 8% (versus OSISAF), and 11% (versus ASI 3.125 and 6.25 km); and for summer months about 15% (NSIDC CDR), 16% (OSISAF), and 16% (ASI 3.125 and 6.25 km). A bias correction method was performed on our product using NSIDC CDR SIC data, which resulted in an improvement of 6% RMSE. The algorithm was also analyzed for 14 regions in the Arctic to gain confidence in the methodology employed. The results show that our SIC is of good quality and can be used as a basic dataset for SIC records.

Published
2023
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3. Harnessing the potential of EOS-04 SAR data for Himalayan and polar cryospheric studies.

Author

Singh, S. K., Thakur, Praveen, Tripathi, Naveen, Joshi, Purvee, Garg, Vaibhav, Dubey, Amit, Ghetiya, Satyesh, Srigyan, Madhukar, Das, Jay, Jayaprasad, P., Shukla, Aparna, and Oza, Sandip R.

Subjects
*CRYOSPHERE, *ICE shelves, *ABLATION (Glaciology), *SNOWMELT, *SYNTHETIC aperture radar, *SEA ice, *ANTARCTIC ice, *SCIENTIFIC expeditions
Abstract

Present study focuses on the utilization of Earth observation satellite-04 (EOS-04) synthetic aperture radar (SAR) data for maintaining the continuity of the first Indian radar imaging satellite (RISAT)-1 SAR derived products along with exploring the potential of capability of the improved sensor over mountain and polar cryospheric region. Backscattering coefficient (σ0) of various snow and ice features over mountain and polar cryosphere have been analysed to understand the interaction mechanism using C-band SAR data. EOS-04 is able to pick up the spatio-temporal variability of SAR backscatters over accumulation and ablation zone of the glacier due to melt-freeze cycles, and observations were in accordance with variation in elevations over the glacier surface. When analysed for Drang-Drung glacier, wet snow zone was found to be prominently centered around 5500 m elevation zone, having sigmanaught backscatter lower than –10 dB in the ablation months, whereas percolation zone was observed at more than 6000 m elevation with higher sigma-naught backscatter of around –4 dB and above as winter started setting in. EOS-04 also showed the potential to classify various polar ice features based on backscattering signature using HH (H, horizontal) (σ0), HV (V, vertical) (σ0) and normalized difference polarization ratio index (NDPRI) respectively. EOS-04 data have been used to implement approaches to retrieve wet snow cover and set up of Weather Research and Forecasting Model Hydrological Modelling System (WRF hydro) model for snow melt runoff studies, interaction mechanism of snow and ice, snow/ice facies extraction, ice shelf monitoring, sea ice properties and sea ice advisory for Indian scientific expedition to Antarctica. Enrichment of EOS-04 data, suitable for cryosphere studies, will be employed to retrieve parameters such as snowpack properties, elevation, ice surface velocities over mountain and polar region, and to further improve comprehensive understanding on regional and global frozen ice dynamics. [ABSTRACT FROM AUTHOR]

Published
2024
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