INSAT-3D low-level atmospheric motion vectors: Capability to capture Indian summer monsoon intra-seasonal variability View Full Text


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Article Info

DATE

2019-03

AUTHORS

Dineshkumar K Sankhala, Sanjib K Deb, V Sathiyamoorthy

ABSTRACT

In India, Atmospheric Motion Vectors (AMVs) are derived operationally from the advanced Indian meteorological geostationary satellite INSAT-3D since July 2013 over Indian Ocean region and are used in the numerical model for forecast improvement. In this study, first-time the low-level monsoon winds derived from INSAT-3D satellite have been used to see how these winds are successful in capturing the intra-seasonal variability over the Indian Ocean region for the year 2016. A validation of AMVs is done on regular basis. In this study, the validation of low-level AMVs with National Center for Environmental Prediction (NCEP) analysis winds carried out during June to September 2016. The observed mean monthly features of the Indian Summer Monsoon (ISM) in July and August 2016 from low-level AMVs from INSAT-3D match well with those of NCEP analysis winds. INSAT-3D low-level AMVs are quite successful in capturing the northward propagation of low level jet and their locations during active and break monsoon conditions which are known features of the ISM. They are also able to explain the two dominant modes of variability: (i) one with a periodicity between 32 and 64 days, and (ii) another with a periodicity between 8 and 16 days, for the monsoon season of 2016 when Morlet wavelet transform analysis is performed for time series analysis. An EOF analysis is performed for the study of the spatial structure of intra-seasonal variability and temporal variability of INSAT-3D low-level AMVs over the Indian Ocean region for ISM 2016 and quantifies the results of EOF analysis by performing RMSE analysis between EOFs of INSAT-3D and NCEP wind data. More... »

PAGES

31

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12040-018-1060-y

DOI

http://dx.doi.org/10.1007/s12040-018-1060-y

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https://app.dimensions.ai/details/publication/pub.1111612567


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