Skip to main content
Log in

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

  • Published:
Journal of Earth System Science Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15

References

  • Bedka K M and Mecikalski J R 2005 Application of satellite-derived atmospheric motion vectors for estimating mesoscale flows; J. Appl. Meteorol. 44(11) 1761–1772.

    Article  Google Scholar 

  • David L M 1983 Empirical orthogonal function analysis of wind vector over Tropical Pacific Region; Am. Meteor. Soc. 64(3) 234–241.

    Article  Google Scholar 

  • Deb S K, Kishtawal C M, Kumar P, Kumar A K, Pal P K, Kaushik N and Sangar G 2016 Atmospheric motion vectors from INSAT-3D: Initial quality assessment and its impact on track forecast of cyclonic storm Nanauk; Atmos. Res. 169 1–16.

    Article  Google Scholar 

  • Deb S K, Kishtawal C M and Pal P K 2010 Impact of Kalpana-1-derived water vapor winds on Indian Ocean tropical cyclone forecasts; Mon. Wea. Rev. 138(3) 987–1003.

    Article  Google Scholar 

  • Goswami B N and Ajaya Mohan R S 2001 Intraseasonal oscillations and inter-annual variability of the Indian summer monsoon; J. Clim. 14(6) 1180–1198.

    Article  Google Scholar 

  • Joseph P V and Sijikumar S 2004 Intraseasonal variability of the low-level jet stream of the Asian summer monsoon; J. Clim. 17(7) 1449–1458.

    Article  Google Scholar 

  • Kaur I, Deb S K, Kishtawal C M, Pal P K and Kumar R 2013 Low level cloud motion vectors from Kalpana-1 visible images; J. Earth Syst. Sci. 122(4) 935–946.

    Article  Google Scholar 

  • Kelly G, McNally A, Thépaut J and Szyndel M 2004 Observing system experiments of all main data types in the ECMWF operational system; The \(3{{\rm rd}}\) WMO Numerical Weather Prediction OSE Workshop, Alpbach, Austria, WMO, Technical Report no. 1228, pp. 63–94.

  • Kishtawal C M, Deb S K, Pal P K and Joshi P C 2009 Estimation of atmospheric motion vectors from Kalpana-1 imagers; J. Appl. Meteor. Climatol. 48(11) 2410–2421.

    Article  Google Scholar 

  • Kumar P, Deb S K, Kishtawal C M and Pal P K 2016 Impact of assimilation of INSAT-3D retrieved atmospheric motion vectors on short-range forecast of summer monsoon 2014 over the South Asian region; Theo. Appl. Climatol. 128(3–4) 575–586.

    Google Scholar 

  • Lau K M and Weng H 1995 Climate signal detection using wavelet transform: How to make a time series sing; Bull. Am. Meteor. Soc. 76(12) 2391–2402.

    Article  Google Scholar 

  • Nieman S J, Menzel W P, Hayden C M, Gray D, Wanzong S T, Velden C S and Daniels J 1997 Fully automated cloud-drift winds in NESDIS operations; Bull. Am. Meteor. Soc. 78(6) 1121–1133.

    Article  Google Scholar 

  • Rao P C S, Pai D S and Mohapatra 2017 Monsoon 2016: A Report; IMD Met. Monograph.

  • Sathiyamoorthy V, Shikakolli R, Gohil B S and Pal P K 2012 Intra-seasonal variability in Oceansat-2 scatterometer sea-surface winds over the Indian summer monsoon region; Meteor. Atmos. Phys. 117 145–152.

    Article  Google Scholar 

  • Schmetz J, Holmlund K, Hoffman J, Strauss B, Mason B, Gaertner V, Koch A and Van de Berg L 1993 Operational cloud-motion winds from Meteosat infrared images; J. Appl. Meteorol. 32(7) 1206–1225.

    Article  Google Scholar 

  • Shimoji K and Hayashi M 2012 A study on the relationship between spatial and temporal image resolutions for AMV derivation with next generation satellites; Proceedings of \(11{{\rm th}}\) International Winds Workshop, Vol. 60.

  • Sikka D and Gadgil S 1980 On the maximum cloud zone and the ITCZ over Indian, longitudes during the southwest monsoon; Mon. Wea. Rev. 108(11) 1840–1853.

    Article  Google Scholar 

  • Velden C S, Hayden C M, Nieman S J, Menzel W P, Wanzong S and Goerss J S 1997 Upper-tropospheric winds derived from geostationary satellite water vapor observations; Bull. Am. Meteor. Soc. 78(2) 173–195.

    Article  Google Scholar 

  • Webster P J, Magana V O, Palmer T N, Shukla J, Tomas R T, Yanai M and Yasunari T 1998 Monsoons: Processes, predictability, and the prospects for prediction; J. Geophys. Res. 103(C7) 14,451–14,510.

    Article  Google Scholar 

  • Zeng L and Wang D 2009 Intraseasonal variability of latent-heat flux in the southern China Sea; Theor. Appl. Climatol. 97(1–2) 53–64.

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank three anonymous reviewers for their critical and insightful comments/valuable suggestions, which were helpful in substantially improving the content and quality of presentation of this manuscript. The authors would like to thank Director, SAC, Deputy Director EPSA, SAC, Group Director AOSG/EPSA and Head ASD/AOSG/EPSA, SAC, ISRO, Ahmedabad for their constant support and guidance. The National Center for Environmental Prediction (NCEP) is acknowledged for providing GDAS analyses through the NOMAD website (http://nomads.ncdc.noaa.gov/). The authors are also thankful to the Meteorological and Oceanographic Satellite Data Archival Centre (MOSDAC) team (www.mosdac.gov.in) of SAC, ISRO, Ahmedabad, for providing us INSAT-3D atmospheric motion vectors data. The first author also acknowledges SAC, ISRO for providing financial support for carrying out the present study.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dineshkumar K Sankhala.

Additional information

Corresponding editor: Ashok Karumuri

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sankhala, D.K., Deb, S.K. & Sathiyamoorthy, V. INSAT-3D low-level atmospheric motion vectors: Capability to capture Indian summer monsoon intra-seasonal variability. J Earth Syst Sci 128, 31 (2019). https://doi.org/10.1007/s12040-018-1060-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12040-018-1060-y

Keywords

Navigation