Abstract
Tropical cyclones or typhoons or hurricanes are basically an intense rotating circular system, characterized by intense low pressure at the center, high winds associated with intense convective cells in the wall cloud, associated heavy rains and storm surge. Tropical cyclones affect the life of millions of people around the globe. INSAT-3D is the second dedicated meteorological satellite which was launched on July 26, 2013, with two meteorological payloads: Imager and Sounder. It is placed at 83 °E over the equator in the geostationary orbit. Receiving, processing and dissemination of imageries and derived products from INSAT-3D are taken care by INSAT Meteorological Data Processing System (IMDPS), which is operational at National Meteorological Satellite Centre, India Meteorological Department, New Delhi. Currently, three satellite-derived rain estimates are generated at IMDPS, New Delhi: Qualitative Precipitation Estimate (QPE), Hydro-Estimator (HE) and INSAT Multi-Spectral Rain Estimate (IMSRA). In this study, the performance of HE and IMSRA over very severe cyclonic storm Vardah and severe cyclonic storm Mora has been evaluated with respect to GPM (IMERG). The statistical and skill score analysis is performed both over half-hourly scale with respect to various stages of cyclone genesis, intensification and weakening and on the daily scale. At the instantaneous time during half-hourly analysis, IMSRA has displayed better relation and skill with GPM (IMERG). However, during daily accumulated analysis, it is found that IMSRA tends to underestimate after a specific value. On a daily scale, HE has exhibited better skill with IMSRA.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Donaldson, R., et al. (1975). Objective evaluator of techniques for predicting severe weather events. SOC 45 BEACON ST, BOSTON, MA.
Emanuel, K. (2005). Increasing destructiveness of tropical cyclones over the past 30 years. Nature, 436(7051), 686–688. https://doi.org/10.1038/nature03906
Guinn, T. A., & Schubert, W. H. (1993). Hurricane spiral bands. Journal of the Atmospheric Sciences, 50(20), 3380–3403. https://doi.org/10.1175/1520-0469(1993)050%3c3380:HSB%3e2.0.CO;2
Hong, Y., Adler, R. F., Negri, A., & Huffman, G. J. (2007). Flood and landslide applications of near real-time satellite rainfall products. Natural Hazards, 43(2), 285–294. https://doi.org/10.1007/s11069-006-9106-x
Huang, J.-H., Li, J.-N., Wei, X.-L., Fong, S.-K., & Wang, A.-Y. (2006). Assimilation of QuikScat data and its impact on prediction of Typhoon Vongfong (2002). Zhongshan Daxue Xuebao/Acta Scientiarum Natralium Universitatis Sunyatseni, 45(4), 116–120.
Karyampudi, V. M., Lai, G. S., & Manobianco, J. (1998). Impact of initial conditions, rainfall assimilation, and cumulus parameterization on simulations of hurricane Florence (1988). Monthly Weather Review, 126(12), 3077–3101. https://doi.org/10.1175/1520-0493(1998)126%3c3077:IOICRA%3e2.0.CO;2
Kidd, C., & Levizzani, V. (2011). Status of satellite precipitation retrievals. Hydrology and Earth System Sciences, 15, 1109–1116. https://doi.org/10.5194/hess-15-1109-2011
Krishna, K. M., & Rao, S. R. (2009). Study of the intensity of super cyclonic storm GONU using satellite observations. International Journal of Applied Earth Observation and Geoinformation, 11(2), 108–113. https://doi.org/10.1016/j.jag.2008.11.001
Kumar Singh, A., Tripathi, J. N., Singh, K. K., Singh, V., & Sateesh, M. (2019). Comparison of different satellite-derived rainfall products with IMD gridded data over Indian meteorological subdivisions during Indian Summer Monsoon (ISM) 2016 at weekly temporal resolution. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2019.02.016
Mitra, A. K., Kaushik, N., Kumar Singh, A., Parihar, S., & Bhan, S. C. (2018). Evaluation of INSAT-3D satellite derived precipitation estimates for heavy rainfall events and its validation with gridded GPM (IMERG) rainfall dataset over the Indian region. Remote Sensing Applications: Society and Environment, 9, 91–99. https://doi.org/10.1016/J.RSASE.2017.12.006
Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I: A discussion of principles. Journal of Hydrology, 10(3), 282–290.
Scofield, R. A., & Kuligowski, R. J. (2003). Status and outlook of operational satellite precipitation algorithms for extreme-precipitation events. Weather and Forecasting, 18(6), 1037–1051. https://doi.org/10.1175/1520-0434(2003)018%3c1037:SAOOOS%3e2.0.CO;2
Singh, A. K., Singh, V., Singh, K. K., Tripathi, J. N., Kumar, A., Sateesh, M., & Peshin, S. K. (2018a). Validation of INSAT-3D derived rainfall estimates (HE and IMSRA), GPM (IMERG) and GLDAS 21 model rainfall product with IMD gridded rainfall and NMSG data over IMD’s meteorological sub-divisions during monsoon. Mausam, 69(2), 177–192. https://doi.org/10.13140/RG.2.2.31727.53927
Singh, A. K., Singh, V., Singh, K. K., Tripathi, J. N., Kumar, A., Soni, A. K., et al. (2018b). A case study: Heavy rainfall event comparison between daily satellite rainfall estimation products with IMD gridded rainfall over Peninsular India during 2015 winter monsoon. Journal of the Indian Society of Remote Sensing. https://doi.org/10.1007/s12524-018-0751-9
Sloughter, J. M. L., Raftery, A. E., Gneiting, T., & Fraley, C. (2007). Probabilistic quantitative precipitation forecasting using Bayesian model averaging. Monthly Weather Review. https://doi.org/10.1175/MWR3441.1
Sorooshian, S., Duan, Q., & Gupta, V. K. (1993). Calibration of rainfall-runoff models: Application of global optimization to the Sacramento soil moisture accounting model. Water Resources Research. https://doi.org/10.1029/92WR02617
Varma, A., & Gairola, R. (2015). Algorithm theoretical basis document (modified): Hydro-estimator, SAC/ISRO internal report. Retrieved from 24 August 2018 https://scholar.google.com/scholar?hl=en&q=Varma+AK%2C+Gairola+RM.+2015.+‘Algorithm+theoretical+basis+document+%28modified%29%3A+Hydro‐estimator%2C+SAC%2FISRO+internal+report.’+SAC%2FEPSA%2FAOSG%2FSR%2F04%2F2015%2C+p.+29.+Space+Applications+Centre%2C+ISRO%3A+Ahmedabad%2C+India.
Willoughby, H. E., Marks, F. D., & Feinberg, R. J. (1984). Stationary and moving convective bands in hurricanes. Journal of the Atmospheric Sciences, 41(22), 3189–3211. https://doi.org/10.1175/1520-0469(1984)041%3c3189:SAMCBI%3e2.0.CO;2
Yapo, P. O., Gupta, H. V., & Sorooshian, S. (1996). Automatic calibration of conceptual rainfall-runoff models: Sensitivity to calibration data. Journal of Hydrology. https://doi.org/10.1016/0022-1694(95)02918-4
Yu, F., Zhuge, X.-Y., & Zhang, C.-W. (2011). Rainfall retrieval and nowcasting based on multispectral satellite images. Part II: Retrieval study on daytime half-hour rain rate. Journal of Hydrometeorology, 12(6), 1271–1285. https://doi.org/10.1175/2011JHM1374.1
Zou X, Kuo Y (1996) Rainfall assimilation through an optimal control of initial and boundary conditions in a limited-area mesoscale model. Monthly Weather Review. DOI: https://doi.org/10.1175/1520-0493(1996)124<2859:RATAOC>2.0.CO;2
Acknowledgements
The authors wish to thank DGM, IMD and Director, SAC for allowing them to use INSAT-3D rainfall estimates dataset. The data used in this study were acquired as part of the mission of NASA’s Earth Science Division and archived and distributed by the Goddard Earth Science (GES) Data and Information Services Center (DISC). The GPM satellite estimated rainfall data were provided by the JAXA, Japan and NASA, USA. We thankfully acknowledge the use of GPM data in this project. The RSMC Bulletins were acquired from the archives hosted on RSMC New Delhi’s website.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Kumar, A., Singh, A.K., Tripathi, J.N. et al. Evaluation of INSAT-3D-derived Hydro-Estimator and INSAT Multi-Spectral Rain Algorithm over Tropical Cyclones. J Indian Soc Remote Sens 49, 1633–1650 (2021). https://doi.org/10.1007/s12524-021-01332-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12524-021-01332-7