Refine your search
Collections
Journals
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Jaiswal, Neeru
- Probing atmospheric phenomena using C-band synthetic aperture radar onboard Earth Observation Satellite-04
Abstract Views :140 |
Authors
Affiliations
1 Atmospheric and Oceanic Sciences Group, Earth and Planetary Sciences and Applications Area, Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, IN
1 Atmospheric and Oceanic Sciences Group, Earth and Planetary Sciences and Applications Area, Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, IN
Source
Current Science, Vol 126, No 9 (2024), Pagination: 1118-1125Abstract
Indian Space Research Organisation (ISRO) has successfully launched its second civilian C-band Synthetic Aperture Radar (SAR) mission onboard Earth Observation Satellite-04 (EOS-04). The SAR data monitors and measures various atmospheric features and parameters. In this paper, we report on the investigation of EOS-04 data for several atmospheric phenomena. One of the crucial parameters for studying atmospheric manifestations in SAR data is ocean surface winds and an algorithm for its retrieval has been developed using EOS-04 data. The wind speed products thus generated are evaluated using observations from the Advanced Scatterometer and subsequently used to study atmospheric phenomena like boundary layer structures. The EOS-04 SAR data is also demonstrated for studying structures associated with tropical cyclones, coupling of rain and wind imprints and distinct signatures of an atmospheric front. The study outcomes are used to interpret atmospheric phenomena and understand backscattering signals from EOS-04 SAR. This indicates the possibility and potential of a gamut of atmospheric phenomena that can be probed using EOS-04 SAR dataKeywords
Atmospheric phenomena, EOS-04, SAR.Full Text
- SCATSAT-1 Wind Products for Tropical Cyclone Monitoring, Prediction and Surface Wind Structure Analysis
Abstract Views :390 |
PDF Views:97
Authors
Affiliations
1 Atmospheric Sciences Division, Atmospheric and Oceanic Sciences Group, Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area, Space Applications Centre (ISRO), Ahmedabad 380 015, IN
1 Atmospheric Sciences Division, Atmospheric and Oceanic Sciences Group, Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area, Space Applications Centre (ISRO), Ahmedabad 380 015, IN
Source
Current Science, Vol 117, No 6 (2019), Pagination: 983-992Abstract
The present study discusses the application of near real-time ocean surface wind vectors retrieved from scatterometer instrument, on-board Indian polar satellite SCATSAT-1, for tropical cyclone (TC) analysis and prediction. The real-time tropical cyclogenesis prediction of cyclonic activities in the North Indian Ocean basin has been presented using SCATSAT-1 wind data. The study also demonstrates the utility of high-resolution surface wind products of the scatterometer in monitoring mesoscale-level features of TCs for centre determination, size estimation and analysis of asymmetric wind radii. Impact of SCATSAT-1 winds for TC prediction using numerical weather prediction model has also been discussed. The shortcomings of ocean surface wind observations from space-based scatterometers are addressed, in addition to the sensor requirements for future satellite missions.Keywords
Cyclogenesis, Scatterometer, Tropical Cyclone, Wind Structure.Full Text
References
- Gray, W. M., Global view of the origin of tropical disturbances and storms. Mon. Weather Rev., 1968, 96(10), 669–700.
- Mohapatra, M., Mandal, G. S., Bandyopadhyay, B. K., Tyagi, A. and Mohanty, U. C., Classification of cyclone hazard prone districts of India. Nat. Hazards, 2012, 63, 1601–1620.
- Mohapatra, M., Bandyopadhyay, B. K. and Tyagi, A., Best track parameters of tropical cyclones over the North Indian Ocean: a review. Nat. Hazards, 2012, 63, 1285–1317.
- Chourasia, M., Asrit, R. G. and George, J. P., Impact of cyclone bogussing and regional assimilation on tropical cyclone track and intensity prediction. Mausam, 2013, 64, 135–148.
- Dube, S. K., Poulose, J. and Rao, A. D., Numerical simulation of storm surge associated with severe cyclonic storms in the Bay of Bengal during 2008–11. Mausam, 2013, 64(1), 193–202.
- Gohil, B. S., Sikhakolli, R. and Gangwar, R. K., Development of geophysicsl model functions for Oceansat-2 scatterometer. IEEE GRSL, 2013, 10(2), 377–380.
- Liu, W. T., Hu, H., Song, Y. T. and Tang, W., Improvement of scatterometer wind vectors – impact on hurricane and coastal studies. In Proceedings of WCRP/SCOR Workshop on Intercomparison and Validation of Ocean–Atmosphere Flux Fields. World Meteorological Organization – Publications WMO TD, 2001, 2001, pp. 197–200.
- Quilfen, Y., Chapron, B., Elfouhaily, T., Katsaros, K. and Tourna, J., Observation of tropical cyclones by high-resolution scatterometry. J. Geophys. Res., 1998, 103, 7767–7786.
- Tang, W., Liu, W. T. and Stiles, B. W., Evaluation of highresolution ocean surface vector winds measured by QuikSCAT scatterometer in coastal regions. IEEE Trans. Geosci. Remote Sensing, 2004, 42, 1762–1769.
- Lindsley, R. D., Blodgett, J. R. and Long, D. G., Analysis and validation of high-resolution wind from ASCAT. IEEE Trans. Geosci. Remote Sensing, 2016, 54, 5699–5711.
- Chelton, D. B., Freilich, M. H., Sienkiewicz, J. M. and Ahn, J. M. V., On the use of QuikSCAT scatterometer measurements of surface winds for marine weather prediction. Bull. Am. Meteorol. Soc., 2006, 134, 2055–2071.
- Brennan, M. J., Hennon, C. C. and Knabb, R. D., The operational use of QuikSCAT Ocean surface vector winds at the national hurricane center. Weather Forecast., 2009, 24, 621–645.
- Stoffelen, A. and Cats, J. C., The impact of seasat-A scatterometer data on high-resolution analyses and forecasts: the development of the QEII storm. Mon. Weather Rev., 1991, 119, 2794–2802.
- Atlas, R., Hoffman, R. N., Leidner, S. M. and Sienkiewicz, J., The effects of marine winds from scatterometer data on weather analysis and forecasting. Bull. Am. Meteorol. Soc., 2001, 82, 1965–1990.
- Isaksen, L. and Janssen, P. A., Impact of ERS scatterometer winds in ECMWF’s assimilation system. Q. J. R. Meteorol. Soc., 2004, 130, 1793–1814.
- Rambabu, G., QuikSCAT scatterometer wind data impact on tropical cyclone forecasts by a mesoscale model. Mausam, 2006, 57, 141.
- Singh, R., Kishtawal, C. M., Pal, P. K. and Joshi, P. C., Assimilation of the multisatellite data into the WRF model for track and intensity simulation of the Indian Ocean tropical cyclones. Meteorol. Atmosph. Phys., 2011, 111(3–4), 103–119.
- Jung, B. J., Kim, H. M., Auligne, T., Zhang, X., Zhang, X. and Huang, X. Y., Adjoint derived observation impact using WRF in the western North Pacific. Mon. Weather Rev., 2013, 141, 4080–4097.
- Prasad, S. V., Gupta, A. and Rajagopal, E. N., Impact of OSCAT surface wind data on T574L64 assimilation and forecasting system – a study involving tropical cyclone Thane. Curr. Sci., 2013, 104, 627–631.
- Greeshma, M. M., Srinivas, C. V., Naisu, C. V., Baskaran, R. and Venkatraman, B., Impact of local data assimilation on tropical cyclone predictions over the Bay of Bengal using the ARW model. Annu. Geophys., 2015, 33, 805–828.
- Dodla, V. B., Srinivas, D., Dasari, H. P. and Gubbala, C. S., Prediction of tropical cyclone over North Indian Ocean using WRF model: sensitivity to scatterometer winds, ATOVS and ATMS radiances. Proc. SPIE: Remote Sensing Model. Atmos., Oceans Interact VI, 2016, 9882, 988213.
- Duan, B., Zhang, W., Yang, X., Dai, H. and Yu, Y., Assimilation of typhoon wind field retrieved from scatterometer and SAR based on the Huber norm quality control. Remote Sensing, 2017, 9, 987.
- Liu, W. T., Hu, H. and Yueh, S., Interplay between wind and rain observed in hurricane Floyd, Trans. AGU, 2000, 81, 253–253.
- Hsu, C. S. and Liu, W. T., Wind and pressure fields near tropical cyclone Oliver derived from scatterometer observations. J. Geophys. Res., 1996, 101, 17021–17027.
- Liu, K. S. and Chan, J. C., Size of tropical cyclones as inferred from ERS-1 and ERS-2 data. Mon. Weather Rev., 1999, 127(12), 2992–3001.
- Chan, K. T. and Chan, J. C., Size and strength of tropical cyclones as inferred from QuikSCAT data. Mon. Weather Rev., 2012, 140(3), 811–824.
- Knaff, J. A., Longmore, S. P. and Molenar, D. A., An objective satellite-based tropical cyclone size climatology. J. Climate, 2014, 27, 455–476.
- Jaiswal, N., Ha, D. T. T. and Kishtawal, C. M., Estimation of size of tropical cyclones in the North Indian Ocean using Oceansat-2 scatterometer high-resolution wind products. Theor. Appl. Climatol., 2019, 136, 45–53.
- Klotz, B. W. and Jiang, H., Global composites of surface wind speeds in tropical cyclones based on a 12 year scatterometer database, Geophys. Res. Lett., 2016, 43, 10480–10488.
- Sharp, R. J., Bourassa, M. A. and O’Brien, J. J., Early detection of tropical cyclones using sea winds-derived vorticity. Bull. Am. Meteorol. Soc., 2002, 83, 879–889.
- Hite, M. M., Bourassa, M. M., Cunningham, P., O’Brien, J. J. and Reasor, P. D., Vorticiy based detection of tropical cyclogenesis. J. Appl. Meteorol. Climatol., 2007, 46, 1214–1229.
- Jaiswal, N. and Kishtawal, C. M., Prediction of tropical cyclogenesis using scatterometer data. IEEE Trans. Geosci. Remote Sensing, 2011, 49, 4904–4909.
- Jaiswal, N., Kishtawal, C. M. and Pal, P. K., Prediction of tropical cyclogenesis in North Indian Ocean using OSCAT data. Meteorol. Atmos. Phys., 2013, 119, 137–149.
- Mohapatra, M. and Sharma, M., Characteristics of surface wind structure of tropical cyclones over the North Indian Ocean. J. Earth Syst. Sci., 2015, 124, 1573–1598.
- Wilks, D. S., Statistical Methods in the Atmospheric Sciences: An Introduction. Academic Press, San Diego, California, 1995, 465.
- Goyal, S., Mohapatra, M., Kumar, A., Dube, S. K., Rajendra, K. and Goswami, P., Validation of a satellite-based cyclogenesis technique over the North Indian Ocean. J. Earth Syst. Sci., 2016, 125, 1353–1363.
- Miller, A. and Anthes, R., Meteorology, Merril Publishing, Columbus, 1985.
- Chavas, D. R. and Emanuel, K. A., A QuikSCAT climatology of tropical cyclone size. Geophys. Res. Lett., 2010, 37(18), L18816.
- Ahrens, C. D., Essentials of Meteorology, Wadsworth Publishing, Belmont, 1998, 2nd edn.
- Chan, J. C. and Yip, C. K., Interannual variations of tropical cyclone size over the western North Pacific. Geophys. Res. Lett., 2003, 30(24), 2267.
- DeMaria, M. et al., Improvements to the operational tropical cyclone wind speed probability model. Weather Forecast., 2013, 28, 586–602.
- Sampson, C. R., Wittmann, P. A. and Tolman, H. L., Consistent tropical cyclone wind and wave forecasts for the US Navy. Weather Forecast., 2010, 25, 1293–1306.
- Bender, M. A., Ginis, I., Tuleya, R., Thomas, B. and Marchok, T., The operational GFDL coupled hurricane–ocean prediction system and summary of its performance. Mon. Weather Rev., 2007, 135, 3965–3989.
- Singh, R., Kumar, P. and Pal, P. K., Assimilation of Oceansact-2 scatterometer derived surface winds in the weather research and forecasting model. IEEE Trans. Geosci. Remote Sensing, 2012, 50(4), 1015–1021.
- Kumar, P., Kishtawal, C. M. and Pal, P. K., Sensitivity analysis of high resolution Oceansat-2 scatterometer winds on Thane cyclone simulation. Int. J. Remote Sensing, 2014, 35(23), 7927–7940.
- Kumar, P. and Varma, A. K., Assimilation of INSAT-3D hydro estimator method retrieved for short range weather prediction. Q. J. R. Meteorol. Soc., 2017, 143, 384–394.
- Kumar, P., Kishtawal, C. M. and Pal, P. K., Impact of ECMWF, NCEP, and NCMRWF global model analysis on the WRF model forecast over Indian region. Theor. Appl. Climatol., 2017, 127, 143–151.