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Chakraborty, Abhisek
- Probing atmospheric phenomena using C-band synthetic aperture radar onboard Earth Observation Satellite-04
Abstract Views :141 |
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
- Post-Launch Calibration–Validation and Data Quality Evaluation of SCATSAT-1
Abstract Views :340 |
PDF Views:101
Authors
Raj Kumar
1,
Suchandra A. Bhowmick
1,
Abhisek Chakraborty
1,
Anuja Sharma
1,
Shweta Sharma
1,
M. Seemanth
1,
Maneesha Gupta
1,
Prantik Chakraborty
1,
Jalpa Modi
1,
Tapan Misra
1
Affiliations
1 Space Applications Centre, ISRO, Ahmedabad 380 015, IN
1 Space Applications Centre, ISRO, Ahmedabad 380 015, IN
Source
Current Science, Vol 117, No 6 (2019), Pagination: 973-982Abstract
Here we provide a brief description of the post-launch data quality evaluation and calibration–validation chain of the SCATSAT-1, the second scatterometers mission of Indian Space Research Organisation. This chain is of absolute importance to monitor the satellite health and its impact on its measurements. It also provides us overview of the suitability of the data for various applications. The results show that the SCATSAT instrument is having nominal behaviour, the measurements are of very high quality and is comparable to the reference mission QuikSCAT. The ocean surface winds derived using SCATSAT-1 are having errors less than 1 m/s and hence it is suitable for all operational meteorological and oceanographic applications.Keywords
Calibration, Data Quality Evaluation, Scatterometers, Validation, Wind Vectors.Full Text
References
- Kumar, R., Bhowmick, S. A., Babu, K. N., Nigam, R. and Sarkar, A., Relative calibration of scatterometer backscattering coefficient using natural land targets – a preparatory study for OCEANSAT-2 scatterometer. IEEE Trans. Geosci. Remote Sensing, 2011, 49(6), 2268–2273.
- Bhowmick, S. A., Kumar, R. and Kiran Kumar, A. S., Crosscalibration of the OceanSAT-2 scatterometer with QuikSCAT scatterometer using natural terrestrial targets. IEEE Trans. Geosci. Remote Sensing, 2014, 52(6), 3393–3398.
- Kumar, R., Chakraborty, A., Parekh, A., Sikhakolli, R., Gohil, B. S. and Kiran Kumar, A. S., Evaluation of Oceansat-2-derived ocean surface winds using observations from global buoys and other scatterometers. IEEE Trans. Geosci. Remote Sensing, 2013, 51(5), 2571–2576.
- Chakraborty, A., Deb, S. K., Sikhakolli, R., Gohil, B. S. and Kumar, R., Intercomparison of OSCAT winds with numericalmodelgenerated winds. IEEE Geosci. Remote Sensing Lett., 2013, 10(2), 260–262.
- Gupta, M., Desai, Y. and Kartikeyan, B., Strategy for quality evaluation of OSCAT data. In Fourth International Conference of Environmental Research, Surat, Gujarat, India, 15–17 December 2011.
- McPhaden, M. J. et al., RAMA: the research moored array for African–Asian–Australian monsoon analysis and prediction. Bull. Am. Meteorol. Soc., 2009, 90, 459–480.
- Meindl, E. A. and Hamilton, G. D., Programs of the National Data Buoy Center. Bull. Am. Meteorol. Soc., 1992, 73(7), 985–993.
- Bourles, B. et al., The PIRATA program history, accomplishments and future directions. Cover story. Bull. Am. Meteorol. Soc., 2008, 89(8), 1111–1125.
- Prasad, V. S. and Indira Rani, S., Data pre-processing for NCMRWF Unified Model (NCUM): Version 2. NCMRWF research report, NMRF/RR/01/2014, 2014; http://www.ncmrwf.gov.in/ncum_obstore_v2.pdf
- De Kloe, J., Stoffelen, A. and Verhoef, A., Improved use of scatterometer measurements by using stress-equivalent reference winds. IEEE J. Sel. Top. Appl. Earth, 2017, 10(5), 2340–2347; doi: 10.1109/JSTARS.2017.2685242.
- Development of Windspeed Retrieval Model using RISAT-1 SAR Cross-Polarized Observations
Abstract Views :269 |
PDF Views:120
Authors
Affiliations
1 Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area, Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, IN
1 Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area, Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, IN
Source
Current Science, Vol 118, No 8 (2020), Pagination: 1282-1286Abstract
In this study, a method for retrieving ocean surface wind speed using C-band cross-polarization SAR observations has been outlined. A linear least square technique has been used to develop a Geophysical Model Function (GMF), C2P. The GMF was derived using NRCS observations from RISAT-1 and wind-speed observations from ASCAT. The correlation between observed and simulated NRCS values obtained from C2P was 0.66, with a negative bias of 0.01 dB and the corresponding ischolar_main mean square difference of 1.13 dB. Subsequently, the developed GMF was tested with 774 RISAT-1 MRS datasets to retrieve wind speed along the Indian coast and also of the tropical cyclone ‘Megh’. The measured intensity and radius of maximum wind speed were 30 m s–1 and 16.65 km respectively. Subsequently, the retrieved wind speed was validated with ASCAT wind-speed observations. The statistical comparison of RISAT-1 and ASCAT observed wind speed showed negative biases of 0.90 and 0.34 m s–1 with the corresponding RMSD of 2.11 and 1.77 m s–1 respectively, for CMOD5.N and C2P. The developed GMF C2P showed 16% more accuracy than that of CMOD5.N.Keywords
Cross-polarization, Geophysical Model Function, Ocean Surface, Wind Speed Retrieval.Full Text
References
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- Zhang, B. and Perrie, W., Cross-polarized synthetic aperture radar: a new potential technique for hurricanes. Bull. Am. Meteorol. Soc., 2012, 93, 531–541; doi:10.1175/BAMS-D-11-00001.1.
- Horstmann, J., Thompson, D. R., Monaldo, F., Iris, S. and Graber, H. C., Can synthetic aperture radars be used to estimate hurricane force winds? Geophys. Res. Lett., 2005, 32(2), L22801-1– L22801-5.
- Shen, H., Perrie, W. and He, Y., A new hurricane wind retrieval algorithm for SAR images. Geophys. Res. Lett., 2006, 33(21), L21812-1–L21812-5.
- Vachon, P. W. and Wolfe, J., C-band cross-polarization wind speed retrieval. IEEE Geosci. Remote Sensing Lett., 2011, 8(3), 456–459.
- Hwang, P. A., Zhang, B. and Perrie, W., Depolarized radar return for breaking wave measurement and hurricane wind retrieval. Geophys. Res. Lett., 2010, 37(1), L01604-1–L01604-4.
- Monaldo, F. M., Jackson, C. and Li, X., On the use of Sentinel-1 cross-polarization imagery for wind speed retrieval. Int. Geosci. Remote Sensing Symp., 2017, 392–395.
- Shao, W. et al., Development of wind speed retrieval from cross-polarization Chinese Gaofen-3 synthetic aperture radar in typhoons. Sensors, 2018, 18, 412.
- Gao, Y. et al.,A wind speed retrieval model for Sentinel-1A EW mode cross-polarization images. Remote Sensing, 2019, 11, 153; doi:10.3390/rs11020153.
- Mouche, A. et al., Copolarized and cross-polarized SAR measurements for high-resolution description of major hurricane wind structures: application to Irma category 5 hurricane. J. Geophys. Res. Oceans, 2019, 124, 3905–3922; https://doi.org/10.1029/2019 JC015056.
- Jagdish et al., An interesting case of persistent rain cells observed by RISAT-1 SAR over the Indian Ocean during a pair of depres-sion–cyclone interactions (August, 2012). Remote Sensing Lett., 2019, 10(6), 545–552; doi:10.1080/2150704X.2019.1579377.
- Jagdish et al., Atmospheric fronts using RISAT-1 SAR data: case studies for shear lines. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sensing, 2018, 11(12), 4711–4717; doi:10.1109/ JSTARS.2018.2878753.
- Zhang, B. et al., Tropical cyclone vector winds from C-band dual-polarization synthetic aperture radar. Int. Geosci. Remote Sensing Symp., 2014, 3522–3525.
- Jiang, Z. et al., A damped Newton variational inversion method for SAR wind retrieval. J. Geophys. Res., 2016, 122(2), 823–845.
- Jagdish, Kumar, S. V. V. A., Chakraborty, A. and Kumar, R., Validation of wind speed retrieval from RISAT-1 SAR images of the North Indian Ocean. Remote Sensing Lett., 2018, 9(5), 421– 428; doi:10.1080/2150704X.2018.1430392.