Current Science

Open Access Open Access  Restricted Access Subscription Access

Identification of Weather Events from INSAT-3D RGB Scheme using RAPID Tool


Affiliations
1 National Satellite Meteorological Centre, India Meteorological Department, New Delhi 110 003, India
2 Banaras Hindu University, Varanasi 221 005, India
 

Real-time analysis of products and information dissemination (RAPID), a web-based quick visualization and analysis tool for INSAT satellite data has been presented for identification of weather events. The combination of channels using red-green-blue (RGB) composites of INSAT-3D satellite and its physical significant value content is presented. The solar reflectance and brightness temperatures are the major components of this scheme. The shortwave thermal infrared (1.6 μm), visible (0.5 μm) and thermal IR channels (10.8 μm) representing cloud microstructure is known as Day Microphysics (DMP) and the brightness temperature (BT) differences between 10.8, 12.0 and 3.9 μm is referred to as Night Microphysics (NMP). The thresholds technique have been developed separately for both the RGB products of two years (2015-17 of December to February) of data for the identification of fog, snow and low clouds. The validation of these thresholds has been carried out against in situ visibility data from IMD observatories. The RGBs, i.e. DMP and NMP have a reasonable good agreement with ground-based observations and Moderate Resolution Imaging Spectroradiometer (MODIS) data. This threshold technique yields a very good probability of fog detection more than 94% and 85% with acceptable false alarm conditions less than 8% and 10% for DMP and NMP respectively. The technique has significantly minimized the misclassification between low clouds, snow, and fog and found useful for day-to-day weather forecast.

Keywords

INSAT-3D, RAPID, DMP, NMP, RGB.
User
Notifications
Font Size

  • Gultepe, I. et al., The fog remote sensing and modelling (FRAM) field project and preliminary report. Bull. Am. Meteorol. Soc., 2009, 90(3), 341-359.

  • Chaurasia, S. and Gohil, B. S., An objective method for detecting night time fog using MODIS data over northern India. J. Geomat., 2016, 10.

  • Tiwari, S., Payra, S., Mohan, M., Verma, S. and Bhisht, D. S., Visibility degradation during foggy period due to anthropogenic urban aerosol at Delhi, India. Atmos. Pollut. Res., 2011, 2, 116-120.

  • Mitra, A. K., Sankar Nath and Sharma, A. K., Fog forecasting using rule-based fuzzy inference system. J. Indian Soc. Remote Sensing, 2008, 36(3), 243.

  • Roy Bhowmik, S. K., Sud, A. M. and Singh, C., Forecasting fog over Delhi - an objective method. MAUSAM-55, 2004, 2, 313, 322.

  • . Brij Bhusan, Trivedi, H. K. and Bhatia, R. C., On the persistence of fog over northern parts of India. MAUSAM-54, 2003, 4, 851-860.

  • Berndt, E. B., Molthan, A. L., Vaughan, W. W. and Fuell, K. K., Transforming satellite data into weather forecasts. AGU EOS, 2017, 98; https://doi.org/10.1029/2017EO064449.

  • Lensky, I. M. and Rosenfeld, D., Clouds-Aerosols-Precipitation Satellite Analysis Tool (CAPSAT). Atmos. Chem. Phys., 2008, 8, 6739-6753.

  • Inoue, T., An instantaneous delineation of convective rainfall areas using split window data of NOAA-7 AVHRR. J. Meteor. Soc. Jpn, 1987, 65, 469-481.

  • Lensky, I. M. and Rosenfeld, D., Satellite-based insights into precipitation formation processes in continental and maritime convective clouds at nighttime. J. Appl. Meteor., 2003, 42, 1227-1233.

  • Lensky, I. M. and Rosenfeld, D., A night rain delineation algorithm for infrared satellite data based on microphysical considerations. J. Appl. Meteor., 2003, 42, 1218-1226.

  • Eyre, J. R., Brownscombe, J. L. and Allam, R. J., Detection of fog at night using advanced very high resolution radiometer. Meteorol. Mag., 1984, 113, 266-271.

  • Bader, M. J., Forbes, G. S., Grant, J. R., Lilly, R. B. E. and Waters, J., Images in Weather Forecasting, Cambridge University Press, 1995, p. 493.

  • Ellord, G. P., Advances in the detection and analysis of fog at night using GOES multi spectral infrared imagery. Weather Forecasting, 1995, 10, 606-619.

  • Bendix, J., A satellite-based climatology of fog and low-level stratus in Germany and adjacent areas. Atmos. Res., 2002, 64, 3-18.

  • Bendix, J. and Bachmann, M., A method for detection of fog using AVHRR-imagery of NOAA satellites suitable for operational purposes. Meteorol. Rund., 1991, 43, 169-178 (in German).

  • Chaurasia, S., Sathiyamoorthy, V., Paul Shukla, B., Simon, B., Joshi, P. C. and Pal, P. K., Night time fog detection using MODIS data over Northern India. Meteorol. Appl., 2011, 8(4), 483-494.


Abstract Views: 195

PDF Views: 82




  • Identification of Weather Events from INSAT-3D RGB Scheme using RAPID Tool

Abstract Views: 195  |  PDF Views: 82

Authors

A. K. Mitra
National Satellite Meteorological Centre, India Meteorological Department, New Delhi 110 003, India
Shailesh Parihar
National Satellite Meteorological Centre, India Meteorological Department, New Delhi 110 003, India
R. Bhatla
Banaras Hindu University, Varanasi 221 005, India
K. J. Ramesh
National Satellite Meteorological Centre, India Meteorological Department, New Delhi 110 003, India

Abstract


Real-time analysis of products and information dissemination (RAPID), a web-based quick visualization and analysis tool for INSAT satellite data has been presented for identification of weather events. The combination of channels using red-green-blue (RGB) composites of INSAT-3D satellite and its physical significant value content is presented. The solar reflectance and brightness temperatures are the major components of this scheme. The shortwave thermal infrared (1.6 μm), visible (0.5 μm) and thermal IR channels (10.8 μm) representing cloud microstructure is known as Day Microphysics (DMP) and the brightness temperature (BT) differences between 10.8, 12.0 and 3.9 μm is referred to as Night Microphysics (NMP). The thresholds technique have been developed separately for both the RGB products of two years (2015-17 of December to February) of data for the identification of fog, snow and low clouds. The validation of these thresholds has been carried out against in situ visibility data from IMD observatories. The RGBs, i.e. DMP and NMP have a reasonable good agreement with ground-based observations and Moderate Resolution Imaging Spectroradiometer (MODIS) data. This threshold technique yields a very good probability of fog detection more than 94% and 85% with acceptable false alarm conditions less than 8% and 10% for DMP and NMP respectively. The technique has significantly minimized the misclassification between low clouds, snow, and fog and found useful for day-to-day weather forecast.

Keywords


INSAT-3D, RAPID, DMP, NMP, RGB.

References





DOI: https://doi.org/10.18520/cs%2Fv115%2Fi7%2F1358-1366