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Journal articles on the topic 'INSAT-3D Imager'

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Published: 3 June 2025
Last updated: 20 July 2025

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1

Prakash, S., D. Jindal, N. Badal, B. Kartikeyan, and B. Gopala Krishna. "Radiometric Quality Evaluation of INSAT-3D Imager Data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 299–305. http://dx.doi.org/10.5194/isprsarchives-xl-8-299-2014.

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INSAT-3D is an advanced meteorological satellite of ISRO which acquires imagery in optical and infra-red (IR) channels for study of weather dynamics in Indian sub-continent region. In this paper, methodology of radiometric quality evaluation for Level-1 products of Imager, one of the payloads onboard INSAT-3D, is described. Firstly, overall visual quality of scene in terms of dynamic range, edge sharpness or modulation transfer function (MTF), presence of striping and other image artefacts is computed. Uniform targets in Desert and Sea region are identified for which detailed radiometric perfo
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Patel, P., H. Bhatt, and A. K. Shukla. "Absolute Vicarious Calibration of recently launched Indian Meteorological Satellite: INSAT-3D imager." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 291–98. http://dx.doi.org/10.5194/isprsarchives-xl-8-291-2014.

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Looking towards the advancements and popularity of remote sensing and an ever increasing need for the development of a variety of new and complex satellite sensors, it has become even more essential to continually upgrade the ability to provide absolute calibration of sensors. This article describes a simple procedure to implement post-launch calibration for VIS and SWIR channels of INSAT-3D imager over land site (Little Rann of Kutch (ROK), Gujarat) on three different days to account for characterization errors or undetermined post-launch changes in spectral response of the sensor. The measur
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Jindal, D., S. Prakash, J. Sanghvi, B. Kartikeyan, and B. Gopala Krishna. "INSAT-3D Quality Analysis System (i3dQAS)." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 27, 2014): 257–63. http://dx.doi.org/10.5194/isprsarchives-xl-8-257-2014.

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INSAT-3D, an advanced meteorological satellite of ISRO carrying Imager and Sounder payloads is designed for acquiring data required for detailed climatological applications. At established data centres, Raw (L-0) data is processed to generate systematically corrected (Level-1) and geo-physical (Level-2) products which are further disseminated for use in climate related studies. Data quality evaluation (DQE) of basic data products of INSAT-3D is being operationally carried out at MOSDAC, Ahmedabad to monitor in-orbit sensor health, quantify data quality and provide feedback to various missions
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4

Venkat Ratnam, Madineni, Alladi Hemanth Kumar, and Achuthan Jayaraman. "Validation of INSAT-3D sounder data with in situ measurements and other similar satellite observations over India." Atmospheric Measurement Techniques 9, no. 12 (2016): 5735–45. http://dx.doi.org/10.5194/amt-9-5735-2016.

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Abstract. To date, several satellites measurements are available which can provide profiles of temperature and water vapour with reasonable accuracies. However, the temporal resolution has remained poor, particularly over the tropics, as most of them are polar orbiting. At this juncture, the launch of INSAT-3D (Indian National Satellite System) by the Indian Space Research Organization (ISRO) on 26 July 2013 carrying a multi-spectral imager covering visible to long-wave infrared made it possible to obtain profiles of temperature and water vapour over India with higher temporal and vertical res
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Gangwar, Rishi Kumar, and Pradeep Kumar Thapliyal. "Variational Based Estimation of Sea Surface Temperature from Split-Window Observations of INSAT-3D/3DR Imager." Remote Sensing 12, no. 19 (2020): 3142. http://dx.doi.org/10.3390/rs12193142.

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Infrared (IR) radiometers from geostationary (GEO) satellites have an advantage over low-earth orbiting (LEO) satellites as they provide continuous observations to monitor the diurnal variations in the sea surface temperature (SST), typically better than 30-minute interval. However, GEO satellite observations suffer from significant diurnal and seasonal biases arising due to varying sun-earth-satellite geometry, leading to biases in SST estimates from conventional non-linear regression-based algorithms (NLSST). The midnight calibration issue occurring in GEO sensors poses a different challenge
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Mishra, M. K., G. Rastogi, and P. Chauhan. "Operational Retrieval of aerosol optical depth over Indian subcontinent and Indian Ocean using INSAT-3D/Imager product validation." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 277–82. http://dx.doi.org/10.5194/isprsarchives-xl-8-277-2014.

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Aerosol optical depth (AOD) over Indian subcontinent and Indian Ocean region is derived operationally for the first time from the geostationary earth orbit (GEO) satellite INSAT-3D Imager data at 0.65 μm wavelength. Single visible channel algorithm based on clear sky composites gives larger retrieval error in AOD than other multiple channel algorithms due to errors in estimating surface reflectance and atmospheric property. However, since MIR channel signal is insensitive to the presence of most aerosols, therefore in present study, AOD retrieval algorithm employs both visible (centred at 0.65
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7

Dey, I., M. V. Shukla, P. K. Thapliyal, and C. M. Kishtawal. "Evaluation of operational INSAT-3D UTH product, using Radiosonde, Meteosat-7 and NCEP Analysis." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 27, 2014): 247–52. http://dx.doi.org/10.5194/isprsarchives-xl-8-247-2014.

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Recently available satellite observations from the water vapor channel (6.5–7.1 μm) of the Imager on-board India's geostationary satellite, INSAT-3D have been used to estimate Upper Tropospheric Humidity (UTH). In this study, operationally retrieved UTH product has been compared and validated for the period of Jan–Jun, 2014, using in-situ and satellite measurements. In-situ measurements of UTH have been indirectly derived using humidity profiles obtained from a network of radiosonde stations from NOAA/ESRL database. Meteosat-7 UTH products have been used as satellite measur
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8

Satapathy, J., P. K. Thapliyal, M. V. Shukla, and C. M. Kishtawal. "Synergistic use of Imager Window observations for Cloud Clearing of Sounder Observation for INSAT-3D." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 315–20. http://dx.doi.org/10.5194/isprsarchives-xl-8-315-2014.

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The retrieval of atmospheric temperature and water vapor profiles from infrared Sounder are severely limited by the presence of cloud. Therefore, retrieval from infrared sounding observations is performed only over clear-sky atmospheric conditions. The probability of finding a clear-sky pixel at spatial resolution of 10 km is found to be very small globally. This study presents a quantitative analysis of the clear-sky probability that is carried out for different months over the Indian region for INSAT-3D Sounder. The probability of a clear-sky is found to be ~7 % for the field of view of 10 k
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9

Kumawat, Nandkishor, K. N. Babu, Mehul R. Pandya, Saurabh Tripathi, and V. Sathiyamoorthy. "Towards Accurate Radiometric Calibration of INSAT-3D and INSAT-3DR IMAGER: Addressing Uncertainty and Error Sources." International Journal of Remote Sensing 44, no. 20 (2023): 6298–328. http://dx.doi.org/10.1080/01431161.2023.2265541.

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10

Ojha, Satya P., and Randhir Singh. "Physical retrieval of sea-surface temperature from INSAT-3D imager observations." Tellus A: Dynamic Meteorology and Oceanography 71, no. 1 (2019): 1554421. http://dx.doi.org/10.1080/16000870.2019.1657767.

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11

M.Sakthivel*1, Manoj Hari2 &. Dharani G3. "ATMOSPHERIC CORRECTION OF VISIBLE CHANNEL OF SATELLITE IMAGE FROM INSAT-3D IMAGER USING SECOND SIMULATION OF THE SATELLITE SIGNAL IN THE SOLAR SPECTRUM - 6S MODEL." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 5, no. 11 (2018): 137–49. https://doi.org/10.5281/zenodo.1493914.

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Atmospheric correction is an important pre-processing step required in many satellite remote sensing applications. Although there are several available atmospheric correction algorithms, there is limited literature available that examines their effectiveness using in situ measurements from spectroradiometers to retrieve reflectance. The aim of this study is to atmospherically correct the satellite image using Second Simulation of a Satellite Signal in the Solar Spectrum-6S Radiative Transfer Model and to conduct an accuracy assessment of the results using in-situ measurements from spectroradio
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12

Radhakrishna, Basivi, T. Narayana Rao, and K. Saikranthi. "Spatial Coherence of Water Vapor and Rainfall over the Indian Subcontinent during Different Monsoon Seasons." Journal of Hydrometeorology 20, no. 1 (2019): 45–58. http://dx.doi.org/10.1175/jhm-d-18-0069.1.

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Abstract Spatial coherency in atmospheric water vapor and rainfall and their association have been studied over the Indian subcontinent utilizing high spatiotemporal resolution data. Total column water vapor (TCWV) values derived from the Indian National Satellite (INSAT) system series (INSAT-3D) are first evaluated against data from an International GNSS Service (IGS) GPS receiver at Bangalore and the Global Precipitation Measurement Microwave Imager (GMI). The bias, correlation coefficient, and RMSE of TCWV between INSAT-3D and GMI show that, except for the south Bay of Bengal, the datasets
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13

Chaurasia, Sasmita, and Rajendra Kumar Jenamani. "Detection of Fog Using Temporally Consistent Algorithm With INSAT-3D Imager Data Over India." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10, no. 12 (2017): 5307–13. http://dx.doi.org/10.1109/jstars.2017.2759197.

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14

Mishra, Manoj K., Prakash Chauhan, and Arvind Sahay. "Detection of Asian dust storms from geostationary satellite observations of the INSAT-3D imager." International Journal of Remote Sensing 36, no. 18 (2015): 4668–82. http://dx.doi.org/10.1080/01431161.2015.1084432.

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15

Singh, Randhir, Charu Singh, Satya P. Ojha, A. Senthil Kumar, C. M. Kishtawal, and A. S. Kiran Kumar. "Land surface temperature from INSAT-3D imager data: Retrieval and assimilation in NWP model." Journal of Geophysical Research: Atmospheres 121, no. 12 (2016): 6909–26. http://dx.doi.org/10.1002/2016jd024752.

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16

Parihar, Shailesh, A. K. Mitra, M. Mohapatra, L. S. Rathore, R. Bhatla, and R. S. Singh. "Use of the Moon to promote on-board calibration of INSAT-3D imager visible channel." Indian Journal of Physics 93, no. 10 (2019): 1241–46. http://dx.doi.org/10.1007/s12648-019-01380-3.

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17

Pandya, Mehul R., Vishal Pathak, Nitesh Kaushik, and Himanshu J. Trivedi. "Algorithm for the Estimation of Continental Scale Land-Surface Broadband Albedo from INSAT-3D Imager Data." Journal of the Indian Society of Remote Sensing 49, no. 9 (2021): 2093–102. http://dx.doi.org/10.1007/s12524-021-01371-0.

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18

Gangwar, Rishi Kumar, and P. K. Thapliyal. "Retrieval of Total Precipitable Water from Thermal Infrared Observations of Insat-3D Imager over the Ocean." Current Science 119, no. 2 (2020): 382. http://dx.doi.org/10.18520/cs/v119/i2/382-389.

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19

KUMAR, AMIT, VIRENDRA SINGH, SUNIL MUKHERJEE, and RANDHIR SINGH. "Quality assessment of Outgoing Longwave Radiation (OLR) derived from INSAT-3D Imager: Impact of GSICS correction." MAUSAM 70, no. 2 (2021): 309–20. http://dx.doi.org/10.54302/mausam.v70i2.182.

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20

Kumar, Prashant, and Munn V. Shukla. "Assimilating INSAT‐3D Thermal Infrared Window Imager Observation With the Particle Filter: A Case Study for Vardah Cyclone." Journal of Geophysical Research: Atmospheres 124, no. 4 (2019): 1897–911. http://dx.doi.org/10.1029/2018jd028827.

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21

VIJAYALAKSHMI, SP. "IMSRA rainfall estimation validation and variability over Indian region during GAJA cyclone." MAUSAM 74, no. 1 (2022): 129–40. http://dx.doi.org/10.54302/mausam.v74i1.4659.

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Rainfall during a cyclonic event is very high compared to the monsoon seasons, which bring heavy damage to the lives of humans and cattle and other severe bruises. To prevent such conditions, accurate prediction of rainfall is necessary. 
 Before prediction, there should be adequate knowledge about how rainfall is distributed spatially, how it varies from time to time, etc., This study attempts to infer the rainfall distribution during GAJA cyclone over the Indian region with the INSAT-3D IMAGER data. The IMRSRA and IMC (IMSRA-Modified) datasets are used for rainfall retrieval during land
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22

Mishra, Manoj K. "Retrieval of Aerosol Optical Depth From INSAT-3D Imager Over Asian Landmass and Adjoining Ocean: Retrieval Uncertainty and Validation." Journal of Geophysical Research: Atmospheres 123, no. 10 (2018): 5484–508. http://dx.doi.org/10.1029/2017jd028116.

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23

Sharma, Rahul, Shiv Kumar, RK Giri, and Laxmi Pathak. "Monitoring of high temperature weather episodes with satellite data." MAUSAM 73, no. 4 (2022): 763–74. http://dx.doi.org/10.54302/mausam.v73i4.5885.

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INSAT-3D/3R Geosatellites are in orbit at 74 and 82 °E positins oerating in staggring mode of 15 minute temporal frequency providing contineous coverage in high spatial domain throgh 6 channel Imager. Satellite derived geophysical parameters like Outgoing Long wave Radiation (OLR), Land Surface Temperature (LST) and Upper Trpospheric Humidity etc are found to very useful in monitoring the high temperature weather episodes affecting almost entie country like Punjab, Hariyam Rajastham Uttar Pradesh (UP), Bihar, Madhra Pradesh (MP)P, , Maharastra, Andhra Paradesh (AP) , Telengana, Tamilnadu Oriss
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PANDYA, M. R., D. B. SHAH, H. J. TRIVEDI, and S. PANIGRAHY. "Simulation of at-sensor radiance over land for proposed thermal channels of Imager payload onboard INSAT-3D satellite using MODTRAN model." Journal of Earth System Science 120, no. 1 (2011): 19–25. http://dx.doi.org/10.1007/s12040-011-0014-4.

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Kiranpure, Ayush. "Cyclone Intensity Prediction Using Deep Learning on INSAT-3D IR Imagery: A Comparative Analysis." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45392.

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This study investigates the effectiveness of deep learning techniques in accurately estimating tropical cyclone intensity using infrared (IR) imagery from the INSAT-3D satellite. We assess the performance of three models—Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and a hybrid CNN-RNN model—comparing them against traditional machine learning methods like Support Vector Machines (SVM) and Random Forests (RF). Results demonstrate that deep learning models significantly outperform traditional approaches, with the CNN-RNN model achieving the highest accuracy. These findings
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Narendra Babu, A., K. S. V. Supraja, A. Praneetha, G. Manoj, G. Kota Lokesh, and P. S. Brahmanandam. "A DEEP LEARNING MODEL FOR EFFECTIVE CYCLONE INTENSITY ESTIMATION." Journal of Engineering, Management and Information Technology 2, no. 3 (2024): 161–68. http://dx.doi.org/10.61552/jemit.2024.03.007.

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Tropical cyclones are extremely dangerous weather phenomena that cause significant damage to human life, property, economy, agriculture, and development. Currently, various methods are being used to estimate cyclone intensity. One such method is the objective deviation angle variance technique, which estimates the intensity of tropical cyclones from satellite infrared imagery by performing statistical analysis of the brightness of those images. The limitation of this method is that it requires images with properly marked cyclone centers. Another method to estimate cyclone intensity involves fe
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Gangwar, Rishi Kumar, and Pradeep Kumar Thapliyal. "Optimal estimation of total precipitable water from INSAT‐3D / 3DR Imagers." Quarterly Journal of the Royal Meteorological Society 148, no. 742 (2021): 466–79. http://dx.doi.org/10.1002/qj.4215.

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Soe, Thet Mar, Fangmin Zhang, Pradeep Thapliyal, Kyaw Lwin Oo, and Tin Mar Htay. "Enhancing Severe Weather Prediction Over Myanmar Using INSAT-3D Sounder and Hyperspectral Satellite Observations." Journal of Sustainability and Environmental Management 3, no. 3 (2024): 129–37. https://doi.org/10.3126/josem.v3i3.76858.

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This study investigates the application of INSAT-3D Sounder and hyperspectral sounders from polar-orbiting satellites (AIRS, IASI, and CrIS) in predicting severe weather over Myanmar, with a focus on two severe weather case studies from Rakhine State. Data on the stability index (Lifted Index, LI) and Total Precipitable Water (TPW) from INSAT-3D and hyperspectral sounders were analyzed following the methodology of Schmit et al. (2009). The workflow involved downloading data from various sources (MOSDAC, NCDC, Mirador) and generating LI and TPW images. Numerical values for specific stations wer
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TOMAR, C. S., RAJIV BHATLA, V. K. SONI, and R. K. GIRI. "Convective weather event monitoring with multispectral image analysis of INSAT-3D/3DR over Indian domain." MAUSAM 74, no. 4 (2023): 1113–30. http://dx.doi.org/10.54302/mausam.v74i4.6176.

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Pre-monsoon season (March to May) is very challenging as convective activities prevails almost throughout the country. Most of the Rabi crops harvesting affected and sometimes suffer great losses due to sudden rain or high winds. INSAT-3D/3DR satellite images and derived products provides continuous support to the forecasters and end users in monitoring such events and thereafter significant value addition improves the prediction. This information was found to be very useful where actual ground based or upper air observations are limited or especially over data sparse or difficult terrain regi
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Shukla, Bipasha Paul, and P. K. Pal. "Automatic smoke detection using satellite imagery: preparatory to smoke detection from Insat‐3D." International Journal of Remote Sensing 30, no. 1 (2008): 9–22. http://dx.doi.org/10.1080/01431160802226059.

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A, Pugazhenthi, and Lakshmi Sutha Kumar. "Automatic cloud segmentation from INSAT-3D satellite image via IKM and IFCM clustering." IET Image Processing 14, no. 7 (2020): 1273–80. http://dx.doi.org/10.1049/iet-ipr.2018.5271.

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32

Subbiah, Manthira Moorthi, Debajyoti Dhar, and Sivakumar Ramamoorthy. "Automatic Registration of INSAT-3D Daily Images Using Mutual Information and Stochastic Optimization Technique." Journal of the Indian Society of Remote Sensing 46, no. 9 (2018): 1527–35. http://dx.doi.org/10.1007/s12524-018-0820-0.

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Manthira Moorthi, Subbiah, and Ramamoorthy Sivakumar. "COMPARISON OF CONTOUR FEATURE BASED AND INTENSITY BASED INSAT-3D MET IMAGES COREGISTRATION FOR SUB PIXEL ACCURACIES." ICTACT Journal on Image and Video Processing 8, no. 4 (2018): 1731–38. http://dx.doi.org/10.21917/ijivp.2018.0243.

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34

Yadav, Monu, and Laxminarayan Das. "Detecting Tropical Cyclone from the basic overview of life cycle of Extremely Severe Cyclonic Storm, Tauktae." Revista de Climatología 23 (December 26, 2023): 149–56. http://dx.doi.org/10.59427/rcli/2023/v23.149-156.

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The Extremely Severe Cyclonic Storm (ESCS) Tauktae, which made landfall on the Gujarat coast on May 17, 2021, is discussed in the current study. The analysis is based on INSAT-3D and passive microwave (PMW) images, fo-cusing on the cyclone’s eye characteristics and intensity. The satellite imag-es and products are utilized to determine the cyclone's intensity and the spe-cific characteristics of its eye. The study showcases the variations in intensi-ty and eye features of the cyclone throughout its life span, providing insights into the process of tropical cyclone intensification. The paper is
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Jadhav, Jagruti, Shilpa Mathur, Sampada Bhonde, and Pranjali Sankhe. "Deep Learning Based Cyclone Intensity Estimation." International Journal of Environmental Sciences 11, no. 6s (2025): 469–74. https://doi.org/10.64252/1eycka02.

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Background/Objectives: Cyclones are perilous, and disaster management depends heavily on accurate intensity estimation. The primary objective of this research is to develop a deep learning-based technique that uses satellite imagery to assess cyclone intensity. The research involves use of Capsule Convolutional Neural Networks (Caps Nets), which are intended to handle complex patterns and preserve spatial hierarchies. With the ability to preserve positional information and enhance the perception of spatial relationships in satellite imagery, Caps Nets overcome certain drawbacks of conventional
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Mehrin, Sariya, Md Mahbub Jahan Khan, Md Abdul Mannan, and Khan Md Golam Rabbani. "Simulation of Radiation Fog Events over Dhaka, Bangladesh Using WRF Model and Validation with METAR and Radiosonde Data." Dhaka University Journal of Earth and Environmental Sciences 9, no. 1 (2021): 39–47. http://dx.doi.org/10.3329/dujees.v9i1.54860.

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Fog causes severe hazards in the fields of aviation, transportation, agriculture and public health over Dhaka, Bangladesh during the winter season every year. The characterization of fog occurrences, its onset, duration and dissipation time over Hazrat Shahjalal International Airport, Dhaka are the topics of interest in the present study. Attempts have therefore been made to investigate the climatological perspectives of fog over Dhaka, Bangladesh by conducting two selected dense fog events occurred during 24-25 December 2019 and 14-15 January 2020 using WRF-ARW model. The model performance is
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Gaurav, S., and P. Jindal. "RADIATIVE TRANSFER MODEL SIMULATIONS TO DETERMINE THE NIGHT TIME FOG DETECTION THRESHOLD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5 (November 19, 2018): 511–17. http://dx.doi.org/10.5194/isprs-archives-xlii-5-511-2018.

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<p><strong>Abstract.</strong> Every winter the Indo-Gangetic plains (IGP) of northern India are severely impacted both socially and economically by fog. For night time fog detection, visible imagery cannot be used. Also, as emissions from ground and fog is almost similar in thermal infrared (TIR, 10.8<span class="thinspace"></span>μm) channel, TIR channel cannot help in identifying fog. However, emission in middle infrared (MIR, 3.9<span class="thinspace"></span>μm) channel is less than emission in TIR channel over foggy area. The
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Kolbe, Christine, Boris Thies, Sebastian Egli, Lukas Lehnert, Hans Schulz, and Jörg Bendix. "Precipitation Retrieval over the Tibetan Plateau from the Geostationary Orbit—Part 1: Precipitation Area Delineation with Elektro-L2 and Insat-3D." Remote Sensing 11, no. 19 (2019): 2302. http://dx.doi.org/10.3390/rs11192302.

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The lack of long term and well distributed precipitation observations on the Tibetan Plateau (TiP) with its complex terrain raises the need for other sources of precipitation data for this area. Satellite-based precipitation retrievals can fill those data gaps. Before precipitation rates can be retrieved from satellite imagery, the precipitating area needs to be classified properly. Here, we present a feasibility study of a precipitation area delineation scheme for the TiP based on multispectral data with data fusion from the geostationary orbit (GEO, Insat-3D and Elektro-L2) and a machine lea
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Samudraiah, D. R. M., M. Saxena, S. Paul, P. Narayanababu, S. Kuriakose, and A. S. Kiran Kumar. "Payload Configurations for Efficient Image Acquisition – Indian Perspective." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 1201–6. http://dx.doi.org/10.5194/isprsarchives-xl-8-1201-2014.

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The world is increasingly depending on remotely sensed data. The data is regularly used for monitoring the earth resources and also for solving problems of the world like disasters, climate degradation, etc. Remotely sensed data has changed our perspective of understanding of other planets. With innovative approaches in data utilization, the demands of remote sensing data are ever increasing. More and more research and developments are taken up for data utilization. The satellite resources are scarce and each launch costs heavily. Each launch is also associated with large effort for developing
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Khan, Aman W., Dilip Ganguly, Sagnik Dey, Atul Kumar Varma, and Prashant Kumar. "Assimilation of INSAT-3D Imager Retrieved Aerosol Optical Depth for Enhanced Dust Storm Prediction Using WRF-Chem Model." Environmental Research Communications, November 25, 2024. http://dx.doi.org/10.1088/2515-7620/ad9712.

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Abstract Frequent dust storms during the pre-monsoon season in northwest India significantly impact weather, air quality, and health, necessitating accurate predictions. This study demonstrates the first-time assimilation of aerosol optical depth (AOD) data from INSAT-3D into the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), alongside previously assimilated AOD data from MODIS AQUA. Focusing on an extreme dust storm over the Indo-Gangetic Plain (IGP) from June 12-17, 2018, identical experiments were conducted with and without AOD assimilation. The Gridpoint Statisti
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41

Kumar Gangwar, Rishi, and Pradeep Kumar Thapliyal. "Retrieval of Total Precipitable Water from INSAT-3D Imager Observations using Deep Neural Network." Advances in Space Research, September 2024. http://dx.doi.org/10.1016/j.asr.2024.09.036.

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42

Pandya, Mehul R., Vishal Pathak, Nitesh Kaushik, and Himanshu J. Trivedi. "Correction to: Algorithm for the Estimation of Continental Scale Land-Surface Broadband Albedo from INSAT-3D Imager Data." Journal of the Indian Society of Remote Sensing, July 19, 2021. http://dx.doi.org/10.1007/s12524-021-01393-8.

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43

Lima, C. B., S. S. Prijith, and S. Suresh Babu. "Characterisation of dust aerosols and their absorbing efficiency over South Asia using infrared observations from INSAT-3D Imager." International Journal of Remote Sensing, December 10, 2024, 1–24. https://doi.org/10.1080/01431161.2024.2431176.

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44

Rani, S. Indira, Ruth Taylor, Priti Sharma, et al. "Assimilation of INSAT-3D imager water vapour clear sky brightness temperature in the NCMRWF’s assimilation and forecast system." Journal of Earth System Science 128, no. 7 (2019). http://dx.doi.org/10.1007/s12040-019-1230-6.

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Gangwar, Rishi Kumar, M. Jishad, and P. K. Thapliyal. "A Machine Learning-Based SST Retrieval from Thermal Infrared Observations of INSAT-3D Imager: Improvement Over Regression-Based NLSST Algorithm." Pure and Applied Geophysics, October 29, 2024. http://dx.doi.org/10.1007/s00024-024-03586-x.

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Shukla, Munn Vinayak, and Pradeep K. Thapliyal. "Development of a Methodology to Generate In-Orbit Electrooptical Module Temperature-Based Calibration Coefficients for INSAT-3D/3DR Infrared Imager Channels." IEEE Transactions on Geoscience and Remote Sensing, 2020, 1–7. http://dx.doi.org/10.1109/tgrs.2020.2998523.

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47

Yadav, Ramashray, R. K. Giri, and S. C. Bhan. "High-Resolution Outgoing Long Wave Radiation data (2014-2020) of INSAT-3D Imager and its comparison with Clouds and Earth’s Radiant Energy System (CERES) data." Advances in Space Research, May 2022. http://dx.doi.org/10.1016/j.asr.2022.05.053.

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48

"Impact of Atmospheric Stability Indices on Convective Systems Over India and Srilanka." International Journal of Recent Technology and Engineering 8, no. 4 (2019): 3142–46. http://dx.doi.org/10.35940/ijrte.d7980.118419.

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Thunderstorms are real-time global phenomena, as their occurrence can take place at anytime at any place. Though their duration is less when compared to large scale processes, their damage is devastating to human life. Thunderstorms are linked with damage factors such as lightning, damaging wind, hails and rain. Real-time satellite data provide atmospheric data which is useful for prediction of thunderstorms. In this paper, an attempt is made to analyze the statistical based stability indices from INSAT-3D, MODIS and ECMWF satellites for the now casting of thunderstorms. The occurrences of sev
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Pal, Soumyajit, Uma Das, and Oishila Bandyopadhyay. "SSN: A Novel CNN-based Architecture for Classification of Tropical Cyclone Images from INSAT-3D." IEEE Transactions on Geoscience and Remote Sensing, 2024, 1. http://dx.doi.org/10.1109/tgrs.2024.3441729.

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"A Review and Analysis of GAN-Based Super-Resolution Approaches for INSAT 3D/3DR Satellite Imagery using Artificial Intelligence." Journal of Scientific & Industrial Research 83, no. 6 (2024). http://dx.doi.org/10.56042/jsir.v83i6.7320.

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