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Journal articles on the topic 'INSAT-3D'

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Published: 5 June 2025
Last updated: 16 July 2025

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1

Parihar, Shailesh, Ashim Kumar Mitra, Mrutyunjay Mohapatra, and Rajjev Bhatla. "Potential of INSAT-3D sounder-derived total precipitable water product for weather forecast." Atmospheric Measurement Techniques 11, no. 11 (2018): 6003–12. http://dx.doi.org/10.5194/amt-11-6003-2018.

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Abstract. The objectives of the INSAT-3D satellite are to enhance the meteorological observations and to monitor the Earth's surface for weather forecasting and disaster warning. One of the weather-monitoring capabilities of the INSAT-3D sounder is the estimation of water vapour in the atmosphere. The amount of water vapour present in the atmospheric column is derived as the total precipitable water (TPW) product from the infrared radiances measured by the INSAT-3D sounder. The present study is based on TPW derived from INSAT-3D sounder, radiosonde (RS) observations and the corresponding National Oceanic and Atmospheric Administration (NOAA) satellite. To assess retrieval performances of INSAT-3D sounder-derived TPW, RS TPW observations are considered for the validation from May to September 2016 from 34 stations belonging to the India Meteorological Department (IMD). The analysis is performed on daily, monthly, and subdivisional bases over the Indian region. The comparison of INSAT-3D TPW with RS TPW on daily and monthly bases shows that the root mean square error (RMSE) and correlation coefficients (CC) are ∼8 mm and 0.8, respectively. However, on subdivisional and overall scales, the RMSE found to be in the range of 1 to 2 mm and CC was around 0.9 in comparison with RS and NOAA. The spatial distribution of INSAT-3D TPW with actual rainfall observation is also investigated. In general, INSAT-3D TPW corresponds well with rainfall observation; however, it has found that heavy rainfall events occur in the presence of high TPW values. In addition, the cases of thunderstorm events were assessed using TPW from INSAT-3D and network of Global Navigation Satellite System (GNSS) receiver. This shows the good agreement between TPW from INSAT-3D and GNSS during the mesoscale activity. The improvement in the estimation of TPW is carried out by applying the GSICS calibration corrections (Global Space-based Inter-Calibration System) to the radiances from infrared (IR) channels of the sounder, which is used by IMDPS (INSAT Meteorological Data Processing System). The current TPW from INSAT-3D satellite can be utilized operationally for weather monitoring and forecast purposes. It can also offer substantial opportunities for improvement in nowcasting studies.
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2

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 resolutions and altitude coverage, besides other parameters. The initial validation of INSAT-3D data is made with the high temporal (3 h) resolution radiosonde observations launched over Gadanki (13.5° N, 79.2° E) during a special campaign and routine evening soundings obtained at 12:00 UTC (17:30 LT). We also compared INSAT-3D data with the radiosonde observations obtained from 34 India Meteorological Department stations. Comparisons were also made over India with data from other satellites like AIRS, MLS and SAPHIR and from ERA-Interim and NCEP reanalysis data sets. INSAT-3D is able to show better coverage over India with high spatial and temporal resolutions as expected. Good correlation in temperature between INSAT-3D and in situ measurements is noticed except in the upper tropospheric and lower stratospheric regions (positive bias of 2–3 K). There is a mean dry bias of 20–30 % in the water vapour mixing ratio. Similar biases are noticed when compared to other satellites and reanalysis data sets. INSAT-3D shows a large positive bias in temperature above 25° N in the lower troposphere. Thus, caution is advised when using these data for tropospheric studies. Finally it is concluded that temperature data from INSAT-3D are of high quality and can be directly assimilated for better forecasts over India.
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3

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 measurements. The validation of INSAT-3D UTH against UTH derived from radiosonde profiles shows reasonable agreement, with linear correlation coefficients ranging from 0.78 to 0.87 and the slope of the regression line ranging from 0.52 to 0.77. The UTH tends to overestimate observed humidity by ~4 % with RMS difference of ~12 %. Comparison of INSAT-3D UTH product with Meteosat-7 UTH product suggests a good match with RMS difference of 7.61% and a mean bias of −0.43 %, linear correlation coefficients varying from 0.88 to 0.93 and slope of the regression line varying from 0.64 to 1.08. The UTH products from INSAT-3D and Meteosat-7 have also been inter-compared by validating the two against the UTH derived from a set of collocated radiosonde observations. INSAT-3D UTH shows a RMSD of 10.65 % and bias of 0.78 % which matches very well with Meteosat-7 UTH with a RMSD of 10.31 % and bias of −0.53 %.
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4

Murali Krishna, Uriya Veerendra, Subrata Kumar Das, Kizhathur Narasimhan Uma, and Govindan Pandithurai. "Retrieval of convective available potential energy from INSAT-3D measurements: comparison with radiosonde data and their spatial–temporal variations." Atmospheric Measurement Techniques 12, no. 2 (2019): 777–90. http://dx.doi.org/10.5194/amt-12-777-2019.

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Abstract. Convective available potential energy (CAPE) is a measure of the amount of energy available for convection in the atmosphere. The satellite-derived data over the ocean and land are used for a better understanding of the atmospheric stability indices. In this work, an attempt is made for the first time to estimate CAPE from high spatial and temporal resolution measurements of the INSAT-3D over the Indian region. The estimated CAPE from the INSAT-3D is comprehensively evaluated using radiosonde derived CAPE and ERA-Interim CAPE. The evaluation shows that the INSAT-3D CAPE reasonably correlated with the radiosonde derived CAPE; however, the magnitude of CAPE shows higher values. Further, the distribution of CAPE is studied for different instability conditions (different range of CAPE values) during different seasons over the Indian region. In addition, the diurnal and seasonal variability in CAPE is also investigated at different geographical locations to understand the spatial variability with respect to different terrains.
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5

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 teams for corrective actions. DQE methodology and quality parameters have been defined keeping in view repeatability of data acquisition and quality requirements of meteorological data. In this paper, we have described INSAT-3D Quality Analysis System (i3dQAS) designed to facilitate effective data quality monitoring through web based portal. Overall context of i3dQAS in a typical data centre set-up, major objectives and design aspects to highlight the architectural and functional view of system are described. Utility of i3dQAS for mission monitoring is described through analysis scenarios for monitoring payload health and geometric accuracy of Imager.
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6

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 μm) and mid-infrared (MIR) band (centred at 3.9 μm) measurements, and allows us to monitor transport of aerosols at higher temporal resolution. Comparisons made between INSAT-3D derived AOD (τ<sub>I</sub>) and MODIS derived AOD (τ<sub>M</sub>) co-located in space (at 1° resolution) and time during January, February and March (JFM) 2014 encompasses 1165, 1052 and 900 pixels, respectively. Good agreement found between τ<sub>I</sub> and τ<sub>M</sub> during JFM 2014 with linear correlation coefficients (R) of 0.87, 0.81 and 0.76, respectively. The extensive validation made during JFM 2014 encompasses 215 co-located AOD in space and time derived by INSAT 3D (τ<sub>I</sub>) and 10 sun-photometers (τ<sub>A</sub>) that includes 9 AERONET (Aerosol Robotic Network) and 1 handheld sun-photometer site. INSAT-3D derived AOD i.e. τ<sub>I</sub>, is found within the retrieval errors of τ<sub>I</sub> = ±0.07 ±0.15τ<sub>A</sub> with linear correlation coefficient (R) of 0.90 and root mean square error equal (RMSE) to 0.06. Present work shows that INSAT-3D aerosol products can be used quantitatively in many applications with caution for possible residual clouds, snow/ice, and water contamination.
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7

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 measurements of field reflectance of study site (of extent ~6 km x 6 km) in the wavelength range 325–2500 nm, along with atmospheric parameters (Aerosol Optical Depth, Total Columnar Ozone, Water Vapor) and sensor spectral response functions, were input to the 6S radiative transfer model to simulate radiance at top of the atmosphere (TOA) for VIS and SWIR bands. The uncertainty in vicarious calibration coefficients due to measured spatial variability of field reflectance along with due to aerosol types were also computed for the INSAT-3D imager. The effect of surface anisotropy on TOA radiance was studied using a MODIS Bidirectional Reflectance Distribution Function (BRDF) product covering the experimental site. The results show that there is no indication of change in calibration coefficients in INSAT- 3D imager, for VIS and SWIR band over Little ROK. Comparison made between the INSAT-3D imager measured radiance and 6S simulated radiance. Analysis shows that for clear sky days, the INSAT-3D imager overestimates TOA radiance in the VIS band by 5.1 % and in the SWIR band by 11.7 % with respect to 6S simulated radiance. For these bands, in the inverse mode, the 6S corrected surface reflectance was closer to field surface reflectance. It was found that site spatial variability was a critical factor in estimating change in sensor calibration coefficients and influencing uncertainty in TOA radiance for Little ROK.
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8

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 altogether. To mitigate these issues, we propose SST estimation from split-window IR observations of INSAT-3D and 3DR Imagers using One-Dimensional Variational (1DVAR) scheme. Prior to SST estimation, the bias correction in Imager observations is carried out using cumulative density function (CDF) matching. Then NLSST and 1DVAR algorithms were applied on six months of INSAT-3D/3DR observations to retrieve the SST. For the assessment of the developed algorithms, the retrieved SST was validated against in-situ SST measurements available from in-situ SST Quality Monitor (iQuam) for the study period. The quantitative assessment confirms the superiority of the 1DVAR technique over the NLSST algorithm. However, both the schemes under-estimate the SST as compared to in-situ SST, which may be primarily due to the differences in the retrieved skin SST versus bulk in-situ SST. The 1DVAR scheme gives similar accuracy of SST for both INSAT-3D and 3DR with a bias of −0.36 K and standard deviation (Std) of 0.63 K. However, the NLSST algorithm provides slightly less accurate SST with bias (Std) of −0.18 K (0.87 K) for INSAT-3DR and −0.27 K (0.95 K) for INSAT-3D. Both the NLSST and 1DVAR algorithms are capable of producing the accurate thermal gradients from the retrieved SST as compared to the gradients calculated from daily Multiscale Ultrahigh Resolution (MUR) level-4 analysis SST acquired from Group for High-Resolution Sea Surface Temperature (GHRSST). Based on these spatial gradients, thermal fronts can be generated that are very useful for predicting potential fishery zones (PFZ), which is available from GEO satellites, INSAT-3D/3DR, in near real-time at 15-minute intervals. Results from the proposed 1DVAR and NLSST algorithms suggest a marked improvement in the SST estimates with reduced diurnal/seasonal biases as compared to the operational NLSST algorithm.
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9

RAMASHRAY, YADAV, PUVIARASAN N, GIRI R K, TOMAR C S, and SINGH VIRENDRA. "Comparison of GNSS and INSAT-3D sounder retrieved precipitable water vapour and validation with the GPS Sonde data over Indian Subcontinent." MAUSAM 71, no. 1 (2021): 1–10. http://dx.doi.org/10.54302/mausam.v71i1.1.

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Precipitable water vapour (PWV) plays a key role in the atmospheric processes from climate change to micrometeorology. Its distribution and quantity are critical for the description of state and evaluation of the atmosphere in NWP model. Lack of precise and continuous water vapour data is one of the major error sources in short term forecast of precipitation. The task of accurately measuring atmospheric water vapour is challenging. Conventional in situ measurements of atmospheric water vapour is provided by GPS Sonde humidity sensors profile twice a day at 0000 and 1200 UTC mainly from limited land regions. In recent years India Meteorological Department (IMD) is computing PWV from 19 channel sounder of INSAT-3D in three layers 1000-900 hPa, 900-700 hPa and 700-300 hPa and total PWV in the vertical column of atmosphere stretching from surface to about 100 hPa under cloud free condition. These data most commonly were validated using spatially and temporally collocated GPS Sonde measurements. In this paper, INSAT-3D satellite retrieved PWV data are validated with column integrated PWV estimates from a network of ground based Global Navigation Satellite System (GNSS) over Indian subcontinent. The PWV retrieved by INSAT-3D sounder platform is very promising, being in a good agreement with the GNSS data recorded over India for the period June, 2017 to May, 2018. The root-mean-square (rms) differences of 5.4 to 7.1 mm, bias of -4.7 to +2.1 mm and correlations coefficient of 0.79 to 0.92 was observed between INSAT-3D and GNSS PWV. The correlations coefficient between GPS Sonde and GNSS derived PWV ranges from 0.85 to 0.98.
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10

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 highlight the potential of deep learning to enhance early warning systems for extreme weather events. Keywords: Deep learning, Machine Learning, Preprocessing , CNN, INSAT 3D Images
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11

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 performance evaluation for IR channels is carried out. Mean brightness temperature (BT) of targets is computed and validated with independently generated radiometric references. Further, diurnal/seasonal trends in target BT values and radiometric uncertainty or sensor noise are studied. Results of radiometric quality evaluation over duration of eight months (January to August 2014) and comparison of radiometric consistency pre/post yaw flip of satellite are presented. Radiometric Analysis indicates that INSAT-3D images have high contrast (MTF > 0.2) and low striping effects. A bias of <4K is observed in the brightness temperature values of TIR-1 channel measured during January–August 2014 indicating consistent radiometric calibration. Diurnal and seasonal analysis shows that Noise equivalent differential temperature (NEdT) for IR channels is consistent and well within specifications.
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12

John, Jinya, Ipshita Dey, Anurag Pushpakar, V. Sathiyamoorthy, and Bipasha Paul Shukla. "INSAT-3D cloud microphysical product: retrieval and validation." International Journal of Remote Sensing 40, no. 4 (2018): 1481–94. http://dx.doi.org/10.1080/01431161.2018.1524606.

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13

Kumar, Amit, Anil Kumar Singh, J. N. Tripathi, M. Sateesh, and Virendra Singh. "Evaluation of INSAT-3D-derived Hydro-Estimator and INSAT Multi-Spectral Rain Algorithm over Tropical Cyclones." Journal of the Indian Society of Remote Sensing 49, no. 7 (2021): 1633–50. http://dx.doi.org/10.1007/s12524-021-01332-7.

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14

Deb, Sanjib K., Dineshkumar K. Sankhala, Prashant Kumar, and Chandra M. Kishtawal. "Retrieval and applications of atmospheric motion vectors derived from Indian geostationary satellites INSAT-3D/INSAT-3DR." Theoretical and Applied Climatology 140, no. 1-2 (2020): 751–65. http://dx.doi.org/10.1007/s00704-020-03120-8.

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15

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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16

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 km corresponding to the INSAT-3D Sounder. This statistical analysis is established using MODIS cloud mask having 95 % confidence level at 1 km resolution spread in the region between 50E–110E and 30S–30N. This necessitates cloud clearing to remove the effect of partial clouds in the Sounder FOV to provide a clear-sky equivalent sounding retrieval. <br><br> Various methods were explored to derive the cloud-cleared radiances using supplementary information such as high resolution infrared or microwave observations. This study presents an effort to use the existing traditional method to derive optimal cloudcleared radiances for INSAT-3D Sounder, by estimating the fractional cloud cover using collocated high resolution INSAT-3D Imager window channel observation. The final Sounder cloud-cleared radiances have been validated with the operational AIRS L2 cloud-cleared radiance products. <br><br> Nevertheless, the statistical analysis of clear-sky probability over Indian region also provides a significant insight towards the dependency of spatial resolution and the considerable field-of-regard (FOR) in obtaining the clear-sky area in the satellite observations. This, in a way, necessitates the cloud-clearing for coarser resolution sensors and at the same time, states the benefits of using very high resolution sensors. It has been observed that FOV of 1km and by choosing a reasonably good FOR can eliminate the cloudy-sky hindrances by increasing the probability of clear-sky from 5 % to 50 %.
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SEHGAL, NISHTHA, TANVI MALHAN, R. K. GIRI, RAMASHRAY YADAV, YOGESH KUMAR, and LAXMI PATHAK. "An analysis of fog events in respect of winter season 2021-2022 using model reanalysis & INSAT-3D/3DR satellite data." MAUSAM 75, no. 1 (2023): 61–72. http://dx.doi.org/10.54302/mausam.v75i1.5916.

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The objective of the study was twofold (i) Review of INSAT-3D/3DR night time fog detection channel differencing (MIR- TIR1) scheme developed by Space Application Centre (SAC) Indian Space Research Centre (ISRO) thresholds which were not uniform for winter season radiation fog and vary geographically over Indian domain & (ii) An analysis of Fog events of 2021-2022 winter season analysis using the anomalies (temperature, wind, moisture, inversion, geo-potential height etc) from NCEP reanalysis and ERA-5 data sets. This study is a way forward to look into the importance of recently introduced model reanalysis data sets to monitor and understand the recent changes of fog events behaviour. It is seen that the fog events winter season (2021-2022) was reduced appreciably and this change is really a concern but 2021-22 winter fog occurrences were very well captured in both models as well as INSAT-3D/3DR data analysis. The results brought out from the model as well as satellite data analysis were found to be very useful for forecasters and end users especially in monitoring and prediction of fog events. However, to quantify the night time fog thresholds based on INSAT data for different regions of India and appreciable reduction of fog events in the recent past needs long term data sets study.
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18

Lekhadiya, H. S., and R. K. Jana. "Assimilation of INSAT-3D Satellite Data in WRF Model." Proceedings of the National Academy of Sciences, India Section A: Physical Sciences 90, no. 3 (2019): 557–64. http://dx.doi.org/10.1007/s40010-019-00606-7.

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Sankhala, Dineshkumar K., Sanjib K. Deb, P. Yasodha, and Amit Kesarkar. "Assessment of INSAT-3D/INSAT-3DR Derived Atmospheric Motion Vectors Using Wind Profiler Observations Located at Gadanki, India." Journal of the Indian Society of Remote Sensing 48, no. 4 (2020): 585–95. http://dx.doi.org/10.1007/s12524-020-01101-y.

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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 compare well. The seasonal mean TCWV shows large values with lower standard deviation during the southwest monsoon (SWM) than in the northeast monsoon (NEM). Different temporal scales that contribute to the TCWV variance at a given point are quantified, and the variability due to 30–60-day oscillations is found to be dominant during both the monsoon seasons. TCWV and rainfall show good correspondence over the whole Indian subcontinent during both monsoon seasons except over the Arabian Sea and southern Myanmar regions, where large TCWV values show less rainfall during the SWM. On the whole, the spatial homogeneity and intergrid correlations in TCWV and rainfall are higher in NEM than in SWM. The decorrelation distance d0 for TCWV is found to be 10 times larger than that for rainfall, indicating that the rainfall homogeneity is generally limited to smaller areas. The large d0 values of TCWV are mainly due to the occurrence of source and sink processes at large spatial scales over those regions.
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Kumawat, Nandkishor, and K. N. Babu. "INSAT-3D and -3DR relative radiometric performance over bright target." Remote Sensing Applications: Society and Environment 25 (January 2022): 100672. http://dx.doi.org/10.1016/j.rsase.2021.100672.

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Kamath, Harsh G., and J. Srinivasan. "Validation of global irradiance derived from INSAT-3D over India." Solar Energy 202 (May 2020): 45–54. http://dx.doi.org/10.1016/j.solener.2020.03.084.

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23

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 were extracted, followed by time series analysis. Results demonstrated that increasing LI values, indicating atmospheric instability, and TPW values exceeding 50 mm were critical predictors of severe weather with a lead time of 3–4 hours. These findings suggest that LI and TPW are valuable indicators for nowcasting severe weather. However, the study also highlighted limitations in temporal resolution due to the observation gaps from polar-orbiting sounders. Future research aims to integrate Himawari-8/9 AHI data with hyperspectral sounder observations to improve nowcasting accuracy and the development of a robust tool for severe weather and thunderstorms in Myanmar.
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Kumar, Amit, Yogesh Kumar, Mamta Bhardwaj, and R. K. Giri. "Evaluation of INSAT-3DR Hydro-Estimator product for monsoon season rainfall at block-level and its utility in forecast verification: A case study in Karnal district, India." MAUSAM 75, no. 3 (2024): 759–68. http://dx.doi.org/10.54302/mausam.v75i3.6216.

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The availability and assimilation of INSAT-3D and INSAT-3DR weather satellite datasets has improved the accuracy of medium range weather forecasts. Conventionally, the forecast is verified against the in-situ observations. But the distribution of in-situ observatories is not uniform for many reasons like, in-habitable conditions, mountain terrains, operational cost etc. The availability of data from Automatic weather stations is also not guaranteed at all times because of maintenance and operational issues. Therefore, in the absence of in-situ data it becomes very difficult to verify the forecast. In the current study, skill of value added rainfall forecast is assessed by carrying out skill score analysis for Assandh (AS), Gharaunda (GD), Indri (ID), Karnal (KA), Nilokheri (NK) blocks using in-situ rain-gauge data for the southwest monsoon season of 2020 and 2021. For Munak (MU), Kunjpura (KJ) and Nissing (NI) blocks, the data from rain-gauges is not available. In order to fill this gap, in the present study, the block level medium-range value added rainfall forecast issued by IMD, is verified by utilizing INSAT-3DR satellite Hydro-estimator (HE) rainfall product. In order to gain confidence in the approach, the INSAT-3DR derived HE rainfall estimate is validated against the available rain-gauges in Karnal district. The study revealed that the rainfall data received from the satellite can be used for better forecasting of daily rainfall at the block level and preparation of agromet advisory bulletin. The accuracy of forecast of weather parameters in advance is found to be useful for farmers for doing appropriate field operations and crop management practices in the form of preparation of Agromet Advisories Bulletin (AAB).
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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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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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27

Chaurasia, Sasmita, and Bhawani Singh Gohil. "Detection of Day Time Fog Over India Using INSAT-3D Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8, no. 9 (2015): 4524–30. http://dx.doi.org/10.1109/jstars.2015.2493000.

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28

Satapathy, Jyotirmayee. "Extreme cyclonic storm monitoring using INSAT-3D/3DR-hyperspectral sounder observations." Remote Sensing Applications: Society and Environment 19 (August 2020): 100339. http://dx.doi.org/10.1016/j.rsase.2020.100339.

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29

Gunti, Sravanthi, J. Narendran, and S. Muralikrishnan. "PWV Estimation Using GPS and its Comparison with INSAT-3D Rainfall Data." Journal of the Indian Society of Remote Sensing 49, no. 6 (2021): 1453–60. http://dx.doi.org/10.1007/s12524-021-01324-7.

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30

BHAN, S. C., A. K. MITRA, A. K. SHARMA, et al. "INSAT-3D vertical profile retrievals at IMDPS, New Delhi : A preliminary evaluation." MAUSAM 66, no. 4 (2021): 687–94. http://dx.doi.org/10.54302/mausam.v66i4.576.

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31

Patel, Piyushkumar N., Hiren Bhatt, A. K. Mathur, R. P. Prajapati, and Geetika Tyagi. "Reflectance-based vicarious calibration of INSAT-3D using high-reflectance ground target." Remote Sensing Applications: Society and Environment 3 (May 2016): 20–35. http://dx.doi.org/10.1016/j.rsase.2015.12.001.

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32

Mitra, A. K., Shailesh Parihar, R. Bhatla, and K. J. Ramesh. "Identification of Weather Events from INSAT-3D RGB Scheme using RAPID Tool." Current Science 115, no. 7 (2018): 1358. http://dx.doi.org/10.18520/cs/v115/i7/1358-1366.

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33

Lima, Prijith, Sesha Sai, Rao, Niranjan, and Ramana. "Retrieval and Validation of Cloud Top Temperature from the Geostationary Satellite INSAT-3D." Remote Sensing 11, no. 23 (2019): 2811. http://dx.doi.org/10.3390/rs11232811.

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Investigation of cloud top temperature (CTT) and its diurnal variation is highly reliant on high spatial and temporal resolution satellite data, which is lacking over the Indian region. An algorithm has been developed for detection of clouds and retrieval of CTT from the geostationary satellite INSAT-3D. These retrievals are validated (inter-compared) with collocated in-situ (satellite) measurements with specific intent to generate climate-quality data. The cloud detection algorithm employs nine different tests, in accordance with solar illumination, satellite angle and surface type conditions to generate pixel-resolution cloud mask. Validation of cloud mask with cloud-aerosol lidar with orthogonal polarization (CALIOP) shows that probability of detection (POD) of cloudy (clear) sky is 81% (85%), with 83% hit rate. The algorithm is also implemented on similar channels of moderate resolution imaging spectroradiometer (MODIS), which provides 88% (83%) POD of cloudy (clear) sky, with 86% hit rate. CTT retrieval is done at the pixel level, for all cloud pixels, by employing appropriate methods for various types of clouds. Comparison of CTT with radiosonde and cloud-aerosol lidar and infrared pathfinder satellite observations (CALIPSO) shows mean absolute error less than 3%. The study also examines sensitivity of retrieved CTT to the cloud classification scheme and retrieval criteria. Validation results and their close agreements with those of similar satellites demonstrate the reliability of the retrieved product for climate studies.
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34

Gairola, R. M., M. T. Bushair, and Raj Kumar. "Synergy between INSAT-3D infra-red and GPM microwave radiometer for precipitation studies." Atmósfera 33, no. 1 (2020): 33–49. http://dx.doi.org/10.20937/atm.52630.

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35

Rawat, Prajjwal, Manish Naja, Pradeep K. Thapliyal, et al. "Assessment of Vertical Ozone Profiles from INSAT-3D Sounder Over The Central Himalaya." Current Science 119, no. 7 (2020): 1113. http://dx.doi.org/10.18520/cs/v119/i7/1113-1122.

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36

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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37

Singh, Tarkeshwar, Rashmi Mittal, and Munn Vinayak Shukla. "Validation of INSAT-3D temperature and moisture sounding retrievals using matched radiosonde measurements." International Journal of Remote Sensing 38, no. 11 (2017): 3333–55. http://dx.doi.org/10.1080/01431161.2017.1294776.

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38

Sankhala, Dineshkumar, Sanjib K. Deb, and Neeru Jaiswal. "Wind derived products using INSAT-3D atmospheric motion vectors and its meteorological applications." International Journal of Remote Sensing 42, no. 4 (2020): 1357–78. http://dx.doi.org/10.1080/01431161.2020.1829153.

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39

Bisht, Jagat Singh Heet, Pradeep Kumar Thapliyal, Munn Vinayak Shukla, and Raj Kumar. "Evaluating possible enhancement of INSAT-3D Sounder water vapour retrieval in the tropics." Remote Sensing Letters 6, no. 9 (2015): 697–706. http://dx.doi.org/10.1080/2150704x.2015.1069902.

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40

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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41

Malhan, Tanvi, Nishtha Sehgal, RK Giri, et al. "Comparative analysis of Sub division wise rainfall INSAT-3D vs Ground based observations." MAUSAM 73, no. 4 (2022): 843–52. http://dx.doi.org/10.54302/mausam.v73i4.5877.

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Rainfall monitoring during south west monsoon season both are very important ad crucial activity. It is important mainly because it is a boon for agriculture, a mirror for future for both social and economic activities and crucial for its measurements (ground as well as remote). In this current works authors made an attempt to know the performance of recently Space Application Centre (SAC), ISRO developed INSAT-3D improved rainfall algorithms (Hydro Estimator and corrected IMSARA) with actual ground based rainfall data by calculating the bias (Actual –Satellite) for each sub-division. The analysis is done for the southwest monsoon season -2021 in by calculating weekly, monthly and seasonal bias for each subdivisions of Indian domain. It is seen that both the algorithms behave similar fashion (both show increase or decrease, simultaneously) with actual data and mostly satellite overestimate with actual data ranges from ~ 20-40 mm. In some subdivisions bias reached within the range 40 -70 mm (except Konkan & Goa ). Almost 40 % of the subdivisions have bias within 0 to 20 mm range, however the variation on weekly, monthly or seasonal differs subdivision and magnitude-wise. Overall, both the algorithms captures and performance well the trends in weekly, monthly and seasonal accumulated rainfall values. Corrected IMSRA (IMC) algorithm perform slightly better (15-20 %) except heavy rainfall episodes during the monsoon season -2021. In both the heavy and very heavy rainfall cases Hydro Estimators pick up well and performs better (~ 10 -12 %) than IMC algorithm especially over orographic areas. In extremely heavy rainfall cases both the algorithms behave in the same manner and captures the events although it is differing magnitude wise. Seasonal analysis of monsoon 2021 rainfall shows that 8 subdivisions have negative biases in the range of 50-60 mm) and 24 subdivisions have negative biases in the range of 0-20 mm, except Konkan Goa, Coastal Karnataka & A & N Islands have positive biases. Therefore, there is need to strengthen the actual observation rainfall measuring network and re-examine the performance of algorithms with larger data sets so that current algorithms retuned as per changing scenario.
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42

Singh, Randhir, Satya P. Ojha, C. M. Kishtawal, P. K. Pal, and A. S. Kiran Kumar. "Impact of the assimilation of INSAT-3D radiances on short-range weather forecasts." Quarterly Journal of the Royal Meteorological Society 142, no. 694 (2015): 120–31. http://dx.doi.org/10.1002/qj.2636.

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43

Atul, Yadav, and RK Giri. "Heat wave episode 24-27 May-2020 monitored through INSAT-3D satellite data over the Indian region." Int. Res. Journal of Science & Engineering, 2023 11, no. 4 (2023): 173–86. https://doi.org/10.5281/zenodo.8286871.

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Satellite derived products play an important role to monitor the weather events in each season throughout the year over India. In the year 2020, during lock down period 24 to 27<sup>th</sup> may-2020 almost all parts of the india witnessed blistering heat wave conditions. This may be attributed to the northward shift of jet stream with ridge aloft can cause many parts of India (especially central India) including Pakistan will face heat wave conditions. This situation is well captured by insat-3d derived products (outgoing longwave radiation, land surface temperature, land surface albedo and upper tropospheric humidity etc) was found to be very useful in diagnosing the heat wave spread over the Indian region. It has been observed that&nbsp;&nbsp; outgoing long wave radiation (OLR) lies&gt;300 watt/m<sup>2</sup>, upper tropospheric humidity (UTH 5-20 %) Land surface albedo (LSA 15-35 %), Land surface temperature (LST &gt; 315&deg;K) and net radiation (600 -800 watt.m<sup>2</sup>) have been noticed during the prevailing heat wave condition over India during 24-27 May-2020.
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44

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 feature engineering and machine learning, however, which is a manual process. To address the limitations associated with the above conventional methods, a new approach is being proposed that uses a deep learning mechanism to design a CYCLONE NETWORK (CY-Net) model. This model will estimate the cyclone intensity by using INSAT-3D infrared (IR) images. The CY-Net model is developed based on structural, intensification, and landfall features along with biasing parameters such as wind speed, sea level pressure, and sea surface temperature. The INSAT 3D data is given as input to the model for training, testing, and validation. It undergoes convolution along with the Re-lu activation function to generate feature maps, max pooling, sub-convolution, and fully connected layers. The stochastic gradient descent factor is measured to implement the backpropagation. The Stochastic gradient and backpropagation network are implemented to obtain the best filter coefficients. The trained, tested, and validated model is deployed in Python-flask for web application and then hosted using the web servers for web application. This approach uses advanced machine learning techniques to estimate cyclone intensity and has the potential to improve accuracy and reduce manual effort.
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45

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 regions. In this work, we have examined three weather events at different Geographical locations (i) Rainfall over Bihar-24-26 June, 2020 (ii) Delhi &amp; NCR region on 17 June, 2022 (iii) NE region activity in 16-18 June, 2022. The Real Time Analysis of Products and Information Dissemination (RAPID) web based tool was utilized in monitoring and diagnosing the convective weather events based on the brightness temperature &amp; derived products like Outgoing longwave radiation, upper tropospheric humidity, insolation etc &amp; RGB imagery composite in terms of day &amp; night time microphysics daily operational products. The time series of the wind derived products for Delhi NCR rainfall and NE rainfall products also generated through RAPID. The synoptic model analysis provides valuable inputs for these mesoscale convective weather events. The southerly wind flow (at 925 hPa) and velocity convergence (at 500 hPa) analysis of European Centre for Medium Range Weather Forecasting (ECMWF) supports the severity of NE event occurred on 16-18 June, 2022. Therefore, utilization of near real time INSAT-3D/3DR products along with appropriate synoptic model analysis can help the forecasters to understand better about such mesoscale convective events &amp; accurate forecast with sufficient lead time can save the life and property.
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46

Swapna, M., R. K. Nayak, T. Santhoshi, M. V. R. Sesha Sai, and S. S. Rajashekhar. "INSAT-3D SST and its diurnal variability assessment using in-situ and MODIS observations." Progress in Oceanography 201 (February 2022): 102739. http://dx.doi.org/10.1016/j.pocean.2022.102739.

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47

Prakash, Satish, Anil Kumar, Rahul Sharma, Shiv Kumar, and R. K. Giri. "Insat-3D/3DR and GNSS derived products & Thunderstorm events over the Indian region." Journal of Science and Technological Researches 4, no. 1 (2022): 44–50. http://dx.doi.org/10.51514/jstr.4.1.2022.44-50.

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48

Alavipanah, Seyed, Qihao Weng, Mehdi Gholamnia, and Reza Khandan. "An Analysis of the Discrepancies between MODIS and INSAT-3D LSTs in High Temperatures." Remote Sensing 9, no. 4 (2017): 347. http://dx.doi.org/10.3390/rs9040347.

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49

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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50

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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