Post-Launch Calibration–Validation and Data Quality Evaluation of SCATSAT-1

Kumar, Raj; Bhowmick, Suchandra Aich; Chakraborty, Abhisek; Sharma, Anuja; Sharma, Shanu; Seemanth, M.; Gupta, Maneesha; Chakraborty, Prantik; Modi, Jalpa; Misra, Tapan

doi:10.18520/cs/v117/i6/973-982

Cited by 23 publications

(10 citation statements)

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“…The SCATSat1 spacecraft was launched on 26 September 2016 on the PSLVC35 vehicle of ISRO at an altitude of 723 km. The main objective of SCATSat‐1 mission is to provide global ocean surface winds acquisition to improve weather forecasting (Bhowmick et al, ; Jaiswal et al, ; Kumar et al, ; Mankad et al, ; Misra et al, ). The instrument is a pencil beam scatterometer operating at Ku−band of 13.515 GHz.…”

Section: Scatsat‐1 Scatterometermentioning

confidence: 99%

“…SCATSat‐1 L2B product used in this study contains wind vectors retrieved from SCATSat‐1 L2A data with sensor‐specific Geophysical Model Function (Gohil et al, , , ; Kumar et al, ). The SCATSat‐1 L2A data contain the radar‐backscattering coefficient and other observing parameters (incidence angle, azimuth angle, polarization, etc.).…”

Section: Scatsat‐1 Scatterometermentioning

confidence: 99%

“…Kumar et al () performed a detailed validation of different resolution SCATSat‐1 retrieved winds against buoys and ASCAT retrieved winds. Kumar et al () and Chakraborty et al () found root‐mean‐square‐difference (RMSD) of 0.9 m s −1 (17°), 0.9 m s −1 (16°), and 1.2 m s −1 (18°) in 50, 25 and 6.25 km resolution retrieved wind speed (wind direction), respectively when compared with ASCAT retrieved wind speed (wind direction). Chakraborty et al () found RMSD of 1.5 m s −1 (1.6 m s −1 ) in wind speed, and 16° (17°) in wind direction when compared 25 km (6.25 km) winds with buoy observations.…”

Section: Scatsat‐1 Scatterometermentioning

confidence: 99%

“…Gopalan et al () developed a mean‐of‐posteriors Bayesian estimation technique for improving rain‐contaminated wind speed estimates from the SCATSat‐1 scatterometer, particularly at low and moderate wind speed. Kumar et al () performed a detailed validation of different resolution SCATSat‐1 retrieved winds against buoys and ASCAT retrieved winds. Kumar et al () and Chakraborty et al () found root‐mean‐square‐difference (RMSD) of 0.9 m s −1 (17°), 0.9 m s −1 (16°), and 1.2 m s −1 (18°) in 50, 25 and 6.25 km resolution retrieved wind speed (wind direction), respectively when compared with ASCAT retrieved wind speed (wind direction).…”

Section: Scatsat‐1 Scatterometermentioning

confidence: 99%

“…In September 2016, Indian Space Research Organization (ISRO) launched an Indian Ocean observing satellite, SCATterometer Satellite‐1 (SCATSat1; Bhowmick et al, ; Jaiswal et al, ; Kumar et al, ; Mankad et al, ; Misra et al, ), which carries a Ku‐band pencil‐beam scatterometer. Impact studies of new observations in the NWP models are a means of assessing the value of observing systems in terms of the benefit they deliver to model forecasts.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Impact of SCATSat‐1 Retrieved Wind Vectors on Short‐Range WRF Model Predictions Over the South Asia Region

Kumar

Gairola

2019

JGR Atmospheres

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The Indian Space Research Organization launched SCATterometer Satellite-1 (SCATSat1) satellite by PSLV-C35 on 26 September 2016 from Sriharikota. In this study, SCATSat-1 retrieved winds are assimilated in the regional atmospheric model subsequent to preliminary evaluation of SCATSat-1 retrieved winds against National Centers for Environmental Prediction final analyses. The Weather Research and Forecasting (WRF) model and its three-and four-dimensional variational (3D-Var and 4D-Var) data assimilation system are used to demonstrate the importance of SCATSat-1 retrieved winds on short-range weather prediction. In the first set of experiments, two parallel runs are performed with and without assimilation of SCATSat-1 retrieved winds using 6-hourly cycling during entire month of December 2016. Results suggest that assimilation of SCATSat-1 retrieved winds improved the WRF model analyses and subsequent forecasts of winds and moisture. In the second set of experiments, parallel assimilation experiments are performed with SCATSat-1 retrieved winds using 3D-Var and 4D-Var method for the entire lifetime of tropical cyclone Ockhi as a case study. Results suggest that assimilation of SCATSat-1 winds has positive impact on Ockhi tropical cyclone prediction with slightly better forecast skill for 4D-Var technique. Moreover, assimilation of SCATSat-1 retrieved winds improved predictions of surface convergence and accumulated rainfall. In the last set of experiments, forecast sensitivity to observations are performed for SCATSat-1 retrieved different resolution winds using the WRF tangent linear and adjoint models for the entire lifetime of tropical cyclone Ockhi. Results suggest that SCATSat-1 retrieved winds have positive impact on tropical cyclone simulation.

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Section: Scatsat‐1 Scatterometermentioning

confidence: 99%

Section: Scatsat‐1 Scatterometermentioning

confidence: 99%

Section: Scatsat‐1 Scatterometermentioning

confidence: 99%

Section: Scatsat‐1 Scatterometermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Impact of SCATSat‐1 Retrieved Wind Vectors on Short‐Range WRF Model Predictions Over the South Asia Region

Kumar

Gairola

2019

JGR Atmospheres

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Monsoonal Variability in the Mesoscale Coupling of Wind and SST in the Arabian Sea

Ratheesh

Chaudhary

Bhowmick

et al. 2021

Pure Appl. Geophys.

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Satellite observations over the Arabian Sea (AS) have revealed a strong signature of air-sea interaction during the Indian Ocean monsoon. In this study, satellite observations of ocean surface winds from the SCATSAT scatterometer are used to study the monsoonal variability in wind and SST (WS) coupling over the AS at the oceanic mesoscale for the period 2017 to 2019. The study investigates the role of critical parameters including SST gradient, wind speed steadiness and wind directional steadiness with respect to coupling during the winter and summer monsoons. Mesoscale WS coupling is stronger in the summer monsoon. A seasonally varying threshold of SST gradient required for the generation of WS coupling is clearly visible. This threshold is higher during the summer monsoon (0.8 °C/100 km) than during the winter monsoon (0.4 °C/100 km). Results reveal the need for a strong background wind field along with SST gradient for the generation of strong WS coupling. While a strong SST gradient ([ 0.8 °C/100 km) is sufficient to overcome the adverse influence of background wind conditions, strong support from the stable background wind fields is necessary in the regions with a relatively weak SST gradient ([ 0.4 °C/100 km) for the generation of coupling. The observed coupling between the SST and wind is quantified using a coupling coefficient. The coupling between wind stress divergence and downwind SST gradient is more prominent. It is observed that large warming of seawater (SST anomaly [ 1 °C) suppressed the positive linear coupling between anomalies of wind speed and SST in the western AS during the summer monsoon.

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