An Assessment of the Performance of ISRO’s SCATSAT-1 Scatterometer

Bhowmick, Suchandra Aich; Cotton, James; Fore, Alexander; Kumar, Raj; Payan, Christophe; Rodríguez, Ernesto; Sharma, Anuja; Stiles, B.; Stoffelen, Ad; Verhoef, Anton

doi:10.18520/cs/v117/i6/959-972

Cited by 23 publications

(11 citation statements)

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“…The  Clearly, the interpretation of the error model and true variance is not identical in the different comparisons between the different systems. Again, one may speculate on what causes the modest differences between the different triple collocation models and may point to ScatSat retrieval problems (Bhowmick et al, 2019;Ebuchi, 1999) Other possible causes are the time difference of 50 min between the ASCAT-A and ASCAT-B overpasses, insufficiency of linear calibration w.r.t. buoys for one or more observation systems, misspecified error covariances between some of the systems, or observation errors that are not independent of the common signal or not constant in expectation, such as ocean currents.…”

Section: Triple Collocationmentioning

confidence: 99%

Quadruple Collocation Analysis of In‐Situ, Scatterometer, and NWP Winds

Vogelzang

Stoffelen

2021

JGR Oceans

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The triple collocation method, introduced by Stoffelen (1998), is a well-established method for calculating the relative linear calibration coefficients and absolute error variances of a data set consisting of triplets of in space and time collocated measurements. The method has, e.g., been applied to ocean vector winds measured by scatterometers (McColl et al., 2014;Stoffelen, 1998;Vogelzang et al., 2011), to ocean surface wind speed from scatterometer and altimeter (Abdalla & De Chiara, 2017), and soil moisture from scatterometers and radiometers (e.g., Gruber et al., 2016b, and references therein).The triple collocation equations are solved assuming that the error covariances are zero. In most cases, this is not the case, because error covariances originate not only from correlated measurement errors, but also from differences in resolution between the various observing systems involved. The latter error covariances are known as representativeness errors, and they can easily be included in the multicollocation formalism by correcting the observed covariances for them. The first estimate of representativeness errors was given by Stoffelen (1998) in the spectral domain assuming a k −5/3 spectrum. This was refined by Vogelzang

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Section: Triple Collocationmentioning

confidence: 99%

Quadruple Collocation Analysis of In‐Situ, Scatterometer, and NWP Winds

Vogelzang

Stoffelen

2021

JGR Oceans

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“…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. More details of SCATSat‐1 retrieved winds are available in Bhowmick et al (), Jaiswal et al (), and Kumar et al ().…”

Section: Scatsat‐1 Scatterometermentioning

confidence: 99%

“…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%

“…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%

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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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“…On the other hand, Ku-band scatterometers are more sensitive to wind speed (especially variations at low wind speed) and direction, but they are subject to much higher variability than C-band scatterometers [14]. Therefore the C-band Advanced Scatterometer (ASCAT) is expected to provide a more robust wind retrieval associated with the outflow boundaries characteristics near precipitation than scatterometer satellite-1 (Scatsat-1; [15][16][17]). In case the cold pool edges are found within the precipitation itself, scatterometer retrievals may be degraded.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Detection of Mesoscale Outflow Boundaries Using Scatterometer Winds at Different Spatial Resolutions

et al. 2021

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Outflow boundaries induced by cold-pools are a key characteristic of convective systems related to microphysical and kinematic processes during the mature stage of their lifecycle. Over the ocean, such kinematic processes are associated with low-level wind modulations that are captured by scatterometers. This study investigates the ability of the Advanced Scatterometer (ASCAT) wind retrievals to detect the outflow boundary associated with an oceanic mesoscale convective system (MCS). Leveraging a new technique to identify cold pools that is based on features that enclose elevated magnitude of the gradient of the wind, termed as `Gradient Feature’ (GF), wind retrievals at 50-, 25- and 7-km spatial resolution were utilized to explore how the characteristics of the outflow boundary vary with resolution. Ground-based radar retrievals were also implemented to assess and correct, when possible, the performance of the ASCAT retrievals. The magnitude of the gradient of the wind for the coarser resolution was an order of magnitude smaller (10−4 s−1) than the finer ones (10−3 s−1). An increase in the magnitude of the gradient wind field associated with the outflow boundary was captured by all resolutions and a respective feature was identified by the GF method. The location of the features relative to the distance from the front edge of the MCS decreased with resolution, indicating the importance of the high resolution ASCAT product to capture their extent, as well as additional smaller scale features. The effect of the background wind field on the selection of the final wind field during the ambiguity removal process for the high-resolution product is also discussed.

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An Assessment of the Performance of ISRO’s SCATSAT-1 Scatterometer

Cited by 23 publications

References 0 publications

Quadruple Collocation Analysis of In‐Situ, Scatterometer, and NWP Winds

Quadruple Collocation Analysis of In‐Situ, Scatterometer, and NWP Winds

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

Evaluating the Detection of Mesoscale Outflow Boundaries Using Scatterometer Winds at Different Spatial Resolutions

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