Indian Coastal Ocean Radar Network

Jena, Basanta Kumar; Arunraj, K. S.; Suseentharan, V.; Kukadiya, Tushar; Karthikeyan, T.

doi:10.18520/cs/v116/i3/372-378

Cited by 21 publications

(5 citation statements)

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“…Ocean current data were not assimilated during any of the experiments. HF-R measures hourly surface currents up to 200 km offshore with a spatial resolution of 6 km (Jena et al, 2019;Paul et al, 2021). We use daily averaged data for estimating RMSE.…”

Section: Resultsmentioning

confidence: 99%

A study of forecast sensitivity to observations in the Bay of Bengal using LETKF

Paul,

Baduru,

Paul

2024

Front. Mar. Sci.

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IntroductionAssimilating all available observations in numerical models may lead to deterioration of the analysis. Ensemble Forecast Sensitivity to Observations (EFSO) is a method that helps to identify all such observations which benefit the analyses. EFSO has never been tested in an ocean data assimilation system because of a lack of robust formulation of a squared norm against which beneficiality of observations can be estimated.MethodsHere, we explore the efficacy of EFSO in the ocean data assimilation system that comprises the ocean model, Regional Ocean Modeling System (ROMS), coupled to the assimilation system Local Ensemble Transform Kalman Filter (LETKF), collectively called LETKF- ROMS, in the Bay of Bengal by envisaging a novel squared norm. The Bay of Bengal is known for its higher stratification and shallow mixed layer depth. In view of baroclinicity representing the stratification of the ocean, we use the modulus of the baroclinic vector as the squared norm to evaluate forecast errors in EFSO.ResultsUsing this approach, we identify beneficial observations. Assimilating only the beneficial observations greatly improves the ocean state. We also show that the improvements are more pronounced in the head of the Bay of Bengal where stratification is much higher compared to the rest of the basin.DiscussionThough this approach doesn’t degrade the ocean state in other regions of the Indian Ocean, a universal squared norm is needed that can be extended beyond the Bay of Bengal basin.

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Section: Resultsmentioning

confidence: 99%

A study of forecast sensitivity to observations in the Bay of Bengal using LETKF

Paul,

Baduru,

Paul

2024

Front. Mar. Sci.

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“…Ocean surface current vectors derived from a pair (separated by ∼100 km) of high frequency (HF) radars installed at the southeastern coast of India (11.7°N, 79.8°E, and 12.5°N and 80.2°E) are also used to describe surface current variability in the southwestern BoB (Jena et al., 2019) (Figure 1; cyan diamonds). The HF radar operating frequency is 4.4 MHz and can provide surface current measurements as far as 200 km from the coast at a spatial resolution of 6 km and a temporal resolution of 1 hr (Jena et al., 2019). Due to variations in the roughness of the sea surface and electromagnetic interference from environmental sources, the HF radar measurements have spatial data gaps.…”

Section: Methodsmentioning

confidence: 99%

Seasonal Variation of the Land Breeze System in the Southwestern Bay of Bengal and Its Influence on Air‐Sea Interactions

et al. 2023

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Sea surface temperature (SST) in the Bay of Bengal (BoB) plays a vital role in determining the spatio-temporal variability of Indian summer monsoon precipitation (Jiang & Li, 2011;Samanta et al., 2018;Shenoi et al., 2002). Due to the persistent occurrence of the relatively thin salt-stratified mixed layers, primarily owing to a large influx of freshwater from intense precipitation and continental rivers (Girishkumar et al., 2011;Rao and Sivakumar, 2003;Shetye et al., 1996;Thadathil et al., 2007), SST in the BoB is highly sensitive to surface fluxes (Duncan & Han, 2009). Moreover, the SST in the BoB is always higher than 28°C (Shenoi et al., 2002), a favorable condition for the formation of deep convection in the tropics. Due to the high mean SST, a small change in its magnitude can significantly feedback to the overlying atmosphere so as to influence the amplitude of intraseasonal and interannual signals over the BoB. Hence, it is imperative to understand the processes affecting air-sea fluxes that modulate SST in the BoB on different temporal scales to better represent ocean-atmosphere interactions in weather and climate forecast models.

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“…High-frequency ground wave radar (HFGWR) can be used to monitor ocean surface sea states with high spatial and temporal resolution [1][2][3]. The working frequency of the HFGWR is in the high-frequency (HF) band .…”

Section: Introductionmentioning

confidence: 99%

High-Frequency Radar Cross Section of Ocean Surface for an FMICW Source

Zhao

Lai

Wang

et al. 2021

JMSE

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The frequency-modulated interrupted continuous waveform (FMICW) has been widely used in remotely sensing sea surface states by high-frequency ground wave radar (HFGWR). However, the radar cross section model of the sea surface for this waveform has not yet been presented. Therefore, the first- and second-order cross section models of the sea surface about this waveform are derived in this study. The derivation begins with the general electric field equations. Subsequently, the FMICW source is introduced as the radar transmitted signal to obtain the FMICW-incorporated backscattered electric field equations. These equations are used to calculate range spectra by Fourier transforming. Therefore, Fourier transformation of the range spectra calculated from successive sweep intervals gives the Doppler spectra or the power spectral densities. The radar cross section model is obtained by directly comparing the Doppler spectra with the standard radar range equation. Moreover, the derived first- and second-order radar cross section models for an FMICW source are simulated and compared with those for a frequency-modulated continuous waveform (FMCW) source. Results show that the cross section models for the FMICW and FMCW cases have different analytical expressions but almost the same numerical results.

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Indian Coastal Ocean Radar Network

Cited by 21 publications

References 0 publications

A study of forecast sensitivity to observations in the Bay of Bengal using LETKF

A study of forecast sensitivity to observations in the Bay of Bengal using LETKF

Seasonal Variation of the Land Breeze System in the Southwestern Bay of Bengal and Its Influence on Air‐Sea Interactions

High-Frequency Radar Cross Section of Ocean Surface for an FMICW Source

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