Impact of satellite observed microwave SST on the simulation of tropical cyclones

Bongirwar, Vishal; Rakesh, V.; Kishtawal, C. M.; Joshi, P. C.

doi:10.1007/s11069-010-9699-y

Cited by 17 publications

(9 citation statements)

References 22 publications

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“…Previously, a good number of studies have appeared in the literature, concentrating on the impact of physics parameterization schemes to the simulation of mesoscale weather events. In most of the articles, it is established that not only the physics options (Rao and Srinivas 2014), but also the initialization and boundary conditions (Bongirwar et al 2011;Liu et al 2013;Xue et al 2013;Anisetty et al 2014) play very crucial role on weather event simulation.…”

Section: Introductionmentioning

confidence: 99%

Tracking a tropical cyclone through WRF–ARW simulation and sensitivity of model physics

et al. 2014

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The Weather Research and Forecasting (WRF) model's Advanced Research WRF (ARW) dynamic solver is one of the most popular regional numerical weather prediction models being used by operational and research personnel. In this study, we simulate a tropical cyclone to reproduce the track direction and strength of the storm that formed at low latitudes in the West Pacific Ocean. The cyclone is known as ''Haiyan'' and assessed as category-5 equivalent super typhoon status due to its strong sustained winds and gusts, making it the strongest tropical cyclone in the region. We study the sensitivity of three different model physics options: the microphysics schemes, the planetary boundary layer schemes, and the impact of cumulus parameterization schemes. The realism of the cyclone simulation for different physics options is assessed through the comparison between the model outputs and the best track data, which are taken from the Japan

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Section: Introductionmentioning

confidence: 99%

Tracking a tropical cyclone through WRF–ARW simulation and sensitivity of model physics

et al. 2014

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“…A large number of observational and modelling studies have focused on the impact of SST on TC intensity over different ocean basins (Evans, ; Cione and Uhlhorn, ; Sun et al, ; ; ; Vecchi and Soden, ; Ramsay and Sobel, ; Hegde et al, ; Xu et al, ). Although these findings are significant, such studies are fewer over the north Indian Ocean (Mandal et al, ; Bongirwar et al, ; Rai et al, ; Srinivas et al, ) and the impact of SST resolution on TC intensity is not addressed. The present study was designed to explore the impact of the horizontal resolution of SST on TC forecast over the BoB.…”

Section: Introductionmentioning

confidence: 99%

“…According to Smith et al (2014), the TC intensification rate is more rapid at low latitudes. Bongirwar et al (2011) performed sensitivity experiments using different sets of SST (e.g. satellite observed microwave SST and weekly averaged Reynold's SST) for two intense TCs formed over the Bay of Bengal (BoB).…”

Section: Introductionmentioning

confidence: 99%

Impact of high resolution sea surface temperature on tropical cyclone characteristics over the Bay of Bengal using model simulations

Rai

Pattnaik

Rajesh

et al. 2018

Meteorological Applications

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The Advanced Weather Research and Forecasting (WRF‐ARW) model is used to carry out three simulations to examine the response of varying resolution of the sea surface temperature (SST) on tropical cyclone characteristics up to 72 hr lead time. They are named CNTL (Control, with default SST), SST1 (with SST data at 0.5° × 0.5°) and SST2 (with SST data at 0.083° × 0.083°). It is found that a lower SST (~0.2–0.4 K) within the effective radius (i.e. 2.5 times the radius of the maximum wind) of the storm leads to a weaker storm (about 7 hPa) for SST2 than for CNTL. The storm size in SST2 is about 5 km larger than in CNTL during the 24–72 forecast hours. The reduced storm size and enhanced diabatic heating in the core region facilitate a stronger storm for CNTL compared with SST1 and SST2. As a result, the central pressure of the tropical cyclone deepens further with a stronger radial pressure gradient, leading to a more rapid intensification in the CNTL than the others. A daily update of SST improved the storm track and intensity by 37% and 41% for SST2 and 9% and 20% for SST1 respectively in comparison to CNTL. The study demonstrates that SST with a higher horizontal resolution (0.083° × 0.083°) serves as a useful input parameter for the model to improve the prediction of a cyclone’s track and intensity.

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“…Similarly, assimilation of surface observations in WRF 3D‐Var has been shown to improve simulation of extreme rainfall events over the Indian region [ Routry et al ., ; Dash et al ., ]. Positive impact of satellite observed sea surface temperature (SST) on the quality of simulations of tropical cyclone over the Indian Ocean has been shown with the WRF model [ Bongirwar et al ., ]. Although, the effectiveness and impact of assimilation of various observational data in forecasts over the Indian region have been addressed in various studies, a number of issues need investigation for effective use of data assimilation in rainfall forecasting.…”

Section: Introductionmentioning

confidence: 99%

Impact of data assimilation on high‐resolution rainfall forecasts: A spatial, seasonal, and category analysis

Rakesh

Goswami

2015

JGR Atmospheres

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In a limited area model (LAM), the impact of data assimilation is likely to depend on the background state through lateral boundary forcing; this may introduce certain seasonality in the impact of data assimilation on rainfall forecasting. It is also likely that the impact of data assimilation on forecasts will have certain spatial variability. Finally, owing to the convective nature of rainfall and the roles of parameterization scheme, the impact of data assimilation may depend on the category (intensity) of rainfall. Here these aspects for rainfall forecasts at high resolution were examined. Using a LAM (An advanced version of Weather Research and Forecasting Model), we have carried out twin simulations with and without data assimilation; the simulations without data assimilation are used as the benchmark for assessing the impact of data assimilation. Analysis of simulations for 40 sample days distributed over the years 2012-2014 over Karnataka (southern state in India) is carried out to estimate impact of data assimilation. Various statistical measures show that data assimilation improved the rainfall prediction in most cases; however, there is also strong seasonality and location dependence in impact of data assimilation. Our results also show that improvement due to data assimilation is higher/lower for lower/higher rainfall categories. Analysis shows that the cases where the initial states with data assimilation depart strongly from the first guess generally result in less or even negative impact. It is pointed out that the results have important implications in design of observation system and assessment of impact of forecasts.

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Impact of satellite observed microwave SST on the simulation of tropical cyclones

Cited by 17 publications

References 22 publications

Tracking a tropical cyclone through WRF–ARW simulation and sensitivity of model physics

Tracking a tropical cyclone through WRF–ARW simulation and sensitivity of model physics

Impact of high resolution sea surface temperature on tropical cyclone characteristics over the Bay of Bengal using model simulations

Impact of data assimilation on high‐resolution rainfall forecasts: A spatial, seasonal, and category analysis

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