The impact of satellite-derived wind data assimilation on track, intensity and structure of tropical cyclones over the North Indian Ocean

Osuri, Krishna K.; Mohanty, U. C.; Routray, Aurobinda; Mohapatra, M.

doi:10.1080/01431161.2011.596849

Cited by 78 publications

(34 citation statements)

References 22 publications

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“…The application of such mesoscale models for TC forecasting over the NIO is a recent development. Osuri et al (2012a) demonstrated the promising ability of the ARW model for realtime prediction of TC track and intensity over the NIO during the 2008 season. This study also demonstrated that the performance of the ARW model was reasonably good in comparison with other global models.…”

Section: Introductionmentioning

confidence: 96%

“…The other core version, the Nonhydrostatic Mesoscale Model (NMM), was developed at the Environmental Modeling Center of the National Centers for Environmental Prediction (NCEP). Over the Indian monsoon region, and indeed globally, the ARW model is being widely used for the simulation of a variety of weather events, such as heavy rainfall (Niyogi et al 2006;Routray et al 2010;Dodla and Ratna 2010;Hong and Lee 2009) and TCs (Osuri et al 2012a;Pattanaik and Rama Rao 2009;Davis et al 2008). The ARW model has been used for real-time TC forecasting since 2007.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Track Prediction of Tropical Cyclones over the North Indian Ocean Using the ARW Model

Osuri¹,

Mohanty²,

Routray³

et al. 2013

Journal of Applied Meteorology and Climatology

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103

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The performance of the Advanced Research version of the Weather Research and Forecasting (ARW) model in real-time prediction of tropical cyclones (TCs) over the north Indian Ocean (NIO) at 27-km resolution is evaluated on the basis of 100 forecasts for 17 TCs during 2007-11. The analyses are carried out with respect to 1) basins of formation, 2) straight-moving and recurving TCs, 3) TC intensity at model initialization, and 4) season of occurrence. The impact of high resolution (18 and 9 km) on TC prediction is also studied. Model results at 27-km resolution indicate that the mean track forecast errors (skill with reference to persistence track) over the NIO were found to vary from 113 to 375 km (7%-51%) for a 12-72-h forecast. The model showed a right/eastward and slow bias in TC movement. The model is more skillful in track prediction when initialized at the intensity stage of severe cyclone or greater than at the intensity stage of cyclone or lower. The model is more efficient in predicting landfall location than landfall time. The higher-resolution (18 and 9 km) predictions yield an improvement in mean track error for the NIO Basin by about 4%-10% and 8%-24%, respectively. The 9-km predictions were found to be more accurate for recurving TC track predictions by ;13%-28% and 5%-15% when compared with the 27-and 18-km runs, respectively. The 9-km runs improve the intensity prediction by 15%-40% over the 18-km predictions. This study highlights the capabilities of the operational ARW model over the Indian monsoon region and the continued need for operational forecasts from high-resolution models.

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Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Real-Time Track Prediction of Tropical Cyclones over the North Indian Ocean Using the ARW Model

Osuri¹,

Mohanty²,

Routray³

et al. 2013

Journal of Applied Meteorology and Climatology

Self Cite

103

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show abstract

“…The impact of data assimilation on simulation of TCs over the NIO has been studied by several authors in the last decade (e.g. Singh et al, 2008Singh et al, , 2011Singh et al, , 2012aXiao et al, 2009;Krishna et al, 2010Krishna et al, , 2012Srinivas et al, 2010;Prashant et al, 2012;among others). However, most of these were individual case studies using various model configurations and with significant differences in the results due to the use of various data sources (satellite, radar, conventional, etc.)…”

Section: M Greeshma Et Al: Impact Of Local Data Assimilation On mentioning

confidence: 99%

“…Several model performance evaluation studies have been conducted for TC predictions in recent times (e.g. Prasad and Rama Rao, 2006;Davis et al, 2008;McNoldy et al, 2010;Yeh et al, 2012;Zhang et al, 2011;Srinivas et al, 2013;Krishna et al, 2012 among others) over different regions. In the USA, the Geophysical Fluid Dynamics Laboratory (GFDL) hurricane model and the Hurricane Weather Research and Forecasting (HWRF) model (Tallapragada et al, 2014) are widely used for operational hurricane predictions.…”

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confidence: 99%

Impact of local data assimilation on tropical cyclone predictions over the Bay of Bengal using the ARW model

et al. 2015

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Abstract. The tropical cyclone (TC) track and intensity predictions over Bay of Bengal (BOB) using the Advanced Research Weather Research and Forecasting (ARW) model are evaluated for a number of data assimilation experiments using various types of data. Eight cyclones that made landfall along the east coast of India during 2008-2013 were simulated. Numerical experiments included a control run (CTL) using the National Centers for Environmental Prediction (NCEP) 3-hourly 0.5 × 0.5 • resolution Global Forecasting System (GFS) analysis as the initial condition, and a series of cycling mode variational assimilation experiments with Weather Research and Forecasting (WRF) data assimilation (WRFDA) system using NCEP global PrepBUFR observations (VARPREP), Atmospheric Motion Vectors (VARAMV), Advanced Microwave Sounding Unit (AMSU) A and B radiances (VARRAD) and a combination of PrepBUFR and RAD (VARPREP+RAD). The impact of different observations is investigated in detail in a case of the strongest TC, Phailin, for intensity, track and structure parameters, and finally also on a larger set of cyclones. The results show that the assimilation of AMSU radiances and Atmospheric Motion Vectors (AMV) improved the intensity and track predictions to a certain extent and the use of operationally available NCEP PrepBUFR data which contains both conventional and satellite observations produced larger impacts leading to improvements in track and intensity forecasts. The forecast improvements are found to be associated with changes in pressure, wind, temperature and humidity distributions in the initial conditions after data assimilation. The assimilation of mass (radiance) and wind (AMV) data showed different impacts. While the motion vectors mainly influenced the track predictions, the radiance data merely influenced forecast intensity. Of various experiments, the VARPREP produced the largest impact with mean errors (India Meteorological Department (IMD) observations less the model values) of 78, 129, 166, 210 km in the vector track position, 10.3, 5.8, 4.8, 9.0 hPa deeper than IMD data in central sea level pressure (CSLP) and 10.8, 3.9, −0.2, 2.3 m s −1 stronger than IMD data in maximum surface winds (MSW) for 24, 48, 72, 96 h forecasts respectively. An improvement of about 3-36 % in track, 6-63 % in CSLP, 26-103 % in MSW and 11-223 % in the radius of maximum winds in 24-96 h lead time forecasts are found with VARPREP over CTL, suggesting the advantages of assimilation of operationally available PrepBUFR data for cyclone predictions. The better predictions with PrepBUFR could be due to quality-controlled observations in addition to containing different types of data (conventional, satellite) covering an effectively larger area. The performance degradation of VARPREP+RAD with the assimilation of all available observations over the domain after 72 h could be due to poor area coverage and bias in the radiance data.

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“…The quality of initial conditions can be improved with the mesoscale data assimilation of high dense observations. Several previous studies have demonstrated that the assimilation of sea surface and upper air satellite-derived winds near and around the centre of the storm can substantially improve the initial analyses of TCs and hence the prediction of track, intensity and structure (Velden et al 1998, Chen 2007, Pu et al 2008, Osuri et al 2012b). …”

Section: Introductionmentioning

confidence: 99%

Simulation of Structure, Track and Landfall of Tropical Cyclone Bijli Using WRF-ARW Model

Rayhun

Quadir

Chowdhury

et al. 2015

J Bangladesh Acad Sci

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The impact of satellite-derived wind data assimilation on track, intensity and structure of tropical cyclones over the North Indian Ocean

Cited by 78 publications

References 22 publications

Real-Time Track Prediction of Tropical Cyclones over the North Indian Ocean Using the ARW Model

Real-Time Track Prediction of Tropical Cyclones over the North Indian Ocean Using the ARW Model

Impact of local data assimilation on tropical cyclone predictions over the Bay of Bengal using the ARW model

Simulation of Structure, Track and Landfall of Tropical Cyclone Bijli Using WRF-ARW Model

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