Tropical cyclone predictions over the Bay of Bengal using the high‐resolution Advanced Research Weather Research and Forecasting (ARW) model

Srinivas, C. V.; Rao, D. V. Subba; Yesubabu, V.; Baskaran, R.; Venkatraman, B.

doi:10.1002/qj.2064

Cited by 124 publications

(63 citation statements)

References 49 publications

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“…1, 2), many studies (e.g., Oouchi et al 2006;Murakami et al 2012;Srinivas et al 2013;Strachan et al 2013) showed the importance of using high-resolution models to capture the observed intensities and tracks of intense TCs. One might argue that the low-resolution simulation results presented in the previous section are less robust on the grounds that the basic structures around the typhoon center, such as the eye wall, are not well reproduced.…”

Section: Results From Nesting Experimentsmentioning

confidence: 99%

Evidence for the significant role of sea surface temperature distributions over remote tropical oceans in tropical cyclone intensity

2015

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Section: Results From Nesting Experimentsmentioning

confidence: 99%

Evidence for the significant role of sea surface temperature distributions over remote tropical oceans in tropical cyclone intensity

2015

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“…The model has several options for spatial discretization, diffusion, nesting, lateral boundary conditions, data assimilation and physics (Skamarock et al, 2008). The ARW for tropical cyclone prediction over the NIO region was customized by simulating 21 past cyclones (Srinivas et al, 2013a) with two nested domains and with physics sensitivity tests. The same domain and physics configuration is adopted for TC predictions over BOB in the present study.…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…The mean forecast errors for operational IMD QLM have been reported as 152, 235 and 375 km for 24, 48 and 72 h forecasts respectively (Prasad and Rama Rao, 2006;Tyagi et al, 2010;Rama Rao et al, 2010). Srinivas et al (2013a) assessed the performance of the ARW model for 21 cases of cyclones that formed between 2000 and 2011 with a horizontal resolution (27, 9 km) similar to operational configuration of IMD WRF and NCEP HWRF for TC predictions over Bay of Bengal (BOB). The mean forecast errors were reported as −2-15 hPa for central sea level pressure (CSLP), 1-22 m s −1 for maximum surface winds (MSW) and 170-250 km for vector track positions corresponding to 24-72 h 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 used physics options for this study are KF2 (Kain 2004) for cumulus convection, WSM3 explicit microphysics, Dudhia scheme (Dudhia 1989) for shortwave radiation processes, RRTM scheme for longwave radiation processes (Mlawer et al 1997), the Yonsei University scheme for the PBL turbulence (Hong et al 2006;Nolan et al 2009aNolan et al , 2009b and five layer soil thermal diffusion scheme for land surface processes. As the main objective of the study is to explain the westward movement of the storm, we used the grid resolution, size of the domains, and the physics options chosen for this study are same as the previous studies (Sathi Devi et al 2006;Bhaskar Rao and Hari Prasad 2006a, b;Srinivas et al 2007Srinivas et al , 2013Bhaskarrao et al 2009Bhaskarrao et al , 2010Hari Prasad and Bhaskar rao 2015) to maintain the consistency with the previous studies.…”

Section: Details Of the Model And Methodologymentioning

confidence: 99%

On the movement of tropical cyclone LEHAR

et al. 2017

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Purpose In this paper, an attempt has been made to delineate the physical processes which lead to the westward movement of the North Indian Ocean tropical cyclone LEHAR. Methods The Advanced Weather Research and Forecasting (ARW) model is used to simulate LEHAR with 27 and 9 km resolutions. In addition to that, all terms of the complete vorticity equation are computed to obtain the contribution of each term for the vorticity tendency. The vorticity tendency is calculated in four sectors, namely northwest, northeast, southwest, and southeast and assumed that the cyclone moves from its existing location to the nearest point where the vortices tendency is maximum. Results The results indicate that the model performed well in simulating the characteristics of cyclone compared with the Satellite and other observations. It is noticed that the vorticity stretching term contributes most to the positive vorticity tendency. The second highest contribution is from the horizontal advection thus indicating the secondary importance of steering. Conclusions The distribution of lightning flash rates are higher in the SW and followed by NW sectors of the cyclone indicate more strong convective clouds are in SW sector. The equivalent potential temperatures (h e ) at different stages of before, during and after the mature stage of the cyclone reveals that the wind-induced surface heat (WISH) exchange process is a plausible mechanism for the intensification of LEHAR.

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Tropical cyclone predictions over the Bay of Bengal using the high‐resolution Advanced Research Weather Research and Forecasting (ARW) model

Cited by 124 publications

References 49 publications

Evidence for the significant role of sea surface temperature distributions over remote tropical oceans in tropical cyclone intensity

Evidence for the significant role of sea surface temperature distributions over remote tropical oceans in tropical cyclone intensity

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

On the movement of tropical cyclone LEHAR

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