Real-time prediction of a severe cyclone ‘Jal’ over Bay of Bengal using a high-resolution mesoscale model WRF (ARW)

Srinivas, C. V.; Yesubabu, V.; Prasad, K. B. R. R. Hari; Ramakrishna, S. S. V. S.; Venkatraman, B.

doi:10.1007/s11069-012-0364-5

Cited by 22 publications

(13 citation statements)

References 37 publications

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“…Several studies on simulation of tropical cyclones with different data sources and different assimilation techniques are reported over North Indian Ocean region (e.g., Mukhopadhyay et al [10], Sandeep et al [11], Vinodkumar et al [3]), andd Srinivas et al [12] did the simulation of BoB cyclone with FDDA technique using Weather Research & Forecasting (WRF) model and reported that the combination of land-based surface, upper-air observations with the satellite winds for assimilation produced better prediction than the assimilation with individual data sets). Similar studies are carried out by Srinivas et al [13] for Jal cyclone over Bay of Bengal.…”

Section: Introductionsupporting

confidence: 74%

Role of Four-Dimensional Data Assimilation on Track and Intensity of Severe Cyclonic Storms

Kanase

Salvekar

2013

ISRN Meteorology

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To examine the role of four dimensional data assimilation (FDDA) on prediction of two Severe Cyclonic Storm (Aila (May 23–26, 2009) andJal (Nov 4–8, 2010)), four numerical experiments are performed using WRF-ARW model with three nested domains having two-way interaction and physical parameterization schemes as CPS-BMJ, MP-WSM6, and PBL-YSU. In each experiment, the model integration is started prior to the formation of depression and continued till the observed landfall. For the experiment without FDDA, NCEP-FNL data alone is used as initial and boundary conditions and for the experiments with FDDA, additional observations are used. In all the experiments, FDDA is considered only in the outer domain upto 24 hrs of integration and then the inner domain is introduced. The results are examined in terms of surface circulation, vorticity, CSLP, MSW, and surface track error. FDDA-produced surface circulation and vorticity showed well-organized structure. For the case of Aila, the surface track (maximum track error: 281 km) and landfall position (88°E/21.73°N) in FDDA experiment are predicted better than experiment without FDDA (track error: 445 km and landfall position 87.13°E/20.37°N) whereas the landfall time experiment without FDDA is closer to observations (between 09 and 12 UTC of May 25) than that of experiment with FDDA(06 UTC of May 25). When CSLP and MSW are examined, the overall intensity is well predicted with FDDA experiment except near to the landfall time. For Jal cyclone, FDDA played significant role to improve the landfall position (80.16°E/13.67°N) with a time lead of ~10 hrs but other parameters remain more or less unchanged.

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Section: Introductionsupporting

confidence: 74%

Role of Four-Dimensional Data Assimilation on Track and Intensity of Severe Cyclonic Storms

Kanase

Salvekar

2013

ISRN Meteorology

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“…This raises an important question as to which type of observation (surface, ship, buoys, radar, upper air and satellite) provides major improvements in predictions. In connection with this, a few recent works (Srinivas et al, 2010(Srinivas et al, , 2012(Srinivas et al, , 2013b indicate the satellite microwave remote-sensing-based scatterometer winds and MODIS temperature and humidity profiles provide larger impacts relative to the conventional data in assimilation. However, the surface observations may also be useful when combined with other 3-D data assimilation systems such as 3D-Var (Yesubabu et al, 2013;Singh et al, 2013).…”

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

confidence: 89%

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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“…It has been found that regional BES assimilation improves the model forecast significantly (Rakesh and Goswami, ; Routray et al, ). The assimilation impact of a variety of observational datasets in the simulation of TCs over the north Indian Ocean has been addressed by researchers for more than a decade (Mandal et al, ; Singh et al, ;; Routray et al, ; Srinivas et al, ; ; Osuri et al . , ).…”

Section: Introductionmentioning

confidence: 99%

Impact of data assimilation and air−sea flux parameterization schemes on the prediction of cyclone Phailin over the Bay of Bengal using the WRF‐ARW model

Singh¹,

Tyagi

2018

Meteorological Applications

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The present study investigates the impact of data assimilation for prediction of the landfalling cyclone Phailin (October 8–13, 2013) over the Bay of Bengal by using the Weather Research and Forecasting Advanced Research model and its 3D variational data assimilation system. The impact of the air−sea flux (ACF) parameterization scheme is also tested with an improved model initial condition. Nine experiments were conducted, six with and without data assimilation at three different initial conditions. Three more experiments were conducted with the available ACF parameterization scheme. In all experiments, the lateral boundary conditions were obtained from the Global Forecast System (GFS) at 0.5° × 0.5° resolution with a temporal resolution of 3 hr, and the initial condition was taken from GFS analysis. The results suggest that assimilation of observations is beneficial for the forecast of tropical cyclone track and intensity. They show that forecasted storm intensity is highly sensitive during the maximum value of intensity to the parameterization of the ACFs, and higher surface intensity was produced by a large surface exchange co‐efficient for moisture. The distribution and magnitude of rainfall and reflectivity of the storm were compared with observed datasets from the Automatic Weather Station and Doppler Weather Radar, respectively. The results show that accumulated rainfall, as well as the structure of the cyclone, are fairly well captured by the model. The predicted higher diabatic heating rate and horizontal eddy diffusivity of heat are also found to be responsible for increasing wind speed of the storm.

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Real-time prediction of a severe cyclone ‘Jal’ over Bay of Bengal using a high-resolution mesoscale model WRF (ARW)

Cited by 22 publications

References 37 publications

Role of Four-Dimensional Data Assimilation on Track and Intensity of Severe Cyclonic Storms

Role of Four-Dimensional Data Assimilation on Track and Intensity of Severe Cyclonic Storms

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

Impact of data assimilation and air−sea flux parameterization schemes on the prediction of cyclone Phailin over the Bay of Bengal using the WRF‐ARW model

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