Multivariate background error covariances in the assimilation of SAPHIR radiances in the simulation of three tropical cyclones over the Bay of Bengal using the WRF model

2017 ◽  
Vol 39 (1) ◽  
pp. 191-209 ◽  
Author(s):  
M. Dhanya ◽  
A. Chandrasekar
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Meenakshi Shenoy ◽  
P. V. S. Raju ◽  
Jagdish Prasad

AbstractEvaluation of appropriate physics parameterization schemes for the Weather Research and Forecasting (WRF) model is vital for accurately forecasting tropical cyclones. Three cyclones Nargis, Titli and Fani have been chosen to investigate the combination of five cloud microphysics (MP), three cumulus convection (CC), and two planetary boundary layer (PBL) schemes of the WRF model (ver. 4.0) with ARW core with respect to track and intensity to determine an optimal combination of these physical schemes. The initial and boundary conditions for sensitivity experiments are drawn from the National Centers for Environmental Prediction (NCEP) global forecasting system (GFS) data. Simulated track and intensity of three cyclonic cases are compared with the India Meteorological Department (IMD) observations. One-way analysis of variance (ANOVA) is applied to check the significance of the data obtained from the model. Further, Tukey’s test is applied for post-hoc analysis in order to identify the cluster of treatments close to IMD observations for all three cyclones. Results are obtained through the statistical analysis; average root means square error (RMSE) of intensity throughout the cyclone period and time error at landfall with the step-by-step elimination method. Through the elimination method, the optimal scheme combination is obtained. The YSU planetary boundary layer with Kain–Fritsch cumulus convection and Ferrier microphysics scheme combination is identified as an optimal combination in this study for the forecasting of tropical cyclones over the Bay of Bengal.


2016 ◽  
Vol 37 (13) ◽  
pp. 3086-3103 ◽  
Author(s):  
M. Dhanya ◽  
Deepak Gopalakrishnan ◽  
A. Chandrasekar ◽  
Sanjeev Kumar Singh ◽  
V.S. Prasad

2010 ◽  
Vol 33 (4) ◽  
pp. 294-314 ◽  
Author(s):  
U. C. Mohanty ◽  
Krishna K. Osuri ◽  
A. Routray ◽  
M. Mohapatra ◽  
Sujata Pattanayak

2018 ◽  
Vol 66 (1) ◽  
pp. 79-86
Author(s):  
Ashik Imran ◽  
Ishtiaque M Syed ◽  
SM Quamrul Hassan ◽  
Kh Hafizur Rahman

Tropical cyclones (TCs) over Bay of Bengal (BoB) have significant socio-economic impacts on the countries bordering the BoB. In this study, we have examined the structure and thermodynamic features of the TC Hudhud (7th -14th October, 2014) using WRF model. Simulated outputs are in good agreement with the available observations of India Meteorological Department and Joint Typhoon warning Center. At maximum intensity stage, the system’s horizontal size is found around 690 km. Wind and vorticity distributions capture the circulation of the system very well. Most strong winds of 60 ms−1 are extended vertically from 850 hPa to about 700 hPa. Simulation has shown intensification of the system above 200 hPa with wind speed of about 30ms−1. Relative humidity of the order of 90 % is found up to 400 hPa. Dhaka Univ. J. Sci. 66(1): 79-86, 2018 (January)


2021 ◽  
Author(s):  
Harish Baki ◽  
Sandeep Chinta ◽  
C. Balaji ◽  
Balaji Srinivasan

Abstract. The present study focuses on identifying the parameters from the Weather Research and Forecasting (WRF) model that strongly influence the prediction of tropical cyclones over the Bay of Bengal (BoB) region. Three global sensitivity analysis (SA) methods, namely the Morris One-at-A-Time (MOAT), Multivariate Adaptive Regression Splines (MARS), and surrogate-based Sobol' are employed to identify the most sensitive parameters out of 24 tunable parameters corresponding to seven parameterization schemes of the WRF model. Ten tropical cyclones across different categories, such as cyclonic storms, severe cyclonic storms, and very severe cyclonic storms over BoB between 2011 and 2018, are selected in this study. The sensitivity scores of 24 parameters are evaluated for eight meteorological variables. The parameter sensitivity results are consistent across three SA methods for all the variables, and 8 out of the 24 parameters contribute 80 %–90 % to the overall sensitivity scores. It is found that the Sobol' method with Gaussian progress regression as a surrogate model can produce reliable sensitivity results when the available samples exceed 200. The parameters with which the model simulations have the least RMSE values when compared with the observations are considered as the optimal parameters. Comparing observations and model simulations with the default and optimal parameters shows that predictions with the optimal set of parameters yield a 16.74 % improvement in the 10 m wind speed, 3.13 % in surface air temperature, 0.73 % in surface air pressure, and 9.18 % in precipitation predictions compared to the default set of parameters.


Radio Science ◽  
2018 ◽  
Vol 53 (11) ◽  
pp. 1356-1367 ◽  
Author(s):  
Gargi Rakshit ◽  
Soumyajyoti Jana ◽  
Animesh Maitra

2007 ◽  
Vol 135 (12) ◽  
pp. 4006-4029 ◽  
Author(s):  
C. A. Reynolds ◽  
M. S. Peng ◽  
S. J. Majumdar ◽  
S. D. Aberson ◽  
C. H. Bishop ◽  
...  

Abstract Adaptive observing guidance products for Atlantic tropical cyclones are compared using composite techniques that allow one to quantitatively examine differences in the spatial structures of the guidance maps and relate these differences to the constraints and approximations of the respective techniques. The guidance maps are produced using the ensemble transform Kalman filter (ETKF) based on ensembles from the National Centers for Environmental Prediction and the European Centre for Medium-Range Weather Forecasts (ECMWF), and total-energy singular vectors (TESVs) produced by ECMWF and the Naval Research Laboratory. Systematic structural differences in the guidance products are linked to the fact that TESVs consider the dynamics of perturbation growth only, while the ETKF combines information on perturbation evolution with error statistics from an ensemble-based data assimilation scheme. The impact of constraining the SVs using different estimates of analysis error variance instead of a total-energy norm, in effect bringing the two methods closer together, is also assessed. When the targets are close to the storm, the TESV products are a maximum in an annulus around the storm, whereas the ETKF products are a maximum at the storm location itself. When the targets are remote from the storm, the TESVs almost always indicate targets northwest of the storm, whereas the ETKF targets are more scattered relative to the storm location and often occur over the northern North Atlantic. The ETKF guidance often coincides with locations in which the ensemble-based analysis error variance is large. As the TESV method is not designed to consider spatial differences in the likely analysis errors, it will produce targets over well-observed regions, such as the continental United States. Constraining the SV calculation using analysis error variance values from an operational 3D variational data assimilation system (with stationary, quasi-isotropic background error statistics) results in a modest modulation of the target areas away from the well-observed regions, and a modest reduction of perturbation growth. Constraining the SVs using the ETKF estimate of analysis error variance produces SV targets similar to ETKF targets and results in a significant reduction in perturbation growth, due to the highly localized nature of the analysis error variance estimates. These results illustrate the strong sensitivity of SVs to the norm (and to the analysis error variance estimate used to define it) and confirm that discrepancies between target areas computed using different methods reflect the mathematical and physical differences between the methods themselves.


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