Krishna K. Osuri

Bio: Krishna K. Osuri is an academic researcher from National Institute of Technology, Rourkela. The author has contributed to research in topics: Tropical cyclone & Weather Research and Forecasting Model. The author has an hindex of 19, co-authored 58 publications receiving 1074 citations. Previous affiliations of Krishna K. Osuri include Indian Institute of Technology Bhubaneswar & Purdue University.

Customization of WRF-ARW model with physical parameterization schemes for the simulation of tropical cyclones over North Indian Ocean

Abstract: The convection and planetary boundary layer (PBL) processes play significant role in the genesis and intensification of tropical cyclones (TCs). Several convection and PBL parameterization schemes incorporate these processes in the numerical weather prediction models. Therefore, a systematic intercomparison of performance of parameterization schemes is essential to customize a model. In this context, six combinations of physical parameterization schemes (2 PBL Schemes, YSU and MYJ, and 3 convection schemes, KF, BM, and GD) of WRF-ARW model are employed to obtain the optimum combination for the prediction of TCs over North Indian Ocean. Five cyclones are studied for sensitivity experiments and the out-coming combination is tested on real-time prediction of TCs during 2008. The tracks are also compared with those provided by the operational centers like NCEP, ECMWF, UKMO, NCMRWF, and IMD. It is found that the combination of YSU PBL scheme with KF convection scheme (YKF) provides a better prediction of intensity, track, and rainfall consistently. The average RMSE of intensity (13 hPa in CSLP and 11 m s−1 in 10-m wind), mean track, and landfall errors is found to be least with YKF combination. The equitable threat score (ETS) of YKF combination is more than 0.2 for the prediction of 24-h accumulated rainfall up to 125 mm. The vertical structural characteristics of cyclone inner core also recommend the YKF combination for Indian seas cyclones. In the real-time prediction of 2008 TCs, the 72-, 48-, and 24-h mean track errors are 172, 129, and 155 km and the mean landfall errors are 125, 73, and 66 km, respectively. Compared with the track of leading operational agencies, the WRF model is competing in 24 h (116 km error) and 72 h (166 km) but superior in 48-h (119 km) track forecast.

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

Abstract: 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 respectto1)basinsofformation,2)straight-movingandrecurvingTCs,3)TCintensityatmodelinitialization, and 4) season of occurrence. The impact of high resolution (18 and 9km) 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 375km (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 9km) 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.

Assessment of the Weather Research and Forecasting (WRF) model for simulation of extreme rainfall events in the upper Ganga Basin

Abstract: Reliable estimates of extreme rainfall events are necessary for an accurate prediction of floods Most of the global rainfall products are available at a coarse resolution, rendering them less desirable for extreme rainfall analysis Therefore, regional mesoscale models such as the advanced research version of the Weather Research and Forecasting (WRF) model are often used to provide rainfall estimates at fine grid spacing Modelling heavy rainfall events is an enduring challenge, as such events depend on multi-scale interactions, and the model configurations such as grid spacing, physical parameterization and initialization With this background, the WRF model is implemented in this study to investigate the impact of different processes on extreme rainfall simulation, by considering a representative event that occurred during 15–18 June 2013 over the Ganga Basin in India, which is located at the foothills of the Himalayas This event is simulated with ensembles involving four different microphysics (MP), two cumulus (CU) parameterizations, two planetary boundary layers (PBLs) and two land surface physics options, as well as different resolutions (grid spacing) within the WRF model The simulated rainfall is evaluated against the observations from 18 rain gauges and the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) 3B42RT version 7 data From the analysis, it should be noted that the choice of MP scheme influences the spatial pattern of rainfall, while the choice of PBL and CU parameterizations influences the magnitude of rainfall in the model simulations Further, the WRF run with Goddard MP, Mellor–Yamada–Janjic PBL and Betts–Miller–Janjic CU scheme is found to perform best in simulating this heavy rain event The selected configuration is evaluated for several heavy to extremely heavy rainfall events that occurred across different months of the monsoon season in the region The model performance improved through incorporation of detailed land surface processes involving prognostic soil moisture evolution in Noah scheme compared to the simple Slab model To analyse the effect of model grid spacing, two sets of downscaling ratios – (i) 1 : 3, global to regional (G2R) scale and (ii) 1 : 9, global to convection-permitting scale (G2C) – are employed Results indicate that a higher downscaling ratio (G2C) causes higher variability and consequently large errors in the simulations Therefore, G2R is adopted as a suitable choice for simulating heavy rainfall event in the present case study Further, the WRF-simulated rainfall is found to exhibit less bias when compared with the NCEP FiNaL (FNL) reanalysis data

Simulation of Bay of Bengal Tropical Cyclones with WRF Model: Impact of Initial and Boundary Conditions

Abstract: An attempt is made to delineate the relative performances and credentials of GFS, FNL, and NCMRWF global analyses/forecast products as initial and boundary conditions (IBCs) to the WRF-ARW model in the simulation of four Bay of Bengal tropical cyclones (TCs). The results suggest that FNL could simulate horizontal advection of vorticity maxima at 850 hPa; hence, the tracks are more realistic with least errors as compared to GFS and NCMRWF. The mean landfall errors for 24-, 48-, and 72-hour forecasts are 73, 41, and 72 km, respectively. The TC intensity is well captured by NCMRWF IBCs, as it could predict 850 hPa vorticity maxima. The 24-hour accumulated rainfall is well simulated with FNL, and equitable threat score is more than 0.2 up to 100 mm with minimum bias.

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

Abstract: In the present satellite era, remote-sensing data are more useful to improve the initial condition of the model and hence the forecast of tropical cyclones (TCs) when they are in the deep oceans, where conventional observations are unavailable. In this study, an attempt is made to assess the impact of remotely sensed satellite-derived winds on initialization and simulation of TCs over the North Indian Ocean (NIO). For this purpose, four TCs, namely, ‘Nargis’, ‘Gonu’, ‘Sidr’ and ‘KhaiMuk’, are considered, with 13 different initial conditions. Two sets of numerical experiments, with and without satellite-derived wind data assimilation, are conducted using a high-resolution weather research and forecasting (WRF) model. The inclusion of satellite-derived winds through a three-dimensional variational (3DVAR) data assimilation system improves the initial position in 11 cases out of 13 by 34%. The 24-, 48-, 72- and 96-hour mean track forecast improves by 28%, 15%, 41% and 47%, respectively, based on 13 cases. Th...

Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model [presentation]

Abstract: Abstract A two-dimensional version of the Pennsylvania State University mesoscale model has been applied to Winter Monsoon Experiment data in order to simulate the diurnally occurring convection observed over the South China Sea. The domain includes a representation of part of Borneo as well as the sea so that the model can simulate the initiation of convection. Also included in the model are parameterizations of mesoscale ice phase and moisture processes and longwave and shortwave radiation with a diurnal cycle. This allows use of the model to test the relative importance of various heating mechanisms to the stratiform cloud deck, which typically occupies several hundred kilometers of the domain. Frank and Cohen's cumulus parameterization scheme is employed to represent vital unresolved vertical transports in the convective area. The major conclusions are: Ice phase processes are important in determining the level of maximum large-scale heating and vertical motion because there is a strong anvil componen...

Rapid development of the tropical cyclone warm core

Abstract: This paper presents a simple theoretical argument to isolate the conditions under which a tropical cyclone can rapidly develop a warm-core thermal structure and subsequently approach a steady state. The theoretical argument is based on the balanced vortex model and, in particular, on the associated transverse circulation equation and the geopotential tendency equation. These second-order partial differential equations contain the diabatic forcing and three spatially varying coefficients: the static stability A, the baroclinity B, and the inertial stability C. Thus, the transverse circulation and the temperature tendency in a tropical vortex depend not only on the diabatic forcing but also on the spatial distributions of A, B, and C. Experience shows that the large radial variations of C are typically the most important effect. Under certain simplifying assumptions as to the vertical structure of the diabatic forcing and the spatial variability of A, B, and C, the transverse circulation equation and the geopotential tendency equation can be solved via separation of variables. The resultingradialstructureequationsretainthedynamicallyimportantradialvariationofCandcanbe solvedin terms of Green’s functions. These analytical solutions show that the vortex response to a delta function in the diabatic heating depends critically on whether the heating occurs in the low-inertial-stability region outside the radius of maximum wind or in the high-inertial-stability region inside the radius of maximum wind. This resultsuggeststhatrapidintensification isfavoredforstormsthathaveat leastsome oftheeyewallconvection inside the radius of maximum wind. The development of an eye partially removes diabatic heating from the high-inertial-stability region of the storm center; however, rapid intensification may continue if the eyewall heating continues to become more efficient. As the warm core matures and static stability increases over the inner core, conditions there become less favorable for deep upright convection and the storm tends to approach a steady state.

Multiple Radar Data Assimilation and Short-range Quantitative Precipitation Forecasting of a Squall Line Observed during IHOP_2002

Abstract: Abstract The impact of multiple–Doppler radar data assimilation on quantitative precipitation forecasting (QPF) is examined in this study. The newly developed Weather Research and Forecasting (WRF) model Advanced Research WRF (ARW) and its three-dimensional variational data assimilation system (WRF 3DVAR) are used. In this study, multiple–Doppler radar data assimilation is applied in WRF 3DVAR cycling mode to initialize a squall-line convective system on 13 June 2002 during the International H2O Project (IHOP_2002) and the ARW QPF skills are evaluated for the case. Numerical experiments demonstrate that WRF 3DVAR can successfully assimilate Doppler radial velocity and reflectivity from multiple radar sites and extract useful information from the radar data to initiate the squall-line convective system. Assimilation of both radial velocity and reflectivity results in sound analyses that show adjustments in both the dynamical and thermodynamical fields that are consistent with the WRF 3DVAR balance constrai...