C.V. Srinivas

Sensitivity Analysis of Atmospheric Dispersion Simulations by FLEXPART to the WRF-Simulated Meteorological Predictions in a Coastal Environment

AbstractIn this study the sensitivity of the natmospheric dispersion model FLEXPART-WRF to the meteorological data inputs simulated by the mesoscale model ARW in the Kalpakkam coastal environment is examined. High-resolution simulations are conducted with ARW using four alternative planetary boundary layer parameterizations (YSU, MYNN, ACM2 and BL) for a typical period 14–22 Sep 2010 characterized with wide variability in atmospheric flow conditions at the coastal site. Observations generated through a field meteorological experiment are used to study the model sensitivity. Wind field, mixed layer depth, temperature and friction velocity are considered as parameters to evaluate dispersion uncertainty from FLEXPART. Results indicate that the simulated dispersion patterns are influenced by meteorological model forecasts using various boundary layer physics options. It has been found that the ensemble mean of all the meteorological members is closer to the observations than the individual cases. Of the various members the YSU and MYNN are found to give best meteorological simulations in the ensemble. The computed dispersion with various meteorological members indicate that ACM2 and BL simulate highest and least diffusivities leading to widely spread plume in the case of ACM2 and relatively narrow plume with BL, respectively. The ensemble mean of all the simulations is found to yield a better representation of the plume under various possible atmospheric conditions. The meteorological members YSU and MYNN are found to give minimum variance and fractional bias with respect to the ensemble. The minimum uncertainty in tracer concentration estimates due to meteorological uncertainty is −30 to 30xa0% obtained with MYNN. The results demonstrate that the dispersion model results are sensitive to the mesoscale model meteorological simulations.

Source term assessment using inverse modeling and environmental radiation measurements for nuclear emergency response

A simple model ‘Assessment of Source Term for Emergency Response (ASTER)’ for estimation of time-dependent atmospheric source term based on real-time environmental gamma dose rate measurements is developed for application in Online Nuclear Emergency Response System (ONERS). The model inverts the measured dose rates to the airborne radioactive release rate using the dilution factors predicted by a random-walk particle atmospheric dispersion model called System for Prediction of Environmental Emergency Dose Information (SPEEDI) taking into account the real-time meteorological observations and location coordinates of the gamma radiation detectors. A point-kernel method for estimating the cloud-gamma dose rates at receptor locations is implemented in SPEEDI for this purpose. ASTER is validated against Ar-41 routine release data from a pressurized heavy-water reactor (PHWR) at Kalpakkam for over 100xa0days covering different seasons during 2014–2015. Comparison of ASTER-simulated overhead plume gamma dose rates with the measured dose rates at detector points gives a correlation of Rxa0=xa00.59, bias of 89xa0nGy/h, and RMSE of 104.09xa0nGy/h indicating a slight overestimation of gamma dose rates at monitor points. Comparison of ASTER-predicted source term with Ar-41 release rates indicated that about 90% of the calculations fall within a factor of 5 of the actual source term. A correlation of 0.584, bias of 0.152xa0TBq/day, and RMSE of 0.192 TBq/day are found between the computed and actual daily source term values indicating reasonable agreement. The slight underestimation in the computed source term is due to the overestimation of the dose rate by the dispersion model, location of few monitors near buildings and tree canopies, uneven distribution of detectors in various wind direction sectors, and the use of daily source term values for comparison.

Impact of Variational Data Assimilation for Simulating Tropical Cyclones over Bay of Bengal Using WRF-ARW

Tropical cyclones, one of the most destructive of all the natural disasters, are capable of causing loss of life and extensive damage to property. The Bay of Bengal is a potentially energetic region for the development of cyclonic storms and about 7% of the global annual tropical storms form over this region with two cyclone seasons in a year. Tropical cyclones have great socio-economic concern for the Indian subcontinent. Precise forecasting of tropical cyclone intensity and track are important for the countries bordering the Bay of Bengal, especially India, Bangladesh and Myanmar due to significant socio-economic impact. There has been remarkable improvement in forecasting of the tropical cyclones with the development of high resolution atmospheric models and the global forecasting systems such as the National Centers for Environmental Predictions (NCEP) Global Forecasting System (GFS). Assimilation of available observations has been considered to be very important for accurate description of initial conditions in numerical models (Park and Zupanski, 2003; Navon, 2009; Pu et al., 2009). In particular, assimilation methods like variational approach has the additional advantage of assimilating observations by satisfying model dynamic and thermodynamic constraints through a set of independent balance equations (in 3DVAR) (Courtier et al., 1998).

Performance of WRF for Simulation of Mesoscale Meteorological Characteristics for Air Quality Assessment over Tropical Coastal City, Chennai

The land–sea breezes (LSBs) play an important role in transporting air pollution from urban areas on the coast. In this study, the Advanced Research WRF (ARW) mesoscale model is used for predicting boundary layer features to understand the transport of pollution in different seasons over the coastal region of Chennai in Southern India. Sensitivity experiments are conducted with two non-local [Yonsei University (YSU) and Asymmetric Convective Model version 2 (ACM2)] and three turbulence kinetic energy (TKE) closure [Mellor–Yamada–Nakanishi and Niino Level 2.5 (MYNN2) and Mellor–Yamada–Janjic (MYJ) and quasi-normal scale elimination (QNSE)], planetary boundary layer (PBL) parameterization schemes for simulating the thermodynamic structure, and low-level atmospheric flow in different seasons. Comparison of simulations with observations from a global positioning system (GPS) radiosonde, meteorological tower, automated weather stations, and Doppler weather radar (DWR)-derived wind data reveals that the characteristics of LSBs vary widely in different seasons and are more prominent during the pre-monsoon and monsoon seasons (March–September) with large horizontal and vertical extents compared to the post-monsoon and winter seasons. The qualitative and quantitative results indicate that simulations with ACM2 followed by MYNN2 and YSU produced various features of the LSBs, boundary layer parameters and the thermo-dynamical structure in better agreement with observations than other tested physical parameterization schemes. Simulations revealed seasonal variation of onset time, vertical extent of LSBs, and mixed layer depth, which would influence the air pollution dispersion in different seasons over the study region.

Turbulence characteristics of surface boundary layer over the Kalpakkam tropical coastal station, India

In this study turbulent fluxes and their intensity features are studied in different seasons at the tropical Indian coastal station, Kalpakkam. Measurements from Ultrasonic anemometer at 10xa0m agl over 30-day period of four seasons (winter 1–30 January; summer/spring 1–30 April; SW monsoon 1–30 July; NE monsoon 1–30 October) in 2013 and 2014 are used for this work. Various surface layer parameters viz, friction velocity (u*), Obukhov length (L), momentum flux (M), turbulent heat flux (H), turbulence kinetic energy (TKE) are computed using eddy correlation method. Results indicate that the study region is highly turbulent in summer followed by NE monsoon, winter and SW monsoon seasons. Derived parameters indicate that shear is the main contributing mechanism for TKE generation during SW monsoon and both shear and buoyancy contributed for the generation of TKE in other seasons. Site specific turbulent intensity relationships were developed by analyzing second order moments of 3D wind components as a function of stability parameter (z/L). The turbulent components of wind followed 1/3 power law in the unstable regime and −u20091 power law in the stable regime. Comparisons with previous studies indicate that the turbulent intensity for horizontal winds at the coastal station is relatively less especially in the unstable conditions. The derived relationships are found to be unique and vary seasonally and suggest their application for improved modeling of atmospheric dispersion in the study domain. Rate of dissipation of TKE (ϕε) for stable and unstable conditions at the observation site is different from the earlier proposed relationships in the literature. Thus, a new relationship is proposed for the better fit of the data at this site.

Data Assimilation Experiments with ARW–3DVAR for Tropical Cyclone Extreme Weather Predictions Over Bay of Bengal

Tropical cyclones (TCs) are highly disastrous weather phenomena characterised with extreme winds, heavy precipitation and storm surges at landfall along coastal lands. Accurate prediction of the TC formation, movement and intensity is vital for early warning and disaster management. The favourable environmental conditions have been identified as presence of an initial disturbance in the form of incipient lows or tropical easterly waves, high sea surface temperature (SST) (≥26.5 °C) for transport of energy through air–sea fluxes, weak vertical wind shear in the 850–200 hPa layer conducive to large-scale cloud development and divergence in the upper atmosphere to facilitate further surface-level convergence and for overall sustenance of the system (Anthes TCs: their evolution, structure and effects. American Meteorological Society, Science press, Ephrata, p 208, 1982; Gray General characteristics of TCs. In: Roger P,Jr, Roger P, Sr (eds) Storms, vol 1. Routledge, 11 New Fetter Lane, London EC4P4EE. pp 145–163, 2000). The annual average frequency of TCs in the North Indian Ocean (NIO) is about five (Asnani Tropical meteorology, vols. 1 and 2, published by Prof. G.C. Asnani, c/o Indian Institute of Tropical Meteorology, Dr. HomiBhabha Road, Pashan, Pune 411008, India, 1993). Numerical prediction of TCs requires accurate specification of initial conditions that define the characteristics of an incipient storm in terms of its location, radius, central pressure, and tangential and vertical winds.