Tropospheric biennial oscillation and south Asian summer monsoon rainfall in a coupled model

In this study Tropospheric Biennial Oscillation (TBO) and south Asian summer monsoon rainfall are examined in the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFSv2) hindcast. High correlation between the observations and model TBO index suggests that the model is able to capture most of the TBO years. Spatial patterns of rainfall anomalies associated with positive TBO over the south Asian region are better represented in the model as in the observations. However, the model predicted rainfall anomaly patterns associated with negative TBO years are improper and magnitudes are underestimated compared to the observations. It is noted that positive (negative) TBO is associated with La Niña (El Niño) like Sea surface temperature (SST) anomalies in the model. This leads to the fact that model TBO is El Niño-Southern Oscillation (ENSO) driven, while in the observations Indian Ocean Dipole (IOD) also plays a role in the negative TBO phase. Detailed analysis suggests that the negative TBO rainfall anomaly pattern in the model is highly influenced by improper teleconnections allied to IOD. Unlike in the observations, rainfall anomalies over the south Asian region are anti-correlated with IOD index in CFSv2. Further, summer monsoon rainfall over south Asian region is highly correlated with IOD western pole than eastern pole in CFSv2 in contrast to the observations. Altogether, the present study highlights the importance of improving Indian Ocean SST teleconnections to south Asian summer rainfall in the model by enhancing the predictability of TBO. This in turn would improve monsoon rainfall prediction skill of the model.     

On the role of the Planetary Boundary Layer in the numerical simulation of a severe cyclonic storm Nargis using a mesoscale model

The very severe cyclonic storm Nargis of 2008 was a strong tropical cyclone that caused the deadliest natural disaster in the history of Myanmar. The time tested NCAR/PSU MM5 model has been used to simulate the Nargis cyclone, which is designed to have two domains covering the Bay of Bengal with horizontal resolutions of 90 and 30?km. The physics options chosen are Kain?CFritsch 2 for convection, Blackadar (BLA), Burk?CThompson, medium range forecast (MRF), Eta Mellor?CYamada (Eta MY) and Gayno?CSeaman (GS) for Planetary Boundary Layer (PBL) and Simple Ice for explicit cloud physics processes. The experiment was conducted with the model integration starting from April 27, 2008, to May 3, 2008. The performance of the five PBL schemes is evaluated in terms of radius height cross-section of the three component winds, surface heat fluxes of sensible heat and latent heat, equivalent potential temperature (?? _e ), precipitation, track and variation of Central Surface Pressure and wind speed with time. The numerical results show a large impact of the PBL schemes on the intensity and movement of the system. The intensity of the storm is examined in terms of pressure drop, strength of the surface wind and rainfall associated with the storm. The results are compared to the India Meteorological Department observations. These experiments indicate that the intensity of the storm is well simulated with the Eta MY and BLA with finer resolution. The simulated track with MRF compared well with the Joint Typhoon Warning Center observation at landfall position both with the 90 and 30?km resolutions.     

Numerical simulation of cyclonic storms FANOOS, NARGIS with assimilation of conventional and satellite observations using 3-DVAR

In this work, the impact of assimilation of conventional and satellite data is studied on the prediction of two cyclonic storms in the Bay of Bengal using the three-dimensional variational data assimilation (3D-VAR) technique. The FANOOS cyclone (December 6?C10, 2005) and the very severe cyclone NARGIS (April 28?CMay 2, 2008) were simulated with a double-nested weather research and forecasting (WRF-ARW) model at a horizontal resolution of 9?km. Three numerical experiments were performed using the WRF model. The back ground error covariance matrix for 3DVAR over the Indian region was generated by running the model for a 30-day period in November 2007. In the control run (CTL), the National Centers for Environmental Prediction (NCEP) global forecast system analysis at 0.5° resolution was used for the initial and boundary conditions. In the second experiment called the VARCON, the conventional surface and upper air observations were used for assimilation. In the third experiment (VARQSCAT), the ocean surface wind vectors from quick scatterometer (QSCAT) were used for assimilation. The CTL and VARCON experiments have produced higher intensity in terms of sea level pressure, winds and vorticity fields but with higher track errors. Assimilation of conventional observations has meager positive impact on the intensity and has led to negative impact on simulated storm tracks. The QSCAT vector winds have given positive impact on the simulations of intensity and track positions of the two storms, the impact is found to be relatively higher for the moderate intense cyclone FANOOS as compared to very severe cyclone NARGIS.     

Impact of period and timescale of FDDA analysis nudging on the numerical simulation of tropical cyclones in the Bay of Bengal

In this study, the impact of four-dimensional data assimilation (FDDA) analysis nudging is examined on the prediction of tropical cyclones (TC) in the Bay of Bengal to determine the optimum period and timescale of nudging. Six TCs (SIDR: November 13–16, 2007; NARGIS: April 29–May 02, 2008; NISHA: November 25–28, 2008; AILA: May 23–26, 2009; LAILA: May 18–21, 2010; JAL: November 04–07, 2010) were simulated with a doubly nested Weather Research and Forecasting (WRF) model with a horizontal resolution of 9 km in the inner domain. In the control run for each cyclone, the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) analysis and forecasts at 0.5° resolution are used for initial and boundary conditions. In the FDDA experiments available surface, upper air observations obtained from NCEP Atmospheric Data Project (ADP) data sets were used for assimilation after merging with the first guess through objective analysis procedure. Analysis nudging experiments with different nudging periods (6, 12, 18, and 24 h) indicated a period of 18 or 24 h of nudging during the pre-forecast stage provides maximum impact on simulations in terms of minimum track and intensity forecasts. To determine the optimum timescale of nudging, two cyclone cases (NARGIS: April 28–May 02, 2008; NISHA: November 25–28, 2008) were simulated varying the inverse timescales as 1.0e?4 to 5.0e?4 s^?1 in steps of 1.0e?4 s^?1. A positive impact of assimilation is found on the simulated characteristics with a nudging coefficient of either 3.0e?4 or 4.0e?4 s^?1 which corresponds to a timescale of about 1 h for nudging dynamic (u,v) and thermodynamical (t,q) fields.     

Hydrogeochemical assessment of groundwater quality along the coastal aquifers of southern Tamil Nadu,India

Hydrogeochemical investigation of groundwater has been carried out in the coastal aquifers of southern Tamil Nadu, India. Seventy-nine dug well samples were collected and analyzed for various physicochemical parameters. The result of the geochemical analysis indicates the groundwater in the study area is slightly alkaline with moderate saline water. The cation and anion concentrations confirm most of the groundwater samples belong to the order of Na⁺ > Mg²⁺ > Ca²⁺ > K⁺ and Cl^? > SO₄ ^2? > HCO₃ ^?. Thereby three major hydrochemical facies (Ca–Cl, mixed Ca–Mg–Cl and Na–Cl) were identified. Based on the US Salinity diagram, majority of the samples fall under medium to very high salinity with low to high sodium hazard. The cross plot of Ca²⁺ + Mg²⁺ versus chloride shows 61 % of the samples fall under saline water category. Higher EC, TDS and Cl concentrations were observed from Tiruchendur to Koodankulam coastal zone. It indicates that these regions are significantly affected by saltwater contamination due to seawater intrusion, saltpan deposits, and beach placer mining activities.     

Impact of assimilation of conventional and satellite meteorological observations on the numerical simulation of a Bay of Bengal Tropical Cyclone of November 2008 near Tamilnadu using WRF model

The objective of this study is to examine the impact of assimilation of conventional and satellite data on the prediction of a severe cyclonic storm that formed in the Bay of Bengal during November 2008 with the four-dimensional data assimilation (FDDA) technique. The Weather Research and Forecasting (WRF ARW) model was used to study the structure, evolution, and intensification of the storm. Five sets of numerical simulations were performed using the WRF. In the first one, called Control run, the National Centers for Environmental Prediction (NCEP) Final Analysis (FNL) was used for the initial and boundary conditions. In the remaining experiments available observations were used to obtain an improved analysis and FDDA grid nudging was performed for a pre-forecast period of 24 h. The second simulation (FDDAALL) was performed with all the data of the Quick Scatterometer (QSCAT), Special Sensor Microwave Imager (SSM/I) winds, conventional surface, and upper air meteorological observations. QSCAT wind alone was used in the third simulation (FDDAQSCAT), the SSM/I wind alone in the fourth (FDDASSMI) and the conventional observations alone in the fifth (FDDAAWS). The FDDAALL with assimilation of all observations, produced sea level pressure pattern closely agreeing with the analysis. Examination of various parameters indicated that the Control run over predicted the intensity of the storm with large error in its track and landfall position. The assimilation experiment with QSCAT winds performed marginally better than the one with SSM/I winds due to better representation of surface wind vectors. The FDDAALL outperformed all the simulations for the intensity, movement, and rainfall associated with the storm. Results suggest that the combination of land-based surface, upper air observations along with satellite winds for assimilation produced better prediction than the assimilation with individual data sets.