Geomagnetic field variation during winter storm at localized southern and northern high latitude

This paper presents the effect of geomagnetic storm on geomagnetic field components at Southern (Maitri) and Northern (Kiruna) Hemispheres. The Indian Antarctic Station Maitri is located at geom. long. 66.03° S; 53.21° E whereas Kiruna is located at geom. long. 67.52° N; 23.38° E. We have studied all the geomagnetic storms that occurred during winter season of the year 2004–2005. We observed that at Southern Hemisphere the variation is large as compared to the Northern Hemisphere. Geomagnetic field components vary when the interplanetary magnetic field is oriented in southward direction. Geomagnetic field components vary in the main phase of the ring current. Due to southward orientation of vertical component of IMF reconnection takes place all across the dayside that transports plasma and magnetic flux which create the geomagnetic field variation.     

Studying Indian ocean typical dynamical phenomena using TOPEX observations

TOPEX altimeter data of 1993 have been analyzed to study the following three types of oceanographic phenomena in the Indian Ocean: (1) sea level variability of the Indian Ocean (20⁼S to 25⁼N. 40⁼E to 100⁼E): (2) sea surface height signals of the Somali eddy; and (3) sea surface slope variations of the equatorial Indian Ocean (EIO) spanning 5⁼S to 5⁼N and 45⁼E to 95⁼E. Root‐mean‐square sea level variability revealed the presence of Rossby waves in the southern Indian Ocean. Fast Fourier technique analysis of a few passes near the Somali region is used to study the formation and dissipation of an anticyclonic eddy.     

Simulated Heat Content Variability in the Upper Layers of the Tropical Indian Ocean

Ocean General Circulation Model (OGCM) simulations from 1970–2007 are used to study the upper ocean heat content variability in the Tropical Indian Ocean (TIO). Model computed heat contents up to 50 m (denoted by HC50 m hereafter) representing upper ocean heat content and 300 m (HC300 m) representing heat content up to thermocline depth are first compared with heat contents computed from observations of two buoys in the TIO. It is found that there is good agreement between the model and observations. Fourier analysis of heat content is carried out in different regions of TIO. The amplitudes of semi-annual variability for HC50 m and HC300 m are observed to be greater than those for the annual variability in the Bay of Bengal, while in the Arabian Sea there is a mixed result. Heat content tendency is known to be governed by net surface heat flux and horizontal as well as vertical heat transports. For understanding the relative importance of these processes, a detailed analysis of these terms in the tendency equation is carried out. Rossby wave is observed in the annual mode of heat transport while equatorial jet and Kelvin waves are observed in the semi-annual mode of heart transport. Finally, the correlation between heat content and sea surface temperature (SST) and sea level anomaly (SLA), taken one at a time, is computed. It is found that the correlation improves significantly when both these quantities are together taken into account.     

Assimilation of Doppler Weather Radar Data in WRF Model for Simulation of Tropical Cyclone Aila

For the accurate and effective forecasting of a cyclone, it is critical to have accurate initial structure of the cyclone in numerical models. In this study, Kolkata Doppler weather radar (DWR) data were assimilated for the numerical simulation of a land-falling Tropical Cyclone Aila (2009) in the Bay of Bengal. To study the impact of radar data on very short-range forecasting of a cyclone's path, intensity and precipitation, both reflectivity and radial velocity were assimilated into the weather research and forecasting (WRF) model through the ARPS data assimilation system (ADAS) and cloud analysis procedure. Numerical experiment results indicated that radar data assimilation significantly improved the simulated structure of Cyclone Aila. Strong influences on hydrometeor structures of the initial vortex and precipitation pattern were observed when radar reflectivity data was assimilated, but a relatively small impact was observed on the wind fields at all height levels. The assimilation of radar wind data significantly improved the prediction of divergence/convergence conditions over the cyclone's inner-core area, as well as its wind field in the low-to-middle troposphere (600–900 hPa), but relatively less impact was observed on analyzed moisture field. Maximum surface wind speed produced from DWR–Vr and DWR–ZVr data assimilation experiments were very close to real-time values. The impact of radar data, after final analysis, on minimum sea level pressure was relatively less because the ADAS system does not adjust for pressure due to the lack of pressure observations, and from not using a 3DVAR balance condition that includes pressure. The greatest impact of radar data on forecasting was realized when both reflectivity and wind data (DWR–ZVr and DWR–ZVr00 experiment) were assimilated. It is concluded that after final analysis, the center of the cyclone was relocated very close to the observed position, and simulated cyclone maintained its intensity for a longer duration. Using this analysis, different stages of the cyclone are better captured, and cyclone structure, intensification, direction of movement, speed and location are significantly improved when both radar reflectivity and wind data are assimilated. As compared to other experiments, the maximum reduction in track error was noticed in the DWR–ZVr and DWR–ZVr00 experiments, and the predicted track in these experiments was very close to the observed track. In the DWR–ZVr and DWR–ZVr00 experiments, rainfall pattern and amount of rainfall forecasts were remarkably improved and were similar to the observation over West Bengal, Orissa and Jharkhand; however, the rainfall over Meghalaya and Bangladesh was missed in all the experiments. The influence of radar data reduces beyond a 12-h forecast, due to the dominance of the flow from large-scale, global forecast system models. This study also demonstrates successful coupling of the data assimilation package ADAS with the WRF model for Indian DWR data.     

An exploratory study on occurrence and impact of climate change on agriculture in Tamil Nadu,India

This study has been undertaken to examine the occurrence of climate change in Tamil Nadu, the southernmost state of India and its impact on rainfall pattern which is a primary constraint for agricultural production. Among the five sample stations examined across the state, the minimum temperature has increased significantly in Coimbatore while the same has decreased significantly in Vellore whereas both minimum and maximum temperatures have increased significantly in Madurai since 1969 with climate change occurring between late 1980s and early 1990s. As a result, the south-west monsoon has been disturbed with August rainfall increasing with more dispersion while September rainfall decreasing with less dispersion. Thus, September, the peak rainfall month of south-west monsoon before climate change, has become the monsoon receding month after climate change. Though there has been no change in the trend of the north-east monsoon, the quantity of October and November rainfall has considerably increased with increased dispersion after climate change. On the whole, south-west monsoon has decreased with decreased dispersion while north-east monsoon has increased with increased dispersion. Consequently, the season window for south-west monsoon crops has shortened while the north-east monsoon crops are left to fend against flood risk during their initial stages. Further, the incoherence in warming, climate change and rainfall impact seen across the state necessitates devising different indigenous and institutional adaptation strategies for different regions to overcome the adverse impacts of climate change on agriculture.

     

Evaluation of statistical bias correction methods for numerical weather prediction model forecasts of maximum and minimum temperatures

Statistical bias correction methods for numerical weather prediction (NWP) forecasts of maximum and minimum temperatures over India in the medium-range time scale (up to 5 days) are proposed in this study. The objective of bias correction is to minimize the systematic error of the next forecast using bias from past errors. The need for bias corrections arises from the many sources of systematic errors in NWP modeling systems. NWP models have shortcomings in the physical parameterization of weather events and have the inability to handle sub-grid phenomena successfully. The statistical algorithms used for minimizing the bias of the next forecast are running-mean (RM) bias correction, best easy systematic estimator, simple linear regression and the nearest neighborhood (NN) weighted mean, as they are suitable for small samples. Bias correction is done for four global NWP model maximum and minimum temperature forecasts. The magnitude of the bias at a grid point depends upon geographical location and season. Validation of the bias correction methodology is carried out using daily observed and bias-corrected model maximum and minimum temperature forecast over India during July–September 2011. The bias-corrected NWP model forecast generally outperforms direct model output (DMO). The spatial distribution of mean absolute error and root-mean squared error for bias-corrected forecast over India indicate that both the RM and NN methods produce the best skill among other bias correction methods. The inter-comparison reveals that statistical bias correction methods improve the DMO forecast in terms of accuracy in forecast and have the potential for operational applications.     

Trace elemental signatures and mineral chemistry of clays associated with the alteration halos of the Paleoproterozoic U mineralization in Bijawars of the Sonrai Basin,Central India

Acta Geochimica - Paleoproterozoic Bijawars of the Sonrai basin consists of (a) Sonrai (mostly carbonate carbonaceous shale and phosphatic breccia) and (b) Solda Formations (commonly chloritic and...     

Spatial modeling and validation of forest cover change in Kanakapura region using GEOMOD

Deforestation is recognized as one of the most significant components in LULCC and global changes scenario. It is imperative to assess its trend and the rate at which it is occurring. The changes will have long-lasting impact on regional climate and in turn on biodiversity. Present study was taken up in Kanakapura and surrounding areas located on the fringes of Western Ghats biodiversity hot-spots. Temporal satellite data from Landsat was classified into forest cover maps. Drivers of forest cover changes such as roads and settlements were used in order to create predicted map of the region using GEOMOD tool in Idrisi Andes. The predicted map was then validated using actual land cover map of same year prepared from Landsat data. The validated map was found to be 84.26 % accurate. The validation was also tested using ROC approach which was found to be 0.614. The model was then further extended to predict forest cover losses for year 2015. The results highlight ongoing deforestation in the areas adjoining Western Ghats. It also presents an application of the tool and the validation methods which can be used in predictive modeling related studies.     

Location specific forecasting of maximum and minimum temperatures over India by using the statistical bias corrected output of global forecasting system

The output from Global Forecasting System (GFS) T574L64 operational at India Meteorological Department (IMD), New Delhi is used for obtaining location specific quantitative forecast of maximum and minimum temperatures over India in the medium range time scale. In this study, a statistical bias correction algorithm has been introduced to reduce the systematic bias in the 24–120 hour GFS model location specific forecast of maximum and minimum temperatures for 98 selected synoptic stations, representing different geographical regions of India. The statistical bias correction algorithm used for minimizing the bias of the next forecast is Decaying Weighted Mean (DWM), as it is suitable for small samples. The main objective of this study is to evaluate the skill of Direct Model Output (DMO) and Bias Corrected (BC) GFS for location specific forecast of maximum and minimum temperatures over India. The performance skill of 24–120 hour DMO and BC forecast of GFS model is evaluated for all the 98 synoptic stations during summer (May-August 2012) and winter (November 2012–February 2013) seasons using different statistical evaluation skill measures. The magnitude of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for BC GFS forecast is lower than DMO during both summer and winter seasons. The BC GFS forecasts have higher skill score as compared to GFS DMO over most of the stations in all day-1 to day-5 forecasts during both summer and winter seasons. It is concluded from the study that the skill of GFS statistical BC forecast improves over the GFS DMO remarkably and hence can be used as an operational weather forecasting system for location specific forecast over India.