Relationship between CME dynamics and solar flare plasma

The relationship between the velocity of CMEs and the plasma temperature of the associated X-ray solar flares is investigated.The velocity of CMEs increases with plasma temperature(R=0.82)and photon index below the break energy(R=0.60)of X-ray flares.The heating of the coronal plasma appears to be significant with respect to the kinetics of a CME from the reconnection region where the flare also occurs.We propose that the initiation and velocity of CMEs perhaps depend upon the dominant process of conversion of the magnetic field energy of the active region to heating/accelerating the coronal plasma in the reconnected loops.Results show that a flare and the associated CME are two components of one energy release system,perhaps,magnetic field free energy.     

Real time wave forecasting using wind time history and numerical model

Operational activities in the ocean like planning for structural repairs or fishing expeditions require real time prediction of waves over typical time duration of say a few hours. Such predictions can be made by using a numerical model or a time series model employing continuously recorded waves. This paper presents another option to do so and it is based on a different time series approach in which the input is in the form of preceding wind speed and wind direction observations. This would be useful for those stations where the costly wave buoys are not deployed and instead only meteorological buoys measuring wind are moored. The technique employs alternative artificial intelligence approaches of an artificial neural network (ANN), genetic programming (GP) and model tree (MT) to carry out the time series modeling of wind to obtain waves. Wind observations at four offshore sites along the east coast of India were used. For calibration purpose the wave data was generated using a numerical model. The predicted waves obtained using the proposed time series models when compared with the numerically generated waves showed good resemblance in terms of the selected error criteria. Large differences across the chosen techniques of ANN, GP, MT were not noticed. Wave hindcasting at the same time step and the predictions over shorter lead times were better than the predictions over longer lead times. The proposed method is a cost effective and convenient option when a site-specific information is desired.     

Carbonaceous and inorganic species in PM<Subscript>10</Subscript> during wintertime over Giridih,Jharkhand (India)

Ambient concentrations of organic carbon (OC), elemental carbon (EC) and water soluble inorganic ionic components (WSIC) of PM₁₀ were studied at Giridih, Jharkhand, a sub-urban site near the Indo Gangatic Plain (IGP) of India during two consecutive winter seasons (November 2011–February 2012 and November 2012–February 2013). The abundance of carbonaceous and water soluble inorganic species of PM₁₀ was recorded at the study site of Giridih. During winter 2011–12, the average concentrations of PM₁₀, OC, EC and WSIC were 180.2?±?46.4; 37.2?±?6.2; 15.2?±?5.4 and 18.0?±?5.1 μg m^?3, respectively. Similar concentrations of PM₁₀, OC, EC and WSIC were also recorded during winter 2012–13. In the present case, a positive linear trend is observed between OC and EC at sampling site of Giridih indicates the coal burning, as well as dispersed coal powder and vehicular emissions may be the source of carbonaceous aerosols. The principal components analysis (PCA) also identifies the contribution of coal burning? +?soil dust, vehicular emissions?+?biomass burning and seconday aerosol to PM₁₀ mass concentration at the study site. Backward trajectoy and potential source contributing function (PSCF) analysis indicated that the aerosols being transported to Giridih from upwind IGP (Punjab, Haryana, Uttar Pradesh and Bihar) and surrounding region.     

Large-Amplitude Oscillation of an Erupting Filament as Seen in EUV,Hα, and Microwave Observations

We present multiwavelength observations of a large-amplitude oscillation of a polar-crown filament on 15 October 2002, which has been reported by Isobe and Tripathi (Astron. Astrophys. 449, L17, 2006). The oscillation occurred during the slow rise (≈1 km s⁻¹) of the filament. It completed three cycles before sudden acceleration and eruption. The oscillation and following eruption were clearly seen in observations recorded by the Extreme-Ultraviolet Imaging Telescope (EIT) onboard the Solar and Heliospheric Observatory (SOHO). The oscillation was seen only in a part of the filament, and it appears to be a standing oscillation rather than a propagating wave. The amplitudes of velocity and spatial displacement of the oscillation in the plane of the sky were about 5 km s⁻¹ and 15 000 km, respectively. The period of oscillation was about two hours and did not change significantly during the oscillation. The oscillation was also observed in Hα by the Flare Monitoring Telescope at the Hida Observatory. We determine the three-dimensional motion of the oscillation from the Hα wing images. The maximum line-of-sight velocity was estimated to be a few tens of kilometers per second, although the uncertainty is large owing to the lack of line-profile information. Furthermore, we also identified the spatial displacement of the oscillation in 17-GHz microwave images from Nobeyama Radio Heliograph (NoRH). The filament oscillation seems to be triggered by magnetic reconnection between a filament barb and nearby emerging magnetic flux as was evident from the MDI magnetogram observations. No flare was observed to be associated with the onset of the oscillation. We also discuss possible implications of the oscillation as a diagnostic tool for the eruption mechanisms. We suggest that in the early phase of eruption a part of the filament lost its equilibrium first, while the remaining part was still in an equilibrium and oscillated.     

A mathematical model on depth-averaged <Emphasis Type="Italic">β</Emphasis>-factor in open-channel turbulent flow

The well-known Rouse equation is the most widely used equation to determine the vertical distribution of suspended sediment concentration in an open-channel flow. The exponent of Rouse equation, known as Rouse number, contains the parameter β defined by the ratio of sediment diffusion coefficient to turbulent diffusion coefficient. As such to measure sediment concentration accurately, an appropriate expression for β is essentially needed. The present study, therefore, focuses on the derivation of depth-averaged β through modified expressions of sediment and turbulent diffusion coefficients. A regression analysis is done to establish the relation between β and normalized settling velocity, and the relation is used to determine suspension concentration.     

Rainfall‐runoff modelling using artificial neural networks: comparison of network types

Growing interest in the use of artificial neural networks (ANNs) in rainfall‐runoff modelling has suggested certain issues that are still not addressed properly. One such concern is the use of network type, as theoretical studies on a multi‐layer perceptron (MLP) with a sigmoid transfer function enlightens certain limitations for its use. Alternatively, there is a strong belief in the general ANN user community that a radial basis function (RBF) network performs better than an MLP, as the former bases its nonlinearities on the training data set. This argument is not yet substantiated by applications in hydrology. This paper presents a comprehensive evaluation of the performance of MLP‐ and RBF‐type neural network models developed for rainfall‐runoff modelling of two Indian river basins. The performance of both the MLP and RBF network models were comprehensively evaluated in terms of their generalization properties, predicted hydrograph characteristics, and predictive uncertainty. The results of the study indicate that the choice of the network type certainly has an impact on the model prediction accuracy. The study suggests that both the networks have merits and limitations. For instance, the MLP requires a long trial‐and‐error procedure to fix the optimal number of hidden nodes, whereas for an RBF the structure of the network can be fixed using an appropriate training algorithm. However, a judgment on which is superior is not clearly possible from this study. Copyright © 2004 John Wiley & Sons, Ltd.