The effect of El Nino and La Nina on lightning activity: its relation with meteorological and cloud microphysical parameters

The decadal variation of lightning activity over Northeast India (NEI) from the year 2002 to 2011 is studied with the help of satellite-based lightning imaging sensor (LIS) data. An anomalous 56 % increase in lightning activity is observed in the year 2010 with respect to the previous years. To investigate the reason behind this increase, the LIS data are analyzed with several meteorological and cloud microphysical parameters. These parameters are convective sources of cloud ice, cloud top temperature, surface temperature, convective precipitation rate and total ice water content and convective available potential energy. On a synoptic scale, the period of anomalous lightning activity could be related to the development of an El Nino event at the middle of 2009 that broke down in the early months of the next year with a rapid transition to La Nina by August 2010. Analyses expose that El Nino Southern Oscillation might have diversely modified the local meteorological and cloud microphysical parameters. It comes out that this rapid transition from El Nino to La Nina condition could be the possible reason behind the dramatic increase in lightning activity, which was not previously documented over NEI.     

Use of regionalisation approach to develop fire frequency curves for Victoria,Australia

It is important to perform fire frequency analysis to obtain fire frequency curves (FFC) based on fire intensity at different parts of Victoria. In this paper fire frequency curves (FFCs) were derived based on forest fire danger index (FFDI). FFDI is a measure related to fire initiation, spreading speed and containment difficulty. The mean temperature (T), relative humidity (RH) and areal extent of open water (LC2) during summer months (Dec–Feb) were identified as the most important parameters for assessing the risk of occurrence of bushfire. Based on these parameters, Andrews’ curve equation was applied to 40 selected meteorological stations to identify homogenous stations to form unique clusters. A methodology using peak FFDI from cluster averaged FFDIs was developed by applying Log Pearson Type III (LPIII) distribution to generate FFCs. A total of nine homogeneous clusters across Victoria were identified, and subsequently their FFC’s were developed in order to estimate the regionalised fire occurrence characteristics.     

Implementing a Spatial Model to Derive Potential Fishing Zones in the Northern Bay of Bengal Lying Adjacent to West Bengal Coast,India

Potential fishing zones (PFZ’s) are those regions where the fishes aggregate due to an abundance of food and they are demarcated by tracing those regions in the ocean, where a sharp sea surface temperature (SST) gradient along with optimal chlorophyll (Chl) concentration co-exists at a given time. In this regard, Indian National Centre for Ocean Information Services (INCOIS) disseminates the daily PFZ forecasts in Bay of Bengal and Arabian Sea to aid the fishermen community. The present study is an endeavor to develop a local spatial model derived Potential Fishing Zone (PFZ) in the northern Bay of Bengal (nBoB) lying adjacent to the West Bengal coast. Satellite derived SST and chlorophyll data obtained for two consecutive winter seasons of 2010–11 and 2011–12 were used to generate line density (LD) raster. Shapefiles of INCOIS predicted PFZs were overlaid on these LD raster to extract the corresponding pixel values. Histogram ranges of the extracted pixels were fixed and same values lying in the LD raster of both SST and chlorophyll other than INCOIS PFZs were detected by a spatial model in ERDAS. The PFZs thus derived were validated against the ground fish catch data and it was observed that good fish catch was seen in the model derived additional PFZs also. The catch per unit effort (CPUE) values was found to be very close to that of the CPUE value of PFZ advisories of INCOIS. However, the CPUE in the non PFZ areas were significantly lower than the former two categories.     

Optimal operation of reservoirs for downstream water quality control using linked simulation optimization

A methodology is developed for optimal operation of reservoirs to control water quality requirements at downstream locations. The physicochemical processes involved are incorporated using a numerical simulation model. This simulation model is then linked externally with an optimization algorithm. This linked simulation–optimization‐based methodology is used to obtain optimal reservoir operation policy. An elitist genetic algorithm is used as the optimization algorithm. This elitist‐genetic‐algorithm‐based linked simulation–optimization model is capable of evolving short‐term optimal operation strategies for controlling water quality downstream of a reservoir. The performance of the methodology developed is evaluated for an illustrative example problem. Different plausible scenarios of management are considered. The operation policies obtained are tested by simulating the resulting pollutant concentrations downstream of the reservoir. These performance evaluations consider various scenarios of inflow, permissible concentration limits, and a number of management periods. These evaluations establish the potential applicability of the developed methodology for optimal control of water quality downstream of a reservoir. Copyright © 2007 John Wiley & Sons, Ltd.     

Forest cover change prediction using hybrid methodology of geoinformatics and Markov chain model: A case study on sub-Himalayan town Gangtok,India

In the Himalayan states of India, with increasing population and activities, large areas of forested land are being converted into other land-use features. There is a definite cause and effect relationship between changing practice for development and changes in land use. So, an estimation of land use dynamics and a futuristic trend pattern is essential. A combination of geospatial and statistical techniques were applied to assess the present and future land use/land cover scenario of Gangtok, the subHimalayan capital of Sikkim. Multi-temporal satellite imageries of the Landsat series were used to map the changes in land use of Gangtok from 1990 to 2010. Only three major land use classes (built-up area and bare land, step cultivated area, and forest) were considered as the most dynamic land use practices of Gangtok. The conventional supervised classification, and spectral indices-based thresholding using NDVI (Normalized Difference Vegetation Index) and SAVI (Soil Adjusted Vegetation Index) were applied along with the accuracy assessments. Markov modelling was applied for prediction of land use/land cover change and was validated. SAVI provides the most accurate estimate, i.e., the difference between predicted and actual data is minimal. Finally, a combination of Markov modelling and SAVI was used to predict the probable land-use scenario in Gangtok in 2020 AD, which indicted that more forest areas will be converted for step cultivation by the year 2020.     

Medium-range forecast of cyclogenesis over North Indian Ocean with multilayer perceptron model using satellite data

An attempt is made in this study to develop a model to forecast the cyclonic depressions leading to cyclonic storms over North Indian Ocean (NIO) with 3 days lead time. A multilayer perceptron (MLP) model is developed for the purpose and the forecast quality of the model is compared with other neural network and multiple linear regression models to assess the forecast skill and performances of the MLP model. The input matrix of the model is prepared with the data of cloud coverage, cloud top temperature, cloud top pressure, cloud optical depth, cloud water path collected from remotely sensed moderate resolution imaging spectro-radiometer (MODIS), and sea surface temperature. The input data are collected 3 days before the cyclogenesis over NIO. The target output is the central pressure, pressure drop, wind speed, and sea surface temperature associated with cyclogenesis over NIO. The models are trained with the data and records from 1998 to 2008. The result of the study reveals that the forecast error with MLP model varies between 0 and 7.2 % for target outputs. The errors with MLP are less than radial basis function network, generalized regression neural network, linear neural network where the errors vary between 0 and 8.4 %, 0.3 and 24.8 %, and 0.3 and 32.4 %, respectively. The forecast with conventional statistical multiple linear regression model, on the other hand, generates error values between 15.9 and 32.4 %. The performances of the models are validated for the cyclonic storms of 2009, 2010, and 2011. The forecast errors with MLP model during validation are also observed to be minimum.     

Surface Temperature and Surface-Layer Turbulence in a Convective Boundary Layer

Previous laboratory and atmospheric experiments have shown that turbulence influences the surface temperature in a convective boundary layer. The main objective of this study is to examine land-atmosphere coupled heat transport mechanism for different stability conditions. High frequency infrared imagery and sonic anemometer measurements were obtained during the boundary layer late afternoon and sunset turbulence (BLLAST) experimental campaign. Temporal turbulence data in the surface-layer are then analyzed jointly with spatial surface-temperature imagery. The surface-temperature structures (identified using surface-temperature fluctuations) are strongly linked to atmospheric turbulence as manifested in several findings. The surface-temperature coherent structures move at an advection speed similar to the upper surface-layer or mixed-layer wind speed, with a decreasing trend with increase in stability. Also, with increasing instability the streamwise surface-temperature structure size decreases and the structures become more circular. The sequencing of surface- and air-temperature patterns is further examined through conditional averaging. Surface heating causes the initiation of warm ejection events followed by cold sweep events that result in surface cooling. The ejection events occur about 25 % of the time, but account for 60–70 % of the total sensible heat flux and cause fluctuations of up to 30 % in the ground heat flux. Cross-correlation analysis between air and surface temperature confirms the validity of a scalar footprint model.     

Intensity forecast of tropical cyclones over North Indian Ocean using multilayer perceptron model: skill and performance verification

The coastal regions of India are profoundly affected by tropical cyclones during both pre- and post-monsoon seasons with enormous loss of life and property leading to natural disasters. The endeavour of the present study is to forecast the intensity of the tropical cyclones that prevail over Arabian Sea and Bay of Bengal of North Indian Ocean (NIO). A multilayer perceptron (MLP) model is developed for the purpose and compared the forecast through MLP model with other neural network and statistical models to assess the forecast skill and performances of MLP model. The central pressure, maximum sustained surface wind speed, pressure drop, total ozone column and sea surface temperature are taken to form the input matrix of the models. The target output is the intensity of the tropical cyclones as per the T??number. The result of the study reveals that the forecast error with MLP model is minimum (4.70?%) whereas the forecast error with radial basis function network (RBFN) is observed to be 14.62?%. The prediction with statistical multiple linear regression and ordinary linear regression are observed to be 9.15 and 9.8?%, respectively. The models provide the forecast beyond 72?h taking care of the change in intensity at every 3-h interval. The performance of MLP model is tested for severe and very severe cyclonic storms like Mala (2006), Sidr (2007), Nargis (2008), Aila (2009), Laila (2010) and Phet (2010). The forecast errors with MLP model for the said cyclones are also observed to be considerably less. Thus, MLP model in forecasting the intensity of tropical cyclones over NIOs may thus be considered to be an alternative of the conventional operational forecast models.     

Effects of Energetic Solar Emissions on the Earth–Ionosphere Cavity of Schumann Resonances

Schumann resonances (SR) are the electromagnetic oscillations of the spherical cavity bounded by the electrically conductive Earth and the conductive but dissipative lower ionosphere (Schumann in Z Naturforsch A 7:6627–6628, 1952). Energetic emissions from the Sun can exert a varied influence on the various parameters of the Earth’s SR: modal frequencies, amplitudes and dissipation parameters. The SR response at multiple receiving stations is considered for two extraordinary solar events from Solar Cycle 23: the Bastille Day event (July 14, 2000) and the Halloween event (October/November 2003). Distinct differences are noted in the ionospheric depths of penetration for X-radiation and solar protons with correspondingly distinct signs of the frequency response. The preferential impact of the protons in the magnetically unshielded polar regions leads to a marked anisotropic frequency response in the two magnetic field components. The general immunity of SR amplitudes to these extreme external perturbations serves to remind us that the amplitude parameter is largely controlled by lightning activity within the Earth–ionosphere cavity.     

CPT-based seismic stability assessment of a hazardous waste site

In areas of high seismicity, seismic stability often controls hazardous and solid waste landfill closure design. The undrained shear strength (S_u) of the waste mass is fundamental to seismic slope stability analyses. The value of S_u for hazardous waste fill is often difficult to characterize. The physical and chemical natures of the waste fill typically preclude laboratory testing of the materials. In certain cases, Cone Penetration Test (CPT) soundings can provide a viable technique for evaluation of S_u provided that the cone shear strength factor N_k can be established. If hazardous waste materials laboratory testing is not an option, N_k may be evaluated based upon results of non-intrusive in situ testing. This paper presents a case history of the seismic stability assessment of a hazardous waste site in which N_k was established from the results of non-intrusive Spectral Analysis of Surface Waves (SASW) soundings and empirical correlations to shear strength of soils. Generalization of the proposed methodology to other sites should be done with caution owing to variability among the parameters used in the analyses.