Surrogate models are becoming increasingly popular for storm surge predictions. Using existing databases of storm simulations, developed typically during regional flood studies, these models provide fast-to-compute, data-driven approximations quantifying the expected storm surge for any new storm (not included in the training database). This paper considers the development of such a surrogate model for Delaware Bay, using a database of 156 simulations driven by synthetic tropical cyclones and offering predictions for a grid that includes close to 300,000 computational nodes within the geographical domain of interest. Kriging (Gaussian Process regression) is adopted as the surrogate modeling technique, and various relevant advancements are established. The appropriate parameterization of the synthetic storm database is examined. For this, instead of the storm features at landfall, the features when the storm is at closest distance to some representative point of the domain of interest are investigated as an alternative parametrization, and are found to produce a better surrogate. For nodes that remained dry for some of the database storms, imputation of the surge using a weighted k nearest neighbor (kNN) interpolation is considered to fill in the missing data. The use of a secondary, classification surrogate model, combining logistic principal component analysis and Kriging, is examined to address instances for which the imputed surge leads to misclassification of the node condition. Finally, concerns related to overfitting for the surrogate model are discussed, stemming from the small size of the available database. These concerns extend to both the calibration of the surrogate model hyper-parameters, as well as to the validation approaches adopted. During this process, the benefits from the use of principal component analysis as a dimensionality reduction technique, and the appropriate transformation and scaling of the surge output are examined in detail.
Tourmaline bearing leucogranite occurs as a pluton with pegmatitic veins intruding the Archaean granodiorite in the Bastipadu area, Kurnool district of Andhra Pradesh. We present field and petrographic relations, mineral chemistry and geochemical data for the leucogranite. It is essentially a two-mica granite, composed of quartz, perthite, microcline, albite, tourmaline and muscovite along with minor biotite and titanite. The euhedral tourmalines are regularly distributed in the rock. The geochemical studies show that the leucogranite is calc-alkaline, peraluminous to metaluminous which formed in a syn-collisional to volcanic arc-related setting. It displays strong ‘S’ type signatures with high K/Na ratios, moderately fractionated light rare earth elements, relatively flat heavy rare earth elements with \(\hbox {[Ce/Yb]}_\mathrm{N} \le 27.8\) and a strong negative Eu anomaly. The geochemical characteristics indicate that the leucogranite melt might have been generated from partial melting of metasediments. Electron probe microanalyser data show the presence of alkali group tourmaline in leucogranite represented by schorl and dravite. Tourmaline compositions plot in the Li-poor granitoids and associated pegmatites and aplites and metapelites/metasammites fields. Partial melting of boron-enriched source rocks is linked with the development of tourmalines in the leucogranite. 相似文献
The present work deals with pre-monsoon thunderstorms over Bhubaneswar belonging to the state of Orissa, India. A Markovian approach has been adopted to discern the probabilistic behavior of the time series of the occurrence and non-occurrence of this hazardous weather event by introducing a dichotomy within the time series. After a painstaking analysis through chi-square tests, we have identified serial independence in a few years and first-order two-state Markovian dependence in a few years (2000, 2001, 2004 and 2006). Finally, for the years of first-order two-state Markovian dependence, it has been observed that the probability of occurrence or non-occurrence of thunderstorm gets higher if the state of the previous day is similar to that of the current day. Furthermore, the probability of getting non-thunderstorm day followed by non-thunderstorm day is higher than the probability of getting thunderstorm day followed by thunderstorm day. It has been also observed that the unconditional climatological probability of the occurrence of severe pre-monsoon thunderstorm implied by the Markov chain is closely in agreement with the observed relative frequencies. However, it could be revealed that Markov chain cannot, in general, be suggested as a predictive tool for pre-monsoon thunderstorms under study without investigating the serial dependence inherent in the time series. 相似文献
Time-series observations were conducted off Visakhapatnam, central west coast of Bay of Bengal, from October 2007 to April 2009 to examine the influence of physical and atmospheric processes on water column nutrients biogeochemistry. The thermal structure displayed inversions of 0.5 to 1.0° C during winter and were weaker in summer. The water column was vertically stratified during the entire study period and was stronger during October–November 2007 and August–December 2008 compared to other study periods. High concentrations of chlorophyll-a and nutrients were associated with the extreme atmospheric events. The strong relationship of nutrients with salinity indicates that physical processes, such as circulation, mixing and river discharge, have a significant control on phytoplankton blooms in the coastal Bay of Bengal. Phosphate seems to be a controlling nutrient during winter whereas availability of light and suspended matter limits production in summer. Formation of low oxygen conditions were observed in the bottom waters due to enhanced primary production by extreme atmospheric events; however, re-oxygenation of bottom waters through sinking of oxygen-rich surface waters by a warm core (anticyclonic) eddy led to its near recovery. This study reveals that atmospheric and physical processes have significant impacts on the water column biogeochemistry in the coastal Bay of Bengal. 相似文献