云南水氡群体异常特征

采用非均匀度方法,分别提取滇西北、滇中北、滇西南、滇东南和滇东北地区水氡观测资料的地震前兆群体异常。结果表明,该方法能满足水氡观测点所在地区成组中强地震的中、短临预报要求;且该方法能自动识别异常,排除人为主观影响,便于在其他震区进行预报效能检验。     

Assessing environmental risks through fuzzy parameterized probabilistic analysis

A fuzzy parameterized probabilistic analysis (FPPA) method was developed in this study to assess risks associated with environmental pollution-control problems. FPPA integrated environmental transport modeling, fuzzy transformation, probabilistic risk assessment, fuzzy risk quantification into a general risk assessment framework, and was capable of handling uncertainties expressed as fuzzy-parameterized stochastic distributions. The proposed method was applied to two environmental pollution problems, with one being about the point-source pollution in a river system with uncertain water quality parameters and the other being concerned with groundwater contaminant plume from waste landfill site with poorly known contaminant physical properties. The study results indicated that the complex uncertain features had significant impacts on modeling and risk-assessment outputs; the degree of impacts of modeling parameters were highly dependent on the level of imprecision of these parameters. The results also implied that FPPA was capable of addressing vagueness or imprecision associated with probabilistic risk evaluation, and help generate risk outputs that could be elucidated under different possibilistic levels. The proposed method could be used by environmental managers to evaluate trade-offs involving risks and costs, as well as identify management solutions that sufficiently hedge against dual uncertainties.     

SVME: an ensemble of support vector machines for detecting oil spills from full resolution MODIS images

This paper addresses oil spill detection from remotely sensed optical images. In particular, it focuses on the automatic classification of regions of interest (ROIs) in two classes, namely oil spills or look-alikes. Candidate regions and the corresponding boundaries have been manually identified from full resolution Moderate Resolution Imaging Spectroradiometer images, related to the Mediterranean Sea over the years 2008 and 2009. Then, a set of features has been extracted from each ROI, allowing to formulate the oil spill detection problem as a two-class classification task on the provided regions (i.e. using a supervised learning strategy). Since ROI classification is challenging, some desired characteristics for the classification algorithm are first identified, such as accuracy, robustness, etc. Then, a solution (called SVME) is provided: it is based on an ensemble of incremental/decremental cost-oriented Support Vector Machines, aggregated with the Receiving Operating Characteristic (ROC) convex hull method in the ROC space. Such a solution addresses all the desired characteristics. Finally, the results obtained on the collected dataset are shown. The importance of this study is the devising of a powerful classification technique that may have an impact on optical oil spill detection from space, especially if fused with satellite synthetic aperture radar data. Moreover, it is shown how the proposed system can be used as a decision support tool, to help a junior operator in making more reliable detections.     

Analysis and interpretation of gravity data from the Aluto-Langano geothermal field of Ethiopia

The Aluto-Langano geothermal field is located in the central southern portion of Ethiopia within the Ethiopian Rift Valley. The gravity of the area was surveyed in an attempt to delineate the subsurface structure and to better understand the relationship between the geothermal systems and the subsurface structure. The gravity data were analyzed using integrated gradient interpretation techniques, such as the Horizontal Gradient (HG), Source Edge Detection (SED), and Euler Deconvolution (ED) methods. These techniques detected many faults that were compared with the mapped faults in the surface geology. The results of the present study will lead to an improved understanding of the geothermal system in the study area and aid the future geothermal exploration of the area.     

Open and coastal seas interactions south of Japan represented by an ensemble Kalman filter

We investigated the feasibility of the ensemble Kalman filter (EnKF) to reproduce oceanic conditions south of Japan. We have adopted the local ensemble transformation Kalman filter algorithm based on 20 members’ ensemble simulations of the parallelized Princeton Ocean Model (the Stony Brook Parallel Ocean Model) with horizontal resolution of 1/36°. By assimilating satellite sea surface height anomaly, satellite sea surface temperature, and in situ temperature and salinity profiles, we reproduced the Kuroshio variation south of Japan for the period from 8 to 28 February 2010. EnKF successfully reproduced the Kuroshio path positions and the water mass property of the Kuroshio waters as observed. It also detected the variation of the steep thermohaline front in the Kii Channel due to the intrusion of the Kuroshio water based on the observation, suggesting efficiency of EnKF for detection of open and coastal seas interactions with highly complicated spatiotemporal variability.     

Combining long- and short-term probabilistic volcanic hazard assessment with cost-benefit analysis to support decision making in a volcanic crisis from the Auckland Volcanic Field,New Zealand

By using BET_VH, we propose a quantitative probabilistic hazard assessment for base surge impact in Auckland, New Zealand. Base surges resulting from phreatomagmatic eruptions are among the most dangerous phenomena likely to be associated with the initial phase of a future eruption in the Auckland Volcanic Field. The assessment is done both in the long-term and in a specific short-term case study, i.e. the simulated pre-eruptive unrest episode during Exercise Ruaumoko, a national civil defence exercise. The most important factors to account for are the uncertainties in the vent location (expected for a volcanic field) and in the run-out distance of base surges. Here, we propose a statistical model of base surge run-out distance based on deposits from past eruptions in Auckland and in analogous volcanoes. We then combine our hazard assessment with an analysis of the costs and benefits of evacuating people (on a 1 × 1-km cell grid). In addition to stressing the practical importance of a cost-benefit analysis in creating a bridge between volcanologists and decision makers, our study highlights some important points. First, in the Exercise Ruaumoko application, the evacuation call seems to be required as soon as the unrest phase is clear; additionally, the evacuation area is much larger than what is recommended in the current contingency plan. Secondly, the evacuation area changes in size with time, due to a reduction in the uncertainty in the vent location and increase in the probability of eruption. It is the tradeoff between these two factors that dictates which cells must be evacuated, and when, thus determining the ultimate size and shape of the area to be evacuated.     

An anomalous cooling event observed in the Bay of Bengal during June 2009

Sea surface temperature (SST) variability over the Bay of Bengal (BoB) has the potential to trigger deep moist convection thereby affecting the active-break cycle of the monsoons. Normally, during the summer monsoon season, SST over the BoB is observed to be greater than 28°C which is a pre-requisite for convection. During June 2009, satellite observations revealed an anomalous basin-wide cooling and the month is noted for reduced rainfall over the Indian subcontinent. In this study, we analyze the likely mechanisms of this cooling event using both satellite and moored buoy observations. Observations showed deepened mixed layer, stronger surface currents, and enhanced heat loss at the surface in the BoB. Mixed layer heat balance analysis is carried out to resolve the relative importance of various processes involved. We show that the cooling event is primarily induced by the heat losses at the surface resulting from the strong wind anomalies, and advection and vertical entrainment playing secondary roles.     

Wave data assimilation using a hybrid approach in the Persian Gulf

The main goal of this study is to develop an efficient approach for the assimilation of the hindcasted wave parameters in the Persian Gulf. Hence, the third generation SWAN model was employed for wave modeling forced by the 6-h ECMWF wind data with a resolution of 0.5°. In situ wave measurements at two stations were utilized to evaluate the assimilation approaches. It was found that since the model errors are not the same for wave height and period, adaptation of model parameter does not result in simultaneous and comprehensive improvement of them. Therefore, an approach based on the error prediction and updating of output variables was employed to modify wave height and period. In this approach, artificial neural networks (ANNs) were used to estimate the deviations between the simulated and measured wave parameters. The results showed that updating of output variables leads to significant improvement in a wide range of the predicted wave characteristics. It was revealed that the best input parameters for error prediction networks are mean wind speed, mean wind direction, wind duration, and the wave parameters. In addition, combination of the ANN estimated error with numerically modeled wave parameters leads to further improvement in the predicted wave parameters in contrast to direct estimation of the parameters by ANN.