Delay times of worldwide global earthquake alerts

Quantitative estimates of earthquake losses are needed as soon as possible after an event. A majority of earthquake-prone countries lack the necessary dense seismograph networks, modern communication, and in some places the experts to assess losses immediately, so the earliest possible warnings must come from global information and international experts. Earthquakes of interest to us are in most areas of the world M ≥ 6. In this article, we have analyzed the response time for distributing source parameter estimates from: National Earthquake Information Center (NEIC) of the US Geological Survey (USGS), the European Mediterranean Seismological Center (EMSC), and Geophysical Institute-Russian Academy of Science, Obninsk (RAS). In terms of earthquake consequences, the Pacific Tsunami Warning Center (TWC) issues assessments of the likelihood of tsunamis, the Joint Research Laboratory in Ispra, Italy (JRC) issues alerts listing sociological aspects of the affected region, and we distribute loss estimates, and recently the USGS has started posting impact assessment information on their PAGER web page. Two years ago, the USGS reduced its median delay of distributing earthquake source parameters by a factor of 2 to the currently observed 26 min, and they distribute information for 99% of the events of interest to us. The median delay of EMSC is 41 min, with 30% of our target events reported. RAS reports after 81 min and 30% of the target events. The first tsunami assessments by TWC reach us 18 min (median) after large earthquakes in the Pacific area. The median delay of alerts by the JRC is 44 min (36 min recently). The World Agency for Planetary Monitoring and Earthquake Risk Reduction (WAPMERR) distributes detailed loss estimates in 41 min (median). Moment tensor solutions of the USGS, which can be helpful for refining loss estimates, reach us in 78 min (median) for 58% of the earthquakes of interest.     

叶城波龙7.20泥石流应急调查及灾害链实时观察

针对叶城柯克亚乡波龙7.20突发泥石流灾害,现场开展了应急科学调查。对沟下游泥石流堆积区进行量测,对上游突发小型滑坡-堰塞湖-泄流全过程开展实时观察。结果发现:泥石流固体物质主要是晚更新世风积土滑坡和中元古变质岩及碳酸盐岩崩塌,水体来源主要是强降雨导致的上游洪水及堰塞湖溃决;沟谷上游以稀性泥石流为主,下游加入了崩塌块体及河道砂砾石,粘度有所增大;堆积区呈块石散落和淤积状态。据现场多个老堰塞坝推测,波龙沟泥石流发生周期约15~20年。现有停车场位于沟口堆积区,需加大极端洪水及泥石流排泄空间,拓宽排导沟槽净空。     

Review of the development of laser fluorosensors for oil spill application

As laser fluorosensors provide their own source of excitation, they are known as active sensors. Being active sensors, laser fluorosensors can be employed around the clock, in daylight or in total darkness. Certain compounds, such as aromatic hydrocarbons, present in petroleum oils absorb ultraviolet laser light and become electronically excited. This excitation is quickly removed by the process of fluorescence emission, primarily in the visible region of the spectrum. By careful choice of the excitation laser wavelength and range-gated detection at selected emission wavelengths, petroleum oils can be detected and classified into three broad categories: light refined, crude or heavy refined.

This paper will review the development of laser fluorosensors for oil spill application, with emphasis on system components such as excitation laser source, and detection schemes that allow these unique sensors to be employed for the detection and classification of petroleum oils. There have been a number of laser fluorosensors developed in recent years, many of which are strictly research and development tools. Certain of these fluorosensors have been ship-borne instruments that have been mounted in aircraft for the occasional airborne mission. Other systems are mounted permanently on aircraft for use in either surveillance or spill response roles.     

机载PMS粒子测量系统实时处理显示技术系统的研制

机载粒子测量系统（PMS）的云降水观测数据只能实时监视并存入磁带，不能在飞机上实时处理显示，用于指挥人工增雨作业。该文介绍了疳原有的数据据磁带记录改为由机载身长机记录并实时处理显示的硬件结构及其工作原理，以及研制的系统及其软件功能。     

90年代动态 GPS 遥感信息对地定位系统

从GPS的特点、发展趋势及应用分类,介绍了GPS的概况及应用趋势。动态GPS遥感信息对地定位系统是GPS技术在遥感方面的应用。文中充分分析了以往遥感信息对地定位存在的问题,预测了GPS在遥感信息对地定位方面应用前景,并提出了发展这一系统的试验研究总体框图。     

A Monte Carlo study of rainfall forecasting with a stochastic model

A procedure for short-term rainfall forecasting in real-time is developed and a study of the role of sampling on forecast ability is conducted. Ground level rainfall fields are forecasted using a stochastic space-time rainfall model in state-space form. Updating of the rainfall field in real-time is accomplished using a distributed parameter Kalman filter to optimally combine measurement information and forecast model estimates. The influence of sampling density on forecast accuracy is evaluated using a series of a simulated rainfall events generated with the same stochastic rainfall model. Sampling was conducted at five different network spatial densities. The results quantify the influence of sampling network density on real-time rainfall field forecasting. Statistical analyses of the rainfall field residuals illustrate improvement in one hour lead time forecasts at higher measurement densities.     

Rainfall duration and debris-flow initiated studies for real-time monitoring

A rainfall-induced debris flow warning is implemented employing real-time rain gauge data. The pre-warning for the time of landslide triggering derives the threshold or critical rainfall from historical events involving regional rainfall patterns and geological conditions. In cases of debris flow, the time taken cumulative runoff, to yield abundant water for debris triggering, is an important index that needs monitoring. In gathered historical cases, rainfall time history data from the nearest rain gauge stations to debris-flow sites connected to debris flow are used to define relationships between the rainfall intensity and duration. The effects by which the regional rainfall patterns (antecedent rainfall, duration, intensity, cumulative rainfall) and geological settings combine together to trigger a debris-flow are analyzed for real-time monitoring. The analyses focused on 61 historical hazard events with the timing of debris flow initiation and rainfall duration to burst debris-flow characteristics recorded. A combination of averaged rainfall intensity and duration is a more practical index for debris-flow monitoring than critical or threshold rainfall intensity. Because, the outburst timing of debris flows correlates closely to the peak hourly rainfall and the forecasting of peak hourly rainfall reached in a meteorological event could be a valuable index for real-time debris-flow warning.     

Identification of hazard conditions for mudflow occurrence by hydrological model: Application of FLaIR model to Sarno warning system

Mathematical models for forecasting landslides and mudflow movements triggered by heavy rainfalls are useful tools to develop warning systems and hazard mitigation strategy for loss reduction.

In the present paper, an application of Forecasting of Landslides Induced by Rainfalls (FLaIR) hydrological model, correlating the rainfall amount and landslide or mudflow movement occurrences, will be performed. Model application presented here refers to the mudflows of Sarno, Southern Italy, and is based on hourly precipitation data available from a real-time rain gauge installed immediately after the catastrophic event that occurred on May 1998.

The application is extended from October 1998 to May 2002. The main objective is to perform a backanalysis in order to verify the reliability of the proposed scheme for use in a warning system.

Among the most interesting results of the application, the relatively few false alarms for populations given by the model may be highlighted.

The FLaIR model is more useful when it is integrated with a probabilistic model for forecasting precipitation depths during a storm event at an hourly scale. By stochastic modelling of hourly precipitation, it is possible to estimate the probability of reaching the alarm threshold before allowing civil protection actions.     

Analysis of long-range network RTK during a severe ionospheric storm

The network-based GPS technique provides a broad spectrum of corrections to support RTK (real-time kinematic) surveying and geodetic applications. The most important among them are the ionospheric corrections generated in the reference network. The accuracy of these corrections depends upon the ionospheric conditions and may not always be sufficient to support ambiguity resolution (AR), and hence accurate GPS positioning. This paper presents the analyses of the network-derived ionospheric correction accuracy under extremely varying – quiet and stormy – geomagnetic and ionospheric conditions. In addition, the influence of the correction accuracy on the instantaneous (single-epoch) and on-the-fly (OTF) AR in long-range RTK GPS positioning is investigated, and the results, based on post-processed GPS data, are provided. The network used here to generate the ionospheric corrections consists of three permanent stations selected from the Ohio Continuously Operating Reference Stations (CORS) network. The average separation between the reference stations was ∼200 km and the test baseline was 121 km long. The results show that, during the severe ionospheric storm, the correction accuracy deteriorates to the point when the instantaneous AR is no longer possible, and the OTF AR requires much more time to fix the integers. The analyses presented here also outline the importance of the correct selection of the stochastic constraints in the rover solution applied to the network-derived ionospheric corrections.