Seasonal Sensitivity on COBEL-ISBA Local Forecast System for Fog and Low Clouds

Skillful low visibility forecasts are essential for air-traffic managers to effectively regulate traffic and to optimize air-traffic control at international airports. For this purpose, the COBEL-ISBA local numerical forecast system has been implemented at Paris CDG international airport. This local approach is robust owing to the assimilation of detailed local observations. However, even with dedicated observations and initialization, uncertainties remain in both initial conditions and mesoscale forcings. The goal of the research presented here is to address the sensitivity of COBEL-ISBA forecast to initial conditions and mesoscale forcings during the winter season 2002–2003. The main sources of uncertainty of COBEL-ISBA input parameters have been estimated and the evaluation of parameter uncertainty on the forecasts has been studied. A budget strategy is applied during the winter season to quantify COBEL-ISBA sensitivity. This study is the first step toward building a local ensemble prediction system based on COBEL-ISBA. The conclusions of this work point out the potential for COBEL-ISBA ensemble forecasting and quantify sources of uncertainty that lead to dispersion.     

Quality Assessment of the Cobel-Isba Numerical Forecast System of Fog and Low Clouds

Short-term forecasting of fog is a difficult issue which can have a large societal impact. Fog appears in the surface boundary layer and is driven by the interactions between land surface and the lower layers of the atmosphere. These interactions are still not well parameterized in current operational NWP models, and a new methodology based on local observations, an adaptive assimilation scheme and a local numerical model is tested. The proposed numerical forecast method of foggy conditions has been run during three years at Paris-CdG international airport. This test over a long-time period allows an in-depth evaluation of the forecast quality. This study demonstrates that detailed 1-D models, including detailed physical parameterizations and high vertical resolution, can reasonably represent the major features of the life cycle of fog (onset, development and dissipation) up to +6 h. The error on the forecast onset and burn-off time is typically 1 h. The major weakness of the methodology is related to the evolution of low clouds (stratus lowering). Even if the occurrence of fog is well forecasted, the value of the horizontal visibility is only crudely forecasted. Improvements in the microphysical parameterization and in the translation algorithm converting NWP prognostic variables into a corresponding horizontal visibility seems necessary to accurately forecast the value of the visibility.     

Seasonal Prediction for the Indian Monsoon Region with FSU Ocean-atmosphere Coupled Model: Model Mean and 2002 Anomalous Drought

Seasonal climate prediction for the Indian summer monsoon season is critical for strategic planning of the region. The mean features of the Indian summer monsoon and its variability, produced by versions of the ‘Florida State University Coupled Ocean-Atmosphere General Circulation Model’ (FSUCGCM) hindcasts, are investigated for the period 1987 to 2002. The coupled system has full global ocean and atmospheric models with coupled assimilation. Four member models were created by choosing different combinations of parameterizations of the physical processes in the atmospheric model component. Lower level wind flow patterns and rainfall associated with the summer monsoon season are examined from this fully coupled model seasonal integrations. By comparing with observations, the mean monsoon condition simulated by this coupled model for the June, July and August periods is seen to be reasonably realistic. The overall spatial low-level wind flow patterns and the precipitation distributions over the Indian continent and adjoining oceanic regions are comparable with the respective analyses. The anomalous below normal large-scale precipitation and the associated anomalous low-level wind circulation pattern for the summer monsoon season of 2002 was predicted by the model three months in advance. For the Indian summer monsoon, the ensemble mean is able to reproduce the mean features better compared to individual member models.     

考虑动态临界加速度的地震边坡永久位移预测模型研究

在区域边坡地震危险性评价中主要采用永久位移预测模型进行地震边坡永久位移计算.永久位移预测模型以Newmark滑块理论为基础,通过大量实测地震时程记录统计拟合得出.针对Newmark理论中滑动面抗剪强度参数保持不变和已有位移预测模型的计算位移小于实测位移的问题,利用动态临界加速度理论,分别构建含有峰值加速度和阿里亚斯强度...     

滇西实验场地震中短临动态跟踪综合预测方案及其实践运用

对滇西实验场1992年以来地下流体前兆观测资料进行分析研究,选取应震效果通过R信度检验（对应MS≥5.0级地震）的8条单项前兆指标。综合考虑单项前兆指标虚报率和漏报率对指标信度的影响,对单项前兆指标利用数学模型进行综合处理,减少了虚报和漏报的次数,预测效果明显提高。利用分级预警模式分别建立了中期（T≤6个月）和短临（T≤3个月）预测模型。综合预测方案还提供了地点判定原则和震级估算原则。最终提供地震短临预测三要素。运用本综合预测方案对2011年腾冲5级双震、2013年洱源5级双震和德钦两次5级地震作用了较为成功的短临预测。     

加勒比板块多学科研究综述及其对地震预测的启示

2010年1月12日海地M_W7.0地震打破了Enriquillo断层近240余年的地震平静期, 可能意味着加勒比地区新的活跃周期的到来。 本文通过收集该地区地质构造演化、 地球物理场、 发震构造、 震源物理、 运动学特征及应力场演化等方面的研究成果, 分析由于海地地震的发生所带来的区域地震危险性等相关认识的改变。 通过对多学科资料的梳理, 分析利用多学科研究成果开展加勒比地区地震危险性分析的基本思路, 以期为未来的中国大陆区域动力学模型构建、 地震预测预报研究提供基础参考。     

东莞市区震害预测与防御对策系统建设

在地震小区划、概率设定地震和广泛收集地震地质、建(构)筑物、生命线工程、地震次生灾害源、社会环境与经济人口数据等的基础上,对东莞市区89.3 km2范围工作区开展震害预测与防御对策研究,得到不同烈度下建(构)筑物、生命线工程的震害分析结果,以及地震次生灾害影响、人员伤亡与经济损失评估结果,对工作区及东莞市的抗震能力进行...     

滇西地震预报实验场及邻区短临预报量化跟踪决策方案

借鉴了数学上降元法解多元方程的思路，分步进行地震三要素的短临预报尝试。首先确定了个较大尺度的研究区域，确定所预报震级下限，用多条短临预报指标和自适应加权综合集成概率模式预报时间；又在有时间预报的前提下，利用地震活动性图象确定最危险的具体地点；要根据前兆异常幅度和持续时间、异常数量以及宏观异常情况估计可能的震级范围，从而初步实现地震的三要素短临预报。该方法具有较好的可操作性。     

无井地震储层预测及烃类检测——以北黄海中部坳陷A构造为例

北黄海盆地是中国东部勘探及研究程度较低的含油气盆地之一,从原始地震资料分析入手,利用研究区波阻抗、道积分、瞬时振幅、瞬时频率等多种地震资料特殊处理方法及AVO油气检测技术,并结合沉积相和区域地质资料,对北黄海中部坳陷A构造的储盖层分布、含油气性等油气地质条件进行分析预测,认为研究区A构造中新生带地层纵向上主要有两套有利的储盖组合,且构造中部和翼部含油气性较好。     

A simple ground‐motion prediction model for cumulative absolute velocity and model validation

Cumulative absolute velocity (CAV) is an important ground motion intensity measure used in seismic hazard analysis. Based on the Next Generation Attenuation strong motion database, a simple ground‐motion prediction equation is proposed for the geometric mean of as‐recorded horizontal components of CAVs using mixed regression analysis. The proposed model employs only four parameters and has a simple functional form. Validation tests are conducted to compare the proposed model with the recently developed Campbell–Bozorgnia (CB10) model using subsets of the strong motion database, as well as several recent earthquakes that are not used in developing the model. It is found that the predictive capability of the proposed model is comparable with the CB10 model, which employs a complex functional form and more parameters. The study also corroborates previous findings that CAV has higher predictability than other intensity measures such as the peak ground acceleration. The high predictability of CAV warrants the use of the proposed simple model as an alternative in seismic hazard analysis. Copyright © 2012 John Wiley & Sons, Ltd.