Progress in Lightning Forecast by Using Numerical Weather Models and Model Outputs

Lightning can threaten human and equipment safety. An indicator of sever convective weather, it plays an important role in atmospheric chemistry. The intensive studies have advanced the lightning forecast in the mesoscale weather models and its application in global climate models. There are three methods to forecast lightning by using numerical weather models: Numerical diagnosis prediction based on synoptic background filed statistical relations; Flash rate parameterization developed with the relationship between dynamical, microphysical and electrification processes, and The numerical weather model coupled with the explicit electrification and lightning parameterization schemes. In this paper, the research progress in lightning forecast with three above-mentioned methods were reviewed, and the future research issues on lightning forecast were also discussed.     

贵州省地质灾害气象预警预报

贵州复杂和脆弱的地质环境使地质灾害频发,危害民生,影响国民经济的健康发展,引起社会的重视。选用适合的地质灾害气象预警预报方法,是主动防灾减灾的手段之一。几年来的预警预报工作证明,效果总体较好。对其重要性、影响、作用、改进的意义做了解读。     

宁夏降水型地质灾害气象条件等级预警系统

利用1982-2004年宁夏地质灾害与降水资料,在分析引发宁夏干旱区主要地质灾害的气象条件基础上,采用统计学方法分区建模,建立了宁夏降水型地质灾害潜势预报模型。依据该模型,在可视化高级编程语言DELPHl环境下,研究开发了一套自动化程度较高的降水型地质灾害气象条件等级预警系统;该系统可以通过网络,以协调一致的工作平台,将气象与地质等相关部门有机连接,实现了联合开展地质灾害预报及指导订正的业务流程。根据实时的雨情及降雨预报,依据所建的分区预报模型,对宁夏地质灾害的发生概率进行快速评价,实现对灾害发生的空间范围、强度及其分布概率的自动实时预警预报;通过人机交互订正,提供位图和GEOS（文本）2种格式的概率预报结论,同时实现了预报预警服务材料的自动化输出。2004,2005年的业务试运行表明,该预警系统基本能满足业务的需求,为新业务领域的拓展提供了技术支持。     

Progress in Researches on Ensemble Forecasting of Extreme Weather Based on Numerical Models

Under the background of climate change, extreme weather events (e.g., heavy rainfall, heat wave, and cold damage) in China have been occurring more frequently with an increasing trend of induced meteorological disasters. Therefore, it is of great importance to carry out research on forecasting of extreme weather. This paper systematically reviewed the primary methodology of extreme weather forecast, current status in development of ensemble weather forecasting based on numerical models and their applications to forecast of extreme weather, as well as progress in approaches for correcting ensemble probabilistic forecast. Nowadays, the forecasting of extreme weather has been generally dominated by methodology using dynamical models. That is to say, the dynamical forecasting methods based on ensemble probabilistic forecast information have become prevailing in current operational extreme weather forecast worldwide. It can be clearly found that the current major directions of research and development in this field are the application of ensemble forecasts based on numerical models to forecasting of extreme weather, and its improvement through bias correction of ensemble probabilistic forecast. Based on a relatively comprehensive review in this paper, some suggestions with respect to development of extreme weather forecast in future were further given in terms of the issues of how to propose effective approaches on improving level of identification and forecasting of extreme events.     

径流长期预报的人工神经网络方法

提出径流长期预报的人工神经网络方法。运用神经网络的一典型模型──“反向传播”模型,以大伙房水库在补水期的径流状况作为研究对象,尝试了神经网络方法的效果。结果表明,该方法预报成功率较高,容错能力较强,可望成为径流长期预报的有效的辅助手段。     

利用人工神经网络原理对钻井中钻速的预测

在对各种钻速预测模式进行分析的基础上，提出利用改进后的人工神经网络BP算法理论完成对钻井过程中机械钻速的预测。     

A Study of the Extended-range Forecast for the Low Frequency Temperature and High Temperature Weather over the Lower Reaches of Yangtze River Valley in Summer

Based on the observational data, the variations of Intraseasonal Oscillation (ISO) of the daily temperatures and its relationships to the high temperature in summer over the lower reaches of the Yangtze River Valley (LYRV) were studied for the period of 1979-2011. It is found that the daily temperatures over LYRV in May-August was mainly of periodic oscillations of 1525, 3060 and 6070 days, and the interannual variation of the intensity of its 3060-day oscillation had a strongly positive correlation with the number of days with daily highest temperature over 35 ℃ in July-August. Low frequency components of daily temperature in the LYRV, and the principal components of the Eastern Asian 850 hPa low frequency temperature, over a time period ranging from 1979 to 2000, were used to establish the Extended Complex Autoregressive model (ECAR) on an extended-range forecast of the 3060-day low frequency temperature over the LYRV. A 11-year independent real-time extended-range forecast was conducted on the extended-range forecast of low frequency component of the temperature over the LYRV in May-August, for the period ranging from 2001 to 2011. These experimental results show that this ECAR model, which is based on a data-driven model, has a good forecast skill at the lead time of approximately 23 days, with a forecast ability superior to the traditional autoregressive (AR) model. Hence, the development and variation of the leading 3060-day modes for the Eastern Asian 850 hPa low frequency temperatures and temporal evolutions of their relationships to low frequency components of the temperature over the LYRV in summer are very helpful in predicting the persistent high temperature over the LYRV at a 20 to 25 days lead.     

An operational multiscale system for hazards prediction, mapping, and response

By definition, a crisis is a situation that requires assistance to be managed. Hence, response to a crisis involves the merging of local and non-local emergency response personnel. In this situation, it is critical that each participant: (1) know the roles and responsibilities of each of the other participants; (2) know the capabilities of each of the participants; and (3) have a common basis for action. For many types of natural disasters, this entails having a common operational picture of the unfolding events, including detailed information on the weather, both current and forecasted, that may impact on either the emergency itself or on response activities. The Consequences Assessment Tool Set (CATS) is a comprehensive package of hazard prediction models and casualty and damage assessment tools that provides a linkage between a modeled or observed effect and the attendant consequences for populations, infrastructure, and resources, and, hence, provides the common operational picture for emergency response. The Operational Multiscale Environment model with Grid Adaptivity (OMEGA) is an atmospheric simulation system that links the latest methods in computational fluid dynamics and high-resolution gridding technologies with numerical weather prediction to provide specific weather analysis and forecast capability that can be merged into the geographic information system framework of CATS. This paper documents the problem of emergency response as an end-to-end system and presents the integrated CATS–OMEGA system as a prototype of such a system that has been used successfully in a number of different situations.     

致洪暴雨中期预报进展

结合我国国情,从防洪减灾的迫切需要出发,进行增长暴雨预见期的研究。采用气象与水文结合的方法,提出“致洪暴雨”的新概念,并简要地论述了近年来国内在致洪暴雨中期预报研究试验方面的若干新进展,其内容包括致洪暴雨过程出现的天气气候特征,大气环流形势背景、卫星云图及OLR场分布特点、大气超长波、长波及合成行星波、低频振荡、大气物理参数诊断、暴雨时空分布特点及其与洪水的关系,以及中期预报方法的探索等。     

新一代天气雷达常用产品在我国人工影响天气工作中的应用

随着新一代天气雷达的组网建设,常用雷达产品逐渐在人工影响天气（以下简称＂人影＂）工作中发挥了重要作用。如利用反射率因子产品分析降水系统的移向、移速、强度以及降水性质等特征为人影作业指挥提供参考;利用径向速度产品选择人影作业的最佳时机和部位;利用垂直积分液态水含量产品识别冰雹云并指导防雹作业;综合应用多种雷达产品建立人影...     

一种提前预测孔内事故的新技术

由于种种原因,在钻井过程中往往会出现一些意想不到的孔内事故,处理孔内事故通常需要浪费大量的时间和资金。介绍了一种能够提前精确预测孔内事故的新技术,该技术利用时间序列方法实时分析钻井过程中钻进参数的变化,依据钻进参数变化曲线来提前预测孔内事故发生。并给出了该技术的一个应用实例以及避免事故发生的补救措施。研究表明,该技术能够提前100~200 min可靠预测将要发生的孔内事故,大幅度降低经济损失,具有重大的经济价值和广泛的应用前景。     

新一代中尺度预报模式（WRF）国内应用进展

随着中尺度大气模式的不断发展,新一代中尺度天气研究和预报模式WRF因其完全开放、可移植性强、更新快等特点在国内外得到了广泛应用。从物理参数化方案研究,实时个例模拟研究及与中尺度大气模式MM5的对比研究3个方面介绍近10年来WRF模式在国内的发展和应用概况,阐明WRF模式在中尺度模拟中的普适性和优越性,展望WRF模式在国...     

Artificial intelligence in seismology:Advent,performance and future trends

Realistically predicting earthquake is critical for seismic risk assessment,prevention and safe design of major structures.Due to the complex nature of seismic events,it is challengeable to efficiently identify the earthquake response and extract indicative features from the continuously detected seismic data.These challenges severely impact the performance of traditional seismic prediction models and obstacle the development of seismology in general.Taking their advantages in data analysis,artificial intelligence(AI) techniques have been utilized as powerful statistical tools to tackle these issues.This typically involves processing massive detected data with severe noise to enhance the seismic performance of structures.From extracting meaningful sensing data to unveiling seismic events that are below the detection level,AI assists in identifying unknown features to more accurately predicting the earthquake activities.In this focus paper,we provide an overview of the recent AI studies in seismology and evaluate the performance of the major AI techniques including machine learning and deep learning in seismic data analysis.Furthermore,we envision the future direction of the AI methods in earthquake engineering which will involve deep learning-enhanced seismology in an internet-of-things(IoT) platform.     

关于平流层异常影响对流层天气系统的研究进展

人们传统上认为大气平流层很少能对对流层产生重要影响,但是,最近几年的观测研究表明这种自上而下的影响是显著的和非常重要的,特别地,近几年关于北极涛动的研究极大推动了这一问题的研究进展。这方面的研究发现平流层异常可以对对流层天气系统产生重要影响,也就是冬季平流层北极涛动（AO）的负异常可以诱发中高纬度地区寒潮天气,而AO的正异常则导致中高纬度地区的温暖晴朗天气。由于观测分析表明平流层AO的异常信号总是领先对流层AO异常,一些学者甚至建议冬季北半球平流层的异常信号可以作为预报对流层天气变化的先行指标,并可以把对流层天气预报的时限提高到3个星期以上。综述这一领域在最近几年的研究进展、阐述平流层异常影响对流层天气系统的物理机制和总结各种不同的学术观点,并对将来研究中应注意的问题提出了建议。     

GM—BP串联组合预测模型在滑坡变形监测中的应用

为了提高滑坡的预测精度,通过对灰色GM（1,1）模型与BP神经网络模型各自优缺点及互补性的分析,建立了GM—BP串联组合预测模型。模型首先采用等维动态GM（1,1）模型进行初步预测,然后利用BP神经网络对初步预测的结果进行训练及仿真,通过数据的归一化处理,参数的判定选取,获得组合模型预测值。以茅坪滑坡为例,对位移进行了预测。通过数据的对比分析,发现GM—BP串联组合预测模型在短期预测精度上高于单一模型。     

Mountain range specific analog weather forecast model for northwest Himalaya in India

Mountain range specific analog weather forecast model is developed utilizing surface weather observations of reference stations in each mountain range in northwest Himalaya (NW-Himalaya). The model searches past similar cases from historical dataset of reference observatory in each mountain range based on current situation. The searched past similar cases of each mountain range are used to draw weather forecast for that mountain range in operational weather forecasting mode, three days in advance. The developed analog weather forecast model is tested with the independent dataset of more than 717 days (542 days for Pir Panjal range in HP) of the past 4 winters (2003–2004 to 2006–2007). Independent test results are reasonably good and suggest that there is some possibility of forecasting weather in operational weather forecasting mode employing analog method over different mountain ranges in NW-Himalaya. Significant difference in overall accuracy of the model is found for prediction of snow day and no-snow day over different mountain ranges, when weather is predicted under snow day and no-snow day weather forecast categories respectively. In the same mountain range, significant difference is also found in overall accuracy of the model for prediction of snow day and no-snow day for different areas. This can be attributed to their geographical position and topographical differences. The analog weather forecast model performs better than persistence and climatological forecast for day-1 predictions for all the mountain ranges except Karakoram range in NW-Himalaya. The developed analog weather forecast model may help as a guidance tool for forecasting weather in operational weather forecasting mode in different mountain ranges in NW-Himalaya.     

SWAT模型中天气发生器与数据库构建及其验证

提出了通过日照时数和太阳辐射量的相关关系来估算逐日辐射量的方法。采用日平均温度和日平均湿度来计算日露点温度，以建立SWAT模型天气发生器。采用插值方法对土壤粒径进行转换，并利用SPAW程序估算土壤水特性参数，建立了SWAT模型土壤属性库。将构建的SWAT模型应用于潮河上游下会水文站以上流域的水文过程模拟，月效率系数≥0．91，确定性系数≥0．93，取得了非常好的模拟效果。结果表明在缺乏详细的气象和土壤数据情况下，可以构建SWAT模型进行水文模拟研究。     

隧道系统预测的多因素模型

建立了隧道系统预测的多因素分析模型 ,弥补了以往单因素时序分析的不足 ,并编制了相应的分析程序。结合沈大高速公路韩家岭隧道部分断面的多种实测数据 ,对隧道系统的多因素模型及稳定性进行了研究 ,对相应预测结果进行了讨论