降水临近预报及其在水文预报中的应用研究进展

随着全球气候变化、自然变迁及陆表生境改变,极端天气频发且呈现出多尺度时空变异特征,对其进行预报和预警一直是气象水文领域关注的焦点。临近预报可较准确地预报未来短时间天气显著变化,是当前预报强降水等极端事件的主要手段。从基于天气雷达0～3 h外推临近预报、融合数值模式0～6 h临近预报的发展历程梳理了临近预报的研究进展,阐述了雷达外推算法的发展进程、雷达外推预报与数值模式预报融合技术进展,指出"取长补短"的0～6 h融合预报在提高降水预报精度、延长降水预见期等多方面有较大的发展潜力,进一步探寻及提升融合技术是未来融合预报发展的核心。将临近预报以气象水文耦合的方式引入水文预报是从源头提高水文预报精度、保障水文预报效果的主要途径,总结了现阶段主流耦合方式、空间尺度匹配技术、水文模型不确定等陆气耦合中的关键问题,阐述了外推临近预报、融合临近预报作为水文预报输入的研究进展,明确了融合临近预报在延长洪水预见期、提高洪水预报精度中存在优势,并讨论了未来的研究重点及发展方向。     

An Overview of Ocean Predictability and Ocean Ensemble Forecast

Ocean is a highly complex and nonlinear dynamical system. The inevitable errors in both data and numerical models lead to uncertainties in ocean numerical prediction. By understanding features and properties in the ocean on multiple scales, it is important to quantify and estimate the predictability of the ocean, and analyze the reasons and mechanism of error growth. The efforts focus on investigating the method to reduce the uncertainties and errors in forecasting and increase the time limit of ocean predictability. The advances will result in improved marine forecasting models and forecasting skill. Understanding limitations and identifying the research needed to increase accuracy will lead to fundamental progress in ocean forecast, which is of great significance. The present study described and illustrated the mechanics and computations involved in modeling and predicting uncertainties for ocean prediction and its modern applications. Firstly, it discussed the fundamental concept and classification of the ocean predictability. The research status of ocean predictability is introduced including the dynamics methodologies and the ocean ensemble prediction. Three of the dynamical computational methodologies including the singular vector, Lyapunov exponent and bred vector method were introduced. Three ocean ensemble prediction methods including initial condition ensemble, multi-model ensemble and atmospheric forcing ensemble were described and illustrated. Finally, this paper gave a future prospective of ocean predictability and its application.     

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.     

Application and Verification of Joint Probability Method in Potential Forecast for Severe Convective Weather in Anhui Province

In consideration of large uncertainties in severe convective weather forecast, ensemble forecasting is a dynamic method developed to quantitatively estimate forecast uncertainty. Based on ensemble output, joint probability is a post-processing method to delineate key areas where weather event may actually occur by taking account of the uncertainty of several important physical parameters. An investigation of the environments of little rainfall convection and strong rainfall convection from April to September (warm season) during 2009-2015 was presented using daily disastrous weather data, precipitation data of 80 stations in Anhui province and NCEP Final Analysis (FNL) data. Through ingredients-based forecasting methodology and statistical analysis,four convective parameters characterizing two types of convection were obtained, respectively, which were used to establish joint probability forecasting together with their corresponding thresholds. Using the ECMWF ensemble forecast and observations from April to September during 2016-2017, systematic verification mainly based on ROC and case study of different weather processes were conducted. The results demonstrate that joint probability method is capable of discriminating little rainfall convection and non-convection with comparable performance for different lead times, which is more favorable to identifying the occurrence of strong rainfall convection. The joint probability of little rainfall convection is a good indication for the occurrence of regional or local convection, but may produce some false alarms. The joint probability of strong rainfall convection is good at indicating regional concentrated short-term heavy precipitation as well as local heavy rainfall. There are also individual missing reports in this method, and in practice, 10% can be roughly used as joint probability threshold to achieve relative high TS score. Overall, ensemble-based joint probability method can provide practical short-term probabilistic guidance for severe convective weather.     

An extended STIRPAT model-based methodology for evaluating the driving forces affecting carbon emissions in existing public building sector: evidence from China in 2000–2015

In recent decades, population growth associated with unplanned urban occupation has increased the vulnerability of the Brazilian population to natural disasters. In susceptible regions, early flood forecasting is essential for risk management. Still, in Brazil, most flood forecast and warning systems are based either on simplified models of flood wave propagation through the drainage network or on stochastic models. This paper presents a methodology for flood forecasting aiming to an operational warning system that proposes to increase the lead time of a warning through the use of an ensemble of meteorological forecasts. The chosen configuration was chosen so it would be feasible for an operational flood forecast and risk management. The methodology was applied to the flood forecast for the Itajaí-Açu River basin, a region which comprises a drainage area of approximately 15,500 km² in the state of Santa Catarina, Brazil, historically affected by floods. Ensemble weather forecasts were used as input to the MHD-INPE hydrological model, and the performance of the methodology was assessed through statistical indicators. Results suggest that flood warnings can be issued up to 48 h in advance, with a low rate of false warnings. Streamflow forecasting through the use of hydrological ensemble prediction systems is still scarce in Brazil. To the best of our knowledge, this is the first time this methodology aiming to an operational flood risk management system has been tested in Brazil.     

数值模式的预报策略和方法研究进展

数值预报经历了半个多世纪的发展,已成为当前主要的客观预报工具。在模式和资料状况给定的情况下,预报效果的改善很大程度上依赖于所采用的预报策略和方法。为此,全面回顾了国内外基于数值模式的预报策略和方法研究进展,认为采取统计—动力相结合、从历史资料中提炼信息的预报策略是提高数值预报水平的可行之路。最后在总结前人工作基础上,着重介绍了动力相似预报策略和方法的相关研究,特别是实际预报中的试验情况。     

水文集合预报试验及其研究进展

水文集合预报是一种既可以给出确定性预报值,又能提供预报值的不确定性信息的概率预报方法。简述了水文集合预报试验(Hydrologic Ensemble Prediction Experiment,HEPEX)国际计划的主要研究内容,回顾了HEPEX研究进展,分析了对水文预报发展有重要意义的3个HEPEX前沿研究:降尺度研究、集合预报系统研究以及不确定性研究。研究表明,动力-统计降尺度法和高分辨率"单一"模式及低分辨率集合相结合是HEPEX未来研究的方向。     

Validating quantitative precipitation forecast for the Flood Meteorological Office,Patna region during 2011–2014

In order to issue an accurate warning for flood, a better or appropriate quantitative forecasting of precipitation is required. In view of this, the present study intends to validate the quantitative precipitation forecast (QPF) issued during southwest monsoon season for six river catchments (basin) under the flood meteorological office, Patna region. The forecast is analysed statistically by computing various skill scores of six different precipitation ranges during the years 2011–2014. The analysis of QPF validation indicates that the multi-model ensemble (MME) based forecasting is more reliable in the precipitation ranges of 1–10 and 11–25 mm. However, the reliability decreases for higher ranges of rainfall and also for the lowest range, i.e., below 1 mm. In order to testify synoptic analogue method based MME forecasting for QPF during an extreme weather event, a case study of tropical cyclone Phailin is performed. It is realized that in case of extreme events like cyclonic storms, the MME forecasting is qualitatively useful for issue of warning for the occurrence of floods, though it may not be reliable for the QPF. However, QPF may be improved using satellite and radar products.     

Mechanism and forecasting method of persistent extreme weather events: review and prospect

Persistent extreme weather is of high disaster causing capability, represents a great threat to the safety of both people and property and results in substantial economic losses. However, the underlying mechanism of such high impact weather remains unclear, and related forecasting methods are quite under studied currently. Based on the comprehensive reviews of the relevant studies about persistent extreme weather, the prediction of such events within the period during 1～2 weeks in advance is believed to be a significant scientific issue. For this scientific problem, the studies of atmospheric low frequency process, the interaction between multi scale systems, the forcing of complicated underlying surface and sea land atmosphere interactions are necessary to be performed. These multi perspective studies will favor the final establishment of the corresponding forecasting theory and method based on the combination of dynamical prediction and statistical predication. It is hoped that the deficiencies in systematic studies about persistent extreme weather may be made up through pertinent studies, which will prolong the time length of forecasting and increase the prediction precision of such high impact events.     

Flood forecasts based on multi-model ensemble precipitation forecasting using a coupled atmospheric-hydrological modeling system

The recent improvement of numerical weather prediction (NWP) models has a strong potential for extending the lead time of precipitation and subsequent flooding. However, uncertainties inherent in precipitation outputs from NWP models are propagated into hydrological forecasts and can also be magnified by the scaling process, contributing considerable uncertainties to flood forecasts. In order to address uncertainties in flood forecasting based on single-model precipitation forecasting, a coupled atmospheric-hydrological modeling system based on multi-model ensemble precipitation forecasting is implemented in a configuration for two episodes of intense precipitation affecting the Wangjiaba sub-region in Huaihe River Basin, China. The present study aimed at comparing high-resolution limited-area meteorological model Canadian regional mesoscale compressible community model (MC2) with the multiple linear regression integrated forecast (MLRF), covering short and medium range. The former is a single-model approach; while the latter one is based on NWP models [(MC2, global environmental multiscale model (GEM), T213L31 global spectral model (T213)] integrating by a multiple linear regression method. Both MC2 and MLRF are coupled with Chinese National Flood Forecasting System (NFFS), MC2-NFFS and MLRF-NFFS, to simulate the discharge of the Wangjiaba sub-basin. The evaluation of the flood forecasts is performed both from a meteorological perspective and in terms of discharge prediction. The encouraging results obtained in this study demonstrate that the coupled system based on multi-model ensemble precipitation forecasting has a promising potential of increasing discharge accuracy and modeling stability in terms of precipitation amount and timing, along with reducing uncertainties in flood forecasts and models. Moreover, the precipitation distribution of MC2 is more problematic in finer temporal and spatial scales, even for the high resolution simulation, which requests further research on storm-scale data assimilation, sub-grid-scale parameterization of clouds and other small-scale atmospheric dynamics.     

Multi-model multi-analysis ensemble weather forecasting on the grid for the South Eastern Mediterranean Region

Weather forecasting is based on the use of numerical weather prediction (NWP) models that are able to perform the necessary calculations that describe/predict the major atmospheric processes. One common problem in weather forecasting derives from the uncertainty related to the chaotic behaviour of the atmosphere. A solution to that problem is to perform in addition to “deterministic” forecasts, “stochastic” forecasts that provide an estimate of the prediction skill. A computationally feasible approach towards this aim is to perform “ensemble forecasts”. Indeed, in the frame of SEE-GRID-SCI EU funded project a Regional scale Multi-model, Multi-analysis ensemble forecasting system (REFS) was built and ported on the Grid infrastructure. REFS is based on the use of four limited area models (namely BOLAM, MM5, ETA, and NMM) that are run using a multitude of initial and boundary conditions over the Mediterranean. This paper presents the tools and procedures followed for developing this application at a production level.     

陕西省地质灾害-气象预报预警系统研制及应用

为了提高地质灾害-气象预报预警工作的自动化程度和产品的质量,文章从计算机系统制作的角度探讨了地质灾害-气象预报预警的方法,论述了陕西省地质灾害-气象预报预警系统的运行环境、软件功能、空间数据库、模型方法等内容,并介绍了陕西省汛期地质灾害-气象预报预警的应用实例。     

陆面过程与天气研究

陆面作为大气运动的下边界,通过动量、热量及物质交换与大气发生复杂的相互作用。陆面过程被认为是影响天气气候的关键过程之一。关于陆面过程对气候的影响已经开展了大量较为深入的研究,相比之下,针对陆面过程对天气的影响研究并没有受到足够的重视。近年来,陆面过程与天气研究也开始受到了越来越多的关注。本文从陆面基本要素、下垫面构成、陆面诱发的局地环流3个方面,回顾了土壤湿度、地形、土地利用、山谷-平原环流等要素和过程对强对流、暴雨、台风、高温热浪等天气事件影响研究的相关进展,以期为今后的研究提供参考。需要指出,尽管此方面的研究已取得了一定进展,但关于陆面过程对天气,尤其是极端(高影响)天气的影响及机制还有待深入研究,进而从陆面过程的角度来理解重要天气形成、发生和发展的机理,从而为数值模式发展和天气预报业务提供更有力的科学支撑。     

Development of Artificial Intelligence Technology in Weather Forecast

Numerical weather prediction, which is the major basis of current weather forecast, has some shortcomings, such as the understanding of the law of atmospheric motion, the assimilation and application of observation data, the expression of model physics, etc., leading to the forecast error of weather. The rapid development of artificial intelligence technology in recent years provides a new possibility for the advancement and innovation of weather forecast. In this paper, the background of the development of artificial intelligence, the current situation of the application of artificial intelligence technology to weather forecast and the future development trend are mainly described to account for this possibility. After that, the idea for development of weather forecast technology based on the integration of artificial intelligence and numerical forecast is put forward. Particularly, this study stresses that, in order to advance the AI algorithm of weather forecast in the future, it is requested to focus on the nonlinear and chaotic characteristics of atmospheric motion leading to the uncertainty of forecast. Starting from the essence of mathematics and physics, we need to realize the hybrid modeling of mathematics and physics, not only to establish the framework of input-output mapping, but also to provide solutions to the bottleneck problems of weather forecast.     

数值天气预报检验方法研究进展

数值天气预报检验是改进及应用数值模式的重要环节。近年来,模式检验中的观念不断更新,适用于不同预报产品及不同用户需求的模式检验方法也不断涌现。首先简单回顾了以列联表为基础的传统的模式检验方法。其次重点总结了伴随高分辨率数值预报而出现的空间诊断检验技术,按照检验目的的不同,诊断方法可以归纳为:①基于滤波技术的分辨模式在不同时空尺度上预报能力的邻域法、尺度分离法;②利用位移偏差诊断模式预报位置、面积、方位、轴角等与观测差异的属性判别法、变形评估法。然后阐述了集合样本成员的概率分布函数(PDF)、集合预报与观测概率分布函数相似程度、事件发生的概率预报等集合预报检验方法。最后论述了空间诊断技术、集合预报检验方法的适用领域,并讨论了模式检验中存在的一些问题及未来的发展方向。     

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.     

Forecasting for flood warning

Advances in flood forecasting have been constrained by the difficulty of estimating rainfall continuously over space, for catchment-, national- and continental-scale areas. This has had a concomitant impact on the choice of appropriate model formulations for given flood-forecasting applications. Whilst weather radar used in combination with raingauges – and extended to utilise satellite remote-sensing and numerical weather prediction models – have offered the prospect of progress, there have been significant problems to be overcome. These problems have curtailed the development and adoption of more complete distributed model formulations that aim to increase forecast accuracy. Advanced systems for weather radar display and processing, and for flood forecast construction, are now available to ease the task of implementation. Applications requiring complex networks of models to make forecasts at many locations can be undertaken without new code development and be readily revised to take account of changing requirements. These systems make use of forecast-updating procedures that assimilate data from telemetry networks to improve flood forecast performance, at the same time coping with the possibility of data loss. Flood forecasting systems that integrate rainfall monitoring and forecasting with flood forecasting and warning are now operational in many areas. Present practice in flood modelling and forecast updating is outlined from a UK perspective. Challenges for improvement are identified, particularly against a background of greater access to spatial datasets on terrain, soils, geology, land-cover, and weather variables. Representing the effective runoff production and translation processes operating at a given grid or catchment scale may prove key to improved flood simulation, and robust application to ungauged basins through physics-based linkages with these spatial datasets. The need to embrace uncertainty in flood-warning decision-making is seen as a major challenge for the future. To cite this article: R.J. Moore et al., C. R. Geoscience 337 (2005).     

海上大气波导研究进展

大气波导是对流层中具有异常大气折射率梯度的大气层,对于评估和预测电磁波传播和海上探测通信系统等具有重要的科学意义和应用价值.以海上发生的大气波导类型为线索总结了与大气波导相关的研究方法.在蒸发波导研究中以相似理论为基础,开发蒸发波导诊断模型为重点,开展区域海域适应性研究;海上悬空波导和表面波导从早期的定性分析到目前精确定量研究过程中,中尺度数值模式逐渐成为极其重要的研究手段,不仅提高了特定天气过程中大气波导模拟预测精度,而且在此基础上开展区域大气波导环境研究,分析其出现规律、气候原因等.针对海上大气波导研究现状,借鉴气象上的手段和技术,开展海上水文气象调查和电波传播实验,结合中尺度数值模式和海气耦合模式,采用同化技术和集合预报等手段,提高海上低空大气波导量化精度.     

石笋记录年际、年代际极端天气/气候事件研究进展

近年来随着极端天气/气候事件的频发,应对极端天气/气候事件的要求极其紧迫。目前气象资料对极端天气/气候事件的研究相对较短,由此作者提出利用石笋记录重建历史年际、年代际极端天气/气候事件的构想。通过对目前已有的石笋极端天气/气候事件研究实例分析总结,认为洞穴石笋沉积速率相对较快,石笋中标志性结构构造特征的存在,有利于石笋记录到极端天气/气候事件。同时就目前的研究现状,作者提出建立准确年代标尺、选择生长速率相对较快和存在标志性结构构造特征、能记录到极端气候的石笋,以及提高采样分辨率和与其他记录相互验证等作为石笋极端气候研究的工作要求,同时就文石笋研究极端天气/气候事件提出文石笋可能更加容易记录到极端天气/气候事件的个人新认识。     

石笋记录年际、年代际极端天气/气候事件研究进展

近年来随着极端天气/气候事件的频发,应对极端天气/气候事件的要求极其紧迫。目前气象资料对极端天气/气候事件的研究相对较短,由此作者提出利用石笋记录重建历史年际、年代际极端天气/气候事件的构想。通过对目前已有的石笋极端天气/气候事件研究实例分析总结,认为洞穴石笋沉积速率相对较快,石笋中标志性结构构造特征的存在,有利于石笋记录到极端天气/气候事件。同时就目前的研究现状,作者提出建立准确年代标尺、选择生长速率相对较快和存在标志性结构构造特征、能记录到极端气候的石笋,以及提高采样分辨率和与其他记录相互验证等作为石笋极端气候研究的工作要求,同时就文石笋研究极端天气/气候事件提出文石笋可能更加容易记录到极端天气/气候事件的个人新认识。