一种基于小数据量的UHF频段电离层闪烁事件人工智能预报新方法

准确的电离层闪烁事件预警是空间天气预报的主要任务之一.针对中国低纬地区特高频(ultra high frequency,UHF)频段电离层闪烁事件预警信息需求,基于小数据量,充分利用经验知识和深度学习算法从电离层闪烁发生前的背景电离层参数中筛选有效的事件发生前兆因子,进而将电离层闪烁事件预报问题转换为观测数据的分类问题,最终基于深度信念网络形成了一种中国低纬地区UHF频段电离层闪烁事件预报新方法.利用该方法分析了多种观测数据组合与UHF频段电离层闪烁事件发生之间的相关性后,首次发现预报地区东侧跨赤道的电子总含量(total electron content,TEC)随纬度变化剖面的时序数据是电离层闪烁事件预报的重要前兆因子之一,对提升预报性能指标有显著帮助.

高影响海-气环境事件预报模式的高分辨率海洋资料同化系统研发

由于国家安全与社会经济可持续发展的迫切需求,MJO、ENSO、IOD和PDO等高影响海-气环境事件的预测一直是一个重要的研究领域。多尺度、多过程的复杂性使这些海气环境事件的预测至今仍然是气候科学最具挑战性的问题之一。由于海洋成分的长期记忆能力,耦合模式海洋成分的预报初始化对它们的预报有非常重要的作用。然而,国内现有的气候预测系统缺乏耦合模式框架下先进的海洋资料同化系统,同化的海洋资料种类也亟待增加。而针对MJO等季节内尺度和PDO等年代际尺度气候变率,现有气候预测系统的研究水平和业务能力更处于薄弱或空白状态,滞后于国际发展水平。针对全球变化的背景,需着重开展耦合模式的海洋资料同化研究,发展全球耦合模式的海洋资料同化系统,从而提高气候预测能力,增强我国气候防灾减灾的手段,提升我国全球变化研究的竞争力和国际地位。基于我国自主研发的海气耦合模式,项目将开展全球和区域多源海洋观测资料同化技术研究,突破耦合资料同化中海洋和大气之间的动力、热力平衡的关键技术问题,发展耦合模式框架下的参数估计和约束系统,研制具有业务化前景的高分辨率海洋模式的耦合同化系统,并开展MJO、IOD、ENSO和PDO等高影响海-气环境事件的综合评估和预测示范应用。

结合光球磁场特征物理量的质子事件短期预报

利用描述太阳活动区光球磁场复杂性和非势性特征的三个物理量(纵向磁场最大水平梯度|▽_hB_z|_m,强梯度中性线长度L,孤立奇点数目η)建立了质子事件短期预报模型,验证了磁场特征物理量对质子事件短期预报的有效性.目前已建立或使用的太阳质子事件短期预报模型中仍然没有正式将磁场特征物理量作为预报因子.由于太阳质子事件是小概率事件,其物理产生机制尚不完全清楚,这些预报模型往往存在虚报率偏高或报准率偏低的问题.本文试图将原有质子事件模型所用的传统因子与磁场特征物理量结合起来,利用神经网络方法建立一个更为有效的质子事件短期预报模型.利用1997-2001年的训练数据集1871个样本建立了输入层为传统预报因子的模型A以及输入层为传统预报因子和磁场特征物理量的模型B.通过对2002 2003年973个样本的测试数据集进行模拟预报发现,模型A与B在具有相同质子事件报准率的情况下,模型B的虚报率明显降低.这进一步验证了磁场特征物理量在质子事件短期预报中的作用,进而可以加强对太阳质子事件的实际预报能力.

太阳光球磁场特征物理量在质子事件短期预报中的应用

利用SOHO/MDI全日面纵向磁图,计算了三个描述太阳活动区磁场复杂性和非势性的特征物理量,即纵向磁场最大水平梯度|▽_hB_z|_m,强梯度中性线长度L,孤立奇点数目η.为检验太阳光球磁场特征在质子事件短期预报中是否有效,采用BP神经网络方法,建立了基于这三个磁场特征物理量简单的太阳质子事件短期(24 h)预报模型.模型在对2002年和2003年连续两年的样本检测中,有很高的准确率(2002年和2003年分别为90%,87.54%)和较高的质子事件报准率(2002年和2003年分别为60%,75%),从而为光球磁场特征物理量作为质子事件预报的有效因子提供了依据.

论滑坡预报

在简单回顾了滑坡时间预报的研究历程,对滑坡预报的一些概念作了进一步的界定和讨论的基础上,指出了目前滑坡时间预报中存在的一些问题。最后,结合多年的研究工作体会,提出了滑坡预报GMD模型的基本思想和技术路线。

应用机器学习方法的太阳质子事件短期预报模型

本文选取三个描述太阳活动区磁场复杂性和非势性的特征物理量纵向磁场最大水平梯度|-hBz|m,强梯度中性线长度L,孤立奇点数目η.对这三个参量统计计算后结果作为预报因子,应用支持向量机作为预报方法建立一个基于磁场特征物理量的太阳质子事件短期预报模型,该模型可以预报活动区未来24小时是否爆发太阳质子事件.2002和2003年连续两年的样本检测并和基于传统预报因子的模型进行了比对,结果显示预报模型具有较高的准确率和较低的虚报率,从而验证了太阳光球磁场参量作为太阳质子事件预报因子的有效性.

Surface Soil Moisture Simulation for a Typical Torrential Event with a Modified Noah LSM Coupling to the NWP Model

Surface soil moisture has great impact on both meso-and microscale atmospheric processes,especially on severe local convection processes and on the dynamics of short-lived torrential rains.To promote the performance of the land surface model (LSM) in surface soil moisture simulations,a hybrid hydrologic runoff parameterization scheme based upon the essential modeling theories of the Xin'anjiang model and Topography based hydrological Model (TOPMODEL) was developed in preference to the simple water balance model (SWB) in the Noah LSM.Using a strategy for coupling and integrating this modified Noah LSM to the Global/Regional Assimilation and Prediction System (GRAPES) analogous to that used with the standard Noah LSM,a simulation of atmosphere-land surface interactions for a torrential event during 2007 in Shandong was attempted.The results suggested that the surface,10-cm depth soil moisture simulated by GRAPES using the modified hydrologic approach agrees well with the observations.Improvements from the simulated results were found,especially over eastern Shandong.The simulated results,compared with the products of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) soil moisture datasets,indicated a consistent spatial pattern over all of China.The temporal variation of surface soil moisture was validated with the data at an observation station,also demonstrated that GRAPES with modified Noah LSM exhibits a more reasonable response to precipitation events,even though biases and systematic trends may still exist.

The Combined Effect of Initial Error and Model Error on ENSO Prediction Uncertainty Generated by the Zebiak-Cane Model

Initial errors and model errors are the source of prediction errors. In this study, the authors compute the conditional nonlinear optimal perturbation （CNOP）-type initial errors and nonlinear forcing singular vector （NFSV）- type tendency errors of the Zebiak-Cane model with respect to El Nifio events and analyze their combined effect on the prediction errors for E1 Nino events. The CNOP- type initial error （NFSV-type tendency error） represents the initial errors （model errors） that have the largest effect on prediction uncertainties for E1 Nifio events under the perfect model （perfect initial conditions） scenario. How- ever, when the CNOP-type initial errors and the NFSV- type tendency errors are simultaneously considered in the model, the prediction errors caused by them are not am- plified as the authors expected. Specifically, the predic- tion errors caused by the combined mode of CNOP-type initial errors and NFSV-type tendency errors are a little larger than those caused by the NFSV-type tendency er- rors. This fact emphasizes a need to investigate the opti- mal combined mode of initial errors and tendency errors that cause the largest prediction error for E1 Nifio events.

Simulation of the Microphysical Processes and Effect of Latent Heat on a Heavy Rainfall Event in Beijing

An extraordinary rainstorm that occurred in Beijing on 21 July 2012 was simulated using the Weather Research and Forecasting model. The results showed that:(1) The two precipitation phases were based on a combination of cold cloud processes and warm cloud processes. The accumulated conversion amount and conversion rate of microphysical processes in the warm-area phase were all much larger than those in the cold front phase.(2) 72.6% of rainwater was from the warm-area phase. Rainwater mainly came from the melting of graupel and the melting of snow, while the accretion of cloud water by rain ranked second.(3) The net heating rate with height appeared as an overall warming with two strong heating centers in the lower and middle layers of the troposphere and a minimum heating center around the melting layer. The net heating effect in the warm-area phase was stronger than that in the cold front phase.(4) Warm cloud processes contributed most to latent heat release, and the thermal effect of cold cloud processes on the storm in the cold front phase was enhanced compared to that in the warm-area phase.(5) The melting of graupel and snow contributed most to latent heat absorption, and the effect of the evaporation of rainwater was significantly reduced in the cold front phase.

Discrepancies in surface temperature between NCEP reanalysis data and station observations over China and their implications

Previous studies show that temporal irreversibility(TI),as an important indicator of the nonlinearity of time series,is almost uniformly overestimated in the daily air temperature anomaly series over China in NCEP reanalysis data,as compared with station observations.Apart from this highly overestimated TI in the NCEP reanalysis,some other important atmospheric metrics,such as predictability and extreme events,might also be overestimated since there are close relations between nonlinearity and predictability/extreme events.In this study,these issues are fully addressed,i.e.,intrinsic predictability,prediction skill,and the number of extreme events.The results show that intrinsic predictability,prediction skill,and the occurrence number of extreme events are also almost uniformly overestimated in the NCEP reanalysis daily minimum and maximum air temperature anomaly series over China.Furthermore,these overestimations of intrinsic predictability,prediction skill,and the number of extreme events are only weakly correlated with the overestimated TI,which indicates that the quality of the NCEP reanalysis should be carefully considered when conclusions on both predictability and extreme events are derived.

Oil Spill Dispersion Forecasting with the Aid of a 3D Simulation Model

The simulation of the transport and fate of an oil slick, accidentally introduced in the marine environment, is the focus of this research. An oil spill dispersion forecasting system （DIAVLOS forecasting system）, based on wind, wave and ocean circulation forecasting models is developed. The 3-D oil spill model, by the University of Thessaloniki, is based on a Lagrangian （tracer） model that accounts for the transport-diffusion-dispersion and physicochemical evolution of an oil slick. The high resolution meteorological, hydrodynamic and wave models are coupled with the operational systems ALERMO and SKIRON of the University of Athens. The modelling system was successfully assembled and tested under theoretical and realistic scenarios, in order to be applied in forecasting mode and be used by local authorities when an accident occurs. As a result, a 48-hours oil spill dispersion forecasting system was synthesized aiming primarily at the oil spill management at the Burgas-Alexandroupolis oil-pipe terminal, part of a greater busy coastal basin in North Aegean.

A deep stratospheric intrusion associated with an intense cut-off low event over East Asia

European Centre for Medium-Range Weather Forecasts Re-Analysis Interim(ERA-Interim)reanalysis data and satellite data,and trajectory model were applied to analyze the dynamical,thermo-dynamical,and chemical structure in the upper troposphere and lower stratosphere(UTLS)of an intense cut-off low(COL)event occurring over East Asia during June 19-23,2010,and to characterize the process and transport pathway of deep stratospheric intrusion.The Atmospheric Infrared Sounder(AIRS)ozone data and the Global Positioning System Ozone(GPSO3)sonde data showed that the air mass originating from the polar formed a region with relatively high values of potential vorticity(PV)and ozone in the center of COL,and a secondary ozone peak appeared in the upper troposphere during mature stage of the COL.Forward trajectory simulation suggested that during the first stage of COL,deep stratospheric intrusion associated with strong northerly wind jet on the west side of the upper-level trough transported ozone-rich air from the polar lower stratosphere into the middle and lower troposphere in the mid-latitude,and increased the ozone concentration there.During the mature stage of the COL,stratospheric air was transported counterclockwise into the troposphere.Backward trajectory model was used to find the source regions of air mass within the COL during its mature stage.Model results show that air masses with high ozone concentration in the center of the COL have two source regions:one is the subpolar vortex which lies in northern part of Center Siberia,where ozone-rich air plays a major role in increasing the ozone concentrations,and the other is the strong shear region which is near by the cyclonic side of the extratropical jet axis(west of 90°E,near 50°N).The air masses with low ozone concentration around the COL also have two source regions:one is the anticyclonic side of the extratropical jet axis,where the air mass with the relatively low ozone concentration at the bottom of the COL is mainly controlled by horizontal movement,and the other is the warm area of the south side of COL,where the air mass on the east and west side of the COL is mainly dominated by upward motion.

Using CMIP5 model outputs to investigate the initial errors that cause the “spring predictability barrier” for El Nio events

Most ocean-atmosphere coupled models have difficulty in predicting the E1 Nifio-Southern Oscillation （ENSO） when starting from the boreal spring season. However, the cause of this spring predictability barrier （SPB） phenomenon remains elusive. We investigated the spatial characteristics of optimal initial errors that cause a significant SPB for E1 Nifio events by using the monthly mean data of the pre-industrial （PI） control runs from several models in CMIP5 experiments. The results indicated that the SPB-related optimal initial errors often present an SST pattern with positive errors in the central-eastern equatorial Pa- cific, and a subsurface temperature pattern with positive errors in the upper layers of the eastern equatorial Pacific, and nega- tive errors in the lower layers of the western equatorial Pacific. The SPB-related optimal initial errors exhibit a typical La Ni- fia-like evolving mode, ultimately causing a large but negative prediction error of the Nifio-3.4 SST anomalies for El Nifio events. The negative prediction errors were found to originate from the lower layers of the western equatorial Pacific and then grow to be large in the eastern equatorial Pacific. It is therefore reasonable to suggest that the E1 Nifio predictions may be most sensitive to the initial errors of temperature in the subsurface layers of the western equatorial Pacific and the Nifio-3.4 region, thus possibly representing sensitive areas for adaptive observation. That is, if additional observations were to be preferentially deployed in these two regions, it might be possible to avoid large prediction errors for E1 Nifio and generate a better forecast than one based on additional observations targeted elsewhere. Moreover, we also confirmed that the SPB-related optimal initial errors bear a strong resemblance to the optimal precursory disturbance for E1 Nifio and La Nifia events. This indicated that im- provement of the observation network by additional observations in the identified sensitive areas would also be helpful in de- tecting the signals provided by the precursory disturbance, which may greatly improve the ENSO prediction skill.

Verification of SPE probability forecasts at the Space Environment Prediction Center(SEPC)

In space weather forecasting, forecast verification is necessary so that the forecast quality can be assessed. This paper provides an example of how to choose and devise verification methods and techniques according to different space weather forecast products. Solar proton events(SPEs) are hazardous space weather events, and forecasting them is one of the major tasks of the Space Environment Prediction Center(SEPC) at the National Space Science Center of the Chinese Academy of Sciences. Through analyzing SPE occurrence characteristics, SPE forecast properties, and verification requirements at SEPC, verification methods for SPE probability forecasts are identified, and verification results obtained. Overall, SPE probability forecasts at SEPC exhibit good accuracy, reliability, and discrimination. Compared with climatology and persistence forecasts, the SPE forecasts are more accurate. However, the forecasts for SPE onset days are substantially underestimated and need to be considerably improved.

On the “spring predictability barrier” for strong El Nio events as derived from an intermediate coupled model ensemble prediction system

Using predictions for the sea surface temperature anomaly(SSTA) generated by an intermediate coupled model(ICM)ensemble prediction system(EPS), we first explore the "spring predictability barrier"(SPB) problem for the 2015/16 strong El Nio event from the perspective of error growth. By analyzing the growth tendency of the prediction errors for ensemble forecast members, we conclude that the prediction errors for the 2015/16 El Nio event tended to show a distinct season-dependent evolution, with prominent growth in spring and/or the beginning of the summer. This finding indicates that the predictions for the 2015/16 El Nio occurred a significant SPB phenomenon. We show that the SPB occurred in the 2015/16 El Nio predictions did not arise because of the uncertainties in the initial conditions but because of model errors. As such, the mean of ensemble forecast members filtered the effect of model errors and weakened the effect of the SPB, ultimately reducing the prediction errors for the 2015/16 El Nio event. By investigating the model errors represented by the tendency errors for the SSTA component,we demonstrate the prominent features of the tendency errors that often cause an SPB for the 2015/16 El Nio event and explain why the 2015/16 El Nio was under-predicted by the ICM EPS. Moreover, we reveal the typical feature of the tendency errors that cause not only a significant SPB but also an aggressively large prediction error. The feature is that the tendency errors present a zonal dipolar pattern with the west poles of positive anomalies in the equatorial western Pacific and the east poles of negative anomalies in the equatorial eastern Pacific. This tendency error bears great similarities with that of the most sensitive nonlinear forcing singular vector(NFSV)-tendency errors reported by Duan et al. and demonstrates the existence of an NFSV tendency error in realistic predictions. For other strong El Nio events, such as those that occurred in 1982/83 and 1997/98, we obtain the tendency errors of the NFSV structure, which cause a significant SPB and yield a much larger prediction error. These results suggest that the forecast skill of the ICM EPS for strong El Nio events could be greatly enhanced by using the NFSV-like tendency error to correct the model.