LTP_DSEF模型对2018年登陆中国热带气旋强降水预报的应用

最近,一种基于路径相似的登陆热带气旋降水动力统计集合预报(LTP_DSEF)模型被发展用来预报登陆热带气旋(LTC)带来的强降水.文章把LTP_DSEF模型应用于2018年登陆中国的10个热带气旋(TC)的强过程降水预报,通过测试模型的3452套预报方案确定了对这10个LTC的最佳方案,然后将其性能与三家动力模式(ECMWF、GFS和GRAPES)进行对比.结果表明:LTP_DSEF模型在预报LTC的较强过程降水方面与三家动力模式相比很有优势,特别是预报250mm以上量级的过程降水;对单TC, LTP_DSEF模型预报LTC过程降水的能力优于或者略逊于三家动力模式,特别在三家动力模式对某些TC的强降水均无预报能力时,模型仍能提供宝贵的大于零的TS值;此外虽然与实况相比该模型预测的强降水范围倾向偏大,但它在多数情况下能合理地捕捉到强降水的落区.初步研究表明,尽管LTP_DSEF模型只引入了TC路径和登陆时间两个相似性变量,但它已能为LTC的强过程降水预报提供非常有用的指导.     

大桥水库4.6级地震前后的库水加卸载响应比变化特征

多年的实测数据表明,大桥水库的坝前水位年变化约40m,库水对库盆的静压变化大约为4×105Pa,比固体潮的作用大2个量级,水库的蓄水和放水是对库区影响最大的加卸载力源。根据加卸载响应比理论(LURR),用库区的地震频次和能量分别研究在大桥水库诱发M_S4.6地震前后加卸载响应比的变化。结果表明:在大桥水库及附近相对较大的范围和水体附近相对较小的范围内,当库水加载过程中发生小震群时,地震频次加卸载响应比Y_n和C_r值显示高值异常,表明研究区岩石处于临近破裂状态,其后发生4.6级水库诱发地震;蓄水前后地震频次库水加卸载响应比Y_n和C_r值的平均值有明显差异;在大桥水库诱发的4.6级地震前,库水加卸载作用导致的地震频次及能量的加卸载响应比在震前存在明显的高值异常,震后异常消失。大桥4.6级震例表明库水加卸载响应比可有效应用于水库诱发地震的监测预报     

新丰江水库应力场及2012年2月16日4.8级地震震源特征研究

新丰江水库1959年截流蓄水,蓄水后第一个月即记录到地震活动.1962年3月19日,在水库蓄水两年半之后,库区发生6.1级强震.随后,库区大坝附近地震活动不断.60年来,共发生有记录的地震十万余次,其中4.0级以上地震58次.新丰江水库地震的发生,开始了中国大陆水库地震的监测和研究.     

大渡河猴子岩水库入库洪水过程预报-实时校正-概率预报集成

与传统确定性预报相比,洪水概率预报能够为防洪调度决策提供更为丰富的信息。以大渡河猴子岩水库以上流域为研究区,建立新安江次洪模型,并采用动态系统响应曲线进行实时洪水预报校正。在确定性预报校正基础上,建立基于水文不确定性处理器（HUP）的次洪概率预报模型,定量分析预报不确定性,实现入库洪水概率预报。结果表明：(1)利用猴子岩流域2009 2019年水文气象资料,建立的新安江次洪模型整体精度较高,率定期和验证期的洪量和洪峰相对误差均在±20%以内,平均确定性系数分别为0.69和0.72;经动态系统响应曲线校正后,洪峰和洪量误差均有降低,率定期和验证期的确定性系数分别提高0.13和0.09。(2)以2020年3场洪水未来48 h预报降雨为输入,新安江模型预报精度不高,且随着预见期增长而降低,但经动态系统响应曲线校正后,整体预报精度有所提高,洪量相对误差减小幅度超50%,确定性系数提高幅度超60%。(3)HUP次洪概率预报模型提供的分布函数中位数Q50的预报精度在一定程度上优于校正后的确定性预报;提供的90%置信区间覆盖率均在90%左右,离散度均在0.40以下,能以相对较窄的区间覆盖大部分实测值...     

卡里巴水库蓄水引起库区应力场变化影响分析

水库地震是近几年国际上地震学和地球动力学领域研究的热点问题。 作为世界上库容最大的卡里巴(Kariba)水库在其蓄水后库区地震活动性不断增加, 并于1963年9月23日发生M6.1地震, 是世界上公认发生过6级以上水库地震的四大水库之一。 对该水库蓄水引起库区应力场变化进行量化估算, 有助于对特大—大型水库蓄水后库区应力场变化及应力触发地震这一基础性科学问题的深入了解。 本文分别应用解析解和数值解方法对卡里巴水库蓄水引起库区弹性应力场、 不排水和排水孔隙压力变化进行了计算分析。 结果表明, 卡里巴水库蓄水引起M6.1地震震源处孔压变化为0.015~0.22 MPa, 库仑应力变化约0.03~0.17 MPa, 触发了此次地震的发生。     

适应水文非一致条件的水库防洪优化调度

为完善变化环境下水库防洪调度方案,建立了集水文非一致性诊断、水文模型还现、设计洪水计算与水库调度规则优化于一体的水库适应性调控方法体系,并应用于沂沭泗水系仕阳水库.结果表明,仕阳水库入库洪水变异点发生在1975年;利用新安江模型对水文序列还现处理,得到满足一致性的设计洪水,其洪峰、最大24 h和最大72 h洪量均值,较原设计分别增大了 19.7%、14.5%和10.0%;建立了以防洪效益最大为目标函数的水库优化调度模型,采用动态规划进行求解.提出的适应性调度方案在20年、100年、1000年和5000年一遇设计洪水条件下,较水库现行设计方案最高洪水位分别降低了0.32、0.11、0.19和0.11 m.     

水库诱发地震与构造地震震源参数特征差异性研究——以龙滩水库为例

首先讨论龙滩库区水库蓄水与地震活动之间的关系,发现龙滩水库诱发地震特征十分明显,地震共分5丛呈丛集分布.利用库区架设的24个固定和流动台站记录的数字记录资料,在研究得到龙滩库区非弹性衰减和台站场地响应的基础上,精确测定得到了该地区总共1616个ML≥0.1级地震的震源参数,比较了水库诱发地震与构造地震震源参数特征的差异,得到了以下主要结论:1)龙滩水库地震活动与水库蓄水关系密切,不同蓄水阶段5丛的地震活动状态不同,局部断裂构造发育以及岩石透水性能影响着地震活动对蓄水过程的响应.2)龙滩水库诱发地震的地震矩M0随震级ML的增大而增大,两者之间存在较好的线性关系,统计关系为LogM0=1.07 ML+10.17.应力降与地震大小之间的关系和Nuttli的板内地震为增加应力降(ISD)模型的结果比较吻合,统计关系为LogΔσ=0.71 ML-2.89.3)龙滩水库地区地震辐射能量和地震视应力均随震级的增大而增大,后者意味着大地震是比小地震更高效率的地震能量辐射体.4)总体上不同丛地震应力降水平存在差异.地震应力降空间分布上与库水深度有较好的一致性,即库水深的区域应力降水平高.5)与同震级的构造地震相比,水库诱发地震的应力降值比前者明显偏低,大约小10倍.这可能是由于水库蓄水造成地下介质孔隙压力增大或者水的润滑作用,从而导致在一个比较低的构造应力情况下发生水库诱发地震.     

紫坪铺水库库区震源波谱参数研究

利用紫坪铺水库数字地震台网和成都区域数字台网中的油榨坪台2004年8月到2008年5月12日汶川大地震前的地震波形资料,采用遗传算法反演计算了紫坪铺库区261次1.6级以上地震的震源波谱参数.结果表明紫坪铺库区中小地震的地震矩Mo在1.35×1012～5.98×1014 N·m之间;应力降随着震级的增加而升高;库区小震震源谱拐角频率与地震矩、震级的依赖关系不明确,这可能与紫坪铺水库蓄水后库区压力增加,且水体沿着龙门山断裂方向扩散和渗透有关.     

台湾强震对福建水口库区的库仑应力触发研究

基于简化的水口库区断裂模型,研究了台湾地区1976年以来13次7级以上地震对福建水口水库库区NE和NW向断裂产生的库仑破裂应力。结果表明,台湾7级以上地震对水口库区NE向断裂产生库仑应力累积,而对水口库区NW向断裂的累积库伦应力作用较小,NW向断裂大多处于库伦破裂应力正、负影响区交汇部位,有的还表现为明显的抑制作用。因而,台湾7级以上地震活动对水口库区NE向断裂小震活动有促进作用,但两者之间不是简单的直接触发关系;对水口库区NW向断裂小震活动的库仑应力触发影响可以忽略。     

二滩库区蓄水前地震活动本底研究

1991年 7月建成投测的二滩水库地震遥测台网 ,经过 7年的连续运行 ,为我们提供了宝贵的第一手资料。笔者根据这些地震记录资料 ,用多种地震学方法进行分析研究后发现 ,该区域内 1～ 2级地震是库区主要的地震活动 ;小震活动基本上符合 G- R震级频度关系式 ,正常背景下的平均 b值为 0 .58;1 .5级以上地震的平均月频度为 9次 ;其能量累计蠕变也具有较好的稳定增长关系 ;地震活动的时空分布与库区几条主要断裂有一定关系。可将它们作为研究蓄水后地震活动的参照本底     

Integrating hydrometeorological information for rainfall‐runoff modelling by artificial neural networks

The major purpose of this study is to effectively construct artificial neural networks‐based multistep ahead flood forecasting by using hydrometeorological and numerical weather prediction (NWP) information. To achieve this goal, we first compare three mean areal precipitation forecasts: radar/NWP multisource‐derived forecasts (P_r), NWP precipitation forecasts (P_n), and improved precipitation forecasts (P_m) by merging P_r and P_n. The analysis shows that the accuracy of P_m is higher than that of P_r and P_n. The analysis also indicates that the NWP precipitation forecasts do provide relative effectiveness to the merging procedure, particularly for forecast lead time of 4–6 h. In sum, the merged products performed well and captured the main tendency of rainfall pattern. Subsequently, a recurrent neural network (RNN)‐based multistep ahead flood forecasting techniques is produced by feeding in the merged precipitation. The evaluation of 1–6‐h flood forecasting schemes strongly shows that the proposed hydrological model provides accurate and stable flood forecasts in comparison with a conventional case, and significantly improves the peak flow forecasts and the time‐lag problem. An important finding is the hydrologic model responses which do not seem to be sensitive to precipitation predictions in lead times of 1–3 h, whereas the runoff forecasts are highly dependent on predicted precipitation information for longer lead times (4–6 h). Overall, the results demonstrate that accurate and consistent multistep ahead flood forecasting can be obtained by integrating predicted precipitation information into ANNs modelling. Copyright © 2009 John Wiley & Sons, Ltd.     

Fuzzy computing based rainfall–runoff model for real time flood forecasting

This paper analyses the skills of fuzzy computing based rainfall–runoff model in real time flood forecasting. The potential of fuzzy computing has been demonstrated by developing a model for forecasting the river flow of Narmada basin in India. This work has demonstrated that fuzzy models can take advantage of their capability to simulate the unknown relationships between a set of relevant hydrological data such as rainfall and river flow. Many combinations of input variables were presented to the model with varying structures as a sensitivity study to verify the conclusions about the coherence between precipitation, upstream runoff and total watershed runoff. The most appropriate set of input variables was determined, and the study suggests that the river flow of Narmada behaves more like an autoregressive process. As the precipitation is weighted only a little by the model, the last time‐steps of measured runoff are dominating the forecast. Thus a forecast based on expected rainfall becomes very inaccurate. Although good results for one‐step‐ahead forecasts are received, the accuracy deteriorates as the lead time increases. Using the one‐step‐ahead forecast model recursively to predict flows at higher lead time, however, produces better results as opposed to different independent fuzzy models to forecast flows at various lead times. Copyright © 2004 John Wiley & Sons, Ltd.     

A variational method for correcting non-systematic errors in numerical weather prediction

A variational method based on previous numerical forecasts is developed to estimate and correct non-systematic component of numerical weather forecast error. In the method, it is assumed that the error is linearly dependent on some combination of the forecast fields, and three types of forecast combination are applied to identifying the forecasting error: 1) the forecasts at the ending time, 2) the combination of initial fields and the forecasts at the ending time, and 3) the combination of the forecasts at...     

Quantitative assessment of short‐term rainfall forecasts from radar nowcasts and MM5 forecasts

Short‐term Quantitative Precipitation Forecasts (QPFs) can be achieved from numerical weather prediction (NWP) models or radar nowcasting, that is the extrapolation of the precipitation at a future time from consecutive radar scans. Hybrid forecasts obtained by merging rainfall forecasts from radar nowcasting and NWP models are potentially more skilful than either radar nowcasts or NWP rainfall forecasts alone. This paper provides an assessment of deterministic and probabilistic high‐resolution QPFs achieved by implementing the Short‐term Ensemble Prediction System developed by the UK Met Office. Both radar nowcasts and hybrid forecasts have been performed. The results show that the performance of both deterministic nowcasts and deterministic hybrid forecasts decreases with increasing rainfall intensity and spatial resolution. The results also show that the blending with the NWP forecasts improves the performance of the forecasting system. Probabilistic hybrid forecasts have been obtained through the modelling of a stochastic noise component to produce a number of equally likely ensemble members, and the comparative assessment of deterministic and probabilistic hybrid forecasts shows that the probabilistic forecasting system is characterised by a higher discrimination accuracy than the deterministic one. Copyright © 2011 John Wiley & Sons, Ltd.     

Probabilistic forecasting of drought: a hidden Markov model aggregated with the RCP 8.5 precipitation projection

The creeping characteristics of drought make it possible to mitigate drought’s effects with accurate forecasting models. Drought forecasts are inevitably plagued by uncertainties, making it necessary to derive forecasts in a probabilistic framework. In this study, we proposed a new probabilistic scheme to forecast droughts that used a discrete-time finite state-space hidden Markov model (HMM) aggregated with the Representative Concentration Pathway 8.5 (RCP) precipitation projection (HMM-RCP). The standardized precipitation index (SPI) with a 3-month time scale was employed to represent the drought status over the selected stations in South Korea. The new scheme used a reversible jump Markov chain Monte Carlo algorithm for inference on the model parameters and performed an RCP precipitation projection transformed SPI (RCP-SPI) weight-corrected post-processing for the HMM-based drought forecasting to perform a probabilistic forecast of SPI at the 3-month time scale that considered uncertainties. The point forecasts which were derived as the HMM-RCP forecast mean values, as measured by forecasting skill scores, were much more accurate than those from conventional models and a climatology reference model at various lead times. We also used probabilistic forecast verification and found that the HMM-RCP provided a probabilistic forecast with satisfactory evaluation for different drought categories, even at long lead times. In a drought event analysis, the HMM-RCP accurately predicted about 71.19 % of drought events during the validation period and forecasted the mean duration with an error of less than 1.8 months and a mean severity error of <0.57. The results showed that the HMM-RCP had good potential in probabilistic drought forecasting.     

The use of scale recursive estimation for short term quantitative precipitation forecast

Scale recursive estimation (SRE) is adopted for short term quantitative precipitation forecast (QPF). The precipitation field is modelled using a lognormal random cascade, well suited to properly represent the scaling properties of rainfall fields. To estimate the random cascade parameters, scale recursive maximum likelihood estimation (MLE) is carried out by the iterative expectation maximization (EM) algorithm. To illustrate the potentiality of the SRE, forecast of a synthetically generated rainfall time series is shown. Adaptive estimation of the process parameters is carried out and precipitation forecasts are issued. The forecasts from the SRE are compared with those from standard ARMA models, showing a good performance. The SRE is then adopted for forecasting of an observed half hourly precipitation series for a two day storm event in northern Italy. The SRE provides good performance and it can therefore be adopted as a tool for short term QPF.     

Adaptive Blending of Model and Observations for Automated Short-Range Forecasting: Examples from the Vancouver 2010 Olympic and Paralympic Winter Games

An automated short-range forecasting system, adaptive blending of observations and model (ABOM), was tested in real time during the 2010 Vancouver Olympic and Paralympic Winter Games in British Columbia. Data at 1-min time resolution were available from a newly established, dense network of surface observation stations. Climatological data were not available at these new stations. This, combined with output from new high-resolution numerical models, provided a unique and exciting setting to test nowcasting systems in mountainous terrain during winter weather conditions. The ABOM method blends extrapolations in time of recent local observations with numerical weather predictions (NWP) model predictions to generate short-range point forecasts of surface variables out to 6 h. The relative weights of the model forecast and the observation extrapolation are based on performance over recent history. The average performance of ABOM nowcasts during February and March 2010 was evaluated using standard scores and thresholds important for Olympic events. Significant improvements over the model forecasts alone were obtained for continuous variables such as temperature, relative humidity and wind speed. The small improvements to forecasts of variables such as visibility and ceiling, subject to discontinuous changes, are attributed to the persistence component of ABOM.     

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.