The prolonged mei-yu/baiu system with anomalous precipitation in the year 2020 has swollen many rivers and lakes,caused flash flooding,urban flooding and landslides,and consistently wreaked havoc across large swathes of China,particularly in the Yangtze River basin.Significant precipitation and flooding anomalies have already been seen in magnitude and extension so far this year,which have been exerting much higher pressure on emergency responses in flood control and mitigation than in other years,even though a rainy season with multiple ongoing serious flood events in different provinces is not that uncommon in China.Instead of delving into the causes of the uniqueness of this year’s extreme precipitation-flooding situation,which certainly warrants in-depth exploration,in this article we provide a short view toward a more general hydrometeorological solution to this annual nationwide problem.A“glocal”(global to local)hydrometeorological solution for floods(GHS-F)is considered to be critical for better preparedness,mitigation,and management of different types of significant precipitation-caused flooding,which happen extensively almost every year in many countries such as China,India and the United States.Such a GHS-F model is necessary from both scientific and operational perspectives,with the strength in providing spatially consistent flood definitions and spatially distributed flood risk classification considering the heterogeneity in vulnerability and resilience across the entire domain.Priorities in the development of such a GHS-F are suggested,emphasizing the user’s requirements and needs according to practical experiences with various flood response agencies. 相似文献
A snow burst event characterized by brief heavy snowfall affected Northeast China and caused serious social impact on 26 January 2017, with the snowband generally aligned with a northeast–southwest-oriented cold front. ECMWF reanalysis data were used to diagnose the possible trigger mechanism. Results showed there were two stages: (a) an initial stage far away from the Changbai Mountains, and (b) an enhancement stage under the influence of high terrain. During the initial stage, the coupling of low-level frontogenesis and a favorable convergence pattern caused strong upward motion, contributing to the release of instability. When the snowband approached the high terrain during the enhancement stage, the various instabilities were triggered by the low-level frontogenesis, terrain circulation, and strong wind shear associated with the low-level jet. Further, a modified Q-vector divergence including generalized potential temperature was calculated to diagnose the vertical motion. It showed that the frontogenesis terms contributed greatly to the negative Q-vector divergence along the moist isentropes, while the pseudo-vorticity terms played a role in the regions with strong wind shear associated with the low-level jet in the warm section, suggesting both were important in stimulating the ascending motion. The regions with negative Q-vector divergence had a close relationship with the vertical structure of convection, indicating the potential to track the development of the snowband in the next few hours.摘要2017年1月26日, 中国东北地区发生了一次短时强降雪过程.本文利用ECMWF再分析数据诊断该过程的可能触发机制.分析表明, 该过程可分两个阶段:初生阶段降雪远离高地形, 低层锋生和有利的辐散场配置激发上升运动释放不稳定;增强阶段雪带接近长白山, 低层锋生,地形环流以及与低空急流有关的风切变共同释放锋前不稳定.本文进一步计算了包含广义位温的修正Q矢量方程.结果表明, 锋生项对沿湿等熵线的负Q矢量散度贡献较大, 而拟涡度项在暖区强风切变区域中比较显著, 两项在激发上升运动中同等重要. 相似文献
Tropical cyclone (TC) annual frequency forecasting is significant for disaster prevention and mitigation in Guangdong Province. Based on the NCEP-NCAR reanalysis and NOAA Extended Reconstructed global sea surface temperature (SST) V5 data in winter, the TC frequency climatic features and prediction models have been studied. During 1951-2019, 353 TCs directly affected Guangdong with an annual average of about 5.1. TCs have experienced an abrupt change from abundance to deficiency in the mid to late 1980 with a slightly decreasing trend and a normal distribution. 338 primary precursors are obtained from statistically significant correlation regions of SST, sea level pressure, 1000hPa air temperature, 850hPa specific humidity, 500hPa geopotential height and zonal wind shear in winter. Then those 338 primary factors are reduced into 19 independent predictors by principal component analysis (PCA). Furthermore, the Multiple Linear Regression (MLR), the Gaussian Process Regression (GPR) and the Long Short-term Memory Networks and Fully Connected Layers (LSTM-FC) models are constructed relying on the above 19 factors. For three different kinds of test sets from 2010 to 2019, 2011 to 2019 and 2010 to 2019, the root mean square errors (RMSEs) of MLR, GPR and LSTM-FC between prediction and observations fluctuate within the range of 1.05-2.45, 1.00-1.93 and 0.71-0.95 as well as the average absolute errors (AAEs) 0.88-1.0, 0.75-1.36 and 0.50-0.70, respectively. As for the 2010-2019 experiment, the mean deviations of the three model outputs from the observation are 0.89, 0.78 and 0.56, together with the average evaluation scores 82.22, 84.44 and 88.89, separately. The prediction skill comparisons unveil that LSTM-FC model has a better performance than MLR and GPR. In conclusion, the deep learning model of LSTM-FC may shed light on improving the accuracy of short-term climate prediction about TC frequency. The current research can provide experience on the development of deep learning in this field and help to achieve further progress of TC disaster prevention and mitigation in Guangdong Province. 相似文献
Abrupt climate change has an important impact on sustainable economic and social development, as well as ecosystem. However, it is very difficult to predict abrupt climate changes because the climate system is a complex and nonlinear system. In the present paper, the nonlinear local Lyapunov exponent (NLLE) is proposed as a new early warning signal for an abrupt climate change. The performance of NLLE as an early warning signal is first verified by those simulated abrupt changes based on four folding models. That is, NLLE in all experiments showed an almost monotonous increasing trend as a dynamic system approached its tipping point. For a well-studied abrupt climate change in North Pacific in 1976/1977, it is also found that NLLE shows an almost monotonous increasing trend since 1970 which give up to 6 years warning before the abrupt climate change. The limit of the predictability for a nonlinear dynamic system can be quantitatively estimated by NLLE, and lager NLLE of the system means less predictability. Therefore, the decreasing predictability may be an effective precursor indicator for abrupt climate change.
This study investigates the interdecadal variability of Quasi-biennial Oscillation (QBO) based on the sounding data in the stratosphere, ERA-40 and ERA-interim reanalysis data in the past 62 years. The QBO periodicity experiences a significant interdecadal variability; the longer (shorter) the mean period, the smaller (larger) the amplitude of variation is. The QBO amplitude varies in a cycle around 10 to 15 years and in an out-of-phase correlation with the period. In addition, there is an increasing trend of the QBO amplitude in 30 to 10 hPa, while a little declining trend in 70 to 40 hPa. The deviation of the QBO zonal wind extremum centers from the equator also shows interdecadal variability. The deviation location of the easterly core is generally in the reverse side to the westerly core, which means that when the easterly core is on one side of a hemisphere, the westerly core is on the other side. 相似文献