基于TCP-GAN的热带气旋路径预测

准确预测热带气旋路径对于中国沿海地区的防灾减灾具有重要作用.卫星数据是预测热带气旋的重要手段,针对现有方法生成的卫星图像不够清晰,很难准确判断热带气旋云系的轮廓,提出了一种基于TCP-GAN的热带气旋路径预测方法.采用了生成对抗网络,并加入了感知损失,使得生成的图像更加细致.模型在不同输入图像序列长度(2、4和6)下进行试验,结果表明,当长度为4时,此时的路径误差是最小的,约为45.36 km.此外,进行了滚动预测,以验证模型在12、18和24 h的预测性能,提出的热带气旋路径预测模型生成的图像能够很好地描述云系的细致纹理结构,预测的路径误差相比于同类方法也更小.

最优子集回归在福建热带气旋年频数预测中的应用

用相关分析方法普查了影响福建省热带气旋（ＴＣ）年频数与前期北半球５００ｈＰａ和１００ｈＰａ月平均位势高度场、北太平洋海温场以及５００ｈＰａ月环流特征量的关系，分析影响福建ＴＣ年频数的前期大气环流及海温场特征，寻找与福建ＴＣ年频数关系密切的预测因子，采用“两段筛选”的思路，选用逐步回归筛选出Ｍ个（１０个左右）预报因子，再用最优子集回归建立预报模型，其效果较为理想。

RESEARCH PROGRESS ON THE STRUCTURE AND INTENSITY CHANGE FOR THE LANDFALLING TROPICAL CYCLONES

Landfalling tropical cyclones(LTCs)include those TCs approaching the land and moving across the coast.Structure and intensity change for LTCs include change of the eye wall,spiral rain band,mesoscale vortices,low-layer shear lines and tornadoes in the envelope region of TC,pre-TC squall lines,remote rain bands,core region intensity and extratropical transition(ET)processes,etc.Structure and intensity change of TC are mainly affected by three aspects,namely,environmental effects,inner core dynamics and underlying surface forcing.Structure and intensity change of coastal TCs will be especially affected by seaboard topography,oceanic stratification above the continental shelf and cold dry continental airflow,etc.Rapid changes of TC intensity,including rapid intensification and sudden weakening and dissipation,are the small probability events which are in lack of effective forecasting techniques up to now.Diagnostic analysis and mechanism study will help improve the understanding and prediction of the rapid change phenomena in TCs.

Preliminary Study of Sensitive Areas for Several Tropical Cyclone Track Prediction Cases in 2007

Conditional （CNOP） obtained by nonlinear optimal perturbation the ensemble-based calculation method is employed to find possible sensitive areas for improving 48-h or more than 48-h tropical cyclone （TC） track predictions in several cases affecting China in 2007. These sensitive areas are examined by observing system simulation experiments （OSSEs）. Results show that these sensitive areas improve TC track predictions for 48 h or more to different extents. Further analysis is performed to determine the distribution characteristics of sensitive areas in these cases. Results show that areas south of Luzon and over surrounding oceans are significant for 48-h or more than 48-h TC track predictions, especially 60-h to 72-h track predictions. Areas over oceans north or east to Taiwan Island seem to be secondary sensitive for 48-h or more than 48-h TC track predictions.

Monthly prediction of tropical cyclone activity over the South China Sea using the FGOALS-f2 ensemble prediction system

The monthly prediction skill for tropical cyclone(TC)activity in the South China Sea(SCS)during the typhoon season(July to November)was evaluated using the FGOALS-f2 ensemble prediction system.Specifically,the prediction skill of the system at a 10-day lead time for monthly TC activity is given based on 35-year(1981–2015)hindcasts with 24 ensemble members.The results show that FGOALS-f2 can capture the climatology of TC track densities in each month,but there is a delay in the monthly southward movement in the area of high track densities of TCs.The temporal correlation coefficient of TC frequency fluctuates across the different months,among which the highest appears in October(0.59)and the lowest in August(0.30).The rank correlation coefficients of TC track densities are relatively higher(R>0.6)in July,September,and November,while those in August and October are relatively lower(R within 0.2 to 0.6).For real-time prediction of TCs in 2020(July to November),FGOALS-f2 demonstrates a skillful probabilistic prediction of TC genesis and movement.Besides,the system successfully forecasts the correct sign of monthly anomalies of TC frequency and accumulated cyclone energy for 2020(July to November)in the SCS.

An Observational and Modeling Study of Extratropical Transition of Hurricane Sandy in 2012

Around 30 October 2012, Hurricane Sandy made landfall along the New Jersey shoreline after its completion of extratropical transition and transformation into an extratropical cyclone. The strong gale induced a catastrophic storm surge, and caused 72 death and damage of more than $50 billion. In this paper, the evolutionary process and spatial structure of the Hurricane Sandy during its extratropical transition were investigated by using Weather Research and Forecasting （WRF） version 3.3.1 modeling resuits and National Center for Environmental Prediction （NCEP） Coupled Forecast System model version 2 reanalysis datasets （CFSv2）. It is found that during the upper-level trough interaction on 29 October, Sandy gradually fused with a pre-existing mid-latitude low-pressure system, and finished the re-intensification. WRF modeling results showed that the second peak occurred mainly due to the enhanced vertical motion, reduced vertical wind shear as well as the supplement of potential vorticity resulting from trough interaction over the southeast of Great Lakes. The cold continental air from the back of trough was encircled within the warm core system cyclonically, forming the characteristic of warm seclusion.

Yearly tropical cyclone potential impact index in China

A new composite index called the yearly tropical cyclone potential impact(YTCPI)is introduced.The relationship between YTCPI and activities of tropical cyclones(TCs)in China,disaster loss,and main ambient fields are investigated to show the potential of YTCPI as a new tool for short-term climate prediction of TCs.YTCPI can indicate TC activity and potential disaster loss.As correlation coefficients between YTCPI and frequency of landfalling TCs,the frequency of TCs traversing or forming inside a 24 h warning line in China from 1971 to 2010 are 0.58 and 0.56,respectively(both are at a statistically significant level,aboveα=0.001).Furthermore,three simple indexes are used to compare with YTCPI.They all have very close relationships with it,with correlation coefficients 0.75,0.82 and 0.78.For economic loss and YTCPI,the correlation coefficient is 0.57 for 1994–2009.Information on principal ambient fields(sea surface temperature,850 and 500 hPa geopotential heights)during the previous winter is reflected in the relationship with YTCPI.Spatial and temporal variabilities of ambient fields are extracted through empirical orthogonal function(EOF)analysis.Spatial distributions of correlation coefficient between YTCPI and ambient fields match the EOF main mode.Correlation coefficients between YTCPI and the EOF time array for the three ambient fields are 0.46,0.44 and 0.4,respectively,all statistically significant,aboveα=0.01.The YTCPI has the overall potential to be an improved prediction tool.