检索结果-南通市图书馆

An Evaluation of Tropical Cyclone Genesis Forecast over the Western North Pacific and the South China Sea from the CMA-TRAMS

引用

Journal of Tropical Meteorology 2024年第1期30卷 20-28页

作者：李梦婕陈子通戴光丰田群梁卓轩林青张艳霞 Guangzhou Institute of Tropical and Marine Meteorology/Guangdong Provincial Key Laboratory of Regional Numerical weather Prediction CMAGuangzhou 510641 China State Key Laboratory of Severe weather Chinese Academy of Meteorological SciencesBeijing 100081 China Guangdong Meteorological Observatory Guangzhou 510641 China

Tropical cyclone(TC) genesis forecasting is essential for daily operational practices during the typhoon *** updated version of the Tropical Regional Atmosphere Model for the South China Sea(CMA-TRAMS) offers forecasters reliable numerical weather prediction(NWP) products with improved configurations and fine resolution. While traditional evaluation of typhoon forecasts has focused on track and intensity, the increasing accuracy of TC genesis forecasts calls for more comprehensive evaluation methods to assess the reliability of these predictions. This study aims to evaluate the effectiveness of the CMA-TRAMS for cyclogenesis forecasts over the western North Pacific and South China Sea. Based on previous research and typhoon observation data over five years, a set of localized, objective criteria has been proposed. The analysis results indicate that the CMA-TRAMS demonstrated superiority in cyclogenesis forecasts, predicting 6 out of 22 TCs with a forecast lead time of up to 144 h. Additionally, over 80% of the total could be predicted 72 h in advance. The model also showed an average TC genesis position error of 218.3 km, comparable to the track errors of operational models according to the annual evaluation. The study also briefly investigated the forecast of Noul(2011). The forecast field of the CMA-TRAMS depicted thermal and dynamical conditions that could trigger typhoon genesis, consistent with the analysis field. The 96-hour forecast field of the CMA-TRAMS displayed a relatively organized threedimensional structure of the typhoon. These results can enhance understanding of the mechanism behind typhoon genesis,fine-tune model configurations and dynamical frameworks, and provide reliable forecasts for forecasters.

关键词： CMA-TRAMS cyclogenesis numerical weather prediction tropical cyclone

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Determination of Surface Precipitation Type Based on the Data Fusion Approach

引用

Advances in Atmospheric Sciences 2021年第3期38卷 387-399页

作者： Marek PÓŁROLNICZAK Leszek KOLENDOWICZ Bartosz CZERNECKI Mateusz TASZAREK Gabriella TÓTH department of Meteorology and climatology Institute of Physical Geography and Environmental PlanningAdam Mickiewicz University61-680 PoznańPoland department of weather forecasting and climatology Hungarian Meteorological ServiceH-1024 BudapestHungary

Hazardous events related to atmospheric precipitation depend not only on the intensity of surface precipitation,but also on its *** related to determination of the precipitation type(PT)leads to financial losses in many areas of human activity,such as the power industry,agriculture,transportation,and many *** this study,we use machine learning(ML)algorithms with the data fusion approach to more accurately determine surface *** on surface synoptic observations,ERA5 reanalysis,and radar data,we distinguish between liquid,mixed,and solid precipitation *** study domain considers the entire area of Poland and a period from 2015 to *** purpose of this work is to address the question:“How can ML techniques applied in observational and NWP data help to improve the recognition of the surface PT?”Despite testing 33 parameters,it was found that a combination of the near-surface air temperature and the depth of the warm layer in the 0-1000 m above ground level(AGL)layer contains most of the signal needed to determine surface *** accrued probability of detection for liquid,solid,and mixed PTs according to the developed Random Forest model is 98.0%,98.8%,and 67.3%,*** application of the ML technique and data fusion approach allows to significantly improve the robustness of PT prediction compared to commonly used baseline models and provides promising results for operational forecasters.

关键词： precipitation type forecast data fusion Random Forest ERA5 Poland

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Improved forecasting via physics-guided machine learning as exemplified using“21·7”extreme rainfall event in Henan

引用

Science China Earth Sciences 2024年第5期67卷 1652-1674页

作者： Qi ZHONG Zhicha ZHANG Xiuping YAO Shaoyu HOU Shenming FU Yong CAO Linguo JING China Meteorological Administration Training Center Beijing 100081China State Key Laboratory of Severe weather Chinese Academy of Meteorological SciencesBeijing 100081China Zhejiang Meteorological Observatory Hangzhou 310017China Hebei Provincial weather Modification Center Shijiazhuang 050020China Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China National Meteorological Center Beijing 100081China School of Computer Science Chengdu University of Information TechnologyChengdu 610225China

As a natural disaster,extreme precipitation is among the most destructive and influential,but predicting its occurrence and evolution accurately is very challenging because of its rarity and *** the example of the“21·7”extreme precipitation event(17–21 July 2021)in Henan Province,this study explores the potential of using physics-guided machine learning to improve the accuracy of forecasting the intensity and location of extreme *** physics-guided ways of embedding physical features,fusing physical model forecasts and revised loss function are used,i.e.,(1)analyzing the anomalous circulation and thermodynamical factors,(2)analyzing the multi-model forecast bias and the associated underlying reasons for it,and(3)using professional forecasting knowledge to design the loss function,and the corresponding results are used as input for machine learning to improve the forecasting *** results indicate that by learning the relationship between anomalous physical features and heavy precipitation,the forecasting of precipitation intensity is improved significantly,but the location is rarely adjusted and more false alarms *** reasons for this are as *** anomalous features used here mainly contain information about large-scale systems and factors which are consistent with the model precipitation deviation;moreover,the samples of extreme precipitation are sparse and so the algorithm used here is ***,by combining“good and different”multi models with machine learning,the advantages of each model are extracted and then the location of the precipitation center in the forecast is improved ***,by combining the appropriate anomalous features with multi-model fusion,an integrated improvement of the forecast of the rainfall intensity and location is ***,this study is a novel exploration to improve the refined forecasting of heavy precipitation with extreme intensity and high variability,

关键词： Extreme precipitation event Refined assessment Anomalous physical features Multi-model fusion Machine learning

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Local Torrential Rainfall Event within a Mei-Yu Season Mesoscale Convective System:Importance of Back-Building Processes

引用

Advances in Atmospheric Sciences 2024年第5期41卷 847-863页

作者： Honglei ZHANG Ming XUE Hangfeng SHEN Xiaofan LI Guoqing ZHAI Zhejiang Institute of Meteorological Sciences Hangzhou 310017China Center for Analysis and Prediction of Storms and School of MeteorologyUniversity of OklahomaNormanOklahoma 73072USA department of Atmospheric Science School of Earth SciencesZhejiang UniversityHangzhou 310027China Hangzhou weather Bureau Hangzhou 310007China

An extreme rainfall event occurred over Hangzhou,China,during the afternoon hours on 24 June *** event occurred under suitable synoptic conditions and the maximum 4-h cumulative rainfall amount was over 150 *** rainfall event had two major *** was caused by a quasi-stationary convective line,and the other by a backbuilding convective line related to the interaction of the outflow boundary from the first rainband and an existing low-level mesoscale convergence line associated with a mei-yu frontal *** rainfall event lasted 4 h,while the back-building process occurred in 2 h when the extreme rainfall center *** far,few studies have examined the back-building processes in the mei-yu season that are caused by the interaction of a mesoscale convergence line and a convective cold *** two rainbands are successfully reproduced by the weather Research and forecasting(WRF)model with fourlevel,two-way interactive *** the model,new cells repeatedly occur at the west side of older cells,and the backbuilding process occurs in an environment with large CAPE,a low LFC,and plenty of water *** from older cells enhance the low-level convergence that forces new *** precipitation efficiency of the back-building training cells leads to accumulated precipitation of over 150 *** experiments without evaporation of rainwater show that the convective cold pool plays an important role in the organization of the back-building process in the current extreme precipitation case.

关键词： torrential rainfall back-building processes numerical simulation trigger mechanism convergence line convective cold pool

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Effect of Meteorological Data Assimilation on Regional Air Quality Forecasts over the Korean Peninsula

引用

Journal of Meteorological Research 2024年第2期38卷 262-284页

作者： Yunjae CHO Hyun Mee KIM Eun-Gyeong YANG Yonghee LEE Jae-Bum LEE Soyoung HA department of Atmospheric Sciences Yonsei UniversitySeoul 03722Republic of Korea Air Quality forecasting Center National Institute of Environmental ResearchIncheon 22689Republic of Korea National Center for Atmospheric Research BoulderCO 80305USA University of Maryland Baltimore County BaltimoreMD 21250USA Global Modeling and Assimilation Office NASA Goddard Space Flight CenterGreenbeltMD 20771USA

The weather Research and forecasting model coupled with Chemistry(WRF-Chem),a type of online coupled chemistry-meteorology model(CCMM),considers the interaction between air quality and meteorology to improve air quality *** data assimilation(DA)can be used to reduce uncertainty in meteorological field,which is one factor causing prediction uncertainty in the *** this study,WRF-Chem and three-dimensional variational DA were used to examine the impact of meteorological DA on air quality and meteorological forecasts over the Korean *** nesting model domains were configured over East Asia(outer domain)and the Korean Peninsula(inner domain).Three experiments were conducted by using different DA domains to determine the optimal model domain for the meteorological *** the meteorological DA was performed in the outer domain or both the outer and inner domains,the root-mean-square error(RMSE),bias of the predicted particulate matter(PM)concentrations,and the RMSE of predicted meteorological variables against the observations were smaller than those in the experiment where the meteorological DA was performed only in the inner *** indicates that the improvement of the synoptic meteorological fields by DA in the outer domain enhanced the meteorological initial and boundary conditions for the inner domain,subsequently improving air quality and meteorological *** to the experiment without meteorological DA,the RMSE and bias of the meteorological and PM variables were smaller in the experiments with *** effect of meteorological DA on the improvement of PM predictions lasted for approximately 58-66 h,depending on the ***,the uncertainty reduction in the meteorological initial condition by the meteorological DA contributed to a reduction of the forecast errors of both meteorology and air quality.

关键词： meteorological data assimilation regional air quality forecast particulate matter concentration optimal model domain forecast error WRF-Chem

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Predictability of the 7·20 extreme rainstorm in Zhengzhou in stochastic kinetic-energy backscatter ensembles

引用

Science China Earth Sciences 2024年第7期67卷 2226-2241页

作者： Min YANG Peilong YU Lifeng ZHANG Xiaobing PAN Quanjia ZHONG Yunying LI College of Meteorology and Oceanography National University of Defense TechnologyChangsha 410073China Key Laboratory of High Impact weather(Special) China Meteorological AdministrationChangsha 410073China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment ChangeNanjing University of Information Science and TechnologyNanjing 210044China department of Ocean Science Hong Kong University of Science and TechnologyHong Kong 999077China

The scale-dependent predictability of the devastating 7·20 extreme rainstorm in Zhengzhou,China in 2021 was investigated via ensemble experiments,which were perturbed on different scales using the stochastic kinetic-energy backscatter(SKEB)scheme in the WRF model,with the innermost domain having a 3-km grid *** daily rainfall(RAIN24h)and the cloudburst during 1600-1700 LST(RAIN1h)were *** demonstrated that with larger perturbation scales,the ensemble spread for the rainfall maximum widens and rainfall forecasts become closer to the *** ensembles with mesoscale or convective-scale perturbations,RAIN1h loses predictability at scales smaller than 20 km and RAIN24h is predictable for all *** in ensembles with synoptic-scale perturbations,the largest scale of predictability loss extends to 60 km for both RAIN1h and ***,the average positional error in forecasting the heaviest rainfall for RAIN24h(RAIN1h)was 400 km(50-60)*** southerly low-level jet near Zhengzhou was assumed to be directly responsible for the forecast uncertainty of *** rapid intensification in low-level cyclonic vorticity,mid-level divergence,and upward motion concomitant with the jet dynamically facilitated the *** analysis of the divergent,rotational and vertical kinetic spectra and the corresponding error spectra showed that the error kinetic energy at smaller scales grows faster than that at larger scales and saturates more quickly in all ***-scale perturbations not only boost larger-scale error growth but are also conducive to error growth at all scales through a downscale cascade,which indicates that improving the accuracy of larger-scale flow forecast may discernibly contributes to the forecast of cloudburst intensity and position.

关键词： Stochastic kinetic-energy backscatter(SKEB) Extreme rainfall Ensemble forecast Predictability

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Advancing flood susceptibility modeling using stacking ensemble machine learning: A multi-model approach

引用

Journal of Geographical Sciences 2024年第8期34卷 1513-1536页

作者： YANG Huilin YAO Rui DONG Linyao SUN Peng ZHANG Qiang WEI Yongqiang SUN Shao AGHAKOUCHAK Amir School of Geography and Tourism Anhui Normal UniversityWuhu 241002AnhuiChina Changjiang River Scientific Research Institute Wuhan 430015China Advanced Interdisciplinary Institute of Environment and Ecology Beijing Normal UniversityZhuhai 519087GuangdongChina Hunan Institute of Water Resources and Hydropower Research Changsha 410007China State Key Laboratory of Severe weather Chinese Academy of Meteorological SciencesBeijing 100081China department of Civil and Environmental Engineering University of California IrvineIrvineCA 92697USA

Flood susceptibility modeling is crucial for rapid flood forecasting, disaster reduction strategies, evacuation planning, and decision-making. Machine learning(ML) models have proven to be effective tools for assessing flood susceptibility. However, most previous studies have focused on individual models or comparative performance, underscoring the unique strengths and weaknesses of each model. In this study, we propose a stacking ensemble learning algorithm that harnesses the strengths of a diverse range of machine learning models. The findings reveal the following:(1) The stacking ensemble learning, using RF-XGBCB-LR model, significantly enhances flood susceptibility simulation.(2) In addition to rainfall,key flood drivers in the study area include NDVI, and impervious surfaces. Over 40% of the study area, primarily in the northeast and southeast, exhibits high flood susceptibility, with higher risks for populations compared to cropland.(3) In the northeast of the study area,heavy precipitation, low terrain, and NDVI values are key indicators contributing to high flood susceptibility, while long-duration precipitation, mountainous topography, and upper reach vegetation are the main drivers in the southeast. This study underscores the effectiveness of ML, particularly ensemble learning, in flood modeling. It identifies vulnerable areas and contributes to improved flood risk management.

关键词： flood susceptibility assessment machine learning stacking ensemble learning flood drivers XiangjiangRiverBasin

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Characteristics of Extreme Rainfall Events over Uganda during September to November Rainfall Season

引用

Journal of Geoscience and Environment Protection 2024年第3期12卷 131-152页

作者： Donnata Alupot Guirong Tan Kokou Romaric Posset Peter Natiko Key Laboratory of Meteorological Disaster Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD) Nanjing University of Information Science and Technology Nanjing China Climate Change department Pan African University Institute for Water and Energy Sciences (Including Climate Change) C/O Univer-sité Abou Bekr Belkaid Tlemcen Campus Chetouane Tlemcen Algeria Directorate of weather forecasting Uganda National Meteorological Authority Port Bell Road Kampala Uganda

Understanding the characteristics of extreme rainfall is crucial for effective flood management planning, as it enables the incorporation of insights from past extreme rainfall patterns and their spatiotemporal distribution. This work investigated the changes in the frequency and pattern of extreme rainfall over Uganda, using daily datasets sourced from Climate Hazard Group InfraRed Precipitation with Station (CHIRPS-v2) for the period 1981 to 2022. The study utilized the extreme weather Indices provided by the Expert Team on Climate Change Detection and Indices (ETCCDI). Attention was directed towards September to November (SON) rainfall season with precise analysis of four indices (Rx1day, Rx5day, R95p, and R99p). The Sequential Mann-Kendall (SQMK) non-parametric test was applied to identify abrupt changes in SON extreme rainfall trends. Results showed that October consistently recorded the highest count of extreme rainfall days across all four indices. The long-term analysis revealed fluctuations in extreme rainfall events across years, with certain periods exhibiting heightened intensity. The analysis portrayed a shift in the decadal variations and region-specific distribution of extreme rainfall, with Eastern Uganda and areas around Lake Victoria standing out compared to other regions. The findings further revealed an increase in extreme rainfall for all indices in the recent decade (2011-2022) with 2019/2020 standing out as the extreme years of SON for the study period. While trendlines suggested a slight increase in intense daily rainfall events, the SQMK tests revealed statistical significance in the trend of prolonged periods of intense daily rainfall. This study contributes to the understanding of the spatiotemporal variability and trends of extreme rainfall events over Uganda during the SON season, which is crucial for the assessment of climate change impacts and adaptation strategies. It provides valuable information for seasonal extreme rainfall forecas

关键词： CHIRPS Variability Region-Specific Early Warning Climate Change

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Effect of the Initial Vortex Structure on Intensity Change During Eyewall Replacement Cycle of Tropical Cyclones:A Numerical Study

引用

Journal of Tropical Meteorology 2024年第2期30卷 106-117页

作者：杨昕玮王玉清王慧徐晶占瑞芬 State Key Laboratory of Severe weather Chinese Academy of Meteorological SciencesChina Meteorological AdministrationBeijing 100081 China University of Chinese Academy of Sciences Beijing 101408 China department of Atmospheric Sciences and International Pacific Research Center School of Earth and Ocean Science and TechnologyUniversity of Hawaii at ManoaHonoluluHI 96822 USA Qingdao Joint Institute of Marine Meteorology Chinese Academy of Meteorological SciencesChina Meteorological AdministrationQingdaoShandong 266034 China department of Atmospheric and Oceanic Sciences&Institute of Atmospheric Sciences Fudan UniversityShanghai 200438 China

This study investigates the effect of the initial tropical cyclone(TC)vortex structure on the intensity change during the eyewall replacement cycle(ERC)of TCs based on two idealized simulations using the weather Research and forecasting(WRF)*** show that an initially smaller TC with weaker outer winds experienced a much more drastic intensity change during the ERC than an initially larger TC with stronger outer *** is found that an initially larger TC vortex with stronger outer winds favored the development of more active spiral rainbands outside the outer eyewall,which slowed down the contraction and intensification of the outer eyewall and thus prolonged the duration of the concentric eyewall and slow intensity *** contrast,the initially smaller TC with weaker outer winds corresponded to higher inertial stability in the inner core and weaker inertial stability but stronger filamentation outside the outer *** led to stronger boundary layer inflow,stronger updraft and convection in the outer eyewall,and suppressed convective activity outside the outer *** resulted in the rapid weakening during the formation of the outer eyewall,followed by a rapid re-intensification of the TC during the *** study demonstrates that accurate initialization of the TC structure in numerical models is crucial for predicting changes in TC intensity during the ***,monitoring the activity of spiral rainbands outside the outer eyewall can help to improve short-term intensity forecasts for TCs experiencing ERCs.

关键词： tropical cyclones concentric eyewall inner eyewall and outer eyewall eyewall replacement cycle intensity change

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Preliminary Model Study for forecasting a Hot weather Process in Guangdong Province Using CMA-TRAMS

引用

Atmospheric and Climate Sciences 2024年第1期14卷 101-117页

作者： Wenfan Wang Jiemin Zhan Dehui Chen Zitong Chen Yanxia Zhang Qing Fan Guangfeng Dai Yingying Luo Aifen Ye department of Applied Mechanics and Engineering Shenzhen Campus of Sun Yat-sen University Shenzhen Guangdong China Guangzhou Institute of Tropical and Marine Meteorology/Guangdong Provincial Key Laboratory of Regional Numerical weather Prediction CMA Guangzhou China

In this paper, the CMA-TRAMS tropical high-resolution system was used to forecast a typical hot weather process in Guangdong, China with different horizontal resolutions and surface coverage. The results of resolutions of 0.02° and 0.06° were presented with the same surface coverage of the GlobeLand30 V2020, companies with the results of resolution 0.02° with the USGS global surface coverage. The results showed that, on the overall assessment the 2 km model performed better in forecasting 2 m temperature, while the 6 km model was more accurate in predicting 10 m wind speed. In the evaluation of representative stations, the 2 km model performed better in forecasting 2 m temperature and 2 m relative humidity at the coastal stations, and the 2 km model was also better in forecasting 2 m pressure at the representative stations. However, the 6 km model performed better in forecasting 10 m wind speed at the representative stations. Furthermore, the 2 km model, owing to its higher horizontal resolution, presented a more detailed stratification of various meteorological field maps, allowing for a more pronounced simulation of local meteorological element variations. And the use of the surface coverage data of the GlobeLand30 V2020 improved the forecasting of 2 m temperature, and 10 m wind speed compared to the USGS surface coverage data.

关键词： High-Resolution Numerical Model CMA-TRAMS Model Test Evaluation Surface Coverage Numerical Simulation

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：