四川地区多模式2m温度预报性能分析及集成方法研究          下载免费PDF全文

冯良敏  周秋雪  陈朝平  范江琳 《高原山地气象研究》2019,39(2):30-36

本文通过分析2017年9~12月四川地区ECMWF(European Centre for Medium-Range Weather Forecasting)细网格模式、GRAPES_GFS(Global and Regional Assimilation and Prediction System)全球模式和西南区域模式(South West Center-WRF ADAS Real-time Modeling System, SWCWARMS)2m温度168h预报时效内的系统性偏差特征,采用滑动双权重平均法分别对三种模式温度预报产品进行偏差订正,并集成得到各时效2m温度的订正场,结果表明:(1)三种模式的预报存在明显的日变化,整体上EC模式的预报最优。(2)三种模式对于低温和高温的预报,在全省均大致呈现负的系统性误差,特别在高原及过渡区表现的尤为明显。(3)订正后三种模式的预报准确率显著提高,均方根误差减小1.4~2.5℃,大部分地区平均误差维持在±0.5℃之间,在系统性偏差较大的地区,订正效果更好。(4)两种集成方案预报结果接近,且均优于三种模式的订正预报。  相似文献   

中国地区集合预报产品自适应递减平均偏差订正法的改进研究     

肖瑶  史一丛  王耸  王新伟 《气象与环境学报》2019,(2):9-14

基于中国地区T213集合预报产品2 m温度预报数据,采用卡尔曼滤波类型的自适应递减平均法进行偏差订正处理,原方案在剧烈降温天气订正效果表现不理想。通过对递减平均参数w的重新构建得到改进的订正方案w(i,p)(i为站点信息,p为天气过程信息),在此基础上进一步优化对历史信息的有效提取,得到改进的方案w(i,p)相似法和w(i,p)统计法,并进行效果检验。结果表明:改进为包含空间和天气过程信息的函数w(i,p)后方案的订正效果得到不同程度的提高,其中24 h剧烈降温预报各成员预报均方根误差平均减小了0. 15℃;而进一步改进的w(i,p)统计法在当前几种剧烈降温预报中订正效果最优,其集合平均偏差与w(i,p)方案相比减小2. 54℃。  相似文献   

基于数值预报模式的乡镇温度预报方法研究     

吴春英  刘多文  钟博  蒋婷婷  于蕙箐  陈佳美  高燕 《气象与环境学报》2019,(1):108-112

利用2015年8月至2017年7月长兴岛站和交流岛站日最高气温、日最低气温实况资料,对ECMWF细网格2 m温度预报值和日本FSFE02(24 h地面形势场预报)、FSFE03(36 h地面形势场预报)进行了检验。结果表明:根据历史回归统计检验,ECMWF细网格模式24 h的2 m最高气温、最低气温预报效果显著,通过了0. 05信度显著性检验。对各月做相关分析,相关性均较好。利用前一日ECMWF细网格2 m温度预报值与长兴岛站实况差值,根据统计的ECMWF细网格2 m温度预报订正值,做出长兴岛站未来24 h的气温预报。交流岛站温度预报是在长兴岛站温度预报的基础上订正做出,经统计分析,交流岛站和长兴岛站的气温差值与地面形势场和风场有较好的对应关系,根据不同类型的地面形势场和风场订正值,做出交流岛站的温度预报。应用Matlab计算机语言的开发功能,提取ECMWF细网格2 m温度预报的最高、最低气温值,并录入当日长兴岛站和交流岛站最高、最低气温实况值,自动预报各站未来24 h最高气温、最低气温。创建可视化预报工作界面,实现乡镇温度预报自动化。  相似文献   

深水稀井区天然气藏储盖组合定量预测方法研究:以琼东南盆地陵水凹陷为例     

刘静静  刘震  孙志鹏  孙晓明 《现代地质》2018,32(4):796

南海北部油气勘探表明天然气是本地区主要的资源类型。与石油相比,天然气藏的形成对盖层要求更为严格,因此开展天然气藏储盖组合研究对油气勘探具有重要意义。通过统计和调研的方法,将砂岩储层孔隙度12%定义为琼东南盆地天然气有效储层评价的下限参数,将泥岩排替压力1 MPa定义为天然气有效泥岩盖层评价的下限参数。利用测井资料开展了单井天然气藏储盖组合窗口定量预测,得出陵水凹陷W22-1井发育单个天然气藏储盖组合窗口。在此基础上,针对稀井或无井区,提出了基于地震反演速度剖面的天然气藏储盖组合定量预测方法,以琼东南盆地陵水凹陷主洼过井测线为例,开展了天然气藏储盖组合窗口地震预测,确定出该凹陷主洼发育单个天然气藏储盖组合窗口,大体上包括陵水组-莺歌海组上部多套地层,拓宽了陵水凹陷天然气勘探层系。这种方法对其它深水区和低勘探新区均有借鉴意义。  相似文献   

A Study of the Extended-range Forecast for the Low Frequency Temperature and High Temperature Weather over the Lower Reaches of Yangtze River Valley in Summer     

Qiuming Yang 《地球科学进展》2018,33(4):385-395

Based on the observational data, the variations of Intraseasonal Oscillation (ISO) of the daily temperatures and its relationships to the high temperature in summer over the lower reaches of the Yangtze River Valley (LYRV) were studied for the period of 1979-2011. It is found that the daily temperatures over LYRV in May-August was mainly of periodic oscillations of 1525, 3060 and 6070 days, and the interannual variation of the intensity of its 3060-day oscillation had a strongly positive correlation with the number of days with daily highest temperature over 35 ℃ in July-August. Low frequency components of daily temperature in the LYRV, and the principal components of the Eastern Asian 850 hPa low frequency temperature, over a time period ranging from 1979 to 2000, were used to establish the Extended Complex Autoregressive model (ECAR) on an extended-range forecast of the 3060-day low frequency temperature over the LYRV. A 11-year independent real-time extended-range forecast was conducted on the extended-range forecast of low frequency component of the temperature over the LYRV in May-August, for the period ranging from 2001 to 2011. These experimental results show that this ECAR model, which is based on a data-driven model, has a good forecast skill at the lead time of approximately 23 days, with a forecast ability superior to the traditional autoregressive (AR) model. Hence, the development and variation of the leading 3060-day modes for the Eastern Asian 850 hPa low frequency temperatures and temporal evolutions of their relationships to low frequency components of the temperature over the LYRV in summer are very helpful in predicting the persistent high temperature over the LYRV at a 20 to 25 days lead.  相似文献   

顾及多因子影响的中国区域T_m模型精化研究     

李浩杰  刘立龙  黄良珂  莫智翔  蔡猛 《大地测量与地球动力学》2022,42(4):393-397

利用中国区域2015~2017年探空数据,建立一种顾及地表温度、地表水汽压、高程和纬度的中国区域大气加权平均温度T_m模型（BET模型）。以2018年探空站T_m数据为参考值,分析BET模型精度,并与Bevis模型和GPT3模型进行对比。结果表明,BET模型年均RMSE与bias分别为3.15 K和0.04 K,相比于Bevis模型、1°×1°分辨率的GPT3模型和5°×5°分辨率的GPT3模型,年均RMSE分别降低29.2%、32.8%和39.1%,年均bias分别降低96.4%、96.7%和97.4%,且该模型在中国区域不同高程和纬度上的精度与稳定性优于Bevis模型和GPT3模型。  相似文献   

The Seasonal Prediction of the Exceptional Yangtze River Rainfall in Summer 2020     

Chaofan LI  Riyu LU  Nick DUNSTONE  Adam A.SCAIFE  Philip E.BETT  Fei ZHENG 《大气科学进展》2021,38(12):2055-2066

During June and July of 2020, the Yangtze River basin suffered from extreme mei-yu rainfall and catastrophic flooding. This study explores the seasonal predictability and associated dynamical causes for this extreme Yangtze River rainfall event, based on forecasts from the Met Office GloSea5 operational forecast system. The forecasts successfully predicted above-average rainfall over the Yangtze River basin, which arose from the successful reproduction of the anomalous western North Pacific subtropical high (WNPSH). Our results indicate that both the Indian Ocean warm sea surface temperature (SST) and local WNP SST gradient were responsible for the westward extension of the WNPSH, and the forecasts captured these tropical signals well. We explore extratropical drivers but find a large model spread among the forecast members regarding the meridional displacements of the East Asian mid-latitude westerly jet (EAJ). The forecast members with an evident southward displacement of the EAJ favored more extreme Yangtze River rainfall. However, the forecast Yangtze River rainfall anomaly was weaker compared to that was observed and no member showed such strong rainfall. In observations, the EAJ displayed an evident acceleration in summer 2020, which could lead to a significant wind convergence in the lower troposphere around the Yangtze River basin, and favor more mei-yu rainfall. The model forecast failed to satisfactorily reproduce these processes. This difference implies that the observed enhancement of the EAJ intensity gave a large boost to the Yangtze River rainfall, hindering a better forecast of the intensity of the event and disaster mitigation.  相似文献   

A CRPS-Based Spatial Technique for the Verification of Ensemble Precipitation Forecasts     

赵滨  张博  李子良 《热带气象学报(英文版)》2021,27(1):24-33

Traditional precipitation skill scores are affected by the well-known“double penalty”problem caused by the slight spatial or temporal mismatches between forecasts and observations. The fuzzy (neighborhood) method has been proposed for deterministic simulations and shown some ability to solve this problem. The increasing resolution of ensemble forecasts of precipitation means that they now have similar problems as deterministic forecasts. We developed an ensemble precipitation verification skill score, i.e., the Spatial Continuous Ranked Probability Score (SCRPS), and used it to extend spatial verification from deterministic into ensemble forecasts. The SCRPS is a spatial technique based on the Continuous Ranked Probability Score (CRPS) and the fuzzy method. A fast binomial random variation generator was used to obtain random indexes based on the climatological mean observed frequency, which were then used in the reference score to calculate the skill score of the SCRPS. The verification results obtained using daily forecast products from the ECMWF ensemble forecasts and quantitative precipitation estimation products from the OPERA datasets during June-August 2018 shows that the spatial score is not affected by the number of ensemble forecast members and that a consistent assessment can be obtained. The score can reflect the performance of ensemble forecasts in modeling precipitation and thus can be widely used.  相似文献   

基于卷积神经网络的逐时降水预报订正方法研究     

陈锦鹏  冯业荣  蒙伟光  文秋实  潘宁  戴光丰 《气象》2021,(1):60-70

应用2017—2018年5—9月福建省观测资料对华南区域中尺度模式(GTRAMS-3 km-RUC)预报进行站点检验,建立和训练基于卷积神经网络的逐时降水分级订正模型,并与频率匹配法进行2017—2018年测试集的对比试验和2019年数据集的模拟业务检验,探讨了试验过程中遇到的样本不均衡、特征变量选取以及模型过拟合问题。结果表明:模式对于15 mm·h-1以上降水的预报能力弱,各订正方法对原始预报均有不同程度的改进作用。从评估指标来看,基于卷积神经网络的订正方法比频率匹配法表现出优势,其中相关系数判别方案下的网络模型对强降水预报的订正效果显著优于其他方法;在输入特征变量选取方面,应用主成分分析方案的模型训练收敛速度比相关系数判别方案更快,最佳训练期有所提前,但也更早进入严重的过拟合状态,而相关系数判别方案能够使网络模型的训练拥有更长的提升期以达到更具“潜力”的状态;基于卷积神经网络的订正方法对减少分类降水预报的漏报率、晴雨和弱降水预报的空报率具有显著作用,其优化程度明显超过频率匹配法。  相似文献   

基于Parsivel天气现象仪资料的郑州“7·20”罕见特大暴雨微物理特征分析          下载免费PDF全文

周丹  周淑玲  田金华 《气象与环境科学》2022,45(1):93-101

基于2016-2018年ECMWF模式温度预报和浙江省72个国家基本站观测资料,根据温度日变化特征,采用K-近邻(KNN)回归算法进行误差订正,改进浙江省172 h精细化温度预报。在KNN回归算法中,将模式起报时刻的温度视作“背景”,由模式预报减去起报时刻温度消除“背景”影响,得到温度日变化曲线,通过温度日变化曲线构建差异指标,选取历史相似个例。根据历史相似个例的误差特征,对温度预报进行订正,得到改进的温度预报。检验结果表明,KNN方案的温度预报平均绝对误差较ECMWF和30 d滑动平均误差订正方案(OCF)的分别减小26.2%和5.2%;日最高和最低温度预报误差绝对值小于2℃,准确率较ECMWF的分别提高14.8%和4.3%,较OCF的分别提高3.0%和1.3%。KNN方案对地形复杂地区的温度预报改进效果更为明显,对冷空气活动和夏季高温等天气过程预报改善效果也较稳定。  相似文献