降水的统计降尺度预报及其空间相关性和时间连续性重建   总被引：2，自引：0，他引：2       下载免费PDF全文

王姝苏  智协飞  俞剑蔚  陈超辉  周红梅  朱寿鹏  赵欢 《大气科学学报》2018,41(1):36-45

利用欧洲中期天气预报中心(ECMWF)、日本气象厅(JMA)、美国国家环境预报中心(NCEP)以及英国气象局(UKMO)四个中心1~7 d日累计降水量集合预报资料,以中国降水融合产品作为"观测值",对我国地面降水量进行统计降尺度预报,并对预报降水的空间相关性和时间连续性进行重建。对降水量进行分级后,建立各个量级的回归方程进行统计降尺度预报。此外,还利用Schaake Shuffle方法重建丢失的空间相关性和时间连续性。结果表明,分级回归比未分级回归后的预报结果相关系数更高,预报误差更小,更接近观测值。Schaake Shuffle方法可以有效地改进降水预报的空间相关性和时间连续性,使之更接近实况观测,集合成员间的相关性也更好。  相似文献   

长江中下游6—7月降水的降尺度模型          下载免费PDF全文

宋进波  陆尔  屠菊青  丁滢  刘思源 《大气科学学报》2018,41(1):85-92

基于偏相关的强迫因子选取方法,以长江中下游6—7月降水为例,进行了降水变率的归因分析,并建立了相应的统计降尺度模型。结果表明,影响长江中下游6—7月降水的强迫因子主要有两个:西太平洋850 h Pa的位势高度(W_(PH8))和黑潮延伸区的海表温度(K_(SST))。W_(PH8)反映的是西太平洋副热带高压对长江中下游降水的影响;K_(SST)反映了黑潮延伸区的变率。基于这两个因子的线性降尺度模型能较好地拟合长江中下游6—7月的降水,在独立检验和模式检验阶段,模型体现出了可靠性,因而可用于长江中下游降水的季节预测。  相似文献   

Predictor Selection for CNN-based Statistical Downscaling of Monthly Precipitation     

Dangfu YANG  Shengjun LIU  Yamin HU  Xinru LIU  Jiehong XIE  Liang ZHAO 《大气科学进展》2023,40(6):1117-1131

Convolutional neural networks(CNNs) have been widely studied and found to obtain favorable results in statistical downscaling to derive high-resolution climate variables from large-scale coarse general circulation models(GCMs).However, there is a lack of research exploring the predictor selection for CNN modeling. This paper presents an effective and efficient greedy elimination algorithm to address this problem. The algorithm has three main steps: predictor importance attribution, predictor rem...  相似文献   

Evaluation of Performance of Polar WRF Model in Simulating Precipitation over Qinghai-Tibet Plateau     

薛建军  肖子牛 《热带气象学报(英文版)》2023,29(4):410-430

Considering the complex topographic forcing and large cryosphere concentration, the present study utilized the polar-optimized WRF model(Polar WRF) to conduct downscaling simulations over the Qinghai-Tibet Plateau(TP) and its surrounding regions. Multi-group experiments with the 10 km horizontal resolution are used to evaluate the modeling of precipitation. Firstly, on the basis of the model ground surface properties upgrade and the optimized Noah-MP, the “betterperforming” configuration suite f...  相似文献   

华北汛期降水分离时间尺度降尺度预测模型的改进   总被引：2，自引：1，他引：1  

阮成卿  李建平 《大气科学》2016,40(1):215-226

本文采用偏相关预报因子挑选法和条件降尺度法,对已有的华北汛期(7~8月)降水时间尺度分离(TSD)降尺度模型进行了改进.利用偏相关法,找到一个新的影响华北汛期降水年际分量的前期预报因子,即6月北大西洋—欧亚遥相关(AEAT).该因子将扰动信号储存于北大西洋三极子结构,并在7~8月释放出来影响下游贝加尔湖低压系统的发展,从而影响华北汛期降水.利用6月Ni?o3指数和AEAT指数,本文建立了条件TSD统计降尺度模型,即按照预报因子的强度进行逐年分类,对于每个分类设计相应的预报模型,从而避免信息较弱因子的干扰.条件TSD降尺度方法显著改善了华北汛期降水的预测技巧,在独立检验阶段,预报降水与观测降水的相关系数由原模型的0.61提高到0.77,符号一致率从70%提高到87%.  相似文献   

白洋淀流域未来日最高最低气温变化的统计降尺度分析     

王洁  王浩  王彬彬 《南京气象学院学报》2016,8(5):446-453

大气环流模型（GCMs）预测的气候变化情景空间分辨率低,不能满足气候变化对水资源影响进行评估的需要.利用统计降尺度模型可以解决GCMs预测的气候变化情景空间分辨率低的缺陷.在白洋淀流域应用统计降尺度模型（SDSM）,选取日平均气温作为预报量,根据NCEP再分析数据与站点实测数据序列的相关关系选择合适的预报因子,建立大气环流因子与各站点日最高气温和最低气温之间的统计关系.将数据序列分为1961-1975年和1976-1990年两个时段,对SDSM进行率定和验证.最后将HadCM3输出的未来情景降尺度到站点尺度,模拟白洋淀流域未来时期三个时段2020s（2010-2039年）、2050s（2040-2069年）和2080s（2070-2099年）的日最高气温和最低气温时间序列.结果表明：SDSM在白洋淀流域的模拟效果较好.白洋淀流域日最高气温和最低气温在A2和B2两种情景下均呈现上升趋势,且A2情景下的增幅高于B2情景,山区的增幅高于平原,日最高气温的增幅大于日最低气温.  相似文献   

基于BP-CCA统计降尺度的中亚春季降水的多模式集合模拟与预估   总被引：1，自引：0，他引：1       下载免费PDF全文

陈鹏翔  江志红  彭冬梅 《Acta Meteorologica Sinica》2017,75(2):236-247

利用中亚地区30个观测台站逐月降水资料及同期ERA-40再分析资料,结合8个CMIP5全球气候模式模拟与未来预估大尺度环流场,使用基于变形典型相关分析的统计降尺度方法（BP-CCA）建立降尺度模型,评估多个气候模式对当前气候下中亚地区春季降水的降尺度模拟能力,并对春季降水进行降尺度集合未来预估。结果表明,建立的降尺度模型能够很好地模拟出交叉检验期内春季降水的时间变化和空间结构：降尺度春季降水与相应观测序列的平均时间相关系数为0.35,最高为0.62,平均空间相关系数为0.87。气候模式对中亚春季降水的模拟能力通过降尺度方法得到了显著提高：8个模式降尺度后模拟的降水气候平均态相对误差绝对值降至0.2%—8%,相比降尺度前减小了10%—60%,模拟的降水量场与相应观测场的空间相关均超过0.77;对比降尺度前多模式集合结果,多模式降尺度集合模拟的相对误差绝对值由64%减小至4%,空间相关系数由0.47增大至0.81,标准化均方根误差降至0.59,且多模式降尺度集合结果优于大部分单个模式降尺度结果。多模式降尺度集合预估结果表明,在RCP4.5排放情景下,21世纪前期（2016—2035年）、中期（2046—2065年）和末期（2081—2100年）的全区平均降水变化率分别为-5.3%、3.0%和17.4%。21世纪前期中亚大部分地区降水呈减少趋势,降水呈增多趋势的站点主要分布在南部。21世纪中期整体降水变化率由减少变为增多趋势,21世纪末期中亚大部分台站降水增多较为明显。21世纪初期和末期可信度高的台站均主要位于中亚西部地区。  相似文献   

Quantification of uncertainty in the assessment of future streamflow under changing climate conditions          下载免费PDF全文

Sohom Mandal  Slobodan P. Simonovic 《水文研究》2017,31(11):2076-2094

Climate change has a significant influence on streamflow variation. The aim of this study is to quantify different sources of uncertainties in future streamflow projections due to climate change. For this purpose, 4 global climate models, 3 greenhouse gas emission scenarios (representative concentration pathways), 6 downscaling models, and a hydrologic model (UBCWM) are used. The assessment work is conducted for 2 different future time periods (2036 to 2065 and 2066 to 2095). Generalized extreme value distribution is used for the analysis of the flow frequency. Strathcona dam in the Campbell River basin, British Columbia, Canada, is used as a case study. The results show that the downscaling models contribute the highest amount of uncertainty to future streamflow predictions when compared to the contributions by global climate models or representative concentration pathways. It is also observed that the summer flows into Strathcona dam will decrease, and winter flows will increase in both future time periods. In addition to these, the flow magnitude becomes more uncertain for higher return periods in the Campbell River system under climate change.  相似文献   

Sensitivity of the Weather Research and Forecasting (WRF) model to downscaling ratios and storm types in rainfall simulation     

Jia Liu  Michaela Bray  Dawei Han 《水文研究》2012,26(20):3012-3031

Accurate information of rainfall is needed for sustainable water management and more reliable flood forecasting. The advances in mesoscale numerical weather modelling and modern computing technologies make it possible to provide rainfall simulations and forecasts at increasingly higher resolutions in space and time. However, being one of the most difficult variables to be modelled, the quality of the rainfall products from the numerical weather model remains unsatisfactory for hydrological applications. In this study, the sensitivity of the Weather Research and Forecasting (WRF) model is investigated using different domain settings and various storm types to improve the model performance of rainfall simulation. Eight 24‐h storm events are selected from the Brue catchment, southwest England, with different spatial and temporal distributions of the rainfall intensity. Five domain configuration scenarios designed with gradually changing downscaling ratios are used to run the WRF model with the ECMWF 40‐year reanalysis data for the periods of the eight events. A two‐dimensional verification scheme is proposed to evaluate the amounts and distributions of simulated rainfall in both spatial and temporal dimensions. The verification scheme consists of both categorical and continuous indices for a first‐level assessment and a more quantitative evaluation of the simulated rainfall. The results reveal a general improvement of the model performance as we downscale from the outermost to the innermost domain. Moderate downscaling ratios of 1:7, 1:5 and 1:3 are found to perform better with the WRF model in giving more reasonable results than smaller ratios. For the sensitivity study on different storm types, the model shows the best performance in reproducing the storm events with spatial and temporal evenness of the observed rainfall, whereas the type of events with highly concentrated rainfall in space and time are found to be the trickiest case for WRF to handle. Finally, the efficiencies of several variability indices are verified in categorising the storm events on the basis of the two‐dimensional rainfall evenness, which could provide a more quantitative way for the event classification that facilitates further studies. It is important that similar studies with various storm events are carried out in other catchments with different geographic and climatic conditions, so that more general error patterns can be found and further improvements can be made to the rainfall products from mesoscale numerical weather models. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

Evaluation of dynamically downscaled reanalysis precipitation data for hydrological application          下载免费PDF全文

Satish Bastola  Vasubandhu Misra 《水文研究》2014,28(4):1989-2002

Skilful and reliable precipitation data are essential for seasonal hydrologic forecasting and generation of hydrological data. Although output from dynamic downscaling methods is used for hydrological application, the existence of systematic errors in dynamically downscaled data adversely affects the skill of hydrologic forecasting. This study evaluates the precipitation data derived by dynamically downscaling the global atmospheric reanalysis data by propagating them through three hydrological models. Hydrological models are calibrated for 28 watersheds located across the southeastern United States that is minimally affected by human intervention. Calibrated hydrological models are forced with five different types of datasets: global atmospheric reanalysis (National Centers for Environmental Prediction/Department of Energy Global Reanalysis and European Centre for Medium‐Range Weather Forecasts 40‐year Reanalysis) at their native resolution; dynamically downscaled global atmospheric reanalysis at 10‐km grid resolution; stochastically generated data from weather generator; bias‐corrected dynamically downscaled; and bias‐corrected global reanalysis. The reanalysis products are considered as surrogates for large‐scale observations. Our study indicates that over the 28 watersheds in the southeastern United States, the simulated hydrological response to the bias‐corrected dynamically downscaled data is superior to the other four meteorological datasets. In comparison with synthetically generated meteorological forcing (from weather generator), the dynamically downscaled data from global atmospheric reanalysis result in more realistic hydrological simulations. Therefore, we conclude that dynamical downscaling of global reanalysis, which offers data for sufficient number of years (in this case 22 years), although resource intensive, is relatively more useful than other sources of meteorological data with comparable period in simulating realistic hydrological response at watershed scales. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献