Evaluation of Tianji and ECMWF high-resolution precipitation forecasts for extreme rainfall event in Henan in July 2021

The extreme rainfall event of July 17 to 22, 2021 in Henan Province, China, led to severe urban waterlogging and flood disasters. This study investigated the performance of high-resolution weather forecasts in predicting this extreme event and the feasibility of weather forecast-based hydrological forecasts. To achieve this goal, high-resolution precipitation forecasts from the Tianji weather system and the forecast system of the European Centre for Medium-Range Weather Forecasts (ECMWF) were evaluated with the spatial verification metrics of structure, amplitude, and location. The results showed that Tianji weather forecasts accurately predicted the amplitude of 12-h accumulated precipitation with a lead time of 12 h. The location and structure of the rainfall areas in Tianji forecasts were closer to the observations than ECMWF forecasts. Tianji hourly precipitation forecasts were also more accurate than ECMWF hourly forecasts, especially at lead times shorter than 8 h. The precipitation forecasts were used as the inputs to a hydrological model to evaluate their hydrological applications. The results showed that the runoff forecasts driven by Tianji weather forecasts could effectively predict the extreme flood event. The runoff forecasts driven by Tianji forecasts were more accurate than those driven by ECMWF forecasts in terms of amplitude and location. This study demonstrates that high-resolution weather forecasts and corresponding hydrological forecasts can provide valuable information in advance for disaster warnings and leave time for people to act on the event. The results encourage further hydrological applications of high-resolution weather forecasts, such as Tianji weather forecasts, in the future.

Projection of Future Precipitation Change over China with a High-Resolution Global Atmospheric Model

Projections of future precipitation change over China are studied based on the output of a global AGCM, ECHAM5, with a high resolution of T319 （equivalent to 40 km）. Evaluation of the model’s performance in simulating present-day precipitation shows encouraging results. The spatial distributions of both mean and extreme precipitation, especially the locations of main precipitation centers, are reproduced reasonably. The simulated annual cycle of precipitation is close to the observed. The performance of the model over eastern China is generally better than that over western China. A weakness of the model is the overestimation of precipitation over northern and western China. Analyses on the potential change in precipitation projected under the A1B scenario show that both annual mean precipitation intensity and extreme precipitation would increase significantly over southeastern China. The percentage increase in extreme precipitation is larger than that of mean precipitation. Meanwhile, decreases in mean and extreme precipitation are evident over the southern Tibetan Plateau. For precipitation days, extreme precipitation days are projected to increase over all of China. Both consecutive dry days over northern China and consecutive wet days over southern China would decrease.

A Strategy for Merging Objective Estimates of Global Daily Precipitation from Gauge Observations, Satellite Estimates, and Numerical Predictions

This paper describes a strategy for merging daily precipitation information from gauge observations, satellite estimates （SEs）, and numerical predictions at the global scale. The strategy is designed to remove systemic bias and random error from each individual daily precipitation source to produce a better gridded global daily precipitation product through three steps. First, a cumulative distribution function matching procedure is performed to remove systemic bias over gauge-located land areas. Then, the overall biases in SEs and model predictions （MPs） over ocean areas are corrected using a rescaled strategy based on monthly precipitation. Third, an optimal interpolation （OI）-based merging scheme （referred as the HL-OI scheme） is used to combine unbiased gahge observations, SEs, and MPs to reduce random error from each source and to produce a gauge--satellite-model merged daily precipitation analysis, called BMEP-d （Beijing Climate Center Merged Estimation of Precipitation with daily resolution）, with complete global coverage. The BMEP-d data from a four-year period （2011- 14） demonstrate the ability of the merging strategy to provide global daily precipitation of substantially improved quality. Benefiting from the advantages of the HL-OI scheme for quantitative error estimates, the better source data can obtain more weights during the merging processes. The BMEP-d data exhibit higher consistency with satellite and gauge source data at middle and low latitudes, and with model source data at high latitudes. Overall, independent validations against GPCP-1DD （GPCP one-degree daily） show that the consistencies between B MEP-d and GPCP-1DD are higher than those of each source dataset in terms of spatial pattern, temporal variability, probability distribution, and statistical precipitation events.

Quantitative estimation of hourly precipitation in the Tianshan Mountains based on area-to-point kriging downscaling and satellite-gauge data merging

Precipitation, a basic component of the water cycle, is significantly important for meteorological, climatological and hydrological research. However, accurate estimation on the precipitation remains considerably challenging because of the sparsity of gauge networks and the large spatial variability of precipitation over mountainous regions. Moreover, meteorological stations in mountainous areas are often dispersed and have difficulty in accurately reflecting the intensity and evolution of precipitation events. In this study,we proposed a novel method to produce high-quality,high-resolution precipitation estimates in the Tianshan Mountains, China, based on area-to-point kriging(ATPK) downscaling and a two-step correction, i.e., probability density function matching-optimum interpolation(PDF-OI). We obtained 1-km hourly precipitation data in the Tianshan Mountains by merging estimates from the Integrated Multisatellite Measurement(IMERG) product with observations from 1065 meteorological stations in the warm season(May to September) during 2016–2018. The spatial resolution and accuracy of the merged precipitation data greatly increased compared to IMERG.According to a cross-validation with gauged observations, the correlation coefficient(CC),probability of detection(POD) and critical success index(CSI) increased from 0.30, 0.50 and 0.24 for IMERG to 0.63, 0.65 and 0.38, respectively, for the merged estimates, and the root mean squared error(RMSE), mean error(ME) and false alarm ratio(FAR)decreased from 0.46 to 0.38 mm/h, 0.06 to 0.05 mm/h and 0.69 to 0.52, respectively. The proposed method will be useful for developing high-resolution precipitation estimates in mountainous areas such as central Asia and the Belt and Road Initiative regions.

Correction of CMPAS Precipitation Products over Complex Terrain Areas with Machine Learning Models

Machine learning models were used to improve the accuracy of China Meteorological Administration Multisource Precipitation Analysis System(CMPAS)in complex terrain areas by combining rain gauge precipitation with topographic factors like altitude,slope,slope direction,slope variability,surface roughness,and meteorological factors like temperature and wind speed.The results of the correction demonstrated that the ensemble learning method has a considerably corrective effect and the three methods(Random Forest,AdaBoost,and Bagging)adopted in the study had similar results.The mean bias between CMPAS and 85%of automatic weather stations has dropped by more than 30%.The plateau region displays the largest accuracy increase,the winter season shows the greatest error reduction,and decreasing precipitation improves the correction outcome.Additionally,the heavy precipitation process’precision has improved to some degree.For individual stations,the revised CMPAS error fluctuation range is significantly reduced.

High-resolution Transmission Electron Microscopy Characterization of the Structure of Cu Precipitate in a Thermal-aged Multicomponent Steel

High-dispersed nanoscale Cu precipitates often contribute to extremely high strength due to precipitation hardening,and whereas usually lead to degraded toughness for especially ferritic steels.Hence,it is important to understand the formation behaviors of the Cu precipitates.High-resolution transmission electron microscopy(TEM)is utilized to investigate the structure of Cu precipitates thermally formed in a high-strength low-alloy(HSLA)steel.The Cu precipitates were generally formed from solid solution and at the crystallographic defects such as martensite lath boundaries and dislocations.The Cu precipitates in the same aging condition have various structure of BCC,9 R and FCC,and the structural evolution does not greatly correlate with the actual sizes.The presence of different structures in an individual Cu precipitate is observed,which reflects the structural transformation occurring locally to relax the strain energy.The multiply additions in the steel possibly make the Cu precipitation more complex compared to the binary or the ternary Fe-Cu alloys with Ni or Mn additions.This research gives constructive suggestions on alloying design of Cu-bearing alloy steels.

Deep learning-based multi-source precipitation merging for the Tibetan Plateau

Due to its complex and diverse terrain, precipitation gauges in the Tibetan Plateau(TP) are sparse, making it difficult to obtain reliable precipitation data for environmental studies. Data merging is a method that can integrate precipitation data from multiple sources to generate high-precision precipitation data. However, the more commonly used methods, such as regression and machine learning, do not usually consider the local correlation of precipitation, so that the spatial pattern of precipitation cannot be reproduced, while deep learning methods do incorporate spatial correlation. To explore the ability of using deep learning methods in merging precipitation data for the TP, this study compared three methods: a deep learning method—a convolutional neural network(CNN) algorithm, a machine learning method—an artificial neural network(ANN) algorithm, and a statistical method based on Extended Triple Collocation(ETC) in merging precipitation from multiple sources(gauged, grid,satellite and dynamic downscaling) over the TP, as well as their performance for hydrological simulations. Dynamic downscaling data driven by global reanalysis data centered on the TP were introduced in the merging process to better reflect the spatial variability of precipitation. The results show that:(1) in terms of the meteorological metrics, the merged data perform better than the gauge interpolation data. By using data merging, the error between the raw multi-source and gauged precipitation can be reduced, and the precipitation detection capability can be greatly improved;(2) The merged precipitation data also perform well in the hydrological evaluation. The Xin’anjiang(XAJ) model parameter calibration experiments at the source of the Yangtze River(SYR) and the source of the Yellow River(SHR) were repeated 300 times to remove uncertainty in the model parameter results. The median Kling-Gupta Efficiency Coefficients(KGE) of simulated runoff from the merged data of the ANN, CNN and ETC methods for the SYR and the SHR are 0.859, 0.864, 0.838 and 0.835, 0.835, 0.789, respectively. Except for the ETC merging data at the SHR, the performance of other merged data was improved compared to the simulation results of the gauged precipitation(KGE=0.807 at the SYR, KGE=0.828 at the SHR);and(3) In contrast to the machine learning ANN method and the statistical ETC method, the deep learning method, CNN, consistently showed better performance.

Parameter Optimization of Regularization Variational Merging and Its Application in GNSS/MET Water Vapor

The paper discusses the core parameters of the 3 D and 4 D variational merging based on L1 norm regularization,namely optimization characteristic correlation length of background error covariance matrix and regularization parameter. Classical 3 D/4 D variational merging is based on the theory that error follows Gaussian distribution. It involves the solution of the objective functional gradient in minimization iteration,which requires the data to have continuity and differentiability. Classic 3 D/4 D-dimensional variational merging method was extended,and L1 norm was used as the constraint coupling to the classical variational merged model. Experiment was carried out by using linear advection-diffusion equation as four-dimensional prediction model,and parameter optimization of this method is discussed. Considering the strong temporal and spatial variation of water vapor,this method is further applied to the precipitable water vapor( PWV) merging by calculating reanalysis data and GNSS retrieval.Parameters were adjusted gradually to analyze the influence of background field on the merging result,and the experiment results show that the mathematical algorithm adopted in this paper is feasible.

Diurnal Variations of Precipitation over the Steep Slopes of the Himalayas Observed by TRMM PR and VIRS

This study investigates diurnal variations of precipitation during May–August, 1998–2012, over the steep slopes of the Himalayas and adjacent regions(flat Gangetic Plains–FGP, foothills of the Himalayas–FHH, the steep slope of the southern Himalayas–SSSH, and the Himalayas-Tibetan Plateau tableland–HTPT). Diurnal variations are analyzed at the pixel level utilizing collocated TRMM precipitation radar and visible infrared data. The results indicate that rain parameters(including rain frequency, rain rate, and storm top altitude) are predominantly characterized by afternoon maxima and morning minima at HTPT and FGP, whereas, maximum rain parameters at FHH typically occur in the early morning. Rain parameters at SSSH are characterized by double peaks;one in the afternoon and one at midnight. Over HTPT and FGP,convective activity is strongest in the afternoon with the thickest crystallization layer. Over FHH, the vertical structure of precipitation develops most vigorously in the early morning when the most intense collision and growth of precipitation particles occurs. Over SSSH, moist convection is stronger in the afternoon and at midnight with strong mixing of ice and water particles. The results of harmonic analysis show that rain bands move southward from lower elevation of SSSH to FHH with apparent southward propagation of the harmonic phase from midnight to early morning. Moreover, the strongest diurnal harmonic is located at HTPT, having a diurnal harmonic percentage variance of up to 90%. Large-scale atmospheric circulation patterns exhibit obvious diurnal variability and correspond well to the distribution of precipitation.

基于多模式预报的四川盆地强降水订正方法

四川盆地地形地貌复杂,强降水预报难度大,对模式降水预报产品进行订正,是提升强降水预报质量的重要手段。本文选取2018—2019年发生在四川盆地的35次强降水过程,对ECMWF、CMA_MESO和SWC_WARMS三种模式的24 h强降水预报采用常规评分和空间平移两个方法进行检验,并利用最优评分、多模式集成和位移订正三种方法进行订正试验。结果表明:最优评分订正方法可以有效改善各模式降水预报的强度,而多模式集成订正法可以改进降水落区和极值预报,在此基础上计算位移偏差,根据最优的位移偏差值对降水预报进行位移订正,可以进一步改进强降水落区预报效果。然后利用2020—2021年强降水过程进行订正效果验证,结果显示:经订正后的降水极值预报更接近实况,各量级降水预报评分明显优于单一模式,暴雨和大暴雨预报的TS评分提高率较最优单模式分别可达24.3%和42.8%,订正后空报率基本维持,漏报率显著下降,订正效果良好。

多模式对四川盆地强降水过程的预报性能检验

为进一步认识当前数值预报模式的预报能力,选取2018—2020年发生在四川盆地的47次强降水过程进行分型,再基于多源降水融合产品和地面观测资料,通过TS评分、时空滑动等方法对欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)数值预报模式、国家气象中心区域中尺度数值预报模式(China Meteorological Administration Mesoscale Model,CMA_MESO)和西南区域数值预报系统(Southwest Center WRF ADAS Real-time Modeling System,SWC_WARMS)在强降水过程范围、强度、极值、时间和位移偏差等方面的预报能力进行检验评估。结果表明,各模式08:00(北京时,下同)预报优于20:00预报,ECMWF对中雨和大雨预报更优,SWC_WARMS的暴雨量级评分更高。各模式对中雨的预报范围普遍较实况偏大,随着降水量级增大,逐渐转为低估,其中SWC_WARMS更接近实况。对于降水强度,ECMWF和CMA_MESO的平均降水量和极值普遍较实况偏小,SWC_WARMS更接近实况。3种模式时间偏差不明显,仅个别起报时次有-6~3 h的时间偏差;ECMWF的位移偏差最小,纬向上ECMWF和SWC_WARMS以偏北为主,经向上ECMWF以偏西为主,CMA_MESO和SWC_WARMS以偏东为主。

多源融合降水实况产品在日照市暴雨过程中的适用性

为评估融合降水实况产品在日照市的适用性,以日照市气象和水文降水观测数据为检验源,对2019—2021年5—10月和2022年5—7月融合降水数据以及发生在该时段内的25次暴雨过程进行评估检验。结果表明:(1)融合降水和观测降水空间分布一致性较好,融合降水产品可以在无降水观测地区提供参考。(2)融合降水和观测降水时间序列一致性较好,相关系数在0.9以上,长序列评估的均方根误差各月均在1.0 mm以内。平均值误差和均方根误差长序列评估结果均随着小时雨强的增大而增大。(3)融合降水存在对暴雨过程降水量和短时强降水的低估,两者差值随着小时雨强的增大而增大,当融合降水大于或小于观测降水时,两者差值较小或较大;在对暴雨极值点评估中融合降水较观测降水偏小,极值差异多在±20 mm以内。

降水融合实况产品在内蒙古冬季强降水过程中的检验评估

利用内蒙古国家站逐小时降水观测资料,针对2021年11月发生在内蒙古冬季的一次强降水过程,采用多种评估指标对业务上的3种降水融合实况产品(FRT_5KM\,RT_1KM\,NRT_1KM)进行多角度检验评估。结果表明:在小时最大降水量方面,3种实况产品均与观测值有较好的对应关系,FRT_5KM在全区大部分地区呈现负偏差,两种1 km产品表现为较低的正偏差;在降水量分级客观检验中,除暴雪外,两种1 km产品的TS优于5 km产品,在准确率方面,NRT_1KM优于RT_1KM优于FRT_5KM,FRT_5KM产品在中雪以上量级具有较高的漏报率,两种1 km产品在中雪、大雪、暴雪的空报率较高;3种实况产品在过程累计降水量方面均能很好地反映此次降水过程,1 km产品降水落区刻画更为细致,和实况有较高的一致性,但在非降水区具有明显空报。综合整体精度检验结果来看,评估时段内NRT_1KM产品的表现情况最好,相对来说FRT_5KM在内蒙古地区的检验精度较低。

四种融合降水产品在广西适用性评估

基于2022年汛期广西陆面的降水资料,对国家气象信息中心的中国区域多源融合实况分析产品(RT1、RT2和NRT)和广西壮族自治区气象信息中心的二源融合降水产品(GXPAS)等四种融合降水产品进行适用性评估,评估指标为统计误差和技巧评分.非独立检验结果表明,融合降水产品与自动站点吻合度高,在观测或估测有降水的情况下,表现最优的NRT产品有89.73%样本实况值等于观测值.独立检验结果表明,GXPAS综合表现稍优,特别是小时雨强[10,20)mm·h^(-1)区间.用最近邻法分析自动站与其邻近自动站的统计误差,分析得出在广西区域,水平距离自动站超过0.01°的位置,融合降水产品优于使用附近自动站观测数据.融合降水产品适用在无观测站区域或自动站数据未能及时上传时,作为降水的“真值”开展业务服务.

多源降水量数据融合分析及评价

利用2018年5月贵州省黔西南州自动气象观测站的降水量资料、兴义站雷达资料和CMORPH卫星降水反演产品资料,采用最优插值法对3种降水量数据开展融合试验.试验设计4种降水量数据融合方案,分别对融合的降水量数据进行区域精度检验.结果表明,经概率密度匹配法订正后的卫星-地面融合降水量与订正之前相比,相关系数由66.19%提升至79.68%,偏差由-0.544mm/h降至-0.247 mm/h,均方根误差由2.944 mm/h降至1.353 mm/h.当融合降水模型仅输入2种降水量数据源时,具有较高精度的一方对融合降水量的精度影响更大.卫星-雷达-地面数据融合降水量的相关系数为81.32%,偏差和均方根误差分别为-0.219、1.037 mm/h,精度高于其他降水量数据融合方案,说明融入更多有效降水量数据源能明显提高面降水量的精度.

高分辨率融合降水驱动下的WRF/WRF-Hydro耦合模拟研究

为了满足陆面水文模式和数值大气模式耦合构建降雨径流模拟系统的需求,构建了基于天气雷达与雨量站观测降水的3种高分辨率降水数据(雷达估测降水、雨量站插值降水和基于雷达与雨量站观测数据的融合降水),采用雨量站插值降水和融合降水数据对陆面水文模式WRF-Hydro的关键产汇流参数进行率定,探讨了不同降水驱动WRF-Hydro开展径流模拟在我国北方山区中小尺度流域的适用性。结果表明:融合降水的误差指标相比传统插值降水有不同程度的改善,融合降水的时空准确性也更为突出;采用雨量站插值降水和融合降水均可有效改善WRF-Hydro的关键产汇流参数,其中融合降水率定参数组的洪水模拟效果要优于雨量站插值降水;以高分辨率融合降水数据为驱动,可获得更准确的WRF-Hydro关键产汇流参数,有效提高WRF/WRF-Hydro陆气耦合系统在研究流域的适用性。

多种融合降水实况分析产品在雅安宝兴暴雨过程中的适用性评估

利用四川省地面逐小时降水观测资料,对国家气象信息中心研制的空间分辨率分别为0.01°和0.05°的三套逐时融合降水实况分析产品,在2019年8月20~22日引发雅安宝兴洪涝灾害的区域性暴雨天气过程中的表现进行评估分析。结果表明:三套融合降水实况分析产品的强降水落区和走向与站点实况基本一致,能较好地反映降水强弱变化和时空分布特征。三套融合降水实况分析产品的平均晴雨准确率都在85%以上,与站点实况的相关系数均在0.8以上,但均较站点实况存在不同程度的低估。相对而言,融合雷达、卫星、站点数据的0.01°产品最优,融合雷达、卫星、站点数据的0.05°产品次之,只融合卫星、站点数据的0.05°产品最差,可见融合站点数据能在一定程度上改善融合降水实况分析产品的质量。总体而言,融合降水实况分析产品的质量较高,在灾害性天气过程中可作为站点实况的有效补充。

CMORPH卫星站点融合降水的沂河流域径流模拟适用性评估

为验证卫星地面融合降水在北方半湿润地区径流模拟中的适用性,对比了2008~2012年沂河流域汛期的CMORPH融合降水和实测网格降水时空分布,并分别驱动HEC-HMS模型进行日径流模拟、洪水模拟,进而评估利用卫星地面融合降水进行洪水预报的可行性。结果表明,在沂河流域,CMORPH卫星站点融合降水与实测网格降水具有较高的相关性,其洪水模拟精度与实测网格降水相当,径流深和洪峰的预报能力更优,在研究区洪水预报中具有很好的适用性。

基于星地融合降水的中小流域洪水模拟

我国气象站点分布不均,导致众多中小流域的降水资料不足,给中小流域的洪水模拟带来一定困难。融合星地降水是提高降水时空分辨率和精度的有效方法,但在中小流域洪水模拟中的适应性需进一步研究。以屯溪流域为例,采用BP神经网络模型,分别将实测降水与GPM时代两种近实时卫星降水产品IMERG_Early及GSMaP_NRT融合,并探讨融合降水在场次洪水模拟中的适用性。通过减少流域内降水站点数量,探讨在缺乏资料地区星地融合降水的应用潜力。结果表明:在不同数量实测站点条件下,融合降水模拟场次洪水的结果均可靠,确定性系数可达0.8,洪峰相对误差合格率在70%以上,峰现时间误差合格率达90%;当站点信息较少时,融合降水相较于实测降水的确定性系数及洪峰相对误差合格率更高。基于千河流域的模拟结果表明,融合了4个站点信息的融合降水的模拟结果与基于12个实测站点的模拟结果一致。融合降水能为中小流域,特别是缺乏降水资料的中小流域,提供可靠的降水数据支撑。

WRF-Hydro模式结合不同降水产品模拟清江流域径流的效果分析

以2016—2017年清江流域2次大径流事件和3次小径流事件为研究对象,首先,分析了CMORPH卫星-地面自动站-雷达三源融合降水产品(CMPAS)、中国全球大气再分析产品(CRA)和雨量站降雨资料(Gauge)3种产品的降水时空分布特征;然后,基于径流事件实况与不同降水产品的特点,设计了两种径流模拟试验方案,对3种产品的降水数据输入WRF-Hydro模式的径流模拟结果进行分析。最后,结合降水时空分布差异,探讨3种降水产品在径流模拟中的应用效果。结果表明:(1)5次径流事件中,3种降水产品探测的降雨中心、雨带位置和走向大致相同,流域内面雨量随时间变化趋势较为一致。(2)两种试验方案下,3种降水产品均能模拟出各次径流事件。对大径流事件,CMPAS的模拟效果最优,相关系数均在0.76以上、纳什效率系数均在0.63以上;对小径流事件,Gauge的模拟效果最优,相关系数均在0.75以上,纳什效率系数均在0.48以上;CRA无论对大、小径流事件,其模拟效果相对都较差,但参数经重新率定后,其模拟效果明显改善。(3)3种降水产品经重新率定参数后(方案2),其在峰现、涨水、退水各时段的径流模拟效果改善不同。对小径流事件,相对涨水和退水时段,各产品在峰现时段的模拟效果改善较为明显,而对大径流事件,3种降水产品在各时段的模拟效果均无明显改善。