Improving the accuracy of precipitation estimates in a typical inland arid area of China using a dynamic Bayesian model averaging approach

Xinjiang Uygur Autonomous Region is a typical inland arid area in China with a sparse and uneven distribution of meteorological stations,limited access to precipitation data,and significant water scarcity.Evaluating and integrating precipitation datasets from different sources to accurately characterize precipitation patterns has become a challenge to provide more accurate and alternative precipitation information for the region,which can even improve the performance of hydrological modelling.This study evaluated the applicability of widely used five satellite-based precipitation products(Climate Hazards Group InfraRed Precipitation with Station(CHIRPS),China Meteorological Forcing Dataset(CMFD),Climate Prediction Center morphing method(CMORPH),Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record(PERSIANN-CDR),and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis(TMPA))and a reanalysis precipitation dataset(ECMWF Reanalysis v5-Land Dataset(ERA5-Land))in Xinjiang using ground-based observational precipitation data from a limited number of meteorological stations.Based on this assessment,we proposed a framework that integrated different precipitation datasets with varying spatial resolutions using a dynamic Bayesian model averaging(DBMA)approach,the expectation-maximization method,and the ordinary Kriging interpolation method.The daily precipitation data merged using the DBMA approach exhibited distinct spatiotemporal variability,with an outstanding performance,as indicated by low root mean square error(RMSE=1.40 mm/d)and high Person's correlation coefficient(CC=0.67).Compared with the traditional simple model averaging(SMA)and individual product data,although the DBMA-fused precipitation data were slightly lower than the best precipitation product(CMFD),the overall performance of DBMA was more robust.The error analysis between DBMA-fused precipitation dataset and the more advanced Integrated Multi-satellite Retrievals for Global Precipitation Measurement Final(IMERG-F)precipitation product,as well as hydrological simulations in the Ebinur Lake Basin,further demonstrated the superior performance of DBMA-fused precipitation dataset in the entire Xinjiang region.The proposed framework for solving the fusion problem of multi-source precipitation data with different spatial resolutions is feasible for application in inland arid areas,and aids in obtaining more accurate regional hydrological information and improving regional water resources management capabilities and meteorological research in these regions.

Simulation of Spatial Distribution of Annual Average Precipitation in Shandong Province

Based on DEM data and conventional data of precipitation at 114 meteorological stations in Shandong Province during 1971 -2010, the statistical model of annual average precipitation in Shandong Province was established using SPSS software; DEM data and raster data of latitude and longitude were substituted into the statistical model, and the spatial distribution of annual average precipitation in Shandong Province based on the statistical model was obtained with the aid of ArcGIS software. Afterwards, the difference between actual value of precipitation at stations used in interpolation and simulated value was interpolated using Kriging interpolation method to obtain residual error of precipitation. Finally, the raster da- ta of annual average precipitation based on the regression model were overlaid with residual error of precipitation to obtain the spatial distribution map of annual average precipitation in Shandong Province. It is verified that the simulation result has high accuracy and can reflect the spatial distri- bution of precipitation in Shandong Province.

Study on the Characteristics of Changes in Air Temperature and Precipitation in Shenyang in Recent 50 Years

Based on the data of monthly average air temperature,extreme maximum,minimum air temperature and precipitation of Shenyang from 1960 to 2009,the climate changes and its characteristics in Shenyang in recent 50 years were comprehensively analyzed and studied.The results showed that the increasing trend of air temperature in recent 50 years was obvious.With the rising of the air temperature,the precipitation in Shenyang City showed a decreasing trend.

Improvement of 6–15 Day Precipitation Forecasts Using a Time-Lagged Ensemble Method

A time-lagged ensemble method is used to improve 6-15 day precipitation forecasts from the Beijing Climate Center Atmospheric General Circulation Model,version 2.0.1.The approach averages the deterministic predictions of precipitation from the most recent model run and from earlier runs,all at the same forecast valid time.This lagged average forecast （LAF） method assigns equal weight to each ensemble member and produces a forecast by taking the ensemble mean.Our analyses of the Equitable Threat Score,the Hanssen and Kuipers Score,and the frequency bias indicate that the LAF using five members at time-lagged intervals of 6 h improves 6-15 day forecasts of precipitation frequency above 1 mm d-1 and 5 mm d-1 in many regions of China,and is more effective than the LAF method with selection of the time-lagged interval of 12 or 24 h between ensemble members.In particular,significant improvements are seen over regions where the frequencies of rainfall days are higher than about 40％-50％ in the summer season; these regions include northeastern and central to southern China,and the southeastem Tibetan Plateau.

Simulation and analysis on seasonal variability of average salinity in the Yellow Sea

The CTD (conductivity, temperature and depth) data collected by six China-Korea joint cruises during 1996-1998 and the climatological data suggest that the seasonal variability of average salinity in the Yellow Sea (Sa) presents a general sinusoid pattern. To study the mechanism of the variability, annual cycles of Sa were simulated and a theoretical analysis based on the governing equations was reported.Three main factors are responsible for the variability: the Yellow Sea Warm Current (YSWC), the Changji-ang (Yangtze) River diluted water (YRDW) and the evaporation minus precipitation (E-P). From December to the next May, the variability of Sa is mainly controlled by the salt transportation of the YSWC. But in early July, the YSWC is overtaken and replaced by the YRDW which then becomes the most important controller in summer. From late September to November, the E-P gradually took the lead. The mass exchange north of the 37癗 line is not significant.

Precipitation Retrieval from Himawari-8 Satellite Infrared Data Based on Dictionary Learning Method and Regular Term Constraint

In this paper,the application of an algorithm for precipitation retrieval based on Himawari-8 (H8) satellite infrared data is studied.Based on GPM precipitation data and H8 Infrared spectrum channel brightness temperature data,corresponding "precipitation field dictionary" and "channel brightness temperature dictionary" are formed.The retrieval of precipitation field based on brightness temperature data is studied through the classification rule of k-nearest neighbor domain (KNN) and regularization constraint.Firstly,the corresponding "dictionary" is constructed according to the training sample database of the matched GPM precipitation data and H8 brightness temperature data.Secondly,according to the fact that precipitation characteristics in small organizations in different storm environments are often repeated,KNN is used to identify the spectral brightness temperature signal of "precipitation" and "non-precipitation" based on "the dictionary".Finally,the precipitation field retrieval is carried out in the precipitation signal "subspace" based on the regular term constraint method.In the process of retrieval,the contribution rate of brightness temperature retrieval of different channels was determined by Bayesian model averaging (BMA) model.The preliminary experimental results based on the "quantitative" evaluation indexes show that the precipitation of H8 retrieval has a good correlation with the GPM truth value,with a small error and similar structure.

Spatial-temporal Analysis and Prediction of Precipitation Extremes: A Case Study in the Weihe River Basin, China

Extreme precipitation events bring considerable risks to the natural ecosystem and human life.Investigating the spatial-temporal characteristics of extreme precipitation and predicting it quantitatively are critical for the flood prevention and water resources planning and management.In this study,daily precipitation data(1957–2019)were collected from 24 meteorological stations in the Weihe River Basin(WRB),Northwest China and its surrounding areas.We first analyzed the spatial-temporal change of precipitation extremes in the WRB based on space-time cube(STC),and then predicted precipitation extremes using long short-term memory(LSTM)network,auto-regressive integrated moving average(ARIMA),and hybrid ensemble empirical mode decomposition(EEMD)-LSTM-ARIMA models.The precipitation extremes increased as the spatial variation from northwest to southeast of the WRB.There were two clusters for each extreme precipitation index,which were distributed in the northwestern and southeastern or northern and southern of the WRB.The precipitation extremes in the WRB present a strong clustering pattern.Spatially,the pattern of only high-high cluster and only low-low cluster were primarily located in lower reaches and upper reaches of the WRB,respectively.Hot spots(25.00%–50.00%)were more than cold spots(4.17%–25.00%)in the WRB.Cold spots were mainly concentrated in the northwestern part,while hot spots were mostly located in the eastern and southern parts.For different extreme precipitation indices,the performances of the different models were different.The accuracy ranking was EEMD-LSTM-ARIMA>LSTM>ARIMA in predicting simple daily intensity index(SDII)and consecutive wet days(CWD),while the accuracy ranking was LSTM>EEMD-LSTM-ARIMA>ARIMA in predicting very wet days(R95 P).The hybrid EEMD-LSTM-ARIMA model proposed was generally superior to single models in the prediction of precipitation extremes.

江西宜丰县2012—2022年短时强降水的雷达回波特征分析

为了做好宜丰县短时强降水“1小时风险叫应”标准研究,使用宜丰雨量、江西雷达拼图等资料,采用统计学、雷达气象学等原理和方法,对宜丰县2012—2022年短时强降水进行分析,结果表明:1)2012—2022年18个气象观测站共出现短时强降水273站次。最多的是板坑水库(19次),最少的是双峰林场(8次);2)在大片絮状回波带中(层状云与积状云混合),存有大于45 dBZ的短带回波,在短带回波覆盖地方,多处会出现短时强降水。絮状回波带大于45 dBZ的短带回波面积都在500 km^(2)以上;3)块状单体回波尺度较小,基本孤立存在,回波强度CR在50~60 dBZ。有时几个块状单体回波聚在一起,但之间有间隔,有时块状单体回波存在于大范围絮状回波团中。块状单体回波在小时平均回波图上的反映差距较大;4)雷暴回波群的45 dBZ回波面积都≥100 km^(2),这个指标为识别短时强降水提供量化阈值。研究结果对宜丰短时强降水“1小时风险叫应”研究具有指导意义。

不同降水年型下旱地春小麦产量及其构成因素对播期和日均温度变化响应的模拟分析

为探讨播期调控及降水量和日均温度变化对旱地春小产量及其构成因素的耦合效应,基于甘肃省陇中地区气象数据、土壤数据和管理数据,通过APSIM模型对不同播期及日均温度变化情景下春小麦产量和生物量进行模拟分析,并利用试验区2013-2018年大田试验的春小麦产量及生物量数据对该模型进行适用性验证。结果表明,春小麦模拟产量及生物量的NRMSE均在8%以内,模型的有效性均大于0.80,说明模型具有较高的准确度和适用性。使用1970-2018年研究区的气象及土壤数据,设置不同播期及日均温度变化的模拟情景[早播(3月3日)、正常播(3月18日)、晚播(3月31日);日平均温度变化范围为降温2.0℃~增温2.0℃,以温度变化量1℃为1个梯度],经回归分析,小麦产量受影响程度表现为降水量>温度>播期,相同气候条件下小麦产量均表现为正常播>早播>晚播。春小麦在湿润年、正常播、日均温度降低2.0℃的条件下可获得最高产量。降水量对春小麦千粒重和籽粒数分别表现出负效应和正效应,温度则分别表现出正效应和负效应,千粒重和籽粒数均随着播期的推迟先增加后减少。综合分析,降水增多可通过增加春小麦籽粒数来提高产量;当地春小麦宜在3月中旬播种且应注重防寒与蓄水保墒,以促进小麦的高产与稳产。

黄土高原近30年降水集中度分析及时空演变特征

选取近30年的黄土高原地区58个气象站点的降水数据,分析了黄土高原年降水以及降水集中度(PCD)和集中期(PCP)的时空变化特征,结果表明:(1)黄土高原地区年均降水量在50~900 mm,空间分布上东南高西北低。降水高值区分布在三门峡站与武功站附近以及五台山站,年均降水量为900 mm;低值区位于临河站附近,年均降水量为50 mm;(2)黄土高原地区的降水集中度分布于0.43~0.76,自东南向西北递增;而降水集中期的变化则不太明显,多集中于夏季的7-8月份。因此在降水集中的7-8月份,水土流失会比较加重,应加强水土流失防治工程,在降水量大的月份做好灾害防护,为工农业生产提供预警,减少工农业生产损失与经济损失。

Probabilistic Precipitation Forecasting Based on Ensemble Output Using Generalized Additive Models and Bayesian Model Averaging

A probabilistic precipitation forecasting model using generalized additive models （GAMs） and Bayesian model averaging （BMA） was proposed in this paper. GAMs were used to fit the spatial-temporal precipitation models to individual ensemble member forecasts. The distributions of the precipitation occurrence and the cumulative precipitation amount were represented simultaneously by a single Tweedie distribution. BMA was then used as a post-processing method to combine the individual models to form a more skillful probabilistic forecasting model. The mixing weights were estimated using the expectation-maximization algorithm. The residual diagnostics was used to examine if the fitted BMA forecasting model had fully captured the spatial and temporal variations of precipitation. The proposed method was applied to daily observations at the Yishusi River basin for July 2007 using the National Centers for Environmental Prediction ensemble forecasts. By applying scoring rules, the BMA forecasts were verified and showed better performances compared with the empirical probabilistic ensemble forecasts, particularly for extreme precipitation. Finally, possible improvements and a^plication of this method to the downscaling of climate change scenarios were discussed.

1956-2020年西昌气候变化特征分析

基于西昌1956—2020年降水量、平均气温和日照时数等气候要素资料,采用线性回归、加权滑动平均法、Mann-Kendall突变检验和小波分析等方法分析西昌近65 a的气候变化特征。结果表明:(1)年降水量呈上升趋势,显著性较低,趋势率为8.75 mm·(10 a)^(-1)。在1998年发生突变,自1964年后降水量持续增加。降水量在57 a尺度上存在周期性变化。春季和夏季降水量呈持续增加的趋势,而秋季和冬季呈减小的趋势。(2)年平均气温呈显著上升趋势,趋势率为0.17℃·(10 a)^(-1)。在2004、2006和2008年均存在突变点,自2009年持续呈显著增加的趋势。四季年平均气温也均呈上升的趋势,冬季趋势率最大为0.24℃·(10 a)^(-1)。平均气温在56 a尺度上存在周期性变化。(3)年日照时数呈显著下降趋势,趋势率为-53.76 h·(10 a)^(-1)。在1982年存在突变点,自1991年持续呈显著减小的趋势。四季的日照时数均呈减小的趋势,夏季下降最快,趋势率为-20.00 h·(10 a)^(-1)。日照时数在57 a尺度上存在周期性变化。预计未来西昌年际和季节的平均气温将呈现出持续性较强的上升趋势,日照时数将呈现出持续性较强的下降趋势。

微生物加固粉土的强度特性及加固机理研究

针对华北地区广泛分布的黄河冲积粉土级配差、强度低的问题,采用微生物诱导碳酸钙沉淀(MICP)技术对其进行加固。通过三轴试验研究加固粉土的强度特性,通过微观结构测试分析其微观机理;结合宏观现象和微观机理揭示强度加固机理。结果表明:MICP加固后粉土的强度得到了大幅提升;其黏聚力和内摩擦角均随加固轮数、胶结液浓度的增加而增大,且增长速率呈现出先快后慢的趋势;在胶结液浓度为0.5 mol/L、加固两轮时加固效率更高。就微观结构而言,土样中的孔隙随胶结液浓度和加固轮数的增大而逐渐减少;土样截面的平均非孔隙面积比随加固轮数、胶结液浓度的增加而增大;黏聚力、内摩擦角与平均非孔隙面积比之间均符合先快后慢的双线性增加关系。黏聚力与内摩擦角提升原因在于MICP加固生成的碳酸钙通过粘结粉土颗粒,填充堵塞颗粒间孔隙,使平均非孔隙面积比增大,进而提升土体强度。

平均降水量算法在山东省水情业务系统中的应用

平均降水量是水利、气象等行业最常用且很重要的指标,各行业自建设雨量站,存在数据标准不统一、数据共享机制不畅通、资源浪费等现象,并且各行业、同一行业不同部门计算方法不一致。为解决平均降水量计算不统一的问题,实现数据快速查询和服务,结合水情业务系统建设,提出算术平均与面积加权相结合,利用系统计算入成果库的解决方法。本方法快速高效,便于维护,成果可作为后期分析多年均值的数据源。后期进一步优化算法,可以实现资源优化,避免重复建设。

青海地区大气加权平均温度模型精度优化

针对当前大气加权平均温度(T_(m))模型在青海省各地区的适用性较差,气象要素顾及较少等问题,提出一种青海地区大气加权平均温度模型精度优化方法:利用青海省4个探空站2014—2018年5 a的数据,通过线性回归的方法构建适用于青海省西宁、都兰、郭勒木得及玉树4个地区的本地化单因子T_(m)模型和多个气象因子参与的本地化多因子T_(m)模型;并通过反演的大气可降水量(PWV)与贝维斯(Bevis)模型和龚绍琦全国模型反演的PWV结果进行对比分析。结果表明,所构建的模型精度均优于现有模型,在反演PWV精度方面也要优于现有模型,可为提高青海地区全球卫星导航系统(GNSS)水汽反演精度提供参考。

迎春水库流域年降水量变化特性分析

本文根据凤庆气象站1959—2022年降水资料分析降水年内分配情况,采用线性回归、Mann-Kendall检验等方法分析迎春水库流域降水年际变化趋势、突变特征;采用高程法拟合罗闸河流域高程与多年平均降水量的关系曲线,通过线性插值计算出迎春水库本区、落拐河引水区、水箐河引水区多年平均年降水量。结果表明,迎春水库流域降水量年内分配不均;年降水量随时间变化整体呈下降趋势,变化不显著;年降水量随着流域平均高程的增加而增加。研究成果可为迎春水库水资源优化配置提供依据。

黑水河流域水文气象要素演变特征分析

黑水河为岷江上游典型流域,流域气候变化敏感,水能资源丰富,开展黑水河流域水文气象要素变化特征的研究工作,有利于成都平原水资源的开发利用与管理。采用有序聚类法、Pettitt法、线性倾向估计法、MK趋势检验法及小波分析法等科学全面探讨了年尺度水文气象要素的演变特征。研究成果可以为流域径流演变归因分析及径流预测等内容提供理论依据,以期加深对岷江上游径流变化规律的认识。

“2023-03-22”乐平大暴雨强降水回波特征与应用

为了更好地预报乐平大暴雨和短时强降水天气,文章使用MICAPS天气图、乐平自动气象站雨量、雷达回波等资料,采用天气学、雷达气象学等原理和图像识别技术,对2023-03-22乐平大暴雨过程和短时强降水的回波进行分析。结果表明:大暴雨的对流性特征明显,伴有强降水、强雷电等强对流天气;探空图上具有典型的“喇叭口”结构,低槽东移,冷空气与西南气流交汇,高层辐散,地面辐合,导致强降水发生;强降水伴有显著强雷电,降水强度最大的乐平雷电密度也最大,在强降水开始时,强雷电一般超前于强降水出现,而在强降水过程之中或即将结束时,强雷电落后于强降水;TBB强中心以南等值线密集地方对流最旺盛,最易诱发强降水;在测站上游出现大范围45 dBZ以上强回波且向测站移来时,强回波面积≥300 km^(2),会导致≥30 mm/h强降水,应提前发布预警信息。研究结果对乐平大暴雨和短时强降水的监测预警、预报服务发挥指导作用。

黄河中游晋西黄土高原近62年气候变化分析

通过对9个地面站的年平均气温和年平均降水量数据进行分析,研究了黄河中游晋西黄土高原近62 a(1957年至2018年)的气候变化情况。结果表明,1957年至2018年,黄河中游晋西黄土高原气温显著升高,年平均增温0.023 0℃。其中,北部区域和中部区域年平均增温为0.028 8℃和0.035 4℃,线性方程达到极显著水平;南部区域年平均增温0.017 6℃,线性方程达到显著水平,增温趋势相对平缓。全区年平均降水量减少0.324 mm,线性方程未达到显著水平,从北向南降水量减少量增加。总体来看,区域气候环境总体趋于暖干化。

气象要素空间插值方法的比较分析

气象要素是草原综合顺序分类中重要的指标,对生态系统中物种的组成、生长、演替更新及干物质的积累有很大的影响。年均温度、年降水量、年积温空间分布的一般规律是随海拔高度和纬度的增加而减少,但不同插值方法所计算出的空间插值之间,却有明显的不同。用ArcMap 8.3地理信息系统软件,对全国及周边地区2 114个气象台(站),1961-1990年30年的年均温度、年积温和年降水量,分别采用样条函数法(Spline)、反距离加权平方法(IDS)和普通克里金法(OK)进行空间插值分析。交叉验证结果表明:当选择合适的站点数目时,3种方法的精度都很高。在克里金法中,除高斯模型精度较低外,球状模型、环状模型、指数模型对同一气象因素插值,其相对平均误差也不大。3种插值方法的比较是,年均温度插值时,OK法优于Spline法,Spline法优于IDS法,相对平均误差分别为7.65%、7.9%和7.95%;年降水量插值时,OK法优于Spline法和IDS法,相对平均误差分别为8.31%、8.76%和8.76%;年积温插值时,OK法优于IDS法,IDS法优于Spline法,相对平均误差分别为5.82%、6.11%和6.13%。