1901～2013年GPCC和CRU降水资料在中国大陆的适用性评估

利用1901~2013年中国大陆地区的气象台站实测降水资料,对东英吉利(East Anglia)大学气候研究中心(Climatic Research Unit,CRU)和全球降水气候中心(Global Precipitation Climatology Centre,GPCC)的降水资料分别从季节、年际和年代际尺度上进行了评估。结果表明:1961~2013年CRU与GPCC降水资料均能较准确地描述中国大陆地区的降水特征,且在东部较西部地区、夏季较冬季与站点实测降水情况更为一致。将中国大陆划分为不同区域并在其季节、年际和年代际时间尺度上通过比较降水偏差绝对值的百分比、均方根误差和相关系数等统计量后发现:CRU在青藏高原和其它较大的山脉附近与站点实测降水的差别较大,且年均降水趋势在西北一带的阿尔金山脉、黄土高原、东南地区和长江下游地区,比实测降水的年均趋势小、甚至出现趋势相反的情况。此外,CRU降水的年代际变化趋势也偏小。而GPCC数据不论是降水量还是降水趋势都更接近实际情况。在1901~1961年,通过与65个长期气象观测站点的降水时间序列比较发现,CRU在110°E以西地区与站点观测的降水资料间的差别较大,而GPCC与站点观测资料的吻合较好。最后,利用1961~2013年两套降水资料和站点实测资料分别计算了标准化降水指数(SPI),简单分析了中国大陆地区的干旱变化,发现GPCC对旱涝的时空变化特征的描述比CRU更接近站点实际观测;并且CRU也没有反映出1997年夏季中国地区出现的严重干旱情况,而GPCC较为准确地反映出了这一干旱事件特征。因此,本文的研究结果认为,就中国大陆地区长时期降水资料而言,GPCC的适用性优于CRU。

Influence of vapor pressure deficit on vegetation growth in China

Vapor pressure deficit(VPD)plays a crucial role in determining plant physiological functions and exerts a substantial influence on vegetation,second only to carbon dioxide(CO_(2)).As a robust indicator of atmospheric water demand,VPD has implications for global water resources,and its significance extends to the structure and functioning of ecosystems.However,the influence of VPD on vegetation growth under climate change remains unclear in China.This study employed empirical equations to estimate the VPD in China from 2000 to 2020 based on meteorological reanalysis data of the Climatic Research Unit(CRU)Time-Series version 4.06(TS4.06)and European Centre for Medium-Range Weather Forecasts(ECMWF)Reanalysis 5(ERA-5).Vegetation growth status was characterized using three vegetation indices,namely gross primary productivity(GPP),leaf area index(LAI),and near-infrared reflectance of vegetation(NIRv).The spatiotemporal dynamics of VPD and vegetation indices were analyzed using the Theil-Sen median trend analysis and Mann-Kendall test.Furthermore,the influence of VPD on vegetation growth and its relative contribution were assessed using a multiple linear regression model.The results indicated an overall negative correlation between VPD and vegetation indices.Three VPD intervals for the correlations between VPD and vegetation indices were identified:a significant positive correlation at VPD below 4.820 hPa,a significant negative correlation at VPD within 4.820–9.000 hPa,and a notable weakening of negative correlation at VPD above 9.000 hPa.VPD exhibited a pronounced negative impact on vegetation growth,surpassing those of temperature,precipitation,and solar radiation in absolute magnitude.CO_(2) contributed most positively to vegetation growth,with VPD offsetting approximately 30.00%of the positive effect of CO_(2).As the rise of VPD decelerated,its relative contribution to vegetation growth diminished.Additionally,the intensification of spatial variations in temperature and precipitation accentuated the spatial heterogeneity in the impact of VPD on vegetation growth in China.This research provides a theoretical foundation for addressing climate change in China,especially regarding the challenges posed by increasing VPD.

Comprehensive applicability evaluation of four precipitation products at multiple spatiotemporal scales in Northwest China

Precipitation plays a crucial role in the water cycle of Northwest China.Obtaining accurate precipitation data is crucial for regional water resource management,hydrological forecasting,flood control and drought relief.Currently,the applicability of multi-source precipitation products for long time series in Northwest China has not been thoroughly evaluated.In this study,precipitation data from 183 meteorological stations in Northwest China from 1979 to 2020 were selected to assess the regional applicability of four precipitation products(the fifth generation of European Centre for Medium-Range Weather Forecasts(ECMWF)atmospheric reanalysis of the global climate(ERA5),Global Precipitation Climatology Centre(GPCC),Climatic Research Unit gridded Time Series Version 4.07(CRU TS v4.07,hereafter CRU),and Tropical Rainfall Measuring Mission(TRMM))based on the following statistical indicators:correlation coefficient,root mean square error(RMSE),relative bias(RB),mean absolute error(MAE),probability of detection(POD),false alarm ratio(FAR),and equitable threat score(ETS).The results showed that precipitation in Northwest China was generally high in the east and low in the west,and exhibited an increasing trend from 1979 to 2020.Compared with the station observations,ERA5 showed a larger spatial distribution difference than the other products.The overall overestimation of multi-year average precipitation was approximately 200.00 mm and the degree of overestimation increased with increasing precipitation intensity.The multi-year average precipitation of GPCC and CRU was relatively close to that of station observations.The trend of annual precipitation of TRMM was overestimated in high-altitude regions and the eastern part of Lanzhou with more precipitation.At the monthly scale,GPCC performed well but underestimated precipitation in the Tarim Basin(RB=-4.11%),while ERA5 and TRMM exhibited poor accuracy in high-altitude regions.ERA5 had a large bias(RB≥120.00%)in winter months and a strong dispersion(RMSE≥35.00 mm)in summer months.TRMM showed a relatively low correlation with station observations in winter months(correlation coefficients≤0.70).The capture performance analysis showed that ERA5,GPCC,and TRMM had lower POD and ETS values and higher FAR values in Northwest China as the precipitation intensity increased.ERA5 showed a high capture performance for small precipitation events and a slower decreasing trend of POD as the precipitation intensity increased.GPCC had the lowest FAR values.TRMM was statistically ineffective for predicting the occurrence of daily precipitation events.The findings provide a reference for data users to select appropriate datasets in Northwest China and for data developers to develop new precipitation products in the future.

Evaluation of Daily Gridded Meteorological Datasets over the Niger Delta Region of Nigeria and Implication to Water Resources Management

Hydro-climatological study is difficult in most of the developing countries due to the paucity of monitoring stations. Gridded climatological data provides an opportunity to extrapolate climate to areas without monitoring stations based on their ability to replicate the Spatio-temporal distribution and variability of observed datasets. Simple correlation and error analyses are not enough to predict the variability and distribution of precipitation and temperature. In this study, the coefficient of correlation (R2), Root mean square error (RMSE), mean bias error (MBE) and mean wet and dry spell lengths were used to evaluate the performance of three widely used daily gridded precipitation, maximum and minimum temperature datasets from the Climatic Research Unit (CRU), Princeton University Global Meteorological Forcing (PGF) and Climate Forecast System Reanalysis (CFSR) datasets available over the Niger Delta part of Nigeria. The Standardised Precipitation Index was used to assess the confidence of using gridded precipitation products on water resource management. Results of correlation, error, and spell length analysis revealed that the CRU and PGF datasets performed much better than the CFSR datasets. SPI values also indicate a good association between station and CRU precipitation products. The CFSR datasets in comparison with the other data products in many years overestimated and underestimated the SPI. This indicates weak accuracy in predictability, hence not reliable for water resource management in the study area. However, CRU data products were found to perform much better in most of the statistical assessments conducted. This makes the methods used in this study to be useful for the assessment of various gridded datasets in various hydrological and climatic applications.

1901~1950年5~9月北半球CRU数据与树轮资料的对比

气候变化的研究依赖于可靠的气候数据。英国东英吉利大学气候研究中心推出的一套依据观测资料插值所得的月平均气候数据集(CRU),是目前使用较为广泛、质量较高的气候数据集。气候观测数据大多始于1950年,已有的研究也表明CRU数据在1950年后更具参考价值,但该数据集在1950年之前的可靠性不清楚。由于1950年之前缺乏器测数据,本研究使用树轮资料,对比每十年树轮重建温度(降水)与树轮附近CRU格点上的温度(降水)的距平和趋势的同号率,来评价CRU温度(降水)数据在不同地区的数据质量(可靠性),由此发现从1930年代开始,CRU温度数据在欧洲和中西伯利亚可信度较高;从1940年代开始,CRU温度数据在美国东部可信度较高;从1940年代开始,CRU降水数据在美国东部与西西伯利亚可信度较高。研究发现,气象站密度对CRU数据质量有很大影响;在气象站密度小的地区,CRU温度或降水数据与树轮序列的差异更为显著,气候数据质量偏低;而数据可信度较高的年代和地区,气象站密度往往较大。

A new method for the prediction of network security situations based on recurrent neural network with gated recurrent unit

Purpose-The purpose of this paper is to solve the shortage of the existing methods for the prediction of network security situations(NSS).Because the conventional methods for the prediction of NSS,such as support vector machine,particle swarm optimization,etc.,lack accuracy,robustness and efficiency,in this study,the authors propose a new method for the prediction of NSS based on recurrent neural network(RNN)with gated recurrent unit.Design/methodology/approach-This method extracts internal and external information features from the original time-series network data for the first time.Then,the extracted features are applied to the deep RNN model for training and validation.After iteration and optimization,the accuracy of predictions of NSS will be obtained by the well-trained model,and the model is robust for the unstable network data.Findings-Experiments on bench marked data set show that the proposed method obtains more accurate and robust prediction results than conventional models.Although the deep RNN models need more time consumption for training,they guarantee the accuracy and robustness of prediction in return for validation.Originality/value-In the prediction of NSS time-series data,the proposed internal and external information features are well described the original data,and the employment of deep RNN model will outperform the state-of-the-arts models.