气象三要素校准表计算及生成软件

气象三要素仪作为辅助观测仪器,需要对其传感器进行现场校准。基于VB平台,开发气象三要素校准表计算及生成软件,具有自动计算并生成气象三要素校准表的功能,可为气象三要素现场校测节省时间,提高效率和准确率,值得推广。     

Dynamic factor analysis and artificial neural network for estimating pan evaporation at multiple stations in northern Taiwan

Abstract

Evaporation is an important reference for managers of water resources. This study proposes a hybrid model (BD) that combines back-propagation neural networks (BPNN) and dynamic factor analysis (DFA) to simultaneously precisely estimate pan evaporation at multiple meteorological stations in northern Taiwan through incorporating a large number of meteorological data sets into the estimation process. The DFA is first used to extract key meteorological factors that are highly related to pan evaporation and to establish the common trend of pan evaporation among meteorological stations. The BPNN is then trained to estimate pan evaporation with the inputs of the key meteorological factors and evaporation estimates given by the DFA. The BD model successfully inherits the advantages from the DFA and BPNN, and effectively enhances its generalization ability and estimation accuracy. The results demonstrate that the proposed BD model has good reliability and applicability in simultaneously estimating pan evaporation for multiple meteorological stations.

Citation Chang, F.J., Sun, W., and Chung, C.H., 2013. Dynamic factor analysis and artificial neural network for estimating pan evaporation at multiple stations in northern Taiwan. Hydrological Sciences Journal, 58 (4), 813–825.     

Consequences of implementing a reservoir operation algorithm in a global hydrological model under multiple meteorological forcing

We investigated dam behaviours during high-flow events and their robustness against perturbations in meteorological conditions using the H08 global hydrological model. Differences in these behaviours were examined by comparing simulation runs, with and without dams and using multiple meteorological datasets, at a case-study site, Fort Peck Dam on the Missouri River, USA. The results demonstrated that dam-regulated river flow reduced temporal variability over large time periods and also dampened inter-forcing discrepancies in river discharge (smoothing effects). However, during wet years, differences in peak flow were accentuated downstream of the dam, resulting in divergence in simulated peak flow across the meteorological forcing (pulsing effect). The pulsing effect was detected at other major dams in global simulations. Depending upon the meteorological forcing, the dams act as a selective filter against high-flow events. Synergy between a generic dam scheme and differences in meteorological forcing data might introduce additional uncertainties in global hydrological simulations.     

Modelling bivariate rainfall distribution and generating bivariate correlated rainfall data in neighbouring meteorological subdivisions using copula

The rainfall patterns of neighbouring meteorological subdivisions of India are similar because of similar climatological and geographical characteristics. Analysing the rainfall pattern separately for these meteorological subdivisions may not always capture the correlation and tail dependence. Furthermore, generating the multivariate rainfall data separately may not preserve the correlation. In this study, copula method is used to derive the bivariate distribution of monsoon rainfall in neighbouring meteorological subdivisions. Different Archimedean copulas are used for this purpose and the best copula is selected based on nonparametric test and tail dependence coefficient. The fitted copula is then applied to derive the bivariate distribution, joint return period and conditional distribution. Bivariate rainfall data is generated with the fitted copula and it is observed with the increase of sample size, the generated data is able to capture the correlation as well as tail dependence. The methodology is demonstrated with the case study of two neighbouring meteorological subdivisions of North‐East India: Assam and Meghalaya meteorological subdivision and Nagaland, Manipur, Mizoram and Tripura meteorological subdivision. Copyright © 2010 John Wiley & Sons, Ltd.     

Distribution and Potential Sources of Surface Aerosol Particles in the Yangtze River Delta Region,China

Based on meteorological and pollution data from January 2017 to December 2019, in this paper the long-term distribution of surface aerosol particles, and the interaction between aerosol pollution and meteorological factors in four cities of the Yangtze River Delta (YRD) region is investigated. The long-term observation shows the law of typical aerosol pollution characteristics. Meteorological factors are significantly different during aerosol-polluted and nonpolluted days. The effect of each meteorological factor on aerosol pollution may vary by different seasons and cities. The changes in meteorological factors are not completely consistent during aerosol fine-mode and coarse-mode polluted days. To distinguish the possible sources of surface aerosol particles, the potential source contribution function and concentration-weighted trajectory models are applied to study transport sources. Based on the detailed analyses, this study will provide a reliable basis for further pollution control in the YRD.     

On the effect of climatic factors on groundwater in the Don-Donetsk basin in the second half of the 20th century

The effect of meteorological factors on the formation of groundwater in the Don-Donetsk groundwater basin is studied. Correlations between meteorological factors and groundwater level are determined, and the effect of these factors on groundwater level is assessed.     

Spatial distribution of monthly potential evaporation over mountainous regions: case of the Lhasa River basin,China

Abstract

This paper develops an algorithm for computing spatially-distributed monthly potential evaporation (PE) over a mountainous region, the Lhasa River basin in China. To develop the algorithm, first, correlation analysis of different meteorological variables was conducted. It was observed that PE is significantly correlated with vapour pressure and temperature differences between the land surface and the atmosphere. Second, the Dalton model, which was developed based on the mass transfer mechanism, was modified by including the influence of the related meteorological variables. Third, the influence of elevation on monthly temperature, vapour pressure and wind velocity was analysed, and functions for extending these meteorological variables to any given altitude were developed. Fourth, the inverse distance weighting method was applied to integrate the extended meteorological variables from five stations adjacent to and within the Lhasa River basin. Finally, using the modified Dalton model and the integrated meteorological variables, we computed the spatially-distributed monthly PE. This study indicated that spatially-distributed PE can be obtained using data from sparse meteorological stations, even if only one station is available; the results show that in the Lhasa River basin PE decreases when elevation increases. The new algorithm, including the modified model and the method for spatially extending meteorological variables can provide the basic inputs for distributed hydrological models.

Editor Z.W. Kundzewicz     

Multivariate hydrologic time series analysis

The objective of this study is to establish a multivariate watershed hydrologic system model involving meteorological data as the input and river flow as the output of the system. Monthly hydrological time series of runoff, temperature and precipitation were selected for analysis. A first-order autoregressive-moving average (ARMA) transfer function model was found adequate to describe the multivariate watershed hydrologic system for the monthly runoff and meteorological time series. The results also indicated that the coordinated use of the meteorological and hydrometric data would enhance the accuracy of estimation of runoff characteristics.     

Stochastic modeling of the impact of meteorological conditions on road traffic accidents

Meteorological conditions have become one of the major factors that influence the frequency and severity of motor vehicle collisions in urban environments. In Kuwait, more than 60,000 accidents occur each year, and about 500 people are killed annually on the roads. This paper is intended to investigate the impact of meteorological conditions on traffic accidents in Kuwait. Stochastic models are developed to analyze and examine the influence of meteorological conditions on the level of road accidents. Normal and lognormal probability densities and their associated cumulative density functions are used to model the meteorological conditions in four different seasons. The results indicate that the most influential meteorological condition that causes accidents is temperature during the fall, spring, and winter seasons. In the summer, wind speed is identified as the most influential factor that accounts for the increased road accidents, with temperature as the second highest meteorological condition affecting accidents. Wind speed and humidity are also found to have significant influence on accident level, following temperature in the fall and winter seasons, respectively. Correlation analyses were also applied and supported the findings obtained using stochastic analyses. The results of this study may help local authorities to reduce the number of accidents and help save people lives.     

Leveraging ensemble meteorological forcing data to improve parameter estimation of hydrologic models

As continental to global scale high-resolution meteorological datasets continue to be developed, there are sufficient meteorological datasets available now for modellers to construct a historical forcing ensemble. The forcing ensemble can be a collection of multiple deterministic meteorological datasets or come from an ensemble meteorological dataset. In hydrological model calibration, the forcing ensemble can be used to represent forcing data uncertainty. This study examines the potential of using the forcing ensemble to identify more robust parameters through model calibration. Specifically, we compare an ensemble forcing-based calibration with two deterministic forcing-based calibrations and investigate their flow simulation and parameter estimation properties and the ability to resist poor-quality forcings. The comparison experiment is conducted with a six-parameter hydrological model for 30 synthetic studies and 20 real data studies to provide a better assessment of the average performance of the deterministic and ensemble forcing-based calibrations. Results show that the ensemble forcing-based calibration generates parameter estimates that are less biased and have higher frequency of covering the true parameter values than the deterministic forcing-based calibration does. Using a forcing ensemble in model calibration reduces the risk of inaccurate flow simulation caused by poor-quality meteorological inputs, and improves the reliability and overall simulation skill of ensemble simulation results. The poor-quality meteorological inputs can be effectively filtered out via our ensemble forcing-based calibration methodology and thus discarded in any post-calibration model applications. The proposed ensemble forcing-based calibration method can be considered as a more generalized framework to include parameter and forcing uncertainties in model calibration.     

Reproduction of Pechora runoff hydrographs with the help of a model of heat and water exchange between the land surface and the atmosphere (SWAP)

The potentialities of a procedure for calculating the Pechora River runoff from the pan-Arctic river basin are studied. The procedure is based on the use of a model describing heat and water exchange between the land surface and the atmosphere and two variants of input data sets relying on global databases on meteorological characteristics and land surface parameters and data of standard measurements of meteorological characteristics in combination with parameters of the land surface of the basin, taken from global databases. In both cases, use was made of the method for optimizing part of the most important model parameters, including both land surface parameters and correction factors for some meteorological elements.     

Simplified methods for real-time prediction of storm surge uncertainty: The city of Venice case study

Providing reliable and accurate storm surge forecasts is important for a wide range of problems related to coastal environments. In order to adequately support decision-making processes, it also become increasingly important to be able to estimate the uncertainty associated with the storm surge forecast. The procedure commonly adopted to do this uses the results of a hydrodynamic model forced by a set of different meteorological forecasts; however, this approach requires a considerable, if not prohibitive, computational cost for real-time application. In the present paper we present two simplified methods for estimating the uncertainty affecting storm surge prediction with moderate computational effort. In the first approach we use a computationally fast, statistical tidal model instead of a hydrodynamic numerical model to estimate storm surge uncertainty. The second approach is based on the observation that the uncertainty in the sea level forecast mainly stems from the uncertainty affecting the meteorological fields; this has led to the idea to estimate forecast uncertainty via a linear combination of suitable meteorological variances, directly extracted from the meteorological fields. The proposed methods were applied to estimate the uncertainty in the storm surge forecast in the Venice Lagoon. The results clearly show that the uncertainty estimated through a linear combination of suitable meteorological variances nicely matches the one obtained using the deterministic approach and overcomes some intrinsic limitations in the use of a statistical tidal model.     

精河定点形变受气象因素影响探索

收集整理新疆精河地震台数字化形变资料和该地区的气象要素观测资料,利用相关和回归分析,研究精河定点形变与气象要素之间的关系和气象因素对精河数字化形变观测资料的影响特征,并给出精河数字化形变观测资料与气象要素之间的回归方程.     

临潼西泉井水冒泡现象原因分析

通过对气象因素和地质结构特征的分析，并依据水溶气观测资料，提出与干旱相关的气象因素引起的地下水位变化，是西泉杜家村井水产生冒泡现象的原因     

Meteorological fields from remote infrared sensing

Modern numerical weather prediction techniques require global observations of the atmospheric state and structure parameters. The current meteorological observing system, which is based on radiosonde balloon observations, has extensive gaps. Remote sensing of the Earth atmosphere emission spectrum from satellites can fill these gaps. The physical basis for extracting information on meteorological fields from such remote observations is explained. The problem reduces to that of solving a linear Fredholm equation of the first kind in the presence of noisy data. There is no unique solution to such a problem. The mathematical techniques-inversion techniques-that are currently used to solve the problem are reviewed. Examples are given of meteorological fields obtained from remote infrared sensing from satellites. Results indicate that meteorological parameters such as temperature and geopotential height of constant pressure surfaces can be measured-in conditions of clear skies-to accuracies approaching that of the radiosonde system. Other meterological variables, e.g., water vapor and ozone, can be determined to a lesser degree of accuracy. Applications of the remotely sensed fields are described. Problem areas and suggested solutions are discussed.     

Tree-based threshold modeling for short-term forecast of daily maximum ozone level

This paper proposes a simple class of threshold autoregressive model for purpose of forecasting daily maximum ozone concentrations in Southern California. Linear time series model has been widely considered in environmental modeling. However, this class of models fails to capture the nonlinearity in ozone process and the complexity of meteorological interactions with ozone. In this article, we used the threshold autoregressive models with two classes of regimes; periodic and meteorological regimes. Days in week were used for the periodic regimes and the regression tree method was used to define the regimes as a function of meteorological variables. As the reference model we used the autoregressive model with lagged ozone and various lagged meteorological variables as the covariates. The proposed models were applied to a 3-year dataset of daily maximum ozone concentrations obtained from five monitoring stations in San Bernardino County, CA and their forecast performances were evaluated using an independent year-long dataset from the same stations. The results showed that the threshold models well capture the nonlinearity in ozone process and remove the nonstationarity in model residuals. The threshold models outperformed the non-threshold autoregressive models in day-ahead forecasts. The tree-based model showed slightly better performance than the periodic threshold model.     

太湖富营养化条件下影响蓝藻水华的主导气象因子

利用2004-2018年卫星遥感解译的太湖蓝藻水华信息构建蓝藻综合指数,采用随机森林机器学习算法分析同期气象因子与蓝藻水华综合指数的关系,定量评估影响蓝藻水华的主要气象因子特征变量的重要性度量和贡献率.结果表明,在光、温、水、风等主要气象要素中,气温对蓝藻水华综合指数起着主导的作用,其次是风速和降水,日照时间的影响或可忽略.其中气温条件中重要性度量最大的是年平均气温,其次是冬、春季节的平均气温;风速因子中影响较大的是7月份的平均风速;水分条件中主导因子是9月累计降水量.优选的随机森林模型模拟值与实际蓝藻水华综合指数的变化趋势基本一致,拟合优度为0.91,通过0.01显著性检验,随机森林模型模拟效果较好.用随机森林模型模拟值对太湖蓝藻水华分等级评估,模型模拟精度达到了86.7%,其中5个重度等级年份模型模拟结果完全一致,中度等级的6个年份模型模拟值有5年与之相符,中度以上等级的模拟精度达90.9%,模型能够反映气象因子对蓝藻水华综合指数的综合影响,对中、重度蓝藻水华的模拟效果更好.随机森林模型有助于理解富营养化状态下影响蓝藻水华的主导气象因子,利用气象因子的可预测性可以促进蓝藻水华预测预警能力的提升.     

Sublimation from thin snow cover at the edge of the Eurasian cryosphere in Mongolia

Sublimation from thin snow cover at the edge of the Eurasian cryosphere in Mongolia was calculated using the aerodynamic profile method and verified by eddy covariance observations using multiple‐level meteorological data from three sites representing a variety of geographic and vegetative conditions in Mongolia. Data were collected in the winter and analysed from three sites. Intense sublimation events, defined by daily sublimation levels of more than 0·4 mm, were predominant in their effect on the temporal variability of sublimation. The dominant meteorological elements affecting sublimation were wind speed and air temperature, with the latter affecting sublimation indirectly through the vapour deficit. Seasonal and interannual variations in sublimation were investigated using long‐interval estimations for 19 years at a mountainous‐area meteorological station and for 24 years at a flat‐plain meteorological station. The general seasonal pattern indicated higher rates of sublimation in both the beginning and ending of the snow‐covered period, when the wind speed and vapour deficit were higher. Annual sublimation averaged 11·7 mm at the flat‐plain meteorological station, or 20·3% of the annual snowfall, and 15·7 mm at the site in the mountains, or 21·6% of snowfall. The sum of snow sublimation and snowmelt evaporation represented 17 to 20% of annual evapotranspiration in a couple observation years. Copyright © 2008 John Wiley & Sons, Ltd.     

Assessment of meteorological climate models as inputs for coastal studies

Modeling studies of future changes in coastal hydrodynamics, in terms of storm surges and wave climate, need appropriate wind and atmospheric forcings, a necessary requirement for the realistic reproduction of the statistics and the resolution of small scale features. This work compares meteorological results from different climate models in the Mediterranean area, with a focus on the Adriatic Sea, in order to assess their capability to reproduce coastal meteorological features and their possibility to be used as forcings for hydrodynamic simulations. Five meteorological datasets are considered. They are obtained from two regional climate models, implemented with different spatial resolutions and setups and are downscaled from two different global climate models. Wind and atmospheric pressure fields are compared with measurements at four stations along the Italian Adriatic coast. The analysis is carried out both on simulations of the control period 1960–1990 and on the A1B Intergovernmental Panel for Climate Change scenario projections (2070–2100), highlighting the ability of each model in reproducing the statistical coastal meteorological behavior and possible changes. The importance of simulated global- and regional-scale meteorological processes, in terms of correct spatial resolution of the phenomena, is also discussed. Within the Adriatic Sea, the meteorological climate is influenced by the local orography that controls the strengthening of north-eastern katabatic winds like Bora. Results show indeed that the increase in spatial resolution provides a more realistic wind forcing for the hydrodynamic simulations. Moreover, the chosen setup and the global climate models that drive the regional downscalings appear to play an important role in reproducing correct atmospheric pressure fields. The comparison between scenario and control simulations shows a small increase in the mean atmospheric pressure values, while a decrease in mean wind speed and in extreme wind events is observed, particularly for the datasets with higher spatial resolution. Finally, results suggest that an ensemble of downscaled climate models is likely to provide the most suitable climatic forcings (wind and atmospheric pressure fields) for coastal hydrodynamic modeling.     

滇池蓝藻水华发生频率与气象因子的关系

蓝藻水华暴发是在一定的营养、气候、水文条件和生态环境下形成的藻类过度繁殖和聚集的现象,是水体环境因子(如总氮、总磷、pH值、溶解氧)和气象因子综合作用的结果.然而滇池周年性水华暴发标志着滇池蓝藻水华在当前水质条件下,气象因子为关键影响因子.为了进一步探究滇池蓝藻水华发生与气象因子的规律,本文利用2010-2011年滇池蓝藻水华遥感监测资料与周边地面气象站逐月资料,研究滇池蓝藻水华月发生频率与月气象因子的关系.结果显示,滇池蓝藻水华发生频率与平均气温、最低气温、平均风速、累计日照时数和降雨量等气象因子均表现为显著相关,其中与日照时数和风速呈显著负相关.各因子中与风速的相关系数最高,说明滇池各月蓝藻水华发生频率高低与风速关系最为密切,进一步验证了在具备蓝藻水华发生所需营养盐条件下,水体稳定性对蓝藻水华发生的影响更为重要的结论.以上结果可为科学预测蓝藻水华发生,并采取相应措施减少其带来的影响提供理论依据.