Low Frequency Variability of Rainfall in Southeastern South America

Summary The climatic variations of precipitation, within the period range of 10 and 100 years, in the Southeastern South America (SSA) region are investigated. A gaussian low-pass filter with a cut-off period at 30 years, was applied to annual precipitation records, in order to smooth high frequencies and to make evident the variability within the searched low frequency ranges (longer-than-30 years period). The principal components analysis (PCA) applied to filtered series allowed the determination of two climatic signals, defined by the first and second principal components (PC ₁ and PC ₂). Both signals reflect remarkable changes in the annual precipitation values (a sustained increase in precipitation), over a large region of SSA (great part of Argentine pampas and Uruguay). The PCA applied to the filtered precipitation records for each quarter, which we have associated to seasonal rainfall, allowed the determination of the incidence of PC ₁ and PC ₂ on the variability of the series. In particular, it is in the summer rainfall where the two first seasonal components, (PC1₁ and PC1₂), show a high degree of similarity (correlations >0.8) with the respective annual components PC ₁ and PC ₂. During the spring season only the first component, (PC4₁), shows high correlation with PC ₁; the rest of the seasonal components do not show correlations exceeding the established values of 0.8. Received May 5, 1997 Revised April 26, 1998     

The Alignment of the Mean Wind and Stress Vectors in the Unstable Surface Layer

A significant non-alignment between the mean horizontal wind vector and the stress vector was observed for turbulence measurements both above the water surface of a large lake, and over a land surface (soybean crop). Possible causes for this discrepancy such as flow distortion, averaging times and the procedure used for extracting the turbulent fluctuations (low-pass filtering and filter widths etc.), were dismissed after a detailed analysis. Minimum averaging times always less than 30 min were established by calculating ogives, and error bounds for the turbulent stresses were derived with three different approaches, based on integral time scales (first-crossing and lag-window estimates) and on a bootstrap technique. It was found that the mean absolute value of the angle between the mean wind and stress vectors is highly related to atmospheric stability, with the non-alignment increasing distinctively with increasing instability. Given a coordinate rotation that aligns the mean wind with the x direction, this behaviour can be explained by the growth of the relative error of the u–w component with instability. As a result, under more unstable conditions the u–w and the v–w components become of the same order of magnitude, and the local stress vector gives the impression of being non-aligned with the mean wind vector. The relative error of the v–w component is large enough to make it undistinguishable from zero throughout the range of stabilities. Therefore, the standard assumptions of Monin–Obukhov similarity theory hold: it is fair to assume that the v–w stress component is actually zero, and that the non-alignment is a purely statistical effect. An analysis of the dimensionless budgets of the u–w and the v–w components confirms this interpretation, with both shear and buoyant production of u–w decreasing with increasing instability. In the v–w budget, shear production is zero by definition, while buoyancy displays very low-intensity fluctuations around zero. As local free convection is approached, the turbulence becomes effectively axisymetrical, and a practical limit seems to exist beyond which it is not possible to measure the u-w component accurately.     

Using the Penman-Monteith approach to extrapolate soybean evapotranspiration

Summary Soybean evapotranspiration (ET) measured by the Bowen ratio energy balance method is used as the basis for the comparison of a commonly used semi-empirical (combination equation) and a pure statistical approach. Surface conductance within the combination equation was fitted to measured conductance by the Bowen ratio energy balance method. A multiple linear regression to vapor pressure deficit, solar radiation and wind speed was found. The same parameters were used to predict soybean evapotranspiration through a direct multiple linear regression. Despite a statistically rather poor forecast of surface conductance during 1987 (64% of variance explained) both evapotranspiration estimates agreed well with measured evapotranspiration (r ² = 0.94). However, for a second year (1991) both, the combination equation and the pure statistical approach based on 1987 statistical relationships yielded only 70% of actual evapotranspiration. ActualET was 70% and 80% of potentialET in 1987 and 1991, respectively. These similar relationships to potentialET and the statistically weak prediction of surface conductance indicate poor physiological control of soybeanET or inadequate parametrization.NoteOn leave from the Department of Agricultural Engineering, Agricultural Faculty of Ankara University, Turkey.With 5 Figures     

基于自组织映射神经网络的吉林省春夏期降水统计模拟研究

利用1997—2015年吉林省春夏期(4—7月)逐日气象站地面观测资料,以气温、气压、相对湿度、水汽压、风速为协变量,建立各站点逐日降水量的基于自组织映射神经网络(Self-Organizing Maps,SOM)的统计预测模型;分析吉林省春夏期的主要天气模态,研究逐日降水和天气模态之间的关系,并基于此关系提出逐日降水量的蒙特卡罗模拟方法。结果表明:SOM对天气模态的分型质量较好,邻近天气模态的累积概率分布较相似,距离较远的天气模态累计概率分布差异较大。各天气模态下无降水的概率与日降水量区间宽度的相关系数为-0. 94,显著性水平小于0. 01。基于降水量累积概率分布,20种天气模态被划分成4类,并与降水易发程度和逐日降水量完全对应。在此基础上,对吉林省24个站点逐日降水量进行蒙特卡罗模拟,并进行预测性能分析。平均绝对误差(Mean Absolute Error,MAE)和均方根误差(Root Mean Square Error,RM SE)的中位数分别为3. 12 mm和6. 13 mm,SBrier和Ssig分别为0. 06和0. 51,站点的逐日降水量预测性能整体较好。MAE和RMSE分布呈现东南大西北小,去除降水自然变异差异的影响,所有站点的误差都较小; SBrier和Ssig没有明显的空间分布特征。     

RMAPS_Chem V1.0系统SO2排放清单优化效果评估

RMAPS_Chem V1.0系统是基于WRF_Chem模式建立的服务于华北区域雾霾等污染预报业务的模式系统,该研究着重针对系统中污染排放清单不确定性带来的SO₂浓度预报偏差较大问题,采用EnKF源反演和误差统计订正相结合的方法对排放清单进行了改进,形成了一套优化后的华北区域SO₂排放清单。通过输入初始清单和优化清单对2017年10月进行模拟,并与华北地区616个地面环境监测站观测值进行对比,结果表明:EnKF源反演结合误差统计订正的排放清单优化方法适用于SO₂排放清单的改进,有效降低了清单系统性偏差,针对主要区域及重点城市的检验显示模拟结果接近观测值;排放清单优化后模拟误差显著降低,如河北南部、山东西部至北京一带模式预报均方根误差与归一化平均绝对误差明显下降,区域内站点模拟误差呈正态分布特征,误差分布范围、最大概率出现范围均明显变窄,且最大误差概率明显上升。     

Estimation and medium-term forecasting of anthropogenic carbon dioxide and methane emission in Russia with statistical methods

A statistical method of estimation and medium-term forecasting of greenhouse gas emission is under consideration. It is shown that availability of significant enough and stable statistical relationships between greenhouse gas emission and a number of indicators of the economy development allows operational estimating and forecasting of greenhouse gas emission in the Russian economy with a lead-time out to several years. It is possible to estimate and forecast both the total emission of all greenhouse gasses in terms of CO₂-equivalent and emission of particular greenhouse gases in different sectors of economy. Both miacroeconomic and industrial indicators of the economy development can be used as predictors for building regression forecasting models.     

多普勒雷达资料动态定量估测台风小时降水量的研究

在充分考虑传统的最优化方法和概率配对法优缺点的基础上,使用一种改进的最佳窗概率配对法计算Z-I关系中的系数A和b,得到了雷达测得的基本反射率因子Z和雨量计实时测到的小时降水量I的动态关系.利用温州多普勒雷达体扫资料和浙江省自动雨量站资料,使用该方法对"海棠"(Haitang)和"麦莎"(Matsa)两个台风分别进行了动态计算,得到了不同系数的Z-I关系,进而对两个台风的小时降水量进行了定量估测.使用变分技术对估测的小时降水量进行了校准.结果表明,不同台风Z-I关系的系数差别较大,因而造成台风小时降水量的很大不同.使用雷达基本反射率来估测台风小时降水量,能够清楚表现出台风的螺旋雨带和其中的中小尺度雨团,估测的台风小时降水量与实况基本接近.经过变分校准的估测降水量可以较好地表现出台风雨带与地面中尺度流场动力结构的对应关系.误差统计分析表明,变分校准后的估测台风小时降水量要明显好于变分校准前的估测台风小时降水量.变分校准法既保留了雷达估测台风小时降水量的分布特征,又使估测的台风小时降水雨量与实况的误差明显减小.     

定量测量降水量的回波结构自适应算法研究

为提高临近预报中对流性降水估测的准确性,分析了统计法估算降水的局限性。依据降水物理机制和降水回波结构,研究雷达反射率因子定量测量降水量(QPE-Qualitative Precipitation Estimate)的自适应算法,旨在减少由于雨滴谱变化导致的Z-I关系不稳定所引起的降水量测量误差。同时考虑组合反射率因子RC和垂直液态水含量VIL两个参量对降水的影响,建立适于城市区域的动态自适应的Z-I关系,改进对强降水严重低估的问题。     

On the determination of zero-plane displacement and roughness length for flow over forest canopies

This paper discusses the importance of the aerodynamic characteristics of forest and other similar canopies to modelling of boundary-layer flow and to estimating the diffusivity coefficients of turbulence transfer mechanisms over such canopies.The hypothesis of Marunich (1971) reported by Tajchman (1981) that the zero-plane displacement, d, equals the upward displacement of the flow trajectory, is critically examined. It is concluded that Marunich's hypothesis is conceptually incorrect and that calculations of d based on Marunich's hypothesis are inherently in error.This paper presents a method based on the mass conservation principle and uses wind profiles in and above a forest canopy as the sole input for determining d, z ₀ and u _*.Sensitivities of calculated results to measurements errors of wind profile data are evaluated. It is found that an error of less than 1% in wind in the logarithmic regime above the canopy can introduce up to 100% errors in calculated values of d, z ₀ and u _*. It is also found that the high sensitivity to wind data accuracy, characteristic of the present method, can be used as a guide for the selection of high quality canopy wind data.     

Comparative measurement of stem flow and transpiration in cotton

Summary Whole-plant transpiration (T) measurements have many applications, but appropriate methods have remained somewhat elusive. A new method using a constant power heat balance gauge, wherein the xylem mass flow rate is calculated from a balance of heat into and out of a stem, has been shown to provide accurate stem flow measurements. To evaluate the applicability of this promising method to field experiments, cotton (Gossypium hirsutum L. GP 3774) stem flow measurements were compared withT measured from a weighing lysimeter. Initially to confirm method accuracy, stem flow values were compared in the glasshouse withT values determined by mass measurements of a potted plant. The root mean square error (RMSE) between the daylight losses from both (n = 16) was 8.6% of the mean measuredT values. In the field, hourly stem flow and lysimeterT values were also similar, but there was a large variation in stem flow values among the different plants. To account for differences in plant size between the plants with gauges and all lysimeter plants, stem flow values were adjusted using a stem area ratio factor, which adjusted values, on the average for the season, by 25%. Before adjustment, daylight stem flow totals were consistently greater than lysimeterT values. After adjustment, the means differed by only 9%, and theRMSE was reduced from 129 to 69 g plant^–1 d^–1. The coefficient of variation of daylight stem flow totals increased throughout the season. In the glasshouse, method accuracy was comparable (errors < ± 10%) to what has been previously determined. In the field, determining method accuracy was confounded by plant-to-plant variability and, possibly, by errors, unique to the gauge design used in this study, at high flow rates. Thus, this method can provide accurate flow measurements from individual herbaceous plants and is a valuable technique for many applications.With 7 Figures     

Estimating the error in vertically interpolated forecast‐observation differences: U‐ and v‐wind components

Abstract

Data assimilation in numerical weather forecasting corrects current forecast values by subtracting a portion of interpolated forecast‐minus‐observation differences at the points of a three‐dimensional grid. Deviations used in updating a forecast data field are forecast errors obtained or derived from observations available at update time. When observations are missing at mandatory levels, construction of full vertical soundings by interpolation introduces extraneous errors. The present paper is concerned with determination of the error in vertical extrapolations of surface winds, and of aircraft and satellite cloud‐tracked winds. In addition it examines the effect on accuracy of using location‐specific statistics compared to averaged statistics as the basis for the interpolation weighting scheme and compares errors of one‐ and two‐variable interpolations.

Interpolation accuracy tests demonstrate the influence of the interpolation scheme on the quality of interpolated information used in forecast updating. The results show that the level of accuracy exceeds the benchmark provided by monthly mean forecast error values only with bivariate interpolation of wind components from off‐level data sources.     

Comparison of artificial intelligence techniques for prediction of soil temperatures in Turkey

Soil temperature is a meteorological data directly affecting the formation and development of plants of all kinds. Soil temperatures are usually estimated with various models including the artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models. Soil temperatures along with other climate data are recorded by the Turkish State Meteorological Service (MGM) at specific locations all over Turkey. Soil temperatures are commonly measured at 5-, 10-, 20-, 50-, and 100-cm depths below the soil surface. In this study, the soil temperature data in monthly units measured at 261 stations in Turkey having records of at least 20 years were used to develop relevant models. Different input combinations were tested in the ANN and ANFIS models to estimate soil temperatures, and the best combination of significant explanatory variables turns out to be monthly minimum and maximum air temperatures, calendar month number, depth of soil, and monthly precipitation. Next, three standard error terms (mean absolute error (MAE, °C), root mean squared error (RMSE, °C), and determination coefficient (R ²)) were employed to check the reliability of the test data results obtained through the ANN, ANFIS, and MLR models. ANFIS (RMSE 1.99; MAE 1.09; R ² 0.98) is found to outperform both ANN and MLR (RMSE 5.80, 8.89; MAE 1.89, 2.36; R ² 0.93, 0.91) in estimating soil temperature in Turkey.

     

The operational weather radar of Fossalon di Grado (Gorizia, Italy): accuracy of reflectivity and differential reflectivity measurements

Summary The error structure of radar measurements should be accurately known in order to provide reliable estimates for a number of quantitative meteorological applications, from rainfall rate estimation to cloud microphysics. The aim of this paper is to give a detailed characterization of Z _H and Z _DR measurements obtained by the weather radar of Fossalon di Grado (Gorizia, Italy). Vertical-looking observations are used to determine the system bias on differential reflectivity and to estimate the measurement error on both Z _H and Z _DR in the rain medium. It is estimated that no bias is affecting Z _DR and the accuracy of Z _H and Z _DR is 0.8 and 0.1 dB, respectively. A similar evaluation is done in the rain medium at larger ranges with the antenna pointing at low elevation angles. The long time stability of the absolute reflectivity calibration is also established by radar-rain gage inter-comparison over almost 200 hours of precipitation data collected during nearly two years. Received June 21, 2001 Revised November 13, 2001     

复杂地形下C波段雷达定量降水估计算法

C波段雷达定量降水估计(QPE)精度受到很多因素的影响,主要包括:(1)雷达标定,(2)非气象回波的干扰,(3)降水物垂直空间变化,(4)地形或地物的严重遮挡,(5)Z-R关系的代表性,(6)雷达拼图的质量,(7)雷达观测回波衰减等.文中雷达定量降水估计算法基于陕西省C波段天气雷达展开,从雷达探测数据质量控制、地形遮挡...     

The influence of cloud cover index on the accuracy of solar irradiance model estimates

Summary Cloud cover index (CCI) obtained from satellite images contains information on cloud amount and their optical thickness. It is the chief climate data for the assessment of solar energy resources in most radiative transfer models, particularly for the model BRASIL-SR that is currently operational at CPTEC. The wide range of climate environments in Brazil turns CCI determination into a challenging activity and great effort has been directed to develop new methods and procedures to improve the accuracy of these estimations from satellite images (Martins 2001; Martins et al. 2003a; Ceballos et al. 2004). This work demonstrates the influence of CCI determination methods on estimates of surface solar irradiances obtained by the model BRASIL-SR comparing deviations among ground data and model results. Three techniques using visible and/or thermal infrared images of GOES-8 were employed to generate the CCI for input into the model BRASIL-SR. The ground-truth data was provided by the solar radiation station located at Caicó/PE, in Brazilian Northeast region, which is part of the UNEP/GEF project SWERA (Solar and Wind Energy Resources Assessment). Results have shown that the application of the bi-spectral techniques have reduced mean bias error up to 66% and root mean square error up to 50% when compared to the usual technique for CCI determination based on the straightforward determination of month-by-month extremes for maximum and minimum cloud states. Correspondence: Fernando R. Martins, Brazilian Institute for Space Research (INPE), P.O. Box 515, S?o José dos Campos, 12245-970 S?o Paulo, Brazil     

利用Nimbus-7行星反射率观测资料估算青藏高原地区的总辐射

本文利用1982年8月—1983年7月期间Nimbus-7行星反射率的月平均资料用“物理模式方法(Raschke and Preuss,1979)”估算了青藏高原及其邻近地区月平均地面总辐射的分布。得到的结果较好地反映了纬度、海拔与云量三个主要因子对总辐射分布的支配作用。根据高原及其邻近地区23个测站的资料,对总辐射的计算值与观测值进行了比较。统计分析表明,相关系数f=0.90,标准误差RMS=27w/m~2,平均绝对误差ABS=21w/m~2(相当于有效平均总辐射的11.7％)。文中还对误差来源和敏感性问题进行了讨论。     

利用ERA-Interim数据构建海区大气加权平均温度模型

针对海洋区域尤其远海缺乏探空资料,且常用的Bevis经验模型在海区存在模型系统误差的问题,研究基于ERA-Interim再分析资料构建海洋区域E-T_m回归模型。利用不同时间的ERA-Interim数据及近海探空资料,对E-T_m回归模型的拟合效果及预报能力进行检验,并与常规T_m获取方法进行比较,结果表明,E-T_m回归模型拟合效果较好,相比Bevis经验模型,其预报稳定性及精度更高;在典型海区与基于探空资料建立的本地化模型进行了预报精度的比较,结果表明,2. 5°×2. 5°分辨率的E-T_m模型与探空资料建立的本地化模型精度相当,可以在无法建立探空站的海域进行使用,仅存在1%的平均转换误差。     

Hourly predictive Levenberg–Marquardt ANN and multi linear regression models for predicting of dew point temperature

In this study, the ability of two models of multi linear regression (MLR) and Levenberg–Marquardt (LM) feed-forward neural network was examined to estimate the hourly dew point temperature. Dew point temperature is the temperature at which water vapor in the air condenses into liquid. This temperature can be useful in estimating meteorological variables such as fog, rain, snow, dew, and evapotranspiration and in investigating agronomical issues as stomatal closure in plants. The availability of hourly records of climatic data (air temperature, relative humidity and pressure) which could be used to predict dew point temperature initiated the practice of modeling. Additionally, the wind vector (wind speed magnitude and direction) and conceptual input of weather condition were employed as other input variables. The three quantitative standard statistical performance evaluation measures, i.e. the root mean squared error, mean absolute error, and absolute logarithmic Nash–Sutcliffe efficiency coefficient $ \left( {\left| {{\text{Log}}({\text{NS}})} \right|} \right) $ were employed to evaluate the performances of the developed models. The results showed that applying wind vector and weather condition as input vectors along with meteorological variables could slightly increase the ANN and MLR predictive accuracy. The results also revealed that LM-NN was superior to MLR model and the best performance was obtained by considering all potential input variables in terms of different evaluation criteria.     

Numerical simulations of Asian dust events: A Lagrangian Dust Model and its applications

An uni-modal Lagrangian Dust Model (LDM) was developed to simulate the dust concentrations and source-receptor (SR) relationships for recent Asian dust events that occurred over the Korean Peninsula. The following dust sources were used for the S-R calculation in this study: S-I) Gurbantunggut desert, S-II) Taklamakan desert, S-III) Tibetan Plateau, S-IV) Mu Us Desert, S-V) Manchuria, and S-VI) Nei Mongol and Gobi Desert. The following two 8-day dust simulation periods were selected for two case studies: (Period A) March 15–22, 2011, and (Period B) April 27–May 4, 2011. During two periods there were highly dense dust onsets observed over a wide area in Korea. Meteorological fields were generated using the WRF (Weather Research and Forecasting) meteorological model, and Lagrangian turbulent properties and dust emission were estimated using FLEXPART model and ADAM2 (Asian Dust Aerosol Model 2), respectively. The simulated dust concentrations are compared with point measurements and Eulerian model outputs. Statistical techniques were also employed to determine the accuracy and uncertainty associated with the model results. The results showed that the LDM compared favorably well with observations for some sites; however, for most sites the model overestimated the observations. Analysis of S-R relationships showed that 38–50% of dust particles originated from Nei Mongol and the Gobi Desert, and 16–25% of dust particles originated from Manchuria, accounting for most of the dust particles in Korea. Because there is no nudging or other artificial forcing included in the LDM, higher error indicators (e.g., root mean square error, absolute gross error) were found for some sites. However, the LDM was able to satisfactorily simulate the maximum timing and starting time of dust events for most sites. Compared with the Eulerian model, ADAM2, the results of LDM found pattern correlations (PCs) equal to 0.78-0.83 and indices of agreement (IOAs) greater than 0.6, suggesting that LDM is capable of estimation of dust concentrations with the quantitative information on the S-R relationships that can be easily obtained by LDM.