Multisite statistical downscaling model for daily precipitation combined by multivariate multiple linear regression and stochastic weather generator

This study provides a multi-site hybrid statistical downscaling procedure combining regression-based and stochastic weather generation approaches for multisite simulation of daily precipitation. In the hybrid model, the multivariate multiple linear regression (MMLR) is employed for simultaneous downscaling of deterministic series of daily precipitation occurrence and amount using large-scale reanalysis predictors over nine different observed stations in southern Québec (Canada). The multivariate normal distribution, the first-order Markov chain model, and the probability distribution mapping technique are employed for reproducing temporal variability and spatial dependency on the multisite observations of precipitation series. The regression-based MMLR model explained 16?%?~?22?% of total variance in daily precipitation occurrence series and 13?%?~?25?% of total variance in daily precipitation amount series of the nine observation sites. Moreover, it constantly over-represented the spatial dependency of daily precipitation occurrence and amount. In generating daily precipitation, the hybrid model showed good temporal reproduction ability for number of wet days, cross-site correlation, and probabilities of consecutive wet days, and maximum 3-days precipitation total amount for all observation sites. However, the reproducing ability of the hybrid model for spatio-temporal variations can be improved, i.e. to further increase the explained variance of the observed precipitation series, as for example by using regional-scale predictors in the MMLR model. However, in all downscaling precipitation results, the hybrid model benefits from the stochastic weather generator procedure with respect to the single use of deterministic component in the MMLR model.     

Stochastic Modeling of the Effects of Large-Scale Circulation on Daily Weather in the Southeastern U.S.

Statistical methodology is devised to model time series of daily weather at individual locations in the southeastern U.S. conditional on patterns in large-scale atmosphere–ocean circulation. In this way, weather information on an appropriate temporal and spatial scale for input to crop–climate models can be generated, consistent with the relationship between circulation and temporally and/or spatially aggregated climate data (an exercise sometimes termed `downscaling'). The Bermuda High, a subtropical Atlantic circulation feature, is found to have the strongest contemporaneous correlation with seasonal mean temperature and total precipitation in the Southeast (in particular, stronger than for the El Niño–Southern Oscillation phenomenon). Stochastic models for time series of daily minimum and maximum temperature and precipitation amount are fitted conditional on an index indicating the average position of the Bermuda High. For precipitation, a multi-site approach involving a statistical technique known as `borrowing strength' is applied, constraining the relationship between daily precipitation and the Bermuda High index to be spatially the same. In winter (the time of greatest correlation), higher daily maximum and minimum temperature means and higher daily probability of occurrence of precipitation are found when there is an easterly shift in the average position of the Bermuda High. Methods for determining aggregative properties of these stochastic models for daily weather (e.g., variance and spatial correlation of seasonal total precipitation) are also described, so that their performance in representing low frequency variations can be readily evaluated.     

Improved multisite stochastic weather generation with applications to historical data in South Korea

In this paper, we propose an improved multisite weather generation with applications to the historical data in South Korea. The proposed method improve the algorithm of Wilks (1998, 1999) by automatically selecting an optimal model that represents precipitation amounts and by providing a procedure to obtain a symmetric positive definite estimate for the covariance matrix. The proposed method is computationally fast, and hence, it can be feasible to handle a massive data. We apply the proposed method to the precipitation and temperature data collected 170 stations in South Korea for the period 1976–2005 which are given by the Korea Meteorological Administration (KMA). Results of the proposed method demonstrate the promising performance in terms of spatial correlation and long-term variation as compared with those of the multisite method of Wilks (1998) and the single-site weather generator.     

High-Frequency and Low-Frequency Variability in Stochastic Daily Weather Generator and Its Effect on Agricultural and Hydrologic Modelling

The high-frequency and low-frequency variabilities, which are often misreproduced by the daily weather generators, have a significant effect on modelling weather-dependent processes. Three modifications are suggested to improve the reproduction of the both variabilities in a four-variate daily weather generator Met&Roll: (i) inclusion of the annual cycle of lag-0 and lag-1 correlations among solar radiation, maximum temperature and minimum temperature, (ii) use of the 3rd order Markov chain to model precipitation occurrence, (iii) applying the monthly generator (based on a first-order autoregressive model) to fit the low-frequency variability. The tests are made to examine the effects of the three new features on (i) a stochastic structure of the synthetic series, and on (ii) outputs from CERES-Wheat crop model (crop yields) and SAC-SMA rainfall-runoff model (monthly streamflow characteristics, distribution of 5-day streamflow) fed by the synthetic weather series. The results are compared with those obtained with the observed weather series.Results: (i) The inclusion of the annual cycle of the correlations has rather ambiguous effect on the temporal structure of the weather characteristics simulated by the generator and only insignificant effect on the output from either simulation model. (ii) Increased order of the Markov chain improves modelling of precipitation occurrence series (especially long dry spells), and correspondingly improves reliability of the output from either simulation model. (iii) Conditioning the daily generator on monthly generator has the most positive effect, especially on the output from the hydrological model: Variability of the monthly streamflow characteristics and the frequency of extreme streamflows are better simulated. (iv) Of the two simulation models, the improvements related to the three modifications are more pronounced in the hydrological simulations. This may be also due to the fact that the crop growth simulations were less affected by the imperfections of the unmodified version of Met&Roll.     

Statistical multisite simulations of summertime precipitation over South Korea and its future change based on observational data

In this study, a weather generator for summer (May 19 – September 15) precipitation over South Korea is developed. Precipitation data for 33 years (1979–2011) observed at 57 stations of Korea Meteorological Administration (KMA) are used to develop a new weather generator. Using the cyclostationary empirical orthogonal function (CSEOF) technique, the observed precipitation data is described as a linear combination of deterministic evolution patterns and corresponding stochastic amplitude (principal component) time series. An autoregressive-moving average (ARMA) model is used to generate one hundred sets of synthetic amplitude time series for the period of 1979–2061 (83 years) with similar statistical properties of the original amplitude time series. Based on these synthetic time series and the annually repeating evolution patterns, one hundred sets of synthetic summer precipitation were generated. Statistical characteristics of the synthetic datasets are examined in comparison with those of the KMA observational record for the period of the observational record. Characteristic changes of synthetic precipitations for a future period are also examined. The seasonal cycle in the synthetic precipitation is reproduced faithfully with typical bimodal peaks of summer precipitation. The spatial correlation patterns of the synthetic precipitation are fairly similar to that of the observational data. The frequency-intensity relationship of the synthetic precipitation also looks similar to that of the observational data. In the future period, precipitation amount increases except in the precipitation range of (0,10) mm day^?1 with nearly no change in the frequency of no-rain days; frequency increase is particularly conspicuous in the range of (100,500) mm day^?1.     

A multi-model ensemble of downscaled spatial climate change scenarios for the Dommel catchment, Western Europe

Regional or local scale hydrological impact studies require high resolution climate change scenarios which should incorporate some assessment of uncertainties in future climate projections. This paper describes a method used to produce a multi-model ensemble of multivariate weather simulations including spatial–temporal rainfall scenarios and single-site temperature and potential evapotranspiration scenarios for hydrological impact assessment in the Dommel catchment (1,350 km²) in The Netherlands and Belgium. A multi-site stochastic rainfall model combined with a rainfall conditioned weather generator have been used for the first time with the change factor approach to downscale projections of change derived from eight Regional Climate Model (RCM) experiments for the SRES A2 emission scenario for the period 2071–2100. For winter, all downscaled scenarios show an increase in mean daily precipitation (catchment average change of +9% to +40%) and typically an increase in the proportion of wet days, while for summer a decrease in mean daily precipitation (−16% to −57%) and proportion of wet days is projected. The range of projected mean temperature is 7.7°C to 9.1°C for winter and 19.9°C to 23.3°C for summer, relative to means for the control period (1961–1990) of 3.8°C and 16.8°C, respectively. Mean annual potential evapotranspiration is projected to increase by between +17% and +36%. The magnitude and seasonal distribution of changes in the downscaled climate change projections are strongly influenced by the General Circulation Model (GCM) providing boundary conditions for the RCM experiments. Therefore, a multi-model ensemble of climate change scenarios based on different RCMs and GCMs provides more robust estimates of precipitation, temperature and evapotranspiration for hydrological impact assessments, at both regional and local scale.     

Postprocessing of simulated precipitation for impact research in West Africa. Part I: model output statistics for monthly data

Rainfall represents an important factor in agriculture and food security, particularly, in the low latitudes. Climatological and hydrological studies which attempt to diagnose the hydrological cycle, require high-quality precipitation data. In West Africa, like in many parts of the world, the density of observational data is low and climate models are needed in order to perform homogeneous and complete data sets. However, climate models tend to produce systematic errors, especially, in terms of rainfall and cloud processes, which are usually approximated by physical parameterizations. In this study, a 25-year climatology of monthly precipitation in West Africa is presented, derived from a regional climate model simulation, and evaluated with respect to observational data. It is found that the model systematically underestimates the rainfall amount and variability and does not capture some details of the seasonal cycle in sub-Saharan West Africa. Thus, in its present form the precipitation climatology is not appropriate to draw a realistic picture of the hydrological cycle in West Africa nor to serve as input data for impact research. Therefore, a statistical model is developed in order to adjust the simulated rainfall data to the characteristics of observed precipitation. Assuming that the regional climate model is much more reliable in terms of atmospheric circulation and thermodynamics, model output statistics is used to correct simulated rainfall by means of other simulated parameters of the near-surface climate like temperature, sea level pressure and wind components. Monthly data is adjusted by a cross-validated multiple regression model. The resulting adjusted rainfall climatology reveals a substantial improvement in terms of the model deficiencies mentioned above. In part II of this publication, the characteristics of simulated daily precipitation is adapted to station data by applying a weather generator. Once the postprocessing approach is trained, it can be extrapolated to simulation periods, for which observational data do not exist like for instance future climate.     

A new daily weather generator to preserve extremes and low-frequency variability

This paper addresses deficiencies of stochastic Weather Generators (WGs) in terms of reproduction of low-frequency variability and extremes, as well as the unanticipated effects of changes to precipitation occurrence under climate change scenarios on secondary variables. A new weather generator (named IWG) is developed in order to resolve such deficiencies and improve WGs performance. The proposed WG is composed of three major components, including a stochastic rainfall model able to reproduce realistic rainfall series containing extremes and inter-annual monthly variability, a multivariate daily temperature model conditioned to the rainfall occurrence, and a suitable multi-variate monthly generator to fit the low-frequency variability of daily maximum and minimum temperature series. The performance of IWG was tested by comparing statistical characteristics of the simulated and observed weather data, and by comparing statistical characteristics of the simulated runoff outputs by a daily rainfall-runoff model fed by the generated and observed weather data. Furthermore, IWG outputs are compared with those of the well-known LARS-WG weather generator. The tested characteristics are a variety of different daily statistics, low-frequency variability, and distribution of extremes. It is concluded that the performance of the IWG is acceptable, better than LARS-WG in the majority of tests, especially in reproduction of extremes and low-frequency variability of weather and runoff series.     

Neural network based daily precipitation generator (NNGEN-P)

Daily weather generators are used in many applications and risk analyses. The present paper explores the potential of neural network architectures to design daily weather generator models. Focusing this first paper on precipitation, we design a collection of neural networks (multi-layer perceptrons in the present case), which are trained so as to approximate the empirical cumulative distribution (CDF) function for the occurrence of wet and dry spells and for the precipitation amounts. This approach contributes to correct some of the biases of the usual two-step weather generator models. As compared to a rainfall occurrence Markov model, NNGEN-P represents fairly well the mean and standard deviation of the number of wet days per month, and it significantly improves the simulation of the longest dry and wet periods. Then, we compared NNGEN-P to three parametric distribution functions usually applied to fit rainfall cumulative distribution functions (Gamma, Weibull and double-exponential). A data set of 19 Argentine stations was used. Also, data corresponding to stations in the United States, in Europe and in the Tropics were included to confirm the results. One of the advantages of NNGEN-P is that it is non-parametric. Unlike other parametric function, which adapt to certain types of climate regimes, NNGEN-P is fully adaptive to the observed cumulative distribution functions, which, on some occasions, may present complex shapes. On-going works will soon produce an extended version of NNGEN to temperature and radiation.     

Parsivel~激光降水粒子谱仪及其在气象领域的应用

降水粒子特性是大气运动和云内微物理过程的综合结果,在云降水物理及人工影响天气领域有着重要的意义。传统的测量方法不适合对大量数据分析寻找规律,德国OTT公司的Parsivel激光降水粒子谱测量系统可以较好解决自动测量难题。该仪器是以激光测量为基础的粒子测量传感器,采用平行激光束和光电管阵列结合,当有降水粒子穿越采样空间时,自动记录遮挡物的宽度,通过穿越时间计算降水粒子的尺度和速度,根据各种参数的综合信息对降水粒子进行分类,并能够以数字形式显示瞬时降水强度、降水粒子总数、累积降水量、降水时的能见度和雷达反射因子,以图形方式显示降水粒子尺度谱、速度谱、降水粒子分类且自动生成天气现象代码,实现天气现象的自动识别。激光降水粒子谱仪主要用于气象水文观测。在雷达气象学领域可用于Z/R关系的拟合修正,比传统的用雨量筒观测数据拟合效果好得多;由降水粒子谱仪测量雨滴的降落速度,可以对天气雷达垂直向上测量的粒子径向速度谱进行校正。人工影响天气的效果检验一直是一个难题,自然降水粒子谱分布形式与人工催化以后的降水粒子谱型理论上应当具有较大的差别,人工增雨作业降水滴谱变化物理响应和降水强度时间变化响应都有明显的区别。如果能够实时检测到这些差别,就能够充分说明人工催化的有效性。未来如果能够进行联网观测记录区域性降水、降雹,就有充分证据表明人影作业的有效性,在定量化作业效果评估以及灾害损失评估等方面应用潜力巨大。利用该仪器已经对一年的自然降水过程进行了连续观测,并将所获得的降水粒子谱、雨滴浓度值随时间变化状况与卫星反演的云顶有效粒子半径时间变化趋势进行了对比,发现有较好的一致性。     

A study on the water vapor transport trend and water vapor source of the Tibetan Plateau

This study presents results of the pilot experiments made with new parametric multi-site multi-variable stochastic daily weather generator (WG) SPAGETTA. The experiments are performed for eight European regions and we focus on spatial characteristics of temperature. The WG is calibrated using the gridded weather data E-OBS. In evaluating the generator, the spatial and temporal temperature autocorrelations derived from the synthetic series were found to perfectly fit the values derived from the calibration data. Next, the WG is validated in terms of the frequency of “spatial hot days” and the annual maximum length of “spatial hot spells”. The results indicate a very good correspondence between characteristics derived from synthetic and calibration data. As part of the validation tests, the performance of the WG is compared with a regional climate model (RCM), which shows a similar performance as the generator. In a final experiment, the use of the WG for the future climate is demonstrated, the WG parameters (including the temperature autocorrelations) calibrated with the observed data are modified according to the RCM-based changes in these parameters. While analyzing synthetic series produced with the modified generator, we discuss partial impacts due to changes in individual WG parameters on the spatial hot days and spells. We show that the impacts are mainly (but not only) due to changes in temperature averages. The projected changes in temperature autocorrelations have also some impacts, larger for the spatial hot spells than for the spatial hot days. Climate change impacts on spatial hot days/spells based on the WG are compared with impacts based on the RCM, and we conclude that the differences are mainly due to simplifying assumptions adopted in our pilot experiment.     

Sensitivity of Ceres-Maize Yields to Statistical Structure of Daily Weather Series

To study impacts of climate variations on cropproduction, the growth models are used to simulateyields in present vs. changed climate conditions.Met&Roll is a four-variate (precipitation amount,solar radiation, minimum and maximum temperatures) stochasticweather generator used to supply synthetic dailyweather series for the crop growth model CERES-Maize.Three groups of experiments were conducted in thisstudy: (1) Validation of Met&Roll reveals some discrepanciesin the statistical structure of synthetic weatherseries, e.g., (i) the frequency of occurrence of longdry spells, extreme values of daily precipitationamount and variability of monthly means areunderestimated by the generator; (ii) correlations andlag-1 correlations among weather characteristicsexhibit a significant annual cycle not assumed by themodel. On the whole, the best fit of the observed andsynthetic weather series is experienced in summermonths. (2) The Wilcoxon test was employed to comparedistributions of maize yields simulated with use ofobserved vs. synthetic weather series. As nostatistically significant differences were detected,it is assumed that the generator imperfections inreproducing the statistical structure of weatherseries negligibly affect the model yields. (3) Thesensitivity of model yields to selectedcharacteristics of the daily weather series wasexamined. Emphasis was placed on the characteristicsnot addressed by typical GCM-based climate changescenarios: daily amplitude of temperature, persistenceof the weather series, shape of the distribution ofdaily precipitation amount, and frequency ofoccurrence of wet days. The results indicate that someof these characteristics may significantly affect cropyields and should therefore be considered in thedevelopment of climate change scenarios.     

Accuracy of TRMM precipitation data in the southwest monsoon region of China

Accurate, high-resolution precipitation data is important for hydrological applications and water resource management, particularly within mountainous areas about which data is presently scarce. The goal of the this study was to assess the accuracy of TRMM 3B43 precipitation data from the southwest monsoon region of China between 1998 and 2011 based on the correlation coefficients, regression, and geostatistical methods. We found a strong correlation between TRMM 3B43 data and observational data obtained from meteorological stations, but the TRMM 3B43 precipitation data was consistently lower than that obtained from the weather stations. The TRMM 3B43 data was significantly different from the data obtained by weather stations located in the northwest and northeast regions of the Hengduan Mountains. The spatial distribution of precipitation obtained from TRMM 3B43 was also different from meteorological data, but the deviation was predominantly distributed along the northern longitude and southern latitude. In addition, the TRMM data more accurately reflected the regional precipitation patterns. Our results indicate that the TRMM 3B43 data should be used for hydrological applications and water resource management at meteorological stations that have a sparse and uneven distribution of observation stations in the southwest monsoon region.

     

Climate change impact assessment on flow regime by incorporating spatial correlation and scenario uncertainty

Flooding risk is increasing in many parts of the world and may worsen under climate change conditions. The accuracy of predicting flooding risk relies on reasonable projection of meteorological data (especially rainfall) at the local scale. The current statistical downscaling approaches face the difficulty of projecting multi-site climate information for future conditions while conserving spatial information. This study presents a combined Long Ashton Research Station Weather Generator (LARS-WG) stochastic weather generator and multi-site rainfall simulator RainSim (CLWRS) approach to investigate flow regimes under future conditions in the Kootenay Watershed, Canada. To understand the uncertainty effect stemming from different scenarios, the climate output is fed into a hydrologic model. The results showed different variation trends of annual peak flows (in 2080–2099) based on different climate change scenarios and demonstrated that the hydrological impact would be driven by the interaction between snowmelt and peak flows. The proposed CLWRS approach is useful where there is a need for projection of potential climate change scenarios.

     

Postprocessing of simulated precipitation for impact research in West Africa. Part II: A weather generator for daily data

Data from global and regional climate models refer to grid cells and, hence, are basically different from station data. This particularly holds for variables with enhanced spatio-temporal variability like precipitation. On the other hand, many applications like for instance hydrological models require atmospheric data with the statistical characteristics of station data. Here, we present a dynamical-statistical tool to construct virtual station data based on regional climate model output for tropical West Africa. This weather generator (WEGE) incorporates daily gridded rainfall from the model, an orographic term and a stochastic term, accounting for the chaotic spatial distribution of local rain events within a model grid box. In addition, the simulated probability density function of daily precipitation is adjusted to available station data in Benin. It is also assured that the generated data are still consistent with other model parameters like cloudiness and atmospheric circulation. The resulting virtual station data are in excellent agreement with various observed characteristics which are not explicitly addressed by the WEGE algorithm. This holds for the mean daily rainfall intensity and variability, the relative number of rainless days and the scaling of precipitation in time. The data set has already been used successfully for various climate impact studies in Benin.     

秦巴山区地质灾害成因及预报预警

利用1955～2007年地质灾害与降水资料以及加密乡镇人工监测站、乡镇自动气象站、水文降水资料,分析秦巴山区地质灾害特点、形成机制、灾害发生与降水强度和降水持续时间的关系,用数理统计方法研究地质灾害不同易发区的临界降水指标,并结合气象部门现行精细化气象预报预警业务流程,研制了秦巴山区地质灾害精细化预报预警思路和制作流程,建成了自动运行的地质灾害预报预警业务系统。2008年应用取得较好的预报效果。     

华东地区夏季不同等级降水变化特征分析

采用华东地区78个气象站点逐日降水资料,根据日降水量的5个等级划分,应用线性趋势分析、相关分析等分析了不同等级降水频率和降水量的空间分布及其变化趋势。结果表明:(1)夏季不同等级降水频率在整个华东地区具有明显的地区差异,区域平均的降水频率由大到小依次为小雨、微量降水、中雨、大雨、暴雨。(2)平均的夏季总降水量呈南多北少的分布,各等级降水对总降水量的贡献率由大到小依次为暴雨、大雨、中雨、小雨,暴雨对夏季总降水量的贡献在某些年份可达50%以上。(3)区域平均的夏季降水日数呈下降趋势,但总降水量却有明显的增大趋势。(4)区域平均的某等级降水频率正异常时,华东地区各地该等级降水频率,亦多表现为正异常,尤其中雨以上等级降水频率异常符号在整个华东地区更为一致。(5)华东区域微量降水和小雨发生频率分别与其他等级降水存在显著的反相关关系,而中雨、大雨、暴雨三者发生频率之间无显著相关。     

一次降雨过程风廓线雷达特征

为应用风廓线雷达监测降水天气,通过对2006年南京地区一次春季降雨过程的边界层风廓线雷达探测数据和自动站雨量数据进行对比分析和相关性统计,研究了降水发生、维持和消亡期间风廓线雷达资料的变化特征,分析风廓线雷达垂直速度、速度谱宽与降雨强度之间的相关性。结果表明：当降雨临近时,风廓线雷达水平风廓线上的空洞逐渐消失,当降雨结束时空洞再次出现,且伴随着低空急流的出现降水明显增强。随着降雨的发生,风廓线雷达产品的垂直速度、速度谱宽和折射率结构常数值均明显增大。整个降水期间,550 m高度层以下的垂直速度与降水量存在显著线性负相关,450-950 m高度层之间的速度谱宽与降水量存在显著线性正相关,可见垂直速度、速度谱宽的变化与降水强度关系密切;当垂直负速度变小或速度谱宽变大时,降水增强的可能性增大。研究结果揭示了风廓线雷达垂直速度、速度谱宽与降雨强度之间的内在联系,可为风廓线雷达应用于降雨天气的监测。     

一次降雨过程风廓线雷达回波特征

为应用风廓线雷达监测降水天气,通过对2006年南京地区一次春季降雨过程的边界层风廓线雷达探测数据与自动站雨量数据进行对比分析和相关性统计,研究降水发生、维持和消亡期间风廓线雷达资料的变化特征,分析风廓线雷达垂直速度、速度谱宽与降雨强度之间的相关性。结果表明：当降雨临近时,风廓线雷达水平风廓线上的空洞逐渐消失,当降雨结束时空洞再次出现,且伴随着低空急流的出现降水明显增强。随着降雨的发生,风廓线雷达产品的垂直速度、速度谱宽和折射率结构常数值均明显增大。整个降水期间,550 m高度层以下的垂直速度与降水量存在显著线性负相关,450—950 m高度层之间的速度谱宽与降水量存在显著线性正相关,可见垂直速度、速度谱宽的变化与降水强度关系密切;当垂直负速度变小或速度谱宽变大时,降水增强的可能性增大。研究结果揭示了风廓线雷达垂直速度、速度谱宽与降雨强度之间的内在联系,可为风廓线雷达应用于降雨天气监测提供参考。