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.     

A new framework for multi-site weather generator: a two-stage model combining a parametric method with a distribution-free shuffle procedure

Keeping the spatial correlation of synthetic precipitation data is of utmost importance for hydrological modeling; however, most present weather generators are single-site models and ignore the spatial dependence in daily weather data. Multi-site weather generator is an effective method to solve this problem. This study proposes a new framework for multi-site weather generator denoted as two-stage weather generator (TSWG), in which the first stage generates the single-site precipitation occurrence and amount with a parametric chain-dependent process, and the second stage rebuilds the spatial correlation of the synthetic data using a post-processing, distribution-free shuffle procedure. Results show that TSWG reproduces the statistical parameters of the parametric stage quite well, such as wet days and precipitation amount, and it almost perfectly preserves the inter-station correlations of precipitation occurrence and amount as well as their dependences. Most important, it matches the input requirement of hydrological model and gives satisfactory hydrological simulations. There are several advantages for this new framework: (1) only one correlation matrix and two simple steps, no more input variables or iterative optimizations, are needed to rebuild the spatial correlation; (2) the statistical parameters of the observed data can be easily preserved; (3) the inter-station correlations can be satisfactorily rebuilt. As a post-processing method, the shuffle procedure used to reconstruct the spatial correlation has some potential extensions, such as turning current single-site weather generator into multi-site models and generating future multi-site climate scenarios.     

基于多变量非线性DAE子系统模型的同步发电机励磁汽门综合控制

同步发电机励磁汽门综合控制由多变量非线性DAE子系统模型来描述.本文扩展了非线性常微分方程系统的反步控制方法,研究了其镇定控制问题.首先若被控系统的向量相对阶存在,那么可通过一个微分同胚和反馈控制实现系统的解耦和等价转化.然后基于等价系统,利用反步方法设计其镇定控制器,使得整个闭环系统渐近稳定.最后基于MATLAB进行了仿真,仿真结果验证了本文所提方法的有效性.     

Combining a weather generator and a standard sensitivity analysis method to quantify the relevance of weather variables on agrometeorological models outputs

Sensitivity analysis (SA) is increasingly used to explain models behaviour in response to inputs variation. Agrometeorologists are used to apply standard SA methods only on model parameters because of the difficulty of applying standard sampling techniques to derive series of weather data where each value cannot be sampled independently from those of the neighbouring days and from other variables in the same day. The impact of weather variability on a crop model was here analysed by coupling the Morris SA method to a weather generator. Spring barley in northern Italy was simulated and different outputs considered. Under the explored conditions, parameters involved with temperature generation resulted the most relevant in determining yield and maturity date. Radiation-related parameters were high-ranked for cumulated drainage and actual evapotranspiration. According to the author, this is the first time the sensitivity of a cropping system model to weather variables is quantified using standard SA techniques.     

天气发生器对陕西降水的模拟

以两状态一阶马尔科夫链和两参数GAMMA分布为天气发生器,利用陕西省6个代表站1961—2000年的逐日降水资料,计算了降水转移概率P(WD)、P(WW)及GAMMA分布参数ALPHA和BETA。根据各地不同月份计算的4个降水模拟参数对各地的逐日降水进行模拟,最后利用1971—2000年的实测数据对30a模拟结果进行了检验,结果较好。     

Temporal statistical downscaling of precipitation and temperature forecasts using a stochastic weather generator

Statistical downscaling is based on the fact that the large-scale climatic state and regional/local physiographic features control the regional climate. In the present paper, a stochastic weather generator is applied to seasonal precipitation and temperature forecasts produced by the International Research Institute for Climate and Society(IRI). In conjunction with the GLM(generalized linear modeling) weather generator, a resampling scheme is used to translate the uncertainty in the seasonal forecasts(the IRI format only specifies probabilities for three categories: below normal, near normal, and above normal) into the corresponding uncertainty for the daily weather statistics. The method is able to generate potentially useful shifts in the probability distributions of seasonally aggregated precipitation and minimum and maximum temperature, as well as more meaningful daily weather statistics for crop yields, such as the number of dry days and the amount of precipitation on wet days. The approach is extended to the case of climate change scenarios, treating a hypothetical return to a previously observed drier regime in the Pampas.     

地面碘化银烟炉在广西人工影响天气作业中的应用

通过对广西人工影响天气工作发展的现状、规模及需求,从作业装备、作业方式、气候特征、地理环境等方面进行具体分析,得出结论:地面碘化银烟炉是对广西现有人工影响天气作业方式局限性的有效补充;新形势下,在广西利用地面碘化银烟炉进行人工增雨作业是必要的和可行的。同时,对桂林资源县十里平坦布设面地面碘化银烟炉进行了可行性分析。     

Links between the spatial structure of weather generator and hydrological modeling

Pediatric vehicular hyperthermia (PVH) persists as the leading cause of non-crash, vehicle-related deaths among US children with an average of 37 children dying after being left unattended in motor vehicles each year. Our study aims to demonstrate the microclimate conditions within an enclosed vehicle that lead infants and small children to reach key physiological heat thresholds: uncompensable heating (>37 °C) and heatstroke (>40 °C) under “worst case” conditions. A modified version of the Man-Environment Heat Exchange Model was used to compute the length of time for an infant to reach these thresholds. Several different scenarios were modeled using different initial cabin air temperatures. Assuming full sun exposure and maximum heating rates, an infant may reach uncompensable heating within 5 min and experience hyperthermia anywhere from 15 to 55 min depending on the starting cabin air temperature. The rapid approach of these heat-related thresholds occurs as enclosed vehicles maximize heating and minimize cooling mechanisms, leading to net heating and increase in core body temperatures. Health experts can use this information to support public health messaging on the topic of PVH by explaining why it is important to never leave a child alone in a car and increase the public perception of severity and susceptibility to this ongoing public health issue.     

A stochastic weather generator applied to hydrological models in climate impact analysis

Summary A pattern recognition methodology for estimating local climate variables such as regional precipitation and air temperature using local observation and scenario information provided by GCMs is presented. We have adopted a three step approach: (a) Feature information extraction of climate variables, where weather patterns are expanded by the Karhunen-Loeve (K-L) orthogonal functional series; (b) Grey associative clustering of the feature vectors; (3) Stochastic weather generation by a Monte Carlo simulation. The methods described in this paper were verified using the temperature and precipitation data set of Wuhan, Yangtze river basin and the Shun Tian catchment, Dongjiang River in China. The proposed method yields good stochastic simulations and also provides useful information on temporal or spatial downscaling and uncertainty.With 4 Figures     

Evaluation of the weather generator CLIGEN with daily precipitation characteristics in Korea

The ability of the CLImate GENerator (CLIGEN) weather generator to reproduce daily precipitation characteristics for Korea was assessed on the basis of 55-year long historical daily precipitation records from eight weather stations (Seoul, Incheon, Daegu, Ulsan, Gwangju, Busan, Kangneung, and Jeonju) representing different parts of the Korean peninsula. The basic statistics of daily precipitation (mean, standard deviation, skewness of daily precipitation, number of rainy days, and the lengths of wet/dry period), probability distribution characteristics of daily precipitation (percentiles and maximum value), and the spatial covariance statistic generated by CLIGEN were compared with those derived from the observed weather series. Significance tests were conducted on the difference between the historical and generated statistics with the 1% significance level. The results show that CLIGEN simulates most of the daily precipitation characteristics satisfactorily with a tendency to slightly underestimate the mean and variability of daily precipitation. Especially, the number of rainy days is perfectly reproduced with mean relative error of 0.4% across all the stations. It is also found that the spatial covariance statistic from eight different stations is well reproduced by CLIGEN with respect to the leading EOF mode of summer season daily precipitation.     

Thermal radiative fluxes through inhomogeneous cloud fields: a sensitivity study using a new stochastic cloud generator

We analyze the effects of flat and bumpy top, fractional and internally inhomogeneous cloud layers on large area-averaged thermal radiative fluxes. Inhomogeneous clouds are generated by a new stochastic model: the tree-driven mass accumulation process (tdMAP). This model is able to provide stratocumulus and cumulus cloud fields with properties close to those observed in real clouds. A sensitivity study of cloud parameters is done by analyzing differences between 3D fluxes simulated by the spherical harmonic discrete ordinate method and three “standard” models likely to be used in general circulation models: plane-parallel homogeneous cloud model (PPH), PPH with fractional cloud coverage model (FCPPH) and independent pixel approximation model (IPA). We show that thermal fluxes are strong functions of fractional cloud coverage, mean optical depth, mean geometrical thickness and cloud base altitude. Fluctuations of “in-cloud” horizontal variability in optical depth and cloud-top bumps have negligible effects in the whole. We also showed that PPH, FCPPH and IPA models are not suitable to compute thermal fluxes of flat top fractional inhomogeneous cloud layer, except for completely overcast cloud. This implies that horizontal transport of photon at thermal wavelengths is important when cloudy cells are separated by optically thin regions.     

毕节新一代天气雷达业务运行监控系统简介

介绍当前毕节新一代天气雷达业务运行监控系统的设计思路、监控内容和报警方式等。该系统结合实际工作环境和应用需要开发,能对拼图、产品和基数据进行有效监控,及时发现故障并报警。     

一次秋风天气过程的分析

主要运用实况资料和数值预报资料 ,分析和总结了 2 0 0 2年 8月 8～ 16日影响我省的秋风天气。着重于对与此次秋风天气预报直接相关的数值预报 850hPa温度场预报和两种数值预报物理量 (水汽通量对水汽输送的预报及散度场对垂直运动的预报 )作出分析解释 ,从而检验了数值预报对秋风等灾害性天气的预报准确性。     

Accuracy evaluation of ClimGen weather generator and daily to hourly disaggregation methods in tropical conditions

Daily and sub-daily weather data are often required for hydrological and environmental modeling. Various weather generator programs have been used to generate synthetic climate data where observed climate data are limited. In this study, a weather data generator, ClimGen, was evaluated for generating information on daily precipitation, temperature, and wind speed at four tropical watersheds located in Hawai??i, USA. We also evaluated different daily to sub-daily weather data disaggregation methods for precipitation, air temperature, dew point temperature, and wind speed at M??kaha watershed. The hydrologic significance values of the different disaggregation methods were evaluated using Distributed Hydrology Soil Vegetation Model. MuDRain and diurnal method performed well over uniform distribution in disaggregating daily precipitation. However, the diurnal method is more consistent if accurate estimates of hourly precipitation intensities are desired. All of the air temperature disaggregation methods performed reasonably well, but goodness-of-fit statistics were slightly better for sine curve model with 2?h lag. Cosine model performed better than random model in disaggregating daily wind speed. The largest differences in annual water balance were related to wind speed followed by precipitation and dew point temperature. Simulated hourly streamflow, evapotranspiration, and groundwater recharge were less sensitive to the method of disaggregating daily air temperature. ClimGen performed well in generating the minimum and maximum temperature and wind speed. However, for precipitation, it clearly underestimated the number of extreme rainfall events with an intensity of >100 mm/day in all four locations. ClimGen was unable to replicate the distribution of observed precipitation at three locations (Honolulu, Kahului, and Hilo). ClimGen was able to reproduce the distributions of observed minimum temperature at Kahului and wind speed at Kahului and Hilo. Although the weather data generation and disaggregation methods were concentrated in a few Hawaiian watersheds, the results presented can be used to similar mountainous location settings, as well as any specific locations aimed at furthering the site-specific performance evaluation of these tested models.     

单站暴雨过程分析

该文利用2005-07-08 T20—09 T20 5个时次(20时、02时、08时、14时、20时)的NECP1°×1°全球再分析场资料,并运用GRADS绘图和云图对此中尺度气旋过程进行分析,说明了低空急流的重要作用。     

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.     

一种抗噪声的天气雷达速度退模糊新算法

径向速度模糊是影响多普勒天气雷达资料质量的一个重要因素。针对模糊的问题,提出了一种抗噪声的速度退模糊新算法:基于速度场的空间连续性,采用无损的连续噪声抑制方案,由噪声分离、曲线拟合退模糊和噪声恢复3个步骤组成。用CINRAD-SA雷达4站3a的全部模糊数据(>40000个体扫文件)进行验证,结果表明:(1)抗噪声的速度退模糊新算法明显优于WSR-88D退模糊算法,正确率比其高30%;(2)以体扫文件为单位的退模糊正确率为89.4%,其中,台风、强对流和弱对流的退模糊正确率达92.9%。