面雨量计算方法的比较分析

本文介绍了计算面雨量的等值线法、数值法、算术平均法.以三种方法计算了1998年8月逐日面雨量,并进行了统计分析,通过分析得出如下几点看法:1.气象上计算面雨量使用算术平均法优于三角形法.2.算术平均法计算面雨量的误差与站点密度密切相关,站点密度越大,计算误差越小.3.算术平均法、三角形法计算的面雨量一般是比等值线法计算的偏高.     

面雨量计算方法的比较分析

本文介绍了计算面雨量的等值线法、数值法、算术平均法。以三种方法计算了1998年8月逐日面雨量，并进行了统计分析，通过分析得出如下几点看法：1、气象上计算面雨量使用算术平均法优于三角形法。2、算术平均法计算面雨量的误差与站点密度密切相关，站点密度越大，计算误差越小。3、算术平均法、三角形法计算的面雨量一般是比等值线法计算的偏高。     

应用3种方法对降水在各尺度空间不均匀性的分离

利用滑动平均、傅里叶分解和小波分析3种方法,对基于COMRPH高分辨卫星降水资料的中国区域2004—2009年夏季平均降水场进行尺度分离,分析了1 000 km以上、500~1 000 km,200~500 km、64~200 km及64 km以下的5个尺度上的降水空间不均匀性,比较了3种方法在降水空间尺度分离上的优缺点和适用范围,并着重分析了长三角地区降水在各尺度空间的不均匀性及其成因。结果表明：空间尺度分离可以得到具有物理意义的降水非均匀分布特征,可以反映出大尺度海陆分布和季风环流、地形、洋流以及城市和城市群等不同尺度因子对降水的影响。空间滑动平均方法简单、易操作,适用性广泛,但是精确度较低。傅里叶分解能进行解析的分离,但是依赖于信号的平稳性,对于小尺度分离存在较大误差。小波分解则可以对非平稳信号进行处理,且具有较好的细节分辨能力。     

Disaggregating radar-derived rainfall measurements in East Azarbaijan,Iran, using a spatial random-cascade model

Theoretical and Applied Climatology - The availability of spatial, high-resolution rainfall data is one of the most essential needs in the study of water resources. These data are extremely...     

Prediction of extreme rainfall event using weather pattern recognition and support vector machine classifier

A major component of flood alert broadcasting is the short-term prediction of extreme rainfall events, which remains a challenging task, even with the improvements of numerical weather prediction models. Such prediction is a high priority research challenge, specifically in highly urbanized areas like Mumbai, India, which is extremely prone to urban flooding. Here, we attempt to develop an algorithm based on a machine learning technique, support vector machine (SVM), to predict extreme rainfall with a lead time of 6–48 h in Mumbai, using mesoscale (20–200 km) and synoptic scale (200–2,000 km) weather patterns. The underlying hypothesis behind this algorithm is that the weather patterns before (6–48 h) extreme events are significantly different from those of normal weather days. The present algorithm attempts to identify those specific patterns for extreme events and applies SVM-based classifiers for extreme rainfall classification and prediction. Here, we develop the anomaly frequency method (AFM), where the predictors (and their patterns) for SVM are identified with the frequency of high anomaly values of weather variables at different pressure levels, which are present before extreme events, but absent for non-extreme conditions. We observe that weather patterns before the extreme rainfall events during nighttime (1800 to 0600Z) is different from those during daytime (0600 to 1800Z) and, accordingly, we develop a two-phase support vector classifier for extreme prediction. Though there are false alarms associated with this prediction method, the model predicts all the extreme events well in advance. The performance is compared with the state-of-the-art statistical technique fingerprinting approach and is observed to be better in terms of false alarm and prediction.     

New climatic zones in Iran: a comparative study of different empirical methods and clustering technique

Theoretical and Applied Climatology - Recently in agricultural and industrial sectors, researchers have started to classify the climate of a region using empirical methods and clustering. This...     

Comparison of statistical clustering techniques for the classification of modelled atmospheric trajectories

In this study, we used and compared three different statistical clustering methods: an hierarchical, a non-hierarchical (K-means) and an artificial neural network technique (self-organizing maps (SOM)). These classification methods were applied to a 4-year dataset of 5 days kinematic back trajectories of air masses arriving in Athens, Greece at 12.00 UTC, in three different heights, above the ground. The atmospheric back trajectories were simulated with the HYSPLIT Vesion 4.7 model of National Oceanic and Atmospheric Administration (NOAA). The meteorological data used for the computation of trajectories were obtained from NOAA reanalysis database. A comparison of the three statistical clustering methods through statistical indices was attempted. It was found that all three statistical methods seem to depend to the arrival height of the trajectories, but the degree of dependence differs substantially. Hierarchical clustering showed the highest level of dependence for fast-moving trajectories to the arrival height, followed by SOM. K-means was found to be the least depended clustering technique on the arrival height. The air quality management applications of these results in relation to PM₁₀ concentrations recorded in Athens, Greece, were also discussed. Differences of PM₁₀ concentrations, during certain clusters, were found statistically different (at 95% confidence level) indicating that these clusters appear to be associated with long-range transportation of particulates. This study can improve the interpretation of modelled atmospheric trajectories, leading to a more reliable analysis of synoptic weather circulation patterns and their impacts on urban air quality.     

Modelling the spatial and temporal distribution of rainfall intensity at local scale

Summary Accurate estimates of rainfall intensity distribution with high temporal and spatial resolution are necessary in most urban hydrological studies, such as planning, simulation or control of sewer networks. Traditionally, these data are obtained from intensity-duration-frequency (IDF) curves at sites with long rainfall intensity time-series, however, little attention is given to the spatial features of precipitation. In this paper, a mathematical model of a local scale storm that takes account of the spatial variability of rainfall and rain-cell movement is proposed. The model has been calibrated with a dense network of raingauges and a long rainfall intensity timeseries (60 years) and its parameters have been calculated for convective storms of return periods up to 15 years with their most frequently-observed rain-cell velocities (1 to 4 m/s).This work has been supported by the DGICYT, Project NAT91-0596.With 6 Figures     

北京降雨过程分型特征及短历时降雨重现期研究

利用北京观象台1961—2004年44 a逐分钟自记降雨资料,采用模糊识别法和统计分析法对北京市降雨过程进行雨型分型,并对5、10、15、20、30、45、60、90、120、180 min共10种不同短历时年最大降雨极值的概率分布利用指数分布、耿贝尔分布和皮尔逊Ⅲ型概率分布函数进行拟合。在此基础上,推算各历时降雨重现期极值。结果表明,暴雨天数年代际差异较大,暴雨量主要集中在50~100 mm范围内;不同月份雨型分布不一致,6月多双峰型,7—9月多单峰型雨型;总体上,北京降雨过程多为单峰型,占80%以上。最大降雨量主要出现在午后至傍晚和凌晨;北京短历时降雨极值的概率分布多数不服从指数分布而服从皮尔逊Ⅲ型分布;重现期降雨极值随历时增加而增大,2 a至100 a重现期内10 min降雨极值取值为10.4~22.6 mm,60 min降雨极值取值为22.3~59.2 mm,180 min取值为53.0~89.8 mm。     

Association between cloudiness and rainfall over Fars province in Iran

Relationship between precipitation sum and cloud properties over Fars province in Iran was analyzed for the cases of light (4 mm), moderate (17 mm), and heavy (62 mm) precipitation. The cloud properties (temperature and pressure at the top, cloud optical thickness and cloud water path) were obtained from satellite data of spectoradiometer MODIS (MODO6). The spatial distribution of rainfall was obtained from the 3-hourly data of TRMM (3B42). The multivariate regression model was developed to predict the spatial distribution of rainfall. A strong significant positive association between the spatial distribution of cloud characteristics and heavy precipitation was found, while no clear correlation was revealed between light precipitation and cloud properties. The developed regression model comprised 64, 47, and 24% of spatial variance of heavy, moderate, and light rainfall, respectively. The influence of cloud water path on the spatial distribution of rainfall dominates.     

Spatial interpolation of monthly and annual rainfall in northeast of Iran

Precipitation maps are the key input to many hydrological models. In this paper different univariate (inverse distance weighing and ordinary kriging) and multivariate (linear regression, ordinary cokriging, simple kriging with varying local mean and kriging with an external drift) interpolation methods are used to map monthly and annual rainfall from sparse data measurements. The study area is Golestan Province, located in northeast of Iran. A digital elevation model is used as complementary information for multivariate approaches. The prediction performance of each method is evaluated through cross-validation and visual examination of the precipitation maps produced. Results indicate that geostatistical algorithms clearly outperform inverse distance weighting and linear regression. Among multivariate techniques, ordinary cokriging or kriging with an external drift yields the smallest error of prediction for months April to October (autumn and winter) for which the correlation between rainfall and elevation is greater than 0.54. For all other months and annual rainfall, ordinary kriging provides the most accurate estimates.     

Correction to: New climatic zones in Iran: a comparative study of different empirical methods and clustering technique

Theoretical and Applied Climatology -     

Temporal and spatial variability of rainfall over Greece

Recent studies have showed that there is a significant decrease in rainfall over Greece during the last half of the pervious century, following an overall decrease of the precipitation at the eastern Mediterranean. However, during the last decade an increase in rainfall was observed in most regions of the country, contrary to the general circulation climate models forecasts. An updated high-resolution dataset of monthly sums and annual daily maxima records derived from 136 stations during the period 1940–2012 allowed us to present some new evidence for the observed change and its statistical significance. The statistical framework used to determine the significance of the slopes in annual rain was not limited to the time independency assumption (Mann-Kendall test), but we also investigated the effect of short- and long-term persistence through Monte Carlo simulation. Our findings show that (a) change occurs in different scales; most regions show a decline since 1950, an increase since 1980 and remain stable during the last 15 years; (b) the significance of the observed decline is highly dependent to the statistical assumptions used; there are indications that the Mann-Kendall test may be the least suitable method; and (c) change in time is strongly linked with the change in space; for scales below 40 years, relatively close regions may develop even opposite trends, while in larger scales change is more uniform.     

Comparison of spatial interpolation methods for gridded bias removal in surface temperature forecasts

All numerical weather prediction (NWP) models inherently have substantial biases, especially in the forecast of near-surface weather variables. Statistical methods can be used to remove the systematic error based on historical bias data at observation stations. However, many end users of weather forecasts need bias corrected forecasts at locations that scarcely have any historical bias data. To circumvent this limitation, the bias of surface temperature forecasts on a regular grid covering Iran is removed, by using the information available at observation stations in the vicinity of any given grid point. To this end, the running mean error method is first used to correct the forecasts at observation stations, then four interpolation methods including inverse distance squared weighting with constant lapse rate (IDSW-CLR), Kriging with constant lapse rate (Kriging-CLR), gradient inverse distance squared with linear lapse rate (GIDS-LR), and gradient inverse distance squared with lapse rate determined by classification and regression tree (GIDS-CART), are employed to interpolate the bias corrected forecasts at neighboring observation stations to any given location. The results show that all four interpolation methods used do reduce the model error significantly, but Kriging-CLR has better performance than the other methods. For Kriging-CLR, root mean square error (RMSE) and mean absolute error (MAE) were decreased by 26% and 29%, respectively, as compared to the raw forecasts. It is found also, that after applying any of the proposed methods, unlike the raw forecasts, the bias corrected forecasts do not show spatial or temporal dependency.     

An analysis of the spatial pattern of summer persistent moderate-to-heavy rainfall regime in Guizhou Province of Southwest China and the control factors

In this study, we investigated spatial and temporal variation patterns of persistent moderate-to-heavy rainfall events in Guizhou Province of southwest China during 1951–2004. We first performed conventional frequency analysis using the annual maximum daily series at 36 weather stations fit to log-normal distribution curves. Then, we examined the frequencies of moderate-to-heavy rainfall events (>?=?20 mm/day) and persistent rainfall events (10–day running sum >?=?100 mm) during the summer season (June through August). Using principal component analysis, we identified various spatial patterns of the rainfall regime and macroscale atmospheric conditions that influence these patterns. It was found that a minor mode of variation in the 500 hPa geopotential height anomaly field over East Asia (the third principal component) had a very good relationship to the dominant regional precipitation regime (Spearman’s correlation coefficient?=?–0.623). This mode of circulation represents the N–S variation of the upper-air pressure gradients over East Asia. During its positive phase, the pressure gradient south of 40°N is reduced and accompanied by a ridge over the East China coast, while the pressure gradient north of this latitude is enhanced. Correspondingly, the study region experiences fewer persistent moderate-to-heavy rainfall events. In its negative phase, the pattern in the 500 hPa geopotential height anomaly field is reversed and the study region experiences more persistent moderate-to-heavy rainfall events. This circulation mode is related to both East Asian and Indian summer monsoons. It is also associated with the northward intrusion of the West Pacific subtropical high, the size of the circumpolar vortex over the Pacific, and the impact of the Tibetan Plateau.     

重庆山地区域气象要素空间插值方法对比

利用重庆地区1999年和2018年气象数据,分别采用薄盘光滑样条、协同克里金、普通克里金、反距离加权4种方法,从年和月两种尺度对气温、降水、太阳总辐射三个要素进行空间插值;采取交叉验证方法,用MAE、MRE、RMSE评估插值精度,确定各要素最优插值方法。结果表明：气温和太阳总辐射最优插值方法为薄盘光滑样条,降水为反距离加权;插值精度上气温、太阳总辐射高值月份优于低值月份,降水则相反,但三个要素均表现出年尺度优于月尺度。MRE检验表明,插值精度为气温>太阳总辐射>降水,1999年年尺度插值精度分别为1.86%、4.60%、6.87%,月尺度插值精度分别为2.79%、5.82%、17.42%;2018年太阳总辐射年、月尺度插值精度分别为3.03%、4.88%,区域站加密后气温、降水年尺度插值精度分别为2.03%、11.20%,月尺度对应插值精度分别为3.20%、23.14%。     

An objective classification of homogeneous rainfall regimes in Sri Lanka

Summary Homogeneous rainfall regimes in Sri Lanka have been devised through multivariate statistical techniques. The nature of previous subjective classifications are reviewed and the need for an objective classification is stressed. Each of the major classifications of rainfall regimes, micro, meso and macro, throw new light on our understanding of the climates of Sri Lanka. New boundaries between dry/wet and dry/arid zones have emerged and are compared with previous examples. Three rainfall dominance regimes are also identified. Finally a brief account of the micro scale rainfall regimes is given.With 5 Figures