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1.
Because drought is a very common and widespread natural disaster, it has attracted a great deal of academic interest. Based on 12-month time scale standardized precipitation indices (SPI12) calculated from precipitation data recorded between 1960 and 2015 at 22 weather stations in the Tarim River Basin (TRB), this study aims to identify the trends of SPI and drought duration, severity, and frequency at various quantiles and to perform cluster analysis of drought events in the TRB. The results indicated that (1) both precipitation and temperature at most stations in the TRB exhibited significant positive trends during 1960–2015; (2) multiple scales of SPIs changed significantly around 1986; (3) based on quantile regression analysis of temporal drought changes, the positive SPI slopes indicated less severe and less frequent droughts at lower quantiles, but clear variation was detected in the drought frequency; and (4) significantly different trends were found in drought frequency probably between severe droughts and drought frequency.  相似文献   

2.
应用国家基本观测站资料、自动站逐时降水资料,基于客观统计检验方法,针对降水(12h、24h累积雨量)、近地面要素(2m温度、10m风)和高空要素(风场、温度场、高度场),分别评估SWCWARMS模式和GRAPES模式对2015年西南地区预报能力,得到如下几点结论:(1)SWCWARMS模式降水ETS评分高于GRAPES模式,除24h小雨外SWCWARMS模式偏差值均高于GRAPES模式,两个模式在不同预报时效内对中雨、大雨、暴雨都表现一定程度的空报;(2)12h降水分段评分上,SWCWARMS模式TS评分均高于GRAPES模式,但SWCWARMS模式预报降水范围过大,随着预报时效增长空报多于GRAPES模式;SWCWARMS模式中雨和大雨空报大于其它量级降水,GRAPES模式对大暴雨漏报较多其它量级降水表现为空报;(3)两模式对高度场和温度场预报优于风场,对对流层中层预报优于中低层,SWCWARMS模式对高度场和温度场预报优于GRAPES模式,夏半年SWCWARMS模式均方根误差小于GRAPES模式;(4)两模式都表现出2m温度均方根误差在秋季增加而春季减小这一特征,SWCWARMS模式近地面要素均方根误差均小于GRAPES模式。   相似文献   

3.
Estimation of pan evaporation (E pan) using black-box models has received a great deal of attention in developing countries where measurements of E pan are spatially and temporally limited. Multilayer perceptron (MLP) and coactive neuro-fuzzy inference system (CANFIS) models were used to predict daily E pan for a semi-arid region of Iran. Six MLP and CANFIS models comprising various combinations of daily meteorological parameters were developed. The performances of the models were tested using correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE) and percentage error of estimate (PE). It was found that the MLP6 model with the Momentum learning algorithm and the Tanh activation function, which requires all input parameters, presented the most accurate E pan predictions (r?=?0.97, RMSE?=?0.81?mm?day?1, MAE?=?0.63?mm?day?1 and PE?=?0.58?%). The results also showed that the most accurate E pan predictions with a CANFIS model can be achieved with the Takagi–Sugeno–Kang (TSK) fuzzy model and the Gaussian membership function. Overall performances revealed that the MLP method was better suited than CANFIS method for modeling the E pan process.  相似文献   

4.
Trends in evaporation of a large subtropical lake   总被引：1，自引：0，他引：1  
In order to further investigate the capability of the Standardized Precipitation Index (SPI) to identify flood/drought events, monthly precipitation data from 26 precipitation stations and monthly discharge data from four hydrological stations from 1960 to 2006 in the Minjiang River basin were used to analyze the correlations between multiple time scales of the SPI and river discharge. The SPI series that had a maximum correlation with discharge was chosen to detect flood/drought events in the basin, and the results were compared to historical flood/drought events. The results indicated the following. (1) High Pearson correlations between the SPI and discharge were identified at shorter time scales (1 to 3 months), and the maximum correlation was found on the time scale of 2 months. (2) Five floods among the six largest historical flood events in the Minjiang River basin were identified with the 2-month SPI, but the SPI does have shortcomings in identifying more general floods. The SPI also identified major drought events that were consistent with historical data. This demonstrates that the 2-month SPI is an effective indicator for the identification of major flood/drought events in the Minjiang River basin.  相似文献   

5.
Western South America is subject to considerable inter-annual variability due to El Ni?o–Southern Oscillation (ENSO) so forecasting inter-annual variations associated with ENSO would provide an opportunity to tailor management decisions more appropriately to the season. On one hand, the self-organizing maps (SOM) method is a suitable technique to explore the association between sea surface temperature and precipitation fields. On the other hand, Wavelet transform is a filtering technique, which allows the identification of relevant frequencies in signals, and also allows localization on time. Taking advantage of both methods, we present a method to forecast monthly precipitation using the SOM trained with filtered SST anomalies. The use of the SOM to forecast precipitation for Chillan showed good agreement between forecasted and measured values, with correlation coefficients (r 2) ranging from 0.72 to 0.91, making the combined use filtered SST fields and SOM a suitable tool to assist water management, for example in agricultural water management. The method can be easily tailored to be applied in other stations or to other variables.  相似文献   

6.
This study employed two artificial neural network (ANN) models, including multi-layer perceptron (MLP) and radial basis function (RBF), as data-driven methods of hourly air temperature at three meteorological stations in Fars province, Iran. MLP was optimized using the Levenberg–Marquardt (MLP_LM) training algorithm with a tangent sigmoid transfer function. Both time series (TS) and randomized (RZ) data were used for training and testing of ANNs. Daily maximum and minimum air temperatures (MM) and antecedent daily maximum and minimum air temperatures (AMM) constituted the input for ANNs. The ANN models were evaluated using the root mean square error (RMSE), the coefficient of determination (R 2) and the mean absolute error. The use of AMM led to a more accurate estimation of hourly temperature compared with the use of MM. The MLP-ANN seemed to have a higher estimation efficiency than the RBF ANN. Furthermore, the ANN testing using randomized data showed more accurate estimation. The RMSE values for MLP with RZ data using daily maximum and minimum air temperatures for testing phase were equal to 1.2°C, 1.8°C, and 1.7°C, respectively, at Arsanjan, Bajgah, and Kooshkak stations. The results of this study showed that hourly air temperature driven using ANNs (proposed models) had less error than the empirical equation.  相似文献   

7.
利用NCEP的气候预报系统第二版(CFSv2)提供的逐日降水模式资料,采用集合预报方法开展区域性夏季降水预报,使用出入梅日期均方根误差(RMSE)、准确率(ACCU),梅雨期长度均方根误差(RMSE)及梅雨雨强距平符号一致率(Pc)等3种方法评估模式资料对湖北省梅雨特征量的预报能力。结果表明:入梅预报提前13 d的ACCU可达0.5以上、RMSE小于3 d,出梅预报提前14 d的ACCU可达0.5以上、RMSE小于3 d,梅雨期长度预报提前14天的RMSE小于5 d,梅雨雨强预报提前14 d的Pc可达0.5以上。梅雨特征量总体预报时效为14 d左右,CFSv2模式资料对区域性夏季降水在梅雨延伸期时段表现出一定的预报技巧。  相似文献   

8.
Weather forecasts by any forecast system are verified using either distributions-oriented or measures-oriented forecast verification measures. Both the forecast verification schemes represent different aspects of the forecast quality, and advantages of them can be utilized to get better insight and to identify particular strengths (deficiencies) in the forecast performance of any forecast system. Keeping this in view, multi-faced verification (binary and continuous) of quantitative precipitation forecasts for consecutive 3 days by a Regional Meso-scale Weather Simulation Model (MM5 Model) has been carried out to get complete insight into its performance. The MM5 model forecasts at 10-km resolution for 792 days of six winters (winter 2003/2004 to winter 2008/2009) are compared with the observational data of six stations in the complex topography of Northwest Himalaya (NWH) in India. The model forecasts are verified using binary categorical forecast verification measures such as Probability of Detection, False Alarm Rate, Miss Rate, Correct Non-occurrence, Critical Success Index and Percent correct, and continuous forecast verification measures such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). BIAS is computed to know over-forecast/under-forecast tendency of a precipitation day (PT day) by the MM5 model. MAE (RMSE) of the MM5 model is computed separately for all days, PT days and no precipitation days (NPT days). MAE (RMSE) of PT days is found to be relatively larger as compared to NPT days and all days. These findings indicate that MAE (RMSE) computed separately for all days, PT days and NPT days provides better insight into the performance of the MM5 model. Results also suggest that the MM5 model shows reasonably good performance for binary forecasts (PT days/NPT days) for day 1 (0–24 h), day 2 (24–48 h) and day 3 (48–72 h). However, large errors are seen in predicting the observed precipitation amount of PT days over NWH.  相似文献   

9.
使用ERA-interim和FNL再分析资料分别驱动WRF,对2013年7月12—13日的一次暴雨过程进行数值模拟,详细比较了WRF模拟结果之间的差异。结果表明:(1)两种资料在次天气尺度上存在着较大差异,并由此造成了模拟结果之间的差异,ERA-interim作为初始场对降水的模拟优于FNL资料,反映了WRF对初、边界条件的敏感性;(2)从区域总降水量来看,湿度场扰动对降水量的影响最大,其次是风场扰动和温度场扰动,最小的是侧边界扰动;(3)从降水误差来看,湿度场扰动引起的降水误差最大,在积分20 h内风场扰动的降水误差大于温度场,积分21~24 h则相反,侧边界扰动引起的降水误差在前期比较小且增长缓慢,积分一段时间之后与单个气象要素扰动引起的降水误差相当。  相似文献   

10.
我国地面降水的分级回归统计降尺度预报研究   总被引：2，自引：1，他引：1       下载免费PDF全文
利用TIGGE资料中欧洲中期天气预报中心(ECMWF,the European Centre for Medium-Range Weather Forecasts)、日本气象厅(JMA,the Japan Meteorological Agency)、美国国家环境预报中心(NCEP,the National Centers for Environmental Prediction)以及英国气象局(UKMO,the UK Met Office)4个中心1~7 d预报的日降水量集合预报资料,并以中国降水融合产品作为"观测值",对我国地面降水量预报进行统计降尺度处理。采用空间滑动窗口增加中雨和大雨雨量样本,建立分级雨量的回归方程,并与未分级雨量的统计降尺度预报进行对比。结果表明,对于不同模式、不同预报时效以及不同降水量级,统计降尺度的预报技巧改进程度不尽相同。统计降尺度的预报技巧依赖于模式本身的预报效果。相比雨量未分级回归,雨量分级回归的统计降尺度预报与观测值的距平相关系数更高,均方根误差更小,不同量级降水的ETS评分明显提高。对雨量分级回归统计降尺度预报结果进行二次订正,可大大减少小雨的空报。  相似文献   

11.

Relations between Tibetan Plateau precipitation and large-scale climate indices are studied based on the Standardized Precipitation Index (SPI) and the boreal summer season. The focus is on the decadal variability of links between the large-scale circulation and the plateau drought and wetness. Analysis of teleconnectivity of the continental northern hemisphere standardized summer precipitation reveals the Tibetan Plateau as a major SPI teleconnectivity center in south-eastern Asia connecting remote correlation patterns over Eurasia. Employing a moving window approach, changes in covariability and synchronizations between Tibetan Plateau summer SPI and climate indices are analyzed on decadal time scales. Decadal variability in the relationships between Tibetan Plateau summer SPI and the large-scale climate system is characterized by three shifts related to changes in the North Atlantic, the Indian Ocean, and the tropical Pacific. Changes in the North Atlantic variability (North Atlantic Oscillation) result in a stable level of Tibetan Plateau summer SPI variability; the response to changes in tropical Pacific variability is prominent in various indices such as Asian monsoon, Pacific/North America, and East Atlantic/Western Russia pattern.

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12.
Climatology of water excesses and shortages in the La Plata Basin   总被引：1，自引：0，他引：1  
This study presents a multitemporal climatology of water excess and shortage during the 20th century in the La Plata Basin. The climatology is based on 0.5o?×?0.5o grid across the region. We transform monthly precipitation series for each point into index series at different time scales using the Standardized Precipitation Index (SPI). A month is under water excess (shortage) conditions at different time scales (i?=?6, 9, 12, and 18 months), when SPI[i](j)?>?1.5 (SPI[i](j)?<?1.5), where j is the current month. Trends in precipitation were determined using mean regional series of average values over the entire basin. A month when more than 30% of the total basin is under water excesses (shortages) is defined as an excess (shortage) critical month. From the vulnerability point of view, we analyzed the occurrence of critical months. The number of excess critical months increase with time scale of index, and almost all the critical months occurred after 1950 as a consequence of the low-frequency precipitation pattern. That means a noticeable increase in the vulnerability to extended excesses (more than 30% of the area under water excesses) after 1950, especially over the Upper Paraná and the Uruguay basins. For shortage critical months, the behavior depends on time scales. At large time scale (18 and 12 months), almost all the shortage critical months occurred in the period 1901–1950 and only at shorter time scale (9 and 6 months), some critical months appeared after 1950. That means a noteworthy decrease in the basin vulnerability to extended water shortage after 1950 and a moderate decrease in vulnerability to generalized shortage. If we analyze the frequency and mean duration of water excess and shortage events across the basin, we can appreciate that there is a tendency to relate higher frequency regions with regions with lower mean duration events, and conversely.  相似文献   

13.
利用基于目标诊断的空间检验方法(MODE)和时空检验方法(MTD)评估了华南3 km高分辨率区域数值模式(GRAPES_GZ3 km)对2019年海南岛暖季非台降水预报性能, 结果显示: (1)模式24 h累积降水预报的空间分布范围偏大、降水强度偏强; (2)模式逐小时降水预报的平均质心总体偏西和偏北, 降水出现时间总体偏早1~3 h, 结束时间总体偏晚2~4 h, 降水持续时间偏长; 预报的降水目标数量偏多, 与实况一致均存在着主峰和次峰形态的昼夜分布特征, 但预报的昼间主峰出现时间比实况偏早2 h; 预报的短时强降水出现频次总体偏多。相对于传统的预报和观测点对点检验评估方法, MODE和MTD方法具有捕捉模式预报偏差特征的优势。   相似文献   

14.

A comprehensive risk management strategy for dealing with drought should include both short-term and long-term planning. The objective of this paper is to present an early warning method to forecast drought using the Standardised Precipitation Index (SPI) and a non-homogeneous Markov chain model. A model such as this is useful for short-term planning. The developed method has been used to forecast droughts at a number of meteorological monitoring stations that have been regionalised into six (6) homogenous clusters with similar drought characteristics based on SPI. The non-homogeneous Markov chain model was used to estimate drought probabilities and drought predictions up to 3 months ahead. The drought severity classes defined using the SPI were computed at a 12-month time scale. The drought probabilities and the predictions were computed for six clusters that depict similar drought characteristics in Victoria, Australia. Overall, the drought severity class predicted was quite similar for all the clusters, with the non-drought class probabilities ranging from 49 to 57 %. For all clusters, the near normal class had a probability of occurrence varying from 27 to 38 %. For the more moderate and severe classes, the probabilities ranged from 2 to 13 % and 3 to 1 %, respectively. The developed model predicted drought situations 1 month ahead reasonably well. However, 2 and 3 months ahead predictions should be used with caution until the models are developed further.

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15.
This study examines the variability of annual-mean precipitation in eight AOGCMs and in observations using empirical orthogonal functions (EOFs). The leading mode of precipitation variability in both models and observations is centered around the low-latitude western Pacific Ocean and Indian Ocean, and is associated with the El Niño-Southern Oscillation (ENSO). The spatial pattern R 2 correlations between model and observed EOF1 range from 0.12 to 0.61. In the observations, the Southern Oscillation Index (SOI) is highly correlated (R 2 = 0.82) with the amplitude of precipitation EOF1, while model R 2 correlations range from 0.17 to 0.83. If grid points near to those used to compute the standard SOI are used to compute alternative SO indices, the correlation with the amplitude of EOF1 ranges from 0.40 to 0.90 when based on the index that maximizes the correlation. Spatial fields of the variation between local precipitation and the SOI or the North Atlantic Oscillation Index are also computed for each model and compared with the observed fields. The model fields have many important similarities with the observed fields.  相似文献   

16.
基于TIGGE资料集下欧洲中期天气预报中心（ECMWF）、日本气象厅（JMA）、英国气象局（UKMO）、美国国家环境预报中心（NCEP）和中国气象局（CMA）5个气象预报中心2016年5月1日—8月31日中国地区逐日起报预报时效为24~168 h的24 h累积降水量集合预报的结果,对各个集合预报成员进行了频率匹配法的订正,并对订正前后的多模式集成预报效果进行评估。结果表明:采用频率匹配法订正后的降水预报,有效改善了集合平均预报中强降水（日降水量25 mm以上）预报由平滑作用产生的量级偏小现象,使预报的降水量级更接近实况,但对降水落区预报改进不明显。基于卡尔曼滤波技术的集成预报效果优于基于线性回归的超级集合预报和消除偏差集合平均预报,对强降水落区的预报较单模式更优。基于集合成员订正的降水多模式集成预报在强降水的落区预报和降水中心的量级预报更接近实况,效果优于原始多模式集成预报与单模式结果。  相似文献   

17.
运用气象观测资料和GRAPES、ECMWF、SWCWARMS_9KM(简称SWC)模式预报资料,对冕宁“6.26”大暴雨天气过程模式预报性能进行检验。结果表明:（1）对于24 h累计降水预报,中尺度区域模式优势明显,量级与落区预报效果均为最好,其中GRAPES_3KM模式预报落区分布与实况重合度较高,暴雨及以上量级降水TS评分最高。（2）GRAPES_3KM模式最大小时雨强10 mm以上降水落区与实况大雨及以上量级降水落区匹配度最高,ECMWF模式24 h累计降水多物理量订正产品及短时强降水概率产品次之。（3）SWC及GRAPES_3KM模式24 h累计降水极值点相比实况略偏北,量级偏小。对于小时降水峰值出现时间,SWC模式偏早4 h,GRAPES_3KM模式偏早3 h。（4）GRAPES_GFS模式环流背景预报更接近实况,SWC模式能较好地预报出冕宁上空中尺度辐合系统的存在。   相似文献   

18.
We have conducted a case study to investigate the performance of support vector machine, multivariate adaptive regression splines, and random forest time series methods in snowfall modeling. These models were applied to a data set of monthly snowfall collected during six cold months at Hamadan Airport sample station located in the Zagros Mountain Range in Iran. We considered monthly data of snowfall from 1981 to 2008 during the period from October/November to April/May as the training set and the data from 2009 to 2015 as the testing set. The root mean square errors (RMSE), mean absolute errors (MAE), determination coefficient (R 2), coefficient of efficiency (E%), and intra-class correlation coefficient (ICC) statistics were used as evaluation criteria. Our results indicated that the random forest time series model outperformed the support vector machine and multivariate adaptive regression splines models in predicting monthly snowfall in terms of several criteria. The RMSE, MAE, R 2, E, and ICC for the testing set were 7.84, 5.52, 0.92, 0.89, and 0.93, respectively. The overall results indicated that the random forest time series model could be successfully used to estimate monthly snowfall values. Moreover, the support vector machine model showed substantial performance as well, suggesting it may also be applied to forecast snowfall in this area.  相似文献   

19.
Droughts in Moldova were evaluated using meteorological data since 1955 and a long time series (1891?C2009). In addition, yields for corn (Zea mays L.), a crop widely grown in Moldova, were used to demonstrate drought impact. The main aim is to propose use of the S i (S i-a and S i-m) drought index while discussing its potential use in studying the evolution of drought severity in Moldova. Also, a new multi-scalar drought index, the standardized precipitation?Cevapotranspiration index (SPEI), is tested for the first time in identifying drought variability in Moldova while comparing it with the commonly used standardized precipitation index (SPI). S i-m, SPI, SPEI, and S i-a indices show an increasing tendency toward more intensive and prolonged severely dry and extremely dry summer months. Drought frequency increased through six decades, which included long dry periods in the 1990s and 2000s. Moreover, the evolution of summer evapotranspiration recorded a positive and significant trend (+3.3?mm/year, R 2?=?0.46; p????0.05) between 1955 and 2009. A yield model based on the S i-a agricultural index and historic corn yields explained 43% of observed variability in corn production when drought occurred in May, July, and August. Increasing severity of the 20-year drought during the critical part of the growing season is raising corn yield losses, as net losses have so far exceeded net gains.  相似文献   

20.
Long-lead prediction of waxing and waning of the Western North Pacific (WNP)-East Asian (EA) summer monsoon (WNP-EASM) precipitation is a major challenge in seasonal time-scale climate prediction. In this study, deficiencies and potential for predicting the WNP-EASM precipitation and circulation one or two seasons ahead were examined using retrospective forecast data for the 26-year period of 1981–2006 from two operational couple models which are the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) and the Bureau of Meteorology Research Center (BMRC) Predictive Ocean–Atmosphere Model for Australia (POAMA). While both coupled models have difficulty in predicting summer mean precipitation anomalies over the region of interest, even for a 0-month lead forecast, they are capable of predicting zonal wind anomalies at 850 hPa several months ahead and, consequently, satisfactorily predict summer monsoon circulation indices for the EA region (EASMI) and for the WNP region (WNPSMI). It should be noted that the two models’ multi-model ensemble (MME) reaches 0.40 of the correlation skill for the EASMI with a January initial condition and 0.75 for the WNPSMI with a February initial condition. Further analysis indicates that prediction reliability of the EASMI is related not only to the preceding El Niño and Southern Oscillation (ENSO) but also to simultaneous local SST variability. On other hand, better prediction of the WNPSMI is accompanied by a more realistic simulation of lead–lag relationship between the index and ENSO. It should also be noted that current coupled models have difficulty in capturing the interannual variability component of the WNP-EASM system which is not correlated with typical ENSO variability. To improve the long-lead seasonal prediction of the WNP-EASM precipitation, a statistical postprocessing was developed based on the multiple linear regression method. The method utilizes the MME prediction of the EASMI and WNPSMI as predictors. It is shown that the statistical postprocessing is able to improve forecast skill for the summer mean precipitation over most of the WNP-EASM region at all forecast leads. It is noteworthy that the MME prediction, after applying statistical postprocessing, shows the best anomaly pattern correlation skill for the EASM precipitation at a 4-month lead (February initial condition) and for the WNPSM precipitation at a 5-month lead (January initial condition), indicating its potential for improving long-lead prediction of the monsoon precipitation.  相似文献   

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