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1.
An investigation of the difference in seasonal
precipitation forecast skills between the multiple linear regression
(MLR) ensemble and the simple multimodel ensemble mean (EM) was
based on the forecast quality of individual models. The possible
causes of difference in previous studies were analyzed. In order to
make the simulation capability of studied regions relatively
uniform, three regions with different temporal correlation
coefficients were chosen for this study. Results show the causes
resulting in the incapability of the MLR approach vary among
different regions. In the Nino3.4 region, strong co-linearity
within individual models is generally the main reason. However, in
the high latitude region, no significant co-linearity can be found
in individual models, but the abilities of single models are so poor
that it makes the MLR approach inappropriate for superensemble
forecasts in this region. In addition, it is important to note that
the use of various score measurements could result in some
discrepancies when we compare the results derived from different
multimodel ensemble approaches. 相似文献
2.
全国248站中—大雨以上降水概率MOS预报及其因子设计 总被引：2，自引：0，他引：2
本文给出了全国中—大雨以上降水概率MOS预报的制作方法、样本过滤条件、预报因子的设计与推导、预报值后处理以及检验与误差原因分析。还论述了MOS预报中采用σ面上的数值预报产品作因子、两维二次插值和预报值变量因子的作用与求取方法。 相似文献
3.
A Short-Range Quantitative Precipitation Forecast Algorithm Using Back-Propagation Neural Network Approach 总被引：5，自引：0，他引：5
A back-propagation neural network （BPNN） was used to establish relationships between the shortrange （0-3-h） rainfall and the predictors ranging from extrapolative forecasts of radar reflectivity, satelliteestimated cloud-top temperature, lightning strike rates, and Nested Grid Model （NGM） outputs. Quan- titative precipitation forecasts （QPF） and the probabilities of categorical precipitation were obtained. Results of the BPNN algorithm were compared to the results obtained from the multiple linear regression algorithm for an independent dataset from the 1999 warm season over the continental United States. A sample forecast was made over the southeastern United States. Results showed that the BPNN categorical rainfall forecasts agreed well with Stage Ⅲ observations in terms of the size and shape of the area of rainfall. The BPNN tended to over-forecast the spatial extent of heavier rainfall amounts, but the positioning of the areas with rainfall ≥25.4 mm was still generally accurate. It appeared that the BPNN and linear regression approaches produce forecasts of very similar quality, although in some respects BPNN slightly outperformed the regression. 相似文献
4.
Seasonal climate prediction for South America with FSU Multi-model Synthetic Superensemble algorithm 总被引：1，自引：0，他引：1
Rosane R. Chaves A. K. Mitra T. N. Krishnamurti 《Meteorology and Atmospheric Physics》2005,89(1-4):37-56
Summary Objective combination schemes of predictions from different models have been applied to seasonal climate forecasts. These schemes are successful in producing a deterministic forecast superior to individual member models and better than the multi-model ensemble mean forecast. Recently, a variant of the conventional superensemble formulation was created to improve skills for seasonal climate forecasts, the Florida State University (FSU) Synthetic Superensemble. The idea of the synthetic algorithm is to generate a new data set from the predicted multimodel datasets for multiple linear regression. The synthetic data is created from the original dataset by finding a consistent spatial pattern between the observed analysis and the forecast data set. This procedure is a multiple linear regression problem in EOF space. The main contribution this paper is to discuss the feasibility of seasonal prediction based on the synthetic superensemble approach and to demonstrate that the use of this method in coupled models dataset can reduce the errors of seasonal climate forecasts over South America. In this study, a suite of FSU coupled atmospheric oceanic models was used. In evaluation the results from the FSU synthetic superensemble demonstrate greater skill for most of the variables tested here. The forecast produced by the proposed method out performs other conventional forecasts. These results suggest that the methodology and database employed are able to improve seasonal climate prediction over South America when compared to the use of single climate models or from the conventional ensemble averaging. The results show that anomalous conditions simulated over South America are reasonably realistic. The negative (positive) precipitation anomalies for the summer monsoon season of 1997/98 (2001/02) were predicted by Synthetic Superensemble formulation quite well. In summary, the forecast produced by the Synthetic Superensemble approach outperforms the other conventional forecasts. 相似文献
5.
该文对比分析概率回归降水等级预报和回归降水等级预报的差异, 2007年秋季至2008年夏季全国平均检验结果表明:概率回归降水等级预报效果好于回归降水等级预报, 尤其是小雨预报, TS评分明显高于回归降水等级预报, 同预报偏差过大情况也有很大改善。进一步分析表明:回归降水等级预报方法在建立小雨预报方程的样本中, 少数较大降水量的样本方差占总方差的百分比过大, 导致预报方程中反映的预报量与预报因子的关系以少数大降水量样本为主, 是造成小雨预报空报过大的原因。与模式降水预报的对比分析表明:概率回归降水等级预报效果好于模式直接降水预报, 模式降水空报较大情况得到改善。 相似文献
6.
Different multimodel ensemble methods are used to forecast precipitations in China, 1998, and their forecast skills are compared with those of individual models. Datasets were obtained from monthly simulations of eight models during the period of January 1979 to December 1998 from the “Climate of the 20th Century Experiment” （20C3M） for the Fourth IPCC Assessment Report. Climate Research Unit （CRU） data were chosen for the observation analysis field. Root mean square （RMS） error and correlation coeffi-cients （R） are used to measure the forecast skills. In addition, superensemble forecasts based on different input data and weights are analyzed. Results show that for original data, superensemble forecasting based on multiple linear regression （MLR） performs best. However, for bias-corrected data, the superensemble based on singular value decomposition （SVD） produces a lower RMS error and a higher R than in the MLR superensemble. It is an interesting result that the SVD superensemble based on bias-corrected data performs better than the MLR superensemble, but that the SVD superensemble based on original data is inferior to the corresponding MLR superensemble. In addition, weights calculated by different data formats are shown to affect the forecast skills of the superensembles. In comparison with the MLR superensemble, a slightly significant effect is present in the SVD superensemble. However, both the SVD and MLR superensembles based on different weight formats outperform the ensemble mean of bias-corrected data. 相似文献
7.
基于TIGGE多模式降水量预报的统计降尺度研究 总被引：7，自引：0，他引：7
利用TIGGE资料中欧洲中期天气预报中心、美国国家环境预报中心、英国气象局以及日本气象厅4个中心,1~7 d预报时效的降水量预报资料,以TRMM/3B42RT降水量作为"观测值",对东亚地区降水量进行统计降尺度处理。首先利用逻辑回归方法将天气分为有雨和无雨,再对有雨的情况,利用线性回归方法对插值后的预报结果进行降尺度订正,最后将4个中心的预报值进行消除偏差集合平均,得到多模式集成的降水量预报场。结果表明:逻辑回归能够有效地改善预报中小雨的空报情况,统计降尺度订正后的预报结果比直接插值更加准确,多模式集成的预报效果优于单模式结果,其改进效果随预报时效的延长逐渐减小。 相似文献
8.
开展了夏半年72 h内逐3 h降水预报试验,针对ECMWF模式预报、基于ECMWF的模式输出统计(MOS)预报、纳入超前空间实况信息的OMOS预报,以及三种预报的最优TS评分订正(OTS)预报,对比分析预报效果,探讨一种多方法结合能够提供良好预报性能的3 h定量降水预报技术方案。结果表明:在短期预报中,MOS预报与OTS订正相结合的MOSOTS综合预报方法的预报性能最好,而且MOS-OTS方法的3 h强降水预报与业务运行的城镇指导预报中融合主客观预报的降水预报相比,也具有一定优势;而在临近3 h预报中,则OMOS预报与OTS订正相结合的OMOS-OTS综合预报方法最优,3 h内0.1、3和10 mm以上降水的TS评分最高,比原始模式预报分别提高73%、198%和483%,Bias评分接近于1,在夏半年的逐日晴雨预报中,OMOS-OTS方法在大部分日期都稳定优于MOS-OTS预报和城镇指导预报。 相似文献
9.
利用人工神经网络模型预测西北太平洋热带气旋生成频数 总被引：1，自引：0，他引：1
通过对60年(1950~2009年)北半球夏、秋季(6~10月)热带气旋(TC)频数与春季(3~5月)大尺度环境变量的相关分析,挑选出8个相关性较高的前期预报因子建立人工神经网络(ANN)模型,对2010~2017年8年夏、秋季TC频数进行回报,并将回报结果与传统多元线性回归(MLR)方法所得结果进行对比分析。结果表明,ANN模型对60年历史数据的拟合精度高,相关系数高达0.99,平均绝对误差低至0.77。在8年回报中,ANN模型相关系数为0.80,平均绝对误差为1.97;而MLR模型相关系数仅为0.46,平均绝对误差为3.30。ANN模型在历史数据拟合和回报中的表现都明显优于MLR模型,未来可考虑应用于实际的业务预测中。 相似文献
10.
The Madden–Julian Oscillation (MJO)/Boreal Summer Intraseasonal Oscillation (BSISO) has been considered an important climate mode of variability on subseasonal timescales for East Asian summer. However, it is unclear how well the MJO/BSISO indices would serve as guidance for subseasonal forecasts. Using a probabilistic forecast model determined through multiple linear regression (MLR) with MJO, ENSO, and long-term trend as predictors, we examine lagged impacts of each predictor on East Asia extended summer (May–October) climate from 1982 to 2015. The forecast skills of surface air temperature (T2m) contributed by each predictor is evaluated for lead times out to five weeks. We also provide a systematic evaluation of three commonly used, real-time MJO/BSISO indices in the context of lagged temperature impacts over East Asia. It is found that the influence of the trend provides substantial summertime skill over broad regions of East Asia on subseasonal timescales. In contrast, the MJO influence shows regional as well as phase dependence outside the tropical band of the main action centers of the MJO convective anomalies. All three MJO/BSISO indices generate forecasts that yield high skill scores for week 1 forecasts. For some initial phases of the MJO/BSISO, skill reemerges over some regions for lead times of 3–5 weeks. This emergence indicates the existence of windows of opportunity for skillful subseasonal forecasts over East Asia in summer. We also explore the dynamics that contribute to the elevated skills at long lead times over Tibet and Taiwan–Philippine regions following the initial state of phases 7 and 5, respectively. The elevated skill is rooted in a wave train forced by the MJO convective heating over the Arabian Sea and feedbacks between MJO convection and SSTs in Taiwan–Philippine region. Two out of the three commonly used MJO/BSISO indices tend to identify MJO events that evolve consistently in time, allowing them to serve as reliable predictors for subseasonal forecasts for up to 5 weeks.
相似文献11.
集成方法有利于提高降水要素预报的准确性和可预报性。本文基于格点实况资料和智能网格预报、西南区域数值预报、ECMWF模式预报、GRAPES模式预报产品，以面雨量为研究对象，采用多元回归法、BP神经网络法、评分权重法、加权集成预报法和算术平均法，得到集成面雨量预报，再运用平均绝对误差、模糊评分、正确率、TS评分、偏差分析等方法，对2020年4—10月金沙江下游面雨量预报效果进行对比分析。结果表明：多元回归集成法和BP神经网络法的预报效果总体上优于其他几种集成方法。在考虑流域面雨量的预报量级时，下游可以采用预报量级较小的模式和集成方法。集成后偏差百分比均有降低，且多元回归法和BP神经网络法对预报量级较小的模式有矫正作用。在面雨量有无、小雨和中雨预报中，多元回归法集成效果较好，在大雨量级预报中，BP神经网络法集成效果较好。这些结论可为流域面雨量预报提供参考借鉴。 相似文献
12.
面向全国2000多个台站，应用数值预报产品释用MOS技术制作温度、降水、相对湿度、风、云量及能见度等要素预报，并实现了预报业务运行。通过建立MOS预报系统，表明预报因子和预报对象的处理、建方程前的参数选择以及预报因子的选取都会影响要素预报的质量，需要做大量的细致工作。预报检验结果显示，降水预报尚未达到可用程度．温度和相对湿度的短期预报在大多数情况下是可用的或是可参考的，但还有待进一步改进。降水预报尚需在预报因子和充分运用多种探测信息方面加以改进。 相似文献
13.
面向全国2000多个台站，应用数值预报产品释用MOS技术制作温度、降水、相对湿度、风、云量及能见度等要素预报，并实现了预报业务运行。通过建立MOS预报系统，表明预报因子和预报对象的处理，建方程前的参数选择以及预报因子的选取都会影响要素预报的质量，需要做大量的细致工作。预报检验结果显示，降水预报尚未达到可用程度，温度和相对湿度的短期预报在大多数情况下是可用的或是可参考的，但还有待进一步改进。降水预报尚需在预报因子和充分运用多种探测信息方面着手加以改进。 相似文献
14.
S. A. Hakeem 《Acta Meteorologica Sinica》1992,6(2):261-264
Radiosonde profiles of temperature and dewpoint temperature from one station are used to forecast 12-h precipita-tion over Nairobi,Kenya.The forecast scheme is based on statistical regression modelling.Four predictors are derivedfrom data to use in a prognostic equation to get 12-h precipitation forecast.Observed and predicted rainfall values areplotted on a graph against time.Forecast verification shows that the forecasts are positively correlated withobservations. 相似文献
15.
Since the last International Union of Geodesy and Geophysics General Assembly(2003),predictability studies in China have made significant progress.For dynamic forecasts,two novel approaches of conditional nonlinear optimal perturbation and nonlinear local Lyapunov exponents were proposed to cope with the predictability problems of weather and climate,which are superior to the corresponding linear theory.A possible mechanism for the"spring predictability barrier"phenomenon for the El Ni(?)o-Southern Oscillation (ENSO)was provided based on a theoretical model.To improve the forecast skill of an intermediate coupled ENSO model,a new initialization scheme was developed,and its applicability was illustrated by hindcast experiments.Using the reconstruction phase space theory and the spatio-temporal series predictive method, Chinese scientists also proposed a new approach to improve dynamical extended range(monthly)prediction and successfully applied it to the monthly-scale predictability of short-term climate variations.In statistical forecasts,it was found that the effects of sea surface temperature on precipitation in China have obvious spatial and temporal distribution features,and that summer precipitation patterns over east China are closely related to the northern atmospheric circulation.For ensemble forecasts,a new initial perturbation method was used to forecast heavy rain in Guangdong and Fujian Provinces on 8 June 1998.Additionally, the ensemble forecast approach was also used for the prediction of a tropical typhoons.A new downscaling model consisting of dynamical and statistical methods was provided to improve the prediction of the monthly mean precipitation.This new downsealing model showed a relatively higher score than the issued operational forecast. 相似文献
16.
An Experiment of a Statistical Downscaling Forecast Model for Summer Precipitation over China 总被引：1，自引：0，他引：1 
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下载免费PDF全文 A combination of the optimal subset regression (OSR) approach, the coupled general circulation model of the National Climate Center (NCC-CGCM) and precipitation observations from 160 stations over China is used to construct a statistical downscaling forecast model for precipitation in summer. Retroactive forecasts are performed to assess the skill of statistical downscaling during the period from 2003 to 2009. The results show a poor simulation for summer precipitation by the NCC- CGCM for China, and the average spatial anomaly correlation coefficient (ACC) is 0.01 in the forecast period. The forecast skill can be improved by OSR statistical downscaling, and the OSR forecast performs better than the NCC-CGCM in most years except 2003. The spatial ACC is more than 0.2 in the years 2008 and 2009, which proves to be relatively skillful. Moreover, the statistical downscaling forecast performs relatively well for the main rain belt of the summer precipitation in some years, including 2005, 2006, 2008, and 2009. However, the forecast skill of statistical downscaling is restricted to some extent by the relatively low skill of the NCC- CGCM. 相似文献
17.
Philip E.BETT Gill M.MARTIN Nick DUNSTONE Adam A.SCAIFE Hazel E.THORNTON Chaofan LI 《大气科学进展》2021,38(12):2212-2220
Seasonal forecasts for Yangtze River basin rainfall in June, May–June–July (MJJ), and June–July–August (JJA) 2020 are presented, based on the Met Office GloSea5 system. The three-month forecasts are based on dynamical predictions of an East Asian Summer Monsoon (EASM) index, which is transformed into regional-mean rainfall through linear regression. The June rainfall forecasts for the middle/lower Yangtze River basin are based on linear regression of precipitation. The forecasts verify well in terms of giving strong, consistent predictions of above-average rainfall at lead times of at least three months. However, the Yangtze region was subject to exceptionally heavy rainfall throughout the summer period, leading to observed values that lie outside the 95％ prediction intervals of the three-month forecasts. The forecasts presented here are consistent with other studies of the 2020 EASM rainfall, whereby the enhanced mei-yu front in early summer is skillfully forecast, but the impact of midlatitude drivers enhancing the rainfall in later summer is not captured. This case study demonstrates both the utility of probabilistic seasonal forecasts for the Yangtze region and the potential limitations in anticipating complex extreme events driven by a combination of coincident factors. 相似文献
18.
ECMWF模式地面气温预报的四种误差订正方法的比较研究 总被引：16，自引：5，他引：11
采用均方根误差对欧洲中期天气预报中心（ECWMF）确定性预报模式2007年1月至2010年12月的地面气温预报结果进行评估,并分别利用一元线性回归、多元线性回归、单时效消除偏差和多时效消除偏差平均的订正方法,对ECMWF模式地面气温预报结果进行订正。结果表明,4种订正方法都能有效地减小地面气温多个时效预报的误差,改进幅度约为1℃。在短期预报中仅考虑最新预报结果的一元线性回归订正方法要优于考虑多个预报结果的多元集成预报订正方法。在中期预报中考虑多个预报结果的多元集成预报订正方法更优,更稳定。在模式预报误差较大的情况下,多时效集成的订正方法能更稳定地减小误差。 相似文献
19.
The aim of the present study is to develop an adaptive neuro-fuzzy inference system (ANFIS) to forecast the peak gust speed associated with thunderstorms during the pre-monsoon season (April?CMay) over Kolkata (22°32??N, 88°20??E), India. The pre-monsoon thunderstorms during 1997?C2008 are considered in this study to train the model. The input parameters are selected from various stability indices using statistical skill score analysis. The most useful and relevant stability indices are taken to form the input matrix of the model. The forecast through the hybrid ANFIS model is compared with non-hybrid radial basis function network (RBFN), multi layer perceptron (MLP) and multiple linear regression (MLR) models. The forecast error analyses of the models in the test cases reveal that ANFIS provides the best forecast of the peak gust speed with 3.52% error, whereas the errors with RBFN, MLP, and MLR models are 10.48, 11.57, and 12.51%, respectively. During the validation with the 2009 observations of the India Meteorological Department (IMD), the ANFIS model confirms its superiority over other comparative models. The forecast error during the validation of the ANFIS model is observed to be 3.69%, with a lead time of <12?h, whereas the errors with RBFN, MLP, and MLR are 12.25, 13.19, and 14.86%, respectively. The ANFIS model may, therefore, be used as an operational model for forecasting the peak gust speed associated with thunderstorms over Kolkata during the pre-monsoon season. 相似文献
20.
J. N. Mutemi L. A. Ogallo T. N. Krishnamurti A. K. Mishra T. S. V. Vijaya Kumar 《Meteorology and Atmospheric Physics》2007,95(1-2):87-113
Summary This study examines the predictability of weather over several regions in Africa using a multimodel superensemble technique
developed at the Florida State University, which is an objective means of combining daily forecasts from multilevel global
models. It is referred to as FSUSE and up to 7 different models are used to construct the superensemble. The benchmark reanalysis
fields used are the precipitation data sets from CMORPH and all other global fields from ECMWF daily operational analysis.
The FSUSE works by using multiple linear regression to derive weights from a comparison of each member model forecast to the
benchmark analysis during a training period of the most recent 120 days, and these weights are passed to the forecast phase.
This procedure removes the bias of each model and allows for an optimal linear combination of the individual model forecasts
by taking account of the relative skill of each model to give a consensus forecast that is superior to the ensemble mean and
all the members.
Results show that bad models and poor analysis fields used during the training phase degrade the skill of the FSUSE. In the
forecasts of rainfall events over all regions of Africa, the FSUSE root-mean-square (R M S) error, equitable threat skill
score (E T S), and bias on the daily forecasts of rainfall were invariably superior to the best member model. The skills deteriorate
as the forecast lead time in days increases, with the degradation being most significant beyond day 3. In all cases, the bias
score of the FSUSE was approximately 1, while the anomaly correlation scores were to the order of 0.9. These scores indicate
the robustness of the FSUSE forecasts. Over East Africa, the FSUSE forecasts were consistent with the spatial-temporal pattern
of the Intertropical Convergence Zone (ITCZ), the main rain bearing synoptic mechanism across tropical Africa. Thus, in addition
to superior forecasts, the use of FSUSE based data sets may provide a better understanding of the dynamical processes within
the ITCZ over the region.
These results could be further improved if the daily series of operational analysis had included gauge data and if the resolution
were higher. It is hardly possible to get uniformly consistent and continuous daily observations over these diverse regions
of Africa. However, given the availability of the satellite based estimates of daily rainfall, such as CMORPH and global analysis
that are exchanged very fast nowadays, the FSUSE scheme for numerical weather predictions (N W P) provides useful medium range
weather forecasts in real-time. 相似文献