利用TMI资料估计西北太平洋热带气旋强度的客观方法

利用2007—2009年热带降雨测量卫星(TRMM)微波成像仪(TMI)观测的亮温资料,建立一种西北太平洋热带气旋强度(Tropical Cyclone,TC)的估计模型,对2010年热带气旋进行独立估计试验,并对估计误差进行分析。结果表明:该模型对强度小于强台风TC的拟合效果较好,均方根误差约为5 m/s,平均绝对误差约为4 m/s;对强台风和超强台风TC的拟合误差较大,均方根误差分别为9.65和6.60 m/s,平均绝对误差分别为7.76和5.49 m/s;对强台风及以上强度的TC,模型的拟合误差在日(夜)间减小(增大),误差最小(大)值为6.00 m/s(11.96 m/s),说明估计值在日(夜)间偏大(小)。     

基于CMIP5资料的东亚夏季环流的BMA预测研究

利用CMIP5的17个全球气候系统模式对500 hPa位势高度场的年代际回报结果,采用距平相关系数、均方根误差、平均绝对误差及连续等级概率评分4种指标,评估了贝叶斯模式平均(Bayesian model average,BMA)预报方法对东亚夏季环流的回报能力,并与最优单模式MIROC5和多模式简单集合平均结果进行了比较。结果表明,BMA方法对东亚夏季500 hPa位势高度场的回报效果是最好的,优于最优单模式MIROC5和简单集合平均的回报结果。BMA模型能产生高集中度的概率密度函数,并包含了多模式集成回报不确定性的定量估计。此外,BMA方法对西太平洋副热带高压的年际变率也有较好的回报效果,对西太平洋副热带高压的预报,选取60~70%概率下的结果更为合理。     

登陆中国的热带气旋强度和频数物理统计预测模型及其应用

使用1949—2008年美国的再分析资料及中国热带气旋年鉴,分析60年来北半球500 hPa高度场和海面温度场与登陆中国热带气旋(以下简称TC)的强度和频数显著相关区的统计特征及其物理意义。选取相关系数高的格点构成组合因子,建立二项式曲线预测模型,制作登陆我国热带气旋的年、月强度和频数预测。检验结果表明,该模型有较高的拟合能力,在业务中可广泛应用。     

基于CMIP5多模式回报资料的地面气温超级集合研究

利用CMIP5的15个全球气候系统模式对东亚及周边地区(70~150°E,0°~60°N)地面气温的回报结果进行超级集合(简称SUP)试验,以欧洲中期天气预报中心ERA逐月气温资料作为观测值,并采用均方根误差(RMSE)、距平相关系数(ACC)、绝对误差(MAE)对多模式集合平均(EMN)以及超级集合(SUP)的回报结果进行检验和评估。结果表明,超级集合回报结果一定程度上取决于训练期的长度。随训练期长度的增加,距平相关系数呈增大的趋势,均方根误差呈减小的趋势,但训练期达到一定长度后,误差不再有明显的减小,甚至出现误差增长。15个全球气候系统模式对东亚及周边地区的地面气温具有一定的回报能力,可以较好地回报出地面气温的年际变化和空间分布,海洋上回报的均方根误差小于陆地。但不同模式回报的结果不尽相同,在单模式中CCSM4对地面气温的回报效果最好。多模式集成的回报效果优于单模式的回报效果,SUP的回报效果优于EMN,其区域平均的均方根误差比多模式集合平均小0.43℃,超级集合极大地改善了地面气温的回报效果。     

基于随机森林模型的雅鲁藏布江流域气温降尺度研究

采用随机森林RF(Random Forest)模型对雅鲁藏布江流域22个站点的日平均气温进行降尺度研究,为了探求在雅鲁藏布江流域更适宜的气温降尺度方法,采用多元线性回归MLR、人工神经网络ANN和支持向量机SVM三种方法作为对比模型,并且采用主成分分析PCA和偏相关分析PAR两种分析方法,进行特征变量筛选。采用纳西效率系数NASH、均方根误差RMSE系数、绝对误差MAE和相关系数r值四种标准来评价模型的模拟效果。结果表明,RF模型的模拟效果要明显优于其他几种方法的模拟结果;采用PAR筛选特征变量的模型计算结果,不仅优于采用PCA筛选特征变量模型的模拟结果,且较稳定,另外,各种模型验证期的NASH效率系数都在0. 86以上,相关系数都在0. 93以上,所用几种模型都能较好地模拟雅江流域平均气温。选取MPI-ESM-LR模式在未来(2016-2050年)两种极端典型浓度路径RCP(Representative Concentration Pathway)排放情景RCP2. 6和RCP8. 5下的试验数据,研究雅鲁藏布江流域未来气温变化趋势表明,雅鲁藏布江流域未来2016-2050年在RCP2. 6和RCP8. 5两种排放情景下,平均气温都呈现出持续上升的趋势,在RCP2. 6排放情景下日平均气温平均上升0. 14℃,在RCP8. 5排放情景下日平均气温平均上升0. 30℃。     

夏季影响海南的热带气旋频数预测

利用1976-2013年夏季影响海南的热带气旋频数(SumTCs)资料、NCEP/NCAR的500 hPa高度场、高低层纬向风切变场、SLP场、OLR场和海温场等再分析资料,研究了前期不同环境场因子对SumTCs的影响,提取有一定物理意义的高相关预测因子群,经因子降维处理后建立SumTCs的模糊神经网络(FNN)预测模型。研究结果表明:1976-2007年SumTCs的交叉检验预测结果与实况SumTCs的相关系数为0.71,平均绝对误差为0.65个。该预测模型在2008-2013年6年的独立样本检验中平均绝对误差为1.5个,评分为76.7分,优于相同因子的逐步回归预测模型和仅基于海温场和500 hPa高度场因子的FNN预测模型。该模型可以投入海南省热带气旋频数的季节预测业务参考使用。     

基于前期观测降水和500 hPa高度场的西北太平洋热带风暴生成频数的新预测方案

利用前期1—2月和4—5月平均的东半球格点降水与500hPa高度场资料,通过多元线性逐步回归,建立了预测西北太平洋年热带风暴生成频数的预测方案。由于分别使用了欧洲中期数值预报中心和美国国家环境预测中心的大气再分析资料,建立了两个预测模型,对1979—2002年的预测交叉检验的距平相关系数分别为0.78和0.74。预测的多年平均绝对误差是3.0和3.2,即多年平均西北太平洋年热带风暴生成频数的10%左右。进一步指出:实际预测中可以把两个模型的预测结果平均作为最后预测结果,这样的话,多年交叉检验的距平相关系数是0.88,多年平均的预测绝对误差是1.92个。这样就可能得到更加准确的预测。本文结果还只是该方案的交叉检验结果,尚需在实际预测中进一步检验其能力。     

乌鲁木齐市MODIS气溶胶光学厚度与空气质量指数相关性分析

利用MODIS气溶胶光学厚度(AOD,Aerosol Optical Depth)产品与同期乌鲁木齐市空气质量指数进行相关性分析,得到二者的相关系数为0.664。对MODIS AOD产品进行垂直和湿度订正后,二者的相关性显著提高,相关系数从0.664提高到0.805。订正后按季节分类统计,春、夏、秋3季的相关系数分别为0.775、0.608和0.822,其中秋季的订正更为有效,可用性更高。这可能受到不同季节气溶胶来源、特征以及数据样本差异的影响。最后分别建立全年、春季、夏季和秋季的线性、对数、一元二次、乘幂和指数5种类型的拟合模型。考虑模型易于利用的因素,依据各拟合模型相关系数的大小得到全年以及各季节最优拟合模型,该模型函数可用来反演和监测乌鲁木齐市空气质量指数。     

CLDAS气温数据在中国区域的适用性评估

以中国48708个地面气象自动站逐小时气温数据为基础,采用平均偏差(Bias)、相关系数、均方根误差(RMSE)、平均绝对误差(MAE)等评估指标,对比分析2017年10月、2018年1月、4月、7月CLDAS-V2.0气温(分辨率为0.0625°),探讨中国8个分区春、夏、秋、冬4个季节CLDAS与站点气温的相关性及...     

影响广西的热带气旋年频数的BP神经网络预测模型

对影响广西的热带气旋(TC)年频数与大气环流的关系进行分析表明,TC年频数与全球范围大气环流异常有密切关系,特别是春季南半球中高纬度环流异常和低纬越赤道气流异常.利用相关分析从春季全球大气环流场中选择初选预报因子,然后对初选预报因子作EOF展开构造综合预报因子,运用BP神经网络方法建立TC年频数预报模型,并对所建立的模型进行独立样本试验.结果表明,该预报模型对历史样本拟合精度高,试报效果优于传统的逐步回归模型,可在汛期预测业务中应用.     

中国登路热带气旋的季节预测模型

The year-to-year increment prediction approach proposed by was applied to forecast the annual number of tropical cyclones (TCs) making landfall over China.The year-to-year increase or decrease in the number of land-falling TCs (LTCs) was first predicted to yield a net number of LTCs between successive years.The statistical prediction scheme for the year-to-year increment of annual LTCs was developed based on data collected from 1977 to 2007,which includes five predictors associated with high latitude circulations in both Hemispheres and the circulation over the local,tropical western North Pacific Ocean.The model shows reasonably high predictive ability,with an average root mean square error (RMSE) of 1.09,a mean absolute error (MAE) of 0.9,and a correlation coefficient between the predicted and observed annual number of LTCs of 0.86,accounting for 74% of the total variance.The cross-validation test further demonstrated the high predictive ability of the model,with an RMSE value of 1.4,an MAE value of 1.2,and a correlation coefficient of 0.74 during this period.     

Annual Frequency of Tropical Cyclones Directly Affecting Guangdong Province:Prediction Based on LSTM-FC

Tropical cyclone(TC) annual frequency forecasting is significant for disaster prevention and mitigation in Guangdong Province. Based on the NCEP-NCAR reanalysis and NOAA Extended Reconstructed global sea surface temperature(SST) V5 data in winter, the TC frequency climatic features and prediction models have been studied.During 1951-2019, 353 TCs directly affected Guangdong with an annual average of about 5.1. TCs have experienced an abrupt change from abundance to deficiency in the mid to late ...     

Experiments of DSAEF_LTP Model with Two Improved Parameters for Accumulated Precipitation of Landfalling Tropical Cyclones over Southeast China

The Dynamical-Statistical-Analog Ensemble Forecast model for landfalling tropical cyclones (TCs) precipitation (DSAEF_LTP) utilises an operational numerical weather prediction (NWP) model for the forecast track, while the precipitation forecast is obtained by finding analog cyclones, and making a precipitation forecast from an ensemble of the analogs. This study addresses TCs that occurred from 2004 to 2019 in Southeast China with 47 TCs as training samples and 18 TCs for independent forecast experiments. Experiments use four model versions. The control experiment DSAEF_LTP_1 includes three factors including TC track, landfall season, and TC intensity to determine analogs. Versions DSAEF_LTP_2, DSAEF_LTP_3, and DSAEF_LTP_4 respectively integrate improved similarity region, improved ensemble method, and improvements in both parameters. Results show that the DSAEF_LTP model with new values of similarity region and ensemble method (DSAEF_LTP_4) performs best in the simulation experiment, while the DSAEF_LTP model with new values only of ensemble method (DSAEF_LTP_3) performs best in the forecast experiment. The reason for the difference between simulation (training sample) and forecast (independent sample) may be that the proportion of TC with typical tracks (southeast to northwest movement or landfall over Southeast China) has changed significantly between samples. Forecast performance is compared with that of three global dynamical models (ECMWF, GRAPES, and GFS) and a regional dynamical model (SMS-WARMS). The DSAEF_LTP model performs better than the dynamical models and tends to produce more false alarms in accumulated forecast precipitation above 250 mm and 100 mm. Compared with TCs without heavy precipitation or typical tracks, TCs with these characteristics are better forecasted by the DSAEF_LTP model.     

Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region

Soil temperature (T _S) strongly influences a wide range of biotic and abiotic processes. As an alternative to direct measurement, indirect determination of T _S from meteorological parameters has been the focus of attention of environmental researchers. The main purpose of this study was to estimate daily T _S at six depths (5, 10, 20, 30, 50 and 100?cm) by using a multilayer perceptron (MLP) artificial neural network (ANN) model and a multivariate linear regression (MLR) method in an arid region of Iran. Mean daily meteorological parameters including air temperature (T _a), solar radiation (R _S), relative humidity (RH) and precipitation (P) were used as input data to the ANN and MLR models. The model results of the MLR model were compared to those of ANN. The accuracy of the predictions was evaluated by the correlation coefficient (r), the root mean-square error (RMSE) and the mean absolute error (MAE) between the measured and predicted T _S values. The results showed that the ANN method forecasts were superior to the corresponding values obtained by the MLR model. The regression analysis indicated that T _a, RH, R _S and P were reasonably correlated with T _S at various depths, but the most effective parameters influencing T _S at different depths were T _a and RH.     

Comparison of artificial intelligence techniques for prediction of soil temperatures in Turkey

Soil temperature is a meteorological data directly affecting the formation and development of plants of all kinds. Soil temperatures are usually estimated with various models including the artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models. Soil temperatures along with other climate data are recorded by the Turkish State Meteorological Service (MGM) at specific locations all over Turkey. Soil temperatures are commonly measured at 5-, 10-, 20-, 50-, and 100-cm depths below the soil surface. In this study, the soil temperature data in monthly units measured at 261 stations in Turkey having records of at least 20 years were used to develop relevant models. Different input combinations were tested in the ANN and ANFIS models to estimate soil temperatures, and the best combination of significant explanatory variables turns out to be monthly minimum and maximum air temperatures, calendar month number, depth of soil, and monthly precipitation. Next, three standard error terms (mean absolute error (MAE, °C), root mean squared error (RMSE, °C), and determination coefficient (R ²)) were employed to check the reliability of the test data results obtained through the ANN, ANFIS, and MLR models. ANFIS (RMSE 1.99; MAE 1.09; R ² 0.98) is found to outperform both ANN and MLR (RMSE 5.80, 8.89; MAE 1.89, 2.36; R ² 0.93, 0.91) in estimating soil temperature in Turkey.

     

多变量时滞回归模型在江南地区初夏降水低频分量延伸期预报中的应用

利用江南地区77个台站的日降水资料及NCEP/NCAR再分析资料,基于不同时间尺度的江南地区降水低频分量和东亚地区850 h Pa低频经向风主成分,建立了多变量时滞回归(Multivariable Lagged Regression,MLR)模型,并对2011年5—7月江南降水低频分量进行延伸期逐日预报试验。结果表明,50~70 d时间尺度的江南低频降水的平均预报技巧高达0.92,可准确预报持续性强降水过程和降水低频位相的正负转换。对利用2001—2012年资料分别构建的MLR模型的历史回报预测试验表明,在50~70 d振荡较强和正常的年份,模型能提前30 d做出初夏江南低频降水分量预报。模型结果也表明,850 h Pa低频经向风的发展和演变是影响初夏江南低频降水未来30 d变化的显著信号,可作为延伸期强降水预报的关键因子。     

AN INVESTIGATION INTO TROPICAL CYCLONE MOVING DIRECTION PREDICTION BASED ON OLR DATA

1 INTRODUCTION The Tropical Cyclone (TC) moving prediction is always difficult and important in operation. Though the numerical prediction and satellite data have contributed to the promotion of prediction capability in this way[1 – 3], it is not as satisfying for the unusual track of TC, and the primary reason is that the TC moving direction is influenced by many complicated factors. Therefore, further study of unusual TC motion using high-resolution satellite data is very important …     

大西洋风暴活动预测的一个潜在高效能的预测方案

A new empirical approach for the seasonal prediction of annual Atlantic tropical storm number (ATSN) was developed using precipitation and 500 hPa geopotential height data from the preceding January-February and April-May. The 2.5º×2.5º resolution reanalysis data from both the US National Center for Environmental Prediction/the National Center for Atmospheric Research (NCEP/NCAR) and the European Center for Medium-Range Weather Forecasting (ECMWF) were applied. The model was cross-validated using data from 1979-2002. The ATSN predictions from the two reanalysis models were correlated with the observations with the anomaly correlation coefficients (ACC) of 0.79 (NCEP/NCAR) and 0.78 (ECMWF) and the multi-year mean absolute prediction errors (MAE) of 1.85 and 1.76, respectively. When the predictions of the two models were averaged, the ACC increased to 0.90 and the MAE decreased to 1.18, an exceptionally high score. Therefore, this new empirical approach has the potential to improve the operational prediction of the annual tropical Atlantic storm frequency.     

最佳子集多元线性回归模型在热带气旋风圈 变化预报中的应用

基于最佳路径(IBTrACS)数据集和欧洲中期天气预报中心(ECMWF)的再分析(ERA-Interim)数据,建立了西北太平洋上(Western North Pacific,WNP)热带气旋(Tropical Cyclone,TC)的七级风圈(R17)变化的最佳子集多元线性回归(bs-MLR)模型.首先根据2001～...     

Influence of the Arctic Oscillation on the TC activity around Taiwan

This study discovered that strong positive correlations exist between the frequency of tropical cyclones (TC) during the summer around Taiwan and the Arctic Oscillation (AO) during the preceding March to May period. In positive AO years, during the preceding spring to summer period, anomalous cyclone and anomalous anticyclone were strongly developed at low and middle latitudes, respectively. Because of such a distribution of pressure system, in Taiwan, Korea, and Japan during the positive AO years, anomalous southeasterlies, which play the role of anomalous steering flows in transferring TCs to these regions, were strengthened. On the other hand, in southern China and the Indochina Peninsula during the positive AO years, anomalous northwesterlies, which prevent the transfer of TCs to these regions, were strengthened. Moreover, such a distribution of pressure system strengthening during the positive AO years led TCs to occur, move, and recurve more eastward in the western North Pacific in positive AO years as compared with the negative AO years. Contrarily, during the negative AO years, TCs showed the tendency to pass over the South China Sea from the Philippines and move west toward southern China and the Indochina Peninsula. Eventually, the intensity of TCs in these years was lower than that of TCs in positive AO years due to the topographic effects from a high TC passage frequency in mainland China.