Delaunay三角剖分法在降水量插值中的应用

Delaunay三角剖分方法在空间分析中具有重要地位,文中简要介绍了Delaunay三角网特性和常用的3类算法,并对随机增长法实现过程进行了详细阐述.根据三角分片线性插值原理,求得插值系数,实现对任意点的三角分片线性插值.利用2008年中国2200个观测站的08时24 h降水量资料,对全中国范围及划分的8个区域内相应的0.28125°×0.28125°降水量格点场,使用交叉检验方法,对比分析了三角分片线性插值和反距离权重法的估值准确率.结果表明:在各区域,三角分片线性插值法的均方根误差偏小;在站点较密集的区域,均方根误差、平均绝对误差比较中,三角分片线性插值都有一定的优势;在平均误差对比中,三角分片线性插值优势明显,在全中国范围交叉检验中,三角分片线性插值法对应的年平均误差是0.005 mm,而反距离权重法为-0.107 mm,对其可能的原因进行了分析,证明了Delaunay三角剖分法的合理性.同时,从图形上展示了降水量的Delaunay三角网的三维结构图和三角分片线性插值后的格点场,在直观上,Delaunay三角剖分后得到降水分布和实况保持一致,并有较好的视觉效果;通过三角分片线性插值得到的格点场降水量分布图,克服了反距离权重法的固有缺陷,使获得的降水量格点场趋于合理,提高了插值精度.最后,探讨了Delaunay三角网在气象领域的应用前景.     

GIS支持下的自动站雨量插值方法比较

介绍常用的地理信息空间分析插值方法,选择"灿都"台风降雨过程,以南宁市及其周边的212个站点分别利用距离反比法、克吕格法和梯度距离平方反比法进行插值,并利用未参与插值的30站点计算每种插值方法结果的平均绝对误差(MAE)、平均相对误差(MRE)、最大相对误差(MMRE),结果表明,梯度距离平方反比法在实验中较好.     

Evaluation of TIGGE Ensemble Forecasts of Precipitation in Distinct Climate Regions in Iran

The application of numerical weather prediction(NWP) products is increasing dramatically. Existing reports indicate that ensemble predictions have better skill than deterministic forecasts. In this study, numerical ensemble precipitation forecasts in the TIGGE database were evaluated using deterministic, dichotomous(yes/no), and probabilistic techniques over Iran for the period 2008–16. Thirteen rain gauges spread over eight homogeneous precipitation regimes were selected for evaluation.The Inverse Distance Weighting and Kriging methods were adopted for interpolation of the prediction values, downscaled to the stations at lead times of one to three days. To enhance the forecast quality, NWP values were post-processed via Bayesian Model Averaging. The results showed that ECMWF had better scores than other products. However, products of all centers underestimated precipitation in high precipitation regions while overestimating precipitation in other regions. This points to a systematic bias in forecasts and demands application of bias correction techniques. Based on dichotomous evaluation,NCEP did better at most stations, although all centers overpredicted the number of precipitation events. Compared to those of ECMWF and NCEP, UKMO yielded higher scores in mountainous regions, but performed poorly at other selected stations.Furthermore, the evaluations showed that all centers had better skill in wet than in dry seasons. The quality of post-processed predictions was better than those of the raw predictions. In conclusion, the accuracy of the NWP predictions made by the selected centers could be classified as medium over Iran, while post-processing of predictions is recommended to improve the quality.     

Meshless Surface Wind Speed Field Reconstruction Based on Machine Learning

We propose a novel machine learning approach to reconstruct meshless surface wind speed fields, i.e., to reconstruct the surface wind speed at any location, based on meteorological background fields and geographical information. The random forest method is selected to develop the machine learning data reconstruction model (MLDRM-RF) for wind speeds over Beijing from 2015–19. We use temporal, geospatial attribute and meteorological background field features as inputs. The wind speed field can be reconstructed at any station in the region not used in the training process to cross-validate model performance. The evaluation considers the spatial distribution of and seasonal variations in the root mean squared error (RMSE) of the reconstructed wind speed field across Beijing. The average RMSE is 1.09 m s^?1, considerably smaller than the result (1.29 m s^?1) obtained with inverse distance weighting (IDW) interpolation. Finally, we extract the important feature permutations by the method of mean decrease in impurity (MDI) and discuss the reasonableness of the model prediction results. MLDRM-RF is a reasonable approach with excellent potential for the improved reconstruction of historical surface wind speed fields with arbitrary grid resolutions. Such a model is needed in many wind applications, such as wind energy and aviation safety assessments.     

A Deep Learning Method for Bias Correction of ECMWF 24–240 h Forecasts

Correcting the forecast bias of numerical weather prediction models is important for severe weather warnings. The refined grid forecast requires direct correction on gridded forecast products, as opposed to correcting forecast data only at individual weather stations. In this study, a deep learning method called CU-net is proposed to correct the gridded forecasts of four weather variables from the European Centre for Medium-Range Weather Forecast Integrated Forecasting System global model(ECMWF-IFS): 2-m temperature, 2-m relative humidity, 10-m wind speed, and 10-m wind direction, with a forecast lead time of 24 h to 240 h in North China. First, the forecast correction problem is transformed into an image-toimage translation problem in deep learning under the CU-net architecture, which is based on convolutional neural networks.Second, the ECMWF-IFS forecasts and ECMWF reanalysis data(ERA5) from 2005 to 2018 are used as training,validation, and testing datasets. The predictors and labels(ground truth) of the model are created using the ECMWF-IFS and ERA5, respectively. Finally, the correction performance of CU-net is compared with a conventional method, anomaly numerical correction with observations(ANO). Results show that forecasts from CU-net have lower root mean square error, bias, mean absolute error, and higher correlation coefficient than those from ANO for all forecast lead times from 24 h to 240 h. CU-net improves upon the ECMWF-IFS forecast for all four weather variables in terms of the above evaluation metrics, whereas ANO improves upon ECMWF-IFS performance only for 2-m temperature and relative humidity. For the correction of the 10-m wind direction forecast, which is often difficult to achieve, CU-net also improves the correction performance.     

Comparison of prediction performance using statistical postprocessing methods

As the 2018 Winter Olympics are to be held in Pyeongchang, both general weather information on Pyeongchang and specific weather information on this region, which can affect game operation and athletic performance, are required. An ensemble prediction system has been applied to provide more accurate weather information, but it has bias and dispersion due to the limitations and uncertainty of its model. In this study, homogeneous and nonhomogeneous regression models as well as Bayesian model averaging (BMA) were used to reduce the bias and dispersion existing in ensemble prediction and to provide probabilistic forecast. Prior to applying the prediction methods, reliability of the ensemble forecasts was tested by using a rank histogram and a residualquantile-quantile plot to identify the ensemble forecasts and the corresponding verifications. The ensemble forecasts had a consistent positive bias, indicating over-forecasting, and were under-dispersed. To correct such biases, statistical post-processing methods were applied using fixed and sliding windows. The prediction skills of methods were compared by using the mean absolute error, root mean square error, continuous ranked probability score, and continuous ranked probability skill score. Under the fixed window, BMA exhibited better prediction skill than the other methods in most observation station. Under the sliding window, on the other hand, homogeneous and non-homogeneous regression models with positive regression coefficients exhibited better prediction skill than BMA. In particular, the homogeneous regression model with positive regression coefficients exhibited the best prediction skill.     

An Experiment on the Prediction of the Surface Wind Speed in Chongli Based on the WRF Model: Evaluation and Calibration

In this study,the ability of the Weather Research and Forecasting(WRF)model to generate accurate near-surface wind speed forecasts at kilometer-to subkilometer-scale resolution along race tracks(RTs)in Chongli during the wintertime is evaluated.The performance of two postprocessing methods,including the decaying-averaging(DA)and analogy-based(AN)methods,is tested to calibrate the near-surface wind speed forecasts.It is found that great uncertainties exist in the model’s raw forecasts of the near-surface wind speed in Chongli.Improvement of the forecast accuracy due to refinement of the horizontal resolution from kilometer to subkilometer scale is limited and not systematic.The RT sites tend to have large bias and centered root mean square error(CRMSE)values and also exhibit notable underestimation of high-wind speeds,notable overestimation or underestimation of the near-surface wind speed at high altitudes,and notable underestimation during daytime.These problems are not resolved by increasing the horizontal resolution and are even exacerbated,which leads to great challenges in the accurate forecasting of the near-surface wind speed in the competition areas in Chongli.The application of postprocessing methods can greatly improve the forecast accuracy of near-surface wind speed.Both methods used in this study have comparable abilities in reducing the(positive or negative)bias,while the AN method is also capable of decreasing the random error reflected by CRMSE.In particular,the large biases for high-wind speeds,wind speeds at high-altitude stations,and wind speeds during the daytime at RT stations can be evidently reduced.     

日降水量空间插值方法研究

采用反距离权重法和普通克里格方法对26°~34°N, 103°~115°E范围内2004年逐日降水量进行空间插值试验分析, 分辨率为1 km×1 km。采用交叉检验方法和准确率方法对两种方法插值的总体效果及不同等级的降水插值效果进行综合对比。结果表明:两种方法插值效果近似, 插值结果与实测值相关系数分别为0.83和0.82。但对日雨量较大的情况, 两种方法插值效果均有所降低, 相关系数为0.66和0.67。两种方法的实测值与插值结果的相关系数在不同季节非常接近, 并且均以春季最大, 其次为冬、秋季, 夏季相关系数最小; 通过采用平均误差、平均绝对误差和均方根3个指标衡量及不同等级雨量的插值准确率统计比较, 普通克里格方法插值效果略好于反距离权重法。     

Spatial Interpolation of Daily Precipitation in China: 1951--2005

Climate research relies heavily on good quality instrumental data; for modeling efforts gridded data are needed. So far, relatively little effort has been made to create gridded climate data for China. This is especially true for high-resolution daily data. This work, focuses on identifying an accurate method to produce gridded daily precipitation in China based on the observed data at 753 stations for the period 1951--2005. Five interpolation methods, including ordinary nearest neighbor, local polynomial, radial basis function, inverse distance weighting, and ordinary kriging, have been used and compared. Cross-validation shows that the ordinary kriging based on seasonal semi-variograms gives the best performance, closely followed by the inverse distance weighting with a power of 2. Finally the ordinary kriging is chosen to interpolate the station data to a 18 km×18 km grid system covering the whole country. Precipitation for each 0.5o×0.5o latitude-longitude block is then obtained by averaging the values at the grid nodes within the block. Owing to the higher station density in the eastern part of the country, the interpolation errors are much smaller than those in the west (west of 100oE). Excluding 145 stations in the western region, the daily, monthly, and annual relative mean absolute errors of the interpolation for the remaining 608 stations are 74%, 29%, and 16%, respectively. The interpolated daily precipitation has been made available on the internet for the scientific community.     

Surface mean temperature from the observational stations and multiple reanalyses over the Tibetan Plateau

The Tibetan Plateau (TP), also called the “Third pole”, is sensitive to climate change due to extensive areas at high elevation presently dominated by snow and ice. In this study, observed surface temperature trends at 150 stations over the TP during 1979–2018 are analyzed and compared with surface temperatures from multiple reanalyses (NCEP1, NCEP2, ERA-Interim, MERRA, JRA55). Observed warming at the stations has a mean annual rate of 0.46 °C/decade during 1979–2018. Although all reanalyses underestimate observed temperatures (cold bias), most reproduce much of the inter-decadal variations of surface temperature shown in the observations. Absolute errors of mean surface temperature (reanalysis minus observation) are closely correlated with elevation errors, suggesting that parts of the cold bias can be interpreted by elevation errors of reanalysis. After elevation-temperature correction, about half of the cold bias is typically eliminated, more for both ERA-Interim and JRA55. Compared with the observations, corrected NCEP2 surface temperatures still have larger cold biases, and fail to capture the overall warming over the TP. Since the elevation-temperature correction fails to improve trend magnitudes even when a significant proportion of the bias has been removed, this suggests that a more sophisticated modeling of the lapse rate in each reanalysis is required to realistically model warming trends across complex topography.

     

Evaluation of methods of spatial interpolation for monthly rainfall data over the state of Rio de Janeiro,Brazil

Five deterministic methods of spatial interpolation of monthly rainfall were compared over the state of Rio de Janeiro, southeast Brazil. The methods were the inverse distance weight (IDW), nearest neighbor (NRN), triangulation with linear interpolation (TLI), natural neighbor (NN), and spline tension (SPT). A set of 110 weather stations was used to test the methods. The selection of stations had two criteria: time series longer than 20 years and period of data from 1960 to 2009. The methods were evaluated using cross-validation, linear regression between values observed and interpolated, root mean square error (RMSE), coefficient of determination (r ²), coefficient of variation (CV, %), and the Willmott index of agreement (d). The results from different methods are influenced by the meteorological systems and their seasonality, as well as by the interaction with the topography. The methods presented higher precision (r ²) and accuracy (d, RMSE) during the summer and transition to autumn, in comparison with the winter or spring months. The SPT had the highest precision and accuracy in relation to other methods, in addition to having a good representation of the spatial patterns expected for rainfall over the complex terrain of the state and its high spatial variability.     

基于Shepard和OI方法对雨量计逐时资料的分析

引入地形影响效应的日降水量气候分析场,分别运用Shepard和最优插值(OI)两种插值方法对广东和广西2007年5月20日—8月30日汛期小时雨量计降水量进行插值,得到0.125°×0.125°分辨率的规范化网格资料。结果表明:无论是直接插值还是用降水比率(地形影响效应的日降水量气候分析场)插值,两种方法均能很好地体现广东和广西雨量计站点观测降水的季内变化和日变化特征。虽然用直接插值方法比用降水比率插值方法得到的降水空间分布更为平滑,但估值精度没有用降水比率插值方法高。通过交叉检验进一步表明,总体上OI方法优于Shepard方法,而考虑地形影响效应的降水比率OI方法为最优,能有效提高相关性,并减少均方根误差和系统误差。     

精细化MOS相对湿度预报方法研究

利用2003年5~9月MM5模式每隔1 h的站点基本要素预报场和物理量诊断场资料,以及相应时段内宁夏25个测站的相对湿度自记观测资料,同时采用多元线性和逐步回归2种MOS统计方法,预报宁夏25个测站5~9月48 h逐时相对湿度。对2004年夏季6~8月预报效果检验表明:MOS方法制作宁夏48 h逐时相对湿度预报结果是可用的或是可参考的;2种MOS统计方法预报结果相近,逐步回归方法比多元线性方法预报效果稍好,08:00预报误差明显低于20:00;当天气形势变化较平稳时,MOS预报结果稳定,平均绝对误差控制在10%左右;当有明显的变温等特殊天气时,误差变率起伏波动大,预报结果不稳定。     

Bias-Corrected Short-Range Ensemble Forecasts for Near-Surface Variables during the Summer Season of 2010 in Northern China

A running mean bias （RMB） correction ap- proach was applied to the forecasts of near-surface variables in a seasonal short-range ensemble forecasting experiment with 57 consecutive cases during summer 2010 in the northern China region. To determine a proper training window length for calculating RMB, window lengths from 2 to 20 days were evaluated, and 16 days was taken as an optimal window length, since it receives most of the benefit from extending the window length. The raw and 16-day RMB corrected ensembles were then evaluated for their ensemble mean forecast skills. The results show that the raw ensemble has obvious bias in all near-surface variables. The RMB correction can remove the bias reasonably well, and generate an unbiased ensemble. The bias correction not only reduces the ensemble mean forecast error, but also results in a better spreaderror relationship. Moreover, two methods for computing calibrated probabilistic forecast （PF） were also evaluated through the 57 case dates： 1） using the relative frequency from the RMB-eorrected ensemble; 2） computing the forecasting probabilities based on a historical rank histogram. The first method outperforms the second one, as it can improve both the reliability and the resolution of the PFs, while the second method only has a small effect on the reliability, indicating the necessity and importance of removing the systematic errors from the ensemble.     

影响统计插值分析误差的若干因素分析

采用理想观测网,用数值方法计算统计插值分析误差。减小观测误差或初估值误差、增加观测数量、恰当减小观测空间间距、以及选取分布合理的观测资料,有利提高分析精度;如果计算使用的归一化观测误差方差、误差相关函数的特征和总体统计平均情况不一致,有可能造成分析误差的明显增加。最后,利用特征向量分析方法,对归一化观测误差方差、观测误差相关属性对于不同尺度统计插值分析误差的影响进行讨论。     

Analysis of mesometeorological temperature fields

Summary An objective procedure of a three-dimensional temperature analysis in a topography following (zeta) coordinate system is presented. Temperature data from various conventional sources (ground stations, RAWIN, synoptic analysis) are interpolated into the grid points of the zeta system (the horizontal grid distance is 1 km, vertical resolution is 11 levels up to 2500 m) by a topography variability considering horizontal interpolation. As the correlations between the synop and also the upper air data from pairs of stations on different sides of a mountain barrier show a rapid decrease (Lanzinger and Steinacker, 1989), the Euclidic distances between the points with measurements and the grid points are corrected according to the variability of the gird points are corrected according to the variability of the topography between the two points. If the topography between the two points is flat, or if there is only a valley, the Euclidean distance is used as an interpolation distance. In case of a ridge between the two poinst the interpolation distance is increased according to the topography height variation and the stability of the air mass. In the vertical interpolations a synthetic temperature profile is used made of the data from Rawin, the measurements from meteorological tower and the measurements from some meteorological stations located at high altitude.The grid point temperature values are adjusted to satisfy quasi-stationarity condition, considering advective and diabatic changes of the temperature. A variational formalism in a weighted least square sense in the zeta coordinate system is developed and the boundary conditions are specified. The results (3-D temperature fields and the fields of local derivative of temperature) for a large pre-alpine basin (the Ljubljana basin in the central part of Slovenia, Yugoslavia, 50×50 km) are presented and the usefullness of the adjustement procedure is discussed.With 5 Figures     

湖北省乡镇温度预报方法初探

为了提高精细站点温度预报水平,本文在中尺度模式精细化温度预报的基础上,结合MOS分县客观预报结果,采用带海拔高度的距离权重温度插值方法,经过灰色预测模型修正温度预报误差,对湖北省717个四要素自动观测站的72小时内日高低温度进行了精细化预报,并且利用乡镇自动站温度观测进行了检验。结果显示：对于没有历史观测资料的站点,采用此预报方法,预报效果明显高于模式预报结果,基本接近客观预报方法,可以进行业务应用。对湖北的四要素自动观测站温度检验的结果还表明：鄂西北的预报误差最大,江汉平原的预报效果最好;从分月预报情况来看,高低温度都是夏季和初秋预报效果最好,低温预报冬春季预报效果最差,湖北各个区域趋势基本都一致,而高温的预报却没有一致的趋势。     

Multischeme ensemble forecasting of surface temperature using neural network over Turkey

The ensemble method has long been used to reduce the errors that are caused by initial conditions and/or parameterizations of models in forecasting problems. In this study, neural network (NN) simulations are applied to ensemble weather forecasting. Temperature forecasts averaged over 2 weeks from four different forecasts are used to develop the NN model. Additionally, an ensemble mean of bias-corrected data is used as the control experiment. Overall, ensemble forecasts weighted by NN with feed forward backpropagation algorithm gave better root mean square error, mean absolute error, and same sign percent skills compared to those of the control experiment in most stations and produced more accurate weather forecasts.     

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%。     

一种基于空间相关性和B样条曲面拟合的地面气温质量控制算法

将B样条曲面拟合算法引入到地面气温观测资料的质量控制当中,考虑到区域内各参考站与目标站观测值之间的空间相关性,提出了一种基于空间相关性和B样条曲面拟合的地面气温观测资料质量控制算法(Spatial Correlation and B-spline Surface Fitting,BSF)。选择2012—2014年南平站、南京站、太原站、拉萨站、景洪站和长春站以及周围300 km内参考站的02:00、08:00、14:00、20:00定时气温作为观测资料,结合平均绝对误差(Mean Absolute Error,MAE)、均方根误差(Root Mean Square Error,RMSE)、一致性指标(Index of Agreement,IOA)和纳什系数(Nash-sutcliffe Model Efficiency Coefficient,NSC)这4种评价参数对目标站地面气温资料进行质量控制分析。将BSF算法的质量控制效果分别与传统的反距离加权法(Inverse Distance Weighted,IDW)和空间回归检验法(Spatial Regression Test,SRT)进行对比,试验结果表明:在不同案例下,BSF算法的质量控制效果均优于IDW算法和SRT算法,能更有效地标记出气温观测数据中的可疑值。