宁波桃树花期预报方法

以宁波奉化桃花为例,应用区域中尺度自动气象站的逐时资料,分析前期光、温、湿条件的变异系数及这些要素与花期的相关系数得出:时积温(度·时)相对日积温(度·日)更能体现其与花期的内在关系,根据变异系数和相关系数的极值来确定预报因子。在此基础上利用欧洲中期天气预报中心(ECMWF)细网格资料的中期预报产品,采用BP神经网络,建立花期精细化预报模型,应用于实际预报。结果表明,利用中期数值预报产品和适当的预报模型进行花期中期预报是可行的,取得了较好的预报效果,提高了气象为农业服务水平。     

自动气象站温湿度传感器更换的影响评估

选取湖北省自动观测记录较长的38个站作为被检站,每个被检站选取3个邻近站,统计相对湿度、水汽压序列与邻近站的相关系数、平均值及方差变化情况,分析由人工观测改变为自动气象站观测后,两者存在的差异,并对其中4个国家基准气候站2003—2011年自动观测和同期人工观测进行对比,得到这4个站9年内每次更换温湿度传感器对相对湿度、水汽压记录的影响情况。结果表明:对比自动观测与人工观测两个序列,被检站与对应邻近站的相对湿度、水汽压的相关系数呈减小趋势,两种观测差值的平均值和方差差异显著;温湿度传感器的更换易产生相对湿度和水汽压记录的跳变;温湿度传感器的检定示值误差是加剧自动观测与人工观测序列显著差异的重要因素;改进观测方法,完善自动气象站检定规程,是自动观测与人工观测序列均一性的重要保证。     

1981-2012年辽宁省春播期土壤相对湿度月尺度数据重建

辽宁省现有测站春播期土壤相对湿度数据存在不连续性及长时间序列缺失问题。以海城站为例,分析现有土壤相对湿度（0-20 cm）与气象因子及临近站点土壤相对湿度的相关关系,构建海城春播期土壤相对湿度统计回归模型,模拟缺测时段春播期土壤相对湿度。进而以此方法重建辽宁省20个观测站1981-2012年春播期土壤相对湿度月尺度数据。结果表明：海城土壤相对湿度与降水量和秋季封冻雨关联较大,相关系数分别超过0.60和0.30,与同期临近站点本溪站土壤相对湿度相关性也超过0.40,依据该3要素构建的4月和5月土壤相湿度统计回归模型复相关系数R2分别达0.79和0.77,模拟结果与实测资料平均相对误差为2.6 %,模拟效果较好;对辽宁省其他数据缺失站点构建的回归模型复相关系数均高于0.50,模型拟合精度优于85 %,拟合值和实测值平均相对误差基本控制在15 %以内,较好完成辽宁省20个测站1981-2012年春播期土壤相对湿度月尺度数据重建。     

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.     

北京奥运交通路段精细预报

利用2007年6月1日-9月30日北京奥运交通路段27个自动气象站（含9个路面自动气象站）的监测资料,对各自动气象站的气象要素与北京市观象台同期气象要素进行统计相关分析,得出97个一元线性关系式,其各气象要素的相关系数（最高气温为0.9103~0.9884,最低气温为0.8777~0.9883,相对湿度为0.7499~0.9682,路面环境最高气温与路面最高温度为0.8502~0.9288,路面环境最低气温与路面最低温度为0.9171~0.9851）均通过F_α=0.01的检验。2008年8月北京奥运会期间,在北京市气象台短期预报产品的基础上,根据线性回归关系式,生成交通路段客观预报产品,并应用于服务。     

微波辐射计温湿廓线反演方法改进试验

为提升地基微波辐射计在不同天气条件下, 特别是云天条件下温湿廓线的反演精度, 利用2011年1月—2016年12月中国气象局北京国家综合气象观测试验基地探空数据, 在微波辐射计反演温湿度廓线的过程中通过区分晴天和云天条件并引入全固态Ka波段测云仪云高及云厚信息, 对反演输入亮温进行质量控制和偏差订正, 建立BP神经网络模型, 采用2017年1月—2018年3月微波辐射计探测数据评估检验, 结果表明:在亮温订正前提下, 晴天温度模型、云天温度模型、晴天相对湿度模型和云天相对湿度模型反演结果与探空的相关系数分别为0.99, 0.99, 0.80和0.78, 均方根误差为2.3℃, 2.3℃, 9%和16%, 较微波辐射计自带产品（LV2产品）减小约0.4℃, 0.3℃, 11%和9%, 准确性提升约30%, 28%, 64%和45%;温度模型偏差在±2℃以内、湿度模型偏差在±20%以内的占比分别为68%, 70%和95%, 78%, 较LV2产品分别提高了7%, 5%和27%, 23%, 其中相对湿度改善明显。可见亮温订正、区分天气类型训练反演模型有利于改善地基微波辐射温湿廓线反演精度。     

影响人工站与自动站水汽压差值原因探究

为探究人工观测气象站与自动站水汽压差值产生原因,利用全国134个1年的基准气象观测站逐时气压、干球气温、湿球温度等资料,分别采用人工站和自动站水汽压计算公式,模拟了本站气压、干球温度、湿球温度、自动站气温、相对湿度等要素产生一增量时,水汽压所对应变化量随各要素的变化规律,并分析了影响水汽压差值与其主导因子定量关系。通过实际观测资料的数据模拟分析,气压和干球温度的系统误差对人工与自动观测站的水汽压差值影响很小,而自动站的相对湿度和气温对水汽压差值影响较大,其中相对湿度的影响最明显。当自动站的相对湿度或气温的系统误差为一固定值时,水汽压差值变化幅度随气温大小变化,气温越高,水汽压差值越大。     

数值与统计方法在广东核事故应急系统中的应用

根据研究区域（310km×220km）三年的气象资料,将每天的天气按季节、天气状况和风向与风速及其日变化等因子划归为具有代表性的不同天气类型,然后用中尺度气象模式模拟这些天气类型的风温场,使得模拟的风温场能反映下垫面动力和热力不均匀性的影响,并以此统计出研究区域内关心点的天气状况、风向和风速与天气类型之间的相关关系和相关判据。在已知关心点的季节、天气状况、风向和风速后,依据这种相关关系,找出相应天气类型的风温场,并用以预报核泄漏物的污染路径、强度和范围。     

Modeling monthly mean air temperature for Brazil

Air temperature is one of the main weather variables influencing agriculture around the world. Its availability, however, is a concern, mainly in Brazil where the weather stations are more concentrated on the coastal regions of the country. Therefore, the present study had as an objective to develop models for estimating monthly and annual mean air temperature for the Brazilian territory using multiple regression and geographic information system techniques. Temperature data from 2,400 stations distributed across the Brazilian territory were used, 1,800 to develop the equations and 600 for validating them, as well as their geographical coordinates and altitude as independent variables for the models. A total of 39 models were developed, relating the dependent variables maximum, mean, and minimum air temperatures (monthly and annual) to the independent variables latitude, longitude, altitude, and their combinations. All regression models were statistically significant (α?≤?0.01). The monthly and annual temperature models presented determination coefficients between 0.54 and 0.96. We obtained an overall spatial correlation higher than 0.9 between the models proposed and the 16 major models already published for some Brazilian regions, considering a total of 3.67?×?10⁸?pixels evaluated. Our national temperature models are recommended to predict air temperature in all Brazilian territories.     

Operative Scheme for the Short-range Complex Forecasting of Surface Air Temperature and Humidity

The statistical scheme is proposed for the forecast of surface air temperature and humidity using operative weather forecasts with 3–5-day lead time from the best forecasting hydrodynamic models as well as the archives of forecasts of these models and observational data from 2800 weather stations of Russia, Eastern Europe, and Central Asia. The output of the scheme includes the forecasts of air temperature for the standard observation moments with the period of 6 hours and extreme temperatures with the lead times of 12–120 hours. The accuracy of temperature and humidity forecasts for the period from July 2014 till June 2017 is much higher than that for the forecasts of original hydrodynamic models. The skill scores for extreme temperature forecasts based on the proposed method are compared with the similar results of the Weather Element Computation (WEC) forecasting scheme and forecasts by weathermen.     

MP-3000A型地基微波辐射计探测性能及其在乌鲁木齐降水天气中的初步应用

利用乌鲁木齐MP-3000A型35通道地基微波辐射计与L波段高空气象探测系统2010年逐日探测的温湿度,分析微波辐射计探测温湿度性能及其在降水天气中的特征。结果表明：微波辐射计与探空测量温、湿度具有很好的相关性和一致性,两者观测58层高度的温度、相对湿度、水汽密度和整层可降水量的相关系数分别为0．99、0．74、0．92和0．94,均达到0．01显著性水平。10km以下垂直廓线分析表明：微波辐射计测量的温度值均小于探空测量值,相对湿度和水汽密度大于探空测量值,存在系统误差。可降水量偏差冬季最小,夏季最大。降水发生时两者探测的温湿度廓线变化趋势一致,温度和2000in以上水汽密度受降水天气影响相对较小,微波辐射计能较好地反映大雨和暴雨天气发生和结束过程中热力和水汽参数的时空分布和剧烈变化特征。     

一种偏差订正方法在平昌冬奥会气象预报的应用

为了提高GRAPES_3 km（Global/Regional Assimilation and Prediction System）模式在2018年平昌冬奥会气象服务中的预报能力,采用一阶自适应的卡尔曼滤波方法对GRAPES_3 km模式的2 m气温、2 m相对湿度和10 m风开展偏差订正。结果表明:偏差订正方法明显提高了地面要素的预报效果,其中2 m气温的均方根误差整体减小到2℃左右,站点订正改善率为10%~60%;10 m风速的均方根误差减小到2 m·s-1左右,站点订正改善率为10%~45%;2 m相对湿度减小到20%以下,站点订正改善率为0~20%。与韩国气象厅LDAPS（Local Data Assimilation and Prediction System）及美国宇航局NU-WRF（NASA-Unified WRF）模式相比,GRAPES_3 km模式的风速预报表现更为优异,各站点整体预报效果明显优于LDAPS和NU-WRF模式。偏差订正方法可有效改善模式在复杂地形条件下的预报能力,是提高精细化预报准确率的重要手段。     

An Observing System Simulation Experiment to Assess the Potential Impact of a Virtual Mobile Communication Tower–based Observation Network on Weather Forecasting Accuracy in China. Part 1: Weather Stations with a Typical Mobile Tower Height of 40 m

The importance of a national or regional network of meteorological stations for improving weather predictions has been recognized for many years. Ground-based automatic weather stations typically observe weather at a height of 2-10 m above ground level(AGL); however, these observations may have two major shortcomings. Large portions of data cannot be used if the station height is significantly lower than the model surface level; and such observations may contain large representativity errors as near-surface observations are often affected by the local environment, such as nearby buildings and tall trees. With the recent introduction of a significant number of mobile communication towers that are typically over40 m AGL in China, a campaign has been proposed to use such towers to build a future observing system with an observing height of 40 m. A series of observing system simulation experiments has been conducted to assess the potential utility of such a future observing system as part of a feasibility study. The experiments were conducted using the Weather Research and Forecasting model and its Rapid Update Cycle data assimilation system. The results revealed the possibility of improving weather forecasting by raising present weather stations to a height of 40 m; this would not only enable more observations to pass the terrain check, but should also reduce interpolation errors. Additionally, improvements for temperature, humidity and wind forecasting could be achieved as the accuracy of the initial conditions increases.     

基于ECMWF产品的站点气温预报集成学习误差订正

提出一种基于数值模式预报产品的气温预报集成学习误差订正方法,通过人工神经网络、长短期记忆网络和线性回归模型组合出新的集成学习模型（ALS模型）,采用2013—2017年的欧洲中期天气预报中心数值天气预报模式2 m气温预报产品和中国部分气象站点数据,利用气象站点气温、风速、气压、相对湿度4个观测要素,挖掘观测数据的时序特征并结合模式2 m气温预报结果训练机器学习模型,对2018年模式2 m气温6~168 h格点预报产品插值到站点后的预报结果进行偏差订正。结果表明:ALS模型可将站点气温预报整体均方根误差由3.11℃降至2.50℃,降幅达0.61℃（19.6%）,而传统的线性回归模型降幅为0.23℃（8.4%）。ALS模型对站点气温预报误差较大的区域和气温峰值预报的订正效果尤为显著,因此,集成学习方法在数值模式预报结果订正中具有较大的应用潜力。     

Temporal and spatial patterns of modern climatic warming: case study of Northern Eurasia

Century-scale near-surface air temperature data from 744 weather stations in Russia and neighboring countries indicate that the temperature variations have distinct temporal patterns. Two periods, near the beginning and at the end of the 20th century, experienced the largest warming rates. Temperature changes in both periods were not uniform in time or space. We used statistical criteria and applied them to data at the weather stations to define a “tipping point” corresponding to the beginning of the modern climatic period. Results indicate that the position of this point depends on location, and in most cases falls into the interval from the early 1970s through the late 1980s. By means of spatial correlation analysis we delineated regions with coherent air temperature changes and calculated the region-specific rates and magnitudes of changes. We compared the distribution of regional tipping points in time and over space with large-scale atmospheric circulation patterns over northern Eurasia. We analyzed the 20th—early 21st century changes in the relative frequencies of the three circulation forms defined by Vangengheim-Girs classification, and found their qualitative correspondence with the spatial temperature patterns and spread of the tipping points in time. These results improve our knowledge about the regional structure and drivers of modern climate change in northern Eurasia, which is likely to hold the fingerprint of the anthropogenic signal. Findings of this study can be used to obtain insight into regional climatic changes in northern Eurasia over the next few decades.     

中国地面相对湿度非均一性检验及订正

利用加拿大环境部气候研究中心研发的PMTred和PMFT方法,选取均一的邻近站为参考站,使用相关系数权重平均构建参考序列,结合元数据信息,对1951-2014年中国2400多个国家级地面站月平均相对湿度进行了非均一性检验与订正,并分析了造成相对湿度序列非均一的主要原因。结果表明,中国地面相对湿度资料存在较严重的非均一问题,68%的台站存在断点,人工观测转自动观测、迁站和时次变化是造成序列非均一的主要原因。整套资料负订正量所占比例较高,订正范围主要集中在-5%～0之间,这种负订正量与人工转自动观测后相对湿度观测值偏低有密切关系。这也使得订正后中国平均相对湿度趋势与订正前存在明显差异,订正前中国平均相对湿度呈下降趋势,订正后相对湿度没有趋势性变化。     

甘肃地区温度变化对±1100 kV特高压直流输电工程线路的影响

为了给准东-华东(皖南)±1100kV特高压直流输电工程线路工程的施工和设备选型提供一定的参考依据,针对线路工程途径甘肃的18个气象站近10年平均温度、最低温度、最高温度进行了统计分析,采用罗曼诺夫斯基准则的数据剔除法进行数据处理,并使用相关系数法得到各气象台站覆盖区域的气候趋势系数。通过spss软件分析18个台站平均温度的变化以及各台站地区平均温度分布,最终分析各个台站地区温度变化趋势以及温度变化对线路工程的一些影响。     

Spatial and temporal variabilities of rainfall data using functional data analysis

The paper analyzes equivalent data for a low density meteorological station network (spatially discontinuous data) and poor temporal homogeneity of thunderstorm observational data. Due to that, a Regional Climate Model (RegCM) dataset was tested. The Most Unstable Convective Available Potential Energy index value (MUCAPE) above the 200 J kg^?1 threshold was selected as a predictor describing favorable conditions for the occurrence of thunderstorms. The quality of the dataset was examined through a comparison between model results and soundings from several aerological stations in Central Europe. Good, statistically significant (0.05 significance level) results were obtained through correlation analysis; the value of Pearson’s correlation coefficient was above 0.8 in every single case. Then, using methods associated with gridded climatology, data series for 44 weather stations were derived and an analysis of correlation between RegCM modeled data and in situ thunderstorm observations was conducted with coefficients in the range of 0.75–0.90. The possibility of employing the dataset in thunderstorm climatology analysis was checked via a few examples by mapping monthly, seasonal, and annual means. Moreover, long-term variability and trend analysis along with modeled MUCAPE data were tested. As a result, the RegCM modeled MUCAPE gridded dataset was proposed as an easily available, suitable, and valuable predictor for thunderstorm climatology analysis and mapping. Finally, some limitations are discussed and recommendations for further improvements are given.     

基于INCA和METRo的江苏省路面高温精细化预报

使用INCA（Integrated Nowcasting through Comprehensive Analysis）多源资料融合分析和短临外推预报系统的预报结果作为气象强迫场,驱动一路面温度理论预报模型（Model of the Environment and Temperature of Roads,METRo）,开展江苏省高速公路夏季路面高温预报试验,并使用公路沿线逐小时的路面温度观测资料对预报结果进行检验。结果表明:该预报方法能够较好地预报出高速公路沿线日最高路面温度的逐日变化趋势,以及日最高路面温度的大范围空间分布特征。平均日最高路面温度预报绝对偏差为4.1℃,平均相对偏差为10.8%。其中,日最高路面温度预报绝对偏差在5℃以内的站次占总数的64.5%,相对偏差在15%以内的站次占总数的74.6%,比常规业务预报方法分别提高了23.1%和25.3%。但该预报方法对较小的温度波动以及局地性较强的极端温度分布特征的预报技巧还需进一步提高。     

1961～2013年中国蒸发皿蒸发量时空分布特征及其影响因素

分析了1961～2013年中国1302个台站的蒸发皿蒸发量（Pan Evaporation,PE）的时空分布特征并探讨了影响PE变化的主要气候因子。结果表明:站点平均PE在全年和四季都呈明显下降趋势,且在1978年发生了突变。PE在华北平原、新疆、广东、广西及海南等地呈现出显著的下降趋势,而在福建、浙江和贵州等地为显著上升的趋势。用年平均PE距平场经验正交函数做经验正交函数（EOF）分解得到:在第一模态（EOF1）中,1981年时间系数由负转正,EOF1的空间模态与PE的变化有较好的一致性;第二模态（EOF2）中PE距平呈南北反向分布,2002年以后PE在北方减小,在南方增大。通过计算PE与近地面5个气象因子（降水、气温、风速、湿度、日照时数）的偏相关系数后发现:除了降水外,其余4个因子都和PE有很好的相关性。风速与PE为显著正相关,且相关系数最大的区域与EOF1中PE变率最大的区域吻合;相对湿度与PE为显著负相关;PE与气温的相关系数都为正值,且相关系数最大的区域对应于PE显著增加的地区,而与日照时数的相关系数在除春季以外的其他季节都大于0.6。进一步分析发现,风速和日照时数与PE的关系受两个气象因子的线性趋势影响较大,以此推断出PE的下降趋势应该很大程度是受风速和日照时数减小的影响。此外,干旱发生时,PE明显偏大,降水、气温、湿度和日照时数的变化也都对PE增大有明显的贡献,PE对干旱有很好的指示作用。