FY-2C云迹风资料时空分布特征分析

利用统计方法分析我国FY-2C静止卫星红外、水汽通道云迹风资料的数量和误差分布特征,通过分别统计昼夜、不同区域、冬夏季和不同年份的资料分布情况,得到以下主要结论:(1)云迹风资料在白天和黑夜80%以上的可用点资料都集中在200~300 hPa,但白天的资料总个数多于黑夜;误差统计结果表明在资料比较集中层次,昼夜的平均误差、标准误差和均方根误差变化趋势相近。(2)不同区域云迹风资料可用点的数量垂直分布差异不明显,而不可用点的数量分布差异很大;同时发现两个区域的3种误差表现出一定的区域性特征,高原地区的误差整体小于低海拔地区的。(3)冬夏季的统计结果表明云迹风资料在冬、夏季资料总个数及垂直分布变化不同,夏季资料个数多,且主要分布在200~300hPa,而冬季主要分布在100hPa和400hPa;误差统计结果表明夏季误差小、冬季误差大。(4)不同年份的数量统计表明:2005年和2008年的资料个数变化不大,但是IR1资料的数量垂直分布差异很大,而IR3的差异不明显;误差统计结果表明2008年云迹风资料的均方根误差整体比2005年的小。(5)在误差分析中发现,无论IR1资料还是IR3资料都存在一定的平均误差,但标准误差,即随机误差仍是产生均方根误差的主要原因。      

A STUDY ON THE APPLICATION OF FY-2E CLOUD DRIFT WIND HEIGHT REASSIGNMENT IN NUMERICAL FORECAST OF TYPHOON CHANTHU (1003) TRACK

In this paper, we first analyzed cloud drift wind(CDW) data distribution in the vertical direction, and then reassigned the height of every CDW in the research domain in terms of background information, and finally, conducted contrast numerical experiments of assimilating the CDW data before and after reassignment to examine the impacts on the forecast of the track of Typhoon Chanthu(1003) from 00:00(Coordinated Universal Time) 21 July to 00:00 UTC23 July, 2010. The analysis results of the CDW data indicate that the number of CDWs is mainly distributed in the midand upper-troposphere above 500 h Pa, with the maximum number at about 300 h Pa. The height reassigning method mentioned in this work may update the height effectively, and the CDW data are distributed reasonably and no obvious contradiction occurs in the horizontal direction after height reassignment. After assimilating the height-reassigned CDW data, especially the water vapor CDW data, the initial wind field around Typhoon Chanthu(1003) became more reasonable, and then the steering current leading the typhoon to move to the correct location became stronger. As a result, the numerical track predictions are improved.     

The impact of spatial and temporal distribution of satellite observations on tropical cyclone data assimilation: Preliminary results

Summary Preliminary work gauging the impact of varying the spatial and temporal resolution of Cloud Drift Wind (CDW) data using different assimilation techniques is presented, particularly within the framework of a generalised inverse data assimilation scheme. Results are presented for the NW Pacific and Australian regions. There were three main findings for the cases examined. Firstly, hourly and 12-hourly highdensity CDWs produced lower mean forecast errors (relative to intermittent assimilation) than those produced using operational CDWs from the Global Telecommunication System (GTS). This is consistent with Le Marshall et al. (1994). Secondly, intermittent assimilation was significantly worse than both the nudging and variational procedures. Finally, there was little improvement using the variational as opposed to the nudging scheme when using 12-hourly data insertion and operational winds from the GTS. There, the variational procedure proved to be about 4 per cent superior to the nudging. This result is of note, given the variational procedure takes about an order of magnitude longer to produce the initial field than does nudging, and should be contrasted with an earlier finding by one of the authors (LML) where the variational procedure was found to be clearly superior to the nudging approach (Bennett et al., 1993) where an enhanced CDW field was used.With 2 Figures     

云导风资料同化在伴随模式同化系统中的应用

以一次发生在长江流域的暴雨过程为例,设计了几种数值试验方案,并进行模拟。结果表明,MM5伴随模式同化系统能有效改善初始场与模式的协调能力,提高模式对于降水场和其它要素场的预报;使用云导风资料修正初始场后直接模拟的效果比未使用时直接模拟的效果要好,对部分区域的强降水预报精度有一定程度的改善;使用伴随模式同化系统后,加入云导风资料的同化试验对其它要素的改善与直接同化常规资料的效果相比,改善优势不明显,但从各要素的误差来看,对于风场的改善最好。     

万年县夏季地面最高温度预报方法初探

利用江西省万年县气象观测站1981—2010年夏季（6—8月）逐日气象观测资料,采用多元统计回归方法,建立了地面最高温度预报模型,并使用1971—1980年夏季逐日观测资料和2013年夏季Meofis统计预报模式输出产品,分别对预报模型进行了检验和试验。结果表明,模型的回算值与实测值具有较好的对应关系,两者决定系数达0.80;模式的预报值与实测值两者变化趋势基本一致,总体平均相对误差和平均绝对误差分别为11.0%和4.9℃。     

基于RS和GIS技术的新疆气温空间插值方法探讨

分析新疆90个气象站点2004年年均气温与经纬度、海拔高度的关系,之间具有较好的线形相关关系(r2=0.897)。通过"回归方程计算 空间残差"的方法对全疆2004年年均气温数据进行插值。验证结果为:插值计算值与实测值间相关系数r2=0.994,平均误差1.53%。插值结果表明:利用"回归方程计算 空间残差"的方法可以生成高精度、高空间分辨率新疆年平均网格温度结果。     

常规地面观测资料在GRAPES同化系统中的误差控制试验

利用GRAPES-3Dvar系统,分别对2004年9月3~5日、2005年7月2~4日、2008年7月20~22日的3个降水个例进行了常规地面资料各同化变量误差倍数改变的质量控制试验。结果表明,改变地面同化变量的误差倍数对进入同化系统的资料条目数是有所改变的;H的误差倍数取4时就能保证80%的资料进入同化系统,而U、V、Q的误差倍数取到2时,就能保证90%的资料进入同化系统;通过对目标函数及其梯度随跌代步数下降趋势对比分析,发现同化各变量的误差倍数取4及其以下时,其代价函数就能满足收敛条件,从而得到分析场;随着误差倍数的增加,进入同化的H资料的条目数越多,其对四川境内的降水预报的改善程度越差,而进入同化的U、V、Q条目数越多,其对四川境内的降水预报的改善程度越好,同时当误差倍数取到3以上时,各同化变量在四川境内的降水雨带与各自的误差倍数取3时一致。在既保证最大程度的利用更多同化资料,又保证其同化质量的前提下,H、U、V、Q的误差倍数的取值分别为4、3、3、3。      

Two-Dimensional Variational Analysis of Near-Surface Moisture from Simulated Radar Refractivity-Related Phase Change Observations

Because they are most sensitive to atmospheric moisture content, radar refractivity observations can provide high-resolution information about the highly variable low-level moisture field. In this study, simulated radar refractivity-related phase-change data were created using a radar simulator from realistic highresolution model simulation data for a dryline case. These data were analyzed using the 2DVAR system developed specifically for the phase-change data. Two sets of experiments with the simulated observations were performed, one assuming a uniform target spacing of 250 m and one assuming nonuniform spacing between 250 m to 4 km. Several sources of observation error were considered, and their impacts were examined. They included errors due to ground target position uncertainty, typical random errors associated with radar measurements, and gross error due to phase wrapping. Without any additional information, the 2DVAR system was incapable of dealing with phase-wrapped data directly. When there was no phase wrapping in the data, the 2DVAR produced excellent analyses, even in the presence of both position uncertainty and random radar measurement errors. When a separate pre-processing step was applied to unwrap the phase-wrapped data, quality moisture analyses were again obtained, although the analyses were smoother due to the reduced effective resolution of the observations by interpolation and smoothing involved in the unwrapping procedure. The unwrapping procedure was effective even when significant differences existed between the analyzed state and the state at a reference time. The results affirm the promise of using radar refractivity phase-change measurements for near-surface moisture analysis.     

在四维变分同化中运用集合协方差的试验

利用浅水方程模式和模式模拟资料进行数值试验比较3种不同的背景误差协方差矩阵处理方法对四维变分(4DVAR)资料同化的影响.3种背景误差协方差矩阵分别是:(1)对单一变量将背景误差协方差矩阵简化为对角矩阵;(2)将背景误差协方差矩阵的作用简化为高斯过滤;(3)由预报集合生成背景误差协方差矩阵并利用奇异值分解技术解决矩阵的求逆.通过一系列数值试验,比较不同观测密度、不同观测误差下3种背景误差协方差处理方法对4DVAR同化效果的影响.结果表明,背景误差协方差的结构对4DVAR有重大影响.当观测资料的空间密度不够高时,采用对角矩阵得不到满意的结果.高斯过滤方案可以明显改善同化结果,但是对背景误差特征长度比较敏感.第3种方法采用的背景误差协方差矩阵是流型依赖的,而且并不以显式的方式出现在目标函数中.避免了对它求逆的复杂运算.由于做了降维处理,在观测点的密度较低和观测误差较大时可望取得较好的同化结果,同化效果较为稳定.     

Hourly air temperature driven using multi-layer perceptron and radial basis function networks in arid and semi-arid regions

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.     

乌鲁木齐市气象塔梯度观测资料质量控制与处理

本文介绍了乌鲁木齐市建成区内5座100 m气象塔地理位置,根据逻辑极值检查、僵值检查、时间一致性检查、空间一致性检查和人工干预检查等组成的一套针对气象塔观测资料的质量控制方法和四点滑动平均插值、要素垂直分布拟合和线性回归方法组成的数据插值方法,对乌鲁木齐市从南至北5座100 m气象塔2012年4月1日至2014年4月30日的资料进行质量控制和数据插值。结果表明:该套方案能很好地找出缺测、错误和可疑的数据,结合人工干预,使得检验结果更为可靠。5座气象塔资料的质量是比较好的,正常数据占总数据的97.17%,其中红光山数据质量最好(正常数据占总数据的99.01%)。非正常数据只是少数情况(占总数据的2.83%),包括缺测和错误数据,其中缺测数据占非正常数据的6.23%(出现在米东和燕南立交),错误数据占非正常数据的93.77%。虚假数据占错误数据的89.99%(大部分为风速、风向),僵值数据占错误数据的5.47%(大部分为气温和湿度).超出逻辑极值数据占错误数据的0.41%(只出现在水塔山气温),不符合一致性数据占错误数据的4.13%(主要为湿度,主要在燕南立交)。利用四点中央插值法、每座气象塔要素垂直分布拟合和不同气象塔之间线性回归方法插值气温和相对湿度的效果较好。     

观测资料的四维质量控制:变分法

利用四维变分资料同化技术，定义了观测资料的误差均差比，并在理论上初步探讨了利用误差均差比来判断资料中重大误差（ｇｒｏｓｓｅｒｒｏｒ）的合理性。利用简单的Ｌｏｒｅｎｚ模式进行了一些数值试验，以检验质量控制方案。     

WRF模式2m温度在山东预报效果的评估

利用山东省内123个国家气象站2017年11月至2018年2月逐时观测地面温度对WRF模式08：00和20：00起报的2 m温度进行检验,评估了预报时效为72 h的逐时温度与日最低（高）温度的预报效果并初步分析了个别站点大值误差成因。结果表明：WRF模式08：00起报2 m温度的准确率要高于20：00起报,白天预报的效果优于夜晚;鲁西北和半岛地区的2 m预报温度的平均绝对误差总体低于鲁中和鲁南地区,全省大部分站点负误差比例高于正误差比例;WRF模式对于日最高温度的预报效果优于日最低温度;模式地形高度误差造成泰山站2 m预报温度正误差较大,基于两种温度梯度方案对泰山站2 m温度进行订正,订正后的平均绝对误差总体下降,利用单一的温度梯度在有的预报时刻出现负的订正效果,利用随预报时刻变化而变化的温度梯度在各预报时刻订正效果更为稳定;泰安站出现焚风时2 m预报温度有较大负误差,这主要是受WRF模式泰山站地形高度误差影响;WRF模式在微山湖区域土地类型与真实土地类型存在差异是薛城站夜间2 m温度负误差较大的重要因素之一。     

Handling error propagation in sequential data assimilation using an evolutionary strategy

An evolutionary strategy-based error parameterization method that searches for the most ideal error adjustment factors was developed to obtain better assimilation results. Numerical experiments were designed using some classical nonlinear models (i.e., the Lorenz-63 model and the Lorenz-96 model). Crossover and mutation error adjustment factors of evolutionary strategy were investigated in four aspects: the initial conditions of the Lorenz model, ensemble sizes, observation covariance, and the observation intervals. The search for error adjustment factors is usually performed using trial-and-error methods. To solve this difficult problem, a new data assimilation system coupled with genetic algorithms was developed. The method was tested in some simplified model frameworks, and the results are encouraging. The evolutionary strategy-based error handling methods performed robustly under both perfect and imperfect model scenarios in the Lorenz-96 model. However, the application of the methodology to more complex atmospheric or land surface models remains to be tested.     

基于ECMWF模式的江西及周边地区锋面暴雨预报检验和订正

利用全国降水资料（包括江西加密降水资料）、探空资料、ECMWF模式72—24 h降水和形势预报资料,采用天气学检验、SAL定量降水预报检验等方法,对2017—2019年江西及附近地区锋面暴雨的实况和模式产品进行检验分析,检验主要影响天气系统预报效果,得出ECMWF模式降水预报误差分布特征及原因,并对模式的暴雨预报进行订正。结果表明：ECMWF模式对2017—2019年锋面暴雨过程预报较实况大多偏北,落区预报误差主要源于大尺度降水。从锋面暴雨三种SAL分析误差可见,落区预报较实况大多偏北,暴雨过程强度多数较实况偏弱,结构较实况偏小。对误差较大个例的分析得出两点订正思路：1） 锋区南侧有较明显动力热力对流发展的弱锋区暴雨,暴雨落区可订正至925 hPa锋区南侧高温高湿区。2） 较强锋面暴雨,当中低层切变辐合抬升区重叠时,暴雨落区可向925 hPa锋区位置调整,暴雨通常不易出现在锋区北侧冷区。     

地形起伏对模式地表长波辐射计算的影响

初步探讨了数值模式中次网格区地形起伏(高度、坡度)对长波辐射计算的影响。结果表明,地形高度的次网格变化对辐射计算结果的影响较小,其相对误差一般<2%,但不同分辨率的地形高度场会产生不同的坡度计算误差,低分辨率地形资料所计算的坡度分布相差不大,但坡度的量级存在较大偏差。地形的分辨率越高,实际计算结果的偏差越小。而高分辨率地形资料计算实例表明,地形坡度引起的区域相对辐射误差可达到10%以上。因此,在数值模式中,网格区地表长波辐射计算结果对地形起伏的敏感性应给予足够的重视。     

四川CLDAS 5 km小时能见度实况产品与能见度观测站资料质量对比评估

利用四川省CLDAS 5km小时能见度实况产品和能见度观测站资料,将智能网格小时能见度产品按双线性和邻近插值的方法插值到四川省156个国家级能见度观测站上,比较两者的相关系数、均方根误差、平均值误差、平均绝对误差等参数,对CLDAS 5km小时能见度实况产品进行质量评估并对数据的可用性做了分析,结果显示:小时能见度产品在四川盆地及攀西地区中部的相关系数较高,普遍在0.7以上,误差值较小,平均值误差普遍为负值,表明有小的低估值,数据可用性较好;而川西高原及攀西地区南部的相关系数小,误差值大,平均值误差普遍为正值,表明多为高估值,数据可用性一般。分级评估表明:随着能见度等级的降低,高原地区的缺测数据越来越多,盆地内的高估值也明显增加,能见度等级为“优”与“良”时的数据可用性较好。      

An online model correction method based on an inverse problem: Part I—Model error estimation by iteration

Errors inevitably exist in numerical weather prediction (NWP) due to imperfect numeric and physical parameterizations. To eliminate these errors, by considering NWP as an inverse problem, an unknown term in the prediction equations can be estimated inversely by using the past data, which are presumed to represent the imperfection of the NWP model (model error, denoted as ME). In this first paper of a two-part series, an iteration method for obtaining the MEs in past intervals is presented, and the results from testing its convergence in idealized experiments are reported. Moreover, two batches of iteration tests were applied in the global forecast system of the Global and Regional Assimilation and Prediction System (GRAPES-GFS) for July-August 2009 and January-February 2010. The datasets associated with the initial conditions and sea surface temperature (SST) were both based on NCEP (National Centers for Environmental Prediction) FNL (final) data. The results showed that 6th h forecast errors were reduced to 10% of their original value after a 20-step iteration. Then, off-line forecast error corrections were estimated linearly based on the 2-month mean MEs and compared with forecast errors. The estimated error corrections agreed well with the forecast errors, but the linear growth rate of the estimation was steeper than the forecast error. The advantage of this iteration method is that the MEs can provide the foundation for online correction. A larger proportion of the forecast errors can be expected to be canceled out by properly introducing the model error correction into GRAPES-GFS.     

A forecast error correction method in numerical weather prediction by using recent multiple-time evolution data

The initial value error and the imperfect numerical model are usually considered as error sources of numerical weather prediction (NWP). By using past multi-time observations and model output, this study proposes a method to estimate imperfect numerical model error. This method can be inversely estimated through expressing the model error as a Lagrange interpolation polynomial, while the coefficients of polynomial are determined by past model performance. However, for practical application in the full NWP model, it is necessary to determine the following criteria: (1) the length of past data sufficient for estimation of the model errors, (2) a proper method of estimating the term "model integration with the exact solution" when solving the inverse problem, and (3) the extent to which this scheme is sensitive to the observational errors. In this study, such issues are resolved using a simple linear model, and an advection-diffusion model is applied to discuss the sensitivity of the method to an artificial error source. The results indicate that the forecast errors can be largely reduced using the proposed method if the proper length of past data is chosen. To address the three problems, it is determined that (1) a few data limited by the order of the corrector can be used, (2) trapezoidal approximation can be employed to estimate the "term" in this study; however, a more accurate method should be explored for an operational NWP model, and (3) the correction is sensitive to observational error.     

统计降尺度方法在天津小时降水和气温精细化预报中的应用

基于ECWMF模式预报数据对2018年3—11月降水和2 m温度进行统计降尺度,利用先频率匹配法、再阈值法对插值后的降水订正,利用Kalman滤波型的递减平均统计降尺度法对插值后的温度订正,最终获得逐小时降水量和温度的预报。结果表明：(1)对于晴雨预报准确率,绝大多数预报时效频率匹配法和阈值法均对其有明显提高,前者最大改进幅度可达20%以上。对于相对误差,阈值法对空报现象有较显著改进。对于1 h降雨量大于等于20 mm的短时强降水,频率匹配法订正后的TS评分有明显提高。对2018年“安比”台风事件,除具有以上改进效果外,频率匹配法提高了降水主体形态和量级的预报水平,阈值法对空报站订正正确。(2)对于温度的ECWMF模式预报检验,几乎在任何预报时效内都是3月的绝对误差最大。通过Kalman滤波型的递减平均统计降尺度法后,各月的绝对误差都有不同程度减小。总体上,订正后的绝对误差曲线仍具有订正前的周期性波动,波峰、波谷位置也与订正前基本一致,且绝对误差越大,订正幅度越大。个例分析也表明订正后保留了温度预报空间分布的准确性,且绝对误差有明显下降。