Numerical weather prediction over the Eastern Caribbean using Florida State University (FSU) global and regional spectral models and multi-model/multi-analysis super-ensemble

Summary A month-long short-range numerical weather prediction experiment using the Florida State University’s (FSU) global and regional models and the multi-model/multi-analysis super-ensemble over the Eastern Caribbean domain is presented in this paper. The paper also investigates weather prediction capabilities of FSU global and regional models by examining the root mean square errors (RMSE) for the wind and precipitation fields. Super-ensemble forecasting, a new statistical approach to weather forecasting, is used over this domain. Here, forecasts from a number of numerical models provide the input and statistical combinations of these forecasts produce the super-ensemble forecast. A similar approach is used for the precipitation field where one model using different rain rate algorithms is used to generate different model outputs. The results show that the super-ensemble method produces forecasts that are superior to those obtained from the ensemble members. Received May 29, 2000/Revised February 15, 2001     

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.     

Improved seasonal climate forecasts for the Caribbean region using the Florida State University Synthetic Superensemble

Summary Climate variations in the Caribbean, largely manifest in rainfall activity, have important consequences for the large-scale water budget, natural vegetation, and land use in the region. The wet and dry seasons will be defined, and the important roles played by the El Ni?o-Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO) in modulating the rainfall during these seasons will be discussed. The seasonal climate forecasts in this paper are made by 13 state of the art coupled atmosphere-ocean general circulation models (CGCMs) and by the Florida State University Synthetic Superensemble (FSUSSE), whose forecasts are obtained by a weighted combination of the individual CGCM forecasts based on a training period. The success of the models in simulating the observed 1989–2001 climatology of the various forecast parameters will be examined and linked to the models’ success in predicting the seasonal climate for individual years. Seasonal forecasts will be examined for precipitation, sea-surface temperature (SST), 2-meter air temperature, and 850 hPa u- and v-wind components during the period 1989–2001. Evaluation metrics include root mean square (RMS) error and Brier skill score. It will be shown that the FSUSSE is superior to the individual CGCMs and their ensemble mean both in simulating the 1989–2001 climatology for the various parameters and in predicting the seasonal climate of the various parameters for individual years. The seasonal climate forecasts of the FSUSSE and of the ensemble mean of the 13 state of the art CGCMs will be evaluated for years (during the period 1989–2001) that have particular ENSO and NAO signals that are known to influence Caribbean weather, particularly the rainfall. It will be shown that the FSUSSE provides superior forecasts of rainfall, SST, 2-meter air temperature, and 850 hPa u- and v-wind components during dry summers that are modulated by negative SOI and/or positive NAO indices. Such summers have become a feature of a twenty-year pattern of drought in the Caribbean region. The results presented in this paper will show that the FSUSSE is a valuable tool for forecasting rainfall and other atmospheric and oceanic variables during such periods of drought.     

Generalized inversion of a global numerical weather prediction model

Summary We construct the generalized inverse of a global numerical weather prediction (NWP) model, in order to prepare initial conditions for the model at time t=0 hrs. The inverse finds a weighted, least-squares best-fit to the dynamics for –24<t<0, to the previous initial condition att=–24, and to data att=–24,t=–18,t=–12 andt=0. That is, the inverse is a weak-constraint, four-dimensional variational assimilation scheme. The best-fit is found by solving the nonlinear Euler-Lagrange (EL) equations which determine the local extrema of a penalty functional. The latter is quadratic in the dynamical, initial and data residuals. The EL equations are solved using iterated representer expansions. The technique yields optimal conditioning of the very large minimization problem, which has 10⁹ hydrodynamical and thermodynamical variables defined on a 4-dimensional, space-time grid.In addition to introducing the inverse NWP model, we demonstrate it on a medium-sized problem, namely, a study of the impact of reprocessed cloud track wind observations (RCTWO) from the 1990 Tropical Cyclone Motion Experiment (TCM-90). The impact is assessed in terms of the improvement of forecasts in the South China Sea att=+48 hours. The calculation shows that the computations are manageable, the iteration scheme converges, and that the RCTWO have a beneficial impact.     

河南\

对1949年以来影响我国的台风暴雨及其预报研究的进展,从台风暴雨形成机理、专题研究、预报研究和研究新动向等四方面进行回顾总结。首先,从环境场、下垫面和内部条件三个角度对台风暴雨形成机理进行简要回顾,并从“75.8”特大暴雨、“莫拉克”极端暴雨、远距离暴雨、台风暴雨的诊断分析和气候特征等五个角度做了专题回顾。同时,简要回顾了台风暴雨预报研究进展,并就台风暴雨研究新动向给出了在云微物理、极端降水和预报方法三个方向上的初步判断。在此基础上,给出了小结与展望。

     

6种数值模式在安徽区域天气预报中的检验

本文检验了从2006年6月到2008年12月,Grapes、MM5、WRF、T213、JMA和Germany共6个模式对安徽区域72 h内降水量、风速和气温的预报。降水量TS评分显示,从小雨到大雨,JMA的参考价值较高,从大雨到大暴雨则是MM5和WRF比较好;Germany和T213的评分均处于中间水平,而Grapes评分最低。冬夏季各模式的预报较好,其他季节预报较差。风速,24 h JMA和T213的预报较好,48、72 h MM5和WRF的参考价值较高。气温,24、48 h MM5和WRF预报较好,而72 h则是MM5和T213好。Grapes对风速和气温的预报相对较差。上述检验结果不仅有助于预报员更好地利用数值模式制作天气预报,而且为数值天气预报的解释应用提供科学依据。     

基于雷达外推临近预报和中尺度数值预报融合技术的短时定量降水预报试验

为克服目前中尺度数值模式在对流尺度定量降水短时预报方面的不足,弥补基于“外推”的临近预报技术在2h以上定量降水预报能力方面的缺陷,研究设计了一种基于“外推”临近预报技术和中尺度数值模式的定量降水预报(QPF)融合技术方案,并进行了试验应用.该方案首先基于雷达探测和自动气象站观测的定量降水估计(QPE)结果,对中尺度数值模式输出的定量降水预报在谱空间进行相位校正,分析计算出数值预报和观测的偏差,导出一个附加的数值预报校正场;其次,根据数值预报校正场满足一定时间变化分布的特征,调整相应时段的数值预报降水区域和强度;最后,利用双曲正切线权重函数,对校正后的数值模式定量降水预报和基于临近预报技术的定量降水预报进行融合,融合权重根据典型环流特征动态变化.融合后的定量降水预报在前1-2h表现出主要依赖“外推”临近预报结果,之后随着融合权重的变化,数值预报对融合结果的贡献逐渐加大,直至融合后5-6h占主导地位.通过对京津冀地区2011年夏季5个及2012年夏季2个典型强降水个例的80次预报试验及其检验,表明融合后的0-6h定量降水预报结果改进较为明显,总体优于单独的临近预报技术或者中尺度数值预报模式的结果.     

Towards urbanisation of the non-hydrostatic numerical weather prediction model Lokalmodell (LM)

Numerical weather prediction models are increasingly employed for providing meteorological data for urban air quality applications. Model resolution, physiographic parameters and surface-layer parameterisations need to be adapted to the requirements of the urban boundary layer. The Lokalmodell of the German Weather Service was triple-nested down to a horizontal grid resolution of 1.1 km, urbanised physiographic parameters were implemented, and an additional anthropogenic heat source was introduced. Results of a sensitivity study for a spring dust episode in Helsinki show a clear urbanisation effect of these measures on temperature, humidity and the partitioning of surface fluxes, leading to an increased Bowen ratio and heat storage and an urban heat island effect.     

Review of numerical methods for nonhydrostatic weather prediction models

Summary ?Currently available computer power allows to run operational numerical weather prediction models at resolutions higher than 10 km. The aim of such high resolution modeling is the prediction of local weather, including orographically induced winds and local precipitation patterns. In this range the hydrostatic approximation is no longer valid and nonhydrostatic models have to be used instead. For several decades these models have been developed for research purposes only, but operational application is now reality. In this paper, the numerical methods used in current nonhydrostatic forecast models will be reviewed and some promising techniques in this field will be discussed. Special attention is given to aspects such as the choice of the vertical coordinate, the efficiency of algebraic solvers for semi-implicit time discretizations, and accurate and non-oscillatory advection schemes. Received July 6, 2001; revision October 12, 2001     

论过去资料在数值天气预报中使用的理论和方法

对中国气象学家在数值天气预报中使用过去实况演变资料的理论和方法作卫概要性总结,其中不仅讨论了近期实况演变资料的应用,还了积累数十年的历史资料的应用问题。文中强调要改变对数值预报问题的提法,回顾了由初值问题改为演变问题,再改为反问题的历史。     

一次秋季大暴雨及秋风天气预报失误原因剖析

受弱的两高切变和高空冷平流的影响，2001年8月10日黔东南州出现了一次秋季大暴雨天气过程，11－12日受地面冷空气从东北路径影响，又出现了秋风天气。由于在预报过程中疏忽了高空冷平流的作用以及出现转折性天气前单站要素的某些重要特征，造成过程漏报。本文对这次大暴雨和秋风天气过程作了分析，得出一些结论，对今后秋季暴雨笔秋风天气的预报有一定参考价值。     

Generalized inversion of a global numerical weather prediction model, II: Analysis and implementation

Summary This is a sequel to Bennett, Chua and Leslie (1996), concerning weak-constraint, four-dimensional variational assimilation of reprocessed cloud-track wind observations (Velden, 1992) into a global, primitive-equation numerical weather prediction model. The assimilation is performed by solving the Euler-Lagrange equations associated with the variational principle. Bennett et al. (1996) assimilate 2436 scalar wind components into their model over a 24-hour interval, yielding a substantially improved estimate of the state of the atmosphere at the end of the interval. This improvement is still in evidence in forecasts for the next 48 hours.The model and variational equations are nonlinear, but are solved as sequence of linear equations. It is shown here that each linear solution is precisely equivalent to optimal or statistical interpolation using a background error covariance derived from the linearized dynamics, from the forcing error covariance, and from the initial error covariance. Bennett et al. (1996) control small-scale flow divergence using divergence dissipation (Talagrand, 1972). It is shown here that this approach is virtually equivalent to including a penalty, for the gradient of divergence, in the variational principle. The linearized variational equations are solved in terms of the representer functions for the wind observations. Diagonalizing the representer matrix yields rotation vectors. The rotated representers are the array modes of the entire system of the model, prior covariances and observations. The modes are the observable degrees of freedom of the atmosphere. Several leading array modes are presented here. Finally, appendices discuss a number of technical implementation issues: time convolutions, convergence in the presence of planetary shear instability, and preconditioning the essential inverse problem.With 9 Figures     

实测风场在数值天气预报中的应用

一、初始场的误差 我国有较稠密的高空风测站网,相当广大的地区又位于中低纬度带,天气系统在风场中比在气压场中有更明显的反映,使用风场资料在天气预报中有很重要的意义。毫无疑问,在数值天气预告中应用这些实测风场也是很重要的。另方面,为了提高预报效能,则又有必要使用较精确的原始方程式组。但这吋如果直接用实测风场作始始场,却会得到紊乱不堪的计算结果。因此必须对问题作深入的研究。     

Direct verification of forecasts from a very high resolution numerical weather prediction (NWP) model

Summary The University of New South Wales (UNSW) High Resolution numerical weather prediction model (HIRES) is run routinely, on a daily basis, at a horizontal resolution of 25 km. The output is made available to the New South Wales (NSW) regional office of the Australian Bureau of Meteorology in Sydney. HIRES has been used to forecast mean wind direction and speed at a height of 12 metres for a number of events in the past. The opportunity was used in December 1997 to run the model for the annual Sydney to Hobart yacht race area. For the 1997 Sydney to Hobart yacht race the model was run at 25 km horizontal resolution and the output was made available to all competitors on the morning of the race, namely December 26th. It was also decided by the authors to expand the verification to include all observations available both from land and sea within the model domain an well as those available from a moving single point at sea, namely a yacht. After the event, the model was run once at the increased resolution of 10 km, out to 5 days ahead. Both model runs were subjected to detailed verification by one of the authors (KLB) who participated in the race aboard the maxi-yacht Nicorette and who carried out a pre-arranged observational program during the race. Surface synoptic weather maps prepared in the NSW office of the Bureau of Meteorology were also consulted in order to extend the verification scheme. The model predicted winds were verified on a six-hourly basis utilising instrumentation on the yacht as well as surface observations plotted in standard World Meterological Organisation (WMO) format on surface synoptic weather maps. The yacht carried wind sensors situated on top of the mast at a height of 30 metres above the water. The authors were most interested in the accuracy of the wind velocity forecast by the model. It is important to note that forecasting for points over the ocean at widely separated time intervals represents a very difficult challenge. The verifications reveal that the model gave overall forecast guidance of very good to excellent quality and was particularly accurate early in the race, when a Southerly Buster event occurred during the evening of the first day. However, a caveat should be added that this forecast, despite its remarkable accuracy, is not a claim that accurate very high resolution regional prediction is a solved problem. Rather, it is a demonstration that in particular instances current models are now capable of achieving high levels of skill a number of days ahead. Received September 28, 1999 Revised November 25, 1999     

The impact of observation systems on medium-range weather forecasting in a global forecast system

To investigate the impact of various types of data on medium-range forecasts, observing system experiments are performed using an assimilation algorithm based on the National Centers for Environmental Prediction (NCEP)/Department of Energy (DOE) reanalysis system. Data-denial experiments for radiosonde, satellite, aircraft, and sea surface observations, and selected data experiments for radiosonde and surface data, are conducted for the boreal summer of 1997 and the boreal winter of 1997/1998. The data assimilation system used in this study is remarkably dependent on radiosonde data, which provides information about the three-dimensional structure of the atmosphere. As expected, the impact of radiosonde observations on medium-range forecasts is strongly positive over the Northern Hemisphere and tropics, whereas the satellite system is most beneficial over the Southern Hemisphere. These results are also found in experiments simulating historical changes in observation systems. Over the tropics, assimilation without radiosonde observations generates unbalanced analyses resulting in unrealistic forecasts that must be corrected by the forecast model. Forecasts based on analysis from the observation data before the era of radiosonde observation are found to be less meaningful. In addition, the impacts on forecasts are closely related to the geographical distribution of observation data. The memory of observation data embedded in the analysis tends to persist throughout forecasts. However, cases exist where the effect of forecast error growth is more dominant than that of analysis error, e.g., over East Asia in summer, and where the deficiency in observations is supplemented or the imbalance in analysis is adjusted by the forecast model during the period of forecasts. Forecast error growth may be related to the synoptic correction performed by the data assimilation system. Over data-rich areas, analysis fields are corrected to a greater extent by the data assimilation system than are those over data-poor areas, which can cause the forecast model to produce more forecast errors in medium-range forecasts. It is found that even one month per season is sufficient for forecast skill verification in data impact experiments. Additionally, the use of upper-air observations is found to benefit areas that are downstream of observation data-rich areas.     

初始场对暴雨数值预报的影响及集合预报试验

以2003年7月3日至4日发生在淮河流域的暴雨过程为例,利用AREM模式,分析了初始场对暴雨预报的影响,提出了暴雨预报中初始场不确定性包含的两层含义,一是被常规观测遗漏的中小尺度信息误差;另一个则是随着环流变化造成的信息误差的不确定性。并针对着初始场的不确定性,设计了一种初值集合预报的方法,它包含了经典的集合预报方法MCF、LAF、BGM的思想。用这种方法进行了集合预报试验,结果表明:集合平均预报的预报技巧高于24 h控制预报,集合预报还可给出降水概率预报、离散度等产品为暴雨可预报性的评估提供参考。     

数值预报环流形势场在一次强降水天气过程中的应用

主要利用ECWMF及T213数值预报产品的形势预报场对2002年6月17－20日连续性强降水天气过程的影响系统的演变情况作一个简单的分析，考察这两种数值预报产品对这次过程的预报能力，可知它们对夏季强的降雨天气过程的形势预报很有参考意义。