浅谈大气科学的发展脉络

大气科学的发展经历了从定性、定量、经验分析、理论分析、以及综合气象科学体系建立和发展的过程,实现了从感性、定性到理性、定量;从局地、分散到全球、集中;从静止、线性到动态、非线性,从单一学科到综合科学体系的转变。近来人工智能和大数据技术的广泛应用,受到世界主流气象科学中心的关注和青睐,也将为大气科学的发展提供新的方法和思路。

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.

An Online Model Correction Method Based on an Inverse Problem:PartⅡ——Systematic Model Error Correction

An online systematic error correction is presented and examined as a technique to improve the accuracy of real-time numerical weather prediction, based on the dataset of model errors （MEs） in past intervals. Given the analyses, the ME in each interval （6 h） between two analyses can be iteratively obtained by introducing an unknown tendency term into the prediction equation, shown in Part I of this two-paper series. In this part, after analyzing the 5-year （2001-2005） GRAPES- GFS （Global Forecast System of the Global and Regional Assimilation and Prediction System） error patterns and evolution, a systematic model error correction is given based on the least-squares approach by firstly using the past MEs. To test the correction, we applied the approach in GRAPES-GFS for July 2009 and January 2010. The datasets associated with the initial condition and SST used in this study were based on NCEP （National Centers for Environmental Prediction） FNL （final） data. The results indicated that the Northern Hemispheric systematically underestimated equator-to-pole geopotential gradient and westerly wind of GRAPES-GFS were largely enhanced, and the biases of temperature and wind in the tropics were strongly reduced. Therefore, the correction results in a more skillful forecast with lower mean bias and root-mean-square error and higher anomaly correlation coefficient.

Forecast scheme and strategy for extended-range predictable components

Although extended-range forecasting has exceeded the limit of daily predictability of weather,there are still partially predictable characteristics of meteorological fields in such forecasts.A targeted forecast scheme and strategy for extended-range predictable components is proposed.Based on chaotic characteristics of the atmosphere,predictable components and unpredictable random components are separated by using the standpoint of error growth in a numerical model.The predictable components are defined as those with slow error growth at a given range,which are not sensitive to small errors in initial conditions. A numerical model for predictable components(NMPC)is established,by filtering random components with poor predictability.The aim is to maintain predictable components and avoid the influence of rapidly growing forecast errors on small scales. Meanwhile,the analogue-dynamical approach(ADA)is used to correct forecast errors of predictable components,to decrease model error and statistically take into account the influence of random components.The scheme is applied to operational dynamical extended-range forecast(DERF)model of the National Climate Center of China Meteorological Administration (NCC/CMA).Prediction results show that the scheme can improve forecast skill of predictable components to some extent, especially in high predictability regions.Forecast skill at zonal wave zero is improved more than for ultra-long waves and synoptic-scale waves.Results show good agreement with predictability of spatial scale.As a result,the scheme can reduce forecast errors and improve forecast skill,which favors operational use.

Dynamic-statistics combined forecast scheme based on the abrupt decadal change component of summer precipitation in East Asia

基于 19832011 CMAP 数据，在东亚的降水异例和它的附近的海区域(此后叫的东亚为短) 在 1999 前表明 ++ 模式并且 + 模式以后；这个十的变化在相应 EOF3 部件主要被包含。然而， NCC_CGCM 预报结果是相当不同的，它在 1999 前揭示 ++ 模式并且 ++ 模式以后。同时，基于这些关键 SST 区域改进 NCC_CGCMs 预报精确性的概率被讨论，并且动态统计的联合预报计划为增加在东亚在夏天降水包含的十的变化的信息被构造。独立样品预报结果显示这个预报计划罐头有效地修改十的变化信息在东亚在夏天降水包含了的 NCC_CGCMs (特别在 30 敢琠敨攠癮 的区域吗？？

Large-scale low-frequency rainfall regimes and their transition modes in summertime over China

Seven large-scale low-frequency rainfall regimes (LFRRs) in summertime are identified for China in a 10-dimensional phase space by using a 40-year daily precipitation dataset. Corresponding to the local extrema of observed probability density in phase space, the LFRRs are characterized by the persistence and transitions that reflect the spread and jump features of low-frequency rainfall centers. The LFRRs are generally consistent with summer monsoon rainbelts on the intraseasonal timescale, and there exist the preferred transition relationships between the LFRRs. Four LFRRs’ transition modes (LFRRTMs), which are the dominant components of the interannual and intraseasonal variability of sum- mer rainfall in China, are further induced. Analyses of atmospheric circulation system show that the anomalies of the subtropical high over the western Pacific (SHWP) and blockings in mid-high latitudes, the low-latitude circulation anomalies, and the wavetrains over the East Asia-Pacific region (EAPWs), play crucial roles in the occurrence and transitions of the LFRRs. Moreover, the evolution of the SHWP and blockings, the distribution and movement of the EAPWs, and the intraseasonal variability of the East Asian-Pacific jet stream may be principal factors of the formations of the LFRRTMs.

Extracting predictable components and forecasting techniques in extended-range numerical weather prediction

This paper refers to the CNOP-related algorithms and formulates the practical method and forecast techniques of extracting predictable components in a numerical model for predictable components on extended-range scales.Model variables are divided into predictable components and unpredictable chaotic components from the angle of model prediction error growth.The predictable components are defined as those with a slow error growth at a given range.A targeted numerical model for predictable components is established based on the operational dynamical extended-range forecast(DERF)model of the National Climate Center.At the same time,useful information in historical data are combined to find the fields for predictable components in the numerical model that are similar to those for the predictable components in historical data,reducing the variable dimensions in a similar judgment process and further correcting prediction errors of predictable components.Historical data is used to obtain the expected value and variance of the chaotic components through the ensemble forecast method.The numerical experiment results show that this method can effectively improve the forecast skill of the atmospheric circulation field in the 10–30 days extended-range numerical model and has good prospects for operational applications.