多尺度大气数值预报的技术进展

随着计算机和计算理论的发展,数值模式正在向全球化、精细化发展,以适应多尺度、多目的应用的要求,从而模糊了大气环流模式和中尺度数值模式的界限,其主要手段就是改进模式动力框架、离散化手段、计算方法、物理过程的普适性。在实际应用中如何提高模式的多尺度计算性能则是问题关键。该文从模式球面坐标系、网格构造、离散化方法的动力特性、垂直坐标和地形处理以及对物理过程的要求出发,探讨分析多尺度大气数值模式的特点:全球/区域可选、非静力近似、具有良好的频散关系和详细的物理过程,垂直高度坐标和"剪切"地形对多尺度通用模式的改良十分重要。除上述特点外,模式所采用的计算方法也应该最大限度地描述大气动力过程特性,采用高性能计算方案有利于多尺度预报。结合当前多尺度预报的国际研究热点和开发前沿,探讨我国新一代多尺度数值预报系统GRAPES的进一步发展及改进方向。     

AREM模式两种初值扰动方案的集合降水预报试验及检验

基于我国科学家自主开发的AREM区域中尺度模式,考虑模式初值的不确定性,初步建立起11个成员37 km分辨率覆盖全国大部地区的短期集合预报系统。采用两套初值扰动试验方案(降尺度方案和BGM方案)分别进行了批量试验,试验结果表明:(1)通过对集合预报离散度和概率预报的检验,表明采用区域模式自身孵化循环产生的初始扰动明显优于直接使用全球集合预报提供的初始扰动。(2)无论采用哪种初值扰动方案,基于集合预报方法的集合平均预报、概率预报等均优于单一的确定性预报和业务预报。(3)离散度与集合平均预报误差的面平均有较好的对应关系;离散度与预报误差的空间相关系数平均为40%～45%。(4)目前集合预报系统离散度与预报误差相比偏小,其部分原因在于没有考虑模式物理过程的不确定性,这将在下一步的工作中逐步改进。     

GRAPES全球集合预报初始条件及模式物理过程不确定性方法研究

为描述GRAPES全球模式初始条件的不确定性,基于适合集合预报应用的GRAPES全球奇异向量技术,依据大气初始误差符合正态分布的特征,采用高斯取样奇异向量来构造全球集合预报初始扰动,在此基础上建立了GRAPES全球集合预报系统(GRAPES-GEPS)。利用GRAPES全球同化分析场,对采用初始扰动的GRAPES-GEPS连续试验预报结果进行检验和分析。结果表明:GRAPES-GEPS中高度场、风场及温度场预报的集合离散度能有效快速增加,集合平均均方根误差与集合离散度的关系合理;相对控制预报的均方根误差,集合平均的预报优势在预报中期非常显著。为进一步体现GRAPES-GEPS中模式物理过程的不确定性,发展了模式物理过程倾向随机扰动技术(SPPT)。试验结果表明:SPPT方案的应用有效提高了GRAPES-GEPS在南、北半球和热带地区等压面要素预报的集合离散度,同时一定程度减小了集合平均误差,进而改进了集合平均误差与集合离散度的关系,其中SPPT方案在热带地区的改进最为显著。本文发展的基于奇异向量的初始扰动方法和模式扰动SPPT方案在中国气象局2018年12月业务化运行的GRAPES-GEPS中得到了应用。     

基于随机全倾向扰动的台风路径集合预报试验

以GRAPES-TCM为试验模式设计3种集合方案对2011年9个台风进行109次72 h的路径集合预报试验。结果显示:在3种集合方案中,路径预报效果总体最好的是方案1,其路径平均绝对误差与台风移速、移向的总体偏差都最小;方案1和方案2的平均绝对误差与台风移速、移向的总体偏差都小于它们的控制预报的结果,集合预报表现出优于确定性预报的预报能力;与实况比较,3种集合方案预报的台风移向总体以偏右为主,台风移速在积分后期以偏慢为主;方案1的路径离散度最大,方案3最小,3种方案的集合离散度比其合理的离散度都明显偏小;与国内外8个业务数值模式的预报结果比较,方案1的路径预报优于除欧洲中心数值模式以外的7个业务模式的预报,表现出一定的业务应用价值。      

基于通用图形处理器的GRAPES长波辐射并行方案

随着通用图形处理器 (GPGPU) 计算技术的快速发展,通过大规模增加处理系统的并发度来提升性能成为计算机高性能计算的最新趋势。目前,通用图形处理器已经被应用到科学计算的诸多领域。长波辐射作为GRAPES模式中极为重要的物理过程,其巨大的计算量对整个GRAPES模式的运行效率有重要影响。该文依托NVIDIA公司计算统一设备架构 (CUDA) 技术平台,以GRAPES全球模式中长波辐射传输方案为例,对其进行了大规模并发设计和优化,在保持系统结果一致的前提下,对比单颗高端CPU,Tesla C1060 GPGPU具有11倍的加速效果,明显提升了GRAPES全球模式的执行速度和预报时效。研究表明:使用通用图形处理器技术提升数值预报模式的执行速度非常有潜力。     

T639全球模式的台风初始化方案升级试验

由于洋面上缺少有效的台风观测资料,台风初始化对全球数值预报业务模式而言,仍然是一个悬而未决的问题。国家气象中心自从2009年将T639全球数值预报系统投入业务运行以来,应用了一套完整可行的台风初始化方案,其技术路线是在台风刚发生时的预报时刻,在背景场嵌入人造bogus涡旋;而在后续的循环滚动预报时刻,采取涡旋重定位和涡旋调整技术方案;因此,第一个预报时刻的涡旋形成技术直接影响到后续时刻的台风涡旋质量乃至预报效果。但是,人造bogus涡旋主要依据统计的经验模型建立,其结构与全球数值模式的动力学、物理学属性并不匹配。基于国家气象中心T639全球资料分析同化-预报循环系统,一套升级版的台风初始化方案被发展了起来,与业务方案的主要区别在于升级方案利用资料同化技术来形成初始涡旋而不是直接嵌入人造bogus涡旋;这在很大程度上减少了人为主观因素对台风初始结构的影响,而更多地是依靠变分资料同化来协调约束产生三维空间的涡旋环流,这样产生的涡旋环流不但与周围环境场比较协调,而且与模式的动力学、物理学属性也比较匹配。应用新方案,本文对生成于西北太平洋2011-2012年27个台风进行了数值试验,初步分析表明,新方案在实现台风初始涡旋环流结构方面有着不错的表现,相比于业务使用的方案而言,新方案台风路径平均预报误差有了不错幅度的降低,2~5 d预报平均路径误差普遍降低了3%~15%。     

利用距离平方反比算法对格点数值预报产品进行插值

随着数值模式理论与方法、大气探测技术和计算机技术的快速发展,数值预报模式表现出对预报的巨大潜力,已经成为目前业务预报领域不可替代的可用参考方法。目前黑龙江省台主要用于业务应用的数值模式预报产品有T639、JMA（日本数值模式）、GRE（德国数值模式）3种全球数值预报模式和中尺度数值预报模式WRF、MM5以及Grapes...     

高分辨有限区数值预报模式的省时显式积分

本文提出利用省时显式方案积分两个高分辨有限区数值预报模式。其结果表明，这一积分方案可以使模式的计算效率大大提高，而由时间积分方案的变化所造成的预报差异比模式空间离散化方案的不同所造成的预报差异要小。同时还讨论了将省时显式方案用于有限区模式的边界处理问题，提出了一个与蛙跃格式等价的边界处理方案。     

GRAPES区域集合预报两种侧边界扰动方法对比试验

中国气象局数值预报中心自2014年建立了区域集合预报业务系统,其使用的侧边界扰动由全球集合预报系统动力降尺度得到。为深入了解侧边界扰动对区域集合预报的影响,基于15 km水平分辨率的区域集合预报模式,使用动力降尺度方法和尺度化滞后平均法(scaled lagged average forecasting,SLAF)设计构造了两种侧边界扰动方案,并开展了2015年7月共6天的集合预报试验,利用集合均方根误差、集合离散度、连续分级概率评分、离群值、Brier Score及相对作用特征曲线面积等概率预报检验方法进行了多方面检验,分析了两种侧边界扰动方案对区域集合预报质量的影响。结果表明:动力降尺度侧边界扰动方案(DOWN)的扰动总能量在各垂直层次均大于SLAF方案,使得边界上前者的离散度大于后者,集合扰动增长更为合理;对于等压面要素和地面要素,DOWN方案的离散度、Outlier、CRPS等评分优于SLAF方案,反映了DOWN方案构造的侧边界扰动更加合理;在降水概率预报技巧方面,SLAF方案在评分上具有一定优势,但评分的提高没有通过显著性水平检验,因此认为两种方案对降水预报的改进基本相当。     

南海热带气旋数值预报模式与全球谱模式的嵌套试验

分析了各嵌套变量的作用和效果，发现采用部分变量嵌套就能取得一定效果，各变量的作用也不完全相同。这样，可以选取作用大，质量好，易处理的变量进行嵌套。并相应地提出了一个边界嵌套处理方案，应用于南海带热带气旋数值预报有限区模式（ＴＬ１０）与全球谐模式（Ｔ６３）的嵌套过程，通过数值预报试验，表明采用该嵌套处理方案能够取得良好的效果。     

全球大气数值模式动力框架研究进展

随着数值计算方法和高性能计算机技术的发展以及大气科学理论的完善,国外研制出了许多全球高分辨率非静力大气数值模式,为了让国内的模式开发者对当前全球大气数值模式的现状有一个清晰的了解,归纳总结2012年8月在美国大气研究中心参与评估测试的全世界17个非静力全球大气数值模式,主要从基本方程组、球面网格、离散方法、守恒性质、参数化物理过程与动力框架的耦合和全球大气模式的评估等进行回顾、归纳和讨论,对国内的模式研发者有一定的参考意义。     

Temporal downscaling: a comparison between artificial neural network and autocorrelation techniques over the Amazon Basin in present and future climate change scenarios

Several studies have been devoted to dynamic and statistical downscaling for both climate variability and climate change. This paper introduces an application of temporal neural networks for downscaling global climate model output and autocorrelation functions. This method is proposed for downscaling daily precipitation time series for a region in the Amazon Basin. The downscaling models were developed and validated using IPCC AR4 model output and observed daily precipitation. In this paper, five AOGCMs for the twentieth century (20C3M; 1970–1999) and three SRES scenarios (A2, A1B, and B1) were used. The performance in downscaling of the temporal neural network was compared to that of an autocorrelation statistical downscaling model with emphasis on its ability to reproduce the observed climate variability and tendency for the period 1970–1999. The model test results indicate that the neural network model significantly outperforms the statistical models for the downscaling of daily precipitation variability.     

A strategy for merging objective estimates of global daily precipitation from gauge observations,satellite estimates,and numerical predictions

This paper describes a strategy for merging daily precipitation information from gauge observations, satellite estimates(SEs), and numerical predictions at the global scale. The strategy is designed to remove systemic bias and random error from each individual daily precipitation source to produce a better gridded global daily precipitation product through three steps.First, a cumulative distribution function matching procedure is performed to remove systemic bias over gauge-located land areas. Then, the overall biases in SEs and model predictions(MPs) over ocean areas are corrected using a rescaled strategy based on monthly precipitation. Third, an optimal interpolation(OI)–based merging scheme(referred as the HL-OI scheme)is used to combine unbiased gauge observations, SEs, and MPs to reduce random error from each source and to produce a gauge—satellite–model merged daily precipitation analysis, called BMEP-d(Beijing Climate Center Merged Estimation of Precipitation with daily resolution), with complete global coverage. The BMEP-d data from a four-year period(2011–14) demonstrate the ability of the merging strategy to provide global daily precipitation of substantially improved quality.Benefiting from the advantages of the HL-OI scheme for quantitative error estimates, the better source data can obtain more weights during the merging processes. The BMEP-d data exhibit higher consistency with satellite and gauge source data at middle and low latitudes, and with model source data at high latitudes. Overall, independent validations against GPCP-1DD(GPCP one-degree daily) show that the consistencies between BMEP-d and GPCP-1DD are higher than those of each source dataset in terms of spatial pattern, temporal variability, probability distribution, and statistical precipitation events.     

Tschebyscheff 函数在大气模式垂直离散问题中的应用

文章就常用全球谱模式中的垂直离散问题进行了讨论，给出了用Tschebyscheff函数积分静力方程、连续方程，以及计算垂直平流、垂直扩散等的方法。还对某些气象要素在垂直方向微商呈现间断的问题进行了分析，指出了其中存在的问题和应采取的措施。     

Assessing impacts of climate change on forests: The state of biological modeling

Models that address the impacts of climate change on forests are reviewed at four levels of biological organization: global, regional or landscape, community, and tree. The models are compared for their ability to assess changes in fluxes of biogenic greenhouse gases, land use, patterns of forest type or species composition, forest resource productivity, forest health, biodiversity, and wildlife habitat. No one model can address all of these impacts, but landscape transition models and regional vegetation and land-use models have been used to consider more impacts than the other models. The development of landscape vegetation dynamics models of functional groups is suggested as a means to integrate the theory of both landscape ecology and individual tree responses to climate change. Risk assessment methodologies can be adapted to deal with the impacts of climate change at various spatial and temporal scales. Four areas of research needing additional effort are identified: (1) linking socioeconomic and ecologic models; (2) interfacing forest models at different scales; (3) obtaining data on susceptibility of trees and forest to changes in climate and disturbance regimes; and (4) relating information from different scales.The U.S. Government right to retain a non-exclusive, royalty-free license in and to any copyright is acknowledged.Managed by Martin Marietta Energy Systems, Inc., for the U.S. Department of Energy under contract DE-AC05-84OR21400.     

Application of regularised optimal fingerprinting to attribution. Part II: application to global near-surface temperature

Attribution of global near-surface temperature changes is revisited using simulations from the coupled model intercomparison project 5 and methodological improvements from the regularised optimal fingerprinting approach. The analysis of global mean temperature shows that changes can be robustly detected and attributed to anthropogenic influence. However, the differences between results from individual models and observations are found to be larger than the simulated internal variability in several cases. Discrimination between greenhouse gases and other anthropogenic forcings, based on the global mean only, is more difficult due to collinearity of temporal response patterns. Using spatio-temporal data provides less robust conclusions with respect to detection and attribution, as the results tend to deteriorate as the spatial resolution increases. More importantly, some inconsistencies between individual models and observations are found in this case. Such behaviour is not observed in a perfect model framework, where pseudo-observations and the expected response patterns are provided by the same model. However, using response patterns from a model other than the one used for pseudo-observations may lead to the same behaviour as real observations. Our results suggest that additional sources of uncertainty, such as modeling uncertainty or observational uncertainty, should not be neglected in detection and attribution.     

High Resolution Global Modeling of the Atmospheric Circulation

An informal review is presented of recent developments in numerical simulation of the global atmospheric circulation with very fine numerical resolution models. The focus is on results obtained recently with versions of the GFDL SKYHI model and the Atmospheric Model for the Earth Simulator (AFES) global atmospheric models. These models have been run with effective horizontal grid resolution of 10–40 km and fine vertical resolution. The results presented demonstrate the utility of such models for the study of a diverse range of phenomena. Specifically the models are shown to simulate the development of tropical cyclones with peak winds and minimum central pressures comparable to those of the most intense hurricanes actually observed. More fundamentally, the spectrum of energy content in the mesoscale in the flow can be reproduced by these models down to near the smallest explicitly-resolved horizontal scales. In the middle atmosphere it is shown that increasing horizontal resolution can lead to significantly improved overall simulation of the global-scale circulation. The application of the models to two specific problems requiring very fine resolution global will be discussed. The spatial and temporal variability of the vertical eddy flux of zonal momentum associated with gravity waves near the tropopause is evaluated in the very fine resolution AFES model. This is a subject of great importance for understanding and modelling the flow in the middle atmosphere. Then the simulation of the small scale variations of the semidiurnal surface pressure oscillation is analyzed, and the signature of significant topographic modulation of the semidiurnal atmospheric tide is identified.     

Methods and approaches to modelling the Anthropocene

The ‘Anthropocene’ concept provides a conceptual framework that encapsulates the current global situation in which society has an ever-greater dominating influence on Earth System functioning. Simulation models used to understand earth system dynamics provide early warning, scenario analysis and evaluation of environmental management and policies. This paper aims to assess the extent to which current models represent the Anthropocene and suggest ways forward. Current models do not fully reflect the typical characteristics of the Anthropocene, such as societal influences and interactions with natural processes, feedbacks and system dynamics, tele-connections, tipping points, thresholds and regime shifts. Based on an analysis of current model representations of Anthropocene dynamics, we identify ways to enhance the role of modeling tools to better help us understand Anthropocene dynamics and address sustainability issues arising from them. To explore sustainable futures (‘safe and operating spaces’), social processes and anthropogenic drivers of biophysical processes must be incorporated, to allow for a spectrum of potential impacts and responses at different societal levels. In this context, model development can play a major role in reconciling the different epistemologies of the disciplines that need to collaborate to capture changes in the functioning of socio-ecological systems. Feedbacks between system functioning and underlying endogenous drivers should be represented, rather than assuming the drivers to be exogenous to the modelled system or stationary in time and space. While global scale assessments are important, the global scale dynamics need to be connected to local realities and vice versa. The diversity of stakeholders and potential questions requires a diversification of models, avoiding the convergence towards single models that are able to answer a wide range of questions, but without sufficient specificity. The novel concept of the Anthropocene can help to develop innovative model representations and model architectures that are better suited to assist in designing sustainable solutions targeted at the users of the models and model results.     

Spatio-temporal regression kriging model of mean daily temperature for Croatia

High resolution gridded mean daily temperature datasets are valuable for research and applications in agronomy, meteorology, hydrology, ecology, and many other disciplines depending on weather or climate. The gridded datasets and the models used for their estimation are being constantly improved as there is always a need for more accurate datasets as well as for datasets with a higher spatial and temporal resolution. We developed a spatio-temporal regression kriging model for Croatia at 1 km spatial resolution by adapting the spatio-temporal regression kriging model developed for global land areas. A geometrical temperature trend, digital elevation model, and topographic wetness index were used as covariates together with measurements from the Croatian national meteorological network for the year 2008. This model performed better than the global model and previously developed models for Croatia, based on MODIS land surface temperature images. The R2 was 97.8% and RMSE was 1.2 °C for leave-one-out and 5-fold cross-validation. The proposed national model still has a high level of uncertainty at higher altitudes leaving it suitable for agricultural areas that are dominant in lower and medium altitudes.     

How well are daily intense rainfall events captured by current climate models over Africa?

The ability of state-of-the-art climate models to capture the mean spatial and temporal characteristics of daily intense rainfall events over Africa is evaluated by analyzing regional climate model (RCM) simulations at 90- and 30-km along with output from four atmospheric general circulation models (AGCMs) and coupled atmosphere–ocean general circulation models (AOGCMs) of the Climate Model Intercomparison Project 5. Daily intense rainfall events are extracted at grid point scale using a 95th percentile threshold approach applied to all rainy days (i.e., daily rainfall ≥1 mm day^?1) over the 1998–2008 period for which two satellite-derived precipitation products are available. Both RCM simulations provide similar results. They accurately capture the spatial and temporal characteristics of intense events, while they tend to overestimate their number and underestimate their intensity. The skill of AGCMs and AOGCMs is generally similar over the African continent and similar to previous global climate model generations. The majority of the AGCMs and AOGCMs greatly overestimate the frequency of intense events, particularly in the tropics, generally fail at simulating the observed intensity, and systematically overestimate their spatial coverage. The RCM performs at least as well as the most accurate global climate model, demonstrating a clear added value to general circulation model simulations and the usefulness of regional modeling for investigating the physics leading to intense events and their change under global warming.