An Effective Approach for Improving the Real-Time Prediction of Summer Rainfall over China

This paper has two purposes. One is to evaluate the ability of an atmospheric general circulation model (IAP9L-AGCM) to predict summer rainfall over China one season in advance. The other is to propose a new approach to improve the predictions made by the model. First, a set of hindcast experiments for summer climate over China during 1982-2010 are performed from the perspective of real-time prediction with the IAP9L-AGCM model and the IAP ENSO prediction system. Then a new approach that effectively combines the hind-cast with its correction is proposed to further improve the model’s predictive ability. A systematic evaluation reveals that the model’s real-time predictions for 41 stations across China show significant improvement using this new approach, especially in the lower reaches between the Yellow River and Yangtze River valleys.     

Numerical Weather Prediction in China in the New Century ——Progress, Problems and Prospects

This paper summarizes the recent progress of numerical weather prediction(NWP)research since the last review was published.The new generation NWP system named GRAPES(the Global and Regional Assimila- tion and Prediction System),which consists of variational or sequential data assimilation and nonhydrostatic prediction model with options of configuration for either global or regional domains,is briefly introduced, with stress on their scientific design and preliminary results during pre-operational implementation.In ad- dition to the development of GRAPES,the achievements in new methodologies of data assimilation,new improvements of model physics such as parameterization of clouds and planetary boundary layer,mesoscale ensemble prediction system and numerical prediction of air quality are presented.The scientific issues which should be emphasized for the future are discussed finally.     

A comparison of the radar ray path equations and approximations for use in radar data assimilation

The radar ray path equations are used to determine the physical location of each radar measurement. These equations are necessary for mapping radar data to computational grids for diagnosis, display and numerical weather prediction (NWP). They are also used to determine the forward operators for assimilation of radar data into forecast models. In this paper, a stepwise ray tracing method is developed. The influence of the atmospheric refractive index on the ray path equations at different locations related to an intense cold front is examined against the ray path derived from the new tracing method. It is shown that the radar ray path is not very sensitive to sharp vertical gradients of refractive index caused by the strong temperature inversion and large moisture gradient in this case. In the paper, the errors caused by using the simplified straight ray path equations are also examined. It is found that there will be significant errors in the physical location of radar measurements if the earth’s curvature is not considered, especially at lower elevation angles. A reduced form of the equation for beam height calculation is derived using Taylor series expansion. It is computationally more efficient and also avoids the need to use double precision variables to mitigate the small difference between two large terms in the original form. The accuracy of this reduced form is found to be sufficient for modeling use.     

A Fast Algorithm for Solving CNOP and Associated Target Observation Tests

Conditional Nonlinear Optimal Perturbation （CNOP） is a new method proposed by Mu et al. in 2003, which generalizes the linear singular vector （LSV） to include nonlinearity. It has become a powerful tool for studying predictability and sensitivity among other issues in nonlinear systems. This is because the CNOP is able to represent, while the LSV is unable to deal with, the fastest developing perturbation in a nonlinear system. The wide application of this new method, however, has been limited due to its large computational cost related to the use of an adjoint technique. In order to greatly reduce the computational cost, we hereby propose a fast algorithm for solving the CNOP based on the empirical orthogonal function （EOF）. The algorithm is tested in target observation experiments of Typhoon Matsa using the Global/Regional Assimilation and PrEdiction System （GRAPES）, an operational regional forecast model of China. The effectivity and feasibility of the algorithm to determine the sensitivity （target） area is evaluated through two observing system simulation experiments （OSSEs）. The results, as expected, show that the energy of the CNOP solved by the new algorithm develops quickly and nonlinearly. The sensitivity area is effectively identified with the CNOP from the new algorithm, using 24 h as the prediction time window. The 24-h accumulated rainfall prediction errors （ARPEs） in the verification region are reduced significantly compared with the ＂true state,＂ when the initial conditions （ICs） in the sensitivity area are replaced with the ＂observations.＂ The decrease of the ARPEs can be achieved for even longer prediction times （e.g., 72 h）. Further analyses reveal that the decrease of the 24-h ARPEs in the verification region is attributable to improved simulations of the typhoon＇s initial warm-core, upper level relative vorticity, water vapor conditions, etc., as a result of the updated ICs in the sensitivity area.     

Move a Tropical Cyclone with 4D-Var and Vortex Dynamical Initialization in WRF Model

Previous studies showed that 4 D-Var technique used for data assimilation could be modified for weather control. This study demonstrates the ability of 4 D-Var to influence the future path of a tropical cyclone by calculating perturbations in WRF simulation. Given the background error covariance matrix, the initial field is improved by the vortex dynamic initialization technique. Our results show that 4 D-Var can be applied to control the trajectory of simulated tropical cyclones by producing "optimal" perturbations. In the numerical simulation experiment of Typhoon Mitag in 2019, after this kind of weather control similar to data assimilation, the tropical cyclone moved obviously,and the damaging wind over the coastline weakened. The prediction results after the initial field modified by 4 D-Var have a great change, and the position of the tropical cyclone moved about 0.5° southeastward after assimilation,which misses the southeast coast of China. Moreover, the damaging wind is also weakened. Since the 4 D-Var is premised on the assumption that the model is perfect and does not consider the model error, then the research plan to consider model error and introduce new methods is discussed in the paper.     

基于优先队列的另一种简单快速的DEM填洼算法

Some depression cells with heights lower than their surrounding cells may often be found in Grid-based digital elevation models （DEM） dataset due to sampling errors. The depression-filling algorithm presented by Planchon and Darboux works very quickly compared to other published methods. Despite its simplicity and delicacy, this algorithm remains difficult to understand due to its three complex subroutines and its recursive execution. Another fast algorithm is presented in this article. The main idea of this new algorithm is as follows： first, the DEM dataset is viewed as an island and the outer space as an ocean; when the ocean level increases, the DEM cells on the island＇s boundary will be inundated; when a cell is inundated for the first time, its elevation is increased to the ocean level at that moment; after the ocean has inun- dated the entire DEM, all of the depressions are filled. The depression-removing processing is performed using a priority queue. Theoretically, this new algorithm is a fast algorithm despite the fact that it runs more slowly than Planchon and Darboux＇s method. Its time-complexity in both the worst case and in an average case is O（8nlog2 （m））, which is close to O（n）. The running speed of this algorithm depends mainly on the insertion operation of the priority queue. As shown by the tests, the depres- sion-filling effects of this algorithm are correct and valid, and the overall time consumption of this algorithm is less than twice the time consumed by Planchon ＆ Darboux＇s method for handling a DEM smaller than 2500×2500 cells. More importantly, this new algorithm is simpler and easier to understand than Planchon and Darboux＇s method. This advantage allows the correct program code to be written quickly.     

Parallel Computing of a Variational Data Assimilation Model for GPS／MET Observation Using the Ray-Tracing Method

The Spectral Statistical Interpolation (SSI) analysis system of NCEP is used to assimilate me-teorological data from the Global Positioning Satellite System (GPS/MET) refraction angles with thevariational technique. Verified by radiosonde, including GPS/ME% observations into the analysis makesan overall improvement to the analysis variables of temperature, winds, and water vapor. However, thevariational model with the ray-tracing method is quite expensive for numerical weather prediction andclimate research. For example, about 4 000 GPS/MET refraction a;~gles need to be assimilated to producean ideal global analysis. Just one iteration of minimization will take more than 24 hours CPU time onthe NCEP‘s Cray C90 computer. Although efforts have been taken to reduce the computational cost, it isstill prohibitive for operational data assimilation. In this paper, a parallel version of the three-dimensional variational data assimilation model of GPS/MET occultation measurement suitable for massive parallelprocessors architectures is developed. The divide-and-conquer strategy is used to achieve parallelism and isimplemented by message passing. The authors present the principles for the code‘s design and examine theperformance on the state-of-the-art parallel computers in China. The results show that this parallel modelscales favorably as the number of processors is increased. With the Memory-IO technique implemented bythe author, the wall clock time per iteration used for assimilating 1420 refraction angles is reduced from45 s to 12 s using 1420 processors. This suggests that the new parallelized code has the potential to beuseful in numerical weather prediction (NWP) and climate studies.     

Research and Operational Development of Numerical Weather Prediction in China

Numerical weather prediction(NWP) is a core technology in weather forecast and disaster mitigation. China's NWP research and operational applications have been attached great importance by the meteorological community.Fundamental achievements have been made in the theories, methods, and NWP model development in China, which are of certain international impacts. In this paper, the scientific and technological progress of NWP in China since1949 is summarized. The current status and recent progress of the domestically developed NWP system—GRAPES(Global/Regional Assimilation and Pr Ediction System) are presented. Through independent research and development in the past 10 years, the operational GRAPES system has been established, which includes both regional and global deterministic and ensemble prediction models, with resolutions of 3–10 km for regional and 25–50 km for global forecasts. Major improvements include establishment of a new non-hydrostatic dynamic core, setup of four-dimensional variational data assimilation, and development of associated satellite application. As members of the GRAPES system, prediction models for atmospheric chemistry and air pollution, tropical cyclones, and ocean waves have also been developed and put into operational use. The GRAPES system has been an important milestone in NWP science and technology in China.     

Causes and Changes of Drought in China:Research Progress and Prospects

Drought is one of the most serious and extensive natural hazards in the world. Subject to monsoon climate variability, China is particularly influenced by drought hazards, especially meteorological drought. Based on a comprehensive understanding of the current status of international drought research, this paper systematically reviews the history and achievements of drought research in China since the founding of the People's Republic of China, from four main perspectives: characteristics and spatiotemporal distribution of historical and recent drought events, drought formation mechanism and change trend, drought hazard risk, and the particular flash drought. The progress and problems of drought research in China are analyzed and future prospects are proposed, with emphasis on the multi-factor synergetic effect for drought formation; the effect of land–atmosphere interaction; identification, monitoring, and prediction of flash drought; categorization of drought and characteristics among various types of drought; the agricultural drought development; drought response to climate warming; and assessment of drought hazard risks. It is suggested that strengthening scientific experimental research on drought in China is imperative. The present review is conducive to strategic planning of drought research and application, and may facilitate further development of drought research in China.     

A case study of the improvement of soil moisture initialization in IAP-PSSCA

A prediction system is employed to investigate the potential use of a soil moisture initialization scheme in seasonal precipitation prediction through a case study of severe floods in 1998. The results show that driving the model with reasonable initial soil moisture distribution is helpful for precipitation prediction,and the initialization scheme is easy to use in operational prediction.     

Vulnerability and resilience: Coalescing or paralleling approaches for sustainability science?

Vulnerability and resilience constitute different but overlapping research themes embraced by sustainability science. As practiced within this science, the two research themes appear to coalesce around one of the foundational pivots of sustainability, the coupled human–environment system. They differ in regard to their attention to two other pivots, environmental services and the tradeoffs of these services with human outcomes. In this essay I briefly review the emergence of sustainability science and the three foundational pivots relevant to vulnerability and resilience. I outline the distinctions and similarities between the two research themes foremost as practiced within sustainability science and especially in regard to the attention given to the three pivots. I conclude with the observation that improvement in the capacity of vulnerability and resilience research to inform sustainability science may hinge on their linkages in addressing tradeoffs.     

未来气候变化科学研究的主要方向和挑战

2014年9月,IPCC联合世界气候研究计划（WCRP）,在瑞士伯尔尼大学召开了一次特别的研讨会,总结过去几年气候变化科学研究中所取得的经验教训。此次会议针对IPCC最新评估报告中的关键不确定性,梳理并总结了未来气候变化研究的主要科学方向和面临的主要挑战,讨论了如何与WCRP计划结合并解决这些问题,以期在未来更好地应对这些挑战。在此次会议上,与会专家提出,未来气候变化科学研究的重大挑战应包括如下8个主题。云、环流与气候敏感度;理解和预测极端天气气候事件;冰冻圈变化;区域气候信息;区域海平面上升及其对沿海地区的影响;水资源可利用量;生物地球化学、气溶胶和大气化学;理解年代际变化：归因与预测。这些主题涵盖了WCRP计划的六大挑战和其他被认为具有挑战性的主题。本文将在此次会议报告的基础上,对相关内容进行介绍,以供当前的气候变化工作参考。     

季风亚洲区域集成研究国际计划(MAIRS)10年回顾

季风亚洲区域集成研究（Monsoon Asia Integrated Regional Study,简称MAIRS）是国际全球变化研究计划（GEC）和地球系统科学联盟（ESSP）的一个子计划,是全球变化邻域第一个由中国科学家提出,并组织实施的国际计划,历时10余年。MAIRS明确提出:不同于过去和正在进行的任何一个关于季风的研究计划,MAIRS研究的主题是人类与季风系统的相互作用。本文从四个方面回顾了这个计划的历程:（1）2003~2006年的可行性研究。包括区域集成研究概念的提出、亚洲区域全球变化研究的快速评估、以及在此基础上确定MAIRS的总体科学目标和起草MAIRS的科学计划。（2）2006年正式发布的MAIRS科学计划的主要内容。包括季风亚洲区域面临的环境变化和研究挑战、集成研究的科学问题、四个最脆弱区域的确定以及计划执行的方法和手段等。（3）2006~2016年MAIRS计划的执行情况。包括组织结构、科学活动、研究项目和成果、以及国际合作网络的建立。（4）MAIRS的国际影响和未来发展。MAIRS与未来地球国际计划（Future Earth）。     

Assessment of climate information needs in the Argentinean Agro-business sector

The present investigation aims at identifying the needs of the Argentinean agro-business sector for climate research and information. Our approach is focused on any type of climate information from past observations to seasonal prediction and climate change projections. We tried, in particular, to evaluate the possible existence of specific research themes that would be of major interest for these agriculture end-users. We interviewed representatives of seventeen companies from three major agrobusiness sectors: (1) regional/national entities such as agricultural associations, cooperatives and commercial trade boards; (2) insurance companies in the Argentinean agro-insurance market; and (3) large national and international companies representing cereal producers, agro-chemistry and agro-seeds. While all the interviewees recognized the strong influence of climate on their activities, they all pointed out that, at the time of making decision, they considered the political and economic risks rather than the climate one. In many aspects, climate is often considered as a fatality against which it is difficult to be protected. An interesting result is the confidence of the private sectors in the climate information provided by public sources (INTA, SMN, Universities) although they contract private consultants for frequent reports and tendencies. Finally, we explain how such sectors could make a better use of climate information (seasonal prediction, climate change scenarios) that could be integrated in their business projections. In particular, the development of new financial contracts (climate derivatives) open in countries with a relatively poor insurance history new ways to protect the different agricultural sectors, from the producer to large companies.     

Early warnings and emerging accountability: Total’s responses to global warming, 1971–2021

Building upon recent work on other major fossil fuel companies, we report new archival research and primary source interviews describing how Total responded to evolving climate science and policy in the last 50 years. We show that Total personnel received warnings of the potential for catastrophic global warming from its products by 1971, became more fully informed of the issue in the 1980s, began promoting doubt regarding the scientific basis for global warming by the late 1980s, and ultimately settled on a position in the late 1990s of publicly accepting climate science while promoting policy delay or policies peripheral to fossil fuel control. Additionally, we find that Exxon, through the International Petroleum Industry Environmental Conservation Association (IPIECA), coordinated an international campaign to dispute climate science and weaken international climate policy, beginning in the 1980s. This represents one of the first longitudinal studies of a major fossil fuel company’s responses to global warming to the present, describing historical stages of awareness, preparation, denial, and delay.     

Science and Prediction of Heavy Rainfall over China: Research Progress since the Reform and Opening-Up of New China

This paper reviews the major progress on development of the science and prediction of heavy rainfall over China since the beginning of the reform and opening-up of new China(roughly between 1980 and 2019). The progress of research on the physical mechanisms of heavy rainfall over China is summarized from three perspectives: 1) the relevant synoptic weather systems, 2) heavy rainfall in major sub-regions of China, and 3) heavy rainfall induced by typhoons. The development and application of forecasting techniques for heavy rainfall are summarized in terms of numerical weather prediction techniques and objective forecasting methods. Greatly aided by the rapid progress in meteorological observing technology and substantial improvement in electronic computing, studies of heavy rainfall in China have advanced to investigating the evolution of heavy-rain-producing storms and observational analysis of the cloud microphysical features. A deeper and more systematic understanding of the synoptic systems of importance to the production of heavy rainfall has also been developed. Operational forecast of heavy rainfall in China has changed from subjective weather event forecasts to a combination of both subjective and objective quantitative precipitation forecasts, and is now advancing toward probabilistic quantitative precipitation forecasts with the provision of forecast uncertainty information.     

Integrative propositions for adapting conservation policy to the impacts of climate change

Conservation policies have changed over time in response to changes in human and ecological drivers. The impacts of climate (and other) concurrent changes prompt consideration of further iterations for both conservation means and objectives. In this paper we bring together previous disparate literatures and apply them to the question of how to adapt conservation polices to suit an era of climate change impacts. Our approach is based on two assertions: (i) that the integration of specific natural and social science insights is essential for understanding and effectively responding to this challenge, and (ii) that in addition to adaptive conservation means (strategies), attention needs to be given to considering adaptive conservation objectives. Specifically, we convert a core set of natural and social science insights into analytical tools known as heuristics or rules of thumb. We then use the heuristics as a basis to offer a list of preliminary propositions that can help inform the development of new means and objectives. The propositions address key considerations including recalibrating management objectives, the role of disturbance in facilitating ecological transitions, and overarching topics relating to governance. The propositions are speculative, and so intended only to outline potential avenues for further empirical research and subsequent refinement. In the spirit of adaptation, we expect and welcome their revision.     

The Present Status and Future of Research of the East Asian Monsoon

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我国短期气候预测的物理基础及其预测思路

短期气候预测依据大气科学原理,运用气候动力学、统计学等手段,在研究气候异常成因的基础上对未来气候趋势进行预测。虽然目前我国短期气候预测的水平还不高,但短期气候预测是国家经济发展和防灾减灾的迫切需求,提高预测准确率是气象科研和业务人员的重要任务。该文从海洋、积雪等外强迫信号及大气环流大尺度变动等大气内部特性等角度概述了短期气候预测的物理基础,简要回顾了近60年来我国短期气候预测的发展历程,并介绍了作者近十几年来研制短期气候预测客观统计学及统计与动力学相结合预测模型的主要思路。     

Status of NCEP CFS vis-a-vis IPCC AR4 models for the simulation of Indian summer monsoon

National Centers for Environmental Prediction (NCEP) Coupled Forecast System (CFS) is selected to play a lead role for monsoon research (seasonal prediction, extended range prediction, climate prediction, etc.) in the ambitious Monsoon Mission project of Government of India. Thus, as a prerequisite, a detail analysis for the performance of NCEP CFS vis-a-vis IPCC AR4 models for the simulation of Indian summer monsoon (ISM) is attempted. It is found that the mean monsoon simulations by CFS in its long run are at par with the IPCC models. The spatial distribution of rainfall in the realm of Indian subcontinent augurs the better results for CFS as compared with the IPCC models. The major drawback of CFS is the bifurcation of rain types; it shows almost 80–90 % rain as convective, contrary to the observation where it is only 50–65 %; however, the same lacuna creeps in other models of IPCC as well. The only respite is that it realistically simulates the proper ratio of convective and stratiform rain over central and southern part of India. In case of local air–sea interaction, it outperforms other models. However, for monsoon teleconnections, it competes with the better models of the IPCC. This study gives us the confidence that CFS can be very well utilized for monsoon studies and can be safely used for the future development for reliable prediction system of ISM.